To sample from complex, high-dimensional distributions, one may choose algorithms based on the Hybrid Monte Carlo (HMC) method. HMC-based algorithms generate nonlocal moves alleviating diffusive behavior. Here, I build on an already defined HMC framework, Hybrid Monte Carlo on Hilbert spaces [A. Beskos, F.J. Pinski, J.-M. Sanz-Serna and A.M. Stuart, Stoch. Proc. Applic. 121, 2201 - 2230 (2011); doi:10.1016/j.spa.2011.06.003] that provides finite-dimensional approximations of measures π which have density with respect to a Gaussian measure on an infinite-dimensional Hilbert (path) space. In all HMC algorithms, one has some freedom to choose the mass operator. The novel feature of the algorithm described in this article lies in the choice of this operator. This new choice defines a Markov Chain Monte Carlo (MCMC) method which is well defined on the Hilbert space itself. As before, the algorithm described herein uses an enlarged phase space Π having the target π as a marginal, together with a Hamiltonian flow that preserves Π. In the previous method, the phase space π was augmented with Brownian bridges. With the new choice for the mass operator, π is augmented with Ornstein-Uhlenbeck (OU) bridges. The covariance of Brownian bridges grows with its length, which has negative effects on the Metropolis-Hasting acceptance rate. This contrasts with the covariance of OU bridges which is independent of the path length. The ingredients of the new algorithm include the definition of the mass operator, the equations for the Hamiltonian flow, the (approximate) numerical integration of the evolution equations, and finally the Metropolis-Hastings acceptance rule. Taken together, these constitute a robust method for sampling the target distribution in an almost dimension-free manner. The behavior of this novel algorithm is demonstrated by computer experiments for a particle moving in two dimensions, between two free-energy basins separated by an entropic barrier.

In this paper, a complete tool for real-time detection of atypical energy behaviors of airplanes is presented. The methodology extends in real time an existing offline process using Dubins trajectories as a predictor of the remaining distance to the runway threshold. Two major contributions are presented in this paper. First, a real-time measure of the aircraft energy behaviour is defined, indicating whether the aircraft is in good condition to intercept the extended runway centreline from its current position. Secondly, a 2D trajectory suggestion is given, allowing safe management of the approach path according to atypical criteria of historical data. Finally, this document proposes a comprehensive tool for air traffic controllers, which is a major step forward in understanding, becoming aware of and resolving critical situations that could lead to accidents.

We investigated layered titanium nitride (TiN) and aluminum nitride (AlN) for color glasses in Building Integrated Photovoltaic (BIPV) systems. AlN and TiN are among suitable and cost-effective optical materials to be used as thin multilayer films, owing to the significant difference in their refractive index. To fabricate the structure, we used radio frequency magnetron deposition method to achieve the target thickness uniformly. A simple, fast, and cheap fabrication method is achieved by depositing the multilayer films in a single sputtering chamber. It is demonstrated that a multilayer stack that allows light to move from a low refractive index layer to a high refractive index layer or vice-versa can effectively create various distinct color reflections for different film thicknesses and multilayer structures. It is investigated from simulation based on wave optics that, TiN/AlN multilayer offers better color design freedom and cheaper fabrication process as compared to AlN/TiN multilayer films. Blue, green, and yellow color glasses with optical transmittance of more than 80% was achieved by ITO coated glass/TiN/AlN multilayer films. This technology exhibits good potential in commercial BIPV system applications.

Background: Forecasting nonlinear stochastic systems most often is quite difficult, without giving in to temptations to simply simplify models for the sake of permitting simple computations. Objective: Here, two basic algorithms, Adaptive Simulated Annealing (ASA) and path-integral codes PATHINT/PATHTREE (and their quantum generalizations qPATHINT/qPATHTREE) are offered to detail such systems. Method: ASA and PATHINT/PATHTREE have been effective to forecast properties in three disparate disciplines in neuroscience, financial markets, and combat analysis. Applications are described for COVID-19. Results: Results of detailed calculations have led to new results and insights not previously obtained. Conclusion: These 3 applications give strong support to a quite generic application of these tools to stochastic nonlinear systems.

This paper proposed a triangular inequality-based rewiring method for the Rapidly exploring Random Tree (RRT)-Connect robot path-planning algorithm that guarantees the planning time compared to the RRT algorithm, to bring it closer to the optimum. To check the proposed algorithm’s performance, this paper compared the RRT and RRT-Connect algorithms in various environments through simulation. From these experimental results, the proposed algorithm shows both quicker planning time and shorter path length than the RRT algorithm and shorter path length than the RRT-Connect algorithm with a similar number of samples and planning time.

This investigation explores a novel path-planning and optimization strategy for multiple cooperative robotic agents, applied in a fully observable and dynamically changing obstacle field. Current dynamic path planning strategies employ static algorithms operating over incremental time-steps. We propose a cooperative multi-agent (CMA) based algorithm, based on natural flocking of animals, using vector operations. It is preferred over more common graph search algorithms like A* as it can be easily applied for dynamic environments. CMA algorithm executes obstacle avoidance using static potential fields around obstacles, that scale based on relative motion. Optimization strategies including interpolation and Bezier curves are applied to the algorithm. To validate effectiveness, CMA algorithm is compared with A* using static obstacles due to lack of equivalent algorithms for dynamic environments. CMA performed comparably to A* with difference ranging from -0.2% to 1.3%. CMA algorithm is applied experimentally to achieve comparable performance, with an error range of -0.5% to 5.2%. These errors are attributed to the limitations of the Kinect V1 sensor used for obstacle detection. The algorithm was finally implemented in a 3D simulated space, indicating that it is possible to apply with drones. This algorithm shows promise for application in warehouse and inventory automation, especially when the workspace is observable.

