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The present study determined the effect of severe and lesser severe periods of heat stress on production and chemical composition of milk in Kundhi buffalos in the environment of Tandojam. For this total ten (10) buffaloes of third mid lactation period were selected and divided into two groups A and B, with five buffaloes in each group. Group A was studied in the severe period of heat stress whereas Group B was observed for a lesser severe period of heat stress in the Department of Livestock Management, Sindh Agriculture University, Tandojam. The data indicates that significantly higher milk production (7.56±0.30 liters) was recorded for group B in comparison to group A (5.35±0.20 liters). Higher moisture content in milk (83.84±0.21%) was recorded for group A in comparison to group B (83.26±0.18%). Slightly higher total solid content (16.74±0.18%) was recorded for group B in comparison to group A (16.15±0.21%). Higher ash content (0.76±0.06%) was recorded for group A in comparison to group B (0.50±0.10%). Higher fat content (4.88±0.14%) was recorded for group B in comparison to group A (6.40±0.13%). Higher protein content (4.65±0.10%) was recorded for group B in comparison of group A (4.34±1.16%). Higher lactose content (5.19±0.18%) was recorded for group B in comparison to group A (4.48±0.16%). The results for physiological parameters showed that significantly higher results were found for respiration rate, body temperature, and pulse rate breaths/minute in group A having severe heat stress as compared to group B with lesser heat stress of Kundhi buffalos The results.  concluded that Kundhi buffalo produce more milk production during cold or lesser heat periods, while during severe heat periods the production of milk is considerably reduced.

This study has been carried out to investigate the adverse effects of climate change on agriculture and its awareness among various stakeholders. This study aims to determine adverse effects and how the agriculture department of Naushahro Feroze District contributes to creating awareness among the stakeholders about the adverse effects of climate change on agriculture. This study uses a purposeful sample of 4 to 5 High-Rank officers and specialists of the Agriculture department were selected to collect the data. The qualitative Method was adopted using Interviews via Semi-structured open-ended questionnaires. The results show that bodies, Organizations, and Institutions working to mitigate losses are not properly aware of the recommendations by International Bodies. Moreover, there is no proper system for creating awareness.

Air pollution causes millions of premature deaths every year, and fugitive emissions of, e.g., methane are major causes of global warming. Correspondingly, air pollution monitoring systems are urgently needed. Mobile, autonomous monitoring can provide adaptive and higher spatial resolution compared with traditional monitoring stations and allows fast deployment and operation in adverse environments. We present a mobile robot solution for autonomous gas detection and gas distribution mapping using remote gas sensing. Our ‘‘Autonomous Remote Methane Explorer’’ (ARMEx) is equipped with an actuated spectroscopy-based remote gas sensor, which collects integral gas measurements along up to 30 m long optical beams. State-of-the-art 3D mapping and robot localization allow the precise location of the optical beams to be determined, which then facilitates gas tomography (tomographic reconstruction of local gas distributions from sets of integral gas measurements). To autonomously obtain informative sampling strategies for gas tomography, we reduce the search space for gas inspection missions by defining a sweep of the remote gas sensor over a selectable field of view as a sensing configuration. We describe two different ways to find sequences of sensing configurations that optimize the criteria for gas detection and gas distribution mapping while minimizing the number of measurements and distance traveled. We evaluated anARMExprototype deployed in a large, challenging indoor environment with eight gas sources. In comparison with human experts teleoperating the platform from a distant building, the autonomous strategy produced better gas maps with a lower number of sensing configurations and a slightly longer route., Funding Agencies:European Commission ICT-23-2014 645101SURVEYOR (Vinnova) 2017-05468project RAISE 20130196

