IntroductionThe rapid growth of e-commerce has increased pressure on postal logistics networks, especially in remote regions.MethodsThis study proposes a fuzzy hyperheuristic genetic algorithm for optimizing a facilitated hub-and-spoke network with drone integration under uncertainty.ResultsThe proposed approach improves service robustness by 25–35% and expands drone coverage to 93.4% of remote demand, with only a modest cost increase (8–12%).DiscussionResults highlight the effectiveness of fuzzy optimization and adaptive hyperheuristics in designing resilient and cost-efficient postal logistics systems.
Approximate Bayesian computation (ABC) constitutes a class of computational methods rooted in Bayesian statistics. In all model-based statistical inference, the likelihood function is of central importance, since it expresses the probability of the observed data under a particular statistical model, and thus quantifies the support data lend to particular values of parameters and to choices among different models. For simple models, an analytical formula for the likelihood function can typically be derived. However, for more complex models, an analytical formula might be elusive or the likelihood function might be computationally very costly to evaluate. ABC methods bypass the evaluation of the likelihood function. In this way, ABC methods widen the realm of models for which statistical inference can be considered. ABC methods are mathematically well-founded, but they inevitably make assumptions and approximations whose impact needs to be carefully assessed. Furthermore, the wider application domain of ABC exacerbates the challenges of parameter estimation and model selection. ABC has rapidly gained popularity over the last years and in particular for the analysis of complex problems arising in biological sciences (e.g., in population genetics, ecology, epidemiology, and systems biology).
tout travail de recherche. Elle se doit d’être la plus pertinente et la plus exhaustive possible.
En sciences de la santé, dans un contexte de facilité d’accès et de surabondance d'informations numériques, il est légitime de se demander comment les universitaires et les professionnels d’aujourd’hui accèdent à une information de qualité et quelle place occupe encore la consultation des documents imprimés de la bibliothèque dans leur recherche.
Objectif : Identifier les pratiques et besoins des internes, résidents et spécialistes de la Faculté de médecine dentaire de Casablanca (FMDC) en terme de recherche documentaire.
Matériel et méthodes : Une étude descriptive transversale a été menée auprès de l'ensemble des internes, résidents et spécialistes de la FMDC depuis octobre jusqu'à décembre 2021. Pour collecter les données, un formulaire comportant 4 sections comprenant l’identification des répondants, les difficultés rencontrées lors de la recherche en ligne et la fréquentation et l’utilisation des services de la bibliothèque de la FMDC a été utilisé.
Résultats : 68 praticiens ont participé à l’étude. Les principales bases de données consultées en ligne étaient PubMed (97,1%), Google scholar (75%) et Sciencedirect (73,5%). 85,3% des répondants rencontraient des difficultés notamment pour identifier les MESH appropriés. 70,1% se rendaient ponctuellement à la bibliothèque pour consulter des livres (85,1%) et des thèses (80,6%). Pour 65,2% la bibliothèque n’offrait pas d’accès facile aux ordinateurs et à Internet. 98,5% souhaiteraient bénéficier de formations de perfectionnement en recherche documentaire.
Conclusion : Pour une utilisation judicieuse des principaux outils de recherche selon une approche méthodologique simplifiée et raisonnée, une formation continue en recherche documentaire est à prévoir à la FMDC, et pour augmenter l’attractivité de la bibliothèque, des efforts restent à faire tels qu’assurer une meilleure flexibilité, améliorer les services d’informatisation à large échelle et diffuser en instantané les nouvelles acquisitions.
The main aim of this paper is to present our approach of obtaining sufficient conditions for convergence of sequences of Henstock-Kurzweil integrable functions. Our approach involves the use of the concept of multiplier functions, where we define a class Φ of multipliers for the Henstock-Kurzweil integral. We consider a sequence (fn) of Henstock-Kurzweil integrable functions on a non-degenerate interval [a, b] and we assume that (fn) converges point wise to a function f. Then we show that f is Henstock-Kurzweil integrable and its integral is equal to the limit of the sequence (∫abfn) if there exists φ∈Φ such that the defined functionals of the type F (φ, fn) satisfy the imposed conditions. Beside the fact that the results regarding the convergence under the integral sign are always of great importance, the method introduced here can be imitated and used to obtain other results on the related areas.
