Hasil untuk "Accounting. Bookkeeping"

Lukasz W. Niparko

Civil society has been deemed by various scholars, such as Robert D. Putnam, to be a predictor and a cornerstone of a robust and consolidated democracy (Putnam et al., 1993). Putnam highlights in his book Bowling Alone (2000) that American civil society has become weaker: people organize less, and literally, they bowl alone. But what if there is yet another aspect to Putnam's story that has not been fully accounted for, namely the rise of Digital Civil Society (DCS)? Perhaps people in the third decade of the 21st century bowl online. They still organize, mobilize, and care for their civil liberties and democratic institutions; however, the public sphere in which this takes place has shifted online to cyberspace (Bernholz et al., 2013) or to what still needs to be conceptualized, the digital public sphere (DPS), which this article attempts to measure and demarcate.

Alan Said

This work examines the role of recommender systems in promoting sustainability, social responsibility, and accountability, with a focus on alignment with the United Nations Sustainable Development Goals (SDGs). As recommender systems become increasingly integrated into daily interactions, they must go beyond personalization to support responsible consumption, reduce environmental impact, and foster social good. We explore strategies to mitigate the carbon footprint of recommendation models, ensure fairness, and implement accountability mechanisms. By adopting these approaches, recommender systems can contribute to sustainable and socially beneficial outcomes, aligning technological advancements with the SDGs focused on environmental sustainability and social well-being.

Egor Podlesnyi, Foteini Oikonomou

We report the results of the search for the high-energy neutrino emission associated with blazar flares, accounting for a possible lag of neutrinos with respect to the electromagnetic emission, either due to the slowness of the proton energy losses in $pγ$ collisions and/or proton acceleration. We perform two tests, cross-matching neutrinos with energies $E_ν \gtrsim 100$ TeV from the public catalogue of neutrino alerts IceCat-1 with active galactic nuclei from two source samples based on 1) the MOJAVE database and 2) the CGRaBS catalogue, and utilising Fermi-LAT light curves from the public light curve repository. We scan over a wide range of values of the jet-frame time delay $t^{\prime}_{\mathrm{delay}}$ between the neutrino arrival and the time of the prior major $γ$-ray flare and find a pre-trial $\sim 2σ$ correlation at $t^{\prime}_{\mathrm{delay}} \sim 10^{3}$ d, which is consistent ($p_{\mathrm{post-trial}} \sim 0.1$) with expectations under the null hypothesis after trial correction.

Haoyu Dong, Pengkun Zhang, Yan Gao et al.

We introduce FinWorkBench (a.k.a. Finch), a benchmark for evaluating agents on real-world, enterprise-grade finance and accounting workflows that interleave data entry, structuring, formatting, web search, cross-file retrieval, calculation, modeling, validation, translation, visualization, and reporting. Finch is built from authentic enterprise workspaces from Enron (15,000 files and 500,000 emails) and other financial institutions spanning 2000 to 2025, preserving the in-the-wild messiness of multimodal artifacts such as tables and charts across diverse domains including budgeting, trading, and asset management. We propose a workflow construction process that combines LLM-assisted mining of workflows from authentic enterprise environments with expert annotation. Specifically, we use LLM-assisted, expert-verified derivation of workflows from real-world email threads and spreadsheet version histories, followed by meticulous workflow annotation requiring more than 700 hours of expert effort. This process yields 172 composite workflows with 384 tasks, involving 1,710 spreadsheets with 27 million cells, along with PDFs and other artifacts, capturing the intrinsically messy, long-horizon, knowledge-intensive, and collaborative nature of enterprise work. We conduct both human and automated evaluations of frontier AI systems, including GPT 5.1, Claude Sonnet/Opus 4.5, Gemini 3 Pro, Grok 4, and Qwen 3 Max. GPT 5.1 Pro spends an average of 16.8 minutes per workflow yet passes only 38.4% of workflows. Comprehensive case studies further highlight the challenges that real-world enterprise workflows pose for AI agents.

