The first neutron diffraction study of in-situ anaerobic burning of human bones is reported, aiming at an interpretation of heat-induced changes in bone, which were previously detected by vibrational spectroscopy, including inelastic neutron scattering techniques. Structural and crystallinity variations were monitored in samples of the human femur and tibia, as well as a reference hydroxyapatite, upon heating under anaerobic conditions. Information on the structural reorganization of the bone matrix as a function of temperature, from room temperature to 1000 °C, was achieved. Noticeable crystallographic and domain size variations, together with O–H bond lengths and background variations, were detected. Above 700 °C, the inorganic bone matrix became highly symmetric, devoid of carbonates and organic constituents, while for the lower temperature range (<700 °C), a considerably lower crystallinity was observed. The present pilot study is expected to contribute to a better understanding of the heat-prompted changes in bone, which can be taken as biomarkers of the burning temperature. This information is paramount for bone analysis in forensic science as well as in archeology and may also have useful applications in other biomaterial studies.

The remarkable features of a carbon fiber paper sensor (CP) were employed for detection of the estrogenic hormone 17α-ethinylestradiol (EE2), considered a contaminant of emerging concern due to its potential ecotoxicity and widespread in the aquatic ecosystems. In this work, an unpreceded CP pre-treatment study was conducted with the (Il)-hexacyanoferrate(III) ion pair, however a bare sensor without pre-treatment revealed higher efficiency on the oxidation of EE2 compared to a chemical and electrochemical pre-treated CP and a gold nanoparticles modified CP, being thus selected for EE2 determinations. The analytical conditions were thoroughly optimized in terms of electrolyte pH (pH 7), differential pulse voltammetry parameters (modulation time 0.003 s, amplitude 0.09 V, interval time 0.1 s and step potential 0.01 V), and analyte preconcentration potential (0.4 V) and time (180 s). The hormone can be determined by the CP in a wide linear range from 0.1 to 1000 nM, achieving a detection limit of 0.14 ± 0.005 nM and an outstanding sensitivity of 1636 ± 232 μA μM−1 cm−2 in the lowest linear zone (0.1–1 nM). The sensor was validated in river water and fish reaching good recoveries (91.2 ± 4.6 to 109.0 ± 7.1%), reproducibility and repeatability. Moreover, the sensor showed high selectivity to EE2 in the presence of several potential interfering compounds and frequently prescribed drugs, though it could not discriminate the similar hormone, 17β-estradiol, being the total concentration obtained in this case. CP-based sensors emerge as efficient electroanalytical tools, suggesting that modification of the surface may not always be beneficial in terms of sensitivity

Sectorization is the process of grouping a set of previously defined basic units (points or small areas) into a fixed number of sectors. Sectorization is also known in the literature as districting or territory design, and is usually performed to optimize one or more criteria regarding the geographic characteristics of the territory and the planning purposes of sectors. The most common criteria are equilibrium, compactness and contiguity, which can be measured in many ways. Sectorization is similar to clustering but with a different motivation. Both aggregate smaller units into groups. But, while clustering strives for inner similarity of data, sectorization aims at outer homogeneity [1]. In clustering, groups should be very different from each other, and similar points are classified in the same cluster. In sectorization, groups should be very similar to each other, and therefore very different points can be grouped in the same sector. We classify sectorization problems into four types: basic sectorization, sectorization with service centers, resectorization, and dynamic sectorization. A Decision Support System for Sectorization, D3S, is being developed to deal with these four types of problems. Multi-objective genetic algorithms were implemented in D3S using Python, and a user-friendly web interface was developed using Django. Several applications can be solved with D3S, such as political districting, sales territory design, delivery service zones, and assignment of fire stations and health services to the population.

A di-urethane cross-linked poly(oxyethylene)/silica hybrid matrix [di-urethanesil, d-Ut(600)], synthesized by the sol-gel process, was doped with lithium triflate (LiCF3SO3) and the 1-butyl-3-methylimidazolium chloride ([Bmim]Cl) ionic liquid. The as-produced xerogel film is amorphous, transparent, flexible, homogeneous, hydrophilic, and has low nanoscale surface roughness. It exhibits an ionic conductivity of 3.64 × 10–6 and 5.00 × 10–4 S cm–1 at 21 and 100°C, respectively. This material was successfully tested as electrolyte in an electrochromic device (ECD) with the glass/ITO/a-WO3/d-Ut(600)10LiCF3SO3[Bmim]Cl/c-NiO/ITO/glass configuration, where a-WO3 and c-NiO stand for amorphous tungsten oxide and crystalline nickel oxide, respectively. The device demonstrated attractive electro-optical performance: fast response times (1–2 s for coloring and 50 s for bleaching), good optical memory [loss of transmittance (T) of only 41% after 3 months, at 555 nm], four mode modulation [bright mode (+3.0 V, T = 77% at 555 nm), semi-bright mode (−1.0 V, T = 60% at 555 nm), dark mode (−1.5 V, T = 38 % at 555 nm), and very dark mode (−2.0 V, T = 11% and −2.5 V, T = 7% at 555 nm)], excellent cycling stability denoting improvement with time, and high coloration efficiency [CEin = −6727 cm2 C–1 (32th cycle) and CEout = +2794 cm2 C–1 (480th cycle), at 555 nm].

