Distribution Of Variables Research Articles

An investigation of torsional surface wave in a piezoelectric fiber-reinforced composite layer imperfectly bonded to a functionally graded half-space

This article presents a comprehensive study of torsional surface wave propagation in a piezoelectric fiber-reinforced composite (PFRC) layer bonded to functionally graded (FG) half-space considering electrically short and open boundary conditions. The Strength of Materials (SM) and Rule of Mixtures (ROM) approaches have been adopted to determine the effective properties of the PFRC. The non-ideal mechanical and electrical interfacial boundary has been considered for the problem which appears in various realistic scenarios. For the torsional surface wave propagation through PFRC bonded to FG half-space, the dispersion equation has been derived analytically using Whittaker function. The obtained results are validated and matched with existing results by considering special cases. The variation of phase velocity due to the influence of fiber volume fraction, interfacial imperfection parameters and functionally grading parameters have been analyzed and illustrated by means of graphs. Furthermore, the distribution of the field variables, stress and electric displacements across the composite are presented through surface plots. The model holds potential applications in designing and manufacturing transducers, actuators and sensors. The use of PFRC can be found in a wide range of cutting-edge technology, including vibration-canceling gadgets, energy harvesters, and pressure sensors.

A comparison between marginal likelihood and data augmented MCMC algorithms for Gaussian hidden Markov models

We provide a comparison between marginal likelihood and data augmented Markov chain Monte Carlo (MCMC) algorithms for Bayesian estimation of hidden Markov models. In particular, we focus on a specification of these models based on a multivariate Gaussian distribution for the response variables. We evaluate the performance of both methods on the basis of simulated samples and an empirical application, where we compare these approaches in terms of standard measures of estimation and time efficiency. The results show that the augmented algorithm is preferable, obtaining optimal convergence properties and producing larger values of the effective sample size. Instead of using variable dimensional techniques for model selection, we suggest a method based on the so-called parallel sampling for this aim.

A fast robust best subset regression

In this paper, we present a novel approach called the Fast Robust Best Subset Regression (FRBSR) procedure, specifically designed to tackle outliers in both covariates and response variables. Through the incorporation of C-steps, the FRBSR procedure not only enhances the robustness of the estimation process, but also relaxes the stringent assumptions imposed by the Enhanced Support Detection and Root Finding (ESDAR, Huang et al. (2018, 2021)) algorithm regarding the distribution of covariates and response variables. More specifically, we propose additional techniques, namely reweighted least squares (REWLS) and one-step weighted least squares (OSWLS), which build upon the L0 regularized least trimmed squares (LTS) method and aim to improve estimation efficiency. Compared to the majorization-minimization (MM, Liu et al. (2021)) principle for solving non-convex loss L2E, our proposed OSWLS requires only one step to compute the L0 regularized weighted least squares, resulting in reduced estimation error and computational complexity. Extensive simulations and real data analysis demonstrate the superior performance of our FRBSR procedure in terms of estimation accuracy and prediction compared to the latest robust methods.

Classification and regression in prescriptive analytics: Development of hybrid models and an example of ship inspection by port state control

In prescriptive analytics, unknown quantities are involved in practical decision-making problems, and these unknown quantities need to be predicted using auxiliary data. A classic approach is to develop machine learning (ML) models to generate point estimates, which are then input to the decision making problem in a deterministic manner to prescribe the optimal decision. However, the limited quantity and inevitable errors in the auxiliary data lead to inaccurate predictions and thus sub-optimal decisions. One viable approach to addressing the above issue is to consider the uncertainties in data by inputting the conditional distributions of the unknown quantities on the auxiliary data to the optimization problem on hand, and the distributions are predicted by regression ML models. Meanwhile, it is observed that the quantitative target in some problems are discrete, and these properties are analogous to categorical targets in classification problems. Considering the fact that describing and estimating the distribution of categorical variables are much easier than quantitative variables, this study innovatively develops random forest (RF) models with regression and classification features to generate the distribution of quantitative targets that are discrete. Especially, nodes splitting criteria in the RF models is in a regression manner, while the outputs of individual decision trees and the whole RF model is in a classification manner. Numerical experiments using real port state control (PSC) inspection records and settings at the Hong Kong port are conducted to validate and compare the above prescriptive analytics approaches. The superiority of applying the newly proposed RF model into the development of prescriptive analytics approaches is also demonstrated.

