Enabling personalized smart tourism with location-based social networks.

With the rapid advance of mobile internet, communication technology and the Internet of Things (IoT), the tourism industry is undergoing unprecedented transformation. Smart tourism offers users personalized and customized services for travel planning and recommendations. Location-based social networks (LBSNs) play a crucial role in smart tourism industry by providing abundant data sources through their social networking attributes.

A few-shot learning method for tobacco abnormality identification.

Tobacco is a valuable crop, but its disease identification is rarely involved in existing works. In this work, we use few-shot learning (FSL) to identify abnormalities in tobacco. FSL is a solution for the data deficiency that has been an obstacle to using deep learning.

Enhancing underwater target localization through proximity-driven recurrent neural networks.

Environmental monitoring, ocean research, and underwater exploration are just a few of the marine applications that require precise underwater target localization. This study goes into the field of underwater target localization using Recurrent Neural Networks (RNNs) enhanced with proximity-based approaches, with a focus on mean estimation error as a performance metric. In complex and dynamic underwater environments, conventional localization systems frequently face challenges such as signal degradation, noise interference, and unstable hydrodynamic conditions.

Low-cost air quality portable sensors and their potential use in respiratory health.

Air pollution is an environmental risk for the general population and for patients with various diseases, particularly respiratory diseases. Little data are available on personal exposure, but the recent emergence of low-cost air quality sensors (LCSs) should enable a better understanding of the health impacts of air pollution at the individual level. However, the reliability and accuracy of most sensors in the market have not been established, and a thorough understanding of their strengths and limitations is needed.

Spirometry practice by French general practitioners between 2010 and 2018 in adults aged 40 to 75 years.

In France, most spirometries are performed by pneumologists. Spirometry is difficult to access due to the distance to medical office and long delays for appointments. This lack of accessibility contributes to the underdiagnosis of chronic obstructive pulmonary disease (COPD) among patients aged between 40 and 75 years.

Underwater Wireless Sensor Networks Performance Comparison Utilizing Telnet and Superframe.

Underwater Wireless Sensor Networks (UWSNs) have recently established themselves as an extremely interesting area of research thanks to the mysterious qualities of the ocean. The UWSN consists of sensor nodes and vehicles working to collect data and complete tasks. The battery capacity of sensor nodes is quite limited, which means that the UWSN network needs to be as efficient as it can possibly be.

Design of a Planar Sensor Based on Split-Ring Resonators for Non-Invasive Permittivity Measurement.

The permittivity of a material is an important parameter to characterize the degree of polarization of a material and identify components and impurities. This paper presents a non-invasive measurement technique to characterize materials in terms of their permittivity based on a modified metamaterial unit-cell sensor. The sensor consists of a complementary split-ring resonator (C-SRR), but its fringe electric field is contained with a conductive shield to intensify the normal component of the electric field.

Acoustic Wave Reflection in Water Affects Underwater Wireless Sensor Networks.

The phenomenon of acoustic wave reflection off fluid-solid surfaces is the focus of this research. This research aims to measure the effect of material physical qualities on oblique incidence acoustic attenuation across a large frequency range. To construct the extensive comparison shown in the supporting documentation, reflection coefficient curves were generated by carefully adjusting the porousness and permeability of the poroelastic solid.

DMNet: A Personalized Risk Assessment Framework for Elderly People With Type 2 Diabetes.

Type 2 diabetes is the most common chronic disease for the elderly people. This disease is difficult to be cured and causes continued medical expenses. The early and personalized risk assessment of type 2 diabetes is necessary.

Effectiveness and safety of ustekinumab in bio-naïve Crohn's disease patients: a multicentre observational retrospective study.

Background: Clinical trials have demonstrated the efficacy and safety of ustekinumab in Crohn's disease (CD). However, more data are necessary on the effectiveness of ustekinumab in bio-naïve patients in real-life studies.

Objectives: The aim of our study was to evaluate the effectiveness and safety of ustekinumab in patients with CD refractory or intolerant to conventional therapy and without previous exposure to biological drugs.

COVID-19 Data Analytics Using Extended Convolutional Technique.

The healthcare system, lifestyle, industrial growth, economy, and livelihood of human beings worldwide were affected due to the triggered global pandemic by the COVID-19 virus that originated and was first reported in Wuhan city, Republic Country of China. COVID cases are difficult to predict and detect in their early stages, and their spread and mortality are uncontrollable. The reverse transcription polymerase chain reaction (RT-PCR) is still the first and foremost diagnostical methodology accepted worldwide; hence, it creates a scope of new diagnostic tools and techniques of detection approach which can produce effective and faster results compared with its predecessor.

Few-Shot Learning for Plant-Disease Recognition in the Frequency Domain.

