This study evaluates the walkability around the bus rapid transit (BRT) system, Metrobus, which will commence operations in Coimbra, Portugal, in 2025. As part of the first phase of an electric mobility system, Metrobus will traverse the city along two primary axes, reshaping urban mobility patterns. Given this shift, assessing the pedestrian environment around BRT stations is crucial to anticipate potential increases in walking trips. This research employs the OS-WALK-EU tool, which operationalises walkability through four sub-indices – amenity proximity, pedestrian-network permeability, accessible green/blue space and residential density – while incorporating a slope-dependent reduction of the effective pedestrian catchment. Using open data sources, the study demonstrates that OS-WALK-EU is an effective method for assessing walkability, with potential applications in other urban contexts. The findings reveal significant asymmetries in walkability along the BRT corridors, highlighting areas where infrastructure improvements are needed to enhance pedestrian accessibility. These insights are particularly relevant for city planners and policymakers, as they underscore the importance of targeted urban interventions to maximise the benefits of the new mobility paradigm. Ensuring high-quality pedestrian access to Metrobus stations will be essential for leveraging sustainable mobility and fostering a more walkable and connected urban environment in Coimbra.

The concept of Digital Twins (DT) has experienced a remarkable surge in popularity over the past few years. A DT is a computer system designed to monitor, simulate and predict various aspects of a specific physical object. In other words, it is like an enhanced digital counterpart of a real object. Human Digital Twins (HDT) have emerged as an evolution of this concept, where the physical object twinned is a human being. Nevertheless, the inherent complexity of human beings turns the creation of their digital representation into a challenging endeavour. In this study, a systematic literature review was conducted that aimed at clarifying the HDT concept and the different aspects a proper HDT should consider. To shed some light on how the different facets of a HDT are addressed in the literature, we delved into its fields of application, the human dimensions a HDT considers, the information handled, its underlying technological frameworks, what quality assessment processes are being applied to HDT, and lastly, those ethical and legal concerns related to HDT. As a result of this systematic literature review, a HDT research agenda is presented to fill the gaps and shortcomings identified in the literature reviewed and highlight some challenges that should be addressed in the near future.

In this study, we present an indoor localization system leveraging Zigbee-based hardware for real-time positioning. We conduct in-house measurements in a controlled environment to generate a robust dataset for the system. A six-step methodology is employed, encompassing received signal strength indicator (RSSI)-to-image transformation, feature extraction using a pre-trained convolutional neural network (CNN) model, dimensionality reduction, clustering, and classification. By converting RSSI values into images, we capture spatial patterns more effectively. A multi-power level fusion strategy combines RSSI data from three distinct power levels into RGB images, improving localization accuracy and robustness against interference. K-means clustering segments the environment into distinct zones, streamlining classification. Experiments demonstrate the significant contributions of power level fusion, clustering, and classification, with the multi-power level fusion yielding improved performance compared to single power levels. Our approach, based on a custom CNN architecture, offers enhanced precision and efficiency for indoor localization tasks, providing valuable insights into the integration of advanced signal processing and deep learning.

The most common input technologies used to interact with touch screens are finger and stylus. Today, people use mobile devices on a daily basis in different conditions of mobility and encumbrance. This research investigates a usability evaluation of stylus and finger during two levels of mobility (standing and walking), four levels of encumbrance (both hands busy, dominant hand busy, non-dominant hand busy, and both hands available), and two types of gestures (tapping and dragging). We measured the usability based on movement time, error rate, and user satisfaction. The experiments show a negative effect of encumbrance and mobility for both stylus and finger. The interaction with stylus takes more time with encumbrance than that of finger; furthermore, the error rate for stylus is greater than finger. Finally, a survey conducted for user satisfaction showed that the user prefers to interact with stylus when both hands are available, and the encumbrance affects stylus more than finger.

To solve the problems of sleep disorders such as difficulty in falling asleep and insufficient sleep depth caused by uncomfortable indoor temperature, this paper proposes a deep reinforcement learning method based on deep Q-network (DQN) with human sleep electroencephalogram (EEG) as input to improve human sleep. Firstly, the EEG is subjected to a short-time Fourier transform to construct a time-frequency feature data set, which is used as input to DQN along with temperature. Secondly, the agent performs environmental interaction actions in each time step and returns a reward value. Finally, the optimal strategy for indoor temperature control is formulated by the agent. The simulation results show that this method can dynamically adjust the indoor temperature to the optimal temperature for human sleep, and can alleviate sleep disorders, which has certain practical significance.