Human-caused greenhouse gas emissions are one of the major sources of global warming, which is threatening to reach a tipping point. Inspection systems that can provide direct information about critical factors causing global warming, such as systems for gas detection and location of gas sources, are urgently needed to analyze the fugitive emissions and take necessary actions. This thesis presents an autonomous robotic system capable of performing efficient exploration by selecting informative sampling positions for gas detection and gas distribution mapping – the Autonomous Remote Methane Explorer (ARMEx). In the design choice of ARMEx, a ground robot carries a spectroscopybased remote gas sensor, such as a Remote Methane Leak Detector (RMLD), that collects integral gas measurements along up to 30 m long optical-beams. The sensor is actuated to sample a large area inside an adjustable field of view, and with the mobility of the robot, adaptive sampling for high spatial resolution in the areas of interest is made possible to inspect large environments. In a typical gas sampling mission, the robot needs to localize itself and plan a traveling path to visit different locations in the area, which is a largely solved problem. However, the state-of-the-art prior to this thesis fell short of providing the capability to select informative sampling positions autonomously. This thesis introduces efficient measurement strategies to bring autonomy to mobile remote gas sensing. The strategies are based on sensor planning algorithms that minimize the number of measurements and distance traveled while optimizing the inspection criteria: full sensing coverage of the area for gas detection, and suitably overlapping sensing coverage of different viewpoints around areas of interest for gas distribution mapping. A prototype implementation of ARMEx was deployed in a large, real-world environment where inspection missions performed by the autonomous system were compared with runs teleop

Human-caused greenhouse gas emissions are one of the major sources of global warming, which is threatening to reach a tipping point. Inspection systems that can provide direct information about critical factors causing global warming, such as systems for gas detection and location of gas sources, are urgently needed to analyze the fugitive emissions and take necessary actions. This thesis presents an autonomous robotic system capable of performing efficient exploration by selecting informative sampling positions for gas detection and gas distribution mapping – the Autonomous Remote Methane Explorer (ARMEx). In the design choice of ARMEx, a ground robot carries a spectroscopybased remote gas sensor, such as a Remote Methane Leak Detector (RMLD), that collects integral gas measurements along up to 30 m long optical-beams. The sensor is actuated to sample a large area inside an adjustable field of view, and with the mobility of the robot, adaptive sampling for high spatial resolution in the areas of interest is made possible to inspect large environments. In a typical gas sampling mission, the robot needs to localize itself and plan a traveling path to visit different locations in the area, which is a largely solved problem. However, the state-of-the-art prior to this thesis fell short of providing the capability to select informative sampling positions autonomously. This thesis introduces efficient measurement strategies to bring autonomy to mobile remote gas sensing. The strategies are based on sensor planning algorithms that minimize the number of measurements and distance traveled while optimizing the inspection criteria: full sensing coverage of the area for gas detection, and suitably overlapping sensing coverage of different viewpoints around areas of interest for gas distribution mapping. A prototype implementation of ARMEx was deployed in a large, real-world environment where inspection missions performed by the autonomous system were compared with runs teleop

Human-caused greenhouse gas emissions are one of the major sources of global warming, which is threatening to reach a tipping point. Inspection systems that can provide direct information about critical factors causing global warming, such as systems for gas detection and location of gas sources, are urgently needed to analyze the fugitive emissions and take necessary actions. This thesis presents an autonomous robotic system capable of performing efficient exploration by selecting informative sampling positions for gas detection and gas distribution mapping – the Autonomous Remote Methane Explorer (ARMEx). In the design choice of ARMEx, a ground robot carries a spectroscopybased remote gas sensor, such as a Remote Methane Leak Detector (RMLD), that collects integral gas measurements along up to 30 m long optical-beams. The sensor is actuated to sample a large area inside an adjustable field of view, and with the mobility of the robot, adaptive sampling for high spatial resolution in the areas of interest is made possible to inspect large environments. In a typical gas sampling mission, the robot needs to localize itself and plan a traveling path to visit different locations in the area, which is a largely solved problem. However, the state-of-the-art prior to this thesis fell short of providing the capability to select informative sampling positions autonomously. This thesis introduces efficient measurement strategies to bring autonomy to mobile remote gas sensing. The strategies are based on sensor planning algorithms that minimize the number of measurements and distance traveled while optimizing the inspection criteria: full sensing coverage of the area for gas detection, and suitably overlapping sensing coverage of different viewpoints around areas of interest for gas distribution mapping. A prototype implementation of ARMEx was deployed in a large, real-world environment where inspection missions performed by the autonomous system were compared with runs teleop

