This study examined the implementation of Microsoft Excel-based bookkeeping digitalization in the Ngargoretno Village-Owned Enterprise (BUMDes) for the 2023 period. The sample used in this study was the Ngargoretno BUMDes, which oversees seven small and medium enterprises (SMEs): Kopi Merah, Kopi Liar, Kopi Arabica, Gula Semut, Teh Sangit, Jahe Instan, and Kunyit Instan. This research employed a qualitative descriptive approach with a case study focus. The findings indicate that the digitalization of bookkeeping has positively impacted the quality of financial recordkeeping within Ngargoretno BUMDes. The new system has made the bookkeeping process more informative and has significantly helped the BUMDes management overcome the challenges previously faced.
Using an improved version of the Newman-Janis algorithm, we obtain metrics of rotating black holes for a set of extended gravity theories that extend general relativity in different ways: the Horndeski model, the bumblebee model, the Gauss-Bonnet scalar gravity, the loop quantum gravity, the conformal gravity, and $f(Q)$ (symmetric teleparallel gravity STEGR). The obtained metrics are used to model black hole shadows. It is shown that for some models the critical values of the angular momentum $a_{crit}$ emerge. The previous conclusion that the extended gravity theory corrects by itself the effects of rotation in both directions is confirmed. This appears to be important for further modelling of shadow profiles taking into account the constantly increasing accuracy of black hole images. Thus, when rotation is taken into account black hole shadow images, as the GW170817 test or the post-Newtonian formalism, can serve as a meaningful way to test and constrain extended theories of gravity.
Business growth is a goal of great importance for its both private and social benefits. Many firms view business growth as an imperative for their survival, stability, and long-term success. Business growth can be socially beneficial, too, as it enables businesses to expand into new territories where they can stimulate economic growth and development, creates more jobs, increase living standards, and better serve their communities by giving back more through Corporate Social Responsibility initiatives. Business growth must be planned reasonably and optimally so that it can effectively achieve its critical ambitions in business practice. The current common practices for planning the supply side of business growth are usually ad-hoc and lack well-established mathematical and economic foundations. The present paper argues that business growth planning can be pursued more structurally, reliably, and meaningfully within the framework of Growth Accounting (GA), which was first introduced by Economics Nobel Laureate Robert Solow to study economic growth. It is shown that, although GA was initially put forth as a procedure to explain "economic growth" ex-post, it can similarly be used to plan "business growth" ex-ante when a general backward approach is taken in its procedure-called Backward Growth Accounting (BGA) in this paper. Taking this well-established economic-mathematical approach to planning business growth will enhance the current practices conceptually and structurally, as it is built on the basis of economic logic and mathematical tools. BGA can help businesses identify and plan for key drivers of output growth and assess shortcomings in the growth process, such as poor productivity, inadequate labor utilization, or insufficient capital investment. The paper outlines an eight-step procedure for planning business growth using BGA and includes appendices with real-world examples.
Automated vehicles can gather information about surrounding traffic and plan safe and energy-efficient driving behavior, which is known as eco-driving. Conventional eco-driving designs only consider preceding vehicles in the same lane as the ego vehicle. In heavy traffic, however, vehicles in adjacent lanes may cut into the ego vehicle's lane, influencing the ego vehicle's eco-driving behavior and compromising the energy-saving performance. Therefore, in this paper, we propose an eco-driving design that accounts for neighbor vehicles that have cut-in intentions. Specifically, we integrate a leader-follower game to predict the interaction between the ego and the cut-in vehicles and a model-predictive controller for planning energy-efficient behavior for the automated ego vehicle. We show that the leader-follower game model can reasonably represent the interactive motion between the ego vehicle and the cut-in vehicle. More importantly, we show that the proposed design can predict and react to neighbor vehicles' cut-in behaviors properly, leading to improved energy efficiency in cut-in scenarios compared to baseline designs that consider preceding vehicles only.
