Non-intrusive load monitoring (NILM) is a core technology for demand response (DR) and energy conservation services. Traditional NILM methods are rarely combined with practical applications, and most studies aim to decompose the whole loads in a household, which leads to low identification accuracy. In this paper, an NILM approach based on multi-feature integrated classification (MFIC) is explored, which combines some non-electrical features such as ON/OFF duration, usage frequency of appliances, and usage period to improve load differentiability. The implementation of MFIC algorithm is consistent with traditional event-based method. The uniqueness of our algorithm is that it designs an event detector based on steady-state segmentation and a linear discriminant classifier group based on multi-feature global similarity. Simulation results using an open-access dataset demonstrate the effectiveness and high accuracy of MFIC algorithm, with the state-of-the-art NILM methods as benchmarks.

Scouring is the most common cause of bridge failure. This study was conducted to evaluate the efficiency of the Artificial Neural Networks (ANN) in determining scour depth around composite bridge piers. The experimental data, attained in different conditions and various pile cap locations, were used to obtain the ANN model and to compare the results of the model with most well-known empirical, HEC-18 and FDOT, methods. The data were divided into training and evaluation sets. The ANN models were trained using the experimental data, and their efficiency was evaluated using statistical test. The results showed that to estimate scour at the composite piers, feedforward propagation network with three neurons in the hidden layer and hyperbolic sigmoid tangent transfer function was with the highest accuracy. The results also indicated a better estimation of the scour depth by the proposed ANN than the empirical methods.

Worldwide the steel corrosion is one of the greatest deterioration problems for reinforced concrete structures. Comparing some literature experimental results with a complex FEM model, the present paper points out the principal aspects those characterize the static behavior of RC elements damaged by corrosion. Moreover, the non-dimensional abaci defined for some specific case studies finalized to the evaluation of the residual flexural strength of corroded elements highlight the dangerousness of the corrosion degradation if the failure of the element is governed by the steel

This research aims to introduce a new technique, off-site and self-form segmental concrete masonry arches fabrication, without the need of construction formwork or centering. The innovative construction method in the current study encompasses two construction materials forms the self-form masonry arches, wedge-shape plain concrete voussoirs, and Carbon Fibers Reinforced Polymers (CFRP) composites. The employment of CFRP fabrics was for two main reasons; bond the voussoirs and forming the masonry arches. However, CFRP proved to be efficient for strengthening the extrados of the arch rings under service loadings. An experimental test conducted on four sophisticated masonry arch specimens. Research parameters were using thicker keystone as well as the partial strengthening of the self-form arch ring at the intrados. Major test finding was the use of thicker Keystone, alter the behavior of the self-form arch, considerably increased the load carrying capacity by 79%. Partial strengthening of the intrados with CFRP fabrics of typical arch ring Keystone resulted considerable increased the debonding load of fabrication CFRP sheets by 81%, increase the localized crushing load by 13%, and considerably increase voussoir sliding load by 107%.

In laser-assisted milling, higher temperature in shear zone softens the material potentially resulting in a shift of mean residual stress, which significantly affects the damage tolerance and fatigue performance of product. In order to guide the selection of laser and cutting parameters based on the preferred mean residual stress, inverse analysis is conducted by predicting residual stress based on guessed process parameters, which is defined as the forward problem, and applying iterative gradient search to find process parameters for next iteration, which is defined as the inverse problem. An analytical inverse analysis is therefore proposed for the mean residual stress in laser-assisted milling. The forward problem is solved by analytical prediction of mean residual stress after laser-assisted milling. The residual stress profile is predicted through the calculation of thermal stress, by treating laser beam as heat source, and plastic stress by first assuming pure elastic stress in loading process, then obtaining true stress with kinematic hardening followed by the stress relaxation. The variance-based recursive method is applied to solve inverse problem by updating process parameters to match the measured mean residual stress. Three cutting parameters including depth of cut, feed per tooth, and cutting speed, and two laser parameters including laser-tool distance and laser power, are updated with respected to the minimization of resulting residual stress and measurement in each iteration. Experimental measurements are referred on the laser-assisted milling of Ti-6Al-4V grade 5 and ELI. The percentage difference between experiments and predictions is less than 5% for both materials, and the selection is completed within 50 loops.

The present work is a theoretical and experimental study of temperature effect on wavelength and threshold current. Since Semiconductor lasers are the type of lasers which uses semiconductor material as a gain medium to achieve stimulated emission of radiation. In this module, the type of semiconductor lasers use is VCSEL and laser diode. Temperature change cause Semiconductor lasers to shift its threshold current, this variation also causes a shift in output wavelength. The experimental results highly agreement with the theoretical calculations.

The article presents a mathematical model to investigate the operability envelopes for subsea production tress (SPT) installation using drill pipe. The finite differential method was used to solve the established governing equations in which the ocean conditions were considered. Based on the evaluations of the ocean wave, ocean current, water depth, specification of drill pipe and SPT weight that might dominate the mechanical behaviors of the pipe, the operability envelopes with permissible ocean conditions for SPT installation were obtained. The results indicate that changes of depths in deep water and SPT weight have few effects on the operation conditions and it would be better to choose smaller pipe to obtain larger permissible ocean conditions during SPT installation.