Unidimensional Continuous Variable Quantum Key Distribution under Fast Fading Channel

AbstractIn this paper, the performance of a unidimensional continuous variable quantum key distribution protocol is analyzed using Gaussian modulated coherent state under fast fading channel. In the fast fading channel, both parties are connected in free space, resulting in a harsh propagation environment and pollution caused by atmospheric turbulence. This pollution makes it difficult for the communicators to determine the channel transmittance, which can only be estimated based on a probability distribution. To address this, only one modulator is used to code, which demonstrates performance equivalent to that of the symmetric modulated Gaussian coherent state protocol. The security of the protocol in the face of collective attacks is analyzed and it is proved that it can accept a certain amount of excessive channel noise while simplifying operations. Simultaneously, the impact of finite‐key length effects on the protocol is analyzed. It is also demonstrated that this protocol can reduce costs within an acceptable range of performance losses, making it more practical.

New daily persistent headache after SARS-CoV-2 infection in Latin America: a cross-sectional study

BackgroundPersistent headache is a frequent symptom after coronavirus disease 2019 (COVID-19) and there is currently limited knowledge about its clinical spectrum and predisposing factors. A subset of patients may be experiencing new daily persistent headache (NDPH) after COVID-19, which is among the most treatment-refractory primary headache syndromes.MethodsWe conducted a cross-sectional study in Latin America to characterize individuals with persistent headache after severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection and to identify factors associated with NDPH. Participants over 18 years old who tested positive for SARS-CoV-2 infection and reported persistent headache among their symptoms completed an online survey that included demographics, past medical history, persistent headache clinical characteristics, and COVID-19 vaccination status. Based on participants’ responses, NDPH diagnostic criteria were used to group participants into NDPH and non-NDPH groups. Participant data was summarized by descriptive statistics. Student’s t and Mann–Whitney U tests were used according to the distribution of quantitative variables. For categorical variables, Pearson’s chi-square and Fisher’s exact tests were used according to the size of expected frequencies. Binomial logistic regression using the backward stepwise selection method was performed to identify factors associated with NDPH.ResultsFour hundred and twenty-one participants from 11 Latin American countries met the inclusion criteria. One in four participants met the NDPH diagnostic criteria. The mean age was 40 years, with most participants being female (82%). Over 90% of the participants reported having had mild/moderate COVID-19. Most participants had a history of headache before developing COVID-19 (58%), mainly migraine type (32%). The most predominant clinical characteristics in the NDPH group were occipital location, severe/unbearable intensity, burning character, and radiating pain (p < 0.05). A higher proportion of anxiety symptoms, sleep problems, myalgia, mental fog, paresthesia, nausea, sweating of the face or forehead, and ageusia or hypogeusia as concomitant symptoms were reported in participants with NDPH (p < 0.05). Palpebral edema as a concomitant symptom during the acute phase of COVID-19, occipital location, and burning character of the headache were risk factors associated with NDPH.ConclusionThis is the first study in Latin America that explored the clinical spectrum of NDPH after SARS-CoV-2 infection and its associated factors. Clinical evaluation of COVID-19 patients presenting with persistent headache should take into consideration NDPH.

