Portuguese Air Transportations (Portugal)

Automated Fiber Placement (AFP) is a critical process in composite manufacturing, where precise fiber tow placement is essential for achieving high-quality and high-performance engineering components. However, deviations in process variables frequently lead to defects such as gaps and overlaps, which can compromise structural integrity. While various monitoring techniques exist, accurately predicting and understanding the formation of these defects from complex sensor data remains challenging. This work introduces a novel application of a Transformer-based deep learning architecture to enhance the estimation of gap widths in AFP. Leveraging a publicly available industrial AFP dataset, our methodology incorporates a customized positional encoding scheme to effectively integrate the critical spatial context of the tow layup process. The model’s predictive performance was evaluated, achieving a Mean Absolute Percentage Error (MAPE) of 1.04% and an R-squared (R2) value of 0.9143, demonstrating its capability for accurate gap width estimation. Furthermore, SHapley Additive exPlanations (SHAP) analysis was employed to assess the complex interplay between sources of manufacturing process variation. This study establishes the Transformer architecture as a promising and interpretable data-driven tool for AFP process monitoring. The results serve as a proof of concept for attention-based virtual metrology, offering a pathway towards deeper process understanding and defect mitigation.

Land abandonment and recurrent wildfires are major drivers of landscape transformation in Mediterranean Europe, creating favorable conditions for the spread of non-native invasive woody species. Among these, Australian wattles (genus Acacia) are particularly widespread and problematic in Portugal. This work analyzed the spatiotemporal dynamics of Acacia spp. in two municipalities of central Portugal (Sertã and Pedrógão-Grande) by combining multitemporal photointerpretation of aerial imagery (2004–2021), generalized additive models (GAMs), and local perception surveys. Results reveal a 417% increase in occupied area over the last two decades. Modeling outcomes indicate a temporal shift in invasion drivers: from an establishment phase (2004–2010), mainly constrained by altitude and proximity to primary introduction sites, to a disturbance-driven expansion phase (2010–2021), influenced by fire recurrence, slope, and land-use context. Spatial clustering persisted throughout, underscoring the role of founder populations. Surveys confirmed high public awareness of Acacia invasiveness and identified abandonment and wildfire as the main perceived triggers of spread. By integrating ecological and social dimensions, this study provides a socioecological perspective on Acacia spp. expansion in Mediterranean rural landscapes and highlights the urgent need for integrated, landscape-scale management strategies.

Abstract The integration of environmental, social and governance (ESG) aspects in organizational activities has become a priority; more and more companies are taking ESG criteria into account in order to successfully manage global challenges and remain competitive. In this context, this article combines a systematic literature review and a bibliometric review in order to provide an overview of the opportunities and risks associated with ESG features, based on relevant articles in the Scopus database. The distribution of articles by year, country, author, journal, and the links that exist between the keywords in the analyzed articles are described in detail in the article. The results indicate a significant increase in interest in ESG research during the period 2021–2024. Moreover, the analyses revealed that the inclusion of ESG aspects at the organizational level can contribute to reducing the risks faced by companies as well as to increasing opportunities. Also, the analysis of the most significant articles in the literature revealed the interdisciplinary nature of ESG criteria; their approach within organizations is not optional, but a necessity for companies to become resilient. Thus, this article represents an important resource for researchers who want to analyze the ESG – risks – opportunities relationship at the organizational level because this study provides an overview of the current situation, highlighting the main gaps as well as future research directions.

Hernia is a physiological condition that significantly impacts patients’ quality of life. Surgical treatment for hernias often involves the use of specialized meshes to support the abdominal wall. While this method is highly effective, it frequently leads to complications such as pain, infections, inflammation, adhesions, and even the need for revision surgeries. According to the Food and Drug Administration (FDA), hernia recurrence rates can reach up to 11%, surgical site infections occur in up to 21% of cases, and chronic pain incidence ranges from 0.3% to 68%. These statistics highlight the urgent need to improve mesh technologies to minimize such complications. The design and material composition of meshes are critical in reducing postoperative complications. Moreover, integrating drug-eluting properties into the meshes could address issues like infections and inflammation by enabling localized delivery of antibiotics and anti-inflammatory agents. Mesh design is equally important, with innovative structures like auxetic designs offering enhanced mechanical properties, flexibility, and tissue integration. These advanced designs can distribute stress more evenly, reduce fatigue, and improve performance in areas subjected to high pressures, such as during intense coughing, sneezing, or heavy lifting. Technological advancements, such as 3D printing, enable the precise fabrication of meshes with tailored designs and properties, providing new opportunities for innovation. By addressing these challenges, the development of next-generation mesh implants has the potential to reduce complications, improve patient outcomes, and significantly enhance quality of life for individuals undergoing hernia repair.

