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The highly temporal variability of the hydrological response in Mediterranean areas affects the operation of hydropower systems, especially in run-of-river (RoR) plants located in mountainous areas. Here, the water flow regime strongly determines failure, defined as no operating days due to inflows below the minimum operating flow. A Bayesian dynamics stochastic model was developed with statistical modeling of both rainfall as the forcing agent and water inflows to the plants as the dependent variable using two approaches—parametric adjustments and non-parametric methods. Failure frequency analysis and its related operationality, along with their uncertainty associated with different time scales, were performed through 250 Monte Carlo stochastic replications of a 20-year period of daily rainfall. Finally, a scenario analysis was performed, including the effects of 3 and 30 days of water storage in a plant loading chamber to minimize the plant's dependence on the river's flow. The approach was applied to a mini-hydropower RoR plant in Poqueira (Southern Spain), located in a semi-arid Mediterranean alpine area. The results reveal that the influence of snow had greater operationality in the spring months when snowmelt was outstanding, with a 25% probability of having fewer than 2 days of failure in May and April, as opposed to 12 days in the winter months. Moreover, the effect of water storage was greater between June and November, when rainfall events are scarce, and snowmelt has almost finished with operationality levels of 0.04–0.74 for 15 days of failure without storage, which increased to 0.1–0.87 with 3 days of storage. The methodology proposed constitutes a simple and useful tool to assess uncertainty in the operationality of RoR plants in Mediterranean mountainous areas where rainfall constitutes the main source of uncertainty in river flows. [ABSTRACT FROM AUTHOR]

Most research on malware focuses mainly on its detection, without paying attention to its propagation trends. However, modeling the spread of malware is an important research problem because it allows us to predict how malware will evolve and to take steps to prevent its propagation, hence the interest in analyzing this spread from a statistical point of view. This work proposes a malware propagation prediction methodology based on multivariate statistical techniques such as HJ-Biplot in combination with closed queuing networks. Datasets generated using individual-based SIRS models are used to validate the proposed methodology, although any other model could have been chosen to test its validity. Experimental results show that the proposed model can effectively predict and classify malware and discover the influence of different model parameters on the malware propagation situation. [ABSTRACT FROM AUTHOR]

Mediterranean mountainous regions constitute a climate change hotspot where snow plays a crucial role in water resources. The characteristic snow-patched distribution over these areas makes spatial resolution the limiting factor for its correct representation. This work assesses the estimation of snow cover area and the contribution of the patchy areas to the seasonal and annual regime of the snow in a semiarid mountainous range, the Sierra Nevada Mountains in southern Spain, by means of Landsat imagery combined with terrestrial photography (TP). Two methodologies were tested: (1) difference indexes to produce binary maps; and (2) spectral mixture analysis (SMA) to obtain fractional maps; their results were validated from “ground-truth” data by means of TP in a small monitored control area. Both methods provided satisfactory results when the snow cover was above 85% of the study area; below this threshold, the use of spectral mixture analysis is clearly recommended. Mixed pixels can reach up to 40% of the area during wet and cold years, their importance being larger as altitude increases, proving the usefulness of TP for assessing the accuracy of remote data sources. Mixed pixels identification allows for determining the more vulnerable areas facing potential changes of the snow regime due to global warming and climate variability. [ABSTRACT FROM AUTHOR]

Run of river (RoR) hydropower systems, despite being one of the most cost-effective and environmentally benign energy technologies, have the disadvantage that production is not constant because it is subject to a high variability in precipitation and snow cover. In addition, the management of RoR plants has to comply with some particular operating conditions, but also with some environmental flow requirements. This work presents the assessment of the main inputs included in a climate service, historical local data and the seasonal forecast of water inflow to RoR plants, which are used to predict the operability and the expected energy production. The analysis is presented through the application in a pilot RoR system located in the south of Spain, in a semi-arid Mediterranean area impacted by snow, where seasonal forecasting is especially challenging. The results show the high interannual variability of the operation in this kind of facilities. The outcomes indicate that seasonal climate forecast information would improve the prediction of observed river streamflow by 7.4% in reliability and 3.2% in sharpness compared to the current operational forecast based on historical data. The climate forecasts thus provide valuable information for the exploitation of available water resources, which generates a significant value for the operation of the plant and the energy production market. [ABSTRACT FROM AUTHOR]

