With the depletion of shallow surface resources, the future mining work will develop towards the deep surface, and the objective conditions such as the mining environment will be more complex and dangerous than now, and the requirements for personnel and equipment will be higher and higher. The efficient exploitation of deep space cannot be separated from such mobile and flexible production and transportation equipment as scraper. In the new era, intelligence is the development trend of the LHD, and path tracking control technology is the key to the intelligent LHD, and it is also an urgent problem to be solved for its unmanned driving. This paper describes the realization of the automatic operation function of articulated LHD from two aspects of mathematical model and trajectory tracking control method, and focuses on the research of the path tracking control scheme in the field of unmanned driving, that is, LQR controller. On this basis, the parameters of the LQR controller are optimized by combining different intelligent cluster algorithms to find the optimal solution of the LQR controller.

As the number of ships for marine transportation increases with the advancement of global trade, encountering multiple ships in marine traffic becomes common. This situation raises the risk of collision of the ships; hence this paper proposes a novel Fuzzy-logic based intelligent conflict detection and resolution algorithm, where the collision courses and possible avoiding actions are analyzed by considering ship motion dynamics and the input and output fuzzy membership functions are derived. As a conflict detection module, the Collision Risk (CR) is measured for each ship by using a scaled nondimensional Distance to the Closest Point of Approach (DCPA) and Time to the Closest Point of Approach (TCPA) as inputs. Afterwards, the decisions for collision avoidance are made based on the calculated CR, encountering angle and relative angle of each ship measured from others. In this regard, the rules for the Fuzzy interface system are defined in accordance with the COLREGs, and the whole system is implemented on the MATLAB Simulink platform. In addition, to deal with the multiple ship encounters, the paper proposes a unique maximum-course and minimum-speed change approach for decision making, which has been found to be efficient to solve Imazu problems, and other complicated multiple-ship encounters.

This paper presents an analysis of linear viscous stress Favre-Averaged turbulence models for computational fluid dynamics (CFD) of fully turbulent round jets with a long straight tube geometry in the near field. Although similar work has been performed in the past with very relevant solutions, considerations were not given for the issues and limitations involved with coupling between an Eulerian and Lagrangian phase, such as in fully two-way coupled CFD-DEM. These issues include limitations on solution domain, mesh cell size, wall modelling, and momentum coupling between the two phases in relation to the particles size. Therefore, within these considerations, solutions are provided to the Navier-Stokes equations with various turbulence models using a three-dimensional wedge long straight tube geometry for fully developed turbulence flow. Simulations are performed with a Reynolds number of 15000 and 50000 using two different tube diameters. It is found that a modified k−ε turbulence model achieved the most agreeable results for both the velocity and turbulent flow fields between these two flow regimes, while a modified k−ω SST/BSL also provided suitable results.

The upgrade of energy infrastructure to those of smart grids, necessarily goes through the provision of integrated technological solutions that ensure the interoperability of their business capabilities and reduce the risk of devaluation of the systems used. The heterogeneity of the infrastructures and the dynamics of their operating environment, requires the continuous reduction of the complexity, the faster execution of the processes and the easy addition of innovative counterparts. Also, the integrated management of the overall ecosystem demands the provision of end-to-end interconnection, quality assurance, the definition of strict security policies, collaborative integration and correlation of events. In this respect, every design detail can be critical to the success or failure of a costly and ambitious project, such as that of smart energy networks. This work presents communication operating standards specific to the smart electricity networks applications, which should be taken into account in the process of planning and implementation of new infrastructures.

This paper describes some of the challenges that need to be addressed in order to develop collaborative spectrum sharing systems. The importance of these challenges stems from the assumption that rules for spectrum sharing can change after the deployment of radio networks and the whole system must be able to adapt to them. To address such a requirement, we used a policy-based approach in which transmissions are controlled by a policy interpreter system, and the policies can be modified during system operation. Our primary goal was to develop a prototype of such a system. In this paper, we outline the implementation of policy interpretation, automatic generation of transmission opportunities in case a request for transmission is denied by the policy reasoner, and the generation of rendezvous channels for the synchronization of otherwise asynchronously running software defined radios.

Microfluidic lab-on-chip devices are widely being developed for chemical and biological studies. One of the most commonly used types of these chips is perfusion microwells for culturing multicellular spheroids. The main challenge in such systems is the formation of substantial necrotic and hypoxic zones within the cultured spheroids. Herein, we propose a novel acoustofluidic integrated platform to tackle this bottleneck problem. We show that such an approach enhances cell viability and shrinks necrotic and hypoxic zones in these spheroid-on-a-chip platforms without the need to increase the flow rate, leading to a significant reduction in costly reagents' consumption. Proof-of-concept, designing procedures, and finite element numerical simulation are discussed in details. Also, the effects of acoustic and hydrodynamic parameters on the cultured cells are investigated. The results show that by increasing acoustic boundary displacement amplitude (d0), the spheroid’s proliferating zone enlarges greatly. Moreover, it is shown that by implementing d0=0.5 nm, the required flow rate to maintain the necrotic zone below 13% will be decreased 12 times compared to non-acoustic chips.

Copper is a heavy metal used in many industries and known for its negative impacts on the environment and human’s health. A novel structured metal organic framework (MOF) was used for copper adsorption for this work. SEM, EDAX, XRD and FTIR were done to confirm the structure of MOF prepared. MOF of 0.05 gm was used to examine its ability in Cu+ 2 removals with different initial concentrations of Cu+2 and pH values (5, 7 and 9). The prepared MOF was able to achieve Cu removal with 94.6%, 93%, 91.5%, and 92.5 % for the initial concentrations of 5, 10, 15, and 20 ppm respectively. It also performed very well for pH 5 and 7 with average removal ranging from 93.9%-95% for pH 5 and 7 for the initial concentrations of 5, 10, and 15 respectively which indicate that the prepared MOF is of high ability in Cu+ 2 removal.

Abstract: To describe the feeder topology and information exchange between intelligent electronic devices (IEDs) for Internet of Things (IoT), it is necessary to extend IEC 61850 and add a logical model. The feeder model described in IEC 61970 is then extended in IEC 61850 to serve as a container for the equipment along the distribution line. For the physical connections in the electrical equipment, both terminal and connectivity node logical models are added. Taking logical node XCBR as the example, its attributes for the terminals are added to the electrical equipment. The logical node LCNN is then further added in order to describe the connectivity node. By next adding the logical node LTPN to describe the topological node, and FTPA to describe the results of the topology analysis, we can efficiently describe the topology analysis results. Based on these new logical nodes, the exchange of topology information between intelligent terminals no longer relies on configuration files, but only requires logical nodes. Through the above expansion, the topology description and information exchange of medium and low voltage feeders are realized.

The object of this paper is the bending analysis of isotropic rectangular Kirchhoff plates subjected to a thermal gradient (TG) using the Fourier transform method. The bottom and top surfaces of the plate are assumed to have different changes in temperature, whereas the change in temperature of the mid-surface is zero. According to classical plate theory, the governing equation of the plate contains second derivatives of the TG; these derivatives are zero by constant value of the TG, which leads to the absence of the TG in the governing equation. This paper presented an approach by which Fourier sine transform was utilized to describe the TG, while the double trigonometric series of Navier and the simple trigonometric series of Lévy were utilized to describe the deflection. Thus, the TG appeared in the governing equation, which simplified the analysis. Rectangular plates simply supported along all edges were analyzed, bending moments, twisting moments, and deflections being determined. In addition, rectangular plates simply supported along two opposite edges were analyzed, the other edges having various support conditions (free, simply supported, and fixed).

In this paper, the Group Method of Data Handling (GMDH) type of neural networks is used for the inductance calculation of variable inductors. The relation between the inductance of the inductor in the linear and nonlinear regions is investigated, and parameters such as the voltage across the inductor, bias current, and ac current are taken into account. The experimental setup is used for generating the data needed for training the neural network. Over 800 experiments were conducted and were used for training and validation of the neural network results. The results are compared with the reluctance equivalent circuit method, and they show a much better accuracy. The proposed method can be used for the calculation of various magnetic components, and it is not limited to variable inductors.

This paper concerns a new methodology for accuracy assessment of global positioning system verified experimentally with LiDAR (Light Detection and Ranging) data alignment at continent scale for autonomous driving safety analysis. Accuracy of GPS (Global Positioning System) positioning of an autonomous driving vehicle within a lane on the road is one of the key safety considerations. Safety is addressed as a geometry of the problem, where the aim is to maintain knowledge that the vehicle (its bounding box) is within its lane. Accuracy of GPS positioning is checked by comparing it with mobile mapping tracks in the recorded high definition source. The aim of the comparison is to see if the GPS positioning remains accurate up to the dimensions of the lane where the vehicle is driving. For this reason, a new methodology is proposed. Methodology is composed of six elements: 1) Mobile mapping system minimal setup, 2) Global positioning data processing, 3) LiDAR data processing, 4) Alignment algorithm, 5) Accuracy assessment confirmation and 6) Autonomous driving safety analysis. The research challenge is to assess positioning accuracy of moving cars taking into account the constraints of the coverage of limited access highways in the United States of America. The available coverage limits the possibility of repeatable measurements and introduces an important challenge being the lack the ground truth data. State-of-the-art methods are not applicable for this particular application, therefore a novel approach is proposed. The method is to align all the available LiDAR car trajectories to confirm the GNSS+INS (Global Navigation Satellite System + Inertial Navigation System) accuracy. For this reason, the use of LiDAR metric measurements for data alignment implemented using SLAM (Simultaneous Localization and Mapping) was investigated, assuring no systematic drift by applying GNSS+INS constraints. SLAM implementation used state-of-the-art observation equations and the Weighted Non-Linear Least Square optimization technique that enables integration of the required constraints. The methodology was verified experimentally using arbitrarily chosen measurement instruments (NovAtel GNSS+INS, LiDAR Velodyne HDL32) mounted onto mobile mapping systems. The accuracy was assessed and confirmed by the alignment of 32785 trajectories with total length of 1,159,956.9~km and of total $186.4*10^{9}$~optimized parameters (six degrees of freedom of poses) that cover the United States region in the 2016--2019 period. It is demonstrated that the alignment improves the trajectories, thus final map is consistent. The proposed methodology extends the existing methods of global positioning system accuracy assessment focusing on realistic environmental and driving conditions. The impact of global positioning system accuracy on autonomous car safety is discussed. It is shown that 99\% of the assessed data satisfies the safety requirements (driving within lanes of 3.6~m) for Mid-Size (width 1.85~m, length 4.87~m) vehicle and 95\% for 6-Wheel Pickup (width 2.03--2.43~m, length 5.32--6.76~m). The conclusion is that this methodology has great potential for global positioning accuracy assessment at global scale for autonomous driving applications. LiDAR data alignment is introduced as a novel approach to GNSS+INS accuracy confirmation. Further research is needed to solve the identified challenges.

As Automated Driving Systems (ADS) technology gets assimilated into the market, the driver’s obligation will be changed to a supervisory role. A key point to consider is the driver’s engagement in the secondary task to maintain the driver/user in the control loop. The paper’s objective is to monitor driver engagement with a game and identify any impacts the task has on hazard recognition. We designed a driving simulation using Unity3D and incorporated three tasks: No-task, AR-Video, and AR-Game tasks. The driver engaged in an AR object interception game while monitoring the road for threatening road scenarios. From the results, there was less than 1 second difference between the means of gaming task (mean = 2.55s, std = 0.1002s) to no-task (mean = 2.55s, std = 0.1002s). Game scoring followed three profiles/phases: learning, saturation, and decline profile. From the profiles, it is possible to quantify/infer drivers’ engagement with the game task. The paper proposes alternative monitoring that has utility, i.e., entertaining the user. Further experiments AR-Game focusing on real-world car environment will be performed to confirm the performance following the recommendations derived from the current test.

