Realistic Assumptions Research Articles

AN INVESTIGATION ON NONLINEAR OPTION PRICING BEHAVIOURS THROUGH A NEW FRÉCHET DERIVATIVE-BASED QUADRATURE APPROACH

Abstract Complicated option pricing models attract much attention in financial industries, as they produce relatively better accurate values by taking into account more realistic assumptions such as market liquidity, uncertain volatility and so forth. We propose a new hybrid method to accurately explore the behaviour of the nonlinear pricing model in illiquid markets, which is important in financial risk management. Our method is based on the Newton iteration technique and the Fréchet derivative to linearize the model. The linearized equation is then discretized by a differential quadrature method in space and a quadratic trapezoid rule in time. It is observed through computations that the accurate solutions for the model emerge using very few grid points and time elements, compared with the finite difference method in the literature. Furthermore, this method also helps to avoid consideration of the convergence issues of the Newton approach applied to the nonlinear algebraic system containing many unknowns at each time step if an implicit method is used in time discretization. It is important to note that the Fréchet derivative supports to enhance the convergence order of the proposed iterative scheme.

An Efficient Algorithm for the 2-Central Path Problem

In this paper we consider the following [Formula: see text]-Central Path Problem (2CPP): Given a set of [Formula: see text] polygonal curves [Formula: see text] in the plane, find two curves [Formula: see text] and [Formula: see text], called [Formula: see text]-central paths, that best represent all curves in [Formula: see text]. Despite its theoretical interest and a wide range of practical applications, 2CPP has not been well studied. In this paper, we first establish criteria that [Formula: see text] and [Formula: see text] ought to meet in order for them to best represent [Formula: see text]. In particular, we require that there exists parametrizations [Formula: see text] and [Formula: see text] ([Formula: see text]) of [Formula: see text] and [Formula: see text] respectively such that the maximum distance from [Formula: see text] to curves in [Formula: see text] is minimized. Then an efficient algorithm is presented to solve 2CPP under certain realistic assumptions. Our algorithm constructs [Formula: see text] and [Formula: see text] in [Formula: see text] time, where [Formula: see text] is the total complexity of [Formula: see text] (i.e., the total number of vertices and edges), [Formula: see text] is the number of curves in [Formula: see text], and [Formula: see text] is the inverse Ackermann function. Our algorithm uses parametric search technique and is considerably faster than arrangement-related algorithms (i.e. [Formula: see text]) when [Formula: see text] as in most real applications.

A PAPR correction scheme for LoRa self‐jamming waveforms

AbstractA LoRa self‐jamming scheme that enables secure and covert communications by adding jamming symbols when transmitting is proposed. The scheme has been validated in simulations. The work is continued by evaluating the scheme on real‐world software defined radio (SDR) equipment. Experiments immediately showed that a demodulation is impossible as the received signal is highly distorted. This comes from the fact that adding symbols at transmission actually generates high peak‐to‐average power ratio (PAPR) preventing proper waveform generation by the equipment's hardware. This paper presents a PAPR correction scheme that modifies the waveform. The properties of this modified waveform are analysed and the performances are assessed in simulations with realistic time and frequency synchronization assumption. For the ideal 0 dB PAPR corrected output, the SER performances are close to that of a legacy LoRa communication, while preserving the physical layer security capabilities of the self‐jamming scheme. It is finally shown that our PAPR correction scheme effectively recovers the self‐jamming properties on SDR devices.

The Fossilised Birth-Death Model is Identifiable.

Time-dependent birth-death sampling models have been used in numerous studies for inferring past evolutionary dynamics in different biological contexts, e.g. speciation and extinction rates in macroevolutionary studies, or effective reproductive number in epidemiological studies. These models are branching processes where lineages can bifurcate, die, or be sampled with time-dependent birth, death, and sampling rates, generating phylogenetic trees. It has been shown that in some subclasses of such models, different sets of rates can result in the same distributions of reconstructed phylogenetic trees, and therefore the rates become unidentifiable from the trees regardless of their size. Here we show that widely used time-dependent fossilised birth-death (FBD) models are identifiable. This subclass of models makes more realistic assumptions about the fossilisation process and certain infectious disease transmission processes than the unidentifiable birth-death sampling models. Namely, FBD models assume that sampled lineages stay in the process rather than being immediately removed upon sampling. Identifiability of the time-dependent FBD model justifies using statistical methods that implement this model to infer the underlying temporal diversification or epidemiological dynamics from phylogenetic trees or directly from molecular or other comparative data. We further show that the time-dependent fossilised-birth-death model with an extra parameter, the removal after sampling probability, is unidentifiable. This implies that in scenarios where we do not know how sampling affects lineages we are unable to infer this extra parameter together with birth, death, and sampling rates solely from trees.

