Did industrial and export complexity drive regional economic growth in Brazil?

Research on productive structures has shown that economic complexity conditions economic growth. However, little is known about which type of complexity, e.g.

Similarity- and neighbourhood-based dynamic models for infection data: Uncovering the complexities of the COVID-19 infection risks.

Understanding spatial and temporal risk dependencies and correlation is crucial when studying infectious diseases which spread out in consecutive waves. By analysing weekly COVID-19 case data collected from the disease's first reported case on March 3, 2020, to April 22, 2021, in 278 municipalities in Mainland Portugal, we demonstrate that the complexity of infection risks varies based on the outbreak's severity, suggesting that a single model definition is insufficient to explain the multifaceted underlying phenomena. This study employs a dynamic, conditionally specified Gaussian Markov random field model with a novel approach to characterise COVID-19 infection risk dependencies through the similarity of areal-level covariates within a Bayesian hierarchical model framework that accounts for each identifiable wave.

Predictive Factors Driving Positive Awake Test in Carotid Endarterectomy Using Machine Learning.

Background: Positive neurologic awake testing during the carotid cross-clamping may be present in around 8% of patients undergoing carotid endarterectomy (CEA). The present work aimed to assess the accuracy of an artificial intelligence (AI)-powered risk calculator in predicting intraoperative neurologic deficits (INDs).

Methods: Data was collected from carotid interventions performed between January 2012 and January 2023 under regional anesthesia.

Top 10+1 indicators for assessing forest ecosystem conditions: A five-decade fragmentation analysis.

Globally, land use change has consistently resulted in greater losses than gains in aboveground biomass (AGB). Forest fragmentation is a primary driver of biodiversity loss and the depletion of natural capital. Measuring landscape characteristics and analyzing changes in forest landscape patterns are essential for accounting for the contributions of forest ecosystems to the economy and human well-being.

A New Research Model for Artificial Intelligence-Based Well-Being Chatbot Engagement: Survey Study.

Background: Artificial intelligence (AI)-based chatbots have emerged as potential tools to assist individuals in reducing anxiety and supporting well-being.

Objective: This study aimed to identify the factors that impact individuals' intention to engage and their engagement behavior with AI-based well-being chatbots by using a novel research model to enhance service levels, thereby improving user experience and mental health intervention effectiveness.

Methods: We conducted a web-based questionnaire survey of adult users of well-being chatbots in China via social media.

Primary healthcare as a strategy for eliminating hepatitis C: the METRIC toolkit.

Aim: This paper presents the development of the METRIC toolkit, aimed at enhancing primary healthcare interventions in the context of hepatitis C control, thus contributing to the World Health Organization's global strategy to achieve the elimination of the disease by 2030.

Background: At the global level, most people living with hepatitis C are unaware of their condition. As such, the eradication of hepatitis C necessitates comprehensive strategies within primary healthcare settings, as it provides an opportunity to reach the general population, facilitates the identification of potential patients who may be unfamiliar with hepatitis C, and guides them toward adequate care.

Humans versus models: a comparative assessment of ecosystem services models and stakeholders' perceptions.

Mapping the production of Ecosystem Services (ES) is imperative for sustainable ecosystem management. Likewise, incorporating expert knowledge enhances ES research. Here, we calculate eight multi-temporal ES indicators for mainland Portugal using a spatial modelling approach.

Comparing SMILES and SELFIES tokenization for enhanced chemical language modeling.

Life sciences research and experimentation are resource-intensive, requiring extensive trials and considerable time. Often, experiments do not achieve their intended objectives, but progress is made through trial and error, eventually leading to breakthroughs. Machine learning is transforming this traditional approach, providing methods to expedite processes and accelerate discoveries.

Deep Learning Features Can Improve Radiomics-Based Prostate Cancer Aggressiveness Prediction.

Purpose: Emerging evidence suggests that the use of artificial intelligence can assist in the timely detection and optimization of therapeutic approach in patients with prostate cancer. The conventional perspective on radiomics encompassing segmentation and the extraction of radiomic features considers it as an independent and sequential process. However, it is not necessary to adhere to this viewpoint.

Mapping the wildland-urban interface at municipal level for wildfire exposure analysis in mainland Portugal.

The Wildland-Urban Interface (WUI), where vegetation and built-up structures intermingle, encompasses a variety of territorial elements that interact spatially, being variable both in space and time. Mapping the WUI at finer scales is paramount to assess wildfire exposure and define tailored mitigation strategies. Our aim was to develop a semi-automated method to map the WUI at municipal level, leveraging recent advances in data and technology.