Effective human-machine collaboration requires machine learning models to externalize uncertainty, so users can reflect and intervene when necessary. For language models, these representations of uncertainty may be impacted by sycophancy bias: proclivity to agree with users, even if they are wrong. For instance, models may be over-confident in (incorrect) problem solutions suggested by a user. We study the relationship between sycophancy and uncertainty estimation for the first time. We propose a generalization of the definition of sycophancy bias to measure downstream impacts on uncertainty estimation, and also propose a new algorithm (SyRoUP) to account for sycophancy in the uncertainty estimation process. Unlike previous works on sycophancy, we study a broad array of user behaviors, varying both correctness and confidence of user suggestions to see how model answers (and their certainty) change. Our experiments across conversation forecasting and question-answering tasks show that user confidence plays a critical role in modulating the effects of sycophancy, and that SyRoUP can better predict these effects. From these results, we argue that externalizing both model and user uncertainty can help to mitigate the impacts of sycophancy bias.
In this paper I show the equivalence, under appropriate assumptions, of two alternative methods to account for the presence of selection biases (also called selection effects) in population studies: one is to include the selection effects in the likelihood directly; the other follows the procedure of first inferring the observed distribution and then removing selection effects a posteriori. Moreover, I investigate a potential bias allegedly induced by the latter approach: I show that this procedure, if applied under the appropriate assumptions, does not produce the aforementioned bias.
This study aims to develop an effective and practical digital module for learning financial accounting I. The development uses the ADDIE model which consists of five stages which include analysis, design, development, implementation, and evaluation. The digital financial accounting learning module 1 developed contains an introduction, learning materials in the form of PPT, working papers in the form of Microsoft excel, summaries, practice questions and assessment rubrics. Based on the results of the evaluation of material experts and original media, the results of the digital media are valid and can be implemented. Digital module developed that can help facilitate students in understanding the material in learning financial accounting I.
A new model is considered to describe the evolution of tissue temperature and water concentration during a thermal ablation process. The aim of this study is to expand the results obtained in Blauth et. al. 2020, where the tissue water concentration is considered as a known function, experimentally obtained. The model consists of two coupled equations: a parabolic partial differential equation for the temperature $θ$ and an ordinary differential equation for the water content $w$. The equation for $θ$ accounts for temperature diffusion, blood perfusion, the heat source provided by the laser applicator and the vaporization effects which act when the temperature approaches 100C. The equation for $w$ accounts for the loss of tissue water that occurs during vaporization. The model equations are solved in a cylindrical domain around the laser applicator and the results are compared with the existing experimental data for porcine liver tissue from Hübner et. al. 2017.
Research aims: This study aimed to determine whether systematic risk and return are related to each other. It answered the research question: Is it realistic for investors to expect high returns when their investments are associated with more riskiness?
Design/Methodology/Approach: Quantitative analysis was applied through a correlational research design. Secondary data were collected from the Integrated Real-time Equity System (IRESS). The Statistical Package for Social Sciences (SPSS) was utilised to measure a Pearson correlation coefficient and execute multiple regression analysis. This was done to test for the relationship between financial measurements of systematic risk and return, of sampled entities.
Research findings: This research found that measures of systematic risk and return are not necessarily related when empirically analysed for sampled entities.
Theoretical contribution/Originality: This paper indicated that the principle of the modern portfolio theory (MPT) should not be accepted as general truth. It should not be assumed that risk and return are linearly related in all financial markets under all economic circumstances. This premise is contrary to general financial management practice, where the MPT is universally accepted and even forms the basis of other financial theories.
Research limitation: The use of the IRESS database posed a limitation in terms of sampling, as the database was frequently unable to present a complete set of data needed for statistical testing. Consequently, only 33 companies were sampled, as IRESS only made a complete set of required data available for these entities.
Recensión del libro «Deliberación y participación ciudadana. Derechos e instrumentos en el ordenamiento constitucional, Enriqueta Expósito Gómez, publicado en 2021 por Marcial Pons.
