Abstract
This study proposes a framework for short-term production planning of a Portuguese company operating as a tier 2 supplier in the automotive sector. The framework is intended to support the decision-making process regarding a single progressive hydraulic press, which is used to manufacture cold-stamped parts for exhaust systems. The framework consists of two sequential levels: (1) a Mixed-Integer Linear Programming (MILP) model to determine the optimal production quantities per week while minimizing the total cost; (2) a dynamic production sequencing rule for scheduling operations on the hydraulic press. The two levels are combined and implemented in Excel, where the MILP model is solved using the Solver add-in, and the second level uses the optimal production quantities as inputs to determine the production sequence using a dynamic priority rule. To validate the framework, a proposed optimal plan was compared to a real plan executed by the company, and it was found that the framework could save up to 22.1% of the total cost observed in reality while still satisfying demand. To address uncertainties, the framework requires a rolling weekly planning horizon. © 2023, The Author(s).

Abstract
The increased use of smartphones and the COVID-19 pandemic directly influenced the development of remote tools in several areas. In the context of oncology, it was no different, as several studies address health care or services related to mobile devices. Apps aimed at the medical field (m-health) focus directly on monitoring symptoms and improving interaction between health professionals and patients, combined with the convenience of smartphones. In this context, this work aims to address recent studies on the use of m-health in the clinical practice of oncological diseases and report the characteristics of the apps involved. For this, a review of m-health focused on oncology was conducted using the PubMed and Science Direct databases. The investigation was carried out using tools inherent in international databases and was limited to articles published between 2015 and 2022. In total, 34 articles were analyzed, with a higher frequency of publications between 2019 and 2022. The resources observed were patient follow-up, prevention of signs and symptoms, monitoring of treatment and aid in prognosis and diagnosis of patients. It is concluded that a close collaboration among patients, health professionals, and information technology professionals is necessary to optimize symptom recognition and improve patient-professional communication. Although the pandemic has intensified the increase in the use of m-health, its use is expected to increase in the post-pandemic scenario, bearing in mind the changes in social dynamics and the growing dissemination of technologies. © 2023 ITMA.

Abstract
When designing a custom-made product it is important to provide the customer with a budget that resembles the final price. In this work it will be developed a simple application in Python to perform automatic data extraction from computer aided design (CAD) files to estimate multiple linear regression models with the intent of obtaining a more accurate cost estimate. The application will provide an estimate of the amount of raw material needed and time taken to produce a simple inflatable and related products. © 2023 ITMA.

Abstract

Abstract
Nowadays, cities face severe problems related to traffic management and mobility in general. Therefore, technologies have been developed that can handle these situations and somehow mitigate the caused impact, such as CCTV cameras. However, the techniques for analyzing the images collected by these cameras are increasingly complex and have numerous applications, being dispersed in the literature. Therefore, this article fills an important research gap by presenting a systematic review of the literature on the possible applications of data collected from CCTV cameras and the image analysis and processing techniques that have been developed and proposed in recent years. This systematic review followed the PRISMA statement guidelines and checklist, and three databases were searched, namely Scopus, Web of Science, and Inspec. From the analysis performed, the following applications were identified: Image/video analysis and traffic estimation, pedestrian detection, traffic data analysis, and forecasting, and traffic management. Regarding the image analysis and processing techniques YOLO (only look once), GMM (Gaussian mixture method), morphological methods, fuzzy logic, and other proprietary methods stand out. After a thorough analysis of traffic data, most works still implemented relatively trivial traffic management systems to generate a series of actions to be eventually applied to traffic controllers. Additionally, it was realized that these techniques could be implemented in industrial products from a future perspective. © 2023, Aracne Editrice. All rights reserved.

Abstract
An autonomous vehicle can sense its environment and operate without human involvement. Its adequate management in an intelligent transportation system could significantly reduce traffic congestion and overall travel time in a network. Adaptive traffic signal controller (ATSC) based on multi-agent systems using state-action-reward-state-action (SARSA (?) are well-known state-of-the-art models to manage autonomous vehicles within urban areas. However, this study found inefficient weights updating mechanisms of the conventional SARSA (?) models. Therefore, it proposes a Gaussian function to regulate the eligibility trace vector's decay mechanism effectively. On the other hand, an efficient understanding of the state of the traffic environment is crucial for an agent to take optimal actions. The conventional models feed the state values to the agents through the MinMax normalization technique, which sometimes shows less efficiency and robustness. So, this study suggests the MaxAbs scaled state values instead of MinMax to address the problem. Furthermore, the combination of the A-star routing algorithm and proposed model demonstrated a good increase in performance relatively to the conventional SARSA (?)-based routing algorithms. The proposed model and the baselines were implemented in a microscopic traffic simulation environment using the SUMO package over a complex real-world-like 21-intersections network to evaluate their performance. The results showed a reduction of the vehicle's average total waiting time and total stops by a mean value of 59.9% and 17.55% compared to the considered baselines. Also, the A-star combined with the proposed controller outperformed the conventional approaches by increasing the vehicle's average trip speed by 3.4%.