Showing papers by "University of Extremadura published in 2019"

Deep learning classifiers for hyperspectral imaging: A review

Abstract: Advances in computing technology have fostered the development of new and powerful deep learning (DL) techniques, which have demonstrated promising results in a wide range of applications. Particularly, DL methods have been successfully used to classify remotely sensed data collected by Earth Observation (EO) instruments. Hyperspectral imaging (HSI) is a hot topic in remote sensing data analysis due to the vast amount of information comprised by this kind of images, which allows for a better characterization and exploitation of the Earth surface by combining rich spectral and spatial information. However, HSI poses major challenges for supervised classification methods due to the high dimensionality of the data and the limited availability of training samples. These issues, together with the high intraclass variability (and interclass similarity) –often present in HSI data– may hamper the effectiveness of classifiers. In order to solve these limitations, several DL-based architectures have been recently developed, exhibiting great potential in HSI data interpretation. This paper provides a comprehensive review of the current-state-of-the-art in DL for HSI classification, analyzing the strengths and weaknesses of the most widely used classifiers in the literature. For each discussed method, we provide quantitative results using several well-known and widely used HSI scenes, thus providing an exhaustive comparison of the discussed techniques. The paper concludes with some remarks and hints about future challenges in the application of DL techniques to HSI classification. The source codes of the methods discussed in this paper are available from: https://github.com/mhaut/hyperspectral_deeplearning_review .

The interplay of landscape composition and configuration: new pathways to manage functional biodiversity and agroecosystem services across Europe

Abstract: Managing agricultural landscapes to support biodiversity and ecosystem services is a key aim of a sustainable agriculture. However, how the spatial arrangement of crop fields and other habitats in landscapes impacts arthropods and their functions is poorly known. Synthesising data from 49 studies (1515 landscapes) across Europe, we examined effects of landscape composition (% habitats) and configuration (edge density) on arthropods in fields and their margins, pest control, pollination and yields. Configuration effects interacted with the proportions of crop and non-crop habitats, and species’ dietary, dispersal and overwintering traits led to contrasting responses to landscape variables. Overall, however, in landscapes with high edge density, 70% of pollinator and 44% of natural enemy species reached highest abundances and pollination and pest control improved 1.7- and 1.4-fold respectively. Arable-dominated landscapes with high edge densities achieved high yields. This suggests that enhancing edge density in European agroecosystems can promote functional biodiversity and yield-enhancing ecosystem services.

Capsule Networks for Hyperspectral Image Classification

Abstract: Convolutional neural networks (CNNs) have recently exhibited an excellent performance in hyperspectral image classification tasks. However, the straightforward CNN-based network architecture still finds obstacles when effectively exploiting the relationships between hyperspectral imaging (HSI) features in the spectral–spatial domain, which is a key factor to deal with the high level of complexity present in remotely sensed HSI data. Despite the fact that deeper architectures try to mitigate these limitations, they also find challenges with the convergence of the network parameters, which eventually limit the classification performance under highly demanding scenarios. In this paper, we propose a new CNN architecture based on spectral–spatial capsule networks in order to achieve a highly accurate classification of HSIs while significantly reducing the network design complexity. Specifically, based on Hinton’s capsule networks, we develop a CNN model extension that redefines the concept of capsule units to become spectral–spatial units specialized in classifying remotely sensed HSI data. The proposed model is composed by several building blocks, called spectral–spatial capsules, which are able to learn HSI spectral–spatial features considering their corresponding spatial positions in the scene, their associated spectral signatures, and also their possible transformations. Our experiments, conducted using five well-known HSI data sets and several state-of-the-art classification methods, reveal that our HSI classification approach based on spectral–spatial capsules is able to provide competitive advantages in terms of both classification accuracy and computational time.

EEG-Based Brain-Computer Interfaces Using Motor-Imagery: Techniques and Challenges.

