Assessing and mapping of carbon in biomass and soil of mangrove forest and competing land uses in the Philippines

Mangrove forests provide many ecosystem goods and services, and are important carbon (C) sinks in the tropics. Yet, land use conversions in mangroves still continue, especially in Southeast Asia. Carbon stocks in biomass and soil as well as the soil emissions of greenhouse gases (GHG) are important parameters to quantify, monitor and map in mangrove area, and are vital inputs for assessing the impact of mangrove conversion on C budget. This study was conducted in a section of tropical intertidal zone in Honda Bay, Philippines, with the following objectives: 1) evaluate the biomass C stocks in mangrove forests and land uses that replaced mangroves, 2) examine the potential of Sentinel satellite radar and multispectral imagery for mapping the aboveground biomass in mangrove area, 3) investigate the soil C stocks and the potential of GIS-based Ordinary Kriging for mapping the C stocks in mangrove soil, and 4) assess the soil fluxes of greenhouse gases and the potential of Ordinary Kriging for mapping the soil GHG fluxes. I used intensive field assessments, combined with laboratory analysis, remote sensing and GIS methods, to achieve the above objectives. To address the first objective, the biomass C stocks of the study land uses were quantified. Their relationships with selected canopy variables were also evaluated. Results reveal that for mangrove forests, the mean biomass was 22.4 to 178.1 Mg ha-1, which store 10 to 80 MgC ha-1 or 47.9 MgC ha-1, on average. Leaf Area Index significantly correlated with mangrove biomass C. In contrast, the biomass C stock of the land uses that replaced mangroves was, on average, 97% less than that in mangrove forests, ranging from zero in salt pond and cleared mangrove, 0.04 Mg C ha-1 in abandoned aquaculture ponds, to 5.7 Mg C ha-1 in the coconut plantation. C losses in biomass from conversion were estimated at 46.5 Mg C ha-1, on average. For the second objective, the potential of Sentinel imagery for the retrieval and predictive mapping of aboveground biomass in mangrove area was evaluated. I used both Sentinel SAR and multispectral imagery. Biomass prediction models were developed through linear regression and Machine Learning algorithms, each from SAR backscatter data, multispectral bands, vegetation indices, and canopy biophysical variables. The results show that the model based on biophysical variable Leaf Area Index (LAI) derived from Sentinel-2 was more accurate in predicting the overall aboveground biomass. However, the SAR-based model was more accurate in predicting the biomass in the usually deficient-to-low vegetation cover replacement land uses. These models had 0.82 to 0.83 correlation/agreement of observed and predicted value. Overall, Sentinel-1 SAR and Sentinel-2 multispectral imagery can provide satisfactory results in the retrieval and predictive mapping of aboveground biomass in mangrove area. In the third objective, the soil C stocks of the study land uses were quantified to estimate C losses in soil owing to conversion. I also evaluated the potential of GIS-based Ordinary Kriging for predictive mapping of the soil C stock distribution in the entire study site. On average, the soil C stock of mangrove forests was 851.9 MgC ha-1 while that of their non-forest competing land uses was less than half at 365.15 MgC ha-1. Aquaculture, salt pond and cleared mangrove had comparable C stocks (453.6, 401, 413 MgC ha-1, respectively) and coconut plantation had the least (42.2 MgC ha-1). Overall, C losses in soil owing to land use conversion in mangrove ranged from 398 to 809 MgC ha-1 (mean: 486.8 MgC ha-1) or a decline of 57% in soil C stock, on average. It was possible to map the site-scale spatial distribution of soil C stock and predict their values with 85% overall certainty using Ordinary Kriging approach. To achieve the fourth objective, the soil fluxes of CO2, CH4 and N2O in the study land uses were investigated using static chamber method. I also evaluated the potential of GIS-based Ordinary Kriging for predictive mapping of the soil GHG fluxes in the entire study site. Results show that the emissions of CO2 and CH4 were higher in mangrove forests by 2.6 and 6.6 times, respectively, while N2O emissions were lower by 34 times compared to the average of non-forest land uses. CH4 and N2O emissions accounted for 0.59% and 0.04% of the total emissions in mangrove forest as compared to 0.23% and 3.07% for non-forest land uses, respectively. Site-scale soil GHG flux distribution could be mapped with 75% to 83% accuracy using Ordinary Kriging. This study has shown that C losses in biomass and soil arising from mangrove conversion are substantial (63%; 571 MgC ha-1). Moreover, mangrove conversion heavily altered the soil-atmosphere fluxes of GHG, increasing the N2O fluxes by 34 times. The use of Sentinel imagery for biomass mapping, as well as the application of Ordinary Kriging for soil mapping of C stocks and GHG fluxes, offer good potentials for mangrove area monitoring. This study advances current knowledge on the C stocks and soil GHG fluxes in mangrove area and the C emissions owing to mangrove conversion. The mapping techniques presented here contribute to advancing the knowledge for mapping the biomass and soil attributes in mangrove ecosystem