Raziki Waldan, Dewa Ruci

Purpose: This article aims to analyze the implementation of Occupational Health and Safety (OHS) in organizational development at PT. Enggang Mill, a palm oil industry company. The primary focus of this study is to evaluate the company's policies, procedures, training, and efforts in fostering a safety culture, as well as to identify challenges in achieving a zero-incident environment. Method: The methodology involves direct interviews with Mr. Adrian Syah, the Head of Safety at PT. Enggang Mill, and an analysis of the OHS policies and procedures in place. The collected data are analyzed qualitatively to understand the effectiveness and challenges in the execution of OHS programs within the factory setting. Research Findings: The findings reveal that PT. Enggang Mill has implemented stringent safety policies and procedures, including regular training, provision of personal protective equipment (PPE), and the installation of warning signs throughout the work areas. However, the greatest challenge remains in cultivating a safety culture among employees, where further efforts are needed to enhance awareness and compliance with OHS procedures. The implications of this research emphasize the importance of management support and active employee participation in creating a safe and productive work environment. The novelty of this study lies in its comprehensive approach, combining an evaluation of safety policies with an analysis of safety culture within the company, providing insights into how safety culture can be a decisive factor in the success of OHS programs. Tujuan Penelitian: Artikel ini bertujuan untuk menganalisis penerapan Kesehatan dan Keselamatan Kerja (K3) dalam pengembangan organisasi di PT. Enggang Mill, sebuah perusahaan industri kelapa sawit. Fokus utama dari studi ini adalah untuk mengevaluasi kebijakan, prosedur, pelatihan, dan upaya perusahaan dalam membangun budaya keselamatan, serta mengidentifikasi tantangan dalam mencapai lingkungan tanpa insiden. Metode: Metode yang digunakan melibatkan wawancara langsung dengan Bapak Adrian Syah, Kepala Kesehatan dan Keselamatan di PT. Enggang Mill, serta analisis kebijakan dan prosedur K3 yang diterapkan. Data yang dikumpulkan dianalisis secara kualitatif untuk memahami efektivitas dan tantangan dalam pelaksanaan program K3 di lingkungan pabrik. Temuan Penelitian: Temuan menunjukkan bahwa PT. Enggang Mill telah menerapkan kebijakan dan prosedur keselamatan yang ketat, termasuk pelatihan rutin, penyediaan alat pelindung diri (APD), dan pemasangan tanda peringatan di seluruh area kerja. Namun, tantangan terbesar tetap pada pembentukan budaya keselamatan di antara karyawan, di mana perlu ada upaya lebih lanjut untuk meningkatkan kesadaran dan kepatuhan terhadap prosedur K3. Implikasi dari penelitian ini menekankan pentingnya dukungan manajemen dan partisipasi aktif karyawan dalam menciPT.akan lingkungan kerja yang aman dan produktif. Kebaruan dari studi ini terletak pada pendekatannya yang komprehensif, menggabungkan evaluasi kebijakan keselamatan dengan analisis budaya keselamatan di dalam perusahaan, memberikan wawasan tentang bagaimana budaya keselamatan dapat menjadi faktor penentu dalam keberhasilan program K3.

Sarah Dean, Evan Dong, Meena Jagadeesan et al.

As AI systems enter into a growing number of societal domains, these systems increasingly shape and are shaped by user preferences, opinions, and behaviors. However, the design of AI systems rarely accounts for how AI and users shape one another. In this position paper, we argue for the development of formal interaction models which mathematically specify how AI and users shape one another. Formal interaction models can be leveraged to (1) specify interactions for implementation, (2) monitor interactions through empirical analysis, (3) anticipate societal impacts via counterfactual analysis, and (4) control societal impacts via interventions. The design space of formal interaction models is vast, and model design requires careful consideration of factors such as style, granularity, mathematical complexity, and measurability. Using content recommender systems as a case study, we critically examine the nascent literature of formal interaction models with respect to these use-cases and design axes. More broadly, we call for the community to leverage formal interaction models when designing, evaluating, or auditing any AI system which interacts with users.

Ian Schneider, Taylor Mattia

This paper presents a methodology for allocating energy consumption to multiple users of shared data center machines, infrastructure, and software. Google uses this methodology to provide carbon reporting data for enterprise customers of multiple Google products, including Google Cloud and Workspace. The approach documented here advances the state-of-the-art of large scale Cloud carbon reporting systems. It uses detailed, granular measurement data on machine energy consumption. In addition, it uses physical factors for allocating energy consumption and carbon emissions--preferred by the Greenhouse Gas Protocol's Scope 3 Reporting Standard. Specifically, the approach described here allocates machine energy consumption based on a combination of data center resource reservations and hourly measured resource usage. It also accounts for Google's own internal use of shared software services, reallocating energy use to the users of those shared services. Finally, it uses hourly, location-specific estimates of carbon intensity to precisely measure carbon emissions of users in a global fleet of data centers.