Abstract: Electroencephalography (EEG)-based brain-computer interfaces (BCIs), particularly those using motor-imagery (MI) data, have the potential to become groundbreaking technologies in both clinical and entertainment settings. MI data is generated when a subject imagines the movement of a limb. This paper reviews state-of-the-art signal processing techniques for MI EEG-based BCIs, with a particular focus on the feature extraction, feature selection and classification techniques used. It also summarizes the main applications of EEG-based BCIs, particularly those based on MI data, and finally presents a detailed discussion of the most prevalent challenges impeding the development and commercialization of EEG-based BCIs.

Adaptive responses of animals to climate change are most likely insufficient

Abstract: Biological responses to climate change have been widely documented across taxa and regions, but it remains unclear whether species are maintaining a good match between phenotype and environment, i.e. whether observed trait changes are adaptive. Here we reviewed 10,090 abstracts and extracted data from 71 studies reported in 58 relevant publications, to assess quantitatively whether phenotypic trait changes associated with climate change are adaptive in animals. A meta-analysis focussing on birds, the taxon best represented in our dataset, suggests that global warming has not systematically affected morphological traits, but has advanced phenological traits. We demonstrate that these advances are adaptive for some species, but imperfect as evidenced by the observed consistent selection for earlier timing. Application of a theoretical model indicates that the evolutionary load imposed by incomplete adaptive responses to ongoing climate change may already be threatening the persistence of species.

Deep Pyramidal Residual Networks for Spectral–Spatial Hyperspectral Image Classification

Abstract: Convolutional neural networks (CNNs) exhibit good performance in image processing tasks, pointing themselves as the current state-of-the-art of deep learning methods. However, the intrinsic complexity of remotely sensed hyperspectral images still limits the performance of many CNN models. The high dimensionality of the HSI data, together with the underlying redundancy and noise, often makes the standard CNN approaches unable to generalize discriminative spectral–spatial features. Moreover, deeper CNN architectures also find challenges when additional layers are added, which hampers the network convergence and produces low classification accuracies. In order to mitigate these issues, this paper presents a new deep CNN architecture specially designed for the HSI data. Our new model pursues to improve the spectral–spatial features uncovered by the convolutional filters of the network. Specifically, the proposed residual-based approach gradually increases the feature map dimension at all convolutional layers, grouped in pyramidal bottleneck residual blocks, in order to involve more locations as the network depth increases while balancing the workload among all units, preserving the time complexity per layer. It can be seen as a pyramid, where the deeper the blocks, the more feature maps can be extracted. Therefore, the diversity of high-level spectral–spatial attributes can be gradually increased across layers to enhance the performance of the proposed network with the HSI data. Our experiments, conducted using four well-known HSI data sets and 10 different classification techniques, reveal that our newly developed HSI pyramidal residual model is able to provide competitive advantages (in terms of both classification accuracy and computational time) over the state-of-the-art HSI classification methods

Wooden-Breast, White Striping, and Spaghetti Meat: Causes, Consequences and Consumer Perception of Emerging Broiler Meat Abnormalities.

Abstract: Ten years ago, the occurrence of macroscopic defects in breasts muscles from fast-growing broilers challenged producers and animal scientists to label and characterize myopathies wholly unknown. The distinctive white striations in breasts affected by white striping disorder, the presence of out-bulging and pale areas of hardened consistency in the so-called wooden breast, and the separation of the fiber bundles in breasts labelled as spaghetti meat, made these myopathies easily identified in chicken carcasses. Yet, the high incidence of these myopathies and the increasing concern by producers and retailers led to an unprecedented flood of questions on the causes and consequences of these abnormal chicken breasts. This review comprehensively collects the most relevant information from studies aimed to understand the pathological mechanisms of these myopathies, their physicochemical and histological characterization and their impact on meat quality and consumer's preferences. Today, it is known that the occurrence is linked to fast-growth rates of the birds and their large breast muscles. The muscle hypertrophy along with an unbalanced growth of supportive connective tissue leads to a compromised blood supply and hypoxia. The occurrence of oxidative stress and mitochondrial dysfunction leads to lipidosis, fibrosis, and overall myodegeneration. Along with the altered appearance, breast muscles affected by the myopathies display poor technological properties, impaired texture properties, and reduced nutritional value. As consumer's awareness on the occurrence of these abnormalities and the concerns on animal welfare arise, efforts are made to inhibit the onset of the myopathies or alleviate the severity of the symptoms. The lack of fully effective dietary strategies leads scientists to propose whether "slow" production systems may alternatively provide with poultry meat free of these myopathies.