Measurements of Soot Temperature in a Diffusion Flame Using a Digital Camera

Temperatures were measured in a laminar axisymmetric diffusion flame with a digital camera. The camera was a Nikon D700, modified to remove the Infra-Red (IR) cut filter and the anti-aliasing filter. Temperatures were obtained using ratio pyrometry at 550, 650, and 900 nm following deconvolution. The camera was calibrated with a blackbody furnace. Measurements were made for an 88 mm high laminar ethylene/air diffusion flame in co-flow on a burner with an 11.1 mm inside diameter. Soot temperatures were measured in the range of 1250 - 2000 K with an estimated uncertainty of ±60 K. The diagnostic requires axisymmetric, optically thin flames, and regions with temperatures greater than 1250 K and soot volume fractions greater than 0.1 ppm. The results agreed with published measurements for this flame. The new diagnostics provide a convenient and economical way to perform these measurements with good accuracy and spatial resolution

Strategies to parallelize a finite element mesh truncation technique on multi-core and many-core architectures

Achieving maximum parallel performance on multi-core CPUs and many-core GPUs is a challenging task depending on multiple factors. These include, for example, the number and granularity of the computations or the use of the memories of the devices. In this paper, we assess those factors by evaluating and comparing different parallelizations of the same problem on a multiprocessor containing a CPU with 40 cores and four P100 GPUs with Pascal architecture. We use, as study case, the convolutional operation behind a non-standard finite element mesh truncation technique in the context of open region electromagnetic wave propagation problems. A total of six parallel algorithms implemented using OpenMP and CUDA have been used to carry out the comparison by leveraging the same levels of parallelism on both types of platforms. Three of the algorithms are presented for the first time in this paper, including a multi-GPU method, and two others are improved versions of algorithms previously developed by some of the authors. This paper presents a thorough experimental evaluation of the parallel algorithms on a radar cross-sectional prediction problem. Results show that performance obtained on the GPU clearly overcomes those obtained in the CPU, much more so if we use multiple GPUs to distribute both data and computations. Accelerations close to 30 have been obtained on the CPU, while with the multi-GPU version accelerations larger than 250 have been achieved.Funding for open access charge: CRUE-Universitat Jaume

Gating Charge Immobilization in Kv4.2 Channels: The Basis of Closed-State Inactivation

Kv4 channels mediate the somatodendritic A-type K+ current (ISA) in neurons. The availability of functional Kv4 channels is dynamically regulated by the membrane potential such that subthreshold depolarizations render Kv4 channels unavailable. The underlying process involves inactivation from closed states along the main activation pathway. Although classical inactivation mechanisms such as N- and P/C-type inactivation have been excluded, a clear understanding of closed-state inactivation in Kv4 channels has remained elusive. This is in part due to the lack of crucial information about the interactions between gating charge (Q) movement, activation, and inactivation. To overcome this limitation, we engineered a charybdotoxin (CTX)-sensitive Kv4.2 channel, which enabled us to obtain the first measurements of Kv4.2 gating currents after blocking K+ conduction with CTX (Dougherty and Covarrubias. 2006J. Gen. Physiol. 128:745–753). Here, we exploited this approach further to investigate the mechanism that links closed-state inactivation to slow Q-immobilization in Kv4 channels. The main observations revealed profound Q-immobilization at steady-state over a range of hyperpolarized voltages (−110 to −75 mV). Depolarization in this range moves <5% of the observable Q associated with activation and is insufficient to open the channels significantly. The kinetics and voltage dependence of Q-immobilization and ionic current inactivation between −153 and −47 mV are similar and independent of the channel's proximal N-terminal region (residues 2–40). A coupled state diagram of closed-state inactivation with a quasi-absorbing inactivated state explained the results from ionic and gating current experiments globally. We conclude that Q-immobilization and closed-state inactivation at hyperpolarized voltages are two manifestations of the same process in Kv4.2 channels, and propose that inactivation in the absence of N- and P/C-type mechanisms involves desensitization to voltage resulting from a slow conformational change of the voltage sensors, which renders the channel's main activation gate reluctant to open