Human-caused greenhouse gas emissions are one of the major sources of global warming, which is threatening to reach a tipping point. Inspection systems that can provide direct information about critical factors causing global warming, such as systems for gas detection and location of gas sources, are urgently needed to analyze the fugitive emissions and take necessary actions. This thesis presents an autonomous robotic system capable of performing efficient exploration by selecting informative sampling positions for gas detection and gas distribution mapping – the Autonomous Remote Methane Explorer (ARMEx). In the design choice of ARMEx, a ground robot carries a spectroscopybased remote gas sensor, such as a Remote Methane Leak Detector (RMLD), that collects integral gas measurements along up to 30 m long optical-beams. The sensor is actuated to sample a large area inside an adjustable field of view, and with the mobility of the robot, adaptive sampling for high spatial resolution in the areas of interest is made possible to inspect large environments. In a typical gas sampling mission, the robot needs to localize itself and plan a traveling path to visit different locations in the area, which is a largely solved problem. However, the state-of-the-art prior to this thesis fell short of providing the capability to select informative sampling positions autonomously. This thesis introduces efficient measurement strategies to bring autonomy to mobile remote gas sensing. The strategies are based on sensor planning algorithms that minimize the number of measurements and distance traveled while optimizing the inspection criteria: full sensing coverage of the area for gas detection, and suitably overlapping sensing coverage of different viewpoints around areas of interest for gas distribution mapping. A prototype implementation of ARMEx was deployed in a large, real-world environment where inspection missions performed by the autonomous system were compared with runs teleop

Hollow-fiber liquid-phase microextraction (HF-LPME) and electromembrane extraction (EME) are miniaturized extraction techniques, and have been coupled with various analytical instruments for trace analysis of heavy metals, drugs and other organic compounds, in recent years. HF-LPME and EME provide high selectivity, efficient sample cleanup and enrichment, and reduce the consumption of organic solvents to a few micro-liters per sample. HF-LPME and EME are compatible with different analytical instruments for chromatography, electrophoresis, atomic spectroscopy, mass spectrometry, and electrochemical detection. HF-LPME and EME have gained significant popularity during the recent years. This review focuses on hollow fiber based techniques (especially HF-LPME and EME) of heavy metals and pharmaceuticals (published 2017 to May 2019), and their combinations with atomic spectroscopy, UV-VIS spectrophotometry, high performance liquid chromatography, gas chromatography, capillary electrophoresis, and voltammetry.

Human-caused greenhouse gas emissions are one of the major sources of global warming, which is threatening to reach a tipping point. Inspection systems that can provide direct information about critical factors causing global warming, such as systems for gas detection and location of gas sources, are urgently needed to analyze the fugitive emissions and take necessary actions. This thesis presents an autonomous robotic system capable of performing efficient exploration by selecting informative sampling positions for gas detection and gas distribution mapping – the Autonomous Remote Methane Explorer (ARMEx). In the design choice of ARMEx, a ground robot carries a spectroscopybased remote gas sensor, such as a Remote Methane Leak Detector (RMLD), that collects integral gas measurements along up to 30 m long optical-beams. The sensor is actuated to sample a large area inside an adjustable field of view, and with the mobility of the robot, adaptive sampling for high spatial resolution in the areas of interest is made possible to inspect large environments. In a typical gas sampling mission, the robot needs to localize itself and plan a traveling path to visit different locations in the area, which is a largely solved problem. However, the state-of-the-art prior to this thesis fell short of providing the capability to select informative sampling positions autonomously. This thesis introduces efficient measurement strategies to bring autonomy to mobile remote gas sensing. The strategies are based on sensor planning algorithms that minimize the number of measurements and distance traveled while optimizing the inspection criteria: full sensing coverage of the area for gas detection, and suitably overlapping sensing coverage of different viewpoints around areas of interest for gas distribution mapping. A prototype implementation of ARMEx was deployed in a large, real-world environment where inspection missions performed by the autonomous system were compared with runs teleop

Hollow-fiber liquid-phase microextraction (HF-LPME) and electromembrane extraction (EME) are miniaturized extraction techniques, and have been coupled with various analytical instruments for trace analysis of heavy metals, drugs and other organic compounds, in recent years. HF-LPME and EME provide high selectivity, efficient sample cleanup and enrichment, and reduce the consumption of organic solvents to a few micro-liters per sample. HF-LPME and EME are compatible with different analytical instruments for chromatography, electrophoresis, atomic spectroscopy, mass spectrometry, and electrochemical detection. HF-LPME and EME have gained significant popularity during the recent years. This review focuses on hollow fiber based techniques (especially HF-LPME and EME) of heavy metals and pharmaceuticals (published 2017 to May 2019), and their combinations with atomic spectroscopy, UV-VIS spectrophotometry, high performance liquid chromatography, gas chromatography, capillary electrophoresis, and voltammetry.

