Information Signals Research Articles

Moderating effect of IFRS adoption on accounting conservatism and cost of equity: evidence from Canadian ESG data

PurposeThe purpose of this paper is to examine the moderating effect of International Financial Reporting Standards (IFRS) adoption on the relationship between accounting conservatism and the cost of equity in Canadian environmental, social, and corporate governance (ESG) firms.Design/methodology/approachPanel data was collected using the Thomson Reuters ASSET4 database on a sample of 284 Canadian ESG companies over the period 2007–2019.FindingsThe results obtained show a negative relationship between conditional conservatism and the cost of equity. The authors also find a negative relationship between unconditional conservatism and the cost of equity. In addition, IFRS adoption moderates the relationship between accounting conservatism and the cost of equity in Canadian ESG firms.Research limitations/implicationsFuture studies may extend the coverage of the study by including other countries and other sectors.Practical implicationsThe results imply that prudent accounting signals information to investors about the quality of a company’s current and future earnings. The rates of return required by investors may be higher for conservative reporting companies that are more susceptible to opportunistic management discretion.Originality/valueAlthough the previous literature has studied the direct correlation between accounting conservatism and the cost of equity, the present work focuses on examining the direct association between accounting conservatism and the cost of equity through the moderator effect of IFRS, which has not been widely used in studies of accounting conservatism until now.

Do dividends still matter? The role of investment opportunities on the ability of dividends to predict future earnings

Research aims: This study investigates the role of firm characteristics explained by the investment opportunity (IO) on the ability of dividends to predict future earnings.Design/Methodology/Approach: This study performed an empirical study on firms listed in the consumer goods sub-sector on the Indonesian Stock Exchange, divided into companies with strong and weak IO categories to clearly see the role of IO by comparing the variable dividend coefficients of the two sample categories. Through purposive sampling, the researcher determined the research sample, totaling 42 firm samples for the weak IO category and 48 firm samples for the strong IO category. Then, the multiple regression analysis utilizing IBM SPSS Statistic Version 23 was employed to analyze the relationship between variables.Research findings: Surprisingly, companies with weak IO showed a more remarkable ability to predict future earnings than companies with strong IO because the dividend coefficient of companies with weak IO was higher than that of strong IO, denoting that the number could explain the strength of ability.Theoretical contribution/Originality: The result provides alternative explanations to the previous inconsistent results from the dividend's ability to predict future earnings. The result also supports the argument that the companies with weak IO may use dividends to convey information signals and compensate the investor for unsatisfied performance, which is called counter-signal when strong IO refrain from doing so and rely on additional information.Practitioner/Policy implication: Investors should notice companies' characteristics, such as investment opportunities, while considering dividends as a signal for future performance to make an investment decision. Research limitation/Implication: The research did not fully capture all companies in Indonesian Stock Exchange, but specifically for the companies’ sub-sector that aggressively paid the dividend. Thus, future research is hoped to provide empirical studies for other sector companies listed on Indonesia Stock Exchange to enrich alternative explanations.

An in silico toxicogenomic approach in constructing the aflatoxin B1-mediated regulatory network of hub genes in hepatocellular carcinoma

Aflatoxin B1 (AFB1) can cause hepatocellular carcinoma (HCC) through a mutagenic mode of action but can also lead to global changes in gene expression; however, the AFB1 network of molecular pathways involved in HCC is not known. Here, we used toxicogenomic data from human liver cells exposed to AFB1 to infer the network of AFB1-responsive molecular pathways involved in HCC. The following computational tools: STRING, MCODE, cytoHubba, iRegulon, kinase enrichment tool KEA3, and DAVID were used to identify protein-protein interaction network, hub genes, transcription factors (TFs), upstream kinases, and biological processes (BPs). Predicted molecular events were validated with an external dataset, whereas the hub genes in HCC were validated using the UALCAN database. The results revealed an association between AFB1 and the hub genes involved in the cell cycle. We identified TFs that regulate the hub genes and linked them with upstream kinases including cyclin-dependent kinases, mitogen-activated protein kinase 1, and AKT. This approach enabled the construction of the AFB1-mediated regulatory network consisting of upstream kinases, TFs, hub genes, and BPs, thus revealing the signaling hierarchy and information flow that may contribute to AFB1-induced HCC. This could be a useful tool in predicting the molecular mechanisms involved in chemical-induced diseases when available toxicogenomic data exist.

