Search Results (17)

The most popular interpretation of the Aharonov–Bohm (AB) effect is that the electromagnetic potential locally affects the complex phase of a charged particle’s wave function in the magnetic field free region. However, since the vector potential is a gauge-variant quantity, multiple researchers suspect that it is just a convenient tool for calculating the force field. This motivates them to explain the AB effect without using the vector potential, which inevitably leads to some sort of non-locality. This frustrating situation is shortly summarized by the statement by Aharonov et al. that the AB effect may be due to a local gauge potential or due to non-local gauge-invariant fields. In the present paper, we shall give several convincing arguments which support the viewpoint that the vector potential is not just a convenient mathematical tool with little physical entity. Despite its gauge arbitrariness, the vector potential certainly contains a gauge-invariant piece, which solely explains the observed AB phase shift. Importantly, this component has a property such that it is basically unique and cannot be eliminated by any regular gauge transformations. To complete the discussion, we also discuss the role of remaining gauge arbitrariness still contained in the entire vector potential. Full article

In this work, we elaborate further on a (3+1)-dimensional cosmological Running-Vacuum-type-Model (RVM) of inflation based on string-inspired Chern-Simons(CS) gravity, involving axions coupled to gravitational-CS(gCS) anomalous terms. Inflation in such models is caused by primordial-gravitational-waves(GW)-induced condensation of the gCS terms, which leads to a linear-axion potential. We demonstrate that this inflationary phase may be metastable, due to the existence of imaginary parts of the gCS condensate. These are quantum effects, proportional to commutators of GW perturbations, hence vanishing in the classical theory. Their existence is quantum-ordering-scheme dependent. We argue in favor of a physical importance of such imaginary parts, which we compute to second order in the GW (tensor) perturbations in the framework of a gauge-fixed effective Lagrangian, within a (mean field) weak-quantum-gravity-path-integral approach. We thus provide estimates of the inflation lifetime. On matching our results with the inflationary phenomenology, we fix the quantum-ordering ambiguities, and obtain an order-of-magnitude constraint on the String-Mass-Scale-to-Planck-Mass ratio, consistent with previous estimates by the authors in the framework of a dynamical-system approach to linear-axion RVM inflation. Finally, we examine the role of periodic modulations in the axion potential induced by non-perturbative effects on the slow-roll inflationary parameters, and find compatibility with the cosmological data. Full article

Cathinone and its synthetic derivatives belong to organic compounds with narcotic properties. Their structural diversity and massive illegal use create the need to develop new analytical methods for their identification in different matrices. NMR spectroscopy is one of the most versatile methods for identifying the structure of organic substances. However, its use could sometimes be very difficult and time-consuming due to the complexity of NMR spectra, as well as the technical limitations of measurements. In such cases, molecular modeling serves as a good supporting technique for interpreting ambiguous spectral data. Theoretical prediction of NMR spectra includes calculation of nuclear magnetic shieldings and sometimes also indirect spin–spin coupling constants (SSCC). The quality of theoretical prediction is strongly dependent on the choice of the theory level. In the current study, cathinone and its 12 fluorinated derivatives were selected for gauge-including atomic orbital (GIAO) NMR calculations using Hartree–Fock (HF) and 28 density functionals combined with 6-311++G** basis set to find the optimal level of theory for ¹H, ¹³C, and ¹⁹F chemical shifts modeling. All calculations were performed in the gas phase, and solutions were modeled with a polarized-continuum model (PCM) and solvation model based on density (SMD). The results were critically compared with available experimental data. Full article

Time series forecasting plays a critical role in business planning by offering insights for a competitive advantage. This study compared three forecasting methods: the Holt–Winters, Bagging Holt–Winters, and Box–Jenkins methods. Ten datasets exhibiting linear and non-linear trends and clear and ambiguous seasonal patterns were selected for analysis. The Holt–Winters method was tested using seven initial configurations, while the Bagging Holt–Winters and Box–Jenkins methods were also evaluated. The model performance was assessed using the Root-Mean-Square Error (RMSE) to identify the most effective model, with the Mean Absolute Percentage Error (MAPE) used to gauge the accuracy. Findings indicate that the Bagging Holt–Winters method consistently outperformed the other methods across all the datasets. It effectively handles linear and non-linear trends and clear and ambiguous seasonal patterns. Moreover, the seventh initial configurationdelivered the most accurate forecasts for the Holt–Winters method and is recommended as the optimal starting point. Full article

