Corporate cash holdings and disclosure violations: an empirical investigation of Jordanian listed companies

Purpose This study aims to examine the impact of low-quality financial statements; that is, disclosure violations reported by the Securities Exchange Commission related to the level of cash holdings (CH) of firms listed on the Amman Stock Exchange (ASE). Design/methodology/approach Using panel data from 107 ASE-listed companies from 2009 to 2018, the study uses generalized method of moment estimation to examine the research hypothesis. This study hypothesize that disclosure violations can affect the level of CH and control for several variables that affect this level. Findings The results show that disclosure violations significantly affect the level of CH and that cash flow, capital expenditure and debt issues have a significantly positive impact on corporate CH. On the other hand, the market to book ratio and sales growth were found to be insignificant. Research limitations/implications The limitations of the research include the fact that information on research and development and equity issues were not available, so were not included in the examination. Practical implications It is recommended that managers enhance the quality of disclosures since this allows them to hold lower levels of cash and exploit more investment opportunities. Policymakers are recommended to supervise firm disclosures closely and create ratings for disclosure quality. Originality/value To the best of the author’s knowledge, this is the first empirical research on the association between proven low-quality disclosures and the level of corporate CH among Jordanian listed companies.

Classification and pattern extraction of incidents: a deep learning-based approach

Classifying or predicting occupational incidents using both structured and unstructured (text) data are an unexplored area of research. Unstructured texts, i.e., incident narratives are often unutilized or underutilized. Besides the explicit information, there exist a large amount of hidden information present in a dataset, which cannot be explored by the traditional machine learning (ML) algorithms. There is a scarcity of studies that reveal the use of deep neural networks (DNNs) in the domain of incident prediction, and its parameter optimization for achieving better prediction power. To address these issues, initially, key terms are extracted from the unstructured texts using LDA-based topic modeling. Then, these key terms are added with the predictor categories to form the feature vector, which is further processed for noise reduction and fed to the adaptive moment estimation (ADAM)-based DNN (i.e., ADNN) for classification, as ADAM is superior to GD, SGD, and RMSProp. To evaluate the effectiveness of our proposed method, a comparative study has been conducted using some state-of-the-arts on five benchmark datasets. Moreover, a case study of an integrated steel plant in India has been demonstrated for the validation of the proposed model. Experimental results reveal that ADNN produces superior performance than others in terms of accuracy. Therefore, the present study offers a robust methodological guide that enables us to handle the issues of unstructured data and hidden information for developing a predictive model.

Multifactor Uncertain Chemical Reaction Equation

The previous uncertain chemical reaction equation describes the time evolution of single reactions. But in many practical cases, a substance is consumed by several different reaction pathways. For the above considerations, this paper extends the discussion to multiple reactions. Specifically, by taking the decomposition of C2H5OH as an example, parallel reactions with one reactant are analyzed with the multifactor uncertain differential equation. The derived equation is called the multifactor uncertain chemical reaction equation. Following that, the parameters in the multifactor uncertain chemical reaction equation are estimated by the generalized moment estimation. Based on the multifactor uncertain chemical reaction equation, half-life of reaction is investigated. Finally, a numerical example is presented to illustrate the usefulness of the multifactor uncertain chemical reaction equation.

Government domestic debt arrears and private investment in Sub-Saharan Africa

PurposeThis study investigates the effect of government domestic payment arrears on private investment. The authors argue that an increase in government domestic arrears can reduce private sector investment owing to the competition for credit.Design/methodology/approachThe prediction is empirically tested using data for 33 Sub-Saharan Africa (SSA) countries for the period 2007–2018 using a panel general methods of moment estimation technique. This is also complemented with impulse responses derived from the standard vector autoregressive model.FindingsThe results show that an increase in government domestic arrears adversely affects private investment in SSA and most subregional communities within SSA. It also revealed that private investment negatively responds to shocks in government domestic arrears.Originality/valueThis is the first study that attempts to investigate the effect of government domestic borrowing arrears on private investment. It seeks to serve as a guide to governments in their domestic borrowing decisions to ensure timely servicing.

