This study aims to validate a new cosmetic ingredient from Salicornia ramosissima S J. Woods through in vitro and ex vivo assays. The halophyte extracts were obtained by subcritical water extraction (SWE) at different temperatures (110, 120, 140, 160 and 180 °C). The antioxidant/radical scavenging activities and the phenolic profile were screened for all extracts. The optimal extract was assessed in keratinocytes and fibroblasts, while permeation assays were performed in Franz cells. The inhibitory activity of hyaluronidase and elastase was also evaluated. The sample extracted at 180 °C presented the highest phenolic content (1739.28 mg/100 g of dry weight (dw)). Despite not being efficient in the sequestration of ABTS•+, this extract scavenged the DPPH• (IC50 = 824.57 µg/mL). The scavenging capacity of superoxide (O2•−) and hypochlorous acid (HOCl) was also considerable (respectively, IC50 = 158.87 µg/mL and IC50 = 5.80 µg/mL). The cell viability assays confirmed the absence of negative effects on keratinocytes, while the fibroblasts’ viability slightly decreased. The ex vivo permeation of rutin, quercetin and syringic acid after 24 h was, respectively, 11, 20 and 11%. Additionally, the extract showed a good elastase and hyaluronidase inhibitory activity. The results obtained support the S. ramosissima bioactivity as a cosmetic ingredient.

The three-dimensional (3D) electrochemical treatment process was studied for the removal of two pharmaceuticals, diclofenac (anti-inflammatory) and sulfamethoxazole (antibiotic), in mono and bi-component systems. Adsorption and conventional two-dimensional electrochemical processes were initially studied and then combined to develop the 3D process. The influence of different operating parameters on the removal efficiency was studied: the distance between the cathode and the anode, the pharmaceutical and electrolyte (NaCl) concentrations, the pH, and the (carbon-based) adsorbent used as particulate electrode (biochar and commercial activated carbon, granulometry, and amount). The energy consumption and the electric energy per order were evaluated. The results demonstrate the efficiency of the 3D process for the removal of diclofenac and sulfamethoxazole from aqueous solutions, both for mono- and bi-component systems, achieving their complete removal respectively in 10 and 30 min, using a Mixed Metal Oxide anode (titanium-coated with RuO2-IrO2-TiO2), a stainless steel cathode, a biochar particulate electrode (1–2 mm), an initial pharmaceutical concentration of 10 mg/L, an inter-electrode distance of 7.5 cm, a pH value of 7 and a current density of 7 mA/cm2. The optimised 3D process was also successfully applied to a wastewater treatment plant effluent, but lower removal efficiencies were observed (after 30 min) for bi-component fortified samples; 49% for DCF and 86% for SMX, with energy consumptions of 1224 and 613 Wh/g and an electric energy per order of 19.1 and 8.77 kWh/m3 respectively. On the other hand, the pharmaceuticals were completely removed from the effluent when real concentrations (i.e. without their addition) were used

The use of energy sharing models in smart grids has been widely addressed in the literature. However, feasible technical solutions that can deploy these models into reality, as well as the correct use of energy forecasts are not properly addressed. This paper proposes a simple, yet viable and feasible, solution to deploy energy management systems on the end-user-side in order to enable not only energy forecasting but also a distributed discriminatory-price auction peer-to-peer energy transaction market. This work also analyses the impact of four energy forecasting models on energy transactions: a mathematical model, a support-vector machine model, an eXtreme Gradient Boosting model, and a TabNet model. To test the proposed solution and models, the system was deployed in five small offices and three residential households, achieving a maximum of energy costs reduction of 10.89% within the community, ranging from 0.24% to 57.43% for each individual agent. The results demonstrated the potential of peer-to-peer energy transactions to promote energy cost reductions and enable the validation of auction-based energy transactions and the use of energy forecasting models in today’s buildings and end-users.

The energy resource management (ERM) problem in today’s energy systems is complex and challenging due to the increasing penetration of distributed energy resources with uncertain behavior. Despite the improvement of forecasting tools, and the development of strategies to deal with this uncertainty (for instance, considering Monte Carlo simulation to generate a set of different possible scenarios), the risk associated with such variable resources cannot be neglected and deserves proper attention to guarantee the correct functioning of the entire system. This paper proposes a risk-based optimization approach for the centralized day-ahead ERM taking into account extreme events. Risk-neutral and risk-averse methodologies are implemented, where the risk-averse strategy considers the worst scenario costs through the conditional value-at-risk ( CVaR ) method. The model is formulated from the perspective of an aggregator that manages multiple technologies such as distributed generation, demand response, energy storage systems, among others. The case study analysis the aggregator’s management inserted in a 13-bus distribution network in the smart grid context with high penetration of renewable energy and electric vehicles. Results show an increase of nearly 4% in the day-ahead operational costs comparing the risk-neutral to the risk-averse strategy, but a reduction of up to 14% in the worst-case scenario cost. Thus, the proposed model can provide safer and more robust solutions incorporating the CVaR tool into the day-ahead management.

The ever-increasing number of electric vehicles (EVs) circulating on the roads and renewable energy production to achieve carbon footprint reduction targets has brought many challenges to the electrical grid. The increasing integration of distributed energy resources (DER) in the grid is causing severe operational challenges, such as congestion and overloading for the grid. Active management of distribution network using the smart grid (SG) technologies and artificial intelligence (AI) techniques can support the grid's operation under such situations. Implementing evolutionary computational algorithms has become possible using SG technologies. This paper proposes an optimal day-ahead resource scheduling to minimize multiple aggregators' operational costs in a SG, considering a high DER penetration. The optimization is achieved considering three metaheuristics (DE, HyDE-DF, CUMDANCauchy++). Results show that CUMDANCauchy++ and HyDE-DF present the best overall results in comparison to the standard DE.