The motivations for the move to electrified vehicles are discussed with reference to their improved energy efficiency, their potential for lower CO2 emissions (if the electricity system is decarbonized), their lower (or zero) NOx/particulate matter (PM) tailpipe emissions, and the lower overall costs for owners. Some of the assumptions made in life-cycle CO2 emissions calculations are discussed and the effect of these assumptions on the CO2 benefits of electric vehicles are made clear. A number of new tribological challenges have emerged, particularly for hybrid vehicles that have both a conventional internal combustion engine and a battery, such as the need to protect against the much greater number of stop-starts that the engine will have during its lifetime. In addition, new lubricants are required for electric vehicle transmissions systems. Although full battery electric vehicles (BEVs) will not require engine oils (as there is no engine) they will require a system to cool the batteries – alternative cooling systems are discussed, and where these are fluid based, the specific fluid requirements are outlined.

Existing Reynolds Averaged Navier-Stokes based transition models do not accurately predict separation induced transition for low pressure turbines. Therefore, in this study, a novel framework based on computational fluids dynamics driven machine learning coupled with multi-expression and multi-objective optimization is explored to develop models which can improve the transition prediction for the T106A low pressure turbine at an isentropic exit Reynolds number of Re2is=100,000. Model formulations are proposed for the transfer and laminar eddy viscosity terms of the laminar kinetic energy transition model using seven non-dimensional pi groups. The multi-objective optimization approach makes use of cost functions based on the suction-side wall-shear stress and the pressure coefficient. A family of solutions is thus developed, whose performance is assessed using Pareto analysis and in terms of physical characteristics of separated-flow transition. Two models are found which bring the wall-shear stress profile in the separated region at least two times closer to the reference high-fidelity data than the baseline transition model. As these models are able to accurately predict the flow coming off the blade trailing edge, they are also able to significantly enhance the wake-mixing prediction over the baseline model.

This paper presents the super-twisting algorithm (STA) direct power control (DPC) scheme for the control of active and reactive powers of grid-connected DFIG. Simulations of 5 KW DFIG has been presented to validate the effectiveness and robustness of the proposed approach in the presence of uncertainties with respect to vector control (VC). The proposed controller schemes with fixed gains are effective in reducing the ripple of active and reactive powers, effectively suppress sliding-mode chattering and the effe This paper presents a comparative study of two approaches for the direct power control (DPC) of doubly-fed induction generator (DFIG) based on wind energy conversion system (WECS). Vector Control (VC) and Sliding Mode Control (SMC). The simulation results of the DFIG of 5 KW in the presence of various uncertainties were carried out to evaluate the capability and robustness of the proposed control scheme. The (SMC) strategy is the most appropriate scheme with the best combination such as reducing high powers ripple, diminishing steady-state error in addition to the fact that the impact of machine parameter variations does not change the system performance. cts of parametric uncertainties not affecting system performance.

Negative feedback to the back gate of MOS devices available in FD-SOI technologies can be used to improve linearity of operational amplifiers. Two operational amplifiers designed and fabricated in a 22nm FD-SOI technology illustrate this technique, its advantages and limitations.

DC-connected parallel inverter systems are gaining popularity in industrial applications. However, such parallel systems generate excess current ripple (harmonics) at the DC-link due to harmonic interactions between the inverters in addition to the harmonics from the PWM switching. These DC-link harmonics cause the failure of fragile components such as DC-link capacitors. This paper proposes an interleaving scheme to minimize the current harmonics induced in the DC-link of such a system. The results show that when the carrier waves of the two inverters are phase-shifted by 90° angle, the maximum high-frequency harmonic ripple cancellation occurs, which reduces the overall RMS value of the DC-capacitor current.The outcome of this proposed solution is a cost-effective DC-harmonics mitigating strategy for the industrial designers to practically configuring multi-inverter systems, even when most of the drives are not operating at rated power levels. Experimental and simulation results presented in this paper verify the effectiveness of the proposed carrier-based phase-shifting scheme for two different configurations of common DC connected multi-converter systems.

Rural clean heating project (RCHP) in China aims to increase flexibility in the rural energy system, enhance the integration of renewable energy and distributed generation, and reduce environmental impact. While RCHP-enabling routes have been studied from a technical perspective, the economic, ecological, regulatory, and policy dimensions of RCHP are yet to be analysed in depth, especially in the underdeveloped areas in China. This paper discusses RCHP in rural Gansu in a multi-dimension approach. We firstly focus on the current issues and challenges of RCHP in rural Gansu. Then the RCHP-enabling areas are briefly zoned into six typical regions based on the resource distribution in Gansu Province, and a matching framework of RCHP is recommended. Then we focus on the economics and sustainability of RCHP-enabling technologies. Based on the medium-term assessment of RCHP in the demonstration provinces, various technical schemes and routes are analysed and compared so as to be adopted in rural Gansu. In addition to technical and economic effects of those schemes, the corresponding ecology, policy, finance, and market implications are also concerned. We briefly discuss how the national regulators incentivise the implementation of RCHP in rural Gansu. Major barriers to RCHP are identified as the sustainability of technology, economy, ecology, policy, finance, and market. Subsequently, some policy solutions to overcome these barriers are proposed.

This work aims to investigate systematically the influence of process temperature, bio-mass-to-water ratio, and production scales (laboratory and pilot) on the chemical composition of aqueous and gaseous phases and mass production of chemical by hydrothermal processing of Açaí (Euterpe Oleraceae, Mart.) seeds. The hydrothermal carbonization carried out at 175, 200, 225, and 250 °C, 2 °C/min, biomass-to-water ratio of 1:10, and at 250 °C, 2 °C/min, and biomass-to-water ratios of 1:10, 1:15, and 1:20, in technical scale, as well as at 200, 225, and 250 °C, 2 °C/min, bio-mass-to-water ratio of 1:10, in laboratory scale. The elemental composition (C, H, N, S) of solid phase determined to compute the HHV. The chemical composition of aqueous phase determined by GC and HPLC and the volumetric composition of gaseous phase by using an infrared gas an-alyzer. For the experiments in pilot scale with constant biomass-to-water ratio of 1:10, the yields of solid, liquid, and gaseous phases varied between 53.39 and 37.01% (wt.), 46.61 and 59.19% (wt.), and 0.00 and 3.80% (wt.), respectively. The yield of solids shows a smooth exponential de-cay with temperature, while that of liquid and gaseous phases a smooth growth. By varying the biomass-to-water ratios, the yields of solid, liquid, and gaseous reaction products varied be-tween 53.39 and 32.09% (wt.), 46.61 and 67.28% (wt.), and 0.00 and 0.634% (wt.), respectively. The yield of solids decreases exponentially with increasing water-to-biomass ratio and that of liquid phase increases in a sigmoid fashion. For constant biomass-to-water ratio, the concentrations of Furfural and HMF decrease drastically with increasing temperature, reaching a minimum at 250 °C, while that of phenols increases. In addition, the concentrations of CH3COOH and total car-boxylic acids increase, reaching a maximum at 250 °C. For constant process temperature, the concentrations of aromatics vary smoothly with the temperature. The concentrations of furfural, HMF, and cathecol decrease with temperature, while that of phenols increases. The concentra-tions of CH3COOH and total carboxylic acids decrease exponentially with temperature. Finally, for the experiments with varying water-to-biomass ratios, the productions of chemicals (furfu-ral, HMF, phenols, cathecol, and acetic acid) in the aqueous phase is highly dependent on the biomass-to-water ratio. For the experiments in laboratory scale with constant biomass-to-water ratio of 1:10, the yields of solid ranged between 55.9 and 51.1% (wt.), showing not only a linear decay with tem-perature, but also a lower degradation grade. The chemical composition of main organic compounds (furfu-ral, HMF, phenols, cathecol, and acetic acid) dissolved in the aqueous phase in laboratory scale shows the same behavior of those in obtained in pilot scale.

The In this paper, a universal method has been developed, which is a combination of an optimization method and a method for assessing the marginal utility. At present, the problem of optimal load distribution in the power system between a hydropower plant (HPP) and thermal power plants (TPP) is solved using the equality of the differential incremental rate characteristics of fuel consumption at TPP and water consumption at HPP by the Lagrangian multiplier method. In this case, the number of iterations can be five or more. The proposed approach is based, first of all, on the correct representation of the differential characteristics and calculation of a hydro resource price for the operational control of the HPP. Based on the comparison of water volume at a HPP and fuel amount at combined heat and power plants (CHPP) used for generation of 1 kW power, it is possible to determine a water price for a HPP. As a result of implementing the developed method for the HPP, a price of sold electricity in the flexible energy market will be comparable with the price for sold electricity produced at CHPPs, being equal to approximately 120 rubles/MW∙h.

A method of direction-of-arrival (DoA) estimation for FMCW radars is presented. In addition to MUSIC and ESPRIT, which are well-known high-resolution DoA estimation algorithms, deep learning has recently emerged as a very promising alternative. It is proposed in this paper to use a 3D convolutional neural network (CNN) for DoA estimation. The 3D-CNN extracts from the radar data cube spectrum features of the region of interest (RoI) centered on the potential positions of the targets, thereby capturing the spectrum phase shift information, which corresponds to DoA, along the antenna axis. Finally, the results of simulations and field experiments are provided to demonstrate the superior performance, as well as the limitations, of the proposed 3D-CNN.

The rapid growth of the Information and Communications Technology (ICT) sector requires additional infrastructure, such as more micro-datacenters and telecom stations, to support the higher internet speeds and low latency requirements of 5G networks. The increased power requirements of the new ICT technologies necessitate the proposal of new power supplies in an attempt to retain the increase in energy demand and running costs. The work analyses how multicell structured power supply units can increase the energy efficiency of the power supply systems and thus minimize running costs of ICT networks. The effect of multicell structures in energy efficiency improvements is verified experimentally and through simulations in PSIM’s thermal module. A new multicell Power Supply Unit (PSU) is proposed for ICT power supplies and it is shown that using multicell structures, higher efficiency at varying load levels can be achieved.

This report is considered different aspects of the concept of the networked distributed engine control system (DECS) of future air vehicles. These aspects include the following: the structure of multiple networks similar to NATO Generic Vehicle Architecture (NGVA), the role of Artificial Intelligence (AI) in DECS, and the use Augmented Reality (AR) as Human-Machine Interface between AI and pilots. Deployment of AI solutions for monitoring equipment in on-board infrastructure can be provided on physical or virtual servers and in the clouds. In this case, it is possible to use various methods of alerting the pilot and ground personnel on the basis of AR. The use of AI allows covering an unlimited set of scenarios, to provide an assessment of the likelihood of equipment failure, classification alarm is normal, and recognition of the development of defects. To collect Big Data from sensors and the pre-processing of this data before a machine learning (ML) procedure it is proposed to form data sets with the help of the face-splitting matrix product. To decrease the time of reaction of Neural Networks it has been suggested the implementation of advanced tensor-matrix theory on the basis of penetrating face product of matrices. Other important results of the report are a possible version of the AR data format for DECS and a proposal about the use of non-orthogonal frequency discrete multiplexing (N-OFDM) signals to data transfer via fibre optics.

A novel adaptive neural network-based fault-tolerant control scheme is proposed for six-degree freedom nonlinear helicopter dynamic. The proposed approach can detect and mitigate sensors' faults in real-time. An adaptive observer-based on neural network (NN) and extended Kalman filter (EKF) is designed, which incorporates the helicopter's dynamic model to detect faults in the navigation sensors. Based on the detected faults, an active fault-tolerant controller, including three loops of dynamic inversion, is designed to compensate for the occurred faults in real-time. The simulation results showed that the proposed approach is able to detect and mitigate different types of faults on the helicopter navigation sensors, and the helicopter tracks the desired trajectory without any interruption.

