This study employs the MT-NARDL model to examine the nonlinear relationship between economic growth, oil prices, renewable energy consumption, and CO₂ emissions in Algeria over 1980–2024. Allowing GDP per capita to switch across low, medium, and high regimes, the model captures asymmetric short- and long-run responses. The Environmental Kuznets Curve is rejected: no regime yields a significant long-run effect on emissions, evidencing the absence of an automatic decoupling threshold. Renewable energy is the only robust driver of permanent carbon reduction, cutting emissions by 0.155% for each additional 1% share, despite an initial “brown-build” uptick. Oil-price shocks exert merely transitory impacts, whereas medium-growth episodes re-ignite emissions after two years. Error-correction is rapid, yet hydrocarbon dependency persists. Aligning development with climate goals, therefore, requires a coherent policy package that couples accelerated clean-energy deployment, stringent efficiency standards, and economic diversification away from carbon-intensive sectors. By foregrounding regime-dependent asymmetries, the paper offers new empirical evidence for resource-rich economies.
Business mathematics. Commercial arithmetic. Including tables, etc., Business records management
By affine arithmetic is meant the set of affine consequences of Peano arithmetic. This is a continuous theory which is studied in the framework of affine logic, a sublogic of continuous logic. Affine arithmetic is undecidable. Also, its models are generally lattice ordered and carry a nontrivial metric. Classical models are then characterized as those which are linearly ordered. In this paper, the affine variants of several classical results in Peano arithmetic are proved. In particular, an affine form of Gaifman's splitting theorem is proved.
Tasks in Predictive Business Process Monitoring (PBPM), such as Next Activity Prediction, focus on generating useful business predictions from historical case logs. Recently, Deep Learning methods, particularly sequence-to-sequence models like Long Short-Term Memory (LSTM), have become a dominant approach for tackling these tasks. However, to enhance model transparency, build trust in the predictions, and gain a deeper understanding of business processes, it is crucial to explain the decisions made by these models. Existing explainability methods for PBPM decisions are typically *post-hoc*, meaning they provide explanations only after the model has been trained. Unfortunately, these post-hoc approaches have shown to face various challenges, including lack of faithfulness, high computational costs and a significant sensitivity to out-of-distribution samples. In this work, we introduce, to the best of our knowledge, the first *self-explaining neural network* architecture for predictive process monitoring. Our framework trains an LSTM model that not only provides predictions but also outputs a concise explanation for each prediction, while adapting the optimization objective to improve the reliability of the explanation. We first demonstrate that incorporating explainability into the training process does not hurt model performance, and in some cases, actually improves it. Additionally, we show that our method outperforms post-hoc approaches in terms of both the faithfulness of the generated explanations and substantial improvements in efficiency.
Purpose: This research aims to analyze the impact of multinationality and tax havens, with audit quality serving as a moderating variable.
Methodology/approach: The population of this study consists of companies listed on the IDX80 index. Sample selection was conducted using purposive sampling methods, resulting in 215 observations. This research utilizes secondary data, which was analyzed using SPSS software.
Findings: The findings indicate that both multinationality and tax havens positively influence tax avoidance. Audit quality is capable of mitigating the effect of multinationality and tax havens on tax avoidance. High-quality auditors can prevent tax avoidance practices in multinational companies and those utilizing tax havens through rigorous oversight and transparent financial reporting.
Practical and Theoretical contribution/Originality: This study contributes to the existing theoretical framework by elucidating Jensen and Meckling's agency theory (1976) within the context of the observed phenomena. It also provides practical guidance for multinational corporations and tax haven users on how to reduce tax avoidance practices.
Research Limitation: The independent variables used in this study do not fully explain tax avoidance behavior. Future researchers are encouraged to explore and incorporate other variables that may influence tax avoidance.
Accounting. Bookkeeping, Business mathematics. Commercial arithmetic. Including tables, etc.
On June 24 and 25 2024, the annual conference of the Foundation for Auditing Research (FAR) took place, led by conference chair Philip Wallage. This year’s conference theme was ‘Auditing and Assurance: The Role of Audit Firm Culture.’ The focus was on the role of audit firm culture in influencing audit quality, emphasizing the challenges of dissecting, fostering and regulating a positive and productive organizational culture. The paper presentations and discussions emphasized the need for continuous dialogue, collaboration and innovation within audit firms to enhance leadership, manage cultural differences across teams, and integrate technology into auditing practices effectively. The two-day program included an introduction to the conference theme, two lectures, two panel sessions, six research presentations and a wrap-up. This article presents summaries of these sessions.1
Business, Business mathematics. Commercial arithmetic. Including tables, etc.