Occupational exposure may involve a variety of toxic compounds. A mutagenicity analysis using the Ames test can provide valuable information regarding the toxicity of absorbed xenobiotics. Through a search of relevant databases, this systematic review gathers and critically discusses the published papers (excluding other types of publications) from 2001–2021 that have assessed urinary mutagenicity (Ames test with Salmonella typhimurium) in an occupational exposure context. Due to the heterogeneity of the study methods, a meta-analysis could not be conducted. The characterized occupations were firefighters, traffic policemen, bus drivers, mail carriers, coke oven and charcoal workers, chemical laboratory staff, farmers, pharmacy workers, and professionals from several other industrial sectors. The genetically modified bacterial strains (histidine dependent) TA98, TA100, YG1041, YG1021, YG1024 and YG1042 have been used for the health risk assessment of individual (e.g., polycyclic aromatic hydrocarbons) and mixtures of compounds (e.g., diesel engine exhaust, fire smoke, industrial fumes/dyes) in different contexts. Although comparison of the data between studies is challenging, urinary mutagenicity can be very informative of possible associations between work-related exposure and the respective mutagenic potential. Careful interpretation of results and their direct use for occupational health risk assessment are crucial and yet complex; the use of several strains is highly recommended since individual and/or synergistic effects of complex exposure to xenobiotics can be overlooked. Future studies should improve the methods used to reach a standardized protocol for specific occupational environments to strengthen the applicability of the urinary mutagenicity assay and reduce inter- and intra-individual variability and exposure source confounders

This paper proposed a Mixed Binary Linear Programming (MBLP) approach to find the optimal size of some components of a Smart Building (SB) attempting to reduce the overall cost. The considered SB is equipped with local resources such as Photovoltaic (PV) panels, Electrical Vehicles (EVs), and the Battery Energy Storage System (BESS). Moreover, the SB is only connected with the grid by an Energy Management System (EMS) in which the whole SB has a single Contract Power (CP) such that EMS manages the power flow among external grid, local resources, apartments, and common services, for the goal of reducing the electricity bill. Hence, the wrong choice of CP and BESS capacity will impose unnecessary charges on the electricity bill. As a results, EMS has played a crucial role in SB in determining the best CP and BESS values. The obtained results of this work show the efficiency of the model in which by finding the optimal capacity of CP and BESS, the electricity bill improves by a 34% reduction

This study aimed the optimization of antioxidant activity, total phenolic compounds (TPC) and total carotenoids of Horned Melon fruit submitted to maceration extraction, using ethanol as solvent. Optimization was performed by Box–Behnken design on three levels and three variables: extraction temperature (25 °C, 55 °C, and 85 °C), extraction time (0.5, 2.0, and 3.5 h) and ethanol concentration (10%, 50%, and 90%). The optimal conditions to improve the carotenoids extraction was 55 °C, 1.5 h and 86% ethanol, whereas the antiradical/antioxidant activity (ABTS and FRAP assays) and TPC were enhanced at 45 °C for 2.0 h and 40% ethanol. The optimal antioxidant extract was characterized regarding the phenolic composition and in vitro neuroprotective activities. The phenolic composition revealed the presence of high amounts of gallic acid (11.7 ± 0.6 mg/100 g dw) and protocatechuic acid (7.69 ± 0.38 mg/100 g dw). However, this extract presented a weak activity against Monoamine oxidase A (11.8 ± 2.3% inhibition at 2.7 mg/mL) and did not display anti-cholinesterase activity at the maximum concentration tested (1.3 mg/mL). Maceration was an efficient extraction technique to recover polyphenols and carotenoids from Horned Melon fruit. The optimized model can be very useful for food and pharmaceutical industries

The fast growth of renewable energy sources in the residential building led to a complex problem related to the energy management system: the uncertainty associated with the forecast of photovoltaic power generation. To solve this challenge, this paper proposes a robust optimization model to obtain the optimal solution for the worst-case scenario of photovoltaic generation. A Mixed Binary Linear Programming problem is transformed into a trackable robust counterpart to provide immunity against the worst-case realization. Through the budget of uncertainty, the risk of the solution can be adjusted. The results demonstrate that the influence of Battery Energy Storage System and Electric Vehicles against uncertainties leads to higher economic gains up to 6% reduction.

The energy management of buildings plays a vital role in the energy sector. With that in mind, and targeting an accurate forecast of electricity consumption, in the present paper is aimed to provide decision on the best prediction algorithm for each context. It may also increase energy usage related with renewables. In this way, the identification of different contexts is an advantage that may improve prediction accuracy. This paper proposes an innovative approach where a decision tree is used to identify different contexts in energy patterns. One week of five-minutes data sampling is used to test the proposed methodology. Each context is evaluated with a decision criterion based on reinforcement learning to find the best suitable forecasting algorithm. Two forecasting models are approached in this paper, based on K-Nearest Neighbor and Artificial Neural Networks, to illustrate the application of the proposed methodology. The reinforcement learning criterion consists of using the Multiarmed Bandit algorithm. The obtained results validate the adequacy of the proposed methodology in two case-studies: building; and industry.