Today, income management is one of the attractive and controversial issues in accounting investigation areas from both investigations and regulatory view. Managers do manage income either to distort information or to defer and report the information related to future incomes. This investigation aims at examining the effect of taxable income management on the information content of taxable income of firms. Tests of research hypotheses were performed with an empirical method based on econometric and using multivariate regression analysis, t-test, Wilcoxon total scores, and specifically by using the panel data model across 147 firms listed on the Tehran Stock Exchange between 2002 and 2011. Findings show that taxable income management reduces the information content of taxable income. In addition, firms manage accounting income to defer information.

To assess a country's economic outlook and achieve higher economic growth, econometric models and prediction techniques are significant tools. Policymakers are always concerned with the correct future estimates of economic variables to take the right economic decisions, design better policies and effectively implement them. Therefore, there is a need to improve the predictive accuracy of the existing models and to use more sophisticated and superior algorithms for accurate forecasting. Deep learning models like recurrent neural networks are considered superior for forecasting as they provide better predictive results as compared to many of the econometric models. Against this backdrop, this paper presents the feasibility of using different deep-learning neural network architectures for trade forecasting. It predicts export trade using different recurrent neural architectures such as 'vanilla recurrent neural network (VRNN)', 'bi-directional long short-term memory network (Bi-LSTM)', 'bi-directional gated recurrent unit (Bi-GRU)' and a hybrid 'bi-directional LSTM and GRU neural network'. The performances of these models are evaluated and compared using different performance metrics such as Mean Square Error (MSE), Mean Absolute Error (MAE) Root Mean Squared Error (RMSE), Root Mean Squared Logarithmic Error (RMSLE) and coefficient of determination <em>R</em>-squared (<em>R</em>²). The results validated the effective export prediction for India.

Copying and duplicating papers for educational purposes is a violation form of copyright in Indonesia. The principle of fair use in education is a form of structured violation. Copying and duplicating the papers of the authors for educational purposes has provided commercial (business) benefits for libraries and universities. The research method is conducted using the observation method in libraries and universities that duplicate papers. The method also uses the normative juridical method that connects duplication of the papers in libraries and universities with the fair use principle. The results explain the authors' loss from copying and duplicating of papers in libraries and universities. Therefore, copying and duplicating the papers can only be done by implementing the responsibility system. Copying and duplicating the papers of the authors' in libraries and universities can be allowed if they fulfil the elements of copyright protection in the new concept.

Using information criteria is a common method for making a decision about which model to use for forecasting. There are many different methods for evaluating forecasting models, such as MAE, RMSE, MAPE, and Theil-U, among others. After the creation of AIC, AICc, HQ, BIC, and BICc, the two criteria that have become the most popular and commonly utilised are Bayesian IC and Akaike's IC. In this investigation, we are innovative in our use of exponential weighting to get the log-likelihood of the information criteria for model selection, which means that we propose assigning greater weight to more recent data in order to reflect their increased precision. All research data is from the major stock markets' daily observations, which include the USA (GSPC, DJI), Europe (FTSE 100, AEX, and FCHI), and Asia (Nikkei).

This paper describes an environment for the “sheer curation” of the experimental data of a group of researchers in the fields of biophysics and structural biology. The approach involves embedding data capture and interpretation within researchers' working practices, so that it is automatic and invisible to the researcher. The environment does not capture just the individual datasets generated by an experiment, but the entire workflow that represent the “story” of the experiment, including intermediate files and provenance metadata, so as to support the verification and reproduction of published results. As the curation environment is decoupled from the researchers’ processing environment, the provenance is inferred from a variety of domain-specific contextual information, using software that implements the knowledge and expertise of the researchers. We also present an approach to publishing the data files and their provenance according to linked data principles by using OAI-ORE (Open Archives Initiative Object Reuse and Exchange) and OPMV.

In this paper, we describe our recent work in using cloud computing to provision digital library services. We consider our original and current motivations, technical details of our implementation, the path we took, and our future work and lessons learned. We also compare our work with other digital library cloud efforts.

Open data is becoming increasingly important in research. While individual researchers are slowlybecoming aware of the value, funding agencies are taking the lead by requiring data be made available, and also by requiring data management plans to ensure the data is available in a useable form. Some journals also require that data be made available. However, in most cases, “available upon request” is considered sufficient. We describe a number of historical examples of data use and discovery, then describe two current test cases at the University of New Mexico. The lessons learned suggest that an instituional data services program needs to not only facilitate fulfilling the mandates of granting agencies but to realize the true value of open data. Librarians and institutional archives should actively collaborate with their researchers. We should also work to find ways to make open data enhance a researchers career. In the long run, better quality data and metadata will result if researchers are engaged and willing participants in the dissemination of their data.