This study investigates the prospect of using rapid prototyping, particularly additive manufacturing, in Pakistan’s construction and manufacturing sectors, aiming to encourage R&D by the analysis of technical feasibility of this technology and collaboration between civil and industrial engineering. To solve this puzzle, we collected data from field experts, academia researchers, and license holders of this technology. Further, analytical hierarchy process (AHP), a sub-branch of multicriteria decision-making method (MCDM), was used to gauge the systematically by prioritizing selection criteria for solving the problem. AHP makes the methodical process more accurate and organized, which helped us to proposed a feasibility study for the technology’s success in Pakistan’s construction and manufacturing industries. The findings show a 79.4% probability, which indicates interaction among both engineering disciplines. Furthermore, a sensitivity analysis was conducted to enhance the dependability of the AHP model, which assists in sound decision making during ambiguous conditions. Apart from economic technical aspects, sustainability plays a very crucial role in the evaluation process. This text shows the environmental effects and sustainability implications associated with the assimilation of rapid prototyping technologies. This supports the integration of rapid prototyping in Pakistan, contributing to discussions on technological innovations in emerging nations. This will also lay a foundation for future interdisciplinary collaboration and technological enrichments in both engineering domains. Full article

Given the computational challenges associated with valuing large variable annuity (VA) portfolios, a variety of data mining frameworks, including metamodeling and active learning, have been proposed in recent years. Active learning, a promising alternative to metamodeling, enhances the efficiency of VA portfolio assessments by adaptively improving a predictive regression model. This is achieved by augmenting data for model training with strategically selected informative samples. Successful application of active learning requires an effective metric in order to gauge the informativeness of data. Current sampling methods, which focus on prediction error-based informativeness, typically rely solely on prediction variance and assume an unbiased predictive model. In this paper, we address the fact that prediction bias can be nonnegligible in large VA portfolio valuation and investigate the impact of prediction bias in both the modeling and sampling stages of active learning. Our experimental results suggest that bias-based sampling can rival the efficacy of traditional ambiguity-based sampling, with its success contingent upon the extent of bias present in the predictive model. Full article

Lyme disease patient organizations have formed to challenge a health system that is failing Canadians who suffer from a disease that is ambiguous in its symptomology and trajectory. The framework of an embodied health movement illustrates the importance of the illness experience in mobilizing patients to oppose a system that is reliant on restrictive guidelines that deny testing and treatment and to seek alliances with researchers, physicians, and politicians who are sympathetic to their goals. The strategies of Lyme disease patient organizations, the importance of experiential knowledge, and the roles of both adversaries and allies are examined through a “small wins” approach to gauge successes and setbacks within a Canadian context. Full article

In the face of persistent challenges posed by urbanization and climate change, the contemporary era has witnessed a growing urgency for urban intelligence and sustainable development. Consequently, a plethora of smart city schedules and policies have emerged, with smart city assessment serving as a pivotal benchmark for gauging policy effectiveness. However, owing to the inherent ambiguity of the smart city definition and the complexity of application scenarios, designers and decision-makers often struggle to ascertain their desired assessment frameworks swiftly and effectively. In this context, our study undertook a comprehensive analysis and comparative assessment of 33 recently introduced or inferred evaluation frameworks, drawn from a broad spectrum of extensive and longstanding research efforts. The overarching goal was to provide valuable reference points for designers and decision-makers navigating this intricate landscape. The assessment was conducted across seven key dimensions: generalizability, comprehensiveness, availability, flexibility, scientific rigor, transparency, and interpretability. These criteria hold the potential not only to guide the development trajectory and focus of upcoming smart city assessment models but also to serve as invaluable guidelines for stakeholders evaluating the outcomes of such models. Furthermore, they can serve as robust support for designers and decision-makers in their pursuit of targeted frameworks. Full article