Global distribution of earthworm diversity

Abstract: Soil organisms, including earthworms, are a key component of terrestrial ecosystems. However, little is known about their diversity, their distribution, and the threats affecting them. We compiled a global dataset of sampled earthworm communities from 6928 sites in 57 countries as a basis for predicting patterns in earthworm diversity, abundance, and biomass. We found that local species richness and abundance typically peaked at higher latitudes, displaying patterns opposite to those observed in aboveground organisms. However, high species dissimilarity across tropical locations may cause diversity across the entirety of the tropics to be higher than elsewhere. Climate variables were found to be more important in shaping earthworm communities than soil properties or habitat cover. These findings suggest that climate change may have serious implications for earthworm communities and for the functions they provide.

DAEN: Deep Autoencoder Networks for Hyperspectral Unmixing

Abstract: Spectral unmixing is a technique for remotely sensed image interpretation that expresses each (possibly mixed) pixel as a combination of pure spectral signatures (endmembers) and their fractional abundances. In this paper, we develop a new technique for unsupervised unmixing which is based on a deep autoencoder network (DAEN). Our newly developed DAEN consists of two parts. The first part of the network adopts stacked autoencoders (SAEs) to learn spectral signatures, so as to generate a good initialization for the unmixing process. In the second part of the network, a variational autoencoder (VAE) is employed to perform blind source separation, aimed at obtaining the endmember signatures and abundance fractions simultaneously. By taking advantage from the SAEs, the robustness of the proposed approach is remarkable as it can unmix data sets with outliers and low signal-to-noise ratio. Moreover, the multihidden layers of the VAE ensure the required constraints (nonnegativity and sum-to-one) when estimating the abundances. The effectiveness of the proposed method is evaluated using both synthetic and real hyperspectral data. When compared with other unmixing methods, the proposed approach demonstrates very competitive performance.

Feature Extraction With Multiscale Covariance Maps for Hyperspectral Image Classification

Abstract: The classification of hyperspectral images (HSIs) using convolutional neural networks (CNNs) has recently drawn significant attention. However, it is important to address the potential overfitting problems that CNN-based methods suffer when dealing with HSIs. Unlike common natural images, HSIs are essentially three-order tensors which contain two spatial dimensions and one spectral dimension. As a result, exploiting both spatial and spectral information is very important for HSI classification. This paper proposes a new hand-crafted feature extraction method, based on multiscale covariance maps (MCMs), that is specifically aimed at improving the classification of HSIs using CNNs. The proposed method has the following distinctive advantages. First, with the use of covariance maps, the spatial and spectral information of the HSI can be jointly exploited. Each entry in the covariance map stands for the covariance between two different spectral bands within a local spatial window, which can absorb and integrate the two kinds of information (spatial and spectral) in a natural way. Second, by means of our multiscale strategy, each sample can be enhanced with spatial information from different scales, increasing the information conveyed by training samples significantly. To verify the effectiveness of our proposed method, we conduct comprehensive experiments on three widely used hyperspectral data sets, using a classical 2-D CNN (2DCNN) model. Our experimental results demonstrate that the proposed method can indeed increase the robustness of the CNN model. Moreover, the proposed MCMs+2DCNN method exhibits better classification performance than other CNN-based classification strategies and several standard techniques for spectral-spatial classification of HSIs.