This paper develops a fertilizer supply index to measure global fertilizer supply disruptions using text-mining analysis of newspaper articles from over 30 countries. The index captures events including export restrictions, tariff changes, raw material shortages, and international sanctions. Using a structural BVAR framework, the analysis examines how fertilizer supply shocks affect Portugal’s food industry across three dimensions: market activity (domestic and foreign turnover), output prices, and labor market dynamics. The results show that fertilizer supply shocks trigger immediate increases in output prices across both domestic and foreign markets, with firms passing input costs to consumers. Labor market responses are delayed: wages and hours worked remain stable initially before rising, consistent with delayed adjustment to inflation expectations. This research also provides new evidence on how a critical agricultural input shocks propagate through food supply chains in a small open economy, with implications for both policymaking and industry strategy. • A fertilizer supply index tracks global disruptions through news analysis. • Fertilizer supply shocks quickly impact the food industry. • The shocks propagate throughout the broader macroeconomy.

This study examined impulsivity in antisocial personality disorder (ASPD) and obsessive-compulsive personality disorder (OCPD) in the context of criminal offending. Participants completed the Demographic, Criminal, and Institutional Behavior Interview, the International Personality Disorder Examination, the Symptom Checklist-90-Revised, and the Impulsive Behavior Scale, that the APD and OCPD groups can commit the same crimes (against people and against objects). The results revealed that ASPD and OCPD groups can commit the same crimes (against human and against objects), although they differ in terms of impulsivity [Negative Urgency (UN), Sensation Seeking (BS) and Positive Urgency (UP)], with the OCPD group scoring lower on these dimensions. In the ASPD group, those who had committed crimes against human scored higher in UN, BS and UP than those who had committed crimes against objects. In the OCPD group, those who had committed crimes against human scored higher on UN and lower on BS and UP than those who had committed crimes against objects. The importance of analyzing personality disorder profiles for a better understanding of criminal behavior is emphasized.

Repetitive movements and the speed of upper limbs increase the risk of musculoskeletal disorders. This study aimed to analyse the risk of shoulder injuries in repetitive tasks by evaluating the humerus angle, angular velocity, and angular acceleration during simulated chicken wing cutting. The study was conducted in a laboratory simulating a real environment. Thirty-six healthy right-handed volunteers were assessed using an electromagnetic tracking device, TrakSTAR, integrated with MotionMonitor™ (Innovative Sports Training, Inc. Chicago, IL) and software to collect 3-D kinematic data developed in the research centre. The equipment measured the angles performed by the upper limbs during the entire movement. The humerus angles were automatically transformed into angular velocity (°·s−1) and angular acceleration (°·s−2). Maximum angular velocities were 27.39°·s−1 (men, right humerus) and 22.39°·s−1 (women, left humerus), both below the safe limit. Maximum accelerations were 25.32°·s−2 (men, left side) and 28.94°·s−2 (women, left side); safety values for these accelerations are not established. Monotony is a risk factor, especially for the dominant side. Future studies should evaluate risk factors simultaneously in repetitive tasks. Repetitiveness exceeds the safe limit according to the OCRA method.

As modern systems are becoming more complex their control strategy cannot longer relay only on measurement information that usually comes from probes but also from mathematical models. Those systems models can lead to unbearable computation times due to their own complexity, turning the control process non-viable, which leads to the implementation of surrogate models that enable to achieve estimates within acceptable time to take decisions. A control trained with Deep Reinforcement Learning algorithm, using a Physics-Informed Neural Network to obtain the temperature map on the following time step, replaces the need of running Direct Numerical Simulations. On this work we considered an 1D heat conduction problem, which temperature distribution feeds a control system to activate a heat source aiming to obtain a constant, previously defined, temperature value. With this approach, control training becomes much faster without the need of performing numerical simulations or laboratory measurements, as well the control is taken based on Neural Network enabling its implementation on simple processors to edge computing.

Abstract Buying online has become a widespread and common activity for consumers, and, for many organizations, e-commerce has become a very profitable alternative to sell their products and services, also allowing them to leverage their strategy in new geographical markets immediately. Although the literature on the subject is comprehensive, there is a gap in identifying the holistic constructs that are the determinants of consumers' choice of an online store. This research resorts to an exploratory study, based on a nonsystematic literature review, seeking to identify these constructs. The results obtained allowed us to identify the following constructs: consumer behavior, customer experience, web content, catalog, terms and conditions, customer support, perceived value, trust, security and privacy, satisfaction, and loyalty. Customer experience, satisfaction, and loyalty constructs stand out from a strategic perspective.