The horizontal storm structure surrounding 92,512 lightning-ignited wildfires is examined in the mid to eastern sections of the United States from 2003 to 2015 using Vaisala’s National Lightning Detection Network (NLDN), NCEP’s Stage IV gauge-corrected radar precipitation mosaic, and the US Forest Service’s Fire Occurrence Database. Though lightning flash density peaks strongly around fire ignitions on the instantaneous 1 km scale, on the hourly 10 km scale, both the lightning and precipitation peaks are typically offset from fire ignitions. Lightning density is higher, and precipitation is lower around ignition points compared to non-ignition points. The average spatial distribution of total lightning flashes around fire ignitions is symmetrical, while that of precipitation and positive flashes is not. Though regression is consistent with the claim that positive flashes have a stronger association with ignition than negative flashes, the statistical significance is ambiguous and is contradicted by an unchanging positive flash fraction in the vicinity of wildfires. Full article

Successful healthcare companies and illness diagnostics require data visualization. Healthcare and medical data analysis are needed to use compound information. Professionals often gather, evaluate, and monitor medical data to gauge risk, performance capability, tiredness, and adaptation to a medical diagnosis. Medical diagnosis data come from EMRs, software systems, hospital administration systems, laboratories, IoT devices, and billing and coding software. Interactive diagnosis data visualization tools enable healthcare professionals to identify trends and interpret data analytics results. Selecting the most trustworthy interactive visualization tool or application is crucial for the reliability of medical diagnosis data. Thus, this study examined the trustworthiness of interactive visualization tools for healthcare data analytics and medical diagnosis. The present study uses a scientific approach for evaluating the trustworthiness of interactive visualization tools for healthcare and medical diagnosis data and provides a novel idea and path for future healthcare experts. Our goal in this research was to make an idealness assessment of the trustworthiness impact of interactive visualization models under fuzzy conditions by using a medical fuzzy expert system based on an analytical network process and technique for ordering preference by similarity to ideal solutions. To eliminate the ambiguities that arose due to the multiple opinions of these experts and to externalize and organize information about the selection context of the interactive visualization models, the study used the proposed hybrid decision model. According to the results achieved through trustworthiness assessments of different visualization tools, BoldBI was found to be the most prioritized and trustworthy visualization tool among other alternatives. The suggested study would aid healthcare and medical professionals in interactive data visualization in identifying, selecting, prioritizing, and evaluating useful and trustworthy visualization-related characteristics, thereby leading to more accurate medical diagnosis profiles. Full article

This study suggests a novel hybrid model for calculating the interrelationships between factors by integrating the Fuzzy set, Delphi, the Grey theory, and Weighted Influence Nonlinear Gauge System (WINGS) approaches in agricultural green supply chain management (AGSCM). Fuzzy Delphi helps to select 12 indicators from 19 factors by defuzzification for ambiguity associated with subjective judgment by 10 experts in data collection. Grey WINGS can illustrate the relationships, direction, and strength of factors simultaneously, which illustrates that environmental law, green consciousness, product quality, and price are the most significant factors of AGSCM. The results can help operators not only to analyze these key influencing factors, but also to understand the complex cause-and-effect relationships between these factors. This integrated model will hopefully provide a useful tool to agricultural policy makers and decision makers for sustainable development. Full article

It remains an unsolved conundrum how social presence affects the neural processes involved in adaptive situation-specific decision-making mechanisms. To investigate this question, brain potential changes via electroencephalography (EEG) and skin conductance responses (SCR) were taken within this study, while participants were exposed to pre-rated pleasant, neutral, and unpleasant pictures, which they had to rate in terms of their perceived arousal. Crucially, they had to—in respective runs—do this alone and in the presence of a significant other. Contrasting respective event-related potentials (ERPs) revealed significantly more negative going potentials peaking at 708 ms post stimulus onset at mid-frontal electrode locations (around FPz and AFz), when participants were exposed to neutral pictures while in the presence of a significant other. SCR results demonstrate higher states of arousal in the presence of a significant other regardless of picture emotion category. Self-reported arousal turned out to be highest in response to neutral pictures within the significant other condition, whereas in the alone condition in response to the pleasant pictures. In light of existing literature on social aspects and the anterior cingulate cortex (ACC), the ERP finding in the significant other condition, while rating emotionally neutral pictures, is interpreted as reflecting heightened ACC activation, which is supported by electrode locations showing significant brain activity differences as well as by source localization results. Neutral pictures are inherently ambiguous, and the current results indicate the presence of another person to change the way one processes, perceives, and acts on them. This is in support for theories proposing the ACC to be part of a larger signal-specification network that gauges relevant stimuli for adequate execution of control. Full article

