Implantation modified deep echo state neural networks and improve harmony clustering algorithm for optimal and energy efficient path in mobile sink

Wireless network sensors based on the mobile sink are regarded to be a common network and used in various fields in the last few years, they are thought to be easy to use, but contain the problem of energy loss and are affected by an energy hole problem, as it depends on batteries. This paper proposes a solution to this problem by using an innovative objective function for a consistent distributing of cluster heads, the enhanced harmony search based routing protocols based on energy equilibrated node clustering protocol. In order to route the data packet among the sink and cluster heads, an enhanced modified deep echo state neural network is suggested. The efficiency of a projected integrated clustering and routing protocol has been investigated at 500 nodes, and the 96 per cent success data for the proposed algorithm is given using the average energy consumption, send and receive packaged and optimum numbers of CH

Incorporating canopy structure from simulated GEDI lidar into bird species distribution models

The Global Ecosystem Dynamics Investigation (GEDI) lidar began data acquisition from the International Space Station in March 2019 and is expected to make over 10 billion measurements of canopy structure and topography over two years. Previously, airborne lidar data with limited spatial coverage have been used to examine relationships between forest canopy structure and faunal diversity, most commonly bird species. GEDI’s latitudinal coverage will permit these types of analyses at larger spatial extents, over the majority of the Earth’s forests, and most importantly in areas where canopy structure is complex and/or poorly understood. In this regional study, we examined the impact that GEDI-derived Canopy Structure variables have on the performance of bird species distribution models (SDMs) in Sonoma County, California. We simulated GEDI waveforms for a two-year period and then interpolated derived Canopy Structure variables to three grid sizes of analysis. In addition to these variables, we also included Phenology, Climate, and other Auxiliary variables to predict the probability of occurrence of 25 common bird species. We used a weighted average ensemble of seven individual machine learning models to make predictions for each species and calculated variable importance. We found that Canopy Structure variables were, on average at our finest resolution of 250 m, the second most important group (32.5%) of predictor variables after Climate variables (35.3%). Canopy Structure variables were most important for predicting probability of occurrence of birds associated with Conifer forest habitat. Regarding spatial analysis scale, we found that finer-scale models more frequently performed better than coarser-scale models, and the importance of Canopy Structure variables was greater at finer spatial resolutions. Overall, GEDI Canopy Structure variables improved SDM performance for at least one spatial resolution for 19 of 25 species and thus show promise for improving models of bird species occurrence and mapping potential habitat

Modelling of riverine ecosystems by integrating models: conceptual approach, a case study and research agenda