Straw mulch as a sustainable solution to decrease runoff and erosion in glyphosate-treated clementine plantations in Eastern Spain. An assessment using rainfall simulation experiments

Abstract: In many Mediterranean areas, citrus orchards exhibit high soil loss rates because of the expansion of drip irrigation that allows cultivation on sloping terrain and the widespread use of glyphosate. To mitigate these non-sustainable soil losses, straw mulch could be applied as an efficient solution but this has been poorly studied. Therefore, the main goal of this paper was to assess the use of straw mulch as a tool to reduce soil losses in clementine plantations, which can be considered representative of a typical Mediterranean citrus orchard. A total of 40 rainfall simulation experiments were carried out on 20 pairs of neighbouring bare and mulched plots. Each experiment involved applying 38.8 mm of rain at a constant rate over 1 h to a circular plot of 0.28 m2 circular plots. The results showed that a cover of 50% of straw (60 g m−2) was able to delay the time to ponding from 32 to 52 s and the time to runoff initiation from 57 to 129 s. Also, the mulching reduced the runoff coefficient from 65.6 to 50.5%. The effect on sediment transport was even more pronounced, as the straw mulch reduced the sediment concentration from 16.7 g l−1 to 3.6 g l−1 and the soil erosion rates from 439 g to 73 g. Our results indicated that mulching can be used as a useful management practice to control soil erosion rates due to the immediate effect on high soil detachment rate and runoff initiation reduction in conventional clementine orchards on sloping land, by slowing down runoff initiation and by reducing runoff generation and, especially, sediment losses. We indirectly concluded that straw mulch is also a sustainable solution in glyphosate-treated citrus plantations.

Visual Attention-Driven Hyperspectral Image Classification

Abstract: Deep neural networks (DNNs), including convolutional neural networks (CNNs) and residual networks (ResNets) models, are able to learn abstract representations from the input data by considering a deep hierarchy of layers that perform advanced feature extraction. The combination of these models with visual attention techniques can assist with the identification of the most representative parts of the data from a visual standpoint, obtained through more detailed filtering of the features extracted by the operational layers of the network. This is of significant interest for analyzing remotely sensed hyperspectral images (HSIs), characterized by their very high spectral dimensionality. However, few efforts have been conducted in the literature in order to adapt visual attention methods to remotely sensed HSI data analysis. In this paper, we introduce a new visual attention-driven technique for the HSI classification. Specifically, we incorporate attention mechanisms to a ResNet in order to better characterize the spectral–spatial information contained in the data. Our newly proposed method calculates a mask that is applied to the features obtained by the network in order to identify the most desirable ones for classification purposes. Our experiments, conducted using four widely used HSI data sets, reveal that the proposed deep attention model provides competitive advantages in terms of classification accuracy when compared to other state-of-the-art methods.

Scale-Free Convolutional Neural Network for Remote Sensing Scene Classification

Abstract: Fine-tuning of pretrained convolutional neural networks (CNNs) has been proven to be an effective strategy for remote sensing image scene classification, particularly when a limited number of labeled data sets are available for training purposes. However, such a fine-tuning process often needs that the input images are resized into a fixed size to generate input vectors of the size required by fully connected layers (FCLs) in the pretrained CNN model. Such a resizing process often discards key information in the scenes and thus deteriorates the classification performance. To address this issue, in this paper, we introduce a scale-free CNN (SF-CNN) for remote sensing scene classification. Specifically, the FCLs in the CNN model are first converted into convolutional layers, which not only allow the input images to be of arbitrary sizes but also retain the ability to extract discriminative features using a traditional sliding-window-based strategy. Then, a global average pooling (GAP) layer is added after the final convolutional layer so that input images of arbitrary size can be mapped to feature maps of uniform size. Finally, we utilize the resulting feature maps to create a new FCL that is fed to a softmax layer for final classification. Our experimental results conducted using several real data sets demonstrate the superiority of the proposed SF-CNN method over several well-known classification methods, including pretrained CNN-based ones.

How the flipped classroom affects knowledge, skills, and engagement in higher education: Effects on students' satisfaction

Abstract: The aim of this research is to present a successful flipped classroom proposal in higher education to better understand its influence in terms of knowledge, skills and engagement. The reason why we focus on these three dimensions is because of their core roles in the international learning conceptual frameworks presented above to increase the employability of Generation Z students in the digital society of the 21st century. In doing so, first, we first develop a measurement scale (4D_FLIPPED) to explore the degree of flipped classroom presence in our higher education learning experience. Then, we present a structural equation model to analyze the causal relationships of knowledge, skills, and engagement with students' satisfaction. The empirical results point out that there are four fundamental dimensions that should be present in the flipped classroom to be successful in the 21st century with Generation Z. This study also confirms that the flipped classroom has positive effects on students’ knowledge, skills, and engagement. Our research provides useful recommendations and insights for academia.

