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Employing a fog computing (FC) network system in the robotic network system is an effective solution to support robotic application issues. The interconnection between robotic devices through an FC network can be referred as the Internet of Robotic Things (IoRT). Although the FC network system can provide number of services closer to IoRT devices, it still faces significant challenges including real-time tracing services and a secure tracing services. Therefore, this paper aims to provide a tracking mobile robot devices in a secure and private manner, with high efficiency performance, is considered essential to ensuring the success of IoRT network applications.

This paper proposes a secure anonymous tracing (SAT) method to support the tracing of IoRT devices through a FC network system based on the Counting Bloom filter (CBF) and elliptic curve cryptography techniques. With the proposed SAT mechanism, a fog node can trace a particular robot device in a secure manner, which means that the fog node can provide a service to a particular robot device without revealing any private data such as the device's identity or location.

Analysis shows that the SAT mechanism is both efficient and resilient against tracing attacks. Simulation results are provided to show that the proposed mechanism is beneficial to support IoRT applications over an FC network system.

This paper represents a SAT method based on CBF and elliptic curve cryptography techniques as an efficient mechanism that is resilient against tracing attacks.

The purpose of this paper is to effectively deal with querying of classification with membership.

The authors propose a scheme comprising a layer of Bloom filter for membership checking and a second layer based on neural network for dealing with the classification requirement.

Not only could false positives be dramatically decreased, but also classification could be achieved with the proposed scheme.

The experimental data were randomly generated instead of real‐world ones.

It is difficult to implement this scheme in a real‐world environment, such as the internet. Second, the neural network requires time to converge to a satisfactory level.

Internet ethic might be compromised by hackers once they find a way around the filtering mechanism.

The neural network was moditified and utilized for the first time to be suitable for our purpose. Second, the two‐layer design shows effectiveness.

In recent years professional accountancy bodies (e.g., CPA), accreditation institutions (e.g., AACSB) and employers have steadily raised, and continue to raise expectations regarding the need for accounting graduates to demonstrate skills in data analytics. One of the obstacles accounting instructors face in seeking to implement data analytics, however, is that they need access to ample teaching materials. Unfortunately, there are few such resources available for advanced programming languages such as R. While skills in commonly used applications such as Excel are no doubt needed, employers often take these for granted and incremental value is only added if graduates can demonstrate knowledge in using more advanced data analytics tools for decision-making such as coding in programming languages. This, together with the current dearth of resources available to accounting instructors to teach advanced programming languages is what drives motivation for this chapter. Specifically, we develop an intuitive, two-dimensional framework for incorporating R (a widely used open-source analytics tool with a powerful embedded programming language) into the accounting curriculum. Our model uses complexity as an integrating theme. We incorporate complexity into this framework at the dataset level (simple and complex datasets) and at the analytics task level (simple and complex tasks). We demonstrate two-dimensional framework by drawing on authentic simple and complex datasets as well as simple and complex tasks that could readily be incorporated into the accounting curriculum and ultimately add value to businesses. R script programming code are provided for all our illustrations.

The purpose of this paper is to examine the applicability and capability of models based on a genetic algorithm and support vector machine (GA-SVM) and a genetic algorithm and relevance vector machine (GA-RVM) for the prediction of phytoplankton abundances associated with algal blooms in a Macau freshwater reservoir, and compare their performances with an artificial neural network (ANN) model.

The hybrid models GA-SVM and GA-RVM were developed for the optimal control of parameters for predicting (based on the current month’s variables) and forecasting (based on the previous three months’ variables) phytoplankton dynamics in a Macau freshwater reservoir, MSR, which has experienced cyanobacterial blooms in recent years. There were 15 environmental parameters, including pH, SiO₂, alkalinity, bicarbonate (HCO₃−), dissolved oxygen (DO), total nitrogen (TN), UV254, turbidity, conductivity, nitrate (NO₃−), orthophosphate (PO₄³⁻), total phosphorus (TP), suspended solids (SS) and total organic carbon (TOC) selected from the correlation analysis, with eight years (2001-2008) of data for training, and the most recent three years (2009-2011) for testing.

For both accuracy performance and generalized performance, the ANN, GA-SVM and GA-RVM had similar predictive powers of R² of 0.73-0.75. However, whereas ANN and GA-RVM models showed very similar forecast performances, GA-SVM models had better forecast performances of R² (0.862), RMSE (0.266) and MAE (0.0710) with the respective parameters of 0.987, 0.161 and 0.032 optimized using GA.

This is the first application of GA-SVM and GA-RVM models for predicting and forecasting algal bloom in freshwater reservoirs. GA-SVM was shown to be an effective new way for monitoring algal bloom problem in water resources.

Following the Supreme Court’s 1988 decision in Basic, securities class plaintiffs can invoke the “rebuttable presumption of reliance on public, material misrepresentations regarding securities traded in an efficient market” [the “fraud-on-the-market” doctrine] to prove classwide reliance. Although this requires plaintiffs to prove that the security traded in an informationally efficient market throughout the class period, Basic did not identify what constituted adequate proof of efficiency for reliance purposes.

Market efficiency cannot be presumed without proof because even large publicly traded stocks do not always trade in efficient markets, as documented in the economic literature that has grown significantly since Basic. For instance, during the recent global financial crisis, lack of liquidity limited arbitrage (the mechanism that renders markets efficient) and led to significant price distortions in many asset markets. Yet, lower courts following Basic have frequently granted class certification based on a mechanical review of some factors that are considered intuitive “proxies” of market efficiency (albeit incorrectly, according to recent studies and our own analysis). Such factors have little probative value and their review does not constitute the rigorous analysis demanded by the Supreme Court.