Aim Highly complex interactions between the hydrosphere and biosphere, as well as multifactorial relationships, characterize the interconnecting role of streams and rivers between different elements of a landscape. Applying species distribution models (SDMs) in these ecosystems requires special attention because rivers are linear systems and their abiotic and biotic conditions are structured in a linear fashion with significant influences from upstream/downstream or lateral influences from adjacent areas. Our aim was to develop a modelling framework for benthic invertebrates in riverine ecosystems and to test our approach in a data-rich study catchment. Location We present a case study of a 9-km section of the lowland Kielstau River located in northern Germany. Methods We linked hydrological, hydraulic and species distribution models to predict the habitat suitability for the bivalve Sphaerium corneum in a riverine system. The results generated by the hydrological model served as inputs into the hydraulic model, which was used to simulate the resulting water levels, velocities and sediment discharge within the stream channel. Results The ensemble model obtained good evaluation scores (area under the receiver operating characteristic curve 0.96; kappa 0.86; true skill statistic 0.95; sensitivity 86.14; specificity 85.75). Mean values for variables at the sampling sites were not significantly different from the values at the predicted distribution (MannWhitney U-test P > 0.05). High occurrence probabilities were predicted in the downstream half of the 9-km section of the Kielstau. The most important variable for the model was sediment discharge (contributing 40%), followed by water depth (30%), flow velocity (19%) and stream power (11%). Main conclusions The hydrological and hydraulic models are able to produce predictors, acting at different spatial scales, which are known to influence riverine organisms; which, in turn, are used by the SDMs as input. Our case study yielded good results, which corresponded well with ecological knowledge about our study organism. Although this method is feasible for making projections of habitat suitability on a local scale (here: a reach in a small catchment), we discuss remaining challenges for future modelling approaches and large-scale applications.Aim Highly complex interactions between the hydrosphere and biosphere, as well as multifactorial relationships, characterize the interconnecting role of streams and rivers between different elements of a landscape. Applying species distribution models (SDMs) in these ecosystems requires special attention because rivers are linear systems and their abiotic and biotic conditions are structured in a linear fashion with significant influences from upstream/downstream or lateral influences from adjacent areas. Our aim was to develop a modelling framework for benthic invertebrates in riverine ecosystems and to test our approach in a data-rich study catchment. Location We present a case study of a 9-km section of the lowland Kielstau River located in northern Germany. Methods We linked hydrological, hydraulic and species distribution models to predict the habitat suitability for the bivalve Sphaerium corneum in a riverine system. The results generated by the hydrological model served as inputs into the hydraulic model, which was used to simulate the resulting water levels, velocities and sediment discharge within the stream channel. Results The ensemble model obtained good evaluation scores (area under the receiver operating characteristic curve 0.96; kappa 0.86; true skill statistic 0.95; sensitivity 86.14; specificity 85.75). Mean values for variables at the sampling sites were not significantly different from the values at the predicted distribution (MannWhitney U-test P > 0.05). High occurrence probabilities were predicted in the downstream half of the 9-km section of the Kielstau. The most important variable for the model was sediment discharge (contributing 40%), followed by water depth (30%), flow velocity (19%) and stream power (11%). Main conclusions The hydrological and hydraulic models are able to produce predictors, acting at different spatial scales, which are known to influence riverine organisms; which, in turn, are used by the SDMs as input. Our case study yielded good results, which corresponded well with ecological knowledge about our study organism. Although this method is feasible for making projections of habitat suitability on a local scale (here: a reach in a small catchment), we discuss remaining challenges for future modelling approaches and large-scale applications

Volumetric Techniques for Product Routing and Loading Optimisation in Industry 4.0: A Review

Industry 4.0 has become a crucial part in the majority of processes, components, and related modelling, as well as predictive tools that allow a more efficient, automated and sustainable approach to industry. The availability of large quantities of data, and the advances in IoT, AI, and data-driven frameworks, have led to an enhanced data gathering, assessment, and extraction of actionable information, resulting in a better decision-making process. Product picking and its subsequent packing is an important area, and has drawn increasing attention for the research community. However, depending of the context, some of the related approaches tend to be either highly mathematical, or applied to a specific context. This article aims to provide a survey on the main methods, techniques, and frameworks relevant to product packing and to highlight the main properties and features that should be further investigated to ensure a more efficient and optimised approach

A Geospatial Modelling Approach Integrating Archaeobotany and Genetics to Trace the Origin and Dispersal of Domesticated Plants

Background: The study of the prehistoric origins and dispersal routes of domesticated plants is often based on the analysis of either archaeobotanical or genetic data. As more data become available, spatially explicit models of crop dispersal can be used to combine different types of evidence. Methodology/Principal Findings: We present a model in which a crop disperses through a landscape that is represented by a conductance matrix. From this matrix, we derive least-cost distances from the geographical origin of the crop and use these to predict the age of archaeological crop remains and the heterozygosity of crop populations. We use measures of the overlap and divergence of dispersal trajectories to predict genetic similarity between crop populations. The conductance matrix is constructed from environmental variables using a number of parameters. Model parameters are determined with multiple-criteria optimization, simultaneously fitting the archaeobotanical and genetic data. The consilience reached by the model is the extent to which it converges around solutions optimal for both archaeobotanical and genetic data. We apply the modelling approach to the dispersal of maize in the Americas. Conclusions/Significance: The approach makes possible the integrative inference of crop dispersal processes, whil