With the rapid development of smart phones, tablets and their operative systems, many positioning enabled sensors have been built into these devices. Users can now accurately fix their location according to the function of GPS receivers. For indoor environments, as in the case we are studying, WiFi based positioning is preferred to GPS due to the attenuation or obstruction of signals. This paper deals with the automatic classification of customers in a Sports Shop Center on the basis of their movements around the shop's premises. To achieve this goal, we start by collecting (x,y) coordinates from customers while they visit the store. Consequently, any costumer's path through the shop is formed by a list of coordinates, obtained with a frequency of one measurement per minute. Then, a guess about the full trajectory is constructed and a number of parameters about these trajectories is calculated before performing an Unsupervised Learning Clustering Process. As a result, we can identify several types of customers, and the dynamics of their behavior inside the shop. This information is of great value to the company, to be used both in the long term and also in short periods of time, monitoring the current state of the shop at any moment, identifying different types of situation appearing during restricted periods, or predicting customer flow conditions

Internet of things (IoT) is a network of smart things. This indicates the ability of these physical things to transfer information with other physical things. The characteristics of these networks, such as topology dynamicity and energy constraint, challenges the routing problem in these networks. Previous routing methods could not achieve the required performance in this type of network. Therefore, developers of this network designed and developed specific methods in order to satisfy the requirements of these networks. One of the routing methods is utilization of multi-path protocols which send data to its destination using routes with separate links. One of such protocols is AOMDV routing protocol. In this paper, this method is improved using gray system theory which chooses the best paths used for separate routes to send packets. To do this, AOMDV packet format is altered and some fields are added to it so that energy criteria, link expiration time, and signal to noise ratio can also be considered while selecting the best route. The proposed method named RMPGST-IoT is introduced which chooses the routes with highest rank for concurrent transmission of data, using a specific routine based on the gray system theory. In order to evaluate and report the results, the proposed RMPGST-IoT method is compared to the ERGID and ADRM-IoT approaches with regard to throughput, packet receiving rate, packet loss rate, average remaining energy, and network lifetime. The results demonstrate the superior performance of the proposed RMPGST-IoT compared to the ERGID and ADRM-IoT approaches.

In the rescue operations the full time of action plays important role. It is a sum of planning, travel, and manipulation (in the action place) phases times. The time minimization of first two phases by autonomous vehicle for remote action is considered in the paper. For known a priori map the path planning consists of local optimal decision collected next in the general algorithm of the optimal path. Such approach significantly reduces time of path planning. The robot features and known sparse obstacles reduce the allowable robot speeds. The time of travel is calculated from allowable velocity profile. So, it can be used to estimate the travel performance. Genetic algorithm and random search-based methods for path finding with travel time optimization are exploited and compared in the paper. All the proposed time optimisation solutions of rescue operation are checked during computer simulations and results of simulation are presented.

Background: Forecasting nonlinear stochastic systems most often is quite difficult, without giving in to temptations to simply simplify models for the sake of permitting simple computations. Objective: Here, two basic algorithms, Adaptive Simulated Annealing (ASA) and path-integral codes PATHINT/PATHTREE (and their quantum generalizations qPATHINT/qPATHTREE) are offered to detail such systems. Method: ASA and PATHINT/PATHTREE have been effective to forecast properties in three disparate disciplines in neuroscience, financial markets, and combat analysis. Applications are described for COVID-19. Results: Results of detailed calculations have led to new results and insights not previously obtained. Conclusion: These 3 applications give strong support to a quite generic application of these tools to stochastic nonlinear systems.

Indoor Positioning Systems (IPSs) are designed to provide solutions for location-based services. Wireless local area network (WLAN)-based positioning systems are the most widespread around the globe and are commonly found to have a ready-to-use infrastructure composed mostly of access points (APs). They provide useful information on signal strength to be processed by adequate location algorithms, which are not always capable of achieving the desired localization error only by themselves. In this sense, this paper proposes a new method to improve the accuracy of IPSs by optimizing some of their most relevant infrastructure components. Included are the arrangement of APs over the environment, the number of reference points (RPs), and the number of samples per location estimation test. A simulation environment is also proposed, in which the impact of key influencing factors on system accuracy is analyzed. Finally, a case study is simulated to validate an optimal combination of design parameters and its compliance with the requirements of localization error and the limited number of access points. Our simulation results clearly show that the desired localization accuracy, which is set as a goal, can be achieved while maintaining the factors already mentioned at minimal levels, which decreases both system deployment costs and computational effort.