Why does strawberry fruit weight distribution show positive skewness? A simulation model reveals the underlying processes of fruit production

It is widely accepted that the weight distribution of plant fruits conforms to a standard normal distribution. However, some overlooked evidence suggests that some fruits, including strawberries, exhibit positive skewness in fruit weight distribution. This intriguing observation has received limited attention thus far. To shed light on this phenomenon, we conducted a comprehensive simulation study utilizing greenhouse-grown strawberries as our research subject. We modeled the entire process from bee pollination to pollen fertilization on the stigma and fruit growth. The experimental results demonstrated the reliability of the proposed simulation model and revealed that the positive skewness of the fruit weight distribution was the result of the multiplication of several complex intermediate variable distributions, which led to an approximately lognormal distribution. The simulation model and the derived conclusions presented in this paper offer a plausible explanation for the weight distribution patterns observed in strawberry production systems. Furthermore, research results have the potential to be applied to other berry plants that undergo similar bee pollination processes, thereby expanding our understanding of fruit weight distributions across different species.

Large Scale Prediction with Decision Trees

This article shows that decision trees constructed with Classification and Regression Trees (CART) and C4.5 methodology are consistent for regression and classification tasks, even when the number of predictor variables grows sub-exponentially with the sample size, under natural 0-norm and 1-norm sparsity constraints. The theory applies to a wide range of models, including (ordinary or logistic) additive regression models with component functions that are continuous, of bounded variation, or, more generally, Borel measurable. Consistency holds for arbitrary joint distributions of the predictor variables, thereby accommodating continuous, discrete, and/or dependent data. Finally, we show that these qualitative properties of individual trees are inherited by Breiman’s random forests. A key step in the analysis is the establishment of an oracle inequality, which allows for a precise characterization of the goodness of fit and complexity tradeoff for a mis-specified model. Supplementary materials for this article are available online.

Quantitative analysis of maritime piracy at global and regional scales to improve maritime security

The shipping industry has long been hindered by piracy, which threatens the lives of the crews of ships as well as maritime security. Understanding the characteristics of maritime piracy across different scales can help optimize global routes to reduce shipping risks and improve maritime security. In this study, we propose a framework for exploring and comparing the characteristics of maritime piracy at global and regional scales. Data on piracy incidents were collected from Global Integrated Shipping Information System, developed by the International Maritime Organization, and were reconstructed by using text mining and geospatial techniques. The distribution of the key variables and the spatiotemporal distribution of incidents of maritime piracy were investigated, and records of these incidents were divided into six categories. The differences in the characteristics of maritime piracy across categories were then quantitatively examined. The results show that the Gulf of Guinea had the largest number of pirates, and the distance from incidents to the shoreline, the weapons used by pirates, the types and status of the attacked ships, and the area that the incidents frequented varied significantly across regions. The results of this study can be used by the shipping industry to reduce the risk of maritime piracy.

Law of Probability Distribution Function of The Module of Difference of Discrete Random Variables

The use of the absolute value symbol in theoretical studies often leads to difficulties in analytical operations. However, from a practical point of view, transformations based on absolute differences are the most visual and effective. The article discusses functional transformations, which are modules of the differences of discrete random variables. The concept of the first difference modulus is given. The laws of the probability distribution of random variables obtained as the modulus of the difference of two independent random variables that have the same discrete distribution (Bernoulli or geometric), as well as the distribution laws of new random variables that are the sums of the moduli of the first difference, have been found. The main numerical characteristics of new random variables are calculated.

New Perspective for Using Antimicrobial and Cell-Penetrating Peptides to Increase Efficacy of Antineoplastic 5-FU in Cancer Cells.

