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Tooth color is a determining factor in the fabrication of dental prostheses. The aim of the present study is to compare two measurement methods used in the field of dentistry: dental guides and spectrophotometry. A total of 2768 natural teeth were measured using the Vita Classical and Vita 3D-Master dental guides (Vita-Zahnfabrik, Bad Säckingen, Germany), as well as a Vita Easyshade Compact spectrophotometer (Vita-Zahnfabrik). The measurements were carried out by one operator under suitable illumination conditions at 5500 degrees Kelvin. The obtained results show that the measurements obtained with the Vita Classical dental guide classifies teeth into the A-B categories, while the spectrophotometer preferentially classifies teeth into the B-C categories. The correlation coefficients obtained with the dental guides ranged from −0.32 to −0.39 (p < 0.01), while those for the spectrophotometer ranged from −0.35 to −0.55 (p < 0.01). Therefore, we can conclude that the spectrophotometer is more reliable and reproducible in its measurements than the dental guides.

Elderly care home residents are particularly vulnerable to COVID-19 due to immune-senescence, pre-existing medical conditions, and the risk of transmission from staff and visitors. This study aimed to describe the outcomes of a COVID-19 outbreak in a long-term care facility for elderly persons following the initial vaccination. A single-center, retrospective, observational design was used to analyze the variables associated with hospitalization and death rate by logistic regression. Adjusted odds ratios (aOR) and their 95% confidence intervals (CI) were calculated. Sixty-eight residents received the first dose of the COVID-19 vaccine. Despite being negative six days after vaccination, the performance of a second test 4 days later revealed 51 positives (75.0%) among residents and 18 among workers (56.3%). A total of 65 of the 68 residents (95.58%) had positive results with symptoms, whereas 34.9% required hospitalization, and 25.8% died. The best-fitting model to explain the distribution of cases reflects three points at the time of infection.. The time from vaccination to symptom onset explains the hospitalization and mortality rates since a day elapsed halves the risk of hospitalization (aOR = 0.57; CI = 0.38−0.75) and the risk of death by a quarter (aOR = 0.74; CI = 0.63−0.88). Nursing homes present an elevated risk of transmission and severity of SARS-CoV-2 infection. Although vaccination reduces the risk of hospitalization and death, extreme prevention and control measures are essential in these institutions despite the high vaccination coverage.

This review aims to establish criteria for identifying an adoption process in an archaeological context. We define adoption as raising an individual who does not belong genetically to the family. Adoption appears in different moments of past societies, and when establishing a “family” nucleus burial place we must consider certain social behaviors, such as burials under the houses, collective burials, or laying bodies in specific positions. After observing these signs, we are carrying out a genetic analysis, in order to confirm a biologically related family nucleus. These traces have been traditionally linked to family nuclei because they have been found previously in burials where biological kinship was confirmed. However, there can be cases where, after carrying out the genetic analyses, it is confirmed that certain individuals are not genetically related. In such cases, an adoption case cannot be ruled out. These cases are not easy to identify due to the differences between societies and cultures, so more in-depth studies should be carried out on the type of funeral practice in which these human remains are found to be able to discriminate an adopted individual from one who was not adopted. Therefore, the study of adoption should be carried out based on an in-depth knowledge of the cultural background, before using a powerful tool such as ancient DNA technology.