Speed-dating and simulation data explain the discrepancy between stated and revealed mate preferences

There is little evidence in speed-dating studies that stated preferences – what people say they prefer in a partner – are associated with revealed preferences – what people actually find attractive in a partner. In Study 1, a high-powered speed-dating study ( n = 1145) revealed that four out of nine traits provided evidence of a correspondence between stated and revealed preferences. In Study 2, simulations based on the constraints of Study 1’s speed-dating design showed that when attractiveness depends on multiple independent traits, the stated preference for an individual trait can only be, on average, minimally related to the revealed preference for that trait. In Study 3, we investigated methods that simultaneously combine multiple traits when testing the association between stated and revealed preferences (e.g. Euclidean distance, pattern metric). All four omnibus methods indicated an apparent association between stated and revealed preferences in our speed-dating data. However, additional analyses and permutation tests suggest that these significant associations reflect statistical artefacts rather than true correspondences. We conclude that detecting any association between stated and revealed preferences will be difficult under realistic assumptions about the number of traits involved in partner evaluation. In this light, we discuss previous findings and provide suggestions for future studies in this vein.

Implementing Real Options Valuation under Macroeconomic Risk and Normally Distributed Cash Flows

The paper highlights the encountered problems in implementing real options under more realistic assumptions such as business cycle risk and normally distributed cash flows. The problems considered include (i) estimating empirical distribution of cash flows from real option investments; (ii) investment decisions across business cycles, and (iii) calculating the probability of investing with the above stated rich features. To this end, we estimate operating cash flows of US corporate firms using a Markov chain model under both geometric and arithmetic Brownian motions assumptions for cash flows and develop a valuation model of real option with normally distributed cash flows. Associated investment valuation models incorporating these estimates reveal that critical cash flow levels significantly differ across models and regimes.

Optimal coordination of directional overcurrent relays in power energy systems with emphasis on the discreteness of variables: Comprehensive comparisons

The challenge related to the coordination of overcurrent relays in a looped network, which is a highly constrained problem, is to find a more optimum solution and at the same time there is no miscoordination. In this problem, the utilized optimization algorithm has a direct effect on finding a more optimal solution. High exploration particle swarm optimization (HEPSO) algorithm, which utilizes multi-crossover mechanism of the genetic algorithm and the bee colony mechanism to increase the exploration capability, has been selected to solve the coordination problem. To validate the HEPSO algorithm, a new algorithm called turbulent flow of water-based optimization (TFWO) is also used, and their results are compared. A new approach to discretization of variables is presented, which prevents miscoordination in the relays coordination. It is a realistic assumption for all relay types, especially digital relays, in preventing miscoordination. Also, the outage of sub-transmission transformers is considered for determining pickup current, which is a practical aspect of power systems operation. The obtained results are compared with the results of other algorithms and methods such as metaheuristic, mathematical and analytical, and hybrid methods. The efficiency of the selected algorithm is proven through comprehensive comparisons. The results show more optimum solutions while there is no miscoordination. Finally, the performance of the selected algorithm and the proposed approaches are tested on IEEE 14 and 30-bus test systems. Since these test systems are large enough, the number of problem variables will increase significantly, effectively validating the power and robustness of the selected algorithm to tackle this complex and constrained problem. The outage of transformers in determining pickup currents had been implemented in this section.