The rapid advances in the Internet of Things (IoT) and the increment of its users have opened a door to put into service IoT in healthcare, known as the Internet of Medical Things (IoMT), which consists of cloud, fog, and edge computing. IoMT has shown to be a flexible framework to remove traditional healthcare limits through utilizing technology/techniques and innovative digital devices to monitor patients’ conditions and to address medical service problems such as inaccuracy and long response time. It also could be helpful within critical and unexpected circumstances like epidemic diseases. Fortunately, some methods, including Edge Computing, were introduced to improve the healthcare system, make it more effective and solve problems. In this work, a Systematic Literature Review (SLR) was used to study Edge Computing solutions in healthcare, evaluate efficient therapeutic approaches, and demonstrate key factors that have not been considered in previous studies.

Smart homes integrate several sensors to facilitate information exchange and the execution of tasks. In addition, with the development of the Internet of Things (IoT) platforms, the control of appliances and remote devices has become possible. This sensor collects data in real time to closely monitor the devices of a user’s household. The present study employs a machine learning methodology to perform a global analysis of energy consumption and efficiency in smart homes. In This work we propose two advanced ensemble models to improve the performance of energy consumption in smart homes, the first one is a voting ensemble model based on a ranking weight averaging that combines following basic machine learning techniques: decision tree (DT), random forest (RF), and eXtreme Gradient Boosting (XGB). The second one is the stacking ensemble model in which the basic models (DT-RF-XGB) are combined through stacked generalization, then uses a secondary layer model or meta-learner (RF) to provide output prediction. The findings obtained show that the proposed ensemble model based on DT-RF-XGB using stacking technique surpasses all other basic algorithms with R2 around 0.9825.

Over the last fifty years, societies across the world have experienced multiple periods of energy insufficiency with the most recent one being the 2022 global energy crisis. In addition, the electric power industry has been experiencing a steady increase in electricity consumption since the second industrial revolution because of the widespread usage of electrical appliances and devices. Newer devices are equipped with sensors and actuators, they can collect a large amount of data that could help in power management. However, current energy management approaches are mostly applied to limited types of devices in specific domains and are difficult to implement in other scenarios. They fail when it comes to their level of autonomy, flexibility, and genericity. To address these shortcomings, we present, in this paper, an automated energy management approach for connected environments based on generating power estimation models, representing a formal description of energy-related knowledge, and using reinforcement learning (RL) techniques to accomplish energy-efficient actions. The architecture of this approach is based on three main components: power estimation models, knowledge base, and intelligence module. Furthermore, we develop algorithms that exploit knowledge from both the power estimator and the ontology, to generate the corresponding RL agent and environment. We also present different reward functions based on user preferences and power consumption. We illustrate our proposal in the smart home domain. An implementation of the approach is developed and two validation experiments are conducted. Both case studies are deployed in the context of smart homes: (a) a living room with a variety of devices and (b) a smart home with a heating system. The obtained results show that our approach performs well given the low convergence period, the high level of user preferences satisfaction, and the significant decrease in energy consumption.

Satellite remote sensing technology has proven effective in monitoring various environmental parameters, but its efficiency in assessing shallow lakes has been limited. This study applies state-of-the-art machine and deep learning algorithms supported by classical statistic methods to analyze remote sensing data to measure chlorophyll-a (Chl-a) concentration levels. Focused on a shallow coastal lagoon, Mar Menor, this work analyzes statistically daily Sentinel 3 information behaviour and compares Machine Learning and Deep Learning techniques to enhance efficiency and accuracy data of this satellite. Convolutional Neural Networks (CNNs) stand out as a robust choice, capable of delivering excellent results even in the presence of anomalous events. Our findings demonstrate that the CNN-based approach directly utilizing satellite data yields promising results in monitoring shallow lakes, offering enhanced efficiency and robustness. This research contributes to optimizing remote sensing data to and produce a continuous information flow addressed to monitoring shallow aquatic ecosystems with potential environmental management and conservation applications.

Nowadays, the object’s volume is essential for monitoring any scene. Technological equipment is evolving, and mobile devices and other devices embed high-resolution cameras. The high-resolution cameras open a window for different research studies, where the volume measurement is vital for different areas. This study aims to identify image processing techniques for measuring the object’s volume. Thus, a systematic review was performed with a Natural Language Processing (NLP)-based framework for identifying studies between 2010 and 2023 related to the measurement of object volume. As a result of this search, this paper reviewed and analyzed 25 studies, verifying that different computer vision methods accurately handle object recognition. Additionally, an evaluation of the databases presented by the studies above is performed to consider further the design of a new approach to infer the volume of objects from an image.