Abstract
The intensification of environmental impacts and the increased economic risks are triggering a technological race towards a low-carbon economy. In this socioeconomic scenario of increasing changes and environmental concerns, microgrids (MGs) play an important role in integrating distributed energy resources. Thus, a planning strategy for grid-connected MGs with distributed energy resources and electric vehicle (EV) charging stations is proposed in this paper. The developedmathematical model aims to defineMGexpansion decisions that satisfy the growing electricity demand (including EV charging demand) at the lowest possible cost; such decisions include investments in PV units, wind turbines, energy storage systems, and EV charging stations. The objective function is based on the interests of the MG owner, considering constraints associated with the main distribution grid. A mixed-integer linear programming model is used to formulate the problem, ensuring the solution's optimality. The applicability of the proposed model is evaluated in the 69-bus distribution grid. Promising results concerning grid-connected MGs were obtained, including the enhancement of energy exchange with the grid according to their needs.

Abstract
Circular economy policies and recycling play a pivotal role in fostering sustainable models for the wood industry capable of reducing the environmental impact of our consumption patterns. The production of Particleboard is a good example of industry that uses high quantities of recycled wood. However, it poses risks since wood often have contaminants that compromise compliance of safety standards. Thus, it is necessary to develop methodologies for rapid analysis of chemical contaminants in wood wastes that allow easy detection of these elements. In this work, the capability of Laser-induced breakdown spectroscopy (LIBS) to detect a set of heavy metals in wood samples was explored. Some advantages of this technique, such as portability, minimal to no sample preparation, and quick analysis are characteristics that make this method one of the most suitable for this purpose of analysis. In the majority of cases, the contamination comes from the pigments used in paints, varnishes, or coatings. Titanium (Ti) e.g. is a common element in white pigments and Chromium (Cr) in red and green pigments. To ensure the presence or absence of Cr and Ti, a set of 3 lines was analysed. The results revealed the presence of these elements and that 30% of the samples seem to be highly contaminated. The LIBS technique proved to be a powerful methodogy for decision-making purposes.

Abstract
Regulatory changes associated with distributed generation have occurred in several countries (e.g., the USA, Germany, the UK, and Australia). However, there is a lack of robust and holistic analytical models that can be used to implement the best regulatory framework among possible options. In this context, the present paper proposes a cutting-edge regulatory framework for distributed generation based on multi-objective optimization, taking into account socioeconomic (socioeconomic welfare created by the regulated electricity market and electricity tariff affordability) and environmental (global warming potential) indicators. Such indicators are modeled primarily based on the optimized tariff model (socioeconomic regulated electricity market model), Bass diffusion model (forecasting model of distributed generation deployment), and life cycle assessment (environmental impact assessment method). The design variables are assumed to be the regulated electricity tariff and remuneration of the electricity injected into the grid over the years. First, the proposed methodology is applied to fifteen large-scale Brazilian concession areas with a significant deployment of distributed generation assuming two approaches, a multi-compensation scenario, where the compensation is set individually for each concession area, and a single-compensation scenario, where the compensation is set equally for all concession areas. Then, the optimal solutions are compared to Ordinary Law 14300, which is a recently implemented regulatory framework for distributed generation in Brazil. Results demonstrate that Ordinary Law 14300 is a dominated or non-optimal solution since it is not located on the optimal Pareto frontiers for any of the assessed concession areas. Assuming the Euclidian knee points, benefits averaging 33% and 15% were achieved in terms of electricity tariff affordability for the multi and single-compensation scenarios, respectively, with small losses of 8% and 3% in terms of socioeconomic welfare and global warming potential. Though the proposed methodology is applied in the Brazilian context, it can also be applied to other countries with regulated electricity markets; thus, it is expected to be valuable for researchers, government institutions, and regulatory agencies worldwide.

Abstract
With the proliferation of Internet of Things (IoT) devices, there is an increasing need to prioritize their security, especially in the context of identity and authentication mechanisms. However, IoT devices have unique limitations in terms of computational capabilities and susceptibility to hardware attacks, which pose significant challenges to establishing strong identity and authentication systems. Paradoxically, the very hardware constraints responsible for these challenges can also offer potential solutions. By incorporating hardware-based identity implementations, it is possible to overcome computational and energy limitations, while bolstering resistance against both hardware and software attacks. This research addresses these challenges by investigating the vulnerabilities and obstacles faced by identity and authentication systems in the IoT context, while also exploring potential technologies to address these issues. Each identified technology underwent meticulous investigation, considering known security attacks, implemented countermeasures, and an assessment of their pros and cons. Furthermore, an extensive literature survey was conducted to identify instances where these technologies have effectively supported device identity. The research also includes a demonstration that evaluates the effectiveness of hardware trust anchors in mitigating various attacks on IoT identity. This empirical evaluation provides valuable insights into the challenges developers encounter when implementing hardware-based identity solutions. Moreover, it underscores the substantial value of these solutions in terms of mitigating attacks and developing robust identity frameworks. By thoroughly examining vulnerabilities, exploring technologies, and conducting empirical evaluations, this research contributes to understanding and promoting the adoption of hardware-based identity and authentication systems in secure IoT environments. The findings emphasize the challenges faced by developers and highlight the significance of hardware trust anchors in enhancing security and facilitating effective identity solutions.

Abstract
[No abstract available]

Abstract
Raman technology offers a cutting-edge approach to measuring glucose solutions, providing precise and non-invasive analysis. By probing the vibrational energy levels of molecular bonds, Raman technology generates a unique spectral fingerprint that allows for the accurate determination of glucose concentrations. This study proposes the use of Raman spectroscopy to identify different glucose concentrations through the detection of Raman fingerprints. As expected, higher concentrations of glucose in the solution conducted to higher peak bands, indicating more glucose molecules interacting with light and consequently increasing the magnitude of inelastic scattering. This non-destructive approach preserves sample integrity and facilitates rapid analysis, making it suitable for various applications in biomedical research, pharmaceutical development, and food science.