Background: The main objective of this study was to assess the safety and efficacy of deep brain stimulation (DBS) in patients with severe anorexia nervosa (AN). Methods: Eight participants received active DBS to the subcallosal cingulate (SCC) or nucleus accumbens (NAcc) depending on comorbidities (affective or anxiety disorders, respectively) and type of AN. The primary outcome measure was body mass index (BMI). Results: Overall, we found no significant difference (p = 0.84) between mean preoperative and postoperative (month 6) BMI. A BMI reference value (BMI-RV) was calculated. In patients that received preoperative inpatient care to raise the BMI, the BMI-RV was defined as the mean BMI value in the 12 months prior to surgery. In patients that did not require inpatient care, the BMI-RV was defined as the mean BMI in the 3-month period before surgery. This value was compared to the postoperative BMI (month 6), revealing a significant increase (p = 0.02). After 6 months of DBS, five participants showed an increase of ≥10% in the BMI-RV. Quality of life was improved (p = 0.03). Three cases presented cutaneous complications. Conclusion: DBS may be effective for some patients with severe AN. Cutaneous complications were observed. Longer term data are needed. [ABSTRACT FROM AUTHOR]

The operation feasibility of small hydropower plants in mountainous sites is subjected to the run-of-river flow, which is also dependent on a high variability in precipitation and snow cover. Moreover, the management of this kind of system has to be performed with some particular operation conditions of the plant (e.g., turbine minimum and maximum discharge) but also some environmental flow requirements. In this context, a technological climate service is conceived in a tight connection with end users, perfectly answering the needs of the management of small hydropower systems in a pilot area, and providing a forecast of the river streamflow together with other operation data. This paper presents an overview of the service but also a set of lessons learnt related to the features, requirements, and considerations to bear in mind from the point of view of climate service developers. In addition, the outcomes give insight into how this kind of service could change the traditional management (normally based on past experience), providing a probability range of the future river flow based on future weather scenarios according to the range of future weather possibilities. This highlights the utility of the co-generation process to implement climate services for water and energy fields but also that seasonal climate forecasting could improve the business as usual of this kind of facility. [ABSTRACT FROM AUTHOR]

Annual grasslands are an essential component of oak savanna ecosystems as the primary source of fodder for livestock and wildlife. Drought resistance adaptation has led them to complete their life cycle before serious soil and plant water deficits develop, resulting in a close link between grass phenology and soil water dynamics. In this work, these links were explored using a combination of terrestrial photography, satellite imagery and hydrological ground measurements. We obtained key phenological parameters of the grass cycle from terrestrial camera data using the Green Chromatic Coordinate (GCCc) index. These parameters were compared with those provided by time-series of vegetation indices (VI) obtained from Sentinel-2 (S2) satellites and time-series of abiotic variables, which defined the hydrology of the system. The results showed that the phenological parameters estimated by the S2 Normalized Difference Vegetation Index (NDVI) (r = 0.83, p < 0.001) and soil moisture (SM) (r = 0.75, p < 0.001) presented the best agreement with ground-derived observations compared to those provided by other vegetation indices and abiotic variables. The study of NDVI and SM dynamics, that was extended over four growing seasons (July 2015–May 2019), showed that the seasonality of both variables was highly synchronized, with the best agreements at the beginning and at the end of the dry seasons. However, stage changes were estimated first by SM, followed by NDVI, with a delay of between 3 and 10 days. These results support the use of a multi-approach method to monitor the phenology and the influence of the soil moisture dynamic under the study conditions. [ABSTRACT FROM AUTHOR]

Climate change is increasing the vulnerability of Mediterranean coniferous plantations. Here, we integrate a Landsat time series with a physically-based distributed hydrological model (Watershed Integrated Management in Mediterranean Environments—WiMMed) to examine spatially-explicit relationships between the mortality processes of Pinus pinaster plantations and the hydrological regime, using different spectral indices of vegetation and machine learning algorithms. The Normalized Burn Ratio (NBR) and Moisture Stress Index (MSI) show the highest correlations with defoliation rates. Random Forest was the most accurate model (R2 = 0.79; RMSE = 0.059), showing a high model performance and prediction. Support vector machines and neural networks also demonstrated a high performance (R2 > 0.7). The main hydrological variables selected by the model to explain defoliation were potential evapotranspiration, winter precipitation and maximum summer temperature (lower Out-of-bag error). These results show the importance of hydrological variables involved in evaporation processes, and on the change in the spatial distribution of seasonal rainfall upon the defoliation processes of P. pinaster. These results underpin the importance of integrating temporal remote sensing data and hydrological models to analyze the drivers of forest defoliation and mortality processes in the Mediterranean climate. [ABSTRACT FROM AUTHOR]