Soil particle aggregation and their associated carbon (C) and nitrogen (N) content can provide valuable diagnostic indicators of changes in soil properties in response to the implementation of different agricultural management practices. In this sense, there is limited knowledge regarding the impact of intercropping on soil organic carbon (SOC) and total nitrogen (TN) pools in aggregates. This study aimed to evaluate the short-term effect (4 years) of three crop diversifications in rainfed olive orchards on soil aggregation, SOC and TN concentration and SOC stocks (SOC-S) under semi-arid Mediterranean conditions. Olive orchards were diversified with Crocus sativus (D-S), Vicia sativa and Avena sativa in rotation (D-O) and Lavandula x intermedia (D-L) and compared with monocropping system (CT). Soil samples were collected at two depths (0–10 and 10–30 cm) and analysed for soil aggregate mass, SOC and TN content in aggregate-size fractions obtained by the wet-sieving method. Changes caused by crop diversifications on SOC-S were also determined. Overall, after 4 years, a reduction in aggregation values was observed. However, D-S increased the macroaggregates (>250 μm) percentage, Mean Weigh Diameter values, and Geometric Mean Value in the 0–10 cm. Across treatments, aggregate-associated C in 0–10 cm was higher in the D-S treatment, while in the 10–30 cm soil layer, the greatest values were found in CT. Regarding the SOC-S, after 4 years, significant losses were recorded under CT management in 0–10 cm (−1.21 Mg ha−1) and 10–30 cm (−0.84 Mg ha−1), while D-O and D-L showed similar values to those obtained at the beginning of the study. The highest increases in SOC-S were found in D-S, with an increase of 5.88% in the 0–10 cm and 14.47% in the 10–30 cm. Our results showed the high potential of the diversified cropping system to increase soil stability and SOC sequestration.

Intensive tillage (CT) application in Mediterranean olive orchards has threatened soil quality and led to soil degradation. No tillage with bare soil (NT+H) has been considered as an alternative practice to this management system; however, there are discrepancies in the literature on the results of the application of this practice. Our main objective was to assess the impact of continuous tillage and zero tillage on soil aggregate stability, SOC and SON stocks. The study was conducted in a Mediterranean rainfed olive grove under semi-arid conditions in a Calcaric Cambisol, for 16 years evaluating complete soil profiles (0–120 cm depth). In the long-term, the management practices CT1 and NT+H significantly affected aggregate particle size by reducing the percentage of macro-aggregates (>250 μm) and promoting a higher number of micro-aggregates (2000 μm) percentage. In relation to these results, the soil structural stability indices showed a significant decrease in both Mean Weight Diameter (MWD) and Geometric Mean Diameter (GMD) values with losses of more than 50% with respect to the initial period (CT0) in the first two horizons. In the long term, both in CT1 and in NT+H, higher SOC concentrations were found in deep horizons showing a C redistribution in depth and important losses in TN values—while, in CT0, macroaggregates contained the highest CPC values, after the long-term both management practices (CT1 and NT+H) affected the C dynamics and were characterised by higher C pool in the microaggregates than in the macroaggregate fractions. Therefore, long-term NT+H and CT1 showed an SOC storage deterioration and increased susceptibility to decomposition, CO2 emissions and fertility losses. This trend i.e., decreases in SOC stocks following NT, confirms previous studies on the subject and points to nutrient balance impacts.

The urgent need to implement agricultural systems that provide greater sustainability and resilience to the challenges of the climate change process has meant that alternative paradigms for agri-food systems and agriculture have become more relevant in recent times. In this study, we present the building process and consolidation of an agro-ecological project (Extiercol) in a rural area of southern Spain, with a prolonged depopulation process and close connections to nearby urban areas. Through participatory action research, the specific objectives of this study are (1) to describe the agroecological collective process from its creation by a youth association to its establishment as a viable agricultural project; (2) to identify the drivers for the development of this type of transition process towards agricultural sustainability and (3) to analyse urban-rural alliances in the establishment of agroecological projects. Finally, the replicability of this project was assessed, with a special focus on the main barriers to be addressed in order to implement this agricultural system such as difficult to land access or a negative perception of sustainable management by farmers. Through this study we have shown how the connection between the food production area and nearby urban areas can be achieved through an agroecological project.

