Insectivorous bats are less active near freeways

Traffic disturbances (i.e. pollution, light, noise, and vibrations) often extend into the area surrounding a road creating a 'road-effect zone'. Habitat within the road-effect zone is degraded or, in severe cases, completely unsuitable for wildlife, resulting in indirect habitat loss. This can have a disproportionate impact on wildlife in highly modified landscapes, where remaining habitat is scarce or occurs predominantly along roadside reserves. In this study, we investigated the road-effect zone for insectivorous bats in highly cleared agricultural landscapes by quantifying the change in call activity with proximity to three major freeways. The activity of seven out of 10 species of bat significantly decreased with proximity to the freeway. We defined the road-effect zone to be the proximity at which call activity declined by at least 20% relative to the maximum detected activity. The overall road-effect zone for bats in this region was 307 m, varying between 123 and 890 m for individual species. Given that this road-effect zone exceeds the typical width of the roadside verges (<50 m), it is possible that much of the vegetation adjacent to freeways in this and similar landscapes provides low-quality habitat for bats. Without accounting for the road-effect zone, the amount of habitat lost or degraded due to roads is underestimated, potentially resulting in the loss of wildlife, ecosystem services and key ecosystem processes (e.g. predator-prey or plant-pollinator interactions) from the landscape. We suggest all future environmental impact assessments include quantifying the road-effect zone for sensitive wildlife, in order to best plan and mitigate the impact of roads on the environment. Mitigating the effects of new and existing roads on wildlife is essential to ensure enough high-quality habitat persists to maintain wildlife populations

Environmental development of the Spanish ceramic tile manufacturing sector over the period 1992–2007

The Spanish tile manufacturing sector has grown steadily over the years covered by the three benchmark studies, carried out in 1992, 2001, and 2007, from which data are compared in this paper. In that period, production output doubled, although since the last study was published, the situation has undergone a radical change and current production output stands at a level similar to that of 1995. Nevertheless, despite the world economic crisis, which has also severely impacted the ceramic wall and floor tile sector, it is worth noting that the sector’s environmental parameters have demonstrated a constant and positive trend, both in companies’ individual environmental performance and in the actual manufacturing processes itself. To a large extent, this situation was forced upon the sector as it had to adapt to numerous environmental regulations, which in general terms call for harsher and more stringent conditions than before. In this sense, the adoption of IPPC regulations, which affect practically the entire ceramic tile sector, and the approval of EU Directive 2003/87 establishing a scheme for greenhouse gas emission allowance trading were significant factor

Tourist spaces and tourism policy in Spain and Portugal

Advances in Cultura, Tourism and Hospitality Research;10, 235-249This study analyses the relationship between the development of the tourism policy of Spain and Portugal and their effects on regional imbalances. Despite the proximity of the two countries and their specialisation in tourism, there are few comparative studies on tourism of the two Iberian countries. The study focuses on the two major phases of tourism policy: the period of mass tourism and post-Fordist stage. In the conclusions we refer the debate on the existence of a model of development based on tourism to the Latin countries of Southern Europe and we note the export process of the Spanish low-cost tourism model to other countries.Financiado por el Gobierno de España, Programa Fundamental de Investigación, Proyecto de I+D (CSO2012-30840) "Geografías de la crisis: análisis de los territorios urbanos y turísticos de las Islas Baleares, Costa del Sol y principales destinos del Caribe y América Central"

Impactos ambientales del ciclo de vida de las baldosas cerámicas: análisis sectorial, identificación de estrategias de mejora y comunicación (I)

El artículo analiza el impacto ambiental que generan las baldosas cerámicas mediante el Análisis de Ciclo de Vida (AC) a nivel sectorial en el que participaron más de 50 empresas españolas. Los resultados han servido para la redacción de las Reglas de Categoría de Producto (RCP) para los recubrimientos de materiales cerámicos, necesarias para la edición de Declaraciones Ambientales de Producto. (Debido a la extensión del artículo recogeremos en esta edición la primera parte, correspondiente a la definición de objetivos y alcance del estudio y el análisis del inventario. La segunda parte, que consta de la evaluación de impactos e interpretación, la identificación de estrategias de mejora, la comunicación ambiental y las conclusiones se publicarán en el número 236 de Piscinas XXI).The article analyses the environmental impact of ceramic tiles by means of a sector-level Life Cycle Analysis (LCA) involving over 50 Spanish firms. The findings were then used to draw up the Product Category Rules (PCR) for ceramic coverings, which are needed to be able to issue Environmental Product Declarations. (Due to the length of the paper, in this edition we will include only the first part, which covers the definition of the aims and scope of the study, as well as the inventory analysis. The second part, which comprises the evaluation of the impacts and interpretation, the identification of the improvement strategies, environmental communication and the conclusions, will be published in issue 236 of Piscinas XXI.

