Quantum software engineering is advancing rapidly in parallel with equally ambitious hardware roadmaps. However, systematic evidence on how online audiences perceive these advances remains scarce. We present an exploratory baseline of Twitter sentiment toward quantum computing, using automated (silver‐standard) labels for benchmarking. Six months of English‐language tweets containing the hashtag #Quantum (December 1, 2022 and May 31, 2023) were processed, with #Quantum treated as a proxy for online discourse on quantum computing. We then applied a transparent natural language processing (NLP) methodology combining two zero‐shot lexicon‐based tools (TextBlob and the Valence Aware Dictionary and sEntiment Reasoner [VADER]) with three lightweight supervised classifiers (multinomial naïve Bayes, Rocchio, and perceptron). Following standard preprocessing and a stratified 70/30 train–test split, we do not aim to measure definitive public opinion; rather, our primary contribution is to establish a transparent and reproducible baseline for future benchmarking. In this context, the multinomial naïve Bayes classifier attained a macro F1‐score of 0.88 on the 30% hold‐out set when benchmarked against the TextBlob silver labels. This score captures internal agreement rather than accuracy against human annotation. All five methods converged on a largely—though not universally—positive sentiment orientation (≈78%–81% of nonneutral tweets, depending on the tool). Grounded in the technology acceptance model (TAM) and the unified theory of acceptance and use of technology (UTAUT), we interpret our results as indicating the constructs of curiosity and perceived usefulness, rather than unequivocal adoption readiness. These constructs were not operationalized and serve only as interpretative lenses. By documenting every preprocessing step and model configuration, and making tweet identifiers and code available upon request, the study delivers a reproducible benchmark against which future work can (i) extend the query vocabulary, (ii) incorporate neutral and fine‐grained emotions, (iii) apply cross‐validation protocols, and (iv) evaluate advanced transformer models on manually annotated data. Addressing these four points is essential before making any definitive claims about public opinion.

Abstract Effective management of development projects is crucial to delivering high‐quality software within time and budget constraints. However, organizing geographically distributed software development activities presents unique challenges, including difficulties in face‐to‐face interaction and coordination. To assist the global software development community in updating and developing new project management techniques, identifying and prioritizing best practices is essential. This study aims to develop a taxonomy based on the prioritization of management practices for software development, drawing from empirical data. Fifty‐five best practices associated with the Project Management Body of Knowledge (PMBOK) fields were identified from the existing literature. The study also empirically examines the acceptability and relevance of these practices within the industry. An analytic hierarchy process within a fuzzy system is employed to prioritize these practices based on their importance in managing global software development.

Regression is often conducted assuming independent model errors. The detection of atypical values in regression (leverage and influential points) assumes independent errors. However, such independence could be unrealistic in geostatistics. In this article, we propose a methodology based on least squares and geostatistics to identify such values in spatial regression. Our procedure uses the hat matrix to detect leverage points. A modified Cook distance is employed to confirm whether these points are influential. The methodology is evaluated with stationary and non-stationary geostatistical data. We apply this methodology to real georeferenced data related to depth, dissolved oxygen, and temperature. First, an autoregressive model is fitted to depth data. Second, a regression between oxygen and temperature is estimated. In both models, spatial correlation is assumed to determine the parameters, leverage, and influential observations. Our methodology can be used in regression with geographical information to avoid misinterpreted results. Not considering this information may under- or over-estimate geographical indicators, such as the mean depth, which can affect the circulation of water masses or dissolved oxygen variability. Our results reveal that including spatial dependence to identify high leverage points is relevant and must be considered in any geostatistical analysis.

Abstract Healthcare systems face various issues related to complex networks of intermediaries and a lack of transaction traceability. The most critical issues are the fragmentation of healthcare data, obstacles in providing efficient research and services, lack of clinical trial reporting, high cost and mismanagement of the drug supply chain, patient data security, and fake drugs. Blockchain technology has the potential to address these criticalities as it has in build traceability mechanisms and promises new business models by enabling incentive structures. This potential of blockchain gathers a high interest in the health industry. However, the implementation of blockchain in healthcare faces various issues as well. Currently, there are no practice‐oriented maturity models to improve such an implementation. In this paper, we present a roadmap to develop a maturity model for blockchain in healthcare (MMBH) based on critical barriers (CBs), critical success factors (CSFs), and the best practices for blockchain implementation in healthcare systems. As a first step to develop the MMBH, in this paper, we present the initial results of a systematic literature review (SLR) to identify critical success factors for implementing blockchain in healthcare systems. We also applied fuzzy technique order preference by similarity to ideal solution (TOPSIS) to prioritize the identified CSFs.

Univariate Birnbaum-Saunders models have been widely applied to fatigue studies. Calculation of fatigue life is of great importance in determining the reliability of materials. We propose and derive new multivariate generalized Birnbaum-Saunders regression models. We use the maximum likelihood method and the EM algorithm to estimate their parameters. We carry out a simulation study to evaluate the performance of the corresponding maximum likelihood estimators. We illustrate the new models with real-world multivariate fatigue data.

We propose goodness-of-fit tests for Birnbaum-Saunders distributions with type-II right censored data. Classical goodness-of-fit tests based on the empirical distribution, such as Anderson-Darling, Cramér-von Misses, and Kolmogorov-Smirnov, are adapted to censored data, and evaluated by means of a simulation study. The obtained results are applied to real-world censored reliability data.

The Birnbaum-Saunders distribution is a useful model for describing fatigue and reliability data. This model allows us to relate the total time until the failure to some type of cumulative damage. The majority of the models based on the Birnbaum-Saunders distribution have assumed fixed-effects, and a few have been investigated for correlated data. In this work, we introduce Birnbaum-Saunders mixed models for censored data. Specifically, we estimate their parameters by means of the Gauss-Hermite quadrature approximation, carry out a residual analysis for these models, and conduct an application using real censored reliability data. This application illustrates the utility of a Birnbaum-Saunders random intercept model.

The Birnbaum-Saunders distribution has largely been applied to material fatigue and reliability studies to relate the time until failure to some type of cumulative damage. This damage is produced by the growth of a dominant crack in material specimens, the propagation of which is due to cyclic patterns of stress. However, the Birnbaum-Saunders model was constructed under restrictive conditions that may not be valid for certain applications. In particular, this model was developed assuming that the crack extension in each cycle has a constants-mean over time. In this article, we assume the crack extensions in each cycle have a non-constants-mean. This leads us to introduce a non-central version of the Birnbaum-Saunders distribution. Specifically, a comprehensive description of the properties and characteristics of this new model is presented. The suitability of this non-central distribution is shown by using real, and simulated data.