Political institutions and public administration (General), Accounting. Bookkeeping
Social media moderation policies are often at the center of public debate, and their implementation and enactment are sometimes surrounded by a veil of mystery. Unsurprisingly, due to limited platform transparency and data access, relatively little research has been devoted to characterizing moderation dynamics, especially in the context of controversial events and the platform activity associated with them. Here, we study the dynamics of account creation and suspension on Twitter during two global political events: Russia's invasion of Ukraine and the 2022 French Presidential election. Leveraging a large-scale dataset of 270M tweets shared by 16M users in multiple languages over several months, we identify peaks of suspicious account creation and suspension, and we characterize behaviours that more frequently lead to account suspension. We show how large numbers of accounts get suspended within days from their creation. Suspended accounts tend to mostly interact with legitimate users, as opposed to other suspicious accounts, often making unwarranted and excessive use of reply and mention features, and predominantly sharing spam and harmful content. While we are only able to speculate about the specific causes leading to a given account suspension, our findings shed light on patterns of platform abuse and subsequent moderation during major events.
Fabian Hinterer, Magdalena C. Schneider, Simon Hubmer
et al.
Single molecule localization microscopy relies on the precise quantification of the position of single dye emitters in a sample. This precision is improved by the number of photons that can be detected from each molecule. It is therefore recommendable to increase illumination times for the recording process. Particularly recording at cryogenic temperatures dramatically reduces photobleaching and thereby allows a massive increase in illumination times to several seconds. As a downside, microscope instabilities may well introduce jitter during such long illuminations, deteriorating the localization precision. In this paper, we theoretically demonstrate that a parallel recording of fiducial marker beads together with a novel fitting approach accounting for the full drift trajectory allows for largely eliminating drift effects for drift magnitudes of several hundred nanometers per frame.
Bao Tran Truong, Oliver Melbourne Allen, Filippo Menczer
The spread of misinformation poses a threat to the social media ecosystem. Effective countermeasures to mitigate this threat require that social media platforms be able to accurately detect low-credibility accounts even before the content they share can be classified as misinformation. Here we present methods to infer account credibility from information diffusion patterns, in particular leveraging two networks: the reshare network, capturing an account's trust in other accounts, and the bipartite account-source network, capturing an account's trust in media sources. We extend network centrality measures and graph embedding techniques, systematically comparing these algorithms on data from diverse contexts and social media platforms. We demonstrate that both kinds of trust networks provide useful signals for estimating account credibility. Some of the proposed methods yield high accuracy, providing promising solutions to promote the dissemination of reliable information in online communities. Two kinds of homophily emerge from our results: accounts tend to have similar credibility if they reshare each other's content or share content from similar sources. Our methodology invites further investigation into the relationship between accounts and news sources to better characterize misinformation spreaders.
The study aimed to identify the impact of the credit query in reducing credit risks from the viewpoint of workers in Jordanian banks, and to achieve the goal of the study, the researchers adopted the descriptive analytical approach, where a questionnaire is designed and distributed to a sample of (176) individuals within the upper and middle administrative levels in Jordanian banks. The study reached a set of results, the most important of which is the existence of a negative correlation between the level of use of the credit query system and each of the financial, administrative and legal credit risks, in addition a statistically significant effect of using the credit query system in reducing the financial, administrative and legal credit risks. The study recommended that Jordanian banks should continue to use the credit system because of its positive effects on the efficiency and effectiveness of granting credit to business organizations or individuals alike.
El gobierno corporativo es un tema amplio e interesante, la investigación académica ha entregado varios hallazgos importantes, mismos que han sido acogidos por varias corporaciones a nivel mundial, de manera que en el Ecuador existen empresas grandes que podrían adoptar estas prácticas para ser competitivos internacionalmente. En el presente estudio se analiza la influencia del gobierno corporativo en las grandes empresas del Ecuador y su aporte en la competitividad internacional, tomando en cuenta los diferentes elementos que están cambiando el mundo globalizado, determinando así, el impacto que este posee dentro de la captación de inversiones, fusiones corporativas, flujos de información y procesos jerárquicos, permitiendo alcanzar una eficiencia y posicionamiento en mercados mundiales. Mediante la aplicación de encuestas y un modelo teórico documental se determinó la influencia de este en el desarrollo nacional e internacional de las organizaciones ecuatorianas, mismas que al manejar buenas prácticas empresariales tienden a ser más competitivas en el mercado.
Mohammad Hammas Saeed, Shiza Ali, Jeremy Blackburn
et al.