Large Language Models (LLMs), while being increasingly dominant on a myriad of knowledge-intensive activities, have only had limited success understanding lengthy table-text mixtures, such as academic papers and financial reports. Recent advances of long-context LLMs have opened up new possibilities for this field. Nonetheless, we identify two roadblocks: (1) Prior benchmarks of table question answering (TableQA) have focused on isolated tables without context, making it hard to evaluate models in real-world scenarios. (2) Prior benchmarks have focused on some narrow skill sets of table comprehension such as table recognition, data manipulation/calculation, table summarization etc., while a skilled human employs those skills collectively. In this work, we introduce TableQuest, a new benchmark designed to evaluate the holistic table comprehension capabilities of LLMs in the natural table-rich context of financial reports. We employ a rigorous data processing and filtering procedure to ensure that the question-answer pairs are logical, reasonable, and diverse. We experiment with 7 state-of-the-art models, and find that despite reasonable accuracy in locating facts, they often falter when required to execute more sophisticated reasoning or multi-step calculations. We conclude with a qualitative study of the failure modes and discuss the challenges of constructing a challenging benchmark. We make the evaluation data, judging procedure and results of this study publicly available to facilitate research in this field.
Many theorems of mathematics have the form that for a certain problem, e.g. a differential equation or polynomial (in)equality, there exists a solution. The sequential version then states that for a sequence of problems, there is a sequence of solutions. The original and sequential theorem can often be proved via the same (or similar) proof and often have the same (or similar) logical properties, esp. if everything is formulated in the language of second-order arithmetic. In this paper, we identify basic theorems of third-order arithmetic, e.g. concerning semi-continuous functions, such that the sequential versions have very different logical properties. In particular, depending on the constructive status of the original theorem, very different and independent choice principles are needed. Despite these differences, the associated Reverse Mathematics, working in Kohlenbach's higher-order framework, is rather elegant and is still based at the core on weak König's lemma.
In a recent paper, Wang and Jiang (At. Data Nucl. Data Tables 148 (2022) 101532) have reported data for energy levels, radiative rates (A-values), and effective collision strengths ($Υ$) for some transitions of five Al-like ions, namely Ga~XIX, Ge~XX, As~XXI, Se~XXII, and Br~XXIII. On a closer examination we find that their reported data for energy levels and A-values are generally correct, but not for $Υ$. Their $Υ$ values, for all transitions (allowed or forbidden) and for all ions, invariably decrease at higher temperatures. This is mainly because they have adopted a limited range of electron energies for the calculations of collision strengths. We demonstrate this with our calculations with the Flexible Atomic Code (FAC), and conclude that their $Υ$ values are inaccurate, unreliable, and should not be adopted in any applications or modelling analysis.
With the continuous development of business process management technology, the increasing business process models are usually owned by large enterprises. In large enterprises, different stakeholders may modify the same business process model. In order to better manage the changeability of processes, they adopt configurable business process models to manage process variants. However, the process variants will vary with the change in enterprise business demands. Therefore, it is necessary to explore the co-evolution of the process variants so as to effectively manage the business process family. To this end, a novel framework for co-evolution between business process variants through a configurable process model is proposed in this work. First, the mapping relationship between process variants and configurable models is standardized in this study. A series of change operations and change propagation operations between process variants and configurable models are further defined for achieving propagation. Then, an overall algorithm is proposed for achieving co-evolution of process variants. Next, a prototype is developed for managing change synchronization between process variants and configurable process models. Finally, the effectiveness and efficiency of our proposed process change propagation method are verified based on experiments on two business process datasets. The experimental results show that our approach implements the co-evolution of process variants with high accuracy and efficiency.
Lance Cooley, Kathleen Amm, Whitney Hischier
et al.
Stakeholders representing concerns of national and global leadership, industries that use superconducting magnets in products, manufacturers of superconducting wires and tapes that supply to industries, and innovation generators from small businesses and universities came together to address stewardship of superconducting magnet technology and assurance of supply of advanced superconductors to the accelerator sector. This report outlines potential public-private partnerships that develop and enhance domestic capabilities to meet the needs of science facilities in the accelerator systems sector and in the broader commercial ecosystem.