Clarifying how increased atmospheric CO2 concentration (eCO2) contributes to accelerated land carbon sequestration remains important since this process is the largest negative feedback in the coupled carbon–climate system. Here, we constrain the sensitivity of the terrestrial carbon sink to eCO2 over the temperate Northern Hemisphere for the past five decades, using 12 terrestrial ecosystem models and data from seven CO2 enrichment experiments. This constraint uses the heuristic finding that the northern temperate carbon sink sensitivity to eCO2 is linearly related to the site-scale sensitivity across the models. The emerging data-constrained eCO2 sensitivity is 0.64 ± 0.28 PgC yr−1 per hundred ppm of eCO2. Extrapolating worldwide, this northern temperate sensitivity projects the global terrestrial carbon sink to increase by 3.5 ± 1.9 PgC yr−1 for an increase in CO2 of 100 ppm. This value suggests that CO2 fertilization alone explains most of the observed increase in global land carbon sink since the 1960s. More CO2 enrichment experiments, particularly in boreal, arctic and tropical ecosystems, are required to explain further the responsible processes.

Clarifying how increased atmospheric CO2 concentration (eCO2) contributes to accelerated land carbon sequestration remains important since this process is the largest negative feedback in the coupled carbon–climate system. Here, we constrain the sensitivity of the terrestrial carbon sink to eCO2 over the temperate Northern Hemisphere for the past five decades, using 12 terrestrial ecosystem models and data from seven CO2 enrichment experiments. This constraint uses the heuristic finding that the northern temperate carbon sink sensitivity to eCO2 is linearly related to the site-scale sensitivity across the models. The emerging data-constrained eCO2 sensitivity is 0.64 ± 0.28 PgC yr−1 per hundred ppm of eCO2. Extrapolating worldwide, this northern temperate sensitivity projects the global terrestrial carbon sink to increase by 3.5 ± 1.9 PgC yr−1 for an increase in CO2 of 100 ppm. This value suggests that CO2 fertilization alone explains most of the observed increase in global land carbon sink since the 1960s. More CO2 enrichment experiments, particularly in boreal, arctic and tropical ecosystems, are required to explain further the responsible processes.

Insecticide resistance is a major challenge in successful insect pest control as the insects have the ability to develop resistance to various widely used insecticides. Butene-fipronil is a novel compound with high toxicity to insects and less toxicity to the non-target organisms. In the present study, the effect of butene-fipronil alone and in combination with three enzyme inhibitors, piperonyl butoxide (PBO), diethyl maleate (DEM), and triphenyl phosphate (TPP), was carried out on larvae and adults of D. melanogaster

Mobile robot platforms equipped with olfaction systems have been used in many gas sensing applications. However, in-field validation of mobile robot olfaction systems is time consuming, expensive, cumbersome and lacks repeatability. In order to address these issues, simulation tools are used. However, the available mobile robot olfaction simulations lack models for remote gas sensors, and the possibility to import geometrical representations of actual real-world environments in a convenient way. In this paper, we describe extensions to an open-source CFD-based filament gas dispersal simulator. These improvements arrow to use robot-created occupancy maps and offer remote sensing capabilities in the simulation loop. We demonstrate the novel features in an example application: we created a 3D map a complex indoor environment, and performed a gas emission monitoring task with a Tunable Diode Laser Absorption Spectroscopy based remote gas sensor in a simulated version of the environment.

An accurate model of gas emissions is of high importance in several real-world applications related to monitoring and surveillance. Gas tomography is a non-intrusive optical method to estimate the spatial distribution of gas concentrations using remote sensors. The choice of sensing geometry, which is the arrangement of sensing positions to perform gas tomography, directly affects the reconstruction quality of the obtained gas distribution maps. In this paper, we present an investigation of criteria that allow to determine suitable sensing geometries for gas tomography. We consider an actuated remote gas sensor installed on a mobile robot, and evaluated a large number of sensing configurations. Experiments in complex settings were conducted using a state-of-the-art CFD-based filament gas dispersal simulator. Our quantitative comparison yields preferred sensing geometries for sensor planning, which allows to better reconstruct gas distributions.