Growing evidence points to recurring influence campaigns on social media, often sponsored by state actors aiming to manipulate public opinion on sensitive political topics. Typically, campaigns are performed through instrumented accounts, known as troll accounts; despite their prominence, however, little work has been done to detect these accounts in the wild. In this paper, we present TROLLMAGNIFIER, a detection system for troll accounts. Our key observation, based on analysis of known Russian-sponsored troll accounts identified by Reddit, is that they show loose coordination, often interacting with each other to further specific narratives. Therefore, troll accounts controlled by the same actor often show similarities that can be leveraged for detection. TROLLMAGNIFIER learns the typical behavior of known troll accounts and identifies more that behave similarly. We train TROLLMAGNIFIER on a set of 335 known troll accounts and run it on a large dataset of Reddit accounts. Our system identifies 1,248 potential troll accounts; we then provide a multi-faceted analysis to corroborate the correctness of our classification. In particular, 66% of the detected accounts show signs of being instrumented by malicious actors (e.g., they were created on the same exact day as a known troll, they have since been suspended by Reddit, etc.). They also discuss similar topics as the known troll accounts and exhibit temporal synchronization in their activity. Overall, we show that using TROLLMAGNIFIER, one can grow the initial knowledge of potential trolls provided by Reddit by over 300%.
Samuel A. Barnett, Thomas L. Griffiths, Robert D. Hawkins
Language is not only used to transmit neutral information; we often seek to persuade by arguing in favor of a particular view. Persuasion raises a number of challenges for classical accounts of belief updating, as information cannot be taken at face value. How should listeners account for a speaker's "hidden agenda" when incorporating new information? Here, we extend recent probabilistic models of recursive social reasoning to allow for persuasive goals and show that our model provides a pragmatic account for why weakly favorable arguments may backfire, a phenomenon known as the weak evidence effect. Critically, this model predicts a systematic relationship between belief updates and expectations about the information source: weak evidence should only backfire when speakers are expected to act under persuasive goals and prefer the strongest evidence. We introduce a simple experimental paradigm called the Stick Contest to measure the extent to which the weak evidence effect depends on speaker expectations, and show that a pragmatic listener model accounts for the empirical data better than alternative models. Our findings suggest further avenues for rational models of social reasoning to illuminate classical decision-making phenomena.
Objective: Many accounting and auditing variables represent forecasts of future events. Accordingly, decision making under conditions of uncertainty is of fundamental importance in accounting and auditing studies. An important aspect of such studies is estimating the risk of corporate bankruptcy. The estimated risk occurs because the probability of the customer going bankrupt is unknown and must be determined statistically. The bankruptcy risk is somehow different from the estimated risk. Some companies can have the same point estimates of bankruptcy while they do not have the same risk estimates. The level of risk estimation indicates the degree of inaccuracy in the point estimates of bankruptcy. We measure the risk estimate using the standard errors of point estimates of the bankruptcy because the risk estimate is the second-moment risk and measures the estimation precision and uncertainty around the point estimates of bankruptcy. In other words, we use the variance-covariance matrix to compute the standard errors of point estimates. In general, given the importance of bankruptcy and its consequences, our research aims to assess auditor's conservatism by measuring the risk estimates and analyzing their impacts on auditor behavior. Methods: In this study, we first obtained the point estimates of bankruptcy and their standard errors, and then we predicted bankruptcy by specific models over rolling windows over the past five years. Then, we estimated multiple regression and probit models using the data set of 110 companies listed in the Tehran Stock Exchange, TSE, from 2007 to 2017. The data set was collected using financial statements, audit reports on the financial statements, the official website of the TSE, and the official data from the Iranian Association of Certified Public Accountants. At last, we performed data analysis to test related research hypotheses. Results: According to the research findings, an increase in the estimated risk of bankruptcy leads to a positive effect on the issuance of going opinion and no effect on audit fees. Conclusion: The main idea of this study is reporting auditors are risk-averse. The estimated risk imposes incremental uncertainty on the risk-averse auditors and consequently, affects their behaviors. On the other hand, our findings suggest that auditors are more conservative when they face a greater risk. Auditors are cautious about their professional responsibility and social accountability related to the bankruptcy of their client firms. Auditors get more conservative along with the increase of the estimated risk.