Electrohysterogram for ANN-Based Prediction of Imminent Labor in Women with Threatened Preterm Labor Undergoing Tocolytic Therapy

[EN] Threatened preterm labor (TPL) is the most common cause of hospitalization in the second half of pregnancy and entails high costs for health systems. Currently, no reliable labor proximity prediction techniques are available for clinical use. Regular checks by uterine electrohysterogram (EHG) for predicting preterm labor have been widely studied. The aim of the present study was to assess the feasibility of predicting labor with a 7- and 14-day time horizon in TPL women, who may be under tocolytic treatment, using EHG and/or obstetric data. Based on 140 EHG recordings, artificial neural networks were used to develop prediction models. Non-linear EHG parameters were found to be more reliable than linear for differentiating labor in under and over 7/14 days. Using EHG and obstetric data, the <7- and <14-day labor prediction models achieved an AUC in the test group of 87.1 +/- 4.3% and 76.2 +/- 5.8%, respectively. These results suggest that EHG can be reliable for predicting imminent labor in TPL women, regardless of the tocolytic therapy stage. This paves the way for the development of diagnostic tools to help obstetricians make better decisions on treatments, hospital stays and admitting TPL women, and can therefore reduce costs and improve maternal and fetal wellbeing.This work was supported by the Spanish Ministry of Economy and Competitiveness, the European Regional Development Fund (MCIU/AEI/FEDER, UE RTI2018-094449-A-I00-AR) and by the Generalitat Valenciana (AICO/2019/220).Mas-Cabo, J.; Prats-Boluda, G.; Garcia-Casado, J.; Alberola Rubio, J.; Monfort-Ortiz, R.; Martinez-Saez, C.; Perales, A.... (2020). Electrohysterogram for ANN-Based Prediction of Imminent Labor in Women with Threatened Preterm Labor Undergoing Tocolytic Therapy. 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(2011). Comparison between approximate entropy, correntropy and time reversibility: Application to uterine electromyogram signals. Medical Engineering & Physics, 33(8), 980-986. doi:10.1016/j.medengphy.2011.03.010Fergus, P., Idowu, I., Hussain, A., & Dobbins, C. (2016). Advanced artificial neural network classification for detecting preterm births using EHG records. Neurocomputing, 188, 42-49. doi:10.1016/j.neucom.2015.01.107Acharya, U. R., Sudarshan, V. K., Rong, S. Q., Tan, Z., Lim, C. M., Koh, J. E., … Bhandary, S. V. (2017). Automated detection of premature delivery using empirical mode and wavelet packet decomposition techniques with uterine electromyogram signals. Computers in Biology and Medicine, 85, 33-42. doi:10.1016/j.compbiomed.2017.04.013Fergus, P., Cheung, P., Hussain, A., Al-Jumeily, D., Dobbins, C., & Iram, S. (2013). Prediction of Preterm Deliveries from EHG Signals Using Machine Learning. 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Identification of Human Term and Preterm Labor using Artificial Neural Networks on Uterine Electromyography Data. Annals of Biomedical Engineering, 35(3), 465-473. doi:10.1007/s10439-006-9248-8Mas-Cabo, J., Prats-Boluda, G., Garcia-Casado, J., Alberola-Rubio, J., Perales, A., & Ye-Lin, Y. (2019). Design and Assessment of a Robust and Generalizable ANN-Based Classifier for the Prediction of Premature Birth by means of Multichannel Electrohysterographic Records. Journal of Sensors, 2019, 1-13. doi:10.1155/2019/5373810Terrien, J., Marque, C., & Karlsson, B. (2007). Spectral characterization of human EHG frequency components based on the extraction and reconstruction of the ridges in the scalogram. 2007 29th Annual International Conference of the IEEE Engineering in Medicine and Biology Society. doi:10.1109/iembs.2007.4352680Rooijakkers, M. J., Rabotti, C., Oei, S. G., Aarts, R. M., & Mischi, M. (2014). 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A compact fluorescence sensor for low-cost non-invasive monitoring of gut permeability in undernutrition