A model of embedded autonomy and asymmetric information

AbstractWe model the concept of embedded autonomy, introduced by Peter Evans, as an interaction between bureaucrats and entrepreneurs, where bureaucrats must approve projects proposed by entrepreneurs. Entrepreneurs are better informed about their own projects than are bureaucrats, but bureaucrats can receive signals about project quality from entrepreneurs. If bureaucrats and entrepreneurs are more closely connected, say through social ties, they receive more informative signals. However, greater closeness will simultaneously relax the standards for project approval. Hence, there is a tradeoff between these two effects of social closeness, which helps capture the concept of embedded autonomy.

Infants' learning from distinct negative emotions.

Here we investigated infants' developing ability to use emotional expressions as signals that guide their learning about objects. To do so, we presented 16- to 21-month-old infants (N = 99) with actors who conveyed anger, fear, or pain, and tested infants' generalization of others' emotional expressions (Study 1) and infants' exploration of objects (Study 2). Our findings suggest that infants attend to the information conveyed by emotional expressions: When two expressions provide different information (e.g., one conveys threat, and the other does not), infants treated those emotions differently, even if they were both negative. Specifically, infants were more likely to generalize negative emotional expressions that conveyed threat compared to nonthreatening negative emotions (Study 1) and were more likely avoid interacting with potentially threatening items compared to items that were merely evaluated negatively (Study 2). But, when two emotional expressions provided the same information (e.g., that an item was threatening) infants responded similarly to those two emotions (Study 1). These findings are in line with evolutionary theories, which posit that emotions are critical information signals that can be used to learn about the world. (PsycInfo Database Record (c) 2022 APA, all rights reserved).

Counteracting the Risks of International Investment in the Conditions of War

The study aims to determine critical components of the protection of foreign investors in time of war by case of Ukraine and with account to various categories of investors. Foreign direct investment (FDI) is very volatile due to the sensitivity of foreign investors to information signals, caused by much higher riskiness of foreign markets than internal ones. Heavy FDI inflows in the past are not a guarantee of their similar inwards in the future and vice versa. The process of taking investment decisions depends on the investors’ aversion and the investment climate in a host country. The aversion of companies is dependent on size, organization and origin, which is a result of varying capabilities of counteracting international risks. The investment climate is conditional on the performance of democratic institutions, the formation of democratic society and the progress in a host country, especially in information technologies. The sensitivity to information signals essentially increases for investment projects in countries at war. The war risks feature much higher uncertainty than the risks of peacetime, being extreme by nature, which means that they may have devastating effects for an investor in spite of a low probability of the occurrence, i. e. cause abnormal losses, partial or total ruining of invested assets, or physical injuries, captivity, hostage-taking or death of company staff. This offers an argument for reasonability of the systemic approach to setting up the public policy on attracting foreign direct investment in time of war with accounting for various investor categories and special emphasis on the risk management tools that are capable to increase the level of investors’ protection and certainty. We believe that the key ones are information support for the process of taking investment decisions, formation of reserve capital by estimating value at risk and conditional value at risk, double diversification of invested assets, risk premium and international insurance of FDI. International insurance is the core component of a scheme for the protection of investors in time of war, and when the aggressor is a country with permanent membership in the UN Security Council, its guarantee will require initiatives collaborated with international partners

Modes of psycho-informational interaction of a human operator with technical, information and intellectual systems