Hyperspectral Remote Sensing Detection of Marine Oil Spills Using an Adaptive Long-Term Moment Estimation Optimizer

Marine oil spills can damage marine ecosystems, economic development, and human health. It is important to accurately identify the type of oil spills and detect the thickness of oil films on the sea surface to obtain the amount of oil spill for on-site emergency responses and scientific decision-making. Optical remote sensing is an important method for marine oil-spill detection and identification. In this study, hyperspectral images of five types of oil spills were obtained using unmanned aerial vehicles (UAV). To address the poor spectral separability between different types of light oils and weak spectral differences in heavy oils with different thicknesses, we propose the adaptive long-term moment estimation (ALTME) optimizer, which cumulatively learns the spectral characteristics and then builds a marine oil-spill detection model based on a one-dimensional convolutional neural network. The results of the detection experiment show that the ALTME optimizer can store in memory multiple batches of long-term oil-spill spectral information, accurately identify the type of oil spills, and detect different thicknesses of oil films. The overall detection accuracy is larger than 98.09%, and the Kappa coefficient is larger than 0.970. The F1-score for the recognition of light-oil types is larger than 0.971, and the F1-score for detecting films of heavy oils with different film thicknesses is larger than 0.980. The proposed optimizer also performs well on a public hyperspectral dataset. We further carried out a feasibility study on oil-spill detection using UAV thermal infrared remote sensing technology, and the results show its potential for oil-spill detection in strong sunlight.

New methodology for flight control system sizing and hinge moment estimation

Flight control surfaces guarantee a safe and precise control of the aircraft. As a result, hinge moments are generated. These moments need to be estimated in order to properly size the aircraft actuators. Control surfaces include the ailerons, rudder, elevator, flaps, slats, and spoilers, and they are moved by electric or hydraulic actuators. Actuator sizing is the key when comparing different flight control system architectures. This fact becomes even more important when developing more-electric aircraft. Hinge moments need to be estimated so that the actuators can be properly sized and their effects on the overall aircraft design are measured. Hinge moments are difficult to estimate on the early stages of the design process due to the large number of required input. Detailed information about the airfoil, wing surfaces, control surfaces, and actuators is needed but yet not known on early design phases. The objective of this paper is to propose a new methodology for flight control system sizing, including mass and power estimation. A surrogate model for the hinge moment estimation is also proposed and used. The main advantage of this new methodology is that all the components and actuators can be properly sized instead of just having overall system results. The whole system can now be sized more in detail during the preliminary design process, which allows to have a more reliable estimation and to perform systems installation analysis. Results show a reliable system mass estimation similar to the results obtained with other known methods and also providing the weight for each component individually.

Optimization of Intrusion Detection Systems Determined by Ameliorated HNADAM-SGD Algorithm

A single Information security is of pivotal concern for consistently streaming information over the widespread internetwork. The bottleneck flow of incoming and outgoing data traffic introduces the issue of malicious activities taken place by intruders, hackers and attackers in the form of authenticity desecration, gridlocking data traffic, vandalizing data and crashing the established network. The issue of emerging suspicious activities is managed by the domain of Intrusion Detection Systems (IDS). The IDS consistently monitors the network for identifica-tion of suspicious activities and generates alarm and indication in presence of malicious threats and worms. The performance of IDS is improved by using different signature based machine learning algorithms. In this paper, the performance of IDS model is determined using hybridization of nestrov-accelerated adaptive moment estimation –stochastic gradient descent (HNADAM-SDG) algorithm. The performance of the algorithm is compared with other classi-fication algorithms as logistic regression, ridge classifier and ensemble algorithm by adapting feature selection and optimization techniques

Traffic Flow Online Prediction Based on a Generative Adversarial Network with Multi-Source Data

Traffic prediction is essential for advanced traffic planning, design, management, and network sustainability. Current prediction methods are mostly offline, which fail to capture the real-time variation of traffic flows. This paper establishes a sustainable online generative adversarial network (GAN) by combining bidirectional long short-term memory (BiLSTM) and a convolutional neural network (CNN) as the generative model and discriminative model, respectively, to keep learning with continuous feedback. BiLSTM constantly generates temporal candidate flows based on valuable memory units, and CNN screens out the best spatial prediction by returning the feedback gradient to BiLSTM. Multi-dimensional indicators are selected to map the multi-view fusion local trend for accurate prediction. To balance computing efficiency and accuracy, different batch sizes are pre-tested and allocated to different lanes. The models are trained with rectified adaptive moment estimation (RAdam) by dividing the dataset into the training and testing sets with a rolling time-domain scheme. In comparison with the autoregressive integrated moving average (ARIMA), BiLSTM, generating adversarial network for traffic flow (GAN-TF), and generating adversarial network for non-signal traffic (GAN-NST), the proposed improved generating adversarial network for traffic flow (IGAN-TF) successfully generates more accurate and stable flows and performs better.