Vivin Nur Aziza, Fatma Hilali Moh'd, Firda Aulia Maghfiroh
et al.
The impact of the COVID-19 pandemic on the air transportation sector, particularly Soekarno-Hatta (Soetta) International Airport, has been quite significant. The number of passengers at Soetta Airport has decreased due to the COVID-19 pandemic, but flight activities are still ongoing to this day. An accurate forecasting model is needed to predict the number of airline passengers at Soetta Airport with the presence of the COVID-19 pandemic as an intervention. In this study we discuss performance comparison of two models namely SARIMA intervention and Prophet in forecasting the number of domestic passengers at Soetta Airport. The research results showed that the best SARIMA intervention model was SARIMA (0,1,1)(1,0,0)12 b = 0, s = 20, r = 0, with a Mean Absolute Percentage Error (MAPE) of 28% and Root Mean Square Error (RMSE) of 433473. On the other hand, the Prophet model yielded a MAPE of 37% and an RMSE of 497154. In terms of MAPE and RMSE, the SARIMA intervention method provides better results than the Prophet model in forecasting the number of domestic passengers at Soetta Airport.
Clustering is a data analysis process which applied to classify the unlabeled data. Fuzzy clustering is a clustering method based on membership value which enclosing set of fuzzy as a measurement base for classification process. Fuzzy Subtractive Clustering (FSC) is included in one of fuzzy clustering method. This research applies Hamming distance and combined Minkowski Chebysev distance as a distance parameter in Fuzzy Subtractive Clustering. The objective of this research is to compare the output quality of the cluster from Fuzzy Subtractive Clustering by using Hamming distance and combine Minkowski Chebysev distance. The comparison of the two distances aims to see how well the clusters are produced from two different distances. The data used is data on hypertension. The variables used are age, gender, systolic pressure, diastolic pressure, and body weight. This research shows that the Partition Coefficient value resulted on Fuzzy Subtractive Clustering by applying combined Minkowski Chebysev distance is higher than the application of Hamming distance. Based on this, it can be concluded that in this study the quality of the cluster output using the combined Minkowski Chebysev distance is better.
In this paper, we obtain exact solutions of a new modification of the Schrödinger equation related to the Bessel q -operator. The theorem is proved on the existence of this solution in the Sobolev-type space Wq2(R+q ) in the q -calculus. The results on correctness in the corresponding spaces of the Sobolev-type are obtained. For simplicity, we give results involving fractional q -difference equations of real order a> 0 and given real numbers in q -calculus. Numerical treatment of fractional q -difference equations is also investigated. The obtained results can be used in this field and be supplement for studies in this field.
Mathematical physics has many facets, of which we shall briefly give a (very partial) description, centered around those of main interest for Elliott and us (Moshe Flato and I). In our case these aspects had as a corollary a variety of "parascientific activities", in particular the foundation of IAMP (the International Association of Mathematical Physics) and of the journal LMP (Letters in Mathematical Physics), both of which were strongly impacted by Elliott, and Elliott's long insistence that publishers do not demand "copyright transfer" as a precondition for publication but are satisfied with "consent to publish", which is increasingly becoming standard. Since this article is testimony to the huge scientific impact of Elliott, the latter intertwined aspects constitute the core of the present contribution. The last part deals briefly with metaphysical and metamathematical considerations related to axioms.
We investigated whether dichotomous data showed the same latent structure as the interval-level data from which they originated. Given constancy of dimensionality and factor loadings reflecting the latent structure of data, the focus was on the variance of the latent variable of a confirmatory factor model. This variance was shown to summarize the information provided by the factor loadings. The results of a simulation study did not reveal exact correspondence of the variances of the latent variables derived from interval-level and dichotomous data but shrinkage. Since shrinkage occurred systematically, methods for recovering the original variance were fleshed out and evaluated.