Instead, to invoke fraud-on-the-market, plaintiffs must first establish that the security traded in a weak-form efficient market (absent which a security cannot, as a logical matter, trade in a “semi-strong form” efficient market, the standard required for reliance purposes) using well-accepted tests. Only then do event study results, which are commonly used to demonstrate “cause and effect” (i.e., prove that the security’s price reacted quickly to news – a hallmark of a semi-strong form efficient market), have any merit. Even then, to claim classwide reliance, plaintiffs must prove such cause-and-effect relationship throughout the class period, not simply on selected disclosure dates identified in the complaint as plaintiffs often do.

These issues have policy implications because, once a class is certified, defendants frequently settle to avoid the magnified costs and risks associated with a trial, and the merits of the case (including the proper application of legal presumptions) are rarely examined at a trial.

The Suez Irrigation Canal is the source of drinking water to a large community. Complaints have been raised regarding the odor and unpleasant taste of drinking water. The problems encountered reveled enrichment of the Canal with nutrients, degraded water quality and nuisance caused by algal growth. This paper aims to investigate these claims by evaluating the interaction between water and sediment with ecological indicators.

Bioassessments were used as a primary tool to evaluate the biological conditions and identify the degree of water quality degradation in the Suez Irrigation Canal. The monitoring program integrates biological, chemical, and physical data assessment. Several field surveys were carried out to these areas during the period between March 2003 and February 2005 (over 23 months) for acquiring all possible information about the current situation and to explore the impact of human activities along the canal banks on the canal ecosystem. Seasonal variations of phytoplankton and zooplankton standing crop, species diversity as well as physico‐chemical characteristics of water, sediment, fish and aquatic weeds at the intakes of drinking plants and from the discharge of agricultural and domestic drains into the Canal were investigated.

Preliminary field investigations showed great amounts of discharged wastes at several locations to the canal water creating unique conditions, which vary with changes of volume and properties of the discharged wastes. Rotifer and green algae for example demonstrated seasonal variable response to the ecological variations. Myriophyllum spicatum, Potamageton nodsus and Polygonum Salicfolium were the most common types of recorded weed. The Myriophyllum spicatum is the dominant submerged plant. The canal was characterized by high concentrations of HCO₃^‐ as well as high pH >8.2 which provides a favorable habitat for the growth of Myriophyllum spicatum. The results illustrated the ability of using the aquatic weed as biomarkers for monitoring heavy metals contaminates in the canal. The evidence suggests that there is a degree of selectivity in metals uptake and partitioning within the plant compartments.

The current paper adopts the idea of utilizing multiple organism groups in the bioassessment to effectively detect ecological change when they occur in one of the most important waterways in Egypt. These different organism groups are suited for detection various stressors, providing warnings and detection of stress impacts at different scales. The study presented provides decision makers with important information that can assist them in making objective decisions related to the design of monitoring programs based on scientific research.

This study finds evidence that a stock return is inversely correlated with downside risk, confirming a pattern of risk-aversion behavior. Evidence from testing a stock return's response to a change in economic policy uncertainty indicates a significantly negative effect in the Chinese stock market; this conclusion holds true for testing the impacts of changes in fiscal and monetary policy uncertainties. However, the data produce a mixed effect for the change in fiscal policy uncertainty. The evidence produced from examining the geopolitical effect on the stock market strongly supports the presence of an adverse effect on stock market performance.

In this work, we estimated the impact that the US VIX, economic policy and epidemic uncertainty indexes had on leisure and recreation stocks. We extended the current literature in two ways: first, we estimated the smoothed probabilities of being in ‘normal’ ( s = 1 ), ‘distress’ ( s = 2 ) and ‘crisis’ ( s = 3 ) episodes in the Refinitiv global leisure and recreation index. Then, we estimated the influence that the VIX and uncertainty indexes had on the generation of distress and crisis episodes in these stocks. By using logit regressions, we found out that only the US Economic policy uncertainty index is a detonator of distress and crisis episodes. We also found that the pandemic (COVID-19) news uncertainty has no significant and direct influence on the smoothed probabilities. Finally, and complementary to the current literature, we found that the volatility spillover effect from the S&P 500 to these stocks generates extreme volatility (crisis) episodes. Our results could be of use for practitioners and scholars and could provide a model to forecast distress and crisis episodes among leisure and recreation stocks. This model could be used for potential portfolio management or economic (tourism) policy purposes.

Researchers commonly utilize coding‐based analysis of classroom asynchronous discussion contributions as part of studies of online learning and instruction. However, this analysis is inconsistent from study to study with over 50 coding schemes and procedures applied in the last eight years. The aim of this article is to provide a basis for more consistent use of coding schemes and to facilitate comparison of studies utilizing different coding schemes.

The paper identifies coding schemes presented in the research literature, classifies these schemes, and presents a list of synthesis codes reflecting the content of the many different schemes for each classification.

Based on the initial and follow‐up literature review, 56 different coding schemes were identified as having been employed within the last eight years. Initial sorting indicated that schemes primarily focused on identifying critical thinking, describing social interactions, or characterizing online discussion.

In addition to offering a comprehensive resource reflecting the coding schemes currently applied to the analysis of online, asynchronous discussion, the meta‐analysis results also inform regarding the current state of research in this area. In addition, current research trends and areas for potential new research and development are revealed.

This article is an edited extract from a new book ‘Image Analysis: Principles and Practice’ about to be published by Joyce Loebl, the image analysis equipment company.