Amin Beheshti, Boualem Benatallah, Hamid Reza Motahari-Nezhad
et al.
In modern enterprises, Business Processes (BPs) are realized over a mix of workflows, IT systems, Web services and direct collaborations of people. Accordingly, process data (i.e., BP execution data such as logs containing events, interaction messages and other process artifacts) is scattered across several systems and data sources, and increasingly show all typical properties of the Big Data. Understanding the execution of process data is challenging as key business insights remain hidden in the interactions among process entities: most objects are interconnected, forming complex, heterogeneous but often semi-structured networks. In the context of business processes, we consider the Big Data problem as a massive number of interconnected data islands from personal, shared and business data. We present a framework to model process data as graphs, i.e., Process Graph, and present abstractions to summarize the process graph and to discover concept hierarchies for entities based on both data objects and their interactions in process graphs. We present a language, namely BP-SPARQL, for the explorative querying and understanding of process graphs from various user perspectives. We have implemented a scalable architecture for querying, exploration and analysis of process graphs. We report on experiments performed on both synthetic and real-world datasets that show the viability and efficiency of the approach.
Marco Pegoraro, Merih Seran Uysal, David Benedikt Georgi
et al.
The real-time prediction of business processes using historical event data is an important capability of modern business process monitoring systems. Existing process prediction methods are able to also exploit the data perspective of recorded events, in addition to the control-flow perspective. However, while well-structured numerical or categorical attributes are considered in many prediction techniques, almost no technique is able to utilize text documents written in natural language, which can hold information critical to the prediction task. In this paper, we illustrate the design, implementation, and evaluation of a novel text-aware process prediction model based on Long Short-Term Memory (LSTM) neural networks and natural language models. The proposed model can take categorical, numerical and textual attributes in event data into account to predict the activity and timestamp of the next event, the outcome, and the cycle time of a running process instance. Experiments show that the text-aware model is able to outperform state-of-the-art process prediction methods on simulated and real-world event logs containing textual data.
This paper aims a novel and a useful multi-objective optimization approach named Non-Dominated Sorting Ions Motion Algorithm (NSIMO) built on the search procedure of Ions Motion Algorithm (IMO). NSIMO uses selective crowding distance and non-dominated sorting method to obtain various non-domination levels and preserve diversity amongst the best set of solutions. The suggested technique is employed to various multi-objective benchmark functions having different characteristics like convex, concave, multimodal, and discontinuous Pareto fronts. The recommended method is analyzed on different engineering problems having distinct features. The results of the proposed approach are compared with other well-regarded and novel algorithms. Furthermore, we present that the projected method is easy to implement, capable of producing a nearly true Pareto front and algorithmically inexpensive.
Analysis, Business mathematics. Commercial arithmetic. Including tables, etc.
The revised Dutch Corporate Governance Code of 2016 (hereafter “the Code”) comprises provisions regarding the existence of an internal audit function. Following the comply or explain principle of the Code, Euronext Amsterdam listed companies with a registered office in the Netherlands either have established an internal audit function or have to explain why they did not. Our research shows that the number of listed companies with an internal audit function has since grown. In 2016 53% of Euronext Amsterdam listed companies with their registered office in the Netherlands have established an internal audit function; in 2018 this figure is 64%. More than half of these listed companies have an in-house independent internal audit function, whereas other companies have internal audit functions with different characteristics, such as a combined internal audit and risk management function or have outsourced the internal audit function. The majority of the companies without an internal audit function provide inadequate arguments for this absence. They thereby do not meet the standards as set forth in the Code. In most cases, the argument for not having an internal audit function is: “the organization is too small”. This is not a valid argument, as the Code specifically addresses this situation stating that in case the size of a company is not suited for an internal audit function, outsourcing may be an appropriate alternative. We conclude that management boards should give this topic better thought and give better insight in their judgement by explaining the arguments. We therefore advocate that the principle of “comply or explain” should be “comply and explain”. Such is the case in the South African corporate governance code (King IV). The effect will be that management boards mindfully have to elaborate on how they obtain independent assurance on the company’s governance, risk management and control systems.
Business, Business mathematics. Commercial arithmetic. Including tables, etc.