Machine Learning Based Predictions of Dissolved Oxygen in a Small Coastal Embayment

Coastal dissolved oxygen (DO) concentrations have a profound impact on nearshore ecosystems and, in recent years, there has been an increased prevalance of low DO hypoxic events that negatively impact nearshore organisms. Even with advanced numerical models, accurate prediction of coastal DO variability is challenging and computationally expensive. Here, we apply machine learning techniques in order to reconstruct and predict nearshore DO concentrations in a small coastal embayment while using a comprehensive set of nearshore and offshore measurements and easily measured input (training) parameters. We show that both random forest regression (RFR) and support vector regression (SVR) models accurately reproduce both the offshore DO and nearshore DO with extremely high accuracy. In general, RFR consistently peformed slightly better than SVR, the latter of which was more difficult to tune and took longer to train. Although each of the nearshore datasets were able to accurately predict DO values using training data from the same site, the model only had moderate success when using training data from one site to predict DO at another site, which was likely due to the the complexities in the underlying dynamics across the sites. We also show that high accuracy can be achieved with relatively little training data, highlighting a potential application for correcting time series with missing DO data due to quality control or sensor issues. This work establishes the ability of machine learning models to accurately reproduce DO concentrations in both offshore and nearshore coastal waters, with important implications for the ability to detect and indirectly measure coastal hypoxic events in near real-time. Future work should explore the ability of machine learning models in order to accurately forecast hypoxic events

Finite difference modelling of rupture propagation with strong velocity-weakening friction

We incorporate rate- and state-dependent friction in explicit finite difference (FD) simulations of mode II dynamic ruptures in elastic media, using the Mimetic Operators Split-Node (MOSN) method, with adjustable order of spatial accuracy (second-, fourth- or mixed-order accurate), including an option that is fourth-order accurate at the fault discontinuity as well as in the elastic volume. At fault points, the rate and state equations combined with the spatially discretized momentum conservation equations form a coupled system of ordinary differential equations (ODEs) for slip velocity and state variable. As a consequence of the rapid damping of velocity perturbations due to the direct effect, this system exhibits numerical stiffness that is inversely proportional to velocity squared. Approximate solutions to this velocity-state system are achieved by two different implicit schemes: (i) a fourth-order Rosenbrock integration of the full system using multiple substeps and (ii) low order integrations (backward Euler and trapezoidal) of the velocity equation, time-staggered with analytic integration of the state equation under the approximation of constant slip velocity over the time step. In assessing the numerical schemes, we use three test problems: ruptures with frictional resistance controlled by (i) a slip evolution law with strong velocity-weakening behaviour at high slip rates, representing thermal weakening due to flash heating of microscopic asperity contacts, (ii) the classic (low-velocity) slip evolution law and (iii) the classic aging evolution law. A convergence analysis is carried out using reference solutions from a spectral boundary integral equation method (BIEM) (a method restricted to homogeneous media, with nominal spectral accuracy in space and second-order accuracy in time for smooth solutions). Errors are measured by root-mean-square differences of fault-plane time histories (slip, slip rate, traction and state). MOSN shows essentially the same convergence rates as BIEM: second-order convergence for slip and state-variable misfits, with slower (but at least first-order) convergence for slip rates and tractions. For a given grid spacing, fourth-order MOSN is as accurate as BIEM for all variables except slip-rate. MOSN-Rosenbrock nominally has fourth-order temporal accuracy for the fault-plane velocity-state ODE integration (compared to lower-order accuracy for the other two MOSN schemes) and therefore provides an important theoretical benchmark. However, it is sensitive to details of the elastic calculation scheme and occasionally its adaptive substepping performs poorly, leading to large excursions from the reference solution. In contrast, MOSN-trapezoidal is robust and reliable, much easier to implement than MOSN-Rosenbrock, and in all cases achieves precision as good as the latter without recourse to substepping. MOSN-Euler has the same advantages as MOSN-trapezoidal, except that its nominal first-order temporal accuracy ultimately leads to larger errors in slip and state variable compared with the higher-order MOSN schemes at sufficiently small grid spacings and time step

