Abstract
Censored data are frequently found in diverse fields including environmental monitoring, medicine, economics and social sciences. Censoring occurs when observations are available only for a restricted range, e.g., due to a detection limit. Ignoring censoring produces biased estimates and unreliable statistical inference. The aim of this work is to contribute to the modelling of time series of counts under censoring using convolution closed infinitely divisible (CCID) models. The emphasis is on estimation and inference problems, using Bayesian approaches with Approximate Bayesian Computation (ABC) and Gibbs sampler with Data Augmentation (GDA) algorithms.

Abstract
The massive industrialization of products in a factory environment requires testing the product at a stage before its exportation to the sales market. For example, the end-of-line tests at Continental Advanced Antenna contribute to the validation of an antenna's functionality, a product manufactured by this organization. In addition, the storage of information from the testing process allows the data manipulation through automated machine learning algorithms in search of a beneficial contribution. Studies in this area (automatic learning/machine learning) lead to the search and development of tools designed with objectives such as preventing anomalies in the production line, predictive maintenance, product quality assurance, forecast demand, forecasting safety problems, increasing resources, proactive maintenance, resource scalability, reduced production time, and anomaly detection, isolation, and correction. Once applied to the manufacturing environment, these advantages make the EOL system more productive, reliable, and less time-consuming. This way, a tool is proposed that allows the visualization and previous detection of trends associated with faults in the antenna testing system. Furthermore, it focuses on predicting failures at Continental's EOL.

Abstract
Water vapor sorption is a powerful tool for the analysis of cement paste, one of the most used substances by mankind. The monitoring of cementitious materials is fundamental for the improvement of infrastructure resilience, which has a deep impact on the economy, the environment, and on society. In this work, a multimode fiber was embedded in cement paste for real-time monitoring of cement paste water vapor sorption. Changes in the reflected light intensity due to the build-up of water in the cement paste's pores were exploited for this purpose. The sample was 7-day moist cured, and the relative humidity was controlled between 8.9% and 97.6%. Reflected light intensity was converted into a specific surface area of cement paste (133 m(2)/g) and thickness of water through the Brunauer-Emmett-Teller (BET) method and into a pore size distribution through the Barret-Joyner-Halenda (BJH) method. The results achieved through reflected light intensity agree with those found in the literature, validating the usage of this setup for the monitoring of water vapor sorption, breaking away from standard gravimetric measurements.

Abstract
Purpose orporate social responsibility (CSR) is an evolving concept which is increasingly being adopted by companies with the purpose of creating sustained organizational growth. However, while the impact of CSR practices on employees' behaviors and attitudes has been recognized over the years, the relationship between CSR practices and employee performance remains underexplored. Design/methodology/approach Drawing on social identity theory and using the partial least squares structural equation method, this research examines the impact of CSR practices on employees' performance in a sample of 171 employees belonging to the construction industry. Findings The findings do not support the existence of a direct relationship between employees' perception of CSR and their performance; instead, they indicate that this relationship is mediated by job satisfaction and organizational trust. Research limitations/implications The data concerns employees' self-reported measures on their perceived CSR and the study was conducted in a single industry. Practical implications Adopting CSR initiatives in company strategies is worthy as the perceptions of employees and their performance is positively influenced by their organization's CSR activities. Managers should properly communicate and involve internal stakeholders in socially responsible practices to increase their awareness. Originality/value This article analyzes the impact of employees' perception of CSR on employees' performance through the roles of employee organizational trust and job satisfaction as mediating variables in a highly socially pressured industry such as construction.

Abstract
Happiness at work has been increasingly attracting the attention of academics and human resources managers. Literature on the topic provides clear evidence of the benefits for companies resulting from the adoption of strategies that promote happiness among employees. Despite its growing popularity, companies that define and implement a happiness strategy within their internal marketing are still scarce, particularly small and medium companies (SMEs). This paper illustrates the impact of happiness at work perceived by employees of SMEs at three levels: in themselves, in customers, and in the business's success, in the particular case of companies that do not implement such strategies. The research question was: what is the perception of employees on happiness at work outcomes when the company has no explicit strategy to promote it? This article includes a qualitative study comprising twelve semi-structured interviews with employees who directly deal with customers while working in various B2C companies that do not have a defined strategy to stimulate happiness at work. The study shows employees' acknowledgment of the multidimensional impacts of happiness at work, which makes them more motivated, productive, and more able to influence their relationships with customers positively. Based on these findings, even when lacking clear corporate strategies to improve happiness at work, the company is still expected to benefit in terms of customer loyalty and overall profitability, as well as in terms of employees' affective commitment.

Abstract
Artificial intelligence is emerging in the field of veterinary medical imaging. The development of this area in medicine has introduced new concepts and scientific terminologies that professionals must be able to have some understanding of, such as the following: machine learning, deep learning, convolutional neural networks, and transfer learning. This paper offers veterinary professionals an overview of artificial intelligence, machine learning, and deep learning focused on imaging diagnosis. A review is provided of the existing literature on artificial intelligence in veterinary imaging of small animals, together with a brief conclusion.Artificial intelligence and machine learning have been increasingly used in the medical imaging field in the past few years. The evaluation of medical images is very subjective and complex, and therefore the application of artificial intelligence and deep learning methods to automatize the analysis process would be very beneficial. A lot of researchers have been applying these methods to image analysis diagnosis, developing software capable of assisting veterinary doctors or radiologists in their daily practice. This article details the main methodologies used to develop software applications on machine learning and how veterinarians with an interest in this field can benefit from such methodologies. The main goal of this study is to offer veterinary professionals a simple guide to enable them to understand the basics of artificial intelligence and machine learning and the concepts such as deep learning, convolutional neural networks, transfer learning, and the performance evaluation method. The language is adapted for medical technicians, and the work already published in this field is reviewed for application in the imaging diagnosis of different animal body systems: musculoskeletal, thoracic, nervous, and abdominal.