This study explores the effectiveness of the antineoplastic agent 5-FU in cancer cells by leveraging the unique properties of cationic antimicrobial peptides (CAMPs) and cell-penetrating peptides (CPPs). Traditional anticancer therapies face substantial limitations, including unfavorable pharmacokinetic profiles and inadequate specificity for tumor sites. These drawbacks often necessitate higher therapeutic agent doses, leading to severe toxicity in normal cells and adverse side effects. Peptides have emerged as promising carriers for targeted drug delivery, with their ability to selectively deliver therapeutics to cells expressing specific receptors. This enhances intracellular drug delivery, minimizes drug resistance, and reduces toxicity. In this research, we comprehensively evaluate the ADMET (absorption, distribution, metabolism, excretion, and toxicity) properties of various AMPs and CPPs to gain insights into their potential as anticancer agents. The peptide synthesis involved a solid-phase synthesis using a Liberty Microwave Peptide Synthesizer. The peptide purity was confirmed via LC-MS and HPLC methods. For the ADMET screening, computational tools were employed, assessing parameters like absorption, distribution, metabolism, excretion, and toxicity. The cell lines A549 and UM-UC-5 were cultured and treated with 5-FU, CAMPs, and CPPs. The cell viability was measured using the MTT assay. The physicochemical properties analysis revealed favorable drug-likeness attributes. The peptides exhibited potential inhibitory activity against CYP3A4. The ADMET predictions indicated variable absorption and distribution characteristics. Furthermore, we assessed the effectiveness of these peptides alone and in combination with 5-FU, a widely used antineoplastic agent, in two distinct cancer cell lines, UM-UC-5 and A549. Our findings indicate that CAMPs can significantly reduce the cell viability in A549 cells, while CPPs exhibit promising results in UM-UC-5 cells. Understanding these multifaceted effects could open new avenues for antiviral and anticancer research. Further, experimental validation is necessary to confirm the mechanism of action of these peptides, especially in combination with 5-FU.

Financial Feasibility Study of Perum Damri Serang Branch Business Study on Transport Trajects of Tanjung Lesung and Sawarna National Tourism Strategic Areas

Transportation refers to the act of relocating or transferring an object from one location to another, with the intention of enhancing its utility or enabling its application for specific purposes in the new destination. Public transportation refers to a form of mass transportation that provides passenger services through a collective travel system. This method of transportation is accessible to the general public, operates on predetermined routes, and requires payment for each individual journey. Public transportation operates on a rental or payment system. The primary function of public transportation is to cater to the mobility needs and interests of the community. Survey research encompasses the examination of both extensive and limited populations through the collection of data from representative samples. Its primary objective is to ascertain the relative prevalence, distribution, and interrelationship of variables. In summary, it may be concluded that both routes did not demonstrate financial feasibility within the initial two-year period, considering a discount rate of 12.5%. While the internal rate of return (IRR) study suggests the possibility of long-term profitability, it is crucial to thoroughly evaluate the short-term risks and losses that are brought to light by the benefit-cost ratio (BCR) and net present value (NPV) calculations. When making decisions on the operation of this route, it is necessary to carefully analyze several elements, such as market conditions, competition, and prevailing economic circumstances. Prior to proceeding with or ceasing route operations, it is imperative to conduct a comprehensive study and engage in strategic planning that aligns with DAMRI's commercial objectives.

ASD and LRFD: Reliability comparison for designs subjected to wind loads

Current design practice in the United States allows engineers to use either Allowable Strength Design (ASD) or Load and Resistance Factor Design (LRFD) method, under the assumption that both methods provide similar levels of economy (available design strengths) and safety (probabilities of failure and reliability indices) regardless of the design conditions. Recent studies have demonstrated that significant differences in economy can arise between ASD- and LRFD-based designs subjected to combinations of gravity and wind loads, suggesting inconsistencies between the safety levels. This study investigates the safety levels provided by the ASD and LRFD methods for such design cases. The safety levels were estimated using a formal reliability analysis with consideration of geometrically non-linear second-order effects. The reliability analysis was performed using Monte Carlo simulations with appropriate probability distributions for the random variables associated with both resistance and loads. The findings of this study indicate that generally accepted uniformity of reliabilities achieved by the ASD and LRFD methods does not hold for design cases governed by wind loads. The safety levels achieved by using the ASD method are less consistent than those achieved by using the LRFD method. The use of the ASD method generally entails the acceptance of failure risks many times greater than for those of the LRFD method, and target safety levels lower than those stipulated by the ASCE 7 Standard. These results highlight the need for a reevaluation of the current design practices to ensure that the required target levels of structural safety are consistently met.

