Predicting people’s concentration and movements in a smart city

With the rapid growth of urbanization and the proliferation of mobile phone usage, smart city initiatives have gained momentum in leveraging data-driven insights to enhance urban planning and resource allocation. This paper proposes a novel approach for predicting people’s concentration and movements within a smart city environment using mobile phone data provided by telecommunication operators. By harnessing the vast amount of anonymized and aggregated mobile phone data, we present a predictive framework that offers valuable insights into urban dynamics. The methodology involves collecting and processing location-based data obtained from telecommunication operators. Using machine learning techniques, including clustering and spatiotemporal analysis, we developed models to identify patterns in people’s movements and concentration across various city regions. Our proposed approach considers factors such as time of day, day of the week, and special events to capture the intricate dynamics of urban activities. The predictive models presented in this paper demonstrate the ability to predict areas of high concentration of people, such as commercial districts during peak hours, as well as the people flow during the time. These insights have significant implications for urban planning, traffic management, and resource allocation. Our approach respects user privacy by working with aggregated and anonymized data, ensuring compliance with privacy regulations and ethical considerations. The proposed models were evaluated using real-world mobile phone data collected from a smart city environment in Lisbon, Portugal. The experimental results demonstrate the accuracy and effectiveness of our approach in predicting people’s movements and concentration. This paper contributes to the growing field of smart city research by providing a data-driven solution for enhancing urban planning and resource allocation strategies. As cities continue to evolve, leveraging mobile phone data from telecommunication operators can lead to more efficient and sustainable urban environmentsThis work was supported by the Fundação para a Ciência e Tecnologia under Grant [UIDB/00315/2020]; and by the project “BLOCKCHAIN.PT (RE-C05-i01.01—Agendas/Alianças Mobilizadoras para a Reindustrialização, Plano de Recuperação e Resiliência de Portugal” in its component 5—Capitalization and Business Innovation and with the Regulation of the Incentive System “Agendas for Business Innovation”, approved by Ordinance No. 43-A/2022 of 19 January 2022)

Georeferenced analysis of urban nightlife and noise based on mobile phone data

Urban environments are characterized by a complex soundscape that varies across different periods and geographical zones. This paper presents a novel approach for analyzing nocturnal urban noise patterns and identifying distinct zones using mobile phone data. Traditional noise-monitoring methods often require specialized equipment and are limited in scope. Our methodology involves gathering audio recordings from city sensors and localization data from mobile phones placed in urban areas over extended periods with a focus on nighttime, when noise profiles shift significantly. By leveraging machine learning techniques, the developed system processes the audio data to extract noise features indicative of different sound sources and intensities. These features are correlated with geographic location data to create comprehensive city noise maps during nighttime hours. Furthermore, this work employs clustering algorithms to identify distinct noise zones within the urban landscape, characterized by their unique noise signatures, reflecting the mix of anthropogenic and environmental noise sources. Our results demonstrate the effectiveness of using mobile phone data for nocturnal noise analysis and zone identification. The derived noise maps and zones identification provide insights into noise pollution patterns and offer valuable information for policymakers, urban planners, and public health officials to make informed decisions about noise mitigation efforts and urban development.This work was supported by the Fundação para a Ciência e Tecnologia under Grant [UIDB/00315/2020]; and by the project “BLOCKCHAIN.PT (RE-C05-i01.01—Agendas/Alianças Mobilizadoras para a Reindustrialização, Plano de Recuperação e Resiliência de Portugal” in its component 5—Capitalization and Business Innovation and with the Regulation of the Incentive System “Agendas for Business Innovation”, approved by Ordinance No. 43-A/2022 of 19 January 2022)

The fish family Muraenidae: an ideal group for testing at small-scale the coherency of Macaronesia as a biogeographic unit, with the first report on separate fishery statistics

: The present study was conceptualized to study the muraenid species (moray eels) occurring around the volcanic archipelagos of the Azores, Madeira, Selvagens, Canary and Cabo Verde islands (eastern-central Atlantic). The biogeographic patterns of these species were analysed and compared. We then hypothesized that this fish family is an ideal group for testing at small-scale the coherency of Macaronesia and its direct biogeographic units: i.e. the Azores, Webbnesia and Cabo Verde, as proposed in recent scientific literature. Additionally, this paper provides for the first time separate fishery statistics for this group in the region that were analysed to contrast the biogeographic results.En prensa1,00

Development of an IoT system with smart charging current control for electric vehicles