Background: Since circa 1980, a model of neocortical interactions, Statistical Mechanics of Neocortical Interactions (SMNI) has been successful in calculating many experimental phenomena, including fits to electroencephalographic (EEG) data in attention tasks, using an importance-sampling code Adaptive Simulated Annealing (ASA). The SMNI model is developed in the context of classical path-integrals, which affords intuitive insights as well as direct numerical benefits, e.g., using the effective Action as a a cost/objective function for parameter fits to data. Objective: Previous authors have fit affective EEG data to neural-network models. This project seeks to use models based on physics and biology to fit this same data. Previous work showed improvements in fits to EEG for attention states; this project extends these methods to affective states. Method: Path integrals are used in both classical and quantum contexts. Classical path integrals are used to define a cost/objective function to fit data, and quantum path integrals are used to derive a closed-form analytic expression for Ca-ion waves in the presence of a magnetic vector potential which is generated by highly synchronous neuronal firings which give rise to EEG. ASA is used to fit EEG data. Results: The mathematical-physics and computer parts of the study are successful, in that cost/objective functions used to fit EEG data using these models are consistent with previous work published by other authors. However, since the SMNI model includes these quantum effects, this is another reason to continue examining these issues. The results here are consistent, not better, than previous work using neural-network models, albeit only one parameter was used here, instead of multiple filters and kernels used previously on such data. Conclusion: Although these quantum effects are highly speculative, explicit calculations have shown them to be consistent with experimental data, at least to date. The current supercomputer project extends this model to affective/emotion data. Results from several authors using neural-network approaches at individual electrode sites show some predictive capabilities; the results given here are consistent with these other results. However, since the SMNI model includes these quantum effects, this is another reason to continue examining these issues.

The extrusion-based additive manufacturing is a popular fabrication method, which has attracted the attention of various industries due to its simplicity, cheapness, ability to produce complex geometric shapes, and high production speed. One of the effective parameters in this process is the feed of filament that is presented in the production G-code. The feed of filament is calculated according to the layer height, the extrusion width and the length of printing path. All the required motion paths and filling patterns created by commercial software are a set of straight lines or circular arcs that are placed next to each other at a fixed distance. In special curved paths, the distance of adjacent ones is not equal at different points, and due to the weakness of common commercial software, it is not possible to create curved paths for proper printing. Therefore, making a special computer code that can be used to create various functions of curved paths is investigated in this research, and also the feed of filament parameter is studied in detail. Next, by introducing a correction technique, the feed of filament is changed during the curved path to distribute the polymer material uniformly. Finally, composite samples (which have variable stiffness) consisting of curved fibers are produced with the proposed method, and the high quality of printed samples confirm the suggested code and technique.

A well known result for the interference of two single-mode fields is that the degree of coherence and the degree of indistinguishablity are same when we consider the detection of a single photon. In this article we present the relation between degree of coherence, path indistinguishability and the fringe visibility considering interference of multiple number of single-mode fields while being interested in the detection of a single photon only . We will also mention how Born’s rule of interference for multiple sources is reflected in these results.

Underwater wireless optical communications (UWOC) have attracted considerable attention in recent years as an alternative means for acoustic communication. However, optical path loss of light propagation from attenuation is large due to absorption and scattering in various water conditions. Identification of environmental effects especially tropical storms on underwater optical path loss is the key to the success of using optics for UWOC. Underwater inherent optical properties (IOPs) such as the beam attenuation coefficient for 470 nm light in the western North Pacific Ocean were measured from the U.S. Naval Oceanographic Office sea gliders with being deployed after Super Typhoon Guchol (7-20 June 2012)‘s passage during 25-30 June 2012 and no any typhoon passage during 9 January – 28 February 2014. The observed two sets (with and without super typhoon) of IOPs are taken as input into a recently developed Radiative Transfer Equation solver. The simulated normalized received powers for the two durations show large impact of typhoon passage on UWOC.

We present the detailed calculations of social distancing requirement. A comparative study of the growth pattern and death tolls in different communities indicates that the growth pattern of infected patients and death rate follow the similar distribution with different parametrizations. Every distribution follows an exponential growth pattern curve, like other microbes, then reaches the saturation point an d eventually decay s However, the argument for the exponential function depends on several parameters unbeknownst, as of yet. However, the slope varies different ial ly for various epicenter s and seems to have a relationship with parameters such as accessibi lity to healthcare facilities, pre existing medical conditions socio economic conditions and lifestyle. The mismatch of the growth pattern is also linked with the impact of various other factors and a premature interpretation of limit ed data. Novel behavi or of the virus brought many surprises, opened up new venues for medical research, and the need for the more detailed study of pathogens in the light of the interaction of RNA and DNA The adaptability to diverse ecological condition s and the relevant modification in the structure is also worth investigation The genetic modification can be studied using quantum mechanical probabilistic approach.

In Ultra-wideband (UWB)-based wireless ranging or distance measurement, differentiation between line-of-sight~(LOS), non-line-of-sight~(NLOS), and multi-path (MP) conditions are important for precise indoor localization. This is because the accuracy of the reported measured distance in UWB ranging systems is directly affected by the measurement conditions (LOS, NLOS or MP). However, the major contributions in literature only address the binary classification between LOS and NLOS in UWB ranging systems. The MP condition is usually ignored. In fact, the MP condition also has a significant impact on the ranging errors of the UWB compared to the direct LOS measurement results. Though, the magnitudes of the error contained in MP conditions are generally lower than completely blocked NLOS scenarios. This paper addresses machine learning techniques for identification of the mentioned three classes (LOS, NLOS, and MP) in the UWB indoor localization system using an experimental data-set. The data-set was collected in different conditions at different scenarios in indoor environments. Using the collected real measurement data, we compare three machine learning (ML) classifiers, i.e., support vector machine (SVM), random forest (RF) based on an ensemble learning method, and multilayer perceptron (MLP) based on a deep artificial neural network, in terms of their performance. The results show that applying ML methods in UWB ranging systems are effective in identification of the above-mentioned three classes. In specific, the overall accuracy reaches up to 91.9% in the best-case scenario and 72.9% in the worst-case scenario. Regarding the F1-score, it is 0.92 in the best-case and 0.69 in the worst-case scenario. For reproducible results and further exploration, we (will) provide the publicly accessible experimental research data discussed in this paper at PUB - Publications at Bielefeld University. The evaluations of the three classifiers are conducted using the open-source python machine learning library scikit-learn.