DNAM-1 and the TIGIT/PVRIG/TACTILE Axis: Novel Immune Checkpoints for Natural Killer Cell-Based Cancer Immunotherapy.

Abstract: Natural killer (NK) cells are lymphocytes of the innate immune response characterized by their role in the destruction of tumor cells. Activation of NK cells depend on a fine balance between activating and inhibitory signals mediated by different receptors. In recent years, a family of paired receptors that interact with ligands of the Nectin/Nectin-like (Necl) family has attracted great interest. Two of these ligands, Necl-5 (usually termed CD155 or PVR) and Nectin-2 (CD112), frequently expressed on different types of tumor cells, are recognized by a group of receptors expressed on T and NK cells that exert opposite functions after interacting with their ligands. These receptors include DNAM-1 (CD226), TIGIT, TACTILE (CD96) and the recently described PVRIG. Whereas activation through DNAM-1 after recognition of CD155 or CD112 enhances NK cell-mediated cytotoxicity against a wide range of tumor cells, TIGIT recognition of these ligands exerts an inhibitory effect on NK cells by diminishing IFN-γ production, as well as NK cell-mediated cytotoxicity. PVRIG has also been identified as an inhibitory receptor that recognizes CD112 but not CD155. However, little is known about the role of TACTILE as modulator of immune responses in humans. TACTILE control of tumor growth and metastases has been reported in murine models, and it has been suggested that it negatively regulates the anti-tumor functions mediated by DNAM-1. In NK cells from patients with solid cancer and leukemia, it has been observed a decreased expression of DNAM-1 that may shift the balance in favor to the inhibitory receptors TIGIT or PVRIG, further contributing to the diminished NK cell-mediated cytotoxic capacity observed in these patients. Analysis of DNAM-1, TIGIT, TACTILE and PVRIG on human NK cells from solid cancer or leukemia patients will clarify the role of these receptors in cancer surveillance. Overall, it can be speculated that in cancer patients the TIGIT/PVRIG pathways are upregulated and represent novel targets for checkpoint blockade immunotherapy.

Antioxidants and Male Fertility: from Molecular Studies to Clinical Evidence.

Abstract: Spermatozoa are physiologically exposed to reactive oxygen species (ROS) that play a pivotal role on several sperm functions through activation of different intracellular mechanisms involved in physiological functions such as sperm capacitation associated-events. However, ROS overproduction depletes sperm antioxidant system, which leads to a condition of oxidative stress (OS). Subfertile and infertile men are known to present higher amount of ROS in the reproductive tract which causes sperm DNA damage and results in lower fertility and pregnancy rates. Thus, there is a growing number of couples seeking fertility treatment and assisted reproductive technologies (ART) due to OS-related problems in the male partner. Interestingly, although ART can be successfully used, it is also related with an increase in ROS production. This has led to a debate if antioxidants should be proposed as part of a fertility treatment in an attempt to decrease non-physiological elevated levels of ROS. However, the rationale behind oral antioxidants intake and positive effects on male reproduction outcome is only supported by few studies. In addition, it is unclear whether negative effects may arise from oral antioxidants intake. Although there are some contrasting reports, oral consumption of compounds with antioxidant activity appears to improve sperm parameters, such as motility and concentration, and decrease DNA damage, but there is not sufficient evidence that fertility rates and live birth really improve after antioxidants intake. Moreover, it depends on the type of antioxidants, treatment duration, and even the diagnostics of the man’s fertility, among other factors. Literature also suggests that the main advantage of antioxidant therapy is to extend sperm preservation to be used during ART. Herein, we discuss ROS production and its relevance in male fertility and antioxidant therapy with focus on molecular mechanisms and clinical evidence.