Creating an accurate model of gas emissions is an important task in monitoring and surveillance applications. A promising solution for a range of real-world applications are gas-sensitive mobile robots with spectroscopy-based remote sensors that are used to create a tomographic reconstruction of the gas distribution. The quality of these reconstructions depends crucially on the chosen sensing geometry. In this paper we address the problem of sensor planning by investigating sensing geometries that minimize reconstruction errors, and then formulate an optimization algorithm that chooses sensing configurations accordingly. The algorithm decouples sensor planning for single high concentration regions (hotspots) and subsequently fuses the individual solutions to a global solution consisting of sensing poses and the shortest path between them. The proposed algorithm compares favorably to a template matching technique in a simple simulation and in a real-world experiment. In the latter, we also compare the proposed sensor planning strategy to the sensing strategy of a human expert and find indications that the quality of the reconstructed map is higher with the proposed algorithm.

Creating an accurate model of gas emissions is an important task in monitoring and surveillance applications. A promising solution for a range of real-world applications are gas-sensitive mobile robots with spectroscopy-based remote sensors that are used to create a tomographic reconstruction of the gas distribution. The quality of these reconstructions depends crucially on the chosen sensing geometry. In this paper we address the problem of sensor planning by investigating sensing geometries that minimize reconstruction errors, and then formulate an optimization algorithm that chooses sensing configurations accordingly. The algorithm decouples sensor planning for single high concentration regions (hotspots) and subsequently fuses the individual solutions to a global solution consisting of sensing poses and the shortest path between them. The proposed algorithm compares favorably to a template matching technique in a simple simulation and in a real-world experiment. In the latter, we also compare the proposed sensor planning strategy to the sensing strategy of a human expert and find indications that the quality of the reconstructed map is higher with the proposed algorithm.

Creating an accurate model of gas emissions is an important task in monitoring and surveillance applications. A promising solution for a range of real-world applications are gas-sensitive mobile robots with spectroscopy-based remote sensors that are used to create a tomographic reconstruction of the gas distribution. The quality of these reconstructions depends crucially on the chosen sensing geometry. In this paper we address the problem of sensor planning by investigating sensing geometries that minimize reconstruction errors, and then formulate an optimization algorithm that chooses sensing configurations accordingly. The algorithm decouples sensor planning for single high concentration regions (hotspots) and subsequently fuses the individual solutions to a global solution consisting of sensing poses and the shortest path between them. The proposed algorithm compares favorably to a template matching technique in a simple simulation and in a real-world experiment. In the latter, we also compare the proposed sensor planning strategy to the sensing strategy of a human expert and find indications that the quality of the reconstructed map is higher with the proposed algorithm.

Creating an accurate model of gas emissions is an important task in monitoring and surveillance applications. A promising solution for a range of real-world applications are gas-sensitive mobile robots with spectroscopy-based remote sensors that are used to create a tomographic reconstruction of the gas distribution. The quality of these reconstructions depends crucially on the chosen sensing geometry. In this paper we address the problem of sensor planning by investigating sensing geometries that minimize reconstruction errors, and then formulate an optimization algorithm that chooses sensing configurations accordingly. The algorithm decouples sensor planning for single high concentration regions (hotspots) and subsequently fuses the individual solutions to a global solution consisting of sensing poses and the shortest path between them. The proposed algorithm compares favorably to a template matching technique in a simple simulation and in a real-world experiment. In the latter, we also compare the proposed sensor planning strategy to the sensing strategy of a human expert and find indications that the quality of the reconstructed map is higher with the proposed algorithm.

Creating an accurate model of gas emissions is an important task in monitoring and surveillance applications. A promising solution for a range of real-world applications are gas-sensitive mobile robots with spectroscopy-based remote sensors that are used to create a tomographic reconstruction of the gas distribution. The quality of these reconstructions depends crucially on the chosen sensing geometry. In this paper we address the problem of sensor planning by investigating sensing geometries that minimize reconstruction errors, and then formulate an optimization algorithm that chooses sensing configurations accordingly. The algorithm decouples sensor planning for single high concentration regions (hotspots) and subsequently fuses the individual solutions to a global solution consisting of sensing poses and the shortest path between them. The proposed algorithm compares favorably to a template matching technique in a simple simulation and in a real-world experiment. In the latter, we also compare the proposed sensor planning strategy to the sensing strategy of a human expert and find indications that the quality of the reconstructed map is higher with the proposed algorithm.