Studies on the characteristics of hazardous air pollutants (HAPs) in the emissions of medium-duty diesel trucks are significantly insufficient compared to that on heavy-duty trucks. This study investigates the characteristics of regulated pollutants and HAPs such as volatile organic compounds (VOCs), aldehydes, and polycyclic aromatic hydrocarbons (PAHs), and estimates non-methane hydrocarbon (NMHC) speciation in the emissions of medium-duty diesel trucks. Ten medium-duty diesel trucks conforming to Euros 5 and 6 were tested for worldwide harmonized light duty driving test cycle (WLTC), new European driving cycle (NEDC), constant volume sampler (CVS)-75, and National Institute of Environmental Research (NIER)-9 using a chassis dynamometer. CO and NMHC emissions were the highest in the NEDC because of its longer low-speed driving time. NOx emissions were the highest in WLTC owing to the influence of thermal NOx in the high-speed phase. Alkanes dominated non-methane volatile compound (NMVOC) emissions owing to the low reaction of the diesel oxidation catalyst. After-treatment system, driving, and engine conditions influenced the individual components of NMVOC emissions. Formaldehyde emissions were the highest among aldehydes irrespective of driving cycles. By sampling the particle-phase of PAHs, we detected benzo(k)fluoranthene and benzo(a)pyrene and estimated the concentrations of the gas-phase PAHs with models to obtain the total PAH concentrations. The toxic equivalency quantities of benzo(k)fluoranthene and benzo(a)pyrene from NIER-9 (cold) for both Euro 5 and Euro 6 vehicles were more than five times higher than that of NIER (hot) and NEDC. In the case of NMHC speciation, formaldehyde emissions were the highest in all the driving cycles. Formaldehyde and benzene must be controlled in the emissions of medium-duty diesel trucks to reduce their health threats. The results of this study will aid in establishing a national emission inventory system for HAPs of mobile sources in Korea.

One of the most important parameters that characterize the traction-coupling properties of a wheeled tractor is its slip. The more tractor's gross traction, the higher its traction-coupling properties. But, this gross traction should not exceed its maximum possible value, which, in turn out, is to be determined by the maximum permissible slip. This article provides the equation to calculate this crucial parameter and establishes the dependencies between the tractor's slip and soil structure coefficient. It was shown that the value basically depends on such soil characteristics as the bulk deformation coefficient and the coefficient of rolling resistance. Calculations showed that for the average value of the soil bulk deformation coefficient at, the average value of rolling resistance coefficient at 0.16, the ratio value of the maximum permissible soil pressure to the tractor wheel rolling radius at the maximum permitted amount slip of the tractor wheels should not exceed 15%. With more slip, the soil structure deteriorates significantly. In this case, its structure coefficient may be less than critical, equal to 0.4.

Aero submarines (aerosubs) are vehicles that can both fly both in air and travel under water. The concept of dual aerial and aquatic vehicles emerged in 1939 when Russian engineer Boris Ushakov proposed the “flying submarine”[1], and this was followed by further developments including RFS1 [2], convair project in 1964[3] , etc. however, to date, limited attempt has been diverted towards the advanced development of such aircraft. This is heavily influenced by challenges associated with the design and operation of the same. Based on the review of literature the authors aim to introduce a theoretical design for an aerosub (QFS-20) with a view to address the design and operation issues including power, entry to and exit from water.

Electric moped scooter sharing services have recently experienced strong growth rates, particularly in Europe. Due to their compactness, environmental-friendliness and convenience, shared e-mopeds are suitable modes of transport in urban mobility to help reduce the environmental impact. However, its traffic-related, economic and environmental effects are merely represented in academic research. We used passenger car traffic data in Berlin generated by the multi-agent transport simulation framework MATSim to develop a python-based simulation, resembling an e-moped sharing system. Based on the results, a total cost of ownership and a life cycle assessment for fleet sizes of 2,500, 10,000 and 50,000 vehicles were conducted. The results indicate that a substantial part of all passenger car trips in Berlin can be substituted. The larger the fleet, the more and longer trips are replaced. Simultaneously, the efficiency in terms of fleet utilization decreases. The scenario with 10,000 e-mopeds offers the lowest total distance-based costs for sharing operators, whereas a fleet consisting of 2,500 vehicles exhibits the lowest environmental emissions per kilometer driven over the expected lifespan of a shared e-moped. Based on the renewable energy potential for 2050 forecasted by the German Federal Environment Agency, a significant overall decline in environmental impacts can be achieved.

The extended finite element method (XFEM) is now widely used in crack simulations and the direction of propagation of the XFEM crack in SS-304 has been quite interesting. This paper discusses the direction,θ(+ve in anti-clockwise direction), of the XFEM crack (under mode-I) propagation based on the von Mises stress-field around the crack-tip for static loading under the light water reactors (LWRs) conditions. The Mises core region from the crack-tip is chosen for β=〖90〗^o, the angle between the load and the initial crack. Experimental data for LWRs’environment is obtained and the simulations have been carried out applying three different static loads and results for the direction of the crack are calculated by measuring the stress-field around the crack-tip. It has been found that the direction of the XFEM varies inversely as the stress-field around the crack tip is increased. The computational results have been compared with the numerical results based on the minimum strain energy density criterion[S-criterion].

The article discusses the issues related to the safety of transport of dangerous goods by road. Research on accidents in transport unambiguously points to the human factor, which is most responsible for causing the accident. Determining the causes of driver unreliability in the hu-man-vehicle-environment system requires thorough research. Unfortunately, in this case, experimental research with human involvement is limited in scope. This leaves modeling and simulation of the behavior of the human factor, i.e., the driver transporting dangerous goods. The human being, due to its complexity, is a challenging element to parameterize. The literature presents various attempts to model human actions. In their work, the authors used heuristic methods, specifically fuzzy set techniques, to build a human factor model. In these models, human actions were specified using a verbal or linguistic description. The specificity of fuzzy sets allows to "naturally" limit the "precision" in describing human behavior. The model was built based on the author's questionnaire and expert research, based on which individual features were selected. Then, the traits were assigned appropriate states. The output parameter of the model is λL - the intensity of human error. The obtained values of the intensity of the accident caused by the driver's error were implemented into the author's method of risk assessment. They constituted one of the factors determining the probability of an accident in the transport of dangerous goods, which allowed to determine the optimal route of transport of these goods characterized by the lowest risk of an undesirable event on the route. The article presents the model's assumptions, structure, and features included in the model, which have the most significant influence on shaping the intensity of human error. The results of the simulation studies showed a diversified effect of the analyzed characteristics on the driver's efficiency.

Two of the most common activation functions (AF) in deep neural networks (DNN) training are Sigmoid and ReLU. Sigmoid was tend to be more popular the previous decades, but it was suffering with the common vanishing gradient problems. ReLU has resolved these problems by using zero gradient and not tiny values for negative weights and the value “1” for all positives. Although it significant resolves the vanishing of the gradients, it poses new issues with dying neurons of the zero values. Recent approaches for improvements are in a similar direction by just proposing variations of the AF, such as Leaky ReLU (LReLU), while maintaining the solution within the same unresolved gradient problems. In this paper, the combining of the Sigmoid and ReLU in one single function is proposed, as a way to take the advantages of the two. The experimental results demonstrate that by using the ReLU’s gradient solution on positive weights, and Sigmoid’s gradient solution on negatives, has a significant improvement on performance of training Neural Networks on image classification of diseases such as COVID-19, text and tabular data classification tasks on five different datasets.

Energy harvesting from wind turbines has been explored by researchers for more than a century from conventional turbines up to the latest bladeless turbines. Amongst these bladeless turbines, vortex bladeless wind turbine (VBT) harvests energy from oscillation of a turbine body. Due to the novelty of this science and the widespread researches around the world, one of the most important issues is to optimize and predict produced power. To enhance the produced output electrical power of VBT, the fluid-solid interactions (FSI) were analyzed to collect a dataset for predicting procedure. Long short-term memory (LSTM) method has been used to predict the produced power of VBT from the collected data. The reason of choosing LSTM from various artificial neural network methods is that the parameters of VBT study are all time- dependent and the LSTM is one of the most accruable algorithms for predicting time series data. In order to find the relationship between the parameter and the variables used in this research, a correlation matrix was presented. According to the value of 0.3 for the root mean square error (RMSE), a comparative analysis between the simulation results and its prediction shows that the LSTM method is very accurate for these types of research. Furthermore, the LSTM method has significantly reduced the computation time so that the prediction time of desired values has been reduced from an average of 2 and a half hours to two minutes. Also, one of the most important achievements of this study is to suggest a mathematical relation of VBT output power which helps to extend it in a different size of VBT with a high range of parameter variations.

This study presents the consecutive modification of orange peel (OP) by NaOH and sodium dodecyl sulfate (SDS) for simultaneous elimination of basic dyes from the binary system and modeling the adsorption process using an intelligent tool. The natural and modified biosorbents were characterized by variety of analyses such as: field emission scanning electron microscopy with energy dispersive X-ray, N2 physisorption and Fourier transform infrared spectroscopy techniques. The influence of various variables on dye removal like pH, the quantity of biosorbents, dyes concentration, contact time, and temperature in the binary system were investigated and optimized by an artificial neural network (ANN) model as an intelligent tool. The optimum quantity of the sorbent was found to be 0.30 g for orange peel (OP) and 0.25 g for NaOH-treated OP (NOP) and SDS-decorated NOP (SNOP) at pH = 7. The kinetics and thermodynamics investigations showed that the removal of dyes obeyed the pseudo-second-order kinetic model and were spontaneous and exothermic in nature. Moreover, in order to describe the mechanism of sorption process, desorption studies of dyes were carried out. The desorption percentages of methylene blue (MB) in water and HCl were found to be in the range of 1.93%–4.76% and 18.87%–28.76%, respectively; in addition, the desorption percentages of crystal violet (CV) in water and HCl were obtained to be in the range of 4.11%–7.41% and 32.84%–43.00%, respectively; which could be a recommendation ion exchange or electrostatic attachment of dyes onto biosorbents. The ANN predictions matched with the experimental data very well (0.95308 < R2 < 0.99191 and 0.98335 < R2 < 0.99773 for MB and CV, respectively) which indicated high accuracy of the ANN model. In addition, the relative importance of each parameter was calculated by Garson’s equation.

Enhancing the degree of functional multiplexing while assuring operational reliability and manufacturability at competitive costs are crucial ingredients for enabling comprehensive sample-to-answer automation, e.g., for use in common, decentralized “Point-of-Care” or “Point-of-Use” scenarios. This paper demonstrates a model-based ‘digital twin’ approach which efficiently supports the algorithmic design optimization of exemplary centrifugo-pneumatic (CP) dissolvable-film (DF) siphon valves towards larger-scale integration (LSI) of well-established “Lab-on-a-Disc” (LoaD) systems. Obviously, the spatial footprint of the valves and their upstream laboratory unit operations (LUOs) have to fit, at a given radial position prescribed by its occurrence in the assay protocol, into the locally accessible disc space. At the same time, the retention rate of a rotationally actuated CP-DF siphon valve and, most challenging, its band width related to unavoidable tolerances of experimental input parameters, need to slot into a defined interval of the practically allowed frequency envelope. To accomplish particular design goals, a set of parametrized metrics is defined, which are to be met within their practical boundaries while (numerically) minimizing the band width in the frequency domain. While each LSI scenario needs to be addressed individually on the basis of the digital twin, a suite of qualitative design rules and instructive showcases structures are presented.

A solid structure, such as a road, building wall or envelop, used as a solar collector is considered an effective and new way to use renewable energy. This paper focused on the temperature characteristics of four structures exposed to sunshine: asphalt, red brick, composite cement and concrete road slab. Furthermore, the collected heat based on a hydraulic system was investigated experimentally. For the four structure slabs, their temperature differences are due to solar radiation absorption varied greatly by the material’s heat absorptance and color. Through the test, asphalt slab attained the highest temperature and had the weakest reflection among the structures. Compared with the others, the temperature of the asphalt slab was greater by 8.1%, 14.9% and 16.4% than the brick, composite cement and concrete, respectively. The reflection intensity growth ratio was defined and indicates the growth potential for absorbing radiation in the solid slab surface. From the experiments, it was concluded that a suitable selection of road materials can greatly improve the thermal absorption, conduction and penetration into the solid slab. The collected heat capability was approximately 250 W/m2 to 350 W/m2 in the natural summer condition. A black coating or a surface modification can collect more heat, reaching greater than 250 W/m2. The solar collecting heat efficiency with a surface configuration of the road slab can reach above 30% in the summer time.