Visualization of the challenges and limitations of the long-term sunspot number record

Abstract: The solar cycle periodically reshapes the magnetic structure and radiative output of the Sun and determines its impact on the heliosphere roughly every 11 years. Besides this main periodicity, it shows century-long variations (including periods of abnormally low solar activity called grand minima). The Maunder Minimum (1645–1715) has generated significant interest as the archetype of a grand minimum in magnetic activity for the Sun and other stars, suggesting a potential link between the Sun and changes in terrestrial climate. Recent reanalyses of sunspot observations have yielded a conflicted view on the evolution of solar activity during the past 400 years (a steady increase versus a constant level). This has ignited a concerted community-wide effort to understand the depth of the Maunder Minimum and the subsequent secular evolution of solar activity. The goal of this Perspective is to review recent work that uses historical data to estimate long-term solar variability, and to provide context to users of these estimates that may not be aware of their limitations. We propose a clear visual guide than can be used to easily assess observational coverage for different periods, as well as the level of disagreement between currently proposed sunspot group number series. The sunspot number time series is an essential tool to determine the secular variations of solar activity, but particular care must be taken to handle and present incomplete temporal coverage. The authors present the current state of research and propose a new way to visualize long-term solar activity data.

Wireless Sensor Network Combined with Cloud Computing for Air Quality Monitoring

Abstract: Low-cost air pollution wireless sensors are emerging in densely distributed networks that provide more spatial resolution than typical traditional systems for monitoring ambient air quality. This paper presents an air quality measurement system that is composed of a distributed sensor network connected to a cloud system forming a wireless sensor network (WSN). Sensor nodes are based on low-power ZigBee motes, and transmit field measurement data to the cloud through a gateway. An optimized cloud computing system has been implemented to store, monitor, process, and visualize the data received from the sensor network. Data processing and analysis is performed in the cloud by applying artificial intelligence techniques to optimize the detection of compounds and contaminants. This proposed system is a low-cost, low-size, and low-power consumption method that can greatly enhance the efficiency of air quality measurements, since a great number of nodes could be deployed and provide relevant information for air quality distribution in different areas. Finally, a laboratory case study demonstrates the applicability of the proposed system for the detection of some common volatile organic compounds, including: benzene, toluene, ethylbenzene, and xylene. Principal component analysis, a multilayer perceptron with backpropagation learning algorithm, and support vector machine have been applied for data processing. The results obtained suggest good performance in discriminating and quantifying the concentration of the volatile organic compounds.

Intake of Oxidized Proteins and Amino Acids and Causative Oxidative Stress and Disease: Recent Scientific Evidences and Hypotheses

Abstract: The exposure to reactive oxygen species (ROS) is an inevitable consequence of living in an aerobic world. The species contribute to the occurrence of oxidative stress in humans in which an uncontrolled production of ROS exceeds the endogenous antioxidant defences leading to the oxidative damage to essential cellular components, such as lipids, proteins, and DNA. The influence of diet on the modulation of the systemic redox status is recognized and, while some dietary components are found to be protective (that is, fruits and vegetables), others are recognized as pro-oxidants (that is, processed meat and other animal-source protein foods). Oxidized proteins and amino acids are potential promoters of luminal and postprandial oxidative stress; preliminary studies have actually reported noxious effects of these species in cultured cells and in experimental animals. However, the underlying pathological mechanisms remain poorly understood. The application of advanced methodological approaches based on mass spectrometric technologies and OMICS disciplines has enabled the elucidation of the molecular basis of the pathological effects of dietary oxidized proteins and amino acids. The present review collects the most recent evidences of the health risks of dietary protein oxidation and proposes reasonable hypotheses and future perspectives on the field.

Agroforestry is paying off – Economic evaluation of ecosystem services in European landscapes with and without agroforestry systems

Abstract: The study assessed the economic performance of marketable ecosystem services (ES) (biomass production) and non-marketable ecosystem services and dis-services (groundwater, nutrient loss, soil loss, carbon sequestration, pollination deficit) in 11 contrasting European landscapes dominated by agroforestry land use compared to business as usual agricultural practice. The productivity and profitability of the farming activities and the associated ES were quantified using environmental modelling and economic valuation. After accounting for labour and machinery costs the financial value of the outputs of Mediterranean agroforestry systems tended to be greater than the corresponding agricultural system; but in Atlantic and Continental regions the agricultural system tended to be more profitable. However, when economic values for the associated ES were included, the relative profitability of agroforestry increased. Agroforestry landscapes: (i) were associated to reduced externalities of pollution from nutrient and soil losses, and (ii) generated additional benefits from carbon capture and storage and thus generated an overall higher economic gain. Our findings underline how a market system that includes the values of broader ES would result in land use change favouring multifunctional agroforestry. Imposing penalties for dis-services or payments for services would reflect their real world prices and would make agroforestry a more financially profitable system.