The paper presents models of path and control planning for parking, docking, and movement of autonomous vehicles at low speeds considering space constraints. Given the low speed of motion, and in order to test and approve the proposed algorithms, vehicle kinematic models are used. Recent works on the development of parking algorithms for autonomous vehicles are reviewed. Bicycle kinematic models for vehicle motion are considered for three basic types of vehicles: passenger car, long wheelbase truck, and articulated vehicles with and without steered semitrailer axes. Mathematical descriptions of systems of differential equations in matrix form and expressions for determining the linearization elements of nonlinear motion equations that increase the speed of finding the optimal solution are presented. Options are proposed for describing the interaction of vehicle overall dimensions with the space boundaries, within which a maneuver should be performed. An original algorithm that considers numerous constraints is developed for determining vehicle permissible positions within the closed boundaries of the parking area, which are directly used in the iterative process of searching for the optimal plan solution using nonlinear model predictive control (NMPC). The process of using NMPC to find the best trajectories and control laws while moving in a semi-limited space of constant curvature (turnabouts, roundabouts) are described. Simulation tests were used to validate the proposed models for both constrained and unconstrained conditions and the output (state-space) and control parameters' dependencies are shown. The proposed models represent an initial effort to model the movement of autonomous vehicles for parking and has the potential for other highway applications.

Internet of things (IoT) is a network of smart things. This indicates the ability of these physical things to transfer information with other physical things. IoT has introduced various services and daily human life depends on its reliable and accessible operation. The characteristics of these networks, such as topology dynamicity and energy constraint, challenges the routing problem in these networks. Previous routing methods could not achieve the required performance in this type of network. Therefore, developers of this network designed and developed specific methods in order to satisfy the requirements of these networks. One of the routing methods is utilization of multi-path protocols which send data to its destination using routs with separate links. One of such protocols is AOMDV routing protocol. AOMDV protocol is a multi-path protocol which uses multiple different paths for sending information in order to maintain the network traffic balance, manage and control node energy, decrease latency, etc. In this paper, this method is improved using gray system theory which chooses the best paths used for separate routes to send packets. To do this, AOMDV packet format is altered and some fields are added to it so that energy criteria, link expiration time, and signal to noise ratio can also be considered while selecting the best route. The proposed method named GSTMPR-IoT is introduced which chooses the routs with highest rank for concurrent transmission of data, using a specific routine based on the gray system theory. In order to evaluate and report the results, the proposed GSTMPR-IoT method is compared to the EECRP and AOMDV approaches with regard to throughput, packet delivery rate, end to end delay, average residual energy, and network lifetime. The results demonstrate the superior performance of the proposed GSTMPR-IoT compared to the EECRP and AOMDV approaches.

Gas Path Analysis and matching turbine engine models to experimental data are inverse problems of mathematical modelling which are characterized by parametric uncertainty. This results from the fact that the number of measured parameters is significantly lower than the number of components’ performance parameters needed to describe the real engine. In these conditions, even small measurement errors can result in a high variation of results, and obtained efficiency, loss factors etc. can appear out of the physical range. The current methods of engine model identification have developed considerably to provide stable, precise and physically adequate solutions. Presented in this work is an estimation method of engine components’ parameters based on multi-criteria identification which provides stable estimations of parameters and their confidence intervals with the known measurement errors. A priori information about the engine, its parameters and performance is used directly in the regularised identification procedure. The mathematical basis for this approach is the fuzzy sets theory. Forming objective functions and scalar convolutions synthesis of these functions is used to estimate gas-path components’ parameters. A comparison of the proposed approach with traditional methods showed that its main advantage is high stability of estimation in the parametric uncertainty conditions. Regularization reduces scattering, excludes incorrect solutions which do not correspond to a priori assumptions, and also helps to implement the Gas Path Analysis at the limited number of measured parameters. The method can be used for matching thermodynamic models to experimental data, Gas Path Analysis and also adapting dynamic models for the needs of the engine control system.

Aiming at the current optimization problem of electric vehicle charging path planning, a charging path optimization strategy for electric vehicles under the "Traffic-Price-Distribution" mode is proposed. This strategy builds an electric vehicle charging and navigation system based on road traffic network model, real-time electricity price model and distribution network model. Based on Dijkstra shortest path algorithm and Monte Carlo time-space prediction method, the goal is to minimize the charging cost of electric vehicles. Optimal charging path. The simulation results of MATLAB and MATPOWER show that the electric vehicle charging path optimization strategy can better solve the local traffic congestion problem and improve the safety and stability of the distribution network on the basic of fully considering the convenience of electric vehicle charging.

