The diaphragm is a muscular membrane separating the abdominal and thoracic cavities, and its motion is directly linked to respiration. In this study, using data from a 59-year-old female cadaver obtained from the Visible Human Project, the diaphragm is reconstructed and, from the corresponding solid object, a shell finite element mesh is generated and used in several analyses performed with the ABAQUS 6.7 software. These analyses consider the direction of the muscle fibres and the incompressibility of the tissue. The constitutive model for the isotropic strain energy as well as the passive and active strain energy stored in the fibres is adapted from Humphrey's model for cardiac muscles. Furthermore, numerical results for the diaphragmatic floor under pressure and active contraction in normal and pathological cases are presented.

Breast cancer is the most common and the fifth deadliest cancer worldwide. In more advanced stages of cancer, cancer cells metastasize through lymphatic and blood vessels. Currently there is no satisfactory neoadjuvant (i.e., preoperative) diagnosis to assess whether cancer has spread to neighboring Axillary Lymph Nodes (ALN). This paper addresses the use of radar Microwave Imaging (MWI) to detect and determine whether ALNs have been metastasized, presenting an analysis of the performance of different artifact removal and beamformer algorithms in distinct anatomical scenarios. We assess distinct axillary region models and the effect of varying the shape of the skin, muscle and subcutaneous adipose tissue layers on single ALN detection. We also study multiple ALN detection and contrast between healthy and metastasized ALNs. We propose a new beamformer algorithm denominated Channel-Ranked Delay-Multiply-And-Sum (CR-DMAS), which allows the successful detection of ALNs in order to achieve better Signal-to-Clutter Ratio, e.g., with the muscle layer up to (Formula presented.) dB, a Signal-to-Mean Ratio of up to (Formula presented.) dB and a Location Error of (Formula presented.) mm. In multiple target detection, CR-DMAS outperformed other well established beamformers used in the context of breast MWI. Overall, this work provides new insights into the performance of algorithms in axillary MWI.

This article is build on the experience of using Google Earth Engine as a development framework for a previous work by the same authors.Being primarily a distributed parallel computing platform, it is designed around a functional language pattern, even though supported on an object model, and a map / reduce distributed workload paradigm.Leveraging the sheer computing power delivered by the Google infrastructure and a multi petabyte remote sensing data repository, GoogleEarth Engine is an efficient development framework that presents itself in two basic flavors: one online integrated development environment which uses the browser Javascript engine; two APIs that can be deployed to a Python or a NodeJS environment.This work emphasizes the comparison between the Javascript browserbased implementation and the Python environment packages.

Kolmogorov-Arnold Networks (KANs) represent a breakthrough in deep learning, diverging from Multi-Layer Perceptrons (MLPs) by generalizing the Kolmogorov-Arnold representation theorem (KAT) to networks of arbitrary depth and width. This theorem facilitates the decomposition of multivariate functions into constituent one-dimensional elements, with learnable activation functions on weights and the sum operator on nodes. KANs have been shown to exhibit robust performance in function approximation, validated across mathematical, physical, and practical domains such as traffic prediction and medical diagnostics. Our study investigates KANs’ efficacy through comprehensive evaluations on OpenML, Kaggle and UCI datasets, with a focus on enhancing Human Activity Recognition systems. They demonstrate high classification performance compared to conventional machine learning approaches and MLPs. These findings underscore KANs’ potential as scalable, interpretable tools in modern machine learning applications given their favorable neural scaling laws.

This article is build on the experience of using Google Earth Engine as a development framework for a previous work by the same authors.Being primarily a distributed parallel computing platform, it is designed around a functional language pattern, even though supported on an object model, and a map / reduce distributed workload paradigm.Leveraging the sheer computing power delivered by the Google infrastructure and a multi petabyte remote sensing data repository, GoogleEarth Engine is an efficient development framework that presents itself in two basic flavors: one online integrated development environment which uses the browser Javascript engine; two APIs that can be deployed to a Python or a NodeJS environment.This work emphasizes the comparison between the Javascript browserbased implementation and the Python environment packages.