A decrease in the attention of the operator or his incorrect reaction during prolonged interaction with the technical system is one of the most serious problems. From the point of view of the theory of information metabolism of the psyche - socionics - this means that the human psyche does not receive adequate information signals that support its necessary working state, concentration of consciousness, level of attention, etc. The solution to this problem is connected with the definition of the information characteristics of the technical system and with the construction of adequate information interaction in the integral complex “operator - technical system”. It is achieved by using the method of socionical analysis and modeling. Knowledge the information structure of a technical system gives opportunity to predict a number of features of its behavior. It gives additional significant information about the parameters that must be taken into account for adequate monitoring of the system's behavior. The second task is related to the form of providing the system with signals to the operator for the subsequent adequate reaction. A human operator has a certain type of information metabolism, and receiving information on some informational aspects is familiar and convenient for him, but on the other is associated with tension. To solve this problem there are identified and considered 16 possible specific modes of psycho-informational interaction of a human operator as a carrier of a certain socionic type of information metabolism with technical or electronic systems, including artificial intelligence systems. These modes can be complementary, resonant, neutral or non-complementary. The specificity of psycho-informational interaction is certainly important from the point of view of efficiency and safety.

Computer-Generated Holography Methods for Data Page Reconstruction Using Phase-Only Medium

Achievements in the field of high-speed spatial modulation electrooptic components provide the possibility to create perspective optical-digital diffractive systems for information storage and processing that outperform modern electronic counterparts by utilizing throughput, energy efficiency, and reliability. This work presents a study of computer-generated holography methods that allow the formation of spatially-modulated information signals (data pages) with high accuracy using phase-only spatial light modulators. Computer-generated Fourier hologram fringe patterns were formed using bipolar intensity and double-phase coding. Numerical and experimental results of both methods’ implementation are compared. It was determined that bipolar intensity holograms provide higher data density on the data page if complex digital modulation methods such as multilevel amplitude and phase or quadrature modulation are used to represent data points. Double-phase coding can offer perspective for multilevel amplitude or multilevel intensity modulated data page reconstruction; however, exact control of phase modulation characteristics is required to obtain high reconstruction quality.

The effect of perceived usefulness of recommender systems and information sources on purchase intention

PurposeThe current study aims to empirically analyze the influence of different information sources, together with the persuasiveness of recommender systems (RSs) on the consumer’s purchase intention (PI). It also expands the research on RSs from the point of view of consumer behavior and psychology, considering perceived usefulness and relevance. In addition, it analyzes how different types of personalized recommendations, along with non-personalized ones, influence PI.Design/methodology/approachThe proposed model has been validated using partial least squares structural equation modeling (PLS-SEM), based on the data collected from 597 online shoppers.FindingsThis study proves that both information search and RSs influence PI, being complementary rather than mutually exclusive. Recommender systems’ findings indicate that the PI is primarily influenced by the perceived relevance of RSs, the information provided by manufacturers and reviews. Moreover, only the influence of the perceived usefulness of personalized recommendations strongly affects PI. Conversely, non-personalized recommendations do not affect PI.Practical implicationsDevelopers should focus on increasing the perceived usefulness and relevance of RSs. Thus, they could adopt the hybridization of RSs with the aggregation of both personal shopping behavior and social network contacts. It should integrate information signals from multiple sources to include sentiment extracted from reviews or links to the manufacturer’s page. Furthermore, the recommendation of discounted products must be only for products preferred by customers, because only these influence the PI.Originality/valueThis research provides a structural model that examines together, for the first time, the influence on the PI of the main RSs and sources of information.

Entropic Dynamics in a Theoretical Framework for Biosystems.

Central to an understanding of the physical nature of biosystems is an apprehension of their ability to control entropy dynamics in their environment. To achieve ongoing stability and survival, living systems must adaptively respond to incoming information signals concerning matter and energy perturbations in their biological continuum (biocontinuum). Entropy dynamics for the living system are then determined by the natural drive for reconciliation of these information divergences in the context of the constraints formed by the geometry of the biocontinuum information space. The configuration of this information geometry is determined by the inherent biological structure, processes and adaptive controls that are necessary for the stable functioning of the organism. The trajectory of this adaptive reconciliation process can be described by an information-theoretic formulation of the living system's procedure for actionable knowledge acquisition that incorporates the axiomatic inference of the Kullback principle of minimum information discrimination (a derivative of Jaynes' principle of maximal entropy). Utilizing relative information for entropic inference provides for the incorporation of a background of the adaptive constraints in biosystems within the operations of Fisher biologic replicator dynamics. This mathematical expression for entropic dynamics within the biocontinuum may then serve as a theoretical framework for the general analysis of biological phenomena.