Pressure retarded osmosis (PRO) power units, which produce electrical energy from salinity gradient sources located at coastlines, are a technology still in the process of maturation; however, there is an expectation that this technology will need to be integrated into electrical distribution networks. Such integration will drive changes in the electric response of the distribution systems which may lead to harmful operating conditions. Power flow analysis is a tool used to reveal the steady-state operating condition of distribution systems and which could be extended to study and address the integration of PRO power units. To the best of the authors’ knowledge, such extension of power flow analysis has not yet been addressed in the literature. Accordingly, this paper comprehensively provides a model to evaluate the electric current and complex power produced by PRO power units. This model is directly embedded in the forward-backward sweep (FBS) method, extending the power flow analysis of electric distribution systems in this way so as to consider the integration of PRO power units. The resulting approach permits revealing of the steady-state operating response of distribution systems and the effects that may be driven by the integration of PRO power units, as corroborated through numerical results on a 14-node test distribution system.

The use of statistical models and machine-learning techniques along satellite-derived aerosol optical depth (AOD) is a promising method to estimate ground-level particulate matter with an aerodynamic diameter of ≤2.5 μm (PM2.5), mainly in urban areas with low air quality monitor density. Nevertheless, the relationship between AOD and ground-level PM2.5 varies spatiotemporally and differences related to spatial domains, temporal schemes, and seasonal variations must be assessed. Here, an ensemble multiple linear regression (EMLR) model and an ensemble neural network (ENN) model were developed to estimate PM2.5 levels in the Monterrey Metropolitan Area (MMA), the second largest urban center in Mexico. Four AOD-SDSs (Scientific Datasets) from MODIS Collection 6 were tested using three spatial domains and two temporal schemes. The best model performance was obtained using AOD at 0.55 µm from MODIS-Aqua at a spatial resolution of 3 km, along meteorological parameters and daily scheme. EMLR yielded a correlation coefficient (R) of ~0.57 and a root mean square error (RMSE) of ~7.00 μg m−3. ENN performed better than EMLR, with an R of ~0.78 and RMSE of ~5.43 μg m−3. Satellite-derived AOD in combination with meteorology data allowed for the estimation of PM2.5 distributions in an urban area with low air quality monitor density.

Urban green spaces (UGSs) provide essential environmental services for the well-being of ecosystems and society. Due to the constant environmental, social, and economic transformations of cities, UGSs pose new challenges for management, particularly in fast-growing metropolitan areas. With technological advancement and the evolution of deep learning, it is possible to optimize the acquisition of UGS inventories through the detection of geometric patterns present in satellite imagery. This research evaluates two deep learning model techniques for semantic segmentation of UGS polygons with the use of different convolutional neural network encoders on the U-Net architecture and very high resolution (VHR) imagery to obtain updated information on UGS polygons at the metropolitan area level. The best model yielded a Dice coefficient of 0.57, IoU of 0.75, recall of 0.80, and kappa coefficient of 0.94 with an overall accuracy of 0.97, which reflects a reliable performance of the network in detecting patterns that make up the varied geometry of UGSs. A complete database of UGS polygons was quantified and categorized by types with location and delimited by municipality, allowing for the standardization of the information at the metropolitan level, which will be useful for comparative analysis with a homogenized and updated database. This is of particular interest to urban planners and UGS decision-makers.

Olive groves are one of the most important agro-systems in the Mediterranean basin, and the Andalusia region produces the highest quantity of olive oil in Europe. The aim of this work was to evaluate the long-term (15 years) influence of two management practices in olive orchards—conventional tillage (CT) and no tillage with bare soil and herbicide application (NT + H)—on soil physical properties, infiltration capacity, erosion rates, and soil productivity. In addition, the short-term (2 years) influence of no tillage with cover crop management (NT-CC) on these parameters was also assessed. In the study area, CT and NT + H management practices showed unsustainable erosion values, 9.82 and 13.88 Mg ha−1 year−1, respectively, while NT-CC inclusion decreased the erosion rates (2.06 Mg ha−1 year−1). The implementation of NT-CC not only reduced erosion rates but also caused a change in the trend of soil productivity loss observed under CT and NT + H. In this sense, NT-CC showed a positive influence on soil quality. However, tillage removal led to a significant reduction in the infiltration capacity of soils under NT + H and NT-CC, which will be a serious handicap for water storage in an environment with continuous processes of water deficit.