This study presents a path planning method for a mobile robot to be effectively operated through a multi-objective decision-making problem. Specifically, the proposed Fuzzy analytic hierarchy process (FAHP) determines an optimal position as a sub-goal within the multi-objective boundary. The key features of the proposed FAHP are evaluating the candidates according to the fuzzified relative importance among objectives to select an optimal solution. In order to incorporate FAHP into path planning, an AHP framework is defined, which includes the highest level (goal), middle level (objectives), and the lowest level (alternatives). The distance to the target, robot’s rotation, and safety against collision between obstacles are considered as objective functions. Comparative results obtained from the artificial potential field and AHP/FAHP simulations show that FAHP is much preferable for the mobile robot’s path planning than typical AHP.

Laser metal deposition with wire (LMD-w) is a developing additive manufacturing (AM) technology that has a high deposition material rate and efficiency, and is suitable for fabrication of large aerospace components. However, control of material properties, geometry, and residual stresses is needed before LMD-w technology can be widely adopted for the construction of critical structural components. In this study, we investigated the effect of interlayer cooling time, clamp constraints, and tool path strategy on part distortion and residual stresses in large-scale laser additive manufactured Ti-6Al-4V components using finite element method (FEM). The simulations were validated with the temperature and the distortion measurements obtained from a real LMD-w process. We found that a shorter interlayer cooling time, full clamping constraints on the build plates, and a bidirectional tool path with 180⁰ rotation minimized part distortion and residual stresses and resulted in symmetric stress distribution.

We generate numerically on a lattice an ensemble of stationary metrics, with spherical symmetry, which have Einstein action S_E « ħ. This is obtained through a Metropolis algorithm with weight exp(-β²S_E²) and β » ħ^-1. The squared action in the exponential allows to circumvene the problem of the non-positivity of S_E. The discretized metrics obtained exhibit a spontaneous polarization in regions of positive and negative scalar curvature. We compare this ensemble with a class of continuous metrics previously found, which satisfy the condition S_E=0 exactly, or in certain cases even the stronger condition R(x)=0 for any x. All these gravitational field configurations are of considerable interest in quantum gravity, because they represent possible vacuum fluctuations and are markedly different from Wheeler's ''spacetime foam''.

We first review the literature on machine behavior which may contain the interaction between machines, and then discuss the methodology and the related case involving collaboration between robots to instantiate the concept with AI empowerment. The navigation case aims to a security application with autonomous control and roadside agent embedded in a mobile edge computing structure. In the studied navigation based on the artificial potential field method, robots need to use position information to calculate the moving direction frequently. In the case of high motion speed as well as GPS positioning error, the path trajectory may show a sharp change of direction. In order to mitigate the trajectory oscillation, this paper proposes a path planning design where training process and motion direction prediction are integrated by using artificial neural network. The auxiliary navigation agent near multiple obstacles can first extract the past movement information of the robot and then determines whether there is a serious path jitter event. Computer simulation analysis shows that the combination of autonomous control and cooperative behavior can effectively reduce the path jitter so as to achieve a fast and safe path planning.

Methods of path integrals are used to develop multi-factor probabilities of bid-ask variables for use in high-frequency trading (HFT). Adaptive Simulated Annealing (ASA) is used to fit the nonlinear forms so developed to a day of BitMEX tick data. Maxima algebraic code is used to develop the path integral codes into C codes, and sampling code is used for the fitting process. After these fits, the resultant C code is very fast and useful for forecasting upcoming ask, bid, midprice, etc., when narrow and wide windows of incoming data are used. A bonus is the availability of canonical momenta indicators (CMI) useful to forecast direction and strengths of these variables.

A Wireless Body Area Sensor Network (WBASN) is combination of numerous sense nodes, positioned onto/close or inside a person body. Wireless Body Area Sensor Networks (WBASN) is a developing automation trend that exploits wireless sensor nodes to put instantaneous wearable well-being of ill person to improve individual’s existence. The sensor nodes might be used outwardly to observe abundant health parameters (like heart activity, blood pressure and cholesterol) of an ill person at a vital site within hospital. Hence the goal of WBASN is much crucial, enhancing the lifetime of nodes is compulsory to sustain many issues such as utility and efficiency. It is essential to evaluate time that when the first node will die it we want to refresh or change the battery reason is that loss of crucial information is not tolerable. The lifetime is termed as the time interval when a first node dies out due to battery exhaustion. In our proposed protocol life time of a network is the main concern as well other protocol related issues such as throughput, path loss, and residual energy. Bio-sensors are used for deployment on human body. Poisson distribution and equilibrium model techniques have been used for attaining the required results. Multi-hop network topology and random network node deployment used to achieve minimum energy consumption and longer network lifetime.

Many causal factors to marine traffic accidents (MTA) influence each other and have associated effects. It is necessary to quantify the correlation path mode of these factors to improve accident prevention measures and their effects. In the application of human factors to the accident mechanisms, the complex structural chains on causes to MTA systems were analyzed combining the Human Failure Analysis and Classification System (HFACS) with theoretical Structural Equation Modeling (SEM). First, the accident causation model was established as a human error analysis classification in sight of MTA, and the constituent elements of the causes of accident was conducted. Second, a hypothetical model of Human factors classification was proposed applying the practice of the structural model. Third, with the data resource from ship accident cases, this hypothetical model was discussed and simulated, and as a result the relationship path dependency mode between the latent independent variable of the accident was quantitatively analyzed based on the observed dependent variable of human behaviors. Application examples show that relationships in HFACS are verified and in line with the path developing mode, and resource management factors have a pronounced influence and a strong relevance to the causal chain of the accidents. Appropriate algorithms for the theoretical model can be used to numerically understand the safety performance of marine traffic systems under different parameters through mathematical analysis. Hierarchical assumptions in the HFACS model are quantitatively verified.