The Administrative Cost of Appeal Systems and Error Correction

Abstract The performance of appeal systems has been frequently been conditioned by the availability of human and financial resources. Scholars have suggested measures for reducing the financial and administrative burden of appeal tribunals; however, such proposals must be assessed considering their impact on the primigenial objectives pursued by these systems, particularly their capacity to reduce the harm produced by judicial errors. This study assesses diverse options for reducing caseload burdens in an administrative appeal system, including abandoning the prohibition of the Reformatio in Peius (RIP) principle, elevating fees for appealing and introducing subsidies and penalties to defendants. The results show that increasing appeal fees may allow a similar effect on the reduction of caseload burden as abandoning no RIP but at a lower cost in terms of noncorrected errors. The results are robust to different assumptions regarding the informational signals sent by defendants to tribunals during the administrative process.

The Influence of Cash Ownership on Financial Performance: An Examination of Disruptors and Acquirers

Cash ownership emits a powerful positive signal. We examine four sources of cash in firms, i.e., cash flows, cash holdings, cash proceeds from debt, and cash proceeds from equity. We examine the effects of cash ownership for firms growing by disruption, and firms growing by acquisition. Information signaling theory maintains that free cash flows may be used to increase shareholder wealth. Two-stage least squares regressions determined the impact of cash funding on disruptors and size of acquisition in the first stage, and cash-funded disruption or cash-funded acquisition in the second stage, for a US sample of 832 disruptor firms and 924 acquirers, from 2000–2020. Disruptions funded by cash holdings, cash flow, and cash proceeds from debt, significantly increased stock returns. A size effect was observed, with small disruptors showing significant effects. Acquisitions funded by cash holdings, cash flow, and cash proceeds from debt, significantly increased stock returns and return on assets. Agency costs significantly reduced returns and profits. Results for disruptions and acquisitions support signaling theory with free cash flows signaling higher share prices for both disruptors and acquirers, and higher profits for acquirers.

Naming as business strategy: an analysis of eponymy and debt contracting

This study proposes that naming a firm eponymously is a mechanism that small private firms can use to signal their superior financial performance and commitment to fulfill debt contract obligations. Using 621,614 small private firms in Europe over the period 2008–2018, we find that small private eponymous firms pay significantly lower interest on their debts and have more long-term debt than non-eponymous firms. Our findings are robust to various controls and placebo tests. Additional analyses show that eponymy lowers the cost of debt and facilitates long-term debt via reputation signaling and private information. We also document that the effect of eponymy on debt contracting is most pronounced when there is less financial development and when firms’ dependence on external financing is low, consistent with the idea that high-quality firms opt for eponymy when they consider less external financing.

Uman-type neurofilament light antibodies are effective reagents for the imaging of neurodegeneration.

Recent work shows that certain antibody-based assays for the neurofilament light chain detect informative signals in the CSF and blood of human and animals affected by a variety of CNS injury and disease states. Much of this work has been performed using two mouse monoclonal antibodies to neurofilament light, UD1 and UD2, also known as Clones 2.1 and 47.3, respectively. These are the essential components of the Uman Diagnostics Neurofilament-Light™ ELISA kit, the Quanterix Simoa™ bead-based assay and others. We show that both antibodies bind to neighbouring epitopes in a short, conserved and unusual peptide in the centre of the neurofilament light Coil 2 segment of the 'rod' domain. We also describe a surprising and useful feature of Uman and similar reagents. While other well-characterized neurofilament antibodies generally show robust staining of countless cells and processes in CNS sections from healthy rats, both Uman antibodies reveal only a minor subset of profiles, presumably spontaneously degenerating or degenerated neurons and their processes. However, following experimental mid-cervical spinal cord injuries to rats, both Uman antibodies recognize numerous profiles in fibre tracts damaged by the injury administered. These profiles were typically swollen, beaded, discontinuous or sinusoidal as expected for degenerating and degenerated processes. We also found that several antibodies to the C-terminal 'tail' region of the neurofilament light protein bind undamaged axonal profiles but fail to recognize the Uman-positive material. The unmasking of the Uman epitopes and the loss of the neurofilament light tail epitopes can be mimicked by treating sections from healthy animals with proteases suggesting that the immunostaining changes we discovered are due to neurodegeneration-induced proteolysis. We have also generated a novel panel of monoclonal and polyclonal antibodies directed against the Uman epitopes that have degeneration-specific staining properties identical to the Uman reagents. Using these, we show that the region to which the Uman reagents bind contains further hidden epitopes distinct from those recognized by the two Uman reagents. We speculate that the Uman-type epitopes are part of a binding region important for higher order neurofilament assembly. The work provides important insights into the properties of the Uman assay, describes novel and useful properties of Uman-type and neurofilament light tail-binding antibodies and provides a hypothesis relevant to further understanding of neurofilament assembly.