Fluidic larger-scale integration (LSI) resides at the heart of comprehensive sample-to-answer automation and parallelization of assay panels for frequent and ubiquitous bioanalytical testing in decentralized the point-of-use / point-of-care settings. This paper develops a novel “digital twin” strategy with an emphasis on rotational, centrifugo-pneumatic flow control. The underlying model systematically connects retention rates of rotationally actuated valves as a key element of LSI to experimental input parameters; for the first time, the concept of band widths in frequency space as the decisive quantity characterizing operationally robustness is introduced, a set of quantitative performance metrics guiding algorithmic optimization of disc layouts is defined, and the engineering principles of advanced, logical flow control and timing are elucidated. Overall, the digital twin enables efficient design for automating multiplexed bioassay protocols on such “Lab-on-a-Disc” (LoaD) systems featuring high packing density, reliability, configurability, modularity and manufacturability to eventually minimize cost, time and risk of development and production.

Classical laws of friction suggest that friction force is proportional to the normal load and independent of the nominal contact area. As a great improvement in this subject, it is now widely accepted that friction force is proportional to the real area in contact, and much work has been conducted based on this hypothesis. In present study, this hypothesis will be carefully revisited by measuring the friction force and real contact area in-site and real-time at both normal loading and unloading stages. Our experiments reveal that the linear relation always holds between friction force and normal load. However, for the relation between friction force and real contact area, the linearity holds only at the loading stage while fails at the unloading stage. This study may improve our understanding of the origin of friction.

In this paper, we propose a new methodology for crack monitoring in concrete structures. This approach is based on a n this paper, we propose a new methodology for monitoring cracks in concrete structures. This approach is based on a multi-resolution analysis of a sample or a specimen of the studied material subjected to several types of solicitation. The image obtained by ultrasonic investigation and processing by a dedicated wavelet will be analyzed according to several scales in order to detect internal cracks and crack initiation. The ultimate goal of this work is to propose an automatic crack type identification scheme based on convolutional neural networks (CNN). In this context, crack propagation can be monitored without access to the concrete surface and the goal is to detect cracks before they are visible on the concrete surface. The key idea allowing such a performance is the combination of two major data analysis tools which are wavelets and Deep Learning. This original procedure allows to reach a high accuracy close to 0.90. In this work, we have also implemented another approach for automatic detection of external cracks by deep learning from publicly available datasets.

In this study, grit blasting pretreatment was used to improve the adhesion and corrosion resistance and microhardness of Ni-W/SiC nanocomposite coatings fabricated using conventional electrodeposition technique. Prior to deposition, grit blasting and polishing (more commonly used) pretreatment were used to prepare the surface of the substrate and the 3D morphology of the pretreated substrates was characterized using laser scanning confocal microscopy. The coatings surface and the cross section morphology were analyzed using scanning electron microscopy (SEM). The chemical composition, crystalline structure, microhardness, adhesion, and the corrosion behavior of the deposited coatings were characterized using energy dispersive spectroscopy (EDS), X-ray diffraction (XRD), microhardness tester, scratch tester and electrochemical workstation, respectively. The results indicated that the grit blasting and SiC addition, improved the microhardness, adhesion and corrosion resistance. The Ni-W-SiC nanocomposites pretreated by grit blasting exhibited the best adhesion strength, up to 36.5 ± 0.75 N. Its hardness was the highest and increased up to 673 ± 5.47Hv and its corrosion resistance was the highest compared to the one pretreated by polishing.

Current, application-driven trends towards larger-scale integration (LSI) of microfluidic systems for comprehensive assay automation and multiplexing pose significant technological and economical challenges to developers. By virtue of their intrinsic capability for powerful sample preparation, centrifugal systems have attracted significant interest in academia and business since the early 1990s. This review models common, rotationally controlled valving schemes at the heart of such “Lab-on-a-Disc” (LoaD) platforms to predict critical spin rates and reliability of flow control mainly based on geometries, location and liquid volumes to be processed, and their experimental tolerances. In absence of larger-scale manufacturing facilities during product development, the method presented here facilitates the provision of efficient simulation tools for virtual prototyping and characterization to greatly expedite design optimization according to key performance metrics. This virtual in silico approach thus significantly accelerates, de-risks and lowers costs along the critical advancement from idea, fluidic testing, bioanalytical validation and scale-up to commercial mass manufacture.

Nitrogen and phosphorous support the ecosystem by supplying nutrients to algae and aquatic plants. Having them in excess results in the eutrophication of waters creating quality problems. In the past, nitrogen has been widely investigated for wells in the context of groundwater flow. However, a national-scale nitrogen assessment in rivers and streams has not received enough attention. In this research, the Wilcoxon rank sum test, as a non-parametric hypothesis testing method, has been applied to nitrogen concentration in the form of nitrate-nitrogen and nitrite-nitrogen in rivers and streams of the Contiguous United States. This approach was particularly selected because of the non-normal and positively skewed nitrogen levels occurring in the surface flow. This method was able to identify the impaired body of waters as well as quantify the confidence, significance, and errors involved. The Northern Appalachians (NAP), Northern Plains (NPL), and Xeric (XER) ecoregions were worsening in the nitrogen-nitrate condition with NAP, and XER needed immediate actions. The nitrite-nitrogen condition did not pose an immediate threat, so mitigation plans should focus more on nitrate-nitrogen remediation. It was shown that the method was superior to the two-sample t-test by yielding lower type II errors.

Currently, life cannot be imagined without the use of bank cards for purchases or money transfers; however, their use provides new opportunities for money launders and terrorist organizations. This paper proposes a Blockchain-enabled transaction scanning (BTS) method for the detection of anomalous actions. The BTS method specifies the rules for outlier detection and rapid movements of funds, which restrict anomalous actions in transactions. The specified rules determine the specific patterns of malicious activities in the transactions. Furthermore, the rules of the BTS method scan the transaction history and provide a list of entities that receive money suspiciously. Finally, the Blockchain-enabled process is used to restrict the money laundering. To validate the performance of the proposed BTS, the Spring Boot application is built based on the Java programming language. Based on experimental results, the proposed BTS method automates the process of investigating transactions and restricts the money laundering incident.

Whilst prior works have characterised the affordances of prototyping methods in terms of generating knowledge about a product or process, the types, or ‘dimensions’ of knowledge towards which they contribute are not fully understood. In this paper we adapt the concept of ‘design domains’ as a method to interpret, and better understand the contributions of different prototyping methods to design knowledge in new product development. We first synthesise a set of ten dimensions for design knowledge from a review of literature in design-related fields. A study was then conducted in which participants from engineering backgrounds completed a Likert-type questionnaire to quantify the perceived contributions to design knowledge of 90 common prototyping methods against each dimension. We statistically analyse results to identify patterns in the knowledge contributions of different methods. Results reveal that methods exhibit significantly different contribution profiles, suggesting different methods to be suited to different knowledge generation. Thus, this paper indicates potential for new methods, methodology and processes to leverage such characterisations for better selection and sequencing of methods in the prototyping process.

In this study, 12 organic waste materials were subjected to TG/DTG thermogravimetric analysis and DSC calorimetric analysis. These analyses provided basic information about thermochemical transformations and degradation rates during organic waste pyrolysis. Waste materials were chosen to cover the most abundant waste that can be found in the municipal solid waste stream. Based on TG results, kinetic parameters according to Coats–Redfern method were determined. The pyrolysis activation energy was the highest for cotton 134.5 kJ∙(mol∙K)-1 and the lowest for leather 25.2 kJ∙(mol∙K)-1. The DSC analysis showed number of transformations occurred during pyrolysis for each material. For each transformation, normalized energy required for transformation, or released during transformation were determined and then summarized to present energy balance. The study found that only for three waste materials: PET (-220.1 J⸱g-1), leather (-66.9 J⸱g-1), and chicken meat (-130.3 J⸱g-1) energy balance was negative, while the highest positive balance value was found for potato peelings (367.8 J⸱g-1). The obtained results may be applied for the modelling of energy and mass balance of municipal solid waste pyrolysis.

Solar reflective index (SRI) is one of the important parameters in the analysis of a building’s energetic and thermal performance, especially for “cool” reflective paints or coatings. However, there exists less information on the typical performance of the cool materials exposed for long term in the Middle East and more specifically in the United Arab Emirates (UAE). In this study, we determined values of reflectance, emittance, solar reflective index (SRI), and color difference, for three different high SRI white paints exposed naturally on low and high sloped racks for three years. After 3 years, reflectance values of exposed paint panels significantly decreased with an increased color difference in comparison with original unexposed paint surfaces. Emissivity of the paint changed very little for all of the exposed samples, but SRI, determined from both the reflectance and emittance, was reduced from between 95 and 110 to between 60 and 90 after three years. This appeared to be related to exposure to high temperatures, UV radiation, and adhesion of airborne contaminants in conjunction with low precipitation. Macroscopically, panels exposed on a low slope were the most discolored with the greatest presence of dust infusion and reduction in increase in surface temperatures which was demonstrated through thermal imaging. Such natural weathering research study is necessary for the development of standard exposure tests and determination of various control elements to increase the durability of cool materials in hot and arid climatic conditions of UAE.

The article presents a modified finite element (FE) based algorithm for nonlinear analysis of 2D beam structures, which takes into account the influence of the shear forces. The method proposed enables using complex materials with nonlinearities without the need of implementing advanced constitutive models in FE routines. It can be directly integrated with commonly available FE software for linear analysis of beam structures, so its functionality can be easily extended also with material nonlinearities. The presented approach adopts the generalized constitutive law algorithm to iteratively modify the stiffness of beam element. To address an influence of a shear stiffness, a Timoshenko beam element was used. The methodology was implemented and its performance was verified on several numerical examples. For validation, the displacements and the ultimate loads were compared with the values from a commercial FE software. The results shown a good correlation between the reference model and the method proposed.

In the paper, a modified nonlinear finite element method for analysis of trapezoidal plates geometrically reduced to shallow-shell Reissner-Mindlin formulation is presented. Due to the method proposed the complex plate cross-section and nonlinear materials may be modelled and no implementation of advanced constitutive law via user subroutines is needed. The generalized nonlinear constitutive law is used to update the stiffness of the plate element. The method enables modeling of complicated cross-sections, such as steel trapezoidal sheets, metal facing sandwich panels or reinforced concrete. Additionally, for those geometrically complex sections an advanced nonlinear material may be adopted. To verify the proposed method, a selected trapezoidal sheets were modeled in a commercial software as full 3D shell structures. By comparing displacements and forces, it was shown that both models behave almost identically, however, the simplified model has about 300-400 times less degrees of freedom, thus it is much more efficient.

This paper presents an improved droop control strategy for grid-connected inverter power stability and power quality under distorted with consideration of grid fluctuation and inter-harmonics. An instantaneous frequency without PLL and amplitude of capacitor voltage feed-forward control strategy is given to power stability control, meanwhile a grid current feedback control is given by an incomplete derivation with a high-pass filter, so that the harmonics and inter-harmonics current can be suppressed. These approaches can provide both good active and reactive power dynamic response under fluctuation of frequency, and rejection ability against harmonic and inter-harmonic voltage. Based on model of inverter, the proposed control strategies are designed in detail. Simulations and experiments are present to validate the effectiveness of proposed method.