This study identifies, through consultation with relevant stakeholders, low-input farming practices that can help to minimize the most pressing agronomic and environmental problems in some of the most relevant Mediterranean woody crops in Spain (almond, citrus, and olive) and enhance their sustainability. The results illustrate stakeholders’ perception regarding how each cropping system could be oriented towards a more sustainable production. Despite each case study’s particularities, there is consensus in the need for a shift towards the use of organic fertilizers, the maintenance of vegetation in the edges of plots and the use of integrated pest control strategies. On the contrary, stakeholders have selected different tillage and soil cover practices that are consistent with the characteristics and problems of each cropping system. This study also identifies relevant strengths and drawbacks for the implementation of low-input agricultural practices in each crop and study area. While stakeholders find the identified low-impact farming practices as easy and not costly, suggesting a significant potential for their successful implementation, the results also point out at the reduced knowledge of the practical benefits of some farming practices and the need for improved technical advice to foster the adoption of others.

The short- and medium—long-term effects of management and hillside position on soil organic carbon (SOC) changes were studied in a centenary Mediterranean rainfed olive grove. One way to measure these changes is to analyze the soil quality, as it assesses soil degradation degree and attempts to identify management practices for sustainable soil use. In this context, the SOC stratification index (SR-COS) is one of the best indicators of soil quality to assess the degradation degree from SOC content without analyzing other soil properties. The SR-SOC was calculated in soil profiles (horizon-by-horizon) to identify the best soil management practices for sustainable use. The following time periods and soil management combinations were tested: (i) in the medium‒long-term (17 years) from conventional tillage (CT) to no-tillage (NT), (ii) in the short-term (2 years) from CT to no-tillage with cover crops (NT-CC), and (iii) the effect in the short-term (from CT to NT-CC) of different topographic positions along a hillside. The results indicate that the SR-SOC increased with depth for all management practices. The SR-SOC ranged from 1.21 to 1.73 in CT0, from 1.48 to 3.01 in CT1, from 1.15 to 2.48 in CT2, from 1.22 to 2.39 in NT-CC and from 0.98 to 4.16 in NT; therefore, the soil quality from the SR-SOC index was not directly linked to the increase or loss of SOC along the soil profile. This demonstrates the time-variability of SR-SOC and that NT improves soil quality in the long-term.

This study aims to investigate noninvasive indices of neuromechanical coupling (NMC) and mechanical efficiency (MEff) of parasternal intercostal muscles. Gold standard assessment of diaphragm NMC requires using invasive techniques, limiting the utility of this procedure. Noninvasive NMC indices of parasternal intercostal muscles can be calculated using surface mechanomyography (sMMGpara) and electromyography (sEMGpara). However, the use of sMMGpara as an inspiratory muscle mechanical output measure, and the relationships between sMMGpara, sEMGpara, and simultaneous invasive and noninvasive pressure measurements have not previously been evaluated. sEMGpara, sMMGpara, and both invasive and noninvasive measurements of pressures were recorded in twelve healthy subjects during an inspiratory loading protocol. The ratios of sMMGpara to sEMGpara, which provided muscle-specific noninvasive NMC indices of parasternal intercostal muscles, showed nonsignificant changes with increasing load, since the relationships between sMMGpara and sEMGpara were linear (R2 = 0.85 (0.75–0.9)). The ratios of mouth pressure (Pmo) to sEMGpara and sMMGpara were also proposed as noninvasive indices of parasternal intercostal muscle NMC and MEff, respectively. These indices, similar to the analogous indices calculated using invasive transdiaphragmatic and esophageal pressures, showed nonsignificant changes during threshold loading, since the relationships between Pmo and both sEMGpara (R2 = 0.84 (0.77–0.93)) and sMMGpara (R2 = 0.89 (0.85–0.91)) were linear. The proposed noninvasive NMC and MEff indices of parasternal intercostal muscles may be of potential clinical value, particularly for the regular assessment of patients with disordered respiratory mechanics using noninvasive wearable and wireless devices.