A protocol for the covalent biofunctionalization of silicon-based biosensors using a UV light-induced thiol–ene coupling (TEC) reaction has been developed. This biofunctionalization approach has been used to immobilize half antibodies (hIgG), which have been obtained by means of a tris(2-carboxyethyl)phosphine (TCEP) reduction at the hinge region, to the surface of a vinyl-activated silicon-on-insulator (SOI) nanophotonic sensing chip. The response of the sensing structures within the nanophotonic chip was monitored in real time during the biofunctionalization process, which has allowed us to confirm that the bioconjugation of the thiol-terminated bioreceptors onto the vinyl-activated sensing surface is only initiated upon UV light photocatalysis. [ABSTRACT FROM AUTHOR]

Solar energy constitutes one of the most effective alternative energy sources for combating climate change. However, the solar potential in a city can vary depending on the urban morphology. The purpose of this paper is to perform a directional statistical analysis of the distribution of the monthly solar potential of rooftops in the city of Cáceres, Spain, in relation to the orientations and slopes of the rooftops. Two residential areas, one in the city center and one on the outskirts of the city, and an industrial zone, all of which exhibit different urban morphologies, have been evaluated. Statistics have been assessed in consideration of the orientation and slope values of the rooftops as circular data, and the radiation values as linear data. The three dissimilar urban morphologies result in different solar potential values, and the monthly disaggregation of the data enables the ability to detect the differences existing in the solar potential between each zone, during each month. The proposed analysis could also be extrapolated to urban planning for the design of more sustainable cities to face the challenges associated with climate change. [ABSTRACT FROM AUTHOR]

Snow constitutes a key component of the water cycle, which is directly affected by changes in climate. Mountainous regions, especially those located in semiarid environments, are highly vulnerable to shifts from snowfall to rainfall. This study evaluates the influence of future climate scenarios on the snowfall regime in the Sierra Nevada Mountains, an Alpine/Mediterranean climate region in southern Spain. Precipitation and temperature projections from two future climate scenarios representative concentration pathway (RCP) 4.5 and RCP 8.5, Fifth Assessment Report of the Intergovernmental Panel for Climate Change (AR5 IPCC)) were used to estimate the projected evolution of the snowfall regime on both annual and decadal scales during the period of 2006–2100. Specific snowfall descriptors of torrentiality are also analyzed. A general decrease of the annual snowfall was estimated, with a significant trend that ranged from 0.21 to 0.55 (mm·year−1)·year−1. These changes are dependent on the scenario and region in the study area. However, the major impact of future climate scenarios on the snowfall regime relates to an increased torrentiality of snowfall occurrence, with a decreased trend of the annual number of snowfall days (RCP 4.5: −0.068 (days·year−1)·year−1 and RCP 8.5: −0.111 (days·year−1)·year−1) and an increased trend in the annual mean snowfall intensity (RCP 4.5: 0.006 (mm·days−1)·year−1 and RCP8.5: 0.01 (mm·days−1)·year−1)) under both scenarios. This enhanced torrentiality is heterogeneously distributed, with the most semiarid region, which is currently the one least influenced by snow, being the region most affected within the study area. [ABSTRACT FROM AUTHOR]

Portable 3D laser scanners are a valuable tool for compiling elaborate digital collections of archaeological objects and analysing the shapes and dimensions of pieces. Although low-cost desktop 3D laser scanners have powerful capacities, it is important to know their limitations. This paper performs an analysis of the uncertainty and repeatability of the NextEngine™ portable low-cost 3D laser scanner by scanning an object 20 times in two different resolution modes-Macro and Wide. Some dimensions of the object were measured using a digital calliper, and these results were used as the "true" or control data. In comparing the true and the scanned data, we verified that the mean uncertainty in the Macro Mode is approximately half that of the Wide Mode, at ± 0.81 mm and ± 1.66 mm, respectively. These experimental results are significantly higher than the accuracy specifications provided by the manufacturer. An analysis of repeatability shows that the successive replicates do not match in the same position. The results are better in Macro Mode than in Wide Mode; it is observed that the repeatability factor is slightly larger than the corresponding mode accuracy, with ± 0.84 vs. ± 0.81 mm in Macro Mode and ± 1.82 vs. ± 1.66 mm in Wide Mode. We suggest several improvements, such as adding an external reference scale or providing a calibrated object to allow for a self-calibration operation of the scanner.