Robotic path planning is a field of research which is gaining traction given the broad domains of interest to which path planning is an important systemic requirement; the aim being to optimise the efficacy of robotic movements in a defined operational environment. For example, robots have been employed in many domains including: cleaning robots (such as vacuum cleaners), automated paint spraying robots, window cleaning robots, forest monitoring robots, agricultural robots (often driven using satellite and geostationary positional satellite data). Additionally, mobile robotic systems have been utilised in disaster areas and locations hazardous to humans (such as war zones in mine clearance). The coverage path planning problem describes an approach which is designed to determine the path that traverses all points in a defined operational environment while avoiding static and dynamic (moving) obstacles. In this paper we present our proposed Smooth-STC model, the aim of the model being to identify an optimal path, avoid all obstacles, prevent (or at least minimise) backtracking, and maximise the coverage in any defined operational environment. The experimental results in a simulation show that, in uncertain environments, our proposed smooth STC method achieves an almost absolute coverage rate and demonstrates improvement when measured against alternative conventional algorithms.

The topic of technology development and its disruptive effects has been the subject of much debate over the last 20 years with numerous theories at both macro and micro scale offering potential models of technology progression and disruption. This paper focuses on how the potential theories of technology progression can be integrated and considers whether suitable indicators of this progression and any subsequent disruptive effects (particularly considering these geographically) might be derived, based on the use of big data analytic techniques. Given the magnitude of the economic, social and political implications of many disruptive technologies, the ability to quantify disruptive change at the earliest possible stage could deliver major returns by reducing uncertainty, assisting public policy intervention and managing the technology transition through disruption into deployment. However, determining when this stage has been reached is problematic because small random effects in the timing, direction of development, the availability of essential supportive technologies or “platform” technologies, market response or government policy can all result in failure of a technology, its form of adoption or optimality of implementation. This paper reviews some of the key models of technology evolution and their disruptive effect including, in particular, the geographical spread of disruption. It suggests a methodology for utilising the recent explosion of open and web-discoverable data to determine a methodology to achieve this earlier determination and considers the potential exploitation of big data modelling and predictive analytical techniques to achieve this goal.

In quantum gravity perturbation theory in Newton's constant $G$ is known to be badly divergent, and as a result not very useful. Nevertheless, some of the most interesting phenomena in physics are often associated with non-analytic behavior in the coupling constant and the existence of nontrivial quantum condensates. It is therefore possible that pathologies encountered in the case of gravity are more likely the result of inadequate analytical treatment, and not necessarily a reflection of some intrinsic insurmountable problem. The nonperturbative treatment of quantum gravity via the Regge-Wheeler lattice path integral formulation reveals the existence of a new phase involving a nontrivial gravitational vacuum condensate, and a new set of scaling exponents characterizing both the running of $G$ and the long-distance behavior of invariant correlation functions. The appearance of such a gravitational condensate is viewed as analogous to the (equally nonperturbative) gluon and chiral condensates known to describe the physical vacuum of QCD. The resulting quantum theory of gravity is highly constrained, and its physical predictions are found to depend only on one adjustable parameter, a genuinely nonperturbative scale $\xi$ in many ways analogous to the scaling violation parameter $\Lambda_{\bar MS} $ of QCD. Recent results point to significant deviations from classical gravity on distance scales approaching the effective infrared cutoff set by the observed cosmological constant. Such subtle quantum effects are expected to be initially small on current cosmological scales, but could become detectable in future high precision satellite experiments.

Difficult-to-cut materials are being increasingly used in many industries because of their superior properties, including high corrosion resistance, heat resistance and specific strength. However, these same properties make the materials difficult to machine using conventional machining techniques. Laser-assisted milling (LAM) is one of the effective methods for machining difficult-to-cut materials. In laser-assisted milling machining occur after the workpiece is locally preheated using a laser heat source. Laser assisted machining has been studied by many researchers on flat workpiece or micro end-milling. However, there is no research on the curved shape using laser assisted milling. This study investigated the use of laser assisted milling to machine a three-dimensional curved shape workpiece based on NURBS. A machining experiment was performed on Inconel 718 using different tool paths (ramping, contouring) under various machining conditions. Finite elements analysis was conducted to determine the depth of cut. Cutting force, specific cutting energy and surface roughness characteristics were measured, analyzed and compared for conventional and LAM machining. LAM significantly improved these machining characteristics, compared to conventional machining. There results can be applied to the laser-assisted machining of various three-dimensional shapes.