Energy quality problems can cause diverse failures of sensitive equipment. Dynamic voltage restorers (DVRs) are devices that have been proposed to protect sensitive loads from voltage sag effects. However, the compensation capacity of DVRs is limited by the amount of energy stored in the restorer. One way to overcome this limitation is to use the interline DVR (IDVR) structure in which two or more DVRs connected to different feeders share a common DC-link. This paper proposes a new IDVR topology based on two three-phase input matrix converters (TTI-MC) without a capacitor in the dc-link. The TTI-MC integrates the power from two feeders and synthesizes the dc-link voltage. The inverters take the dc-link voltage and generate the appropriate compensation voltages to keep the load voltages stable. Each one of the inverters has its own modulation algorithm which are synchronized with the TTI-MC control. Inverter control is carried out in the reference frame dq and a modified space vector pulse width modulation (MSV-PWM) technique is used. TTI-MC control is performed in the dq reference frame with proportional-integral controllers and the modulation is based on the carrier-based pulse width modulation (MCB-PWM) technique. The proposed Two Input Interline DVR (TI-IDVR) extends its compensation range and has multifunctional capabilities. The efficiency of the proposed TI-IDVR is corroborated by simulations on MATLAB/Simulink.

Aerosol and meteorological remote sensing data could be used to assess the distribution of urban and regional fine particulate matter (PM2.5), especially in locations where there are few or no ground-based observations, such as Latin America. The objective of this study is to evaluate the ability of Modern-Era Retrospective Analysis for Research and Application, version 2 (MERRA-2) aerosol components to represent PM2.5 ground concentrations and to develop and validate an ensemble neural network (ENN) model that uses MERRA-2 aerosol and meteorology products to estimate the monthly average of PM2.5 ground concentrations in the Monterrey Metropolitan Area (MMA), which is the main urban area in Northeastern Mexico (NEM). The project involves the application of the ENN model to a regional domain that includes not only the MMA but also other municipalities in NEM in the period from January 2010 to December 2014. Aerosol optical depth (AOD), temperature, relative humidity, dust PM2.5, sea salt PM2.5, black carbon (BC), organic carbon (OC), and sulfate (SO42−) reanalysis data were identified as factors that significantly influenced PM2.5 concentrations. The ENN estimated a PM2.5 monthly mean of 25.62 μg m−3 during the entire period. The results of the comparison between the ENN and ground measurements were as follows: correlation coefficient R ~ 0.90; root mean square error = 1.81 μg m−3; mean absolute error = 1.31 μg m−3. Overall, the PM2.5 levels were higher in winter and spring. The highest PM2.5 levels were located in the MMA, which is the major source of air pollution throughout this area. The estimated data indicated that PM2.5 was not distributed uniformly throughout the region but varied both spatially and temporally. These results led to the conclusion that the magnitude of air pollution varies among seasons and regions, and it is correlated with meteorological factors. The methodology developed in this study could be used to identify new monitoring sites and address information gaps.

Fixed sample entropy (fSampEn) has been successfully applied to myographic signals for inspiratory muscle activity estimation, attenuating interference from cardiac activity. However, several values have been suggested for fSampEn parameters depending on the application, and there is no consensus standard for optimum values. This study aimed to perform a thorough evaluation of the performance of the most relevant fSampEn parameters in myographic respiratory signals, and to propose, for the first time, a set of optimal general fSampEn parameters for a proper estimation of inspiratory muscle activity. Different combinations of fSampEn parameters were used to calculate fSampEn in both non-invasive and the gold standard invasive myographic respiratory signals. All signals were recorded in a heterogeneous population of healthy subjects and chronic obstructive pulmonary disease patients during loaded breathing, thus allowing the performance of fSampEn to be evaluated for a variety of inspiratory muscle activation levels. The performance of fSampEn was assessed by means of the cross-covariance of fSampEn time-series and both mouth and transdiaphragmatic pressures generated by inspiratory muscles. A set of optimal general fSampEn parameters was proposed, allowing fSampEn of different subjects to be compared and contributing to improving the assessment of inspiratory muscle activity in health and disease.