محمد غفاری فرد, سید حسن قوامی, یزدان علی رضایی

در دهه‌های اخیر رسیدن به رشد اقتصادی به همراه توزیع برابر و بالا رفتن کیفیت زندگی در قالب رشد فراگیر در ادبیات اقتصادی شکل گرفته است. پژوهش حاضر به بیان بررسی عوامل مؤثر بر رشد فراگیر با تأکید بر تبعیض در توزیع بودجه در استان‌های ایران با استفاده از روش پانل دیتا (داده‌های تابلویی) از طریق نرم‌افزار ایویوز پرداخته است. نتایج پژوهش نشان می‌دهد که یک درصد افزایش مشارکت اقتصادی نیروی کار، رشد فراگیر استان‌های ایران را به‌اندازه 0.847 درصد افزایش می‌دهد. با یک درصد افزایش در نسبت اعتبارات بانکی، رشد فراگیر استان‌های ایران به‌اندازه 0.255 درصد افزایش می‌کند. این بدان معنی است که نسبت اعتبارات بانکی یک مؤلفه تأثیرگذار مثبت بر رشد فراگیر استان‌های ایران است. ضریب متغیر تبعیض در توزیع بودجه نیز دارای اثرات منفی بر رشد فراگیر با ضریب 0.155- درصد می‌باشد. متغیر تورم اثر منفی و معنی‌دار بر رشد فراگیر استان‌های ایران گذاشته است. با یک درصد افزایش در نرخ تورم، رشد فراگیر استان‌های ایران به‌اندازه 0.123- درصد کاهش می‌یابد. بنابراین سیاست‌گذران اقتصادی جهت افزایش رشد فراگیر در استان‌های ایران تمرکززدایی در اعتبارات بانکی، مشارکت اقتصادی به همراه توازن در توزیع اعتبارات بودجه و توجه به مزیت‌های اقتصادی هر منطقه را مورد توجه قرار دهند.

Jan Kaczmarzyk

The Monte Carlo simulation is the ultimate solution for considering nearly all possible scenarios in presumably any discounted cash flow valuation. This paper argues that a discount rate expresses an investor’s current requirement and should be respectively perceived as a parameter only. The consequences of qualifying a required rate of return (a discount rate) as a risk factor in a discounted cash flow valuation are described in the paper using a free cash flow financial model of an asset being a hypothetical publicly traded enterprise. The case study is a discounted cash flow valuation using the Monte Carlo simulation for risk analysis. The various sets of assumptions are considered to explain the consequences of qualifying a required rate of return in a discounted cash flow model as a risk factor. As indicated in the paper, the discount rate as an additional risk factor with an attributed probability distribution increases the volatility of a risk variable, then the distribution of a risk variable becomes more flattened. In previous studies, some authors indicated that a discount rate could be considered a risk factor in the Monte Carlo simulation (Krysiak 2000; Damodaran 2018).

Kamil Erdayandi, Lucas C. Cordeiro, Mustafa A. Mustafa

This paper proposes a privacy-preserving and accountable billing (PA-Bill) protocol for trading in peer-to-peer energy markets, addressing situations where there may be discrepancies between the volume of energy committed and delivered. Such discrepancies can lead to challenges in providing both privacy and accountability while maintaining accurate billing. To overcome these challenges, a universal cost splitting mechanism is proposed that prioritises privacy and accountability. It leverages a homomorphic encryption cryptosystem to provide privacy and employs blockchain technology to establish accountability. A dispute resolution mechanism is also introduced to minimise the occurrence of erroneous bill calculations while ensuring accountability and non-repudiation throughout the billing process. Our evaluation demonstrates that PA-Bill offers an effective billing mechanism that maintains privacy and accountability in peer-to-peer energy markets utilising a semi-decentralised approach.

Maryam Barzegar, Seyed Mostafa Alavi Alavi

The main purpose of this study is to investigate the impact of corporate governance and company strategy on the performance of family-owned companies among companies listed on the Tehran Stock Exchange.The present study is in the group of descriptive-correlational researchers in terms of applied purpose and terms of the data collection method. Also, to select the sample size, a systematic elimination sampling method (screening) was used, which according to the considered conditions, finally 183 companies in a period of 6 years between 1394 to 1399 (a total of 1098 years- Companies) were analyzed. To test the hypotheses, multivariate regression methods have been used using Eviews8 software and using panel data method - fixed effects and integrated data method.The results obtained in this study show that in general, family ownership has an inverse and significant relationship with the financial performance of companies. The effect of institutional ownership on the relationship between board independence, managers' rewards, and company diversity with corporate financial performance has been positive and significant

Aurora Christine Hofman, Lars Espeland, Ingelin Steinsland et al.