Stability analysis of dual solutions for nonlinear radiative magnetohydrodynamic flow of [formula omitted] hybrid nanofluid over a nonlinearly shrinking surface

The present work explores the existence and temporal stability of dual solutions with the consequence of nonlinear thermal radiation on hybrid nanofluid flow (including Silver and Titanium dioxide nanoparticles in water) past a permeable shrinking sheet. The analysis also considers the united outcomes of suction/injection, Joule heating, viscous dissipation, and magnetohydrodynamics. Employing realistic assumptions and suitable similarity transformations, the governing nonlinear partial differential equations are formulated and altered into a nonlinear ordinary differential equations system. These equations are then solved numerically using the Lobatto IIIA technique. It is observed that dual solutions can occur within a precise range of the shrinking surface parameter. Analyzing the temporal stability of these dual solutions under small disturbances shows that the upper solution branch is stable and represents a physically realistic solution to the problem. In contrast, the lower solution branch is unstable and not physically realizable. No solution exists beyond the critical λc=−1.3915 for the shrinking parameter. The critical values of shrinking parameter λc=−1.2565,−1.3188, and −1.4161 correspond to volume fractions of hybrid nanofluid with φAg−TiO2 of 2%,4%, and 8%, respectively. No solution exists beyond this critical point. The graphical representation and quantitative explanation demonstrate how various emergent characteristics affect momentum and thermal boundary layer profiles, Nusselt number, and skin friction. The velocity and temperature profile of hybrid nanofluid can be improved by adding a small volume of nanoparticle volume fraction. Thermal boundary layer thickness is amplified by an accumulation in the shrinking parameter, power index of velocity component, thermal radiation parameter, and temperature difference ratio. Sheering stress and heat transfer coefficient improve with adding more nanoparticles in the base fluid for the first solution braches, but opposite effects are found in the second solutions. The contributions of this research are significant both theoretically, in terms of advancing the mathematical modeling of hybrid nanofluid flow with heat transfer in engineering systems, and practically, in terms of applications to engineering cooling systems.

Workplace Democracy as the Liberal Republican Default

Liberal republican justice requires adequate, reasonably equal protection against domination while giving everybody a reasonably equal opportunity to live their lives as they see fit. This article investigates which central policy follows from it for workplace non-domination, under realistic assumptions about the nature of our working lives and some structural characteristics of advanced economies (especially the presence of large firms). It argues for a legal default of workplace democracy, understood as any arrangement that gives workers equal individual control rights over the management of their firm, and, as a body, more control rights than any other groups of enfranchised stakeholders, taken together. The argument has two steps. The first demonstrates where the most promising alternative liberal strategy – that of enhancing all individuals’ power to exit from their job through granting unconditional resources – fails. The second shows how this failure leads to the specific conception of default workplace democracy outlined.

Analyzing Burr-X competing risk model using adaptive progressive Type-II censored binomial removal data with application to electrodes and electronics

The aim of this paper is to investigate the Burr-X competing risks model in the context of adaptive progressively Type-II censored samples. In this scenario, the removal pattern is assumed to be a random variable that follows the binomial distribution, which is a more realistic assumption compared to assuming a fixed removal pattern. In this study, we explore both classical and Bayesian estimation approaches to estimate the parameters of the Burr-X competing risks model, as well as the reliability parameter and the parameter of the binomial distribution. The interval ranges of different parameters are determined by utilizing the asymptotic normality of the maximum likelihood estimators. Furthermore, the Bayes credible intervals are calculated by sampling from the joint posterior distribution using the Markov Chain Monte Carlo procedure. To assess the efficiency of the acquired estimators, a comprehensive simulation study that considered various types of experimental designs is conducted. Finally, two applications are considered by analyzing data sets of electrodes and electronics.

Extending speech emotion recognition systems to non-prototypical emotions using mixed-emotion model

In the conventional approach to speech emotion recognition (SER), the classifier is usually trained on acted emotional speech data to predict individual basic emotions. In this work, we extend the SER systems with the realistic assumption of the coexistence of multiple basic emotions in an utterance. We utilize the MSP-Podcast database for developing the SER system, which contains spontaneous speech utterances. From the primary and secondary emotion annotations of this database, we organize six prototypical and seven non-prototypical emotions. We then propose the mixed-emotion model and express the non-prototypical emotions as a linear combination of prototypical emotions. The combination weights of the mixed-emotion model are computed using a normalized dominance scale based algorithm inspired by the integration of basic emotion theory and dimensional emotion theory in human psychology. We first train a prototypical SER model using the ECAPA-TDNN architecture. The softmax predictions from this model serve as emotion profile inputs to the mixed-emotion model, which then predicts the non-prototypical emotions. Assuming the coexistence of multiple emotions, we only apply the utterances with uniform emotion profiles to the mixed-emotion model. The developed system continues with the conventional SER model if the emotion profile tends to delta function owing to the probable occurrence of a single prototypical emotion. We develop the proposed mixed-emotion model based SER framework using MFCC and wav2vec 2.0 extracted features. Further, we show that due to human perception variations, there exist prominent annotation variations in the non-prototypical emotion ground truths. To address that, we extend the supervised evaluation protocols in four different formulations that capture the subjective variability at different levels. The proposed system shows a best-case performance improvement of 7.10% and 8.39% over the conventional prototypical SER model for the MFCC and wav2vec 2.0 features, respectively.