This research work uses a simplified approach to combine location information from beacons propagation signal interaction with a mobile device with local building information to give real-time location and guidance to a user inside a building. This is an interactive process with visualisation information that can help user’s orientation inside unknown buildings and the data stored from different users can provide useful information about users movements inside a public building. Beacons installed on the building at specific pre-defined position emit signals that give a geographic position with an associated imprecision, related with Bluetooth’s range. This uncertainty is handled by building layout and users’ movement in a developed system that maps users’ position, gives guidance and store user movements. This system is based on an App (Find Me!) for Android OS (Operating System) which captures the Bluetooth Low Energy (BLE) signal coming from the beacon(s) and shows, through a map, the location of the user ‘s smartphone and guide him to the desired destination. Also, the beacons can deliver relevant context information. The application was tested by a panel of new and habitual campus users against traditional wayfinding alternatives yielding navigation times about 30% smaller, respectively.

The recent provision of energy-harvesting capabilities to wireless sensor networks has entailed the redefinition of design objectives. Specifically, the traditional goal of maximizing network lifetime has been replaced by optimizing network performance, namely delay and throughput. The present paper contributes to this reformulation by considering the routing problem for the class of time-driven energy-harvesting WSN (EH-WSN) under regular or quasi-periodic energy sources. In particular, this paper shows that the minimum hop count (MHC) criterion maximizes the average duty cycle that can be sustained by nodes in this type of scenarios. This is a primary objective in EH-WSN, since large duty cycles lead to enhanced performance. Based on a previous result, a general expression is first obtained which gives mathematical form to the relationship between duty cycle and traffic load for any node in a time-driven EH-WSN fed by a regular energy source. This expression reveals that the duty cycle achievable by a node decreases as its traffic load increases. Then, it is shown that MHC minimizes the average traffic load over the network, and thus it maximizes the average duty cycle of nodes. This result is numerically validated via simulation by comparison with other well-known routing strategies. Accordingly, this paper suggests assigning top priority to the MHC criterion in the development of routing protocols for time-driven EH-WSN under regular energy sources.

After developed the formulation of a "general relativity" in C⁴ [2], we proceed with the formulation of a Hamilton-Jacobi equation in C⁴. We argue that in this consideration, the usual problems of the ADM formalism, do not exist, due to the complex time as it exists in our consideration. Specically, we can derive a suitable dispersion relation in order to work with and nd a generalised super Hamiltonian

Robotic decision-support systems must facilitate a robots interactions with their environment, this demands adaptability. Adaptability relates to awareness of the environment and `self-awareness', human behaviour exemplifies the concept of awareness to arrive at an optimal choice of action or decision based on reasoning and inference with learned preferences. A similar conceptual approach is required to implement awareness in autonomous robotic systems which must adapt to the current dynamic environment (the context of use). By incorporating `self-awareness' with knowledge of a Robot's preferences (in decision making) the decision maker interface should adapt to the current context of use. This paper proposes a novel approach to enable an autonomous robotics which implements path planning combining adaptation with knowledge reasoning techniques and hedge algebra to enable an autonomous robot to realise optimal coverage path planning under dynamic uncertainty. The results for a cleaning robot show that using our proposed approach demonstrated the capability to avoid both static and dynamic obstacles while achieving optimal path planning with increased efficiency. The proposed approach achieves the multiple decision-making objectives (path planning) with a high-coverage and low repetition rates. Compared to other current approaches, the proposed approach has demonstrated improved performance over the conventional robot control algorithms.

The generalization of Farey graphs and extended Farey graphs are all originated from Farey graph and scale-free and small-world simultaneously. We propose a labeling of the vertices for it that allows determining all the shortest paths routing between any two vertices only based on their labels. The maximum number of shortest paths between any two vertices is huge as the product of two Fibonacci numbers, however, the label-based routing algorithm runs in linear time O(n). The existence of an efficient routing protocol for Farey-type models should help the understanding of several physical dynamic processes on it.

In this paper, we investigate the use of fat tissue as a communication channel between in-body, implanted devices at R-band frequencies (1.7–2.6 GHz). The proposed fat channel is based on an anatomical model of the human body. We propose a novel probe that is optimized to efficiently radiate the R-band frequencies into the fat tissue. We use our probe to evaluate the path loss of the fat channel by studying the channel transmission coefficient over the R-band frequencies. We conduct extensive simulation studies and validate our results by experimentation on phantom and ex-vivo porcine tissue, with good agreement between simulations and experiments. We demonstrate a performance comparison between the fat channel and similar waveguide structures. Our characterization of the fat channel reveals propagation path loss of 1.4 dB and 3.8 dB per 20 mm for phantom and ex-vivo porcine tissue, respectively. These results demonstrate that fat tissue can be used as a communication channel for high data rate intra-body networks.

In the field of autonomous vehicles, lane detection and control plays an important role. In autonomous driving the vehicle has to follow the path to avoid the collision. A deep learning technique is used to detect the curved path in autonomous vehicles. In this paper a customized lane detection algorithm was implemented to detect the curvature of the lane. A ground truth labelling tool box for deep learning is used to detect the curved path in autonomous vehicle. By mapping point to point in each frame 80-90% computing efficiency and accuracy is achieved in detecting path.