In this work, blood pressure eleven years ahead is modeled using data from a longitudinal population-based health survey, the Trondelag Health (HUNT) Study, while accounting for missing data due to dropout between consecutive surveys (20-50 %). We propose and validate a shared parameter model (SPM) in the Bayesian framework with age, sex, body mass index, and initial blood pressure as explanatory variables. Further, we propose a novel evaluation scheme to assess data missing not at random (MNAR) by comparing the predictive performance of the fitted SPM with and without conditioning on the missing process. The results demonstrate that the SPM is suitable for inference for a dataset of this size (cohort of 64385 participants) and structure. The SPM indicates data MNAR and gives different parameter estimates than a naive model assuming data missing at random. The SPM and naive models are compared based on predictive performance in a validation dataset. The naive model performs slightly better than the SPM for the present participants. This is in accordance with results from a simulation study based on the SPM where we find that the naive model performs better for the present participants, while the SPM performs better for the dropouts.

Rob Procter, Peter Tolmie, Mark Rouncefield

The need for AI systems to provide explanations for their behaviour is now widely recognised as key to their adoption. In this paper, we examine the problem of trustworthy AI and explore what delivering this means in practice, with a focus on healthcare applications. Work in this area typically treats trustworthy AI as a problem of Human-Computer Interaction involving the individual user and an AI system. However, we argue here that this overlooks the important part played by organisational accountability in how people reason about and trust AI in socio-technical settings. To illustrate the importance of organisational accountability, we present findings from ethnographic studies of breast cancer screening and cancer treatment planning in multidisciplinary team meetings to show how participants made themselves accountable both to each other and to the organisations of which they are members. We use these findings to enrich existing understandings of the requirements for trustworthy AI and to outline some candidate solutions to the problems of making AI accountable both to individual users and organisationally. We conclude by outlining the implications of this for future work on the development of trustworthy AI, including ways in which our proposed solutions may be re-used in different application settings.

Jeniffer Paola Gracia Rojas, Oscar Orlando Beltrán Rodríguez, Erika Juliana Quintero Márquez

Desde la creación y puesta en operación de la Planta de Tratamiento de Agua Potable “Acuatocarema” del municipio de Cachipay Cundinamarca, esta cuenta con diferentes problemas que no ha podido superar por ausencia de datos e información que permitan realizar la óptima operación, seguimiento y control de las labores que se realizan, lo que impide tener control de sus capacidades, alcances operativos y gestión del recurso hídrico. El objetivo principal de esta investigación fue diseñar una metodología que permita estandarizar los procesos de dicha planta mediante la herramienta Balanced Scorecard. Para ello se desarrollan cuatro fases metodológicas: Identificación y clasificación de los procesos, seguimiento y medición de la estrategia corporativa usando el cuadro de mando integral, análisis de los indicadores de gestión mediante matriz de evaluación; análisis de las no conformidades y formulación de acciones correctivas. Se estableció la estrategia corporativa de manera gráfica, al igual que indicadores como herramienta de seguimiento y medición, una matriz de evaluación y el planteamiento de alternativas de solución. Los indicadores señalan la existencia de peligros en cuanto a la cantidad de clientes, es riesgoso que no midan los indicadores de calidad del agua establecidos en la normatividad vigente, y no desarrollen actividades para fortalecer el ahorro del agua.

Johannes Kunke, Stephan Wiefling, Markus Ullmann et al.

Threats to passwords are still very relevant due to attacks like phishing or credential stuffing. One way to solve this problem is to remove passwords completely. User studies on passwordless FIDO2 authentication using security tokens demonstrated the potential to replace passwords. However, widespread acceptance of FIDO2 depends, among other things, on how user accounts can be recovered when the security token becomes permanently unavailable. For this reason, we provide a heuristic evaluation of 12 account recovery mechanisms regarding their properties for FIDO2 passwordless authentication. Our results show that the currently used methods have many drawbacks. Some even rely on passwords, taking passwordless authentication ad absurdum. Still, our evaluation identifies promising account recovery solutions and provides recommendations for further studies.

Dong Liu, Brice Lecampion

The quasi-brittle nature of rocks challenges the basic assumptions of linear hydraulic fracture mechanics (LHFM): linear elastic fracture mechanics and smooth parallel plates lubrication fluid flow. We relax these hypotheses and investigate the growth of a plane-strain hydraulic fracture in an impermeable medium accounting for a rough cohesive zone and a fluid lag. In addition to a dimensionless toughness and the time-scale of coalescence of the fluid and fracture fronts as in the LHFM case, the solution now also depends on the in-situ-to-cohesive stress ratio and the intensity of the flow deviation induced by aperture roughness. The solution is appropriately described by a nucleation time-scale, which delineates the fracture growth into a nucleation phase, an intermediate stage and a late time stage where convergence toward LHFM predictions finally occurs. A highly non-linear hydro-mechanical coupling takes place as the fluid front enters the rough cohesive zone which itself evolves during the nucleation and intermediate stages. This coupling leads to significant additional viscous flow dissipation. As a result, the fracture evolution deviates from LHFM solutions with shorter fracture lengths, larger widths and net pressures. These deviations ultimately decrease at late times as the lag and cohesive zone fractions both become smaller. The deviations increase with larger dimensionless toughness and in-situ-to-cohesive stress ratio, as both further localize viscous dissipation near the fluid front located in the rough cohesive zone. The convergence toward LHFM can occur at very late time for realistic values of in-situ-to-cohesive stress ratio encountered at depth. The impact of a rough cohesive zone appears to be prominent for laboratory experiments and short in-situ injections in quasi-brittle rocks with ultimately a larger energy demand compared to LHFM predictions.

