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A complete and fully developed theory of all optical phenomena (refraction, reflection, absorption, and transparency) and the corresponding optical properties of ultrathin crystalline films (optical indices) are presented in this paper, especially along the direction in which the structure is spatially limited (perpendicular to surfaces). While these indices depend on the position of the crystallographic plane (where the mentioned optical phenomena occur) with respect to the two interfaces, these values can be measured/ determined in experiments only for the film as a whole. For these reasons, it is important to answer the question of how to define these optical indices precisely.

A novel methodology is proposed for the development of neural network models for complex engineering systems exhibiting nonlinearity. This method performs neural network modeling by first establishing some fundamental nonlinear functions from a priori engineering knowledge, which are then constructed and coded into appropriate chromosome representations. Given a suitable fitness function, using evolutionary approaches such as genetic algorithms, a population of chromosomes evolves for a certain number of generations to finally produce a neural network model best fitting the system data. The objective is to improve the transparency of the neural networks, i.e. to produce physically meaningful "white box" neural network model with better generalization performance. In this paper, the problem formulation, the neural network configuration, and the associated optimization software are discussed in detail. This methodology is then applied to a practical real-world system to illustrate its effectiveness.

Software metrics based maintainability prediction is leading to development of new sophisticated techniques to construct prediction models. This paper proposes a new software maintainability prediction framework, which bases on Fuzzy Network, a novel exploratory modeling technique. The proposed framework utilizes both the metric data collected from software system and the subjective appraisals from experts. An application example of the framework is shown. In comparison to the Standard Fuzzy System based models, Fuzzy Network based models improves the transparency more than 71.3% and the accuracy more than 11.0%. It is confirmed that Fuzzy Network based framework is more appropriate for constructing SMP model.

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Understanding the impacts of transparency in capital markets is important for determining the trading mechanism and for evaluating market efficiency and market fairness. The recent reforms covering trade transparency on the Taiwan Stock Exchange give us an opportunity to address and examine the relevant arguments. Evidence from this study indicates that increasing trade transparency may create a more efficient market due to decreased effective spreads compared with before the event. Asymmetric information also declines after transparency is liberalized. However, the movement direction of the realized spread does not seem obvious or pronounced. The implication from our results is that the exact effects from trade transparency may be dependent on the stock market's structure itself.

Hydrophobic nano silica sol (HNSS) was incorporated into polyvinylmethylsiloxane to prepare reinforced high-temperature vulcanized (HTV) silicone rubber. HTV silicone rubber filled with 40 phr HNSS showed excellent mechanical and optical properties: the tensile strength reached 11.7 MPa and the optical transmittance was higher than 90%. Possible reasons for reinforcement and transparency were discussed on the basis of the bound rubber percentage, total crosslink density, and SEM analysis. Our work suggests that HNSS is effective for reinforcement of HTV silicone rubber to endow excellent mechanical and optical properties.

Corporate governance (CG) reformists typically presume better-governed companies are more transparent to investors. We focus on CG and transparency in Japan, where CG has been an ongoing issue. Using local ratings of Japanese companies’ CG and data on corporate disclosures and their associated stock returns, we do find better-governed Japanese companies have made more frequent and timelier disclosures, and their share prices have reflected value-relevant information earlier. While these results hold for good news, they do not hold for bad. Consequently, governance guidance in Japan may not have resulted in both timelier and more balanced release of newsworthy information.

The goal of online examination is to ensure convenience, scalability, expanded reach, and customization. Despite the popularity of online exams, there have been some demonstrations against using them. The existing system is vulnerable and faces failures like question paper leakage, answer deception, trying to forge scorecards, difficulty in ensuring the integrity and consistency of scorecards by the exam board, and so on. Blockchain technology is the greatest and unsurpassed option for continuous authentication, confidentiality, and reliability. A blockchain-based framework for an online examination system is proposed to collect, register, and maintain a growing list of records using cryptography. It is also a peer-to-peer system that manages or stores time-stamped transactions such that no one can alter them. The proposed model combines time lock and multi-signature techniques and ensures the encrypted questions are locked till the exam starts, candidates’ answers are not altered, and scorecards are not manipulated. The system is open to the public, and the system’s processes are transparent. The costs associated are analyzed with the suggested solution’s on-chain and off-chain evaluation.

Many of the artificial intelligence (AI) systems used nowadays have a very high level of accuracy but fail to explain their decisions. This is critical, especially in sensitive areas such as medicine and the health area at large but also for applications of the law, finance etc., where explanations for certain decisions are needed and are often useful and valuable as the decision itself. This paper presents a review of four different methods for creating transparency in AI systems. It also suggests a list of criteria under which circumstances one should use which methods.

Mineral wealth tends to make countries less democratic and more likely to experience a civil war. Many countries also find it hard to use their natural resource revenues to make high-quality, growth-enhancing investments. I argue that these problems are caused, in part, by the unusual qualities of resource revenues — their great size, their non-tax source, their lack of stability, and their secrecy. While there is no universal formula for changing these four qualities, I present a menu of policies that could make natural resource revenues smaller, smoother and more transparent, and hence easier for governments to invest productively.