Goodness Of Fit Test In Structural Equation Modeling with Unweighted Least Square (ULS) Estimation Method

Structural equation model (SEM) is a multivariate analysis method that is used to describe a linear relationship simultaneously between indicator variables and latent variables. There are several estimation methods in SEM, one of them is Unweighted Least Square (ULS). The method doesn‟t have specific assumptions about the distribution of variables. This study aims to estimate the model using the ULS method and see the influence of employee competency variables and library facilities on the quality of service at the University of Lampung library. Survey of quality of service in the library of Lampung University is used in the research. Based on the results of the study, it is found that from the three suitability tests, namely the overall model test, the structural model test and the measurement model test using ULS estimation give good results in explaining the compatibility between the model and observation results.&#x0D;

Methods for combining probability and nonprobability samples under unknown overlaps

Nonprobability (convenience) samples are increasingly sought to reduce the estimation variance for one or more population variables of interest that are estimated using a randomized survey (reference) sample by increasing the effective sample size. Estimation of a population quantity derived from a convenience sample will typically result in bias since the distribution of variables of interest in the convenience sample is different from the population distribution. A recent set of approaches estimates inclusion probabilities for convenience sample units by specifying reference sample-weighted pseudo likelihoods. This paper introduces a novel approach that derives the propensity score for the observed sample as a function of inclusion probabilities for the reference and convenience samples as our main result. Our approach allows specification of a likelihood directly for the observed sample as opposed to the approximate or pseudo likelihood. We construct a Bayesian hierarchical formulation that simultaneously estimates sample propensity scores and the convenience sample inclusion probabilities. We use a Monte Carlo simulation study to compare our likelihood based results with the pseudo likelihood based approaches considered in the literature.

Practical, high-speed Gaussian coherent state continuous variable quantum key distribution with real-time parameter monitoring, optimised slicing, and post-processed key distillation

Gaussian coherent state continuous variable quantum key distribution has gained interest owing to its security and compatibility with classical coherent optical fibre networks. For successful system deployment it is necessary to implement practical high speed systems which distil keys efficiently. Here, we demonstrate a Gaussian coherent state continuous variable quantum key distribution system at a 50 MHz symbol rate. Unlike most demonstrations to date which measure excess noise and infer key rates from this, we record signals in real time and distil keys. We also demonstrate, for the first time, slice reconciliation with optimised guard bands to maximise achievable secret key rates. Using this optimisation with multilevel slicing, a record 5 Mb/s secret key rate after a transmission distance of 25 km is achieved. This is a significant improvement on the 3 Mb/s secret key rate which is achieved with single level optimised slice reconciliation.

Systematic framework for handling uncertainty in probabilistic failure analysis of corroded concretes

Due to ambiguous correlations between input random variables and multi-source uncertainties from the electrochemical model, significant differences between deterministic corrosion prediction models and actual measurements are often observed. A systematic framework of quantification of uncertainties is developed for structures with correlated random variables originated from multiple sources, which allows efficiently estimating the failure probability distribution of the steel corrosion over time considering the randomness of the cover depth, the surface chloride concentration, and the chloride diffusion coefficient. After partitioning correlated random variables into different groups based on their uncertain sources, the Morris one-step-at-a-time and Sobol model is established to rank with respect to the importance of each correlated random variable. Based on polynomial chaos expansions and genetic programming methods, a more condensed set of random variables is created to propagate parametric problems. The unknown probability distribution of the input random variables is formulated by the Markov chain Monte Carlo to realize rigorous uncertainty quantification of the structural reliability. The application of the systematic framework to a set of numerical examples of steel corrosion includes experimental validation and uncertainty quantification and propagation of environmental, material and geometric properties. The results show that the framework can be integrated with parametric electrochemical models to allow robustness and reliability of corrosion prediction.

Robust frame synchronization for continuous-variable quantum key distribution with coherent states.