The problem of gas detection is relevant to many real-world applications, such as leak detection in industrial settings and landfill monitoring. In this paper, we address the problem of gas detection in large areas with a mobile robotic platform equipped with a remote gas sensor. We propose an algorithm that leverages a novel method based on convex relaxation for quickly solving sensor placement problems, and for generating an efficient exploration plan for the robot. To demonstrate the applicability of our method to real-world environments, we performed a large number of experimental trials, both on randomly generated maps and on the map of a real environment. Our approach proves to be highly efficient in terms of computational requirements and to provide nearly-optimal solutions.

The problem of gas detection is relevant to many real-world applications, such as leak detection in industrial settings and landfill monitoring. In this paper, we address the problem of gas detection in large areas with a mobile robotic platform equipped with a remote gas sensor. We propose an algorithm that leverages a novel method based on convex relaxation for quickly solving sensor placement problems, and for generating an efficient exploration plan for the robot. To demonstrate the applicability of our method to real-world environments, we performed a large number of experimental trials, both on randomly generated maps and on the map of a real environment. Our approach proves to be highly efficient in terms of computational requirements and to provide nearly-optimal solutions.

The problem of gas detection is relevant to manyreal-world applications, such as leak detection in industrialsettings and surveillance. In this paper we address the problemof gas detection in large areas with a mobile robotic platformequipped with a remote gas sensor. We propose a novelmethod based on convex relaxation for quickly finding anexploration plan that guarantees a complete coverage of theenvironment. Our method proves to be highly efficient in termsof computational requirements and to provide nearly-optimalsolutions. We validate our approach both in simulation andin real environments, thus demonstrating its applicability toreal-world problems.

The problem of gas detection is relevant to manyreal-world applications, such as leak detection in industrialsettings and surveillance. In this paper we address the problemof gas detection in large areas with a mobile robotic platformequipped with a remote gas sensor. We propose a novelmethod based on convex relaxation for quickly finding anexploration plan that guarantees a complete coverage of theenvironment. Our method proves to be highly efficient in termsof computational requirements and to provide nearly-optimalsolutions. We validate our approach both in simulation andin real environments, thus demonstrating its applicability toreal-world problems.

The problem of gas detection is relevant to many real-world applications, such as leak detection in industrial settings and landfill monitoring. In this paper, we address the problem of gas detection in large areas with a mobile robotic platform equipped with a remote gas sensor. We propose an algorithm that leverages a novel method based on convex relaxation for quickly solving sensor placement problems, and for generating an efficient exploration plan for the robot. To demonstrate the applicability of our method to real-world environments, we performed a large number of experimental trials, both on randomly generated maps and on the map of a real environment. Our approach proves to be highly efficient in terms of computational requirements and to provide nearly-optimal solutions.

The problem of gas detection is relevant to many real-world applications, such as leak detection in industrial settings and landfill monitoring. In this paper, we address the problem of gas detection in large areas with a mobile robotic platform equipped with a remote gas sensor. We propose an algorithm that leverages a novel method based on convex relaxation for quickly solving sensor placement problems, and for generating an efficient exploration plan for the robot. To demonstrate the applicability of our method to real-world environments, we performed a large number of experimental trials, both on randomly generated maps and on the map of a real environment. Our approach proves to be highly efficient in terms of computational requirements and to provide nearly-optimal solutions.

The problem of gas detection is relevant to manyreal-world applications, such as leak detection in industrialsettings and surveillance. In this paper we address the problemof gas detection in large areas with a mobile robotic platformequipped with a remote gas sensor. We propose a novelmethod based on convex relaxation for quickly finding anexploration plan that guarantees a complete coverage of theenvironment. Our method proves to be highly efficient in termsof computational requirements and to provide nearly-optimalsolutions. We validate our approach both in simulation andin real environments, thus demonstrating its applicability toreal-world problems.

The problem of gas detection is relevant to many real-world applications, such as leak detection in industrial settings and landfill monitoring. In this paper, we address the problem of gas detection in large areas with a mobile robotic platform equipped with a remote gas sensor. We propose an algorithm that leverages a novel method based on convex relaxation for quickly solving sensor placement problems, and for generating an efficient exploration plan for the robot. To demonstrate the applicability of our method to real-world environments, we performed a large number of experimental trials, both on randomly generated maps and on the map of a real environment. Our approach proves to be highly efficient in terms of computational requirements and to provide nearly-optimal solutions.