We present a geometrical derivation of the relativistic dynamics of the superfluid inner crust of a neutron star. The resulting model is analogous to the Hall-Vinen-Bekarevich-Khalatnikov hydrodynamics for a single-component superfluid at finite temperature, but particular attention should be paid to the fact that some fraction of the neutrons is locked to the motion of the protons in nuclei. This gives rise to an ambiguity in the definition of the two currents (the normal and the superfluid one) on which the model is built, a problem that manifests itself as a chemical gauge freedom of the theory. To ensure chemical gauge covariance of the hydrodynamic model, the phenomenological equation of motion for a quantized vortex should contain an extra transverse force, that is the relativistic version of the Iordanskii force discussed in the context of superfluid Helium. Hence, we extend the mutual friction model of Langlois et al. (1998) to account for the possible presence of this Iordanskii-like force. Furthermore, we propose that a better understanding of the (still not completely settled) controversy around the presence of the Iordanskii force in superfluid Helium, as well as in neutron stars, may be achieved by considering that the different incompatible results present in the literature pertain to two, opposite, dynamical regimes of the fluid system. Full article

In recent years, there has been a preliminary research on monitoring rainfall information based on polarimetric Global Navigation Satellite System (GNSS) signals, which is a quite novel concept. After previous theoretical research on monitoring rain based on polarimetric phase shift of GNSS signals, the paper aims to detect rain using polarimetric GNSS signals from a ground-based experiment. Firstly, a conical horn antenna specially designed for receiving dual-polarized (H, horizontal, and V, vertical) GNSS signals was developed, and an experimental system for polarimetric GNSS rain detection was built. Then, taking Global Positioning System (GPS) satellites as signal source, a ground-based experiment was carried out at a mountain in Nanjing, where heavy rain tends to occur frequently in rainy season. Additionally, a data processing algorithm mainly following Padullés et al. (2016) to solve the problems of quality control, unlocking, hardware effect, phase ambiguity, multipath effect was applied independently to this ground-based data from the polarimetric GNSS rain detection system. Also, the multi-source data from nearby weather radar and weather stations was used for verification. Results from 14 GPS satellites show that the obtained phase shift is zero in all no-rain days while it is not zero during rainy days, which is in accordance with the actual situation. Compared with weather radar and rain gauges’ data, the results verify that the phase shift is caused by rain. Besides, when individual cases are examined, many show that their tendencies of accumulated phase shift are quite similar to that of a weather station’s rainfall data, even some correlation coefficients are up to 0.99. These demonstrate the reliability of our experimental system and the feasibility of the data processing algorithm. This study will provide technical support for future spaceborne experiment, which has promising applications in global rain monitoring. Full article

Digital image correlation (DIC) has emerged as a popular full-field surface profiling technique for analyzing both in-plane and out-of-plane dynamic structures. However, conventional DIC-based surface 3D profilometry often yields erroneous contours along surface edges. Boundary edge detection remains one of the key issues in DIC because a discontinuous surface edge cannot be detected due to optical diffraction and height ambiguity. To resolve the ambiguity of edge measurement in optical surface profilometry, this study develops a novel edge detection approach that incorporates a new algorithm using both the boundary subset and corner subset for accurate edge reconstruction. A pre-calibrated gauge block and a circle target were reconstructed to prove the feasibility of the proposed approach. Experiments on industrial objects with various surface reflective characteristics were also conducted. The results showed that the developed method achieved a 15-fold improvement in detection accuracy, with measurement error controlled within 1%. Full article