A comparison of a GPS device and a multi-camera video technology during official soccer matches: Agreement between systems.

Abstract: The aim of this study was to compare the agreement of the movement demands data during a soccer match (total distance, distance per minute, average speed, maximum speed and distance covered in different speed sectors) between an optical tracking system (Mediacoach System) and a GPS device (Wimu Pro). Participants were twenty-six male professional soccer players (age: 21.65 ± 2.03 years; height: 180.00 ± 7.47 cm; weight: 73.81 ± 5.65 kg) from FC Barcelona B, of whom were recorded a total of 759 measurements during 38 official matches in the Spanish second division. The Mediacoach System and the Wimu Pro were compared using the standardized mean bias, standard error of estimate, intraclass correlation coefficients (ICC), coefficient of variation (%), and the regression equation to estimate data for each variable. In terms of agreement between systems, the magnitude of the ICC was almost perfect (> 0.90-1.00) for all variables analyzed. The coefficient of the variations between devices was close to zero (< 5%) for total distance, distance per minute, average speed, maximum speed, and walking and jogging, and between 9% and 15% for running, intense running, and sprinting at low and at high intensities. It can be observed that, compared to Wimu Pro the Mediacoach System slightly overestimated all the variables analyzed except for average speed, maximum speed, and walking variables. In conclusion, both systems can be used, and the information they provide in the analyzed variables can be interchanged, with the benefits implied for practitioners and researchers.

Cross-site analysis of perceived ecosystem service benefits in multifunctional landscapes

Abstract: Rural development policies in many Organization for Economic Co-operation and Development (OECD) member countries promote sustainable landscape management with the intention of providing multiple ecosystem services (ES). Yet, it remains unclear which ES benefits are perceived in different landscapes and by different people. We present an assessment of ES benefits perceived and mapped by residents (n = 2,301) across 13 multifunctional (deep rural to peri-urban) landscapes in Europe. We identify the most intensively perceived ES benefits, their spatial patterns, and the respondent and landscape characteristics that determine ES benefit perception. We find outdoor recreation, aesthetic values and social interactions are the key ES benefits at local scales. Settlement areas are ES benefit hotspots but many benefits are also related to forests, waters and mosaic landscapes. We find some ES benefits (e.g. culture and heritage values) are spatially clustered, while many others (e.g. aesthetic values) are dispersed. ES benefit perception is linked to people’s relationship with and accessibility to a landscape. Our study discusses how a local perspective can contribute to the development of contextualized and socially acceptable policies for sustainable ES management. We also address conceptual confusion in ES framework and present argumentation regarding the links from services to benefits, and from benefits to different types of values.

Unlocking pre-1850 instrumental meteorological records: A global inventory

Abstract: Instrumental meteorological measurements from periods prior to the start of national weather services are designated “early instrumental data.” They have played an important role in climate...

The impact of environmental, social, and governance performance on stock prices: Evidence from the banking industry

Abstract: After the global financial crisis, commercial banks have increased their social responsibility activities with the aim of reinforcing the credibility and trust that their stakeholders have in them. However, prior research about the value relevance for their financial stakeholders of these sustainable practices is scarce. In this context, the aim of this research is to examine whether environmental, social, and governance (ESG) performance of commercial banks listed on 20 different stock markets provides relevant information and has a significant impact on stock prices over the 2002–2015 period. Our overall results reveal that stock market investors value the three ESG pillars in a different manner. We also observe that the value relevance of ESG performance is significantly higher for banks from common law countries and after the global financial crisis. These findings could have several implications for internal and external stakeholders such as managers, investors, and market regulators.