A critique of some central themes in Pentti Haikonen's recent book, Consciousness and Robot Sentience, is offered. Haikonen maintains that the crucial question concerning consciousness is how the inner workings of the brain or an artificial system can appear, not as inner workings, but as subjective experience. It is argued here that Haikonen's own account fails to answer this question, and that the question is not in fact the right one to ask anyway. It is argued that making the required changes to the question reveals an important lacuna in Haikonen's explanation of consciousness.

We show that new trade restrictions implemented since the onset of the global financial crisis have had — in the limited products targeted — a strong distortionary impact on trade. Their aggregate impact is modest, however, as most countries have resisted a widespread resort to protectionism. Looking ahead, sustained high unemployment, uneven growth, and an unwinding of government stimulus measures suggest that protectionist pressures may rise. Continuing and further enhancing monitoring of all protectionist measures will help, but the surest way to avoid such a downside scenario is to tighten multilateral trade commitments by completing the World Trade Organization (WTO) Doha Round.

Can fiscal transparency, accountability and macro-fiscal stabilization be imposed by adopting a fiscal responsibility law (FRL)? Skeptics argue that law is impotent or that the existing legal framework for the budget system suffices. In Europe, supranational fiscal rules were previously seen to be adequate. After reviewing the experience with FRLs around the world, this paper concludes that FRLs enhance fiscal transparency and accountability. However, the adoption of a FRL for attaining fiscal stability goals succeeds only if there is strong political commitment to fiscal discipline. Also, the inclusion of quantitative fiscal rules in FRLs is a high-risk undertaking.

We examine the impact of iceberg orders on the price and order flow dynamics in limit order books. Iceberg orders allow traders to simultaneously hide a large portion of their order size and signal their interest in trading to the market. We show that when market participants detect iceberg orders they tend to strongly respond by submitting matching market orders consistent with iceberg orders facilitating the search for latent liquidity. The greater the fraction of an iceberg order that is executed, the smaller is its price impact consistent with liquidity rather than informed trading. The presence of iceberg orders is associated with increased trading consistent with a positive liquidity externality, but the reduced order book transparency associated with iceberg orders also creates an adverse selection cost for limit orders that may partly offset any gains.

We discuss a recently proposed interpretation of some model descriptions of the proton-proton elastic scattering data as a manifestation of alleged relative transparency of the central part of the interaction region in the impact parameter space. We argue that the presence of nonzero real part of the elastic scattering amplitude in the unitarity condition enables to conserve the traditional interpretation.

This paper analyzes a firm’s incentives to disclose private information related to its market situation when there is a potential competitor. However, I adopt a more realistic definition of transparency that has been mostly overlooked by the earlier literature. In a realistic situation, financial transparency does not imply that all the relevant information are automatically transmitted to the receiver of the signal but instead the available information needs to be understood. When the model is adjusted to incorporate this realistic definition of transparency, fully revealing equilibrium associated with the “Revelation Principle” does not exist anymore. It is observed that the model with interpretation costs of transparency yields both pooling and partially pooling equilibria.

Explainability and comprehensibility of AI are important requirements for intelligent systems deployed in real-world domains. Users want and frequently need to understand how decisions impacting them are made. Similarly, it is important to understand how an intelligent system functions for safety and security reasons. In this paper, we describe two complementary impossibility results (Unexplainability and Incomprehensibility), essentially showing that advanced AIs would not be able to accurately explain some of their decisions and for the decisions they could explain people would not understand some of those explanations.

The desirability of transparency in its varied aspects is not always certain particularly for developing countries. This paper empirically investigates the link between forecast transparency and inflation volatility in a relatively homogenous sample of 41 developing economies over 1998–2007. It classifies central banks into transparent and opaque groups on the basis of their forecast transparency. The differential effect of the forecast transparency is then estimated both at a point in time and over time while controlling for the relevant factors. The main finding of the paper is that forecast transparency stabilizes inflation. In addition, we find that the stabilizing effect of disclosing numerical forecasts gets stronger over time supporting the theoretical insight that forecast transparency allows reputational externalities, and minimizes the likelihood of policy inconsistency. The results are robust against different specifications of the empirical model.

This paper evaluates sovereign wealth funds in light of the extreme volatility of energy prices and the severe global recession that began in 2008. A recent paper by Das characterized the assets of funds as showing steady growth in the past and likely increased importance in the future. However, recent developments have reduced the relative importance of funds and have demonstrated the sensitivity of the funds to energy prices and world business cycles. Investments by sovereign wealth funds have the potential to introduce political influence into corporate governance, but this potential is much smaller than the interventions into corporate governance by governments of the United States and elsewhere connected to corporate bail-outs during the recession. Lack of transparency remains a problem for certain sovereign wealth funds, but anti-recession interventions by governments have been characterized by extreme lack of transparency.

It is easier than ever to run modern machine learning (ML) models. However, developing and implementing systems that support real-world risk management applications in a bank is a significant challenge. It is partly because ML models are not transparent and explainable. The framework presented here covers the leading eXplainable AI (XAI) methods. Practical challenges in implementing these methods are discussed.