Chestnut (Castanea sativa) shells (CSS) are a source of bioactive compounds with well demonstrated in-vitro antioxidant properties. Nevertheless, no in-vivo studies have already evaluated this effect. This study evaluated the effects of the oral daily administration of an eco-friendly CSS extract (50 and 100 mg/kg per body weight (b. w.)) to rats regarding in-vivo antioxidant activity, glucose and lipids levels, and metabolomic profiling of poly- phenols by LC-ESI-LTQ-Orbitrap-MS. The results demonstrated the in-vivo antioxidant properties in the animals liver, kidney and blood serum, as well as protective effects against hemolysis and rising of blood glucose and lipids levels. New insights on metabolomic profiling of polyphenols proved their absorption and further biotransformation by phase I (hydrogenation and hydroxylation) and II reactions (glucuronidation, methylation and sulfation). This is the first study that attempted to validate a novel nutraceutical ingredient extracted from CSS by in-vivo assays, corroborating the outcomes screened by in-vitro assays

Chestnut (Castanea sativa) shells (CSs), an undervalued agro-industrial biowaste, have arisen as a source of bioactive compounds with promising health-promoting effects. This study attempted, for the first time, to develop a functional food, namely cookies, using a CS extract obtained by an eco-friendly technology (subcritical water extraction). The cookies were characterized regarding their nutritional composition, total phenolic and flavonoid contents (TPC and TFC, respectively), antioxidant/antiradical activities, phenolic profile, and sensory evaluation. The results demonstrated that the CS-extract-enriched cookies were mainly composed of carbohydrates (53.92% on dry weight (dw)), fat (32.62% dw), and fiber (5.15% dw). The phenolic profile outlined by HPLC-PDA revealed the presence of phenolic acids, flavonoids, and hydrolysable tannins, attesting to the high TPC and TFC. The in vitro antioxidant/antiradical effects proved the bioactivity of the functional cookies, while the sensory evaluation unveiled excellent scores on all attributes (≥6.25). The heatmap diagram corroborated strong correlations between the TPC and antioxidant/antiradical properties, predicting that the appreciated sensory attributes were closely correlated with high carbohydrates and phenolic compounds. This study encourages the sustainable recovery of antioxidants from CSs and their further employment as an active nutraceutical ingredient in functional cookies.

Nowadays there is a growing concern with the environment and sustainability, which means that better methods, including pollutants analysis, with less consumption of materials, organic solvents, and energy, need to be developed. Considering the almost inexistent information about the topic, the main goal of this work was to compare the environmental impacts of two analytical methods, a traditional one based on liquid chromatography with fluorescence detection and a newly developed carbon paper sensor. The selected analyte was 17α-ethinylestradiol, which is a contaminant of emergent concern in aquatic ecosystems due to its endocrine disruptor behavior. The life cycle assessment data showed that the sensor detection presents an almost negligible environmental impact when compared with the extraction step (the same for both methods) and the liquid chromatographic determination (roughly 80 times higher than with the sensor). The sensor values for all categories of damage are below 3% of the total method impacts, i.e., 1.6, 1.9, 2.4, and 2.9% for resources, climate change, human health, and ecosystem quality. The extraction represents 98.1% of the sensor environmental impacts (and 99.6% of its life cycle costing) and 38.8% of the chromatographic method. This study evidences the need of developing and applying greener analytical (detection and extraction) strategies

The world is moving toward demand response programs, energy optimization, and using renewable energy resources more than the past. Buildings are responsible for a significant part of energy consumption and they are considered as suitable cases for reducing energy usage. Therefore, energy management in buildings should be improved. This paper presents a multi-period optimization algorithm with the aim of reducing power consumption of the lights and maintain user comfort. For this purpose, several comfort constraints are considered, and several parameters are defined. The case study of the paper uses real data monitored by an implemented automation infrastructure in a building. Four scenarios are presented to validate the impacts of proposed algorithm. Each scenario surveys different aspects of user comfort constraint and the obtained results illustrate the performance of algorithm.

This paper presents the application of collaborative reinforcement learning models to enable the distributed learning of energy contracts negotiation strategies. The learning model combines the learning process on the best negotiation strategies to apply against each opponent, in each context, from multiple learning sources. The diverse learning sources are the learning processes of several agents, which learn the same problem under different perspectives. By combining the different independent learning processes, it is possible to gather the diverse knowledge and reach a final decision on the most suitable negotiation strategy to be applied. The reinforcement learning process is based on the application of the Q-Learning algorithm; and the continuous combination of the different learning results applies and compares several collaborative learning algorithms, namely BEST-Q, Average (AVE)-Q; Particle Swarm Optimization (PSO)-Q, and Weighted Strategy Sharing (WSS)-Q. Results show that the collaborative learning process enables players’ to correctly identify the negotiation strategy to apply in each moment, context and against each opponent.

Active communities are emerging thanks to the necessity of creating a cleaner and safer energy system. The growing concern regarding climate change urges a solution to remove fossil fuels from the production equation. The Distributed Generation (DG) technologies are presented as a substitute, but the main resources’ behavior is highly uncertain. Flexibility from the demand side is needed. In this way, the authors resort to mixed-integer linear programming optimization to schedule the active resources introduced by the Smart Grid concept: DG, Demand Response programs, and Energy Storage Systems. In this study, the last one is the focus where the impact of these technologies in an active community is analyzed and discussed. The authors performed a clustering method to identify patterns on Energy Storage System (ESS) profiles, finding the optimal number of clusters first. The results show the importance of ESS from both Aggregator and active consumer perspectives.