Design of a reverse logistics network for medical waste considering location and routing decisions

Medical waste generated in healthcare facilities is categorized as hazardous due to its infectious, toxic, or radioactive properties, posing substantial risks to human health and the environment. This research proposes a bi-objective Mixed-Integer Linear Programming model for designing reverse logistics networks that enable both economical and safe medical waste management. Said model determines optimal quantity, depot locations, and routes for transporting waste from its generating points (hospitals) to the treatment and disposal sites (depots).This research addresses a bi-objective location routing problem, with multiple depots, time windows, capacity constraints, and variated waste types, across a multi-period horizon. It is innovative because no previous works have successfully dealt with such a combination of pragmatic features. The Location Routing Problem is an already well-known NP-Hard problem, with the additional aforementioned features further adding to its complexity. Thus, a genetic algorithm to solve large-sized realistic instances within reasonable computing times had been developed.Numerical experiments considered 20 random instances contrasting exact and approximate solution approaches. Also, a medical waste collection case study of a public hospital network in Atlántico Department, Colombia, delivering a Pareto frontier solution set, was introduced. The results demonstrated the efficacy of the proposed genetic algorithm in successfully addressing small instances of the problem, delivering outcomes comparable to the Mixed-Integer Linear Programming model. Furthermore, it yields good-quality solutions in a reduced computational time for larger instances deemed unfeasible for the Mixed-Integer Linear Programming model.This article contributes to both scientific literature and practitioners by presenting a decision-making tool designed to address the medical waste reverse logistics challenge under realistic scenarios. It approaches the issue from a bi-objective standpoint, jointly considering economic factors and biomedical safety. Additionally, the article introduces realistic and pertinent assumptions that have not been explored in previous literature.

On the intransitivity of the win ratio

The win-ratio analysis of controlled clinical trials uses pairwise comparisons between patients in the treatment and control group based on a primary outcome, say time to death, with indeterminacies resolved where possible by a secondary outcome, say time to hospitalization. The resulting preferences may not be transitive. Intransitivity occurs when potential follow-up times vary between patients and rankings from the primary events differ from those from secondary events. We characterize the structure of closed loops, derive some general properties of win-ratio preferences and provide simple numerical illustrations. Under realistic assumptions, unless all potential follow-up times are equal, intransitivities are certain to occur in sufficiently large samples, but their overall frequency is low.

The Benefits of Future Quantum Accelerometers for Satellite Gravimetry

AbstractWe investigate the benefits of future quantum accelerometers based on cold atom interferometry (CAI) on current and upcoming satellite gravity mission concepts. These mission concepts include satellite‐to‐satellite tracking (SST) in a single‐pair (GRACE‐like) and double‐pair constellation as well as satellite gravity gradiometry (SGG, single satellite, GOCE‐like). Regarding instruments, four scenarios are considered: current‐generation electrostatic (GRACE‐, GOCE‐like), next‐generation electrostatic, conservative hybrid/CAI and optimistic hybrid/CAI. For SST, it is shown that temporal aliasing poses currently the dominating error source in simulated global gravity field solutions independent of the investigated instrument and constellation. To still quantify the advantages of CAI instruments on the gravity functional itself, additional simulations are performed where the impact of temporal aliasing is synthetically reduced. When neglecting temporal aliasing, future accelerometers in conjunction with future ranging instruments can substantially improve the retrieval performance of the Earth's gravity field (depending on instrument and constellation). These simulation results are further investigated regarding possible benefit for hydrological use cases where these improvements can also be observed (when omitting temporal aliasing). For SGG, it is demonstrated that, with realistic instrument assumptions, one is still mostly insensitive to time‐variable gravity and not competitive with the SST principle. However, due to the improved instrument sensitivity of quantum gradiometers compared to the GOCE mission, static gravity field solutions can be improved significantly.