The intricate nature of congenital heart disease requires understanding of complex, patient-specific three-dimensional dynamic anatomy of the heart, from imaging data such as three-dimensional echocardiography for successful outcomes from surgical and interventional procedures. Conventional clinical systems use flat screens and therefore display remains two-dimensional, which undermines the full understanding of the three-dimensional dynamic data. Additionally, control of three-dimensional visualisation with two-dimensional tools is often difficult, so used only by imaging specialists. In this paper we describe a virtual reality system for immersive surgery planning using dynamic three-dimensional echocardiography , which enables fast prototyping for visualisation such as volume rendering, multi-planar reformatting, flow visualisation, and advanced interaction such as three-dimensional cropping, windowing, measurement, haptic feedback, automatic image orientation, and multi-user interactions. The available features were evaluated by imaging and non-imaging clinicians, showing that the virtual reality system can help improve understanding and communication of the three-dimensional echocardiography imaging and benefit congenital heart disease treatment.

Economic dispatch (ED) is an essential part of any power system network. ED is how to schedule the real power outputs from the available generators to get the minimum cost with satisfying all constraints of the network. Also it can be explained as allocating generation among the committed units with the most effective minimum way in accordance with all constraints of the system. There are many traditional methods for solving ED as the Newton-Raphson Method, Lambda-Iterative technique, Gaussian-Seidel Method, etc. All these traditional methods need the generators’ incremental fuel cost curves to be increasing linearly. But practically the input-output characteristics of a generator are highly non-linear. This causes a challenging non-convex optimization problem. Recent techniques like genetic algorithms, artificial intelligence, dynamic programming and particle swarm optimization solve nonconvex optimization problems in a powerful way and obtain a rapid and near global optimum solution. In addition, renewable energy resources as wind and photovoltaic have been a promising option due to the environmental concerns as the fossil fuels reserves are being consumed and fuel price increases rapidly and emissions are getting higher. Therefore, the world tends to replace the old power stations into renewable ones or hybrid stations. In this paper, we attempt to enhance the operation of electrical power system networks via economic dispatch. An ED problem has been solved using various techniques as particle swarm optimization and a sine-cosine algorithm and the results have been compared. Moreover, case studies have been executed using a photovoltaic-based distributed generator with constant penetration level on the IEEE 14 bus system and results are observed. All the analyses have been made on MATLAB software.

This paper shows the proposal of the use of linked data for smart metering systems. The aim of linked data in smart grid applications such as smart metering systems is to give semantic knowledge of the energy transactions between all participators in the power markets. In combination with other technologies like blockchain, the linked data could improve decentralization and security in the world's energetic future.

We discuss two methods to detect the presence and location of a person in a small-scale room and compare the performances. The first method is Direct Intersection, which determines a coordinate point based on the intersection of spheroids defined by observed distances of high-intensity reverberations. The second method, Sonogram analysis, overlays all channel’s room impulse responses to generate an intensity map for the observed environment. We demonstrate that the former method has lower computation complexity and higher accuracy for small numbers of channels, while the latter performs more robustly.

The incorporation of renewable energy into power systems poses serious challenges to the transmission and distribution power system operators (TSOs and DSOs). To fully leverage these resources there is a need for a new market design with improved coordination between TSOs and DSOs. In this paper we propose two coordination schemes between TSOs and DSOs: one centralised and another decentralised that facilitate the integration of distributed based generation; minimise operational cost; relieve congestion; and promote a sustainable system. To this end, we approximate the power equations with linearised equations so that the resulting optimal power flows (OPFs) in both the TSO and DSO become convex optimisation problems. In the resulting decentralised scheme, the TSO and DSO collaborate to optimally allocate all resources in the system. In particular, we propose an iterative bi-level optimisation technique where the upper level is the TSO that solves its own OPF and determines the locational marginal prices at substations. We demonstrate numerically that the algorithm converges to a near optimal solution. We study the interaction of TSOs and DSOs and the existence of any conflicting objectives with the centralised scheme. More specifically, we approximate the Pareto front of the multi-objective optimal power flow problem where the entire system, i.e., transmission and distribution systems, is modelled. The proposed ideas are illustrated through a five bus transmission system connected with distribution systems, represented by the IEEE 33 and 69 bus feeders.

Holographic subsurface radar (HSR) is currently not in widespread usage. This is due to an historical perspective in the ground penetrating radar (GPR) community that the high attenuation of electromagnetic waves in most media of interest, and the inability to apply time-varying gain to the continuous wave (CW) HSR signal precludes sufficient effective penetration depth. While it is true that the fundamental physics of HSR, with its use of a CW signal, does not allow amplification of later (i.e. deeper) arrivals in lossy media (as is possible with impulse subsurface radar — ISR), HSR has distinct some distinctive advantages. The most important of these is the ability to do shallow subsurface imaging with a resolution that is not possible with ISR. In addition, the design of an HSR system is simpler than for ISR due to the relatively low-tech transmitting and receiving antennae. This paper provides a review of the main principles of HSR through an optical analogy and describes possible algorithms for radar hologram reconstruction. We also present a review of the history of development of systems and applications for HSR of the “RASCAN” type which is possibly the only holographic subsurface radar that is produced in lots. Among the subsurface imaging and remote sensing applications considered are humanitarian demining, construction inspection, surveys of historic architecture and artworks, nondestructive testing of dielectric aerospace materials, security applications, paleontology, detection of wood-boring insect damage, and others. Each application is illustrated with relevant data acquired in laboratory and/or field experiments.

The aim of this study was to build a tool to analyze, using artificial intelligence, the sentiment perception of users who answered two questions from the CSQ – 8 questionnaires with raw Spanish free-text. Their responses are related to mindfulness, which is a novel technique used to control stress and anxiety caused by different factors in daily life. As such, we proposed an online course where this method was applied in order to improve the quality of life of health care professionals in COVID 19 pandemic times. We also carried out an evaluation of the satis-faction level of the participants involved, with a view to establishing strategies to improve fu-ture experiences. To automatically perform this task, we used Natural Language Processing (NLP) models such as swivel embedding, neural networks and transfer learning, so as to classify the inputs into the following 3 categories: negative, neutral and positive. Due to the lim-ited amount of data available - 86 registers for the first and 68 for the second - transfer learning techniques were required. The length of the text had no limit from the user’s standpoint, and our approach attained a maximum accuracy of 93.02 % and 90.53 % respectively based on ground truth labeled by 3 experts. Finally, we proposed a complementary analysis, using com-puter graphic text representation based on word frequency, to help researchers identify relevant information about the opinions with an objective approach to sentiment. The main conclusion drawn from this work is that the application of NLP techniques in small amounts of data using transfer learning is able to obtain enough accuracy in sentiment analysis and text classification stages

In European Seveso Legislation for the control of the hazard of major accidents (Directive 2015/12/UE), the Safety Management System SMS is an essential obligation for managers and the authorities are required to periodically verify its adequateness through periodical inspections at Seveso sites. One of the pillars of the SMS is the collection and analysis of documents on accidents, near misses and possibly anomalies, in order to identify weaknesses and implement continuous improvement. In Italy, for a few years, the documents, gathered from all Italian Seveso sites by the inspectors, have been archived and used for research purposes. The archive currently contains some 4000 reports, collected in five years by some 100 inspectors throughout Italy. The paper discusses in the detail the challenges faced to extract the knowledge hidden in the documents and make it usable through the design of a robust model. For this aim, Machine Learning techniques have been used as a preprocessing of the reports for extracting the concepts and their relations, organized into an entity-relation model. The effectiveness of this methodology and its potentiality are pointed out by investigating a few hot topics, exploiting the information contained in the repository.

The booming wearable market and recent advances in material science has led to the rapid development of the various wearable sensors, actuators, and devices that can be worn, embedded in fabric or accessories, or tattoos directly onto the skin. Wearable actuators, a subcategory of wearable technology, have attracted enormous interest from researchers in various disciplines and many wearable actuators and devices have been developed in the past few decades to assist and improve people's everyday lives. In this paper, we review the actuation mechanisms, structures, applications, and limitations of recently developed wearable actuators including pneumatic and hydraulic actuators, shape memory alloys and polymers, thermal and hygroscopic materials, dielectric elastomers, ionic and conducting polymers, piezoelectric actuators, electromagnetic actuators, liquid crystal elastomers, etc. Examples of the recent applications such as wearable soft robots, haptic devices, and personal thermal regulation textiles are highlighted. Finally, we point out the current bottleneck and suggest the prospective future research directions for wearable actuators.

Due to the increase in the emission of greenhouse gases, the hydrologic cycle is being altered on the daily basis. This has affected the variations in relations of intensity, duration, and frequency of rainfall events. Intensity Duration Frequency (IDF) curves describe the relationship between rainfall intensity, rainfall duration and return period. IDF curves are one of the most often applied implements in water resource engineering, in areas such as for operating, planning and designing of water resource projects, or for numerous engineering projects aimed at controlling floods. In particular, IDF curves for precipitation answer problems of improper drainage systems or conditions and extreme characters of precipitation which are the main cause of floods in Nyabugogo catchment. This study aims to establish Rainfall IDF empirical equations, curves and hydrological discharge (predicted peak rate of runoff (Qlogy)) equations for eight Districts that will be used for designing an appropriate and sustainable hydraulic structures for controlling flood to reduce potential loss of human and aquatic life, degradation of water, air and soil quality and property damage and economic lessen caused by flood in Nyabugogo catchment. However Goodness of Fit tests revealed that Gumbel’s Extreme-Value Distribution method appears to have the most appropriate fit compared with Pearson type III distribution for validating the Intensity-Duration-Frequency curves and equations through the use of daily annual for each meteorological station. The findings of the study show that the intensity of rainfall increases with a decrease in rainfall duration. Additionally, a rainfall of every known duration will have a higher intensity if its return period is high, while the predicted peak rate of runoff (Qlogy) increases also with an increase in the intensity of rainfall.

The mean flow and turbulence statistics of the flow through a simplified urban environment, which is an active research area in order to improve the knowledge of turbulent flow in cities, is investigated. This is useful for civil engineering, pedestrian comfort and for health concerns caused by pollutant spreading. In this work, we provide analysis of the turbulence statistics obtained from well-resolved large-eddy simulations (LES). A detailed analysis of this database reveals the impact of the geometry of the urban array on the flow characteristics and provides for a good description of the turbulent features of the flow within a simplified urban environment. The most prominent features of this complex flow include coherent vortical structures such as the so-called arch vortex, the horseshoe vortex and the roof vortex. These structures of the flow have been identified by an analysis of the turbulence statistics. The influence of the geometry of the urban environment (and particularly the street width and the building height) on the overall flow behavior have also been studied. Finally, the well-resolved LES results were compared with the experimental database from Monnier et al. to discuss differences and similarities between the respective urban configurations.

This paper proposes several LabVIEW applications to accomplish the data acquisition, processing, features extraction and real-time classification of the electroencephalographic (EEG) signal detected by the embedded sensor of the NeuroSky Mindwave Mobile headset. The LabVIEW applications are aimed at the implementation of a Brain-Computer Interface system, which is necessary to people with neuromotor disabilities. It is analyzed a novel approach regarding the preparation and automatic generation of the EEG dataset by identifying the most relevant multiple mixtures between selected EEG rhythms (both time and frequency domains of raw signal, delta, theta, alpha, beta, gamma) and extracted statistical features (mean, median, standard deviation, route mean square, Kurtosis coefficient and others). The acquired raw EEG signal is processed and segmented into temporal sequences corresponding to the detection of the multiple voluntary eye-blinks EEG patterns. The main LabVIEW application accomplished the optimal real-time artificial neural networks techniques for the classification of the EEG temporal sequences corresponding to the four states: 0 - No Eye-Blink Detected; 1 - One Eye-Blink Detected; 2 – Two Eye-Blinks Detected and 3 – Three Eye-Blinks Detected. Nevertheless, the application can be used to classify other EEG patterns corresponding to different cognitive tasks, since the whole functionality and working principle could estimate the labels associated with various classes.