In the realm of digital data, vendor-supplied cloud systems will still leave the user with responsibility for curation of digital data. Some of the very tasks users thought they were delegating to the cloud vendor may be a requirement for users after all. For example, cloud vendors most often require that users maintain archival copies. Beyond the better known vendor cloud model, we examine curation in two other models: inhouse clouds, and what we call "open" clouds—which are neither inhouse nor vendor. In open clouds, users come aboard as participants or partners—for example, by invitation. In open cloud systems users can develop their own software and data management, control access, and purchase their own hardware while running securely in the cloud environment. To do so will still require working within the rules of the cloud system, but in some open cloud systems those restrictions and limitations can be walked around easily with surprisingly little loss of freedom. It is in this context that REDDnet (Research and Education Data Depot network) is presented as the place where the Texas Tech University (TTU)) Libraries have been conducting research on long-term digital archival storage. The REDDnet network by year's end will be at 1.2 petabytes (PB) with an additional 1.4 PB for a related project (Compact Muon Soleniod Heavy Ion [CMS-HI]); additionally there are over 200 TB of tape storage. These numbers exclude any disk space which TTU will be purchasing during the year. National Science Foundation (NSF) funding covering REDDnet and CMS-HI was in excess of $850,000 with $850,000 earmarked toward REDDnet. In the terminology we used above, REDDnet is an open cloud system that invited TTU Libraries to participate. This means that we run software which fits the REDDnet structure. We are beginning to complete the final design of our system, and starting to move into the first stages of construction. And we have made a decision to move forward and purchase one-half petabyte of disk storage in the initial phase. The concerns, deliberations and testing are presented here along with our initial approach.

This study investigates the nexus among the commodities: bitcoin, copper, gold, silver, crude oil, and iron ore. Previous studies on establishing the plausibility and the dynamic nexus among commodities are rare. This research attempts to fill this gap. This study investigates whether there are long-term and short-term links between commodities for the period 2010-2022 by applying the bounds testing method to co-integration and ECM, built using an ARDL model and establishing both short-term and long-term relationships among the economic variables analysed. The ECM confirmed the presence of some co-integration relationship for all the variables, both in the short and long term. A strong correlation was discovered among the commodities, which were greatly influenced by their lagged values. The results of this study provides an opportunity for policymakers and researchers to understand the nature of the relationship between the analysed variables and further support the development of new policies for economic sustainability.

Multi-criteria inventory classification (MCIC) is popularly used to aid managers in categorising the inventory. Researchers have used numerous mathematical models and approaches, but few resorted to unsupervised machine-learning techniques to address MCIC. This study uses the expectation-maximisation (EM) algorithm to estimate the parameters of the Gaussian mixture model (GMM), a popular unsupervised machine learning algorithm, for ABC inventory classification. The EM-GMM algorithm is sensitive to initialisation, which in turn affects the results. To address this issue, two different initialisation procedures have been proposed for the EM-GMM algorithm. Inventory classification outcomes from 14 existing MCIC models have been given as inputs to study the significance of the two proposed initialisation procedures of the EM-GMM algorithm. The effectiveness of these initialisation procedures corresponding to various inputs has been analysed toward inventory management performance measures, i.e., fill rate, total relevant cost, and inventory turnover ratio.

The surge of learner enrolment in massive open online courses (MOOCs) has led to a wealth of learner-generated data, such as online course reviews that document learner experience. To unveil learner experience with MOOCs, this research uses machine learning methods to extract prominent topics from MOOC reviews and assess the sentiments expressed by learners within them. Furthermore, this research investigates the cooccurrence of the topics using association rule mining. The findings reveal six central topics discussed in MOOC reviews, such as "instructor", "design", "material", "assignment", "platform", and "experience". Notably, most learners express positive sentiments in their reviews. The sentiment indicated in reviews of skill-seeking MOOCs is higher than that in reviews of knowledge-seeking MOOCs. Furthermore, the association rule mining identifies four meaningful association rules. The findings offer valuable insights for MOOC instructors to enhance course design and for platform operators to ensure the long-term viability and success of MOOC platforms.