Abstract
Exposure to airborne microorganisms has been linked to the development of health detriments, particularly in children. Microbial pollution can constitute a relevant health concern indoors, where levels of airborne microorganisms may be specially increased. This work aimed to characterize the airborne bacterial levels, and fungal concentration and diversity to which twins are exposed in their bedrooms (n = 30) during the first year of life. Bacterial and fungal levels varied widely across the studied bedrooms, with 10% of the rooms presenting values exceeding the national limit for both indoor bacterial and fungal counts. Cladosporium was the predominant genera, but Penicillium, Aspergillus, Alternaria, Trichoderma and Chrysonilia were also identified in the samples collected. In addition, two toxicogenic species, A. flavus and T. viride, were identified at counts that exceeded the established limit (12 CFU/m(3)) in 3 and 7% of the bedrooms surveyed, respectively. Based on indoor-to-outdoor concentration ratios, outdoor air seemed to be the main contributor to the total load of fungi found indoors, while airborne bacteria appeared to be mainly linked to indoor sources. Higher indoor nitrogen dioxide levels were negatively correlated with indoor fungi concentrations, whereas particulate matter and volatile organic compounds concentrations were associated with an increase in fungal prevalence. In addition, rooms with small carpets or located near outdoor agriculture sources presented significantly greater total fungal concentrations. Multiple linear regression models showed that outdoor levels were the single significant predictor identified, explaining 38.6 and 53.6% of the Cladosporium sp. and total fungi counts, respectively. The results also suggest the existence of additional factors contributing to airborne biologicals load in infants' bedrooms that deserve further investigation. Findings stress the need for investigating the existence of declared interactive effects between chemical and biological air pollutants to accurately understand the health risk that the assessed levels can represent to infants.

Abstract
Satellite gravimetry requires sub-ng acceleration measurement at frequencies below 100mHz. To bring the performance of a MEMS accelerometer closer to this level, one must decrease noise sources and maximize sensitivity (to decrease input-referred electronic noise). Electrostatic pull-in based operation has great potential for high sensitivity since it relies on time transduction. Devices were fabricated with maximized proof mass (170mg over a 13x14mm2 footprint) and tuned damping coefficient (trade-off between noise and sensitivity – pull-in operation requires low Q-factors). Novel stopper designs and caps limit both in-plane and out-of-plane displacements. Devices tested using pull-in voltage-based transduction showed sensitivity of 218 V/g. © 2022 TRF.

Abstract

Abstract
The building façade has a crucial role in acting as the interface between the environment and the indoor ambient, and from an engineering and architecture perspective, in the last years, there has been a growing focus on the strategic development of building façades. In this sense, this work aims to present a literature review for the Building Integrated Solar Energy Systems (BI-SES) for façades, subdivided into three categories: thermal, photovoltaic and hybrid (both thermal and photovoltaic). The methodology used corresponds to a systematic review method. A sample of 75 works was reviewed (16 works on thermal BI-SES, 37 works on photovoltaic BI-SES, 22 works on hybrid BI-SES). This article summarises the works and later classifies them according to the type of study (numerical or experimental), simulation tool, parametric analysis and performance when applied.

Abstract
Power systems are going through a transition period. Consumers want more active participation in electric system management, namely assuming the role of producers-consumers, prosumers in short. The prosumers' energy production is heavily based on renewable energy sources, which, besides recognized environmental benefits, entails energy management challenges. For instance, energy consumption of appliances in a home can lead to misleading patterns. Another challenge is related to energy costs since inefficient systems or unbalanced energy control may represent economic loss to the prosumer. The so-called home energy management systems (HEMS) emerge as a solution. When well-designed HEMS allow prosumers to reach higher levels of energy management, this ensures optimal management of assets and appliances. This paper aims to present a comprehensive systematic review of the literature on optimization techniques recently used in the development of HEMS, also taking into account the key factors that can influence the development of HEMS at a technical and computational level. The systematic review covers the period 2018-2021. As a result of the review, the major developments in the field of HEMS in recent years are presented in an integrated manner. In addition, the techniques are divided into four broad categories: traditional techniques, model predictive control, heuristics and metaheuristics, and other techniques.

Abstract
Wildfires threaten and kill people, destroy urban and rural property, degrade air quality, ravage forest ecosystems, and contribute to global warming. Wildfire management decision support models are thus important for avoiding or mitigating the effects of these events. In this context, this paper aims at providing a review of recent applications of machine learning methods for wildfire management decision support. The emphasis is on providing a summary of these applications with a classification according to the case study type, machine learning method, case study location, and performance metrics. The review considers documents published in the last four years, using a sample of 135 documents (review articles and research articles). It is concluded that the adoption of machine learning methods may contribute to enhancing support in different fire management phases.