The corrugated board packaging industry is increasingly using advanced numerical tools to design and estimate the load capacity of its products. That is why numerical analyzes are becoming a common standard in this branch of manufacturing. Such trend causes either the use of advanced computational models that take into account the full 3D geometry of the flat and wavy layers of corrugated board, or the use of homogenization techniques to simplify the numerical model. The article presents theoretical considerations that extend the numerical homogenization technique already presented in our previous work. The proposed here homogenization procedure also takes into account the creasing and / or perforation of corrugated board, i.e. processes that undoubtedly weaken the stiffness and strength of the corrugated board locally. However, it is not always easy to estimate how exactly these processes affect the bending or torsional stiffness. What is known for sure is that the degradation of stiffness depends, among other things, on the type of cut, its shape, the depth of creasing, as well as their position or direction in relation to the corrugation direction. The method proposed here can be successfully applied to model smeared degradation in a finite element or to define degraded interface stiffnesses on a crease line or a perforation line.

Wireless sensor networks are attractive largely because they need no wired infrastructure. But precisely this feature makes them energy constrained. Recent studies find that sensing behaviors that are otherwise deemed efficient consume comparable energy with communication. The duty cycle scheduling is perceived as contributing to achieving energy efficiency of sensing. Because of different research assumptions and objectives, various scheduling schemes have various emphases. This paper designed an adaptive sensing scheduling strategy. The objective function of the scheduling strategy includes minimizing average energy expenditure and maximizing sensing coverage (reducing event miss-rate), and it requires relatively loose assumptions. We determine the functional relationship between the variables of the objective function and the step-size parameters of the proposed strategy through the numerical fitting. We found that the objective function aggregated by the fitting functions is a bivariate multi-peak function that favors the Fibonacci tree optimization algorithm. Once the optimization of parameters is done, the strategy can be easily deployed and behaves consistently in the coming hours. We name the proposed strategy as “FTOS”. The experimental results show that the Fibonacci tree optimization algorithm gets a better optimistic effect than the comprehensive learning particle swarm optimization (CLPSO) algorithm and differential evolution (DE) algorithm. The FTOS strategy is superior to the fixed time scheduling strategy in achieving the scheduling objectives. It also outperforms other strategies with the same scheduling objectives such as LDAS, BS, DSS and PECAS.

The article is focused on detailed framework process for hierarchized construction of periodic freight train paths (PFTPs) – allocation of pre-arranged railway capacity to freight rail operators. The framework process considers fluctuations in demand for capacity from freight rail opera-tors, so the quality of a freight train path is related with its construction priority. Introduced framework process aims to offer freight rail operators attractive train paths, with low number of scheduled stops, and this way enhance competitiveness and decrease energy consumption of freight railway as a factor for sustainable development. The proposed generic process is in-tended for all time horizons of capacity allocation. Correctness of the framework process is tested on the example of mainline Prague – Dresden, in the context of prospective (denser) mod-el passenger timetable.

An accurate understanding of jointed rock mass behavior is important in many applications ranging from deep geological disposal of nuclear waste to deep mining to urban geoengineering projects. The roughness of rock fractures and the matching of the fracture surfaces are the key contributors to the shear strength of rock fractures. In this research, push shear tests with three normal stress levels of 3.6, 6.0, and 8.5 kPa were conducted with two granite samples with artificially induced well-matching tensile fractures with sizes of 500 mm × 250 mm and 1000 mm × 500 mm. The large sample reached on average a -60 % weaker peak shear stress than the medium-sized sample, and a strong negative scale effect was observed in the peak shear strength. The roughness of the surfaces was measured using a profilometer and photogrammetry. The scale-corrected profilometer-based method (JRC) underestimates the peak friction angle for the medium-sized slabs by -27 % for the medium sample and -9 % for the large sample. The photogrammetry-based (Z’2) method produces an estimate with -7% (medium) and +12 % (large) errors. The photogrammetry-based Z’2 is an objective method that consistently produces usable estimates for the JRC and peak friction angle.

Multiaxial fatigue experiments under asynchronous loadings with four different loading frequency ratios were carried out on 30CrMnSiA steel. The experimental results show that the fatigue life decreases when the axial or torsion frequency increases from 1 to 2, while there is no significant change when the axial or torsion frequency increases from 2 to 4. The surface crack paths are observed and show that cracks initiate on the maximum shear stress amplitude planes, propagate approximately tens of microns, and then turn to propagate along the maximum normal stress planes. The number of secondary cracks increases when the axial or torsion frequency increases. Subsequently, the Bannantine-Socie and Wang-Brown cycle counting methods along with various multiaxial fatigue criteria and Palmgren-Miner’s cumulative damage rule were used for fatigue life prediction. The experimental results are consistent with the fatigue life predicted by the Bannantine-Socie method with the section critical plane criterion for 30CrMnSiA steel under asynchronous loading paths.

This research presents the design and characteristics of new wideband polarizer based on the square waveguide with irises. Matching and polarization characteristics of the polarizer have been simulated and optimized. Frequency dependences of the characteristics are presented. Developed polarizer can be applied in satellite information systems.

Highlights: A new material with a bending strength higher than that of concrete was developed using vegetable or fruit waste. The new material maintains the color, taste, and flavor of the original vegetable or fruit. Without water resistant treatment, the material is edible and can be conditioned with seasonings

AAs Solar Photovoltaics in buildings reaches maturity, grid integration and economic yield are topics of greater interest. The price reduction of photovoltaics has driven a shift in support policies of PV installations in buildings from the feed-in-tariff model, to net-metering and net-billing schemes. The traditional design of photovoltaic installations has considered the optimal orientation of photovoltaic modules to be that which yields the maximum annual energy production. The influence of the consumption patterns and the hourly-variable electricity prices implies that this traditional optimal design might not be the most profitable. Using a full-year dataset for a residential installation, alternative installations using canopies and modules attached to the façades are simulated. Evaluating these alternative installations against a traditional optimal installation, under the Spanish regulation, it is found that the canopy and façade installations offer comparable economic performance despite having a 23% and 39% lower energy yield respectively. The economic evaluation under the new electricity tariffs in Spain shows a better profit for PV self-consumption, reducing by more than 2 years the time of return on investment. The analysis of different alternatives for an industrial PV during the design stage, has allowed us to identify several benefits for these orientations.

In contemporary design practices, there is a disconnect between the design techniques used for early-stage design experimentation and performance analysis, and those used for the manufacture and construction. This study addresses the problems in developing an integrated digital design workflow and provides a research framework for integrating environmental performance requirements with robotic manufacturing processes on a construction site. The proposed method enables the user to import a design surface, identify design parameters, set several environmental performance goals, and thereafter simulate and select a robotic building strategy. Based on these inputs, design alternatives are developed and evaluated, considering their robotically simulated constructibility, in terms of their performance criteria. To validate the proposed method, the design is evaluated in an experiment wherein a double-skin facade perforation is generated using the proposed methodology. The results suggest a heuristic feature to improve the simulated robotic constructibility. Moreover, the functionality of the prototype is demonstrated.

Combined systems for power production and thermally activated cooling have a high potential for improving the efficiency and utilisation of thermal systems. In this regard, various configurations have been proposed and are comprehensively reviewed. They are primarily based on absorption systems and the implementation of multiple levels of complexity and flexibility. The configuration of the absorption power and cooling combined cycle proposed herein has wide commercial applicability owing to its simplicity. The configuration of the components is not new. However, the utilisation of aqueous salt solutions, the comparison with ammonia chiller and with absorption power cycles, the focus on parameters that are important for real-life applications, and the comparison of the performances for constant heat input and waste heat recovery are novel. The proposed cycle is also compared with a system based on the organic Rankine cycle and vapour compression cycle. An investigation of its performance proves that the system is suitable for a given range of boundary conditions from a thermodynamic standpoint, as well as in terms of system complexity and technical feasibility. New possibilities with regard to added power production have the potential to improve the economics and promote the use of absorption chiller systems.

Cemented rock fill (CRF) is commonly used in cut-and-fill stoping operation in underground mining. This allows for the maximum recovery of ore. Backfilling can improve stope stability in underground workings, and then improve ground stability of the whole mine site. Backfilling step scenarios vary from site to site. This paper presents the investigation of five different backfilling step scenarios and their impacts on the stability of stopes at four different mining levels. A comprehensive comparison of displacements, major principal stress and stress concentration factor (SCF) was conducted. The results show that different backfilling step scenarios have little influence on the final displacement for displacement in the stopes. Among the five backfilling scenarios, the major principal stress and stress concentration factor (SCF) have almost the same final results. The backfilling scenario SCN-1 is the optimum option among these five backfilling scenarios. It can immediately prevent the increase of the displacement and reduce the sidewall stress concentration, thereby preventing possible failures. Using the same strength of CRF can achieve same effects among the four mining levels. Applying backfilling CRF of the same strength at different mining depths is acceptable and feasible to improve the stability of the stopes.

Pneumonia is a leading cause of death worldwide, and one of the most significant approaches to diagnose pneumonia is Chest X-ray (CXR) since it was used in clinical scenes. Convolutional neural networks (CNNs) have been widely used in computer vision community. Along with the development of CNNs, we want to make use of CNNs to recognize CXR of people who get pneumonia and make classification. It is important, especially during epidemic period. In this paper, we present a new type of residual learning framework, PEPX-Resnet, which makes use of a type of lightweight residual, and apply this network to CXR dataset. The result shows that PEPX-Resnet is easier to optimize and can have better results, especially for COVID-19 cases. PEPX-Resnet could reach higher accuracy, f1 score and some other evaluations for CXR dataset.

The distributed long-range sensing system using the standard telecommunication single-mode optical fiber in a function of a distributed sensor for sensing of mechanical vibrations is described. Various events generating vibrations such as walking or running person, moving cars, trains and others can be detected, localized and classified. The sensor and related sensing system components were designed and constructed and the system was tested both in the laboratory and in the real situation with 88 km telecom optical link, and the results are presented.

The change in the value of the breaking energy is discussed here for selected steel grades used in building structures after subjecting the samples made of them to episodes of heating in the steady-state heating regime and then cooling in a simulated fire conditions. These changes were recorded based on the instrumented Charpy impact tests, in relation to the material initial state. The S355J2+N, 1H18N9T steels and also X2CrNiMoN22-5-3 duplex steel were selected for detailed analysis. The fire conditions were modelled experimentally by heating the samples and then keeping them for a specified time at a constant temperature of: 600°C (first series) and 800°C (second series), respectively. Two alternative cooling variants were investigated in the experiment: slow cooling of the samples in the furnace, simulating the natural fire progress, without any external extinguishing action, and cooling them in water mist simulating an extinguishing action by a fire brigade. The temperature of the tested samples was set at the level -20oC and alternatively at the level + 20oC. The conducted analysis is aimed at assessing the risk of sudden, catastrophic fracture of load-bearing structure made of steel degraded as a result of a fire previously occurred with different development scenarios.

Current, application-driven trends towards larger-scale integration (LSI) of microfluidic systems for comprehensive assay automation and multiplexing pose significant technological and economical challenges to developers. By virtue of their intrinsic capability for powerful sample preparation, centrifugal systems have attracted significant interest in academia and business since the early 1990s. This review models common, rotationally controlled valving schemes at the heart of such “Lab-on-a-Disc” (LoaD) platforms to predict critical spin rates and reliability of flow control mainly based on geometries, location and liquid volumes to be processed, and their experimental tolerances. In absence of larger-scale manufacturing facilities during product development, the method presented here facilitates the provision of efficient simulation tools for virtual prototyping and characterization to greatly expedite design optimization according to key performance metrics. This virtual in silico approach thus significantly accelerates, de-risks and lowers costs along the critical advancement from idea, fluidic testing, bioanalytical validation and scale-up to commercial mass manufacture.