In a practical continuous variable quantum key distribution (CVQKD) system, frame synchronization is crucial for its operation, especially in conditions of low signal-to-noise ratio (SNR) and phase drift. This paper introduces a robust frame synchronization scheme for CVQKD systems that only utilizes quantum signals. The proposed scheme effectively employs randomly selected segments of quantum signals to achieve frame synchronization, eliminating the need for additional modulation. The performance of this scheme applied in a local local oscillator scenario is thoroughly analyzed through numerical simulations. The results demonstrate that the proposed scheme is capable of withstanding low SNR and arbitrary slow phase drift, as well as fast phase drift originates from two independent lasers, while also slightly improving the secret key rate compared to the scheme using inserted synchronization frames. These findings validate the feasibility of implementing the proposed scheme for long-distance CVQKD in practical scenarios.

Fault-displacement models for aggregate and principal displacements

New fault-displacement models (FDMs) are developed for the aggregate and principal net surface displacement using the database developed by the Fault Displacement Hazard Initiative Project. An FDM for the aggregate displacement is developed, which is then partitioned into principal and distributed displacements. The model for the aggregate displacement is first formulated in the wavenumber domain to incorporate seismology-based constraints for the extrapolation of the magnitude scaling of median displacements to large-magnitude events. The results from the wavenumber-domain model are then adjusted to fit the empirical moderate-magnitude scaling (M &lt; 7) and the shape of the displacement profile at the ends of the rupture. Segments are used in the model development to better capture the complexity of the variability of the surface-displacement profile along strike, including regions with zero displacements. For applications in which segments cannot be identified, simplified FDMs without segments are developed that treat the number, lengths, and locations of the segments as aleatory variability. The principal-displacement FDM is then developed as an adjustment to the aggregate-displacement FDM. The segmentation and the magnitude dependence of the taper length of individual segments lead to non-self-similar scaling of the median profile along the entire rupture that can have a significant impact on probabilistic fault-displacement hazard analysis (PFDHA) for sites near the ends of faults. A key feature of the new FDMs is the use of a power-normal [Formula: see text] distribution for the aleatory variability, which leads to narrower distributions of the displacement for large-magnitude earthquakes compared to the commonly used lognormal distribution. For large-magnitude earthquakes, the expected maximum displacements computed using the power-normal distribution of the FDM are consistent with the observed maximum displacements along strike, supporting the narrower shape of the upper tail of the power-normal distribution for large-magnitude events.

Electro-sequence valorization of specific enablers of aquifer vulnerability and contamination: A case of index-based model approach for ascertaining the threats to quality groundwater in sedimentary beds

This work was set up to appraise the quality of groundwater within a newly established university using index-based techniques and the intention is to generate vulnerability potential maps that can guide in avoiding vulnerable aquifer systems. The study used geo-electrical technology to evaluate the lithological sequence of the hydrogeological units and their first-and second-order geo-electric properties, as well as the geochemical constituents of groundwater. The results showed that the hydrogeological units are composed of sequences of sands, including fine to gravelly sands minimally laced with sandy clay, according to the results of geo-electrical technology. Layers 2 and 3 were accessible by the twenty VES (V1-V20) in which current gained access to designated economic and prospective aquifer systems. The aquifer systems considered have resistivity values ranging from 26.3 to 3576.5Ωm and mean value of 832.97Ωm. The five vulnerability models deployed showed very high spatial variability and moderate negative and positive binary correlations, construed to be due to subjectivity in the weight-rate assignment. The coefficients of variation (CV) from sensitivity analysis (SA) indicated variation in the vulnerability index due to topography (9.7%) and the impact of the vadose zone (4.3%), which are marginally higher than the 1.6% displayed by other parameters. The groundwater displayed WHO's disallowed concentrations of heavy ions, and the SA indicated that CV for the groundwater geochemical contamination is variably high in cadmium ions (140.9%) followed by manganese ions (132%) and copper ions (88. 2%) across the wells. The moderately variable contribution distributions across the wells are nickel ions (48.8%), lead ions (41.6%), chromium ions (39.8%) and iron ions (35.0%). All the geophysical and geochemical indices affirm that the groundwater is vulnerable and contaminated and therefore requires monitoring and management.