Ilia Karpov, Ekaterina Glazkova

The widespread of Online Social Networks and the opportunity to commercialize popular accounts have attracted a large number of automated programs, known as artificial accounts. This paper focuses on the classification of human and fake accounts on the social network, by employing several graph neural networks, to efficiently encode attributes and network graph features of the account. Our work uses both network structure and attributes to distinguish human and artificial accounts and compares attributed and traditional graph embeddings. Separating complex, human-like artificial accounts into a standalone task demonstrates significant limitations of profile-based algorithms for bot detection and shows the efficiency of network structure-based methods for detecting sophisticated bot accounts. Experiments show that our approach can achieve competitive performance compared with existing state-of-the-art bot detection systems with only network-driven features. The source code of this paper is available at: http://github.com/karpovilia/botdetection.

Maxim Dvornikov, V. B. Semikoz, D. D. Sokoloff

We propose the mean field dynamo model for the generation of strongest magnetic fields, $B\sim 10^{15}\,{\rm G}$, in a neutron star (NS) accounting for the chiral magnetic effect (CME) driven by the shock in a supernova (SN) progenitor of that NS. The temperature jump at a narrow shock front, where an initial magnetic field existing in inflowing matter rises sharply, is the source of the CME that prevails significantly the erasure of the CME due to the spin-flip through Coulomb collisions in plasma. The growth of the magnetic field just behind the shock given by the instability term $\nabla\times (α{\bf B})$ in induction equation, stops after a successful SN explosion that throws out the mantle of a protoneutron star. As a result, such an explosion interrupts the transfer of strongly magnetized plasma from the shock onto NS surface and leads to the saturation of the magnetic field. Assuming the rigid protostar rotation, we employ the mean field dynamo, which is similar to the $α^2$-dynamo known in the standard magnetohydrodynamics (MHD). The novelty of our model is that $α^2$-dynamo is based on concepts of particle physics, applied in MHD, rather than by a mirror asymmetry of convective vortices in the rotating convection.

Qing Cheng, Baoluo Sun, Yingcun Xia et al.

Mendelian randomization (MR) is a powerful approach to examine the causal relationships between health risk factors and outcomes from observational studies. Due to the proliferation of genome-wide association studies (GWASs) and abundant fully accessible GWASs summary statistics, a variety of two-sample MR methods for summary data have been developed to either detect or account for horizontal pleiotropy, primarily based on the assumption that the effects of variants on exposure (γ) and horizontal pleiotropy (α) are independent. This assumption is too strict and can be easily violated because of the correlated horizontal pleiotropy (CHP). To account for this CHP, we propose a Bayesian approach, MR-Corr2, that uses the orthogonal projection to reparameterize the bivariate normal distribution for γ and α, and a spike-slab prior to mitigate the impact of CHP. We develop an efficient algorithm with paralleled Gibbs sampling. To demonstrate the advantages of MR-Corr2 over existing methods, we conducted comprehensive simulation studies to compare for both type-I error control and point estimates in various scenarios. By applying MR-Corr2 to study the relationships between pairs in two sets of complex traits, we did not identify the contradictory causal relationship between HDL-c and CAD. Moreover, the results provide a new perspective of the causal network among complex traits. The developed R package and code to reproduce all the results are available at https://github.com/QingCheng0218/MR.Corr2.

Bella Nadya, Dyah Purnamasari

This study aims to determine the influence of sales growth and leverage on tax avoidance on coal sub-sector mining companies listed on the IDX in 2014-2018. The data used were secondary data and the samples were financial statements from 10 coal sub-sector mining companies listed on the IDX in 2014-2018. The method of sample selection was purposive sampling, while the data analysis included panel data regression analysis. The data were analyzed using Eviews 10 software. The results of this study show that sales growth and leverage affect tax avoidance. Suggestions for further research is to add research model variables that influence tax avoidance.