Few-shot learning (FSL) is suitable for plant-disease recognition due to the shortage of data. However, the limitations of feature representation and the demanding generalization requirements are still pressing issues that need to be addressed. The recent studies reveal that the frequency representation contains rich patterns for image understanding.

Few-shot learning approach with multi-scale feature fusion and attention for plant disease recognition.

Image-based deep learning method for plant disease diagnosing is promising but relies on large-scale dataset. Currently, the shortage of data has become an obstacle to leverage deep learning methods. Few-shot learning can generalize to new categories with the supports of few samples, which is very helpful for those plant disease categories where only few samples are available.

Weed cutting in a large river reduces ecosystem metabolic rates in the case of River Gudenå (Denmark).

Problems related to extensive macrophyte growth are widespread both in modified and man-made canals and streams, and in streams with natural morphology and rich vegetation. The weed cutting is a common management practice in order to reduce flood risk and enhance water conveyance. Although the short- and long-term impacts on the stream physical habitats and biota have been extensively studied, only little information exists on the effects of weed cutting on ecosystem metabolism, especially for larger rivers.

Air Quality and Comfort Characterisation within an Electric Vehicle Cabin in Heating and Cooling Operations.

This work is aimed at the experimental characterisation of air quality and thermal profile within an electric vehicle cabin, measuring at the same time the HVAC system energy consumption. Pollutant concentrations in the vehicle cabin are measured by means of a low-cost system of sensors. The effects of the HVAC system configuration, such as fresh-air and recirculation mode, on cabin air quality, are discussed.

Study and Investigation on 5G Technology: A Systematic Review.

In wireless communication, Fifth Generation (5G) Technology is a recent generation of mobile networks. In this paper, evaluations in the field of mobile communication technology are presented. In each evolution, multiple challenges were faced that were captured with the help of next-generation mobile networks.

Bluetooth 5.1: An Analysis of Direction Finding Capability for High-Precision Location Services.

This paper presents an in-depth overview of the Bluetooth 5.1 Direction Finding standard's potentials, thanks to enhancing the Bluetooth Low Energy (BLE) firmware. This improvement allows producers to create location applications based on the Angle of Departure (AoD) and the Angle of Arrival (AoA).

Drone Secure Communication Protocol for Future Sensitive Applications in Military Zone.

Unmanned Aerial Vehicle (UAV) plays a paramount role in various fields, such as military, aerospace, reconnaissance, agriculture, and many more. The development and implementation of these devices have become vital in terms of usability and reachability. Unfortunately, as they become widespread and their demand grows, they are becoming more and more vulnerable to several security attacks, including, but not limited to, jamming, information leakage, and spoofing.

Academically Produced Air Pollution Sensors for Personal Exposure Assessment: The Canarin Project.

The World Health Organization has estimated that air pollution is a major threat to health, causing approximately nine million premature deaths every year. Each individual has, over their lifetime, a unique exposure to air pollution through their habits, working and living conditions. Medical research requires dedicated tools to assess and understand individual exposure to air pollution in view of investigating its health effects.

Selected Papers from the First International Symposium on Future ICT (Future-ICT 2019) in Conjunction with the 4th International Symposium on Mobile Internet Security (MobiSec 2019).

The International Symposium on the Future ICT (Future-ICT 2019) in conjunction with the 4th International Symposium on Mobile Internet Security (MobiSec 2019) has been held on 17-19 October 2019 in Taichung, Taiwan [...

RaveGuard: A Noise Monitoring Platform Using Low-End Microphones and Machine Learning.

Urban noise is one of the most serious and underestimated environmental problems. According to the World Health Organization, noise pollution from traffic and other human activities, negatively impact the population health and life quality. Monitoring noise usually requires the use of professional and expensive instruments, called phonometers, able to accurately measure sound pressure levels.

Special Issue "Internet of Things for Smart Homes".

Smart homes represent one of the principal points in the new ecosystem of the Internet of Things (IoT), both for the centrality of the home in the life of individuals and the significant potential concerning the diffusion of smart objects and innovative services. While IoT-oriented smart homes can revise how inhabitants interact with the domestic environment, each well-defined piece of technology necessitates precise network performance and distinct levels of security based on the sensitivity of the controlled system and the information it handles. This editorial presents a review of the papers accepted in the special issue.

POLLAR: Impact of air POLLution on Asthma and Rhinitis; a European Institute of Innovation and Technology Health (EIT Health) project.

Allergic rhinitis (AR) is impacted by allergens and air pollution but interactions between air pollution, sleep and allergic diseases are insufficiently understood. POLLAR (Impact of air POLLution on sleep, Asthma and Rhinitis) is a project of the European Institute of Innovation and Technology (EIT Health). It will use a freely-existing application for AR monitoring that has been tested in 23 countries (the , iOS and Android, 17,000 users, TLR8).

Next Generation Wireless Technologies for Internet of Things.

In the fast-growing Internet of Things (IoT)[...