The problem of gas detection is relevant to manyreal-world applications, such as leak detection in industrialsettings and surveillance. In this paper we address the problemof gas detection in large areas with a mobile robotic platformequipped with a remote gas sensor. We propose a novelmethod based on convex relaxation for quickly finding anexploration plan that guarantees a complete coverage of theenvironment. Our method proves to be highly efficient in termsof computational requirements and to provide nearly-optimalsolutions. We validate our approach both in simulation andin real environments, thus demonstrating its applicability toreal-world problems.

The problem of gas detection is relevant to manyreal-world applications, such as leak detection in industrialsettings and surveillance. In this paper we address the problemof gas detection in large areas with a mobile robotic platformequipped with a remote gas sensor. We propose a novelmethod based on convex relaxation for quickly finding anexploration plan that guarantees a complete coverage of theenvironment. Our method proves to be highly efficient in termsof computational requirements and to provide nearly-optimalsolutions. We validate our approach both in simulation andin real environments, thus demonstrating its applicability toreal-world problems.

Path planning for multi-DoF legged robots is achallenging task due to the high dimensionality and complexityof the planning space. We present our first attempt to builda path planning framework for the hydraulic quadruped -HyQ. Our approach adopts a similar strategy to [1], whereplanning is divided into a task-space and a joint-space part.The task-space planner finds a path for the center of gravity(COG) of the robot, while then the footstep planner generates theappropriate footholds under reachability and stability criteria.Next the joint-space planner translates the task-space COGtrajectories into robot joint angles. We present a comparisonof a set of search-based planning algorithms; Dijkstra, A* andARA*, and evaluate these over a set of given terrains and anumber of varying start and end points. All test runs supportthat our approach is a simple yet robust solution. We reportcomparisons in path length, computation time, and path cost,between the aforementioned planning algorithms.

Path planning for multi-DoF legged robots is achallenging task due to the high dimensionality and complexityof the planning space. We present our first attempt to builda path planning framework for the hydraulic quadruped -HyQ. Our approach adopts a similar strategy to [1], whereplanning is divided into a task-space and a joint-space part.The task-space planner finds a path for the center of gravity(COG) of the robot, while then the footstep planner generates theappropriate footholds under reachability and stability criteria.Next the joint-space planner translates the task-space COGtrajectories into robot joint angles. We present a comparisonof a set of search-based planning algorithms; Dijkstra, A* andARA*, and evaluate these over a set of given terrains and anumber of varying start and end points. All test runs supportthat our approach is a simple yet robust solution. We reportcomparisons in path length, computation time, and path cost,between the aforementioned planning algorithms.

Path planning for multi-DoF legged robots is achallenging task due to the high dimensionality and complexityof the planning space. We present our first attempt to builda path planning framework for the hydraulic quadruped -HyQ. Our approach adopts a similar strategy to [1], whereplanning is divided into a task-space and a joint-space part.The task-space planner finds a path for the center of gravity(COG) of the robot, while then the footstep planner generates theappropriate footholds under reachability and stability criteria.Next the joint-space planner translates the task-space COGtrajectories into robot joint angles. We present a comparisonof a set of search-based planning algorithms; Dijkstra, A* andARA*, and evaluate these over a set of given terrains and anumber of varying start and end points. All test runs supportthat our approach is a simple yet robust solution. We reportcomparisons in path length, computation time, and path cost,between the aforementioned planning algorithms.

Path planning for multi-DoF legged robots is achallenging task due to the high dimensionality and complexityof the planning space. We present our first attempt to builda path planning framework for the hydraulic quadruped -HyQ. Our approach adopts a similar strategy to [1], whereplanning is divided into a task-space and a joint-space part.The task-space planner finds a path for the center of gravity(COG) of the robot, while then the footstep planner generates theappropriate footholds under reachability and stability criteria.Next the joint-space planner translates the task-space COGtrajectories into robot joint angles. We present a comparisonof a set of search-based planning algorithms; Dijkstra, A* andARA*, and evaluate these over a set of given terrains and anumber of varying start and end points. All test runs supportthat our approach is a simple yet robust solution. We reportcomparisons in path length, computation time, and path cost,between the aforementioned planning algorithms.