Long-term Eulerian Sea surface temperature records along the Portuguese Coast.

Monitoring ocean surface temperature is critical to infer the variability of the upper layers of the ocean, from short temporal scales to climatic change scales. Analysis of the climatological trends and anomalies is fundamental to comprehend the long-term effects of climate change on marine ecosystems and coastal regions. The original data for the dataset presented was collected by the Portuguese Hydrographic Institute () using seven Ondograph and Meteo-oceanography buoys anchored offshore along the Portuguese coast to acquire ocean surface temperatures.

Multiparametric MRI dataset for susceptibility-based radiomic feature extraction and analysis.

Multiple sclerosis (MS) is a progressive demyelinating disease impacting the central nervous system. Conventional Magnetic Resonance Imaging (MRI) techniques (e.g.

Fisheries Inspection in Portuguese Waters from 2015 to 2023.

As a coastal state, Portugal must ensure active surveillance over its maritime area, ensuring its proper control and inspection. One of the most critical inspection activities is the fishery inspection. To protect biodiversity, we must ensure that all the ships comply with the existing safety regulations and respect the current fishing quotas.

Assessing coastal vulnerability at the village level using a robust framework, the example of Canacona in South Goa, India.

Climate change poses a significant threat to coastal regions worldwide. This study presents and applies a modified Coastal Vulnerability Index (CVI) to assess coastal vulnerability at the village level, focusing on Canacona, a taluka in South Goa, India. It adapts the existing CVI methodology by incorporating additional variables to better represent the various dimensions of vulnerability, resulting in 21 variables split into a Physical Vulnerability Index (PVI) and a Social Vulnerability Index (SoVI).

Expectation management in AI: A framework for understanding stakeholder trust and acceptance of artificial intelligence systems.

As artificial intelligence systems gain traction, their trustworthiness becomes paramount to harness their benefits and mitigate risks. This study underscores the pressing need for an expectation management framework to align stakeholder anticipations before any system-related activities, such as data collection, modeling, or implementation. To this end, we introduce a comprehensive framework tailored to capture end-user expectations specifically for trustworthy artificial intelligence systems.

Binge-watching Uncovered: Examining the interplay of perceived usefulness, habit, and regret in continuous viewing.

Binge-watching has become one of the most popular ways for people to spend their free time. Binge-watching refers to watching more than two episodes of a television show in a single sitting. This pattern of behavior can be seen in people of a wide range of ages, but it is particularly widespread among people of millennial age and younger.

Analysis of domain shift in whole prostate gland, zonal and lesions segmentation and detection, using multicentric retrospective data.

Despite being one of the most prevalent forms of cancer, prostate cancer (PCa) shows a significantly high survival rate, provided there is timely detection and treatment. Computational methods can help make this detection process considerably faster and more robust. However, some modern machine-learning approaches require accurate segmentation of the prostate gland and the index lesion.

Side-scan sonar imaging data of underwater vehicles for mine detection.

Unmanned vehicles have become increasingly popular in the underwater domain in the last decade, as they provide better operation reliability by minimizing human involvement in most tasks. Perception of the environment is crucial for safety and other tasks, such as guidance and trajectory control, mainly when operating underwater. Mine detection is one of the riskiest operations since it involves systems that can easily damage vehicles and endanger human lives if manned.

Adoption of digital twins as a sustainable energy solution: Determinants to adoption in household.

Digital Twin (DT) consists of a recent technology that can enable sustainability. However, Digital Twins are still in early stages of adoption, especially in households, and so the determinants to this adoption have not yet been determined. The aim of this study is to fill this research gap through providing a conceptual model of the drivers to the adoption of Digital Twins in households and it's relation to well-being.

A secure cross-domain interaction scheme for blockchain-based intelligent transportation systems.

In the intelligent transportation system (ITS), secure and efficient data communication among vehicles, road testing equipment, computing nodes, and transportation agencies is important for building a smart city-integrated transportation system. However, the traditional centralized processing approach may face threats in terms of data leakage and trust. The use of distributed, tamper-proof blockchain technology can improve the decentralized storage and security of data in the ITS network.

Georeferenced dataset of maritime piracy in the Gulf of Guinea from 2010 to 2021.

Piracy has been a global concern and a threat to the safety of people performing maritime trade around the globe. Since ancient times maritime piracy has been a common practice that, unfortunately, has not ended in the current days. A georeferenced dataset providing the position, meteorologic conditions, and a description of the occurrence can provide essential information for analyzing this global phenomenon.