Whenever vegetated areas are monitored over time, phenological changes in land cover should be decoupled from changes in acquisition conditions, like atmospheric components, sun and satellite heights, and imaging instrument. This especially holds when the multispectral (MS) bands are sharpened for spatial resolution enhancement by means of a panchromatic (Pan) image of higher resolution, a process referred to as pansharpening. In this paper, we provide evidence that pansharpening of visible/near-infrared (VNIR) bands takes advantage from a correction of the path radiance term introduced by the atmosphere, during the fusion process. This holds whenever the fusion mechanism emulates the radiative transfer model ruling the acquisition of the Earth’s surface from space, that is, for methods exploiting a multiplicative, or contrast-based, injection model of spatial details extracted from the panchromatic (Pan) image into the interpolated multispectral (MS) bands. The path radiance should be estimated and subtracted from each band before the product by Pan is accomplished. Both empirical and model-based estimation techniques of MS path radiances are compared within the framework of optimized algorithms. Simulations carried out on two GeoEye-1 observations of the same agricultural landscape at different dates highlight that the de-hazing of MS before fusion is beneficial for an accurate detection of seasonal changes in the scene, as measured by the normalized differential vegetation index (NDVI).

A general framework for inference in dynamical systems is described, based on the language of Bayesian probability theory and making use of the maximum entropy principle. Taking as fundamental the concept of a path, the continuity equation and Cauchy's equation for fluid dynamics arise naturally, while the specific information about the system can be included using the Maximum Caliber (or maximum path entropy) principle.

Landing is one of the most dangerous tasks in all the operations on the aircraft carrier, and the landing safety is very important to the pilot and the flight deck operation. The problem of landing path selection is studied in this paper as there several candidates corresponding to different situations. A fuzzy path selection strategy is proposed to solve the problem considering the fuzziness of environmental information and human judgment, and the goal is to provide the pilot with more reasonable decision. The strategy is based on Fuzzy Multi-attribute Group Decision Making (FMAGDM), which has been widely used in industry. Firstly, the background of the path selection problem is given. Then the essential elements of the problem are abstracted to build the conceptual model. A group decision-making method is applied to denote the preference of each decision maker for each alternative route, and the optimal landing path under the current environment is determined taking into account the knowledge and the weight of both decision makers. Experimental studies under different setups, i.e., different environments, are carried out. The results demonstrate that the proposed path selection strategy is validated in different environments, and the optimal landing paths corresponding to different environments can be determined.

A controlled ammonia (NH₃) release experiment was performed at a grassland site to quantify the effect of dry deposition, at the field scale between the source and the receptors (NH₃ measurement locations), on the estimates of emission rates by means of inverse dispersion modelling. NH₃ was released for 3 hours at a constant rate of Q = 6.29 mg s⁻¹ from a grid of 36 orifices spread over an area of 250 m². The increase in line-integrated NH₃ concentration was measured with open-path optical miniDOAS devices at different locations downwind of the artificial source. Using a backward Lagrangian stochastic (bLS) dispersion model (bLSmodelR), the fraction of the modelled release rate to the emitted NH₃ (Q_bLS/Q) was calculated from the measurements of the individual instruments. Q_bLS/Q was found to be systematically lower than 1, on average between 0.69 and 0.91, depending on the location of the receptor. We hypothesized that NH₃ dry deposition to grass and soil surfaces was the main factor responsible for the observed depletion of NH₃ between source and receptor. A dry deposition algorithm based on a deposition velocity approach was included in the bLS modelling. Model deposition velocities were evaluated from a ‘big‑leaf’ canopy resistance analogy. Canopy resistances (generally termed Rc) that provided Q_bLS/Q = 1 ranged from 75 to 290 s m⁻¹, showing that surface removal of NH₃ by dry deposition can plausibly explain the original underestimation of Q_bLS/Q. The inclusion of a dry deposition process in dispersion modelling is crucial for emission estimates, which are based on concentration measurements of depositing tracers downwind of homogeneous area sources or heterogeneously distributed hot spots, such as e.g. urine patches on pastures in the case of NH₃.

The RPL protocol is a routing protocol for low power and lossy networks. In such a network, energy is a very scarce resource; so many studies are focused on minimizing global energy consumption. End-to-end latency is another important performance indicator of the network, but existing research tends to focus more on energy consumption and ignore the end-to-end delay of data transmission. In this paper, we propose a kind of energy equalization routing protocol to maximize the surviving time of the restricted nodes so that the energy consumed by each node is close to each other. At the same time, a multi-path forwarding route is proposed based on the cache utilization. The data is sent to the sink node through different parent nodes at a certain probability, not only by selecting the preferred parent node, thus avoiding buffer overflow and reducing end-to-end delay. Finally, the two algorithms are combined to accommodate different application scenarios. The experimental results show that the proposed three improved schemes improve the reliability of the routing, extend the lifetime of the network, reduce the end-to-end delay, and reduce the number of DAG reconfiguration.

To eliminate the influence of the inconsistency on the cycle life and the available capacity of the battery pack, and improve the balancing speed, a novel inductor-based layered bidirectional equalizer (IBLBE) is proposed. The equalizer is composed of the bottom balancing circuits and the upper balancing circuits, and the two layer circuits both consist of a plurality of balancing sub-circuits, which allow the dynamic adjustment of equalization path and equalization threshold. The battery string is modularized by layered balancing circuits to realize fast active equalization, especially for long battery strings. By controlling the bottom balancing circuits, the individual cells can be balanced in each module. At the same time, the equalization between battery modules can be realized by controlling the upper balancing circuits. Simulation and experimental results demonstrate that the proposed equalizer can achieve fast active equalization for a long battery string, and has the characteristics of multi balancing path, large balancing current and high accuracy. The advantages of the proposed equalizer are further verified by a comparison with existing active equalizer.