: A new active-contraction visco-elastic numerical model of the pelvic floor (skeletal) muscle is presented. Our model includes all elements that represent the muscle constitutive behavior, contraction and relaxation. In contrast with the previous models, the activation function can be null. The complete equations are shown and exactly linearized. Small verification and validation tests are performed and the pelvis is modeled using the data from the intra-abdominal pressure tests

The diaphragm is a muscular membrane separating the abdominal and thoracic cavities, and its motion is directly linked to respiration. In this study, using data from a 59-year-old female cadaver obtained from the Visible Human Project, the diaphragm is reconstructed and, from the corresponding solid object, a shell finite element mesh is generated and used in several analyses performed with the ABAQUS 6.7 software. These analyses consider the direction of the muscle fibres and the incompressibility of the tissue. The constitutive model for the isotropic strain energy as well as the passive and active strain energy stored in the fibres is adapted from Humphrey's model for cardiac muscles. Furthermore, numerical results for the diaphragmatic floor under pressure and active contraction in normal and pathological cases are presented.

This paper presents IRONEDGE, an architectural framework that can be used in different edge Stream Processing solutions for “Smart Infrastructure” scenarios, on a case-by-case basis. The architectural framework identifies the common components that any such solution should implement and a generic processing pipeline. In particular, the framework is considered in the context of a study case regarding Internet of Things (IoT) devices to be attached to rolling stock in a railway. A lack of computation and storage resources available in edge devices and infrequent network connectivity are not often seen in the existing literature, but were considered in this paper. Two distinct implementations of IRONEDGE were considered and tested. One, identified as Apache Kafka with Kafka Connect (K0-WC), uses Kafka Connect to pass messages from MQ Telemetry Transport (MQTT) to Apache Kafka. The second scenario, identified as Apache Kafka with No Kafka Connect (K1-NC), allows Apache Storm to consume messages directly. When the data rate increased, K0-WC showed low throughput resulting from high losses, whereas K1-NC displayed an increase in throughput, but did not match the input rate for the Data Reports. The results showed that the framework can be used for defining new solutions for edge Stream Processing scenarios and identified a reference implementation for the considered study case. In future work, the authors propose to extend the evaluation of the architectural variation of K1-NC.

O parque automóvel circulante em Portugal tem sofrido um crescimento constante, quer em número de veículos, quer na idade média dos veículos. Os congestionamentos de trânsito, com particular incidência nos centros urbanos, e.g. a cidade de Lisboa, resultam em impactos negativos na vida dos cidadãos, onde se incluem problemas de saúde, económicos, sociais e ambientais. Com recurso a diferentes formas de sensorização é possível estudar, compreender e prever fluxos de tráfego em zonas de interesse nos centros urbanos. A partir de modelos de aprendizagem automática, neste trabalho é possível criar e utilizar modelos preditivos de indicadores de tráfego, para diferentes momentos e pontos de interesse na cidade de Lisboa. Os resultados preliminares mostraram que, com o algoritmo XGBoost, é possível prever o tempo de atraso causado por um congestionamento com erros a variar, aproximadamente, entre os 2 e os 3 minutos, verificando-se ainda que a partir da fusão de dados de tráfego, de meteorologia e sociais estes são melhores. Estes modelos podem ser integrados com a plataforma de gestão integrada de Lisboa (PGIL) e contribuir para as tomadas de decisão relativas à mobilidade. São, assim, mais uma ferramenta que permite antecipar futuros congestionamentos e melhorar o planeamento e gestão urbana para que seja possível reduzir os congestionamentos e mitigar os seus consequentes impactos.

This article introduces the rationale behind the usage of the Google Earth Engine, and the advantages it offers, as an alternative to handle large volumes of georeferenced data using the existing tools we know as Geographic Information Systems on premises. Google Earth Engine is an efficient development framework that presents itself in two basic flavors: one online integrated development environment which uses the browser JavaScript’s engine; and two APIs that can be deployed on either a Python or a NodeJS environment. After presenting a limited number of use cases, representative of the Google Earth Engine design patterns, and building a prototype class using both variants, we conclude that both platforms are merely proxy APIs to the Google Earth Engine and do not have any measurable performance difference. However, since they run on fundamentally diverse contexts — a JavaScript’s engine on an internet browser, that integrates seamlessly with Google Maps, and a Python environment — it is argued that their utility depends on the user requirements instead of being true alternatives.