A Cayley-Sudoku table of a finite group G is a Cayley table for G subdivided into uniformly sized rectangular blocks, in such a way that each group element appears once in each block. They were introduced by J. Carmichael, K. Schloeman, and M. B. Ward [CSW], who also gave three ways to construct them. This paper has four aims. First, we review Constructions 1 and 2 of [CSW] and uncover their unexpected heritage in the work of R. Baer and J. Denes. Next we turn to some new instances of Construction 2 inspired by Baer, which answer an open question in [CSW]. Third, we provide a very brief outline of recent reinventions of special cases of Construction 1 in the literature. We conclude with an invitation to seek out the heritage of Construction 3. Portions of this paper appear in: Kady Hossner Boden and Michael B. Ward (2019) A New Class of Cayley-Sudoku Tables, Mathematics Magazine, 92:4, 243-251, DOI: 10.1080/0025570X.2019.1613949
Paul A. Smith, Chiara Bocci, Nikos Tzavidis
et al.
Small area (or small domain) estimation is still rarely applied in business statistics, because of challenges arising from the skewness and variability of variables such as turnover. We examine a range of small area estimation methods as the basis for estimating the activity of industries within the retail sector in the Netherlands. We use tax register data and a sampling procedure which replicates the sampling for the retail sector of Statistics Netherlands' Structural Business Survey as a basis for investigating the properties of small area estimators. In particular, we consider the use of the EBLUP under a random effects model and variations of the EBLUP derived under (a) a random effects model that includes a complex specification for the level 1 variance and (b) a random effects model that is fitted by using the survey weights. Although accounting for the survey weights in estimation is important, the impact of influential data points remains the main challenge in this case. The paper further explores the use of outlier robust estimators in business surveys, in particular a robust version of the EBLUP, M-regression based synthetic estimators, and M-quantile small area estimators. The latter family of small area estimators includes robust projective (without and with survey weights) and robust predictive versions. M-quantile methods have the lowest empirical mean squared error and are substantially better than direct estimators, though there is an open question about how to choose the tuning constant for bias adjustment in practice. The paper makes a further contribution by exploring a doubly robust approach comprising the use of survey weights in conjunction with outlier robust methods in small area estimation.
G. Bruce Berriman, William O'Mullane, Arik Mitschang
et al.
Commercial cloud platforms are a powerful technology for astronomical research. Despite the benefits of cloud computing -- such as on-demand scalability and reduction of systems management overhead -- confusion over how to manage costs remains, for many, one of the biggest barriers to entry. This confusion is exacerbated by the rapid growth in services offered by commercial providers, by the growth in the number of these providers, and by storage, compute, and I/O metered at separate rates -- all of which can change without notice. As a rule, processing is very cheap, storage is more expensive, and downloading is very expensive. Thus, an application that produces large image data sets for download will be far more expensive than an application that performs extensive processing on a small data set. This Birds of a Feather (BoF) session aimed to quantify the above statement by presenting case studies of the costing of astronomy applications on commercial clouds that covered a range of processing scenarios; these presentations were the basis for discussion by the attendees.
Manuel Camargo, Marlon Dumas, Oscar González-Rojas
Business process simulation is a versatile technique to estimate the performance of a process under multiple scenarios. This, in turn, allows analysts to compare alternative options to improve a business process. A common roadblock for business process simulation is that constructing accurate simulation models is cumbersome and error-prone. Modern information systems store detailed execution logs of the business processes they support. Previous work has shown that these logs can be used to discover simulation models. However, existing methods for log-based discovery of simulation models do not seek to optimize the accuracy of the resulting models. Instead they leave it to the user to manually tune the simulation model to achieve the desired level of accuracy. This article presents an accuracy-optimized method to discover business process simulation models from execution logs. The method decomposes the problem into a series of steps with associated configuration parameters. A hyper-parameter optimization method is used to search through the space of possible configurations so as to maximize the similarity between the behavior of the simulation model and the behavior observed in the log. The method has been implemented as a tool and evaluated using logs from different domains.
Guenter Hesse, Christoph Matthies, Werner Sinzig
et al.
Industry 4.0 and the Internet of Things are recent developments that have lead to the creation of new kinds of manufacturing data. Linking this new kind of sensor data to traditional business information is crucial for enterprises to take advantage of the data's full potential. In this paper, we present a demo which allows experiencing this data integration, both vertically between technical and business contexts and horizontally along the value chain. The tool simulates a manufacturing company, continuously producing both business and sensor data, and supports issuing ad-hoc queries that answer specific questions related to the business. In order to adapt to different environments, users can configure sensor characteristics to their needs.