Statistical tolerance intervals are commonly employed in biomedical and pharmaceutical research, such as in lifetime analysis, the assessment of biosimilarity of branded and generic versions of biopharmaceutical drugs, and in quality control of drug products to ensure that a specified proportion of the products are covered within established acceptance limits. Exact two‐sided parametric tolerance intervals are only available for the normal distribution, while exact one‐sided parametric tolerance limits are available for a limited number of distributions. Approximations to two‐sided parametric tolerance intervals often use the Bonferroni correction on the one‐sided tolerance interval calculation; however, this often incurs a higher coverage probability than the nominal level. Recently, the usage of a bootstrap calibration has been demonstrated as a way to improve coverage probabilities of tolerance intervals for very specific and complex distributional settings. We present a focused treatment on using a single‐layer bootstrap calibration to improve the coverage probabilities of two‐sided parametric tolerance intervals. Simulation results clearly demonstrate the improved coverage probabilities towards the nominal level over the uncalibrated setting. Applications to medical data for various parametric distributions also highlight the utility of constructing these calibrated tolerance intervals.
Nelson Kiprono Bii, Christopher Ouma Onyango, John Odhiambo
Nonresponse is a potential source of errors in sample surveys. It introduces bias and large variance in the estimation of finite population parameters. Regression models have been recognized as one of the techniques of reducing bias and variance due to random nonresponse using auxiliary data. In this study, it is assumed that random nonresponse occurs in the survey variable in the second stage of cluster sampling, assuming full auxiliary information is available throughout. Auxiliary information is used at the estimation stage via a regression model to address the problem of random nonresponse. In particular, auxiliary information is used via an improved Nadaraya–Watson kernel regression technique to compensate for random nonresponse. The asymptotic bias and mean squared error of the estimator proposed are derived. Besides, a simulation study conducted indicates that the proposed estimator has smaller values of the bias and smaller mean squared error values compared to existing estimators of a finite population mean. The proposed estimator is also shown to have tighter confidence interval lengths at 95% coverage rate. The results obtained in this study are useful for instance in choosing efficient estimators of a finite population mean in demographic sample surveys.
In this paper, two known theorems dealing with $|\bar{N},p_{n}|_{k}$ summability of infinite series and Fourier series have been generalized to ${\varphi}-|\bar{N},p_{n};\beta|_{k}$ summability.
Azid Maarof Nur Niswah Naslina, Arasan Jayanthi, Zulkafli Hani Syahida
et al.
This research focuses on assessing the goodness of fit for the Gompertz model in the presence of right and interval censored data with covariate. The performance of the maximum likelihood estimates was evaluated via a simulation study at various censoring proportions and sample sizes. The conclusions were drawn based on the results of bias, standard error and root mean square error at different settings. Following that, another simulation study was carried out to compare the performance of the proposed modifications to the Cox-Snell residuals for both censored and uncensored observations at different combinations of sample sizes and censoring levels. The results show that standard error and root mean square error values of the parameter estimates increase with the increase in censoring proportions and decrease in the number of sample size. This indicates that the estimates perform better when sample sizes are larger and censoring proportions are lower. The performance of the proposed modifications of the Cox-Snell residuals showed that they perform slightly better than existing method.
The subject of probability and statistics is easily dismissed by students as assemblages of formulae to be rote-memorized. We propose here an integration of simulation-based activities with certain mathematical paradoxes using patchwork assessment to first-year undergraduates, such that they can better appreciate the real-world context of probability and statistics. The proposed examples alongside various facilitation skills for the instructor are discussed. We also provide an original spreadsheet simulation program in Excel and Visual Basic for Applications to reproduce the numerical experiments. This program is capable of running Monte Carlo simulations for all three seminal Parrondo’s paradox variants, and can be easily used by students and instructors; moreover, the computed datasets and code are fully-transparent, thereby allowing interactive discussions, modifications and extensions, and further analyses. Our findings suggest that the proposed teaching strategy is useful, and we hope that this work will initiate the significant adoption of paradoxical simulations in teaching practice. The interactive program is freely available on open science framework.