Finding Respondents from Minority Groups

The recruitment of respondents belonging to ethnic minorities poses important challenges in social and health research. This paper reflects on the enablers and barriers to recruitment that we encountered in our research work with persons belonging to ethnic minorities. Additionally, we applied the Matching Model of Recruitment, a theoretical framework concerning minority recruitment, to guide our reflection. We also explored its applicability as a research design tool. In assessing our research experience, we learned that minority recruitment in social and health research is influenced by the social context of all key players involved in the research. Also, there are enablers and barriers within that social context facilitating or delaying the recruitment process. The main enablers to recruit respondents belonging to ethnic minorities include working with community agencies and gatekeepers who share a common vision with researchers and the latter’s ability to gain the trust of potential respondents. The main barriers include demanding too much from these same community agencies and gatekeepers and ignoring factors that could delay the completion of the research. Although we found the Matching Model of Recruitment to be an effective tool in assessing the processes of recruiting respondents belonging to ethnic minorities, further empirical research is needed to explore its usefulness during the research planning phase

Conceptualization of emotional behavior and addiction to social networks

Las redes sociales se han convertido en muy pocos años en un instrumento de influencia y comunicación en todos los rangos de edad. Los preadolescentes y adolescentes siguen siendo el grupo más vulnerable porque carecen de estrategias para defenderse de determinadas agresiones de este medio. Los canales de información y formación para el manejo de las redes sociales siguen siendo aún escasas y controvertidas y su poder de influencia social se evidencia en diferentes ámbitos de la sociedad, generando corrientes de opinión y modificación de actitudes y creencias. La gran versatilidad y facilidad de utilización, las convierte en instrumentos de uso continuado. Tenemos que añadir al proceso el desconocimiento por parte de los padres de cómo educar a los hijos en su uso, teniendo en cuenta que existe una brecha digital bastante pronunciada. Los estados emocionales en este grupo de edad se encuentran en continuo desarrollo y cualquier incidente puede repercutir positiva o negativamente en su configuración y, por otra parte, las consecuencias de un uso abusivo de las mismas, puede conllevar una conducta de adicción. En este trabajo pretendemos abordar, a partir de la revisión de la literatura y desde un punto de vista teórico, la influencia de las redes sociales virtuales en el comportamiento emocional de los jóvenes y su posible relación con el proceso de adicciónIn a few years, social networks have become an instrument of influence and communication in all age ranges. Preteens and adolescents remain the most vulnerable group because they lack strategies to defend themselves against certain aggressions of this environment. The information and training channels for the management of social networks are still scarce and controversial and their power of social influence is evident in different areas of society, generating currents of opinion and modification of attitudes and beliefs. The great versatility and ease of use, makes them instruments for continued use. We have to add to the process the ignorance on the part of the parents of how to educate the children in their use, taking into account that there is a quite pronounced digital divide. The emotional states in this age group are in continuous development and any incident can have a positive or negative impact on their configuration and, on the other hand, the consequences of an abusive use of them can lead to addiction behavior. In this work we intend to address, from the review of the literature and from a theoretical point of view, the influence of virtual social networks on the emotional behavior of young people and their possible relationship with the addiction process.info:eu-repo/semantics/publishedVersio