Tautology explains evolution without variation and selection. A Comment on: 'An evolutionary process without variation and selection' (2021), by Gabora et al.

Gabora and Steel (Gabora L, Steel M. 2021 An evolutionary process without variation and selection. J. R. Soc. Interface 18, 20210334. [doi:10.1098/rsif.2021.0334]) claim that cumulative adaptive evolution is possible without natural selection, that is, without variation and competition. To support this claim, the authors modelled a theoretical process called self-other reorganization (SOR) that envisages a population of reflexively autocatalytic sets that can accumulate beneficial changes without any form of birth, death or selection, that is without population dynamics. The authors claim that despite being non-Darwinian, adaptive evolution happens in SOR, deeming it relevant to the origin of life and to cultural evolution. We analysed SOR and the claim that it implements evolution without variation and selection. We found that the authors, by design, ignore the growth and/or degradation of autocatalytic sets or their components, assuming all effects are beneficial and all entities in SOR are identical and immutable. We prove that due to these assumptions, SOR is a trivial model of horizontal percolation of beneficial effects over a static population. We implemented an extended model of SOR including more realistic assumptions to prove that accounting for any of the ignored processes inevitably leads to conventional Darwinian dynamics. Our analysis directly challenges the authors' claims, revealing evidence of an overly fragile foundation. While the best-case scenario the authors incorrectly generalize from may be mathematically valid, stripping away their unrealistic assumptions reveals that SOR does not represent real entities (e.g. protocells) but rather models the triviality that fast horizontal diffusion of effects can effectively equalize a population. Adaptation in SOR is solely because the authors only consider beneficial effects. The omission of death/growth dynamics and maladaptive effects renders SOR unrealistic and its relevance to evolution, cultural or biological, questionable.

Outpatient appointment systems: A new heuristic with patient classification

PurposeThis study aims to develop a heuristic for an outpatient appointment system considering patient classification. Design/methodology/approachThe proposed heuristic was applied in simulations with eighteen scenarios, combining different environmental factors. Total cost was adopted as a performance metric, composed of the patient's wait time and the service provider's idleness and overtime. The patients were divided into two classes according to their no-show probability, in an arrivals sequence with a binomial distribution. As a significance test of the results, Bonferroni-adjusted repeated measures analysis was applied. FindingsHaving Dome rule as baseline, an increase in performance in terms of total cost (TC) was observed, which varied between 0.46 % and 5.94 % among the means of the simulated environments, validated using the proposed significance test. The greatest benefits were obtained in the scenarios with lower ratios between service provider costs and patient costs (CR), as well as lower coefficients of variation for service times (Cv). It was also found that the heuristic is more efficient when patients from the class with the highest no-show rate predominate in the session. OriginalityThe single study identified in the literature that contemplates recalculations adopts deterministic service times to make its model viable. The present research, in turn, makes more realistic assumptions for the simulated environments, considering the variables and probability distributions most commonly observed in practical contexts Practical implicationsThe proposed heuristic provided a significant increase in performance for some combinations of environmental factors analyzed, preserving flexibility in the choice of appointment slots and covering a wide range of healthcare services found in practice.

Enhancing the pull-out behavior of ribbed steel bars in CNT-modified UHPFRC using recycled steel fibers from waste tires: a multiscale finite element study

