Considering the population growth rate of recent years, a doubling of the current worldwide crop productivity is expected to be needed by 2050. Pests and diseases are a major obstacle to achieving this productivity outcome. Therefore, it is very important to develop efficient methods for the automatic detection, identification, and prediction of pests and diseases in agricultural crops. To perform such automation, Machine Learning (ML) techniques can be used to derive knowledge and relationships from the data that is being worked on. This paper presents a literature review on ML techniques used in the agricultural sector, focusing on the tasks of classification, detection, and prediction of diseases and pests, with an emphasis on tomato crops. This survey aims to contribute to the development of smart farming and precision agriculture by promoting the development of techniques that will allow farmers to decrease the use of pesticides and chemicals while preserving and improving their crop quality and production.

Green infrastructure is a strategically planned network of natural and semi-natural areas, including green and blue spaces and other ecosystems, designed and managed to deliver a wide range of ecosystem services at various scales. Apart from the ecological functions, green infrastructure, as a planning tool, contributes to social and economic benefits, leading to the achievement of sustainable, resilient, inclusive and competitive urban areas. Despite recent developments, there is still no consensus among researchers and practitioners regarding the concept of green infrastructure as well as its implementation approaches, which makes it often difficult for urban planners and other professionals in the field to develop a robust green infrastructure in some parts of the world. To address this issue, an integrative literature review was conducted to identify which green infrastructure planning principles should be acknowledged in spatial planning practices to promote sustainability and resilience. As a result of this literature review, the most common eight green infrastructure planning principles were selected—connectivity, multifunctionality, applicability, integration, diversity, multiscale, governance, and continuity. These principles intend to promote and simplify the development and use of green infrastructure by different academic and implementation organizations and provide a more defined model for sustainable landscape management in order to help practitioners and decision makers during the conceptualization and planning of green infrastructure.

Background: The digital age, with digital sensors, the Internet of Things (IoT), and big data tools, has opened new opportunities for improving the delivery of health care services, with remote monitoring systems playing a crucial role and improving access to patients. The versatility of these systems has been demonstrated during the current COVID-19 pandemic. Health remote monitoring systems (HRMS) present various advantages such as the reduction in patient load at hospitals and health centers. Patients that would most benefit from HRMS are those with chronic diseases, older adults, and patients that experience less severe symptoms recovering from SARS-CoV-2 viral infection. Objective: This paper aimed to perform a systematic review of the literature of HRMS in primary health care (PHC) settings, identifying the current status of the digitalization of health processes, remote data acquisition, and interactions between health care personnel and patients. Methods: A systematic literature review was conducted using PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analysis) guidelines to identify articles that explored interventions with HRMS in patients with chronic diseases in the PHC setting. Results: The literature review yielded 123 publications, 18 of which met the predefined inclusion criteria. The selected articles highlighted that sensors and wearables are already being used in multiple scenarios related to chronic disease management at the PHC level. The studies focused mostly on patients with diabetes (9/26, 35%) and cardiovascular diseases (7/26, 27%). During the evaluation of the implementation of these interventions, the major difficulty that stood out was the integration of information into already existing systems in the PHC infrastructure and in changing working processes of PHC professionals (83%). Conclusions: The PHC context integrates multidisciplinary teams and patients with often complex, chronic pathologies. Despite the theoretical framework, objective identification of problems, and involvement of stakeholders in the design and implementation processes, these interventions mostly fail to scale up. Despite the inherent limitations of conducting a systematic literature review, the small number of studies in the PHC context is a relevant limitation. This study aimed to demonstrate the importance of matching technological development to the working PHC processes in interventions regarding the use of sensors and wearables for remote monitoring as a source of information for chronic disease management, so that information with clinical value is not lost along the way.

The experimental validation of a novel architecture of an off-board, three-phase fast battery charger for electric vehicles (EVs) with innovative operation modes is presented in this paper. The proposed EV fast battery charger is based on a dual-stage power converter (ac-dc and dc-dc) sharing the same dc link. The ac-dc stage is used as an interface between the power grid and the dc link. It is composed of the parallel association of two full-bridge voltage-source converters, and allows control of the grid current and of the dc-link voltage. The dc-dc stage is used as an interface between the dc link and the batteries. It is constituted by a bidirectional three-level asymmetrical voltage-source converter, and controls the flux of current during the EV battery charging process. Compared with the traditional solutions used for EV fast battery chargers, the proposed architecture operates as an interleaved converter, facilitating the reduction of the passive filters size, and the grid current harmonic distortion for the same switching frequency. Throughout the paper, the ac-dc and dc-dc stages, and the digital control algorithms are described in detail. The experimental validation was performed in a laboratory using a developed EV fast battery charger prototype, operating through the grid-to-vehicle and the proposed charger-to-grid modes, exchanging active, and reactive power with the power grid.

The smart city concept, in which data from different systems are available, contains a multitude of critical infrastructures. This data availability opens new research opportunities in the study of the interdependency between those critical infrastructures and cascading effects solutions and focuses on the smart city as a network of critical infrastructures. This paper proposes an integrated resilience system linking interconnected critical infrastructures in a smart city to improve disaster resilience. A data-driven approach is considered, using artificial intelligence and methods to minimize cascading effects and the destruction of failing critical infrastructures and their components (at a city level). The proposed approach allows rapid recovery of infrastructures’ service performance levels after disasters while keeping the coverage of the assessment of risks, prevention, detection, response, and mitigation of consequences. The proposed approach has the originality and the practical implication of providing a decision support system that handles the infrastructures that will support the city disaster management system—make the city prepare, adapt, absorb, respond, and recover from disasters by taking advantage of the interconnections between its various critical infrastructures to increase the overall resilience capacity. The city of Lisbon (Portugal) is used as a case to show the practical application of the approach.

This work presents the efforts on optimizing energy consumption by deploying an energy management system using the current IoT component/system/platform integration trends through a layered architecture. LoBEMS (LoRa Building and Energy Management System), the proposed platform, was built with the mindset of proving a common platform that would integrate multiple vendor locked-in systems together with custom sensor devices, providing critical data in order to improve overall building efficiency. The actions that led to the energy savings were implemented with a ruleset that would control the already installed air conditioning and lighting control systems. This approach was validated in a kindergarten school during a three-year period, resulting in a publicly available dataset that is useful for future and related research. The sensors that feed environmental data to the custom energy management system are composed by a set of battery operated sensors tied to a System on Chip with a LoRa communication interface. These sensors acquire environmental data such as temperature, humidity, luminosity, air quality but also motion. An already existing energy monitoring solution was also integrated. This flexible approach can easily be deployed to any building facility, including buildings with existing solutions, without requiring any remote automation facilities. The platform includes data visualization templates that create an overall dashboard, allowing management to identify actions that lead to savings using a set of pre-defined actions or even a manual mode if desired. The integration of the multiple systems (air-conditioning, lighting and energy monitoring) is a key differentiator of the proposed solution, especially when the top energy consumers for modern buildings are cooling and heating systems. As an outcome, the evaluation of the proposed platform resulted in a 20% energy saving based on these combined energy saving actions.