Joint Uplink Power Control, Downlink Beamforming, and Mode Selection for Secrecy Cell-Free Massive MIMO With Network-Assisted Full Duplexing

In this article, we investigate a secrecy cell-free massive multiple-input multiple-output system with network-assisted full duplexing for both noncolluding eavesdropper (Eve) and colluding Eve cases, where the access points (APs) are designed to serve downlink users and uplink users simultaneously and flexibly in the presence of Eves. To confirm the secrecy transmission, we consider the problem of jointly optimizing duplex mode selection and secrecy transceivers to maximize the overall secrecy spectral efficiency, where the information signals at APs are injected with artificial noise (AN) to prevent interception of information by Eves. Considering that downlink secure beamforming, uplink transmission power, and uplink receivers are tightly coupled in both the objective function and the constraints, we propose a two-loop strategy to solve the optimization problem. In particular, we propose a quantum-inspired tabu search algorithm to find a better duplex mode in the outer loop for each iteration. Then, for the inner loop, we propose a successive convex approximation based iterative algorithm to optimize the transceivers and AN by fixing the duplex mode selection vectors obtained from the outer loop. To address the proposed inner loop design subproblem, we resolve the subproblem by a series of nonconvex-convex approximate methods, such as the equivalent transformation and the iterative concave-convex procedure. The simulation results show that the proposed solution can achieve a higher secrecy spectral efficiency gain than the fixed-mode duplex scheme and simple greedy duplex mode selection schemes and achieve secrecy spectral efficiency performance similar to that of the optimal exhaustive search solution for both noncolluding Eve and colluding Eve cases.

Quantitative Analysis of the Cu Element Enhanced by AgNPs in a Single Microsized Suspended Particle Based on Optical Trapping-LIBS and Machine Learning.

Extremely severe and persistent particulate pollution caused by industrialization and urbanization impacts air quality, regional and global climates, and human health. The unstable and complex spectral signal of laser-induced breakdown spectroscopy (LIBS) with minimal feature information and interference signals considerably influences the accuracy of qualitative and quantitative analysis. In response to overcome this phenomenon, in this work, quantitative analysis of Cu element enhanced by silver nanoparticles (AgNPs) in a single microsized suspended particle was proposed herein using optical trapping-LIBS and machine learning method was proposed. Initially, the optimal AgNPs enhancement conditions were optimized. The LIBS spectra of 15 polluted black carbon samples were collected and various spectral pretreatment methods were compared to optimize the LIBS spectra. Variable selection methods include variable importance measurement (VIM), variable importance projection (VIP), VIM-successive projections algorithm (VIM-SPA), VIM-genetic algorithm (VIM-GA), and VIM-mutual information (VIM-MI). Finally, several hybrid variable selection methods were implemented in random forest (RF) calibration models. In particular, a wavelet transform (WT)-VIM-SPA-RF calibration model has constructed under the WT spectral pretreatment method and the selected and optimized input variables (VIM-SPA). Results elucidate that the WT-VIM-SPA-RF calibration model (R2P = 0.9858, MREP = 0.0396) have the best prediction performance than the WT-RF and Raw-RF models in predicting the Cu level in a single microsized black carbon particle. Compared to the WT-RF and Raw-RF models, MREP values decreased by 37% and 62%, respectively. The values of RSD, RPD, and RER of this calibration model are 2.8%, 8.39%, and 17.79%, respectively. The aforementioned results demonstrate that the WT-VIM-SPA-RF calibration model with accuracy, stability, and robustness is a promising approach for improving the quantitative accuracy of the Cu level in carbon black particles.