In recent years, electrochemical methods utilizing reactive electrochemical membranes (REM) have been recognized as the most promising technologies for the removal of organic pollutants from water. In this paper, we propose a 1D convection-diffusion-reaction model concerning the transport and oxidation of oxalic acid (OA) and oxygen evolution in the flow-through electrochemical oxidation system with REM. It allows the determination of unknown parameters of the system by treatment of experimental data and predicts the behavior of the electrolysis setup. There is a good agreement in calculated and experimental data at different transmembrane pressures and initial concentrations of OA. The model provides an understanding of the processes occurring in the system and gives the concentration, current density, potential and overpotential distributions in REM. The dispersion coefficient was determined as a fitting parameter and it is in good agreement with literary data for similar REMs. It is shown, that the oxygen evolution reaction plays an important role in the process even under the kinetic limit, and its contribution decreases with increasing total organic carbon flux through the REM.

The present study aims to develop a risk-based approach to find optimal solutions for life extension management for offshore wind farms based on Markowitz’s modern portfolio theory, adapted from finance. The developed risk-based approach assumes that the offshore wind turbines (OWT) can be considered as cash-producing tangible assets providing positive return from the initial investment (capital) with a given risk attaining the targeted (expected) return. In this regard, the present study performs a techno-economic life extension analysis within the scope of the multi-objective optimisation problem. The first objective is to maximise the return from the overall wind assets, while the latter aims to minimise the risk associated with obtaining the return. In formulating the multi-dimensional optimisation problem, the life-extension assessment considers the results of a detailed structural integrity analysis, free-cash-flow analysis, and probability of project failure, local and global economic constraints. Further, the risk is identified as the variance from the expected mean of return on investment. The risk-return diagram is utilised to classify the OWTs of different classes using an unsupervised machine learning algorithm. The optimal portfolios for the various required rate of return are recommended for different stages of life extension.

The increasing amount of municipal sludge in China requires safe and effective management to protect human health and ensure environmental sustainability. Pyrolysis is a thermochemical process that that decompose organic matter at elevated temperature and under anaerobic conditions, and it has attracted an increasing attention in sludge treatment in the recent years. However, comprehensive environmental and economic assessment of sludge pyrolysis in China's context is rare, due to the small quantities of full-scale sludge pyrolysis plant. In this paper, we applied our design and operation parameters of full-scale sludge pyrolysis plants to generate the material and energy consumptions of the pyrolysis system under various of conditions, including sludge organic content and moisture content, system size, system energy distribution, and whether or not heat substitution is applied. Life cycle assessment and techno-economic assessment were then applied to investigate the environmental and economic performance of the system Our results demonstrate the significant environmental and economic impacts associated with sludge properties and system size. Generally, sludge with higher organic content and lower moisture content requires less natural gas consumption, which leads to a simultaneous improvement of the system environmental and economic performance. The system economic performance is more sensitive to the system size, and centralized sludge handling using a larger pyrolysis is more economic favorable. In the most ideal case, the average global warming potential and minimum sludge handling price of sludge pyrolysis could be as low as -32.5 kg CO₂-Eq/t DS and 188.8 $/t DS, respectively. Based on these results, we discussed the pathways that could be taken to further optimize the environmental and economic performances of the pyrolysis system.

Drilling of wells for oil and gas production is a highly complex and expensive part of reservoir development. Thus, together with injury prevention, there is a goal to save cost expenditures on downtime and repair of drilling equipment. Nowadays companies have begun to look for ways to improve the efficiency of drilling and minimize non-production time with the help of new technologies. To support decisions in a narrow time frame, it is valuable to have an early warning system. Such a decision support system will help an engineer to intervene in the drilling process and prevent high expenses of unproductive time and equipment repair due to a problem. This work is describing a comparison of machine learning algorithms for anomaly detection during well drilling. Tested models classify drilling problems based on historical data from previously drilled wells. To validate anomaly detection algorithms, we use historical logs of drilling problems for 67 wells at a large brownfield in Siberia, Russia. Wells with problems were selected and analyzed. It should be noted that out of the 67 wells, 20 wells were drilled without expenses for unproductive time. Experiential results illustrated that a model based on gradient boosting can classify the complications in the drilling process best of all.

Binary switches, which are the primitive units of all digital computing and information processing hardware, are usually benchmarked on the basis of their ‘energy-delay product’ which is the product of the energy dissipated in completing the switching action and the time it takes to complete that action. The lower the energy-delay product, the better the switch (supposedly). This approach ignores the fact that lower energy dissipation and faster switching usually come at the cost of poorer reliability (i. e. higher switching error rate) and hence the energy-delay product alone cannot be a good metric for benchmarking switches. Here, we show the trade-off between energy dissipation, energy-delay product and error-probability, for both an electronic switch (a metal oxide semiconductor field effect transistor) and a magnetic switch (a magnetic tunnel junction switched with spin transfer torque). As expected, reducing energy dissipation and/or energy-delay-product generally results in increased switching error probability and reduced reliability.

This work aims to contribute to better understanding the use of public street spaces. (1) Background: In this sense, with a multidisciplinary approach, the objective of this work is to propose an experimental and reproducible method on a large scale. (2) Study area: The applied methodology uses artificial intelligence to analyze Google Street View (GSV) images at street level. (3) Method: The purpose is to validate a methodology that allows to characterize and quantify the use (pedestrians and cars) of some squares in Rome belonging to different historical periods. (4) Results: Through the use of machine vision techniques, typical of artificial intelligence and which use convolutional neural networks, a historical reading of some selected squares is proposed with the aim of interpreting the dynamics of use and identifying some critical issues in progress. (5) Conclusions: This work validated the usefulness of a method applied to the use of artificial intelligence for the analysis of GSV images at street level.

Delay-tolerant networks (DTN) is an approach to deal with scarce network connectivity found in sparse mobile ad-hoc networks (MANETs) which makes the problem of routing messages a challenging task. DTNs have find their usefulness in many challenging environments such as tactical networks, underwater sensor networks, wildlife monitoring, disaster recovery etc. Pocket Switched Networks (PSNs) have emerged as a new application of the delay tolerant networks where network nodes are computing devices carried by humans. Hence, the study of how humans interact in their day-to-day life, the places they visit frequently, the people they meet frequently, the social groups in which they participate on regular basis etc. can help improve routing process in PSNs. This type of routing inspired from the way humans interact with each other is referred to as social based routing and had been a recent topic of research in the field of DTNs. This paper presents a comprehensive survey of the various social-based algorithms that have been designed for Delay Tolerant Networks.

Wearable vital signs monitoring and specially the electrocardiogram have taken important role due to the information that provide about high-risk diseases, it has been evidenced by the needed to increase the health service coverage in home care as has been encouraged by WHO. Some wearables devices have been developed to monitor the ECG in which the location of the measurement electrodes is modified respect to the Einthoven model. However, mislocation of the electrodes on the torso can lead to the modification of acquired signals, diagnostic mistakes and misinterpretation of the information in the signal. This work presents a volume conductor evaluation and an ECG signal waveform comparison when the location of electrodes is changed, to find a electrodes’ location that reduces distortion of interest signals. In addition, effect of motion artifacts and electrodes’ location on the signal acquisition are evaluated. A group of volunteers was recorded to obtain ECG signals, the result was compared with a computational model of the heart behavior through the EA ECG, DTW and SNR methods to quantitatively determine the signal distortion. It was found that while the Einthoven method is followed, it is possible to acquire the ECG signal from the patient’s torso or back without a significant difference, and the electrodes position can be moved 6cm at most from the suggested location by the Einthoven triangle in Mason-Likar’s method.

The motivations for the move to electrified vehicles are discussed with reference to their improved energy efficiency, their potential for lower CO2 emissions (if the electricity system is decarbonized), their lower (or zero) NOx/particulate matter (PM) tailpipe emissions, and the lower overall costs for owners. Some of the assumptions made in life-cycle CO2 emissions calculations are discussed and the effect of these assumptions on the CO2 benefits of electric vehicles are made clear. A number of new tribological challenges have emerged, particularly for hybrid vehicles that have both a conventional internal combustion engine and a battery, such as the need to protect against the much greater number of stop-starts that the engine will have during its lifetime. In addition, new lubricants are required for electric vehicle transmissions systems. Although full battery electric vehicles (BEVs) will not require engine oils (as there is no engine) they will require a system to cool the batteries – alternative cooling systems are discussed, and where these are fluid based, the specific fluid requirements are outlined.

Even though groundwater-based irrigation has increased agricultural productivity and economic activity in the United States (US), the current rate of groundwater withdrawals from major aquifers could significantly affect the sustainability of agricultural production systems in the near future. In the major agricultural regions, producers are now facing challenges to irrigate to meet full crop water needs. There is an increasing need to strategize irrigation management under various climatic and environmental conditions to optimize water use in agriculture while optimizing crop yields. This study reviews some of the major challenges facing irrigated agriculture in the US and the potential measures to ensure the sustainability of groundwater-based irrigation. Identified challenges included diminishing quantity and quality of groundwater resources, frequent droughts, low adoption rates of precision irrigation technologies, and rising energy requirements for irrigation abstractions. Irrigation efficiency improvements, shifting to high water use efficiency crops, wastewater reuse, groundwater monitoring, availing incentives, and policy changes, were identified as promising water management strategies to ensure irrigation sustainability. The success of these strategies will depend on the uptake and adoption by the producers in the affected agricultural regions.

Ferric oxide containing waterworks sludge can be used to reduce formation of hydrogen sulfide during anaerobic digestion. The ferric compound is reduced biochemically in the digester and forms insoluble pyrite in digester sludge. Often virgin ferric chloride is used to solve the hydrogen sulfide problem. Since 2013, Sydvatten AB has supplied a growing number of digestion plants in Sweden with ferric containing dewatered waterworks sludge from the drinking water treatment plant Ringsjöverket to limit the formation of hydrogen sulfide. At the waterworks, ferric chloride is added to enhance coagulation of organic matter from the source water. The sludge formed in this process is dewatered and landfilled, but also recycled in the biogas production in order to reduce the hydrogen sulfide concentration. In this study, the use of sludge, for hydrogen sulfide removal in digesters, was technically and economically evaluated from cases studies from 13 full-scale digesters in Sweden. Compared with use of fresh ferric chloride, the operational costs are reduced up to 50 % by using sludge. The quality of the sludge is high and its content on metals is low or very low, especially when compared with requirements from different certification standards on biosolid reuse applied in Sweden. An addition of waterworks sludge containing iron to a digester for removal of dissolved hydrogen sulfide is a technically and economic good alternative when producing biogas. It is also one step closer to a circular economy, when replacing use of virgin chemicals with the by-product waterworks sludge which saves energy, material and reduces the carbon foot-print of the waterworks.

The concept of this prototype is to create a cold air film layer, in indoor side of the window. The function of this extra cold air layer is to increase the amount of window resistance and decrease its U-Value. It provide air film layer by using the same concept of desert cooler or wind catcher. They both bring the hot air from outside then let it go throw straw miost with cold water. After hot air move through straw, it would become cold, thus increase the window resistance and decrease the haet gian, therefore, decrease the cooling load. So, this window model works by heating the air in air room by sun radiation, then after a while, air pressure will rise and its volume will increase then it will impulse to wet hay, and become cold. Then there is fins to guide air flow to its correct direction, thus inner side of window will become cold, not like other type of window which when temperature rise, the inner side of window become hot. So, this window prevents heat transition and creare regenereted trubouland air flim in the inner side of the window. Using calculation to find air film characteristics and WINDOW7.7 software to calculate heat gain through window, shows that it is expected from this prototype to reduce heat gain by 6.85 % for double glazed clear window.

The application of electrode-based microfluidic devices in biological entity often imposes a problem due to joule heating. The strong applied potentials or micro channels having narrow cross sections generate undesirable temperature inside the microfluidic channels leading to strong thermal distribution inside the micro channel. When intrinsic distribution of temperature, if not fix with threshold value, causes device damage or cell loss. In this work, we investigate the effects of temperature generated due to joules heating effects and we attempt to address the design constraints for minimizing the joule heating effects in the microfluidic device for developing effective microfluidic device. The device reliability was analyzed under different parametric constraints for various types of substrate materials (PDMS, PMMA, Polyimide and glass). We also attempt to investigate the effects of cell reliability due to strong temperature gradients generated through different applied potentials on different cell types. Furthermore, the response of the device performance due to different electrode configuration and different conductivity of the medium was also studied. Our investigation will eventually provide guidelines for microfluidic researchers to fabricate efficient electrode based microfluidic device which will ultimately help to choose a critical channel dimensions, threshold potentials, and conductivity of solutions in order to avoid device damage and cell loss.