This paper presents the development and test of an Internet of Things (IoT) system for monitoring and control of electric vehicles. The IoT architecture, which was developed using the Firebase platform, allows the synchronization of the vehicles' data to the online server, as well as the access to the data outside of the vehicle, though the Internet. The smart charging system proposed in this paper allows the control of the electric vehicle's battery charging current in real time, based on the demand at the residence (home current), which is measured using a residential wireless sensor network (WSN). An Android mobile app was developed to access the vehicle's data. This app communicates with the wireless sensor nodes of an intra-vehicular wireless sensor network (IVWSN), which was developed using the Bluetooth Low Energy (RLE) protocol. A real time notification system was also implemented to alert users about certain events, such as low battery and full battery charge. The main features of the proposed IoT system are validated through experimental results.This work is supported by FCT with the reference project UID/EEA/04436/2013, COMPETE 2020 with the code POCI 01-0145-FEDER-006941

Bottleneck identification and scheduling in multithreaded applications

Abstract Performance of multithreaded applications is limited by a variety of bottlenecks, e.g. critical sections, barriers and slow pipeline stages. These bottlenecks serialize execution, waste valuable execution cycles, and limit scalability of applications. This paper proposes Bottleneck Identification and Scheduling (BIS), a cooperative software-hardware mechanism to identify and accelerate the most critical bottlenecks. BIS identifies which bottlenecks are likely to reduce performance by measuring the number of cycles threads have to wait for each bottleneck, and accelerates those bottlenecks using one or more fast cores on an Asymmetric Chip MultiProcessor (ACMP). Unlike previous work that targets specific bottlenecks, BIS can identify and accelerate bottlenecks regardless of their type. We compare BIS to four previous approaches and show that it outperforms the best of them by 15% on average. BIS' performance improvement increases as the number of cores and the number of fast cores in the system increase

Drivers of variability in Blue Carbon stocks and burial rates across European estuarine habitats

The implementation of climate change mitigation strategies based on the conservation and restoration of Blue Carbon ecosystems requires a deep understanding of the magnitude and variability in organic carbon (Corg) storage across and within these ecosystems. This study explored the variability in soil Corg stocks and burial rates across and within intertidal estuarine habitats of the Atlantic European coast and its relation to biotic and abiotic drivers. A total of 136 soil cores were collected across saltmarshes located at different tidal zones (high marsh, N = 45; low marsh, N = 30), seagrass meadows (N = 17) and tidal flats (N = 44), and from the inner to the outer sections of five estuaries characterized by different basin land uses. Soil Corg stocks were higher in high-marsh communities (65 ± 3 Mg ha−1) than in low-marsh communities (38 ± 3 Mg ha−1), seagrass meadows (40 ± 5 Mg ha−1) and unvegetated tidal flats (46 ± 3 Mg ha−1) whereas Corg burial rates also tended to be higher in high marshes (62 ± 13 g m−2 y−1) compared to low marshes (43 ± 15 g m−2 y−1) and tidal flats (35 ± 9 g m−2 y−1). Soil Corg stocks and burial rates decreased from inner to outer estuarine sections in most estuaries reflecting the decrease in the river influence towards the estuary mouth. Higher soil Corg stocks were related to higher content of silt and clay and higher proportion of forest and natural land within the river basin, pointing at new opportunities for protecting coastal natural carbon sinks based on the conservation and restoration of upland ecosystems. Our study contributes to the global inventory of Blue Carbon by adding data from unexplored regions and habitats in Europe, and by identifying drivers of variability across and within estuaries

Neudesin is involved in anxiety behavior: structural and neurochemical correlates

Neudesin (also known as neuron derived neurotrophic factor, Nenf) is a scarcely studied putative non-canonical neurotrophic factor. In order to understand its function in the brain, we performed an extensive behavioral characterization (motor, emotional, and cognitive dimensions) of neudesin-null mice. The absence of neudesin leads to an anxious-like behavior as assessed in the elevated plus maze (EPM), light/dark box (LDB) and novelty suppressed feeding (NSF) tests, but not in the acoustic startle (AS) test. This anxious phenotype is associated with reduced dopaminergic input and impoverished dendritic arborizations in the dentate gyrus granule neurons of the ventral hippocampus. Interestingly, shorter dendrites are also observed in the bed nucleus of the stria terminalis (BNST) of neudesin-null mice. These findings lead us to suggest that neudesin is a novel relevant player in the maintenance of the anxiety circuitry.This work is supported by a grant from FCT (PTDC/SAU-OSM/104475/2008) under POCTI-COMPETE funds. Ashley Novais, Ana Catarina Ferreira, Ana David-Pereira and Filipa L. Campos are recipients of doctoral fellowships and Fernanda Marques is a recipient of postdoctoral fellowship from Fundacao para a Ciencia e Tecnologia (FCT), Portugal. We acknowledge Merck Serono for providing the neudesin-null mouse strain. We are thankful to Despina Papasava and Vasileios Kafetzopoulos for the assistance given in the HPLC analysis of neurotransmitters