State-Estimation-Based Control Strategy Design for Connected Cruise Control With Delays

The research on connected and automated vehicles attracts much attention recently. In connected vehicle systems, connected cruise control (CCC) is a wildly concerned technique to improve traffic safety and mitigate congestion. In this article, an optimal state-estimation-based (SE-based) control algorithm is proposed in a backward recursion manner to regulate CCC vehicle's longitudinal motion under stochastic communication delays, state noises, and channel interferences. Based on the analysis of error dynamics of vehicles and optimal state estimation strategy, the SE-based system dynamics is first formulated by using the Kalman filtering method. Then, a two-step SE-based control algorithm aiming at minimizing the quadratic system cost is proposed to enhance the vehicular platoon stability. In particular, the optimal control strategy can be generated online as a linear function of the estimated state information and previous control signals, and the corresponding coefficient can be iteratively calculated in an offline step. Finally, the results are successfully extended to the case of arbitrary communication delays, and the condition for the asymptotically mean square stability of the proposed optimal SE-based CCC algorithm is derived. Simulation results indicate that the proposed SE-based control algorithm has better system performance and control stability compared with existing works.

Acoustic Study of Changes in the Breath during Human Mental Activity

Acoustic signals coming from the human head during mental activity are experimentally studied. Acoustic signals that differ from the signals during relaxation are revealed. Calculation of the multiplication table in the mind is used as mental activity. It is shown that the differences are due to a variation in the breathing pattern. It is also shown that the most informative signals related to respiration are observed from the crown region (at a point close to Cz used in encephalography) and the signals related to pulse activity are observed from the temple region (at point T3).

Wireless Energy Harvesting for Autonomous Reconfigurable Intelligent Surfaces

In the current contribution, we examine the feasibility of fully-energy-autonomous operation of reconfigurable intelligent surfaces (RIS) through wireless energy harvesting (EH) from incident information signals. Towards this, we first identify the main RIS energy-consuming components and present a suitable and accurate energy-consumption model that is based on the recently proposed integrated controller architecture and includes the energy consumption needed for channel estimation. Building on this model, we introduce a novel RIS architecture that enables EH through RIS unit-cell (UC) splitting. Subsequently, we introduce an EH policy, where a subset of the UCs is used for beamsteering, while the remaining UCs absorb energy. In particular, we formulate a subset al.ocation optimization problem that aims at maximizing the signal-to-noise ratio (SNR) at the receiver without violating the RIS’s energy consumption demands. As a problem solution, we present low-complexity heuristic algorithms. The presented numerical results reveal the feasibility of the proposed architecture and the efficiency of the presented algorithms with respect to both the optimal and very high-complexity brute-force approach and the one corresponding to random subset selection. Furthermore, the results reveal how important the placement of the RIS as close to the transmitter as possible is, for increasing the harvesting effectiveness.

Achievable Rates and Resource Allocation for CDMA-Based Overlay Cognitive Radio With RF Energy Harvesting

This article introduced a cognitive wireless powered communication network with an uplink (UL) code-division multiple access (CDMA) scheme consisting of one hybrid access point (H-AP) and multiple energy harvesting secondary users (SUs), which are coexisted with a primary wireless network. The frame structure is coordinated through the two-phase harvest-then-transmit protocol. The energy-limited SUs first harvest wireless energy in the downlink phase and then transmit their information signals simultaneously to H-AP with the CDMA scheme in the UL phase. An overlay spectrum sharing model is also considered where H-AP has access to the primary transmitter's codebook and primary network's channel. Our goal is to maximize the sum-throughput optimization problem of SUs via jointly optimizing power and time allocation. The considered joint optimization problem is generally nonconvex due to coupled variables. As a solution, we first transform it into the equivalent convex form using proper variables and then we solve it using the convex optimization technique. The closed-form solutions for time and power allocations are given. We also confirm from various simulations’ results that our proposed CDMA scheme is superior to the conventional time-division multiple access schemes.