Path planning for multi-DoF legged robots is achallenging task due to the high dimensionality and complexityof the planning space. We present our first attempt to builda path planning framework for the hydraulic quadruped -HyQ. Our approach adopts a similar strategy to [1], whereplanning is divided into a task-space and a joint-space part.The task-space planner finds a path for the center of gravity(COG) of the robot, while then the footstep planner generates theappropriate footholds under reachability and stability criteria.Next the joint-space planner translates the task-space COGtrajectories into robot joint angles. We present a comparisonof a set of search-based planning algorithms; Dijkstra, A* andARA*, and evaluate these over a set of given terrains and anumber of varying start and end points. All test runs supportthat our approach is a simple yet robust solution. We reportcomparisons in path length, computation time, and path cost,between the aforementioned planning algorithms.

Magneto-rheological damper is a nonlinear system. In this case study, system has been identified using Neural Network tool. Optimization between number of neurons in the hidden layer and number of epochs has been achieved and discussed by using multilayer perceptron Neural Network.

Magneto-rheological damper is a nonlinear system. In this case study, system has been identified using Neural Network tool. Optimization between number of neurons in the hidden layer and number of epochs has been achieved and discussed by using multilayer perceptron Neural Network.

Magneto-rheological damper is a nonlinear system. In this case study, system has been identified using Neural Network tool. Optimization between number of neurons in the hidden layer and number of epochs has been achieved and discussed by using multilayer perceptron Neural Network.

Magneto-rheological damper is a nonlinear system. In this case study, system has been identified using Neural Network tool. Optimization between number of neurons in the hidden layer and number of epochs has been achieved and discussed by using multilayer perceptron Neural Network.

Magneto-rheological damper is a nonlinear system. In this case study, system has been identified using Neural Network tool. Optimization between number of neurons in the hidden layer and number of epochs has been achieved and discussed by using multilayer perceptron Neural Network.

Magneto-rheological damper is a nonlinear system. In this case study, system has been identified using Neural Network tool. Optimization between number of neurons in the hidden layer and number of epochs has been achieved and discussed by using multilayer perceptron Neural Network.

The aggregation of heterogeneous land surface cover parameters to calculate effective area-average energy fluxes at microscale using a realistic coupled land surfaceatmosphere model are been investigated in this study. The Biosphere-Atmosphere Transfer Scheme (BATS) was merged with a two dimensional atmospheric boundary layer simulation model (ABLE) to develop a coupled model, BAT-ABLE. Observed land surface and atmospheric forcing data from the FIFE site in Kansas were used to initialize and run the time series of this model. The initial model states were obtained from seasonal runs of a stand-alone version of BATS using observed data. The horizontal model domain was set to 2 km, while the size of each patch was 1 km. The height of the first model grid level above the ground was set to 2 m to match the screen height at the FIFE site. Simple aggregation rules were used to aggregate BATS parameters to obtain area-average energy fluxes. Four cover types (short grass, long grass, mixed crop and irrigated crop) with five different patch combinations were tested using July 7, August 15, and October 11, 1987 observed data. The aggregation scheme worked well in almost all cases for these different days and times (am, noon, pm). However the aggregation scheme failed in the particular cases of artificially wet soil patches set in a landscape of dry soil. However, analysis of fluxes in this situation showed that this failure is not due to a difference in efficiency of atmospheric advection in this case, rather it is due to a real net change in the area-average turbulent fluxes returned to the atmosphere.

The aggregation of heterogeneous land surface cover parameters to calculate effective area-average energy fluxes at microscale using a realistic coupled land surfaceatmosphere model are been investigated in this study. The Biosphere-Atmosphere Transfer Scheme (BATS) was merged with a two dimensional atmospheric boundary layer simulation model (ABLE) to develop a coupled model, BAT-ABLE. Observed land surface and atmospheric forcing data from the FIFE site in Kansas were used to initialize and run the time series of this model. The initial model states were obtained from seasonal runs of a stand-alone version of BATS using observed data. The horizontal model domain was set to 2 km, while the size of each patch was 1 km. The height of the first model grid level above the ground was set to 2 m to match the screen height at the FIFE site. Simple aggregation rules were used to aggregate BATS parameters to obtain area-average energy fluxes. Four cover types (short grass, long grass, mixed crop and irrigated crop) with five different patch combinations were tested using July 7, August 15, and October 11, 1987 observed data. The aggregation scheme worked well in almost all cases for these different days and times (am, noon, pm). However the aggregation scheme failed in the particular cases of artificially wet soil patches set in a landscape of dry soil. However, analysis of fluxes in this situation showed that this failure is not due to a difference in efficiency of atmospheric advection in this case, rather it is due to a real net change in the area-average turbulent fluxes returned to the atmosphere.