As of 2020, public data for game ratings provided by Game Ratings And Administration Committee(GRAC) are more limited than public data for movie and video ratings provided by Korea Media Ratings Board and do not provide data which allow us to see information on ratings clearly and in detail. To get information on game ratings, we need to find information by searching for specific target on homepage which is inconvenient for us. In order to improve such inconvenience and extend scope of provision in public data, the author of this paper intends to study public data API which has been extended based on information on video ratings. To draw items to be extended, this study analyzes data for ratings on homepage of GRAC and designs collection system to build database. This study intends to implement system that provides data collected based on extended public data items in a form which users want. This study is expected to provide information on ratings to GRAC which will strengthen fairness and satisfy game users and people’s rights to know and contribute to promotion and development of game industry.

The concept of DC power distribution has gained interest within the research community in the past years; especially due to rapid prevalence of solar PVs as a tool for distributed generation in DC microgrids. Various efficiency analyses have been presented for the DC distribution paradigm, in comparison to the AC counterpart, considering a variety of scenarios. However, even after a number of such comparative efficiency studies, there seems to be a disparity in the results of research efforts - wherein a definite verdict is still unavailable: 'Is DC distribution a more efficient choice as compared to the conventional AC system?' A final verdict is absent primarily due to conflicting results. In this regard, system modeling and the assumptions made in different studies play a significant role in affecting the results of the study. The current paper is an attempt to critically observe the modeling and assumptions used in the efficiency studies related to the DC distribution system. Several research efforts will be analyzed for their approach towards the system upon which they have performed efficiency studies. Subsequently, the paper aims to propose a model that may alleviate the shortcomings in earlier research efforts and be able to give a definite verdict regarding the comparative efficiency of DC and AC networks for residential power distribution.

The transition from Building Information Modelling (BIM) to Heritage Building Information Modelling (H-BIM) is intended to pursue an adequate knowledge of the artefact that is to be preserved, progressively replacing the traditional methods of the restoration and structural reinforcement projects with new tools for managing both existing information and new interventions. The aim of the paper is to show the application of the H-BIM method to a stone pavement road located in the Archaeological Site of Pompeii. In detail, starting from a Laser Scanner based survey, juxtaposed by coordinated points georeferenced through a total station, points clouds were handled by means of several BIM-based tools to perform the road design process, starting from the digital elevation model (DEM) to the corridor representation. Subsequently, a visual programming application based on Python language was adopted to update corridor information by means of the object property set. As preliminary results, a tool, complete with graphical and non-graphical information, is proposed to be used in conservation, maintenance and restoration project.

Lignocellulosic fibers are widely applied as composite reinforcement due to their properties. The thermal degradation behavior determines the maximum temperature in which the fiber can be applied without significant mass loss. It is possible to determine these temperatures using Thermogravimetric Analysis (TG). In particular, when curves are obtained at different heating rates, kinetic parameters can be determined and more detailed characteristics of the material are obtained. However, every curve obtained at a distinct heating rate demands material, cost, and time. Methods to predict thermogravimetric curves can be very useful in the materials science field and in this sense mathematical approaches are powerful tools if well employed. For this reason, in the present study, curaua TG curves were obtained at three different heating rates (5, 10, 20, and 40 °C.min-1) and Vyazovkin kinetic parameters were obtained. After, the experimental curves were fitted using an artificial neural network (ANN) approach followed by a Surface Response Methodology (SRM). Curves at any heating rate between the minimum and maximum experimental heating rates were obtained with high reliability. Finally, Vyazovkin kinetic parameters were tested again with the new curves showing similar kinetic parameters from the experimental ones. In conclusion, due to the capability to learn from the own data, ANN combined with SRM seems to be an excellent alternative to predict TG curves that do not test experimentally, opening the range of applications.

AAs Solar Photovoltaics in buildings reaches maturity, grid integration and economic yield are topics of greater interest. The price reduction of photovoltaics has driven a shift in support policies of PV installations in buildings from the feed-in-tariff model, to net-metering and net-billing schemes. The traditional design of photovoltaic installations has considered the optimal orientation of photovoltaic modules to be that which yields the maximum annual energy production. The influence of the consumption patterns and the hourly-variable electricity prices implies that this traditional optimal design might not be the most profitable. Using a full-year dataset for a residential installation, alternative installations using canopies and modules attached to the façades are simulated. Evaluating these alternative installations against a traditional optimal installation, under the Spanish regulation, it is found that the canopy and façade installations offer comparable economic performance despite having a 23% and 39% lower energy yield respectively. The economic evaluation under the new electricity tariffs in Spain shows a better profit for PV self-consumption, reducing by more than 2 years the time of return on investment. The analysis of different alternatives for an industrial PV during the design stage, has allowed us to identify several benefits for these orientations.

This paper proposes a novel efficient multistage algorithm to extract source speech signals from a noisy convolutive mixture. The proposed approach comprises of two stages named Blind Source Separation (BSS) and De-noising. A hybrid source prior model separates the source signals from the noisy reverberant mixture in the BSS stage. Moreover, we model the low and high-energy components by generalized multivariate Gaussian and super-Gaussian models, respectively. We use Minimum Mean Square Error (MMSE) to reduce noise in the noisy convolutive mixture signal in the de-noising stage. Furthermore, two proposed models investigate the performance gain. In the first model, the speech signal is separated from the observed noisy convolutive mixture in the BSS stage, followed by suppression of noise in the estimated source signals in the de-noising module. In the second approach, the noise is reduced using the MMSE filtering technique in the received noisy convolutive mixture at the de-noising stage, followed by separation of source signals from the de-noised reverberant mixture at the BSS stage. We evaluate the performance of the proposed scheme in terms of signal-to-distortion ratio (SDR) with respect to other well-known multistage BSS methods. The results show the superior performance of the proposed algorithm over the other state-of-the-art methods.

This study investigates the hybridization scenario of a single flash geothermal power plant with a biomass driven sCO2-steam Rankine combined cycle where a solid local biomass source, olive residue, is used as a fuel. The hybrid power plant is modeled using the simulation software EB-SILON®Professional. A topping sCO2 cycle is specifically chosen for its potential for flexible elec-tricity generation. A synergy between the topping sCO2 and bottoming steam Rankine cycles is achieved by a good temperature match between the coupling heat exchanger where the waste heat from the topping cycle is utilized in the bottoming cycle. The high temperature heat addition problem common sCO2 cycles is also eliminated by utilizing the heat in the flue gas in the bottoming cycle. Combined cycle thermal efficiency and biomass to electricity conversion efficiency of 24.9% and 22.4% are achieved, respectively. The corresponding fuel consumption of the hybridized plant is found as 2.2 kg/s.

Building Engineering or Architectural engineering, is a major of science that deals with engineering aspects in buildings, such as structure, construction, site management, mechanical, electric, lighting, acoustic, and energy efficiency. Building engineering is one of unique sciences that is strongly connected with all other types of engineering majors. In addition, it is connected to human basic life directly, and its application is able to noticed easily. However, this research will focus on the field of energy efficiency and opening construction in buildings. The reason why building energy conservation grab more researchers ‘attention is the fact of its relation with all other engineering aspects, and it is a building property that is very important along building life cycle. Currently humanity are living in world that energy market is biggest market in the world after weapons(Levy 2010). Nevertheless, energy in middle east is an important factor, especially when we know that country like Saudi Arabia, 75% of inside energy consumption goes to residential buildings(Of and Audits n.d.). And the reason why these buildings consume that much refers to high usage of energy in building applications, and leak of using eco-friendly building material that able to reduce energy consumption in buildings.

The significance of virtual reality (AR/VR/MR) technology stands out because it can be used in various construction fields such as urban design, construction review, maintenance and education, etc. As to prove this, conglomerates such as Facebook, Google, Sony, Microsoft, and Samsung are increasing their investments to preoccupy the virtual reality market and are competing to dominate the contents and platform market. Virtual reality technology has a concept that collectively refers to VR (Virtual Reality), AR (Augmented Reality), and MR (Mixed Reality), but technically, the two technologies are strictly separated. While VR technology is an immersive virtual environment using computer graphic technology, AR technology is a more advanced technology that combines real data and VR data, and MR technology is a technology that combines both AR and VR. In Korea, Republic of, the use of BIM (Building Information Modeling), a 3D information model, becomes mandatory and based on this, the demand for a new market where VR/AR/MR technologies and advanced sensing equipment are combined increases. This study implemented a building maintenance platform using AR based on 5G, and developed matching technology between BIM of the building maintenance platform and AR equipment. Besides this study implemented the technology to enhance the matching rate of the matching technology and carried out the process to enhance the matching rate empirically through continuous tests. As a result, the initial target of 90% matching rate could be improved to 96%. Based on this, it is expected that effective EV (Optimum Life Cycle Cost) could be achieved at a lower cost compared to the previous one by using it for maintenance monitoring after construction and completion of the building, and can be used as an effective solution in the aspect of building maintenance as well.

The forecast of electricity demand has been a recurrent research topic for decades, due to its economical and strategic relevance. Several Machine Learning (ML) techniques have evolved in parallel with the complexity of the electric grid. This paper reviews a wide selection of approaches that have used Artificial Neural Networks (ANN) to forecast electricity demand, aiming to help newcomers and experienced researchers to appraise the common practices and to detect areas where there is room for improvement in the face of the current widespread deployment of smart meters and sensors, which yields an unprecedented amount of data to work with. The review looks at the specific problems tackled by each one of the selected papers, at the results attained by their algorithms, and at the strategies followed to validate and compare the results. This way, it is possible to highlight some peculiarities and algorithm configurations that seem to consistently outperform others in specific settings.

Integration of battery energy storage in photovoltaic (PV) systems can reduce the electricity costs and provide desirable flexibility and reliability to these systems decreasing renewable energy fluctuations. This paper presents a review of the PV-battery application in Brazil, highlighting the challenges and prospects based on the state-of-art. A PV-battery systems description is presented in this work, as well as the most applied battery technology and its comparison. The paper also describes the set of applications such as voltage and frequency regulation, renewable energy integration, power quality, etc. In the Brazilian scenario, there are applications of PV-battery systems, most of them part of research and development projects (R&amp;D’s), and some real cases are shown, including its goals, applied equipment, operation modes, strategies, and perspectives. Additionally, this work evaluates the Brazilian scenario regarding the energy storage systems implementation challenges, such as regulatory barriers, business models, and opportunities for R&amp;D in the energy market. In conclusion, it is need develop proper regulatory models to expand PV-battery systems and make them visible to the agents in the electricity sector.

Climate change has emerged as one of the defining issues of the early 21st century. It is now more certain than ever and poses a serious threat to sustainable urban development. Climate change has many destructive effects on cities. Land subsidence, rising air pollutants, severe storms, dust and water scarcity are just some effects of this phenomenon in the urban area of ​​Tehran. Urban management must be prepared to deal with these effects and adopt policies to mitigate and adapt to climate change. One of these tools in urban planning is urban development plans. The plans can have a great impact on controlling and counteracting with the effects of climate change. In this research, using content analysis method, 6 dimensions and 31 indicators have been used to evaluate 8 city and region development plans of Tehran from the view of reflecting the effects of climate change. Indicators were scored after studying the content of the plans. Findings indicate that among the reviewed plans, the second and third five-year development plans of Tehran with 61.9% and 61.3% as the highest rate and plan to reduce air pollution in Tehran and the transportation master plan with 20.6% and 23.2%, respectively have the least attention to the effects of climate change. Also, among the indicators, the urban transportation sector with 54.3% as the highest and the energy sector with 20.5% have the least attention to mitigation and adaptation policies.