Compounding distributions is the most common method in lifetime analysis to obtain more flexible families of distributions. Based on the beta Marshall–Olkin generated family, we present a new four-parameter distribution, so-called the beta Marshall–Olkin Lomax, for lifetime applications. We obtain some of its properties from those of well-established distributions. We provide a simulation study to illustrate the performance of the maximum likelihood estimates. An application to uncensored data is carried out and we use some goodness-of-fit statistics to study the flexibility of the new distribution, proving empirically that this model can be appropriate for lifetime applications.
Rahim Mahmoudvand , Paulo Canas Rodrigues , Masoud Yarmohammadi
In this paper, daily exchange rates in four of the BRICS emerging economies: Brazil, India, China and South Africa, over the period 2001 to 2015 are considered. In order to predict the future of exchange rate in these countries, it is possible to use both univariate and multivariate time series techniques. Among different time series analysis methods, we choose singular spectrum analysis (SSA), as it is a relatively powerful non-parametric technique and requires the fewest assumptions to be hold in practice. Both multivariate and univariate versions of SSA are considered to predict the daily currency exchange rates. The results show the superiority of MSSA, when compared with univariate SSA, in terms of mean squared error.
Think-aloud interviews have been a valuable but underused tool in statistics education research. Think-alouds, in which students narrate their reasoning in real time while solving problems, differ in important ways from other types of cognitive interviews and related education research methods. Beyond the uses already found in the statistics literature -- mostly validating the wording of statistical concept inventory questions and studying student misconceptions -- we suggest other possible use cases for think-alouds and summarize best-practice guidelines for designing think-aloud interview studies. Using examples from our own experiences studying the local student body for our introductory statistics courses, we illustrate how research goals should inform study-design decisions and what kinds of insights think-alouds can provide. We hope that our overview of think-alouds encourages more statistics educators and researchers to begin using this method.
Lecturer Evaluation System (e-CEvas) is used to measure lecturers’ strength and weaknesses every end of semester by the students. However, the current lecturer evaluation system had many problems such as slow performance, limited interactivity, loss of states and the data transmission had redundant code that was unnecessary in each page. To overcome those stated problem, AJAX technology is used to make the lecturer evaluation system much faster, easier to use, more reliable and increase the performance. This study applied AJAX technology in the development together with Visual Studio Ultimate 2012 as the editor, ASP.NET C# language for coding and SQL Server as the database. V-model framework approach was used in developing this research study which involves requirement analysis, system design, architectural design, module design, unit testing, integration testing, system testing and acceptance testing. Microsoft Visual Studio Web Performance and Load Testing software is used to measure the performance of AJAX technology and the result shows that differences of response time proved that AJAX is powerful web development technique for programming. User Acceptance Test was carried out to measure the satisfaction of users when using e-CEvas which the result shown that most respondents are agree to all instructions in the system and the organization of information on the screens is clear.
Corinne A. Jones, Corinne A. Jones, Corinne A. Jones
et al.
Any movement performed repeatedly will be executed with inter-trial variability. Oropharyngeal swallowing is a complex sensorimotor action, and swallow-to-swallow variability can have consequences that impact swallowing safety. Our aim was to determine an appropriate method to measure swallowing pressure waveform variability. An ideal variability metric must be sensitive to known deviations in waveform amplitude, duration, and overall shape, without being biased by waveforms that have both positive and sub-atmospheric pressure profiles. Through systematic analysis of model waveforms, we found a coefficient of variability (CV) parameter on waveforms adjusted such that the overall mean was 0 to be best suited for swallowing pressure variability analysis. We then investigated pharyngeal swallowing pressure variability using high-resolution manometry data from healthy individuals to assess impacts of waveform alignment, pharyngeal region, and number of swallows investigated. The alignment that resulted in the lowest overall swallowing pressure variability was when the superior-most sensor in the upper esophageal sphincter (UES) reached half its maximum pressure. Pressures in the tongue base region of the pharynx were least variable and pressures in the hypopharynx region were most variable. Sets of 3–10 consecutive swallows had no overall difference in variability, but sets of two swallows resulted in significantly less variability than the other dataset sizes. This study identified variability in swallowing pressure waveform shape throughout the pharynx in healthy adults; we discuss implications for swallowing motor control.