In the current investigation, the effect of recycled steel fibers recovered from waste tires on the pull-out response of ribbed steel bars from carbon nanotube (CNT)-modified ultrahigh performance fiber reinforced concrete (UHPFRC) was considered using the multiscale finite element method (MSFEM). The MSFEM is based on three phases to simulate CNT-modified UHPC, recycled steel fibers (RSFs), and ribbed steel bars. For the first time, a bar ribbed has been simulated to make more realistic assumptions, and RSFs have been distributed in the form of curved cylinders of different lengths and with a random distribution within a concrete matrix. The interaction of the steel bar and the RSFs with the concrete is applied by the cohesive zone model (CZM). After confirming the simulation outcomes with the experimental results, the steel bar pull-out response is investigated using load-slip curves. The impact of the CNT content, RSFs and their aspect ratio on the bond strength of steel bars and CNT-modified UHPFRC was assessed. The results show that using RSFs with a lower aspect ratio (steel microfibers) significantly improves the pull-out characteristics of steel bars from concrete. Accordingly, the proposed MSFEM is considered for simulating the effects of different parameters on the pull-out response of ribbed steel bars from concrete without causing complex, time-consuming, or costly experiments. The results indicated that waste fiber or RSF can be used as a toughening component in CNT-modified ultrahigh-performance concrete and as a replacement for industrial steel fibers.

A lightweight anonymity protocol at terabit speeds on programmable switches

Anonymous communication protocols are integral tools for people to exercise the right to privacy in their online activities. Such tools are urged to offer maximum protection against various classes of adversaries, including rogue criminals, exploitative companies, and authoritarian or paternalistic regimes, while respecting the diversity of users’ activities, from e-mail and web browsing to file transfer and video streaming. In such a context, lightweight anonymity protocols are a promising solution to provide well-balanced anonymity and performance for daily use against a class of adversaries with a relaxed but realistic assumption of their capability. However, as the existing instantiations of these protocols solely target software implementation, they did not fully exhibit their advantages in performance by the relaxation of the adversarial model. This paper presents P5HI, a novel instantiation and implementation of a lightweight anonymity protocol on hardware programmable switches. Its realization requires a rethinking of all aspects of the system, beginning with the realization of bit-rotation on the switch, through the construction of a cryptographic scheme and attack prevention, to hardware-friendly procedures during path setup and data transmission. We deploy the full-fledged design of the lightweight anonymity protocol on an actual programmable switch, and our prototype demonstrates its operation at a speed of over 3.0 Tbps.

Derivation and dynamics of discrete population models with distributed delay in reproduction

We introduce a class of discrete single species models with distributed delay in the reproductive process and a cohort dependent survival function that accounts for survival pressure during that delay period. These delay recurrences track the mature population for species in which individuals reach maturity after at least τ and at most τ+τM breeding cycles. Under realistic model assumptions, we prove the existence of a critical delay threshold, τ˜c. For given delay kernel length τM, if each individual takes at least τ˜c time units to reach maturity, then the population is predicted to go extinct. We show that the positive equilibrium is decreasing in both τ and τM. In the case of a constant reproductive rate, we provide an equation to determine τ˜c for fixed τM, and similarly, provide a lower bound on the kernel length, τ˜M for fixed τ such that the population goes extinct if τM≥τ˜M. We compare these critical thresholds for different maturation distributions and show that if all else is the same, to avoid extinction it is best if all individuals in the population have the shortest delay possible. We apply the model derivation to a Beverton–Holt model and discuss its global dynamics. For this model with kernels that share the same mean delay, we show that populations with the largest variance in the time required to reach maturity have higher population levels and lower chances of extinction.

Rethinking the mobile edge for vehicular services

The growing connected car market requires mobile network operators (MNOs) to rethink their network architecture to deliver ultra-reliable low-latency communications. In response, Multi-Access Edge Computing (MEC) has emerged as a solution, enabling the deployment of computing resources at the network edge. For MNOs to tap into the potential benefits of MEC, they need to transform their networks accordingly. Consequently, the primary objective of this study is to design a realistic MEC architecture and corresponding optimal deployment strategy – deciding on the placement and configuration of computing resources – as opposed to prior studies focusing on MEC run-time management and orchestration (e.g., service placement, computation offloading, and user allocation). To cater to the heterogeneous demands of vehicular services, we propose a multi-tier MEC architecture aligned with 5G and Beyond-5G radio access network deployments. Therefore, we frame MEC deployment as an optimization problem within this architecture, assuming 3 MEC tiers. Our data-driven evaluation, grounded in realistic assumptions about network architecture, usage, latency, and cost models, relies on datasets from a major MNO in the UK. We show the benefits of adopting a 3-tier MEC architecture over single-tier (centralized or distributed) architectures for heterogeneous vehicular services, in terms of deployment cost, energy consumption, and robustness.