Undernutrition is associated with approximately 45% of deaths among children under the age of 5. Furthermore, in 2020, around 149 million children suffered impaired physical/cognitive development due to lack of adequate nutrition. Environmental enteropathy (EE) is associated with undernutrition and is characterized by a multifaceted breakdown in gut function, including an increase in intestinal permeability that can lead to inflammatory responses. However, the role and mechanisms associated with EE (particularly gut permeability) are not well understood. This is partly because current techniques to assess changes in gut permeability, such as endoscopic biopsies, histopathology and chemical tests such as Lactulose:Mannitol assays, are either highly invasive, unreliable or difficult to perform on specific groups of patients (such as infants and patients with urine retention problems). Therefore, low-cost, non-invasive and reliable diagnostic tools are urgently needed for better evaluation of intestinal permeability. Here, we present a compact transcutaneous fluorescence spectroscopy sensor for non-invasive evaluation of gut permeability and report the first in vivo data collected from volunteers in an undernutrition trial. Using this technique and device, fluorescence signals are detected transcutaneously after oral ingestion of a fluorescent solution. Preliminary results demonstrate the potential use of the presented sensor for clinical assessment of gut permeability in low-income settings

Dispersion Entropy: A Measure of Electrohysterographic Complexity for Preterm Labor Discrimination

[EN] Although preterm labor is a major cause of neonatal death and often leaves health sequels in the survivors, there are no accurate and reliable clinical tools for preterm labor prediction. The Electrohysterogram (EHG) has arisen as a promising alternative that provides relevant information on uterine activity that could be useful in predicting preterm labor. In this work, we optimized and assessed the performance of the Dispersion Entropy (DispEn) metric and compared it to conventional Sample Entropy (SampEn) in EHG recordings to discriminate term from preterm deliveries. For this, we used the two public databases TPEHG and TPEHGT DS of EHG recordings collected from women during regular checkups. The 10th, 50th and 90th percentiles of entropy metrics were computed on whole (WBW) and fast wave high (FWH) EHG bandwidths, sweeping the DispEn and SampEn internal parameters to optimize term/preterm discrimination. The results revealed that for both the FWH and WBW bandwidths the best separability was reached when computing the 10th percentile, achieving a p-value (0.00007) for DispEn in FWH, c = 7 and m = 2, associated with lower complexity preterm deliveries, indicating that DispEn is a promising parameter for preterm labor prediction.This work was supported by the Spanish ministry of economy and competitiveness, the European Regional Development Fund (MCIU/AEI/FEDER, UE RTI2018-094449-A-I00-AR) and the Generalitat Valenciana (AICO/2019/220).Nieto-Del-Amor, F.; Ye Lin, Y.; Garcia-Casado, J.; Díaz-Martínez, MDA.; González Martínez, M.; Monfort-Ortiz, R.; Prats-Boluda, G. (2021). Dispersion Entropy: A Measure of Electrohysterographic Complexity for Preterm Labor Discrimination. SCITEPRESS. 260-267. https://doi.org/10.5220/0010316602600267S26026

Automated Control Systems and Methods for Underground Crop Harvesters

Methods and devices for automated adjustment of a digging implement during harvest of underground crops are described. Utilizing the devices, a digging implement, e.g., a blade, can be located and maintained at a desired depth as a harvester travels across a field. During use, the digging implement depth controls can be varied as the harvester travels within a single field under different operating conditions, e.g., different soil friability, consistency, etc., thereby preventing crop loss and improving crop yield

Exploring deep learning for complex trait genomic prediction in polyploid outcrossing species

Genomic prediction (GP) is the procedure whereby the genetic merits of untested candidates are predicted using genome wide marker information. Although numerous examples of GP exist in plants and animals, applications to polyploid organisms are still scarce, partly due to limited genome resources and the complexity of this system. Deep learning (DL) techniques comprise a heterogeneous collection of machine learning algorithms that have excelled at many prediction tasks. A potential advantage of DL for GP over standard linear model methods is that DL can potentially take into account all genetic interactions, including dominance and epistasis, which are expected to be of special relevance in most polyploids. In this study, we evaluated the predictive accuracy of linear and DL techniques in two important small fruits or berries: strawberry and blueberry. The two datasets contained a total of 1,358 allopolyploid strawberry (2n=8x=112) and 1,802 autopolyploid blueberry (2n=4x=48) individuals, genotyped for 9,908 and 73,045 single nucleotide polymorphism (SNP) markers, respectively, and phenotyped for five agronomic traits each. DL depends on numerous parameters that influence performance and optimizing hyperparameter values can be a critical step. Here we show that interactions between hyperparameter combinations should be expected and that the number of convolutional filters and regularization in the first layers can have an important effect on model performance. In terms of genomic prediction, we did not find an advantage of DL over linear model methods, except when the epistasis component was important. Linear Bayesian models were better than convolutional neural networks for the full additive architecture, whereas the opposite was observed under strong epistasis. However, by using a parameterization capable of taking into account these non-linear effects, Bayesian linear models can match or exceed the predictive accuracy of DL. A semiautomatic implementation of the DL pipeline is available at https://github.com/lauzingaretti/deepGP/