The rapid development of multicopters has led to many security problems. In order to prevent multicopters from invading restricted areas or famous buildings, an Anti-UAV Defense System (AUDS) has been developed and become a research topic of interest. Topics under research in relation to this include electromagnetic interference guns for unmanned aerial vehicles (UAVs), high-energy laser guns, US military net warheads, and AUDSs with net guns. However, these AUDSs use either manual aiming or expensive radar to track UAVs. This paper proposes a dual-axis rotary platform with UAV automatic tracking. The tracking platform uses visual image processing technology to track and lock the dynamic displacement of a UAV. When a target UAV is locked, the system uses a nine-axis attitude meter and laser rangefinders to measure its flight altitude and calculates its longitude and latitude coordinates through sphere coordinates to provide UAV monitoring for further defense or attack missions. Tracking tests of UAV flights in the air were carried out using a DJI MAVIC UAV at a height of 30 meters to 100 meters. It was set up for UAV image capture and visual recognition for tracking under various weather conditions by a thermal imager and a full-color camera respectively. When there was no cloud during the daytime, the images captured by the thermal imager and full-color camera provide a high-quality image recognition result. However, under dark weather, black clouds will emit radiant energy and seriously affect the capture of images by a thermal imager. When there is no cloud at night, the thermal imager performs well in UAV image capture. When the UAV is tracked and locked, the system can effectively obtain the flight altitude and longitude and latitude coordinate values.

The fetal heart rate (FHR) is a screening signal for preventing fetal hypoxia during labor. When experts analyze this signal, they have to position a baseline and then identify decelerations and accelerations. These steps can potentially be automated and made more objective by signal processing analysis. Various methods have been described in the literature but there are no open-source programs for performing these steps. The MATLAB toolbox presented here comprises a standard signal pre-processing function, 11 re-coded literature methods for fetal heart rate analysis, a signal viewer (enabling annotation by an expert) and an evaluation procedure with various criteria measuring intrarater agreement.

The use of new developed high-strength steel in concrete members can reduce steel bars congestion and construction costs. This research aims to study the behavior of concrete columns reinforced with new developed high-strength steel under eccentric loading. Ten reinforced concrete columns were fabricated and tested. The test variables are transverse reinforcement amount and yield strength, eccentricity, and longitudinal reinforcement yield strength. The failure patterns are compression and tensile failure for columns subjected to small eccentricity and large eccentricity, respectively. The same level of post-peak deformability and ductility only can be obtained with lower amount of transverse reinforcement when high-strength transverse reinforcements are used in columns subjected to small eccentricity. The high-strength longitudinal reinforcement can improve bearing capacity and post-peak deformability of concrete columns. Besides, three different equivalent rectangular stress block (ERSB) parameters in predicting bearing capacity of columns with high-strength steel were discussed based on test and simulated results. It is concluded that the Code of GB 50010-2010 overestimates the bearing capacity of columns with high-strength steel, whereas bearing capacities computed using Codes of ACI 318-14 and CSA A23.3-04 agree well with test results.

In this paper, precise examination of multi-port antennas with large mutual coupling between the ports is studied. For this purpose, some vital parameters such as active reflection coefficients and realised gain are emphasized. The main motivation of this research is the ignorance of this issue in several recently published papers. Hence, corrections and criticisms to some recently reported results are presented. At first, a microstrip patch consisting of two inverted U-slots is re-examined, which has been proposed in a recently published paper for wideband applications. However, the authors did not present satisfactory results to indicate the performance of the proposed structure. In reverse, some important parameters such as the antenna gain appear to be incorrectly reported. In this paper, the characteristics of the antenna are compared with a simple 50 Ω microstrip line excited by two probes, indicating no significant differences. In the remainder of this paper, some corrections are provided on two examples of wide-beam circularly polarised multi-fed antennas for satellite applications. The first example is the multi-fed spiral antennas with folded arms, which have been proposed as the widebeam antennas at 435 MHz. It is indicated that the antenna height should be increased for better performance. The second example is the quadrifilar square helical antennas with folded arms, which have been proposed as the widebeam antennas for 435 MHz with minor responses at 145 MHz. Some corrections are presented on the previously reported minor responses at 145 MHz.

This paper presents the design of a high frequency zero voltage switching (ZVS) full-bridge converter with a phase-shifted driving signal for photovoltaic applications. The resonant power converter can provide high-power capacity under high-frequency operation. The proposed power converter can also reduce the size of the transformer under the same power rating. The high-frequency transformer was developed by using the resonant and switching frequencies of the power converter to reduce the switching loss and to improve the system efficiency. Phase-shifted modulation was selected to drive the switches of a full-bridge power converter based on the switching loss minimization method. The desired output voltage was controlled using a closed-loop controller under a loop gain stability margin. The simulation results showed that the output voltage can be controlled to the desired constant when the input voltage changes from 30 VDC to 60 VDC. The desired output voltage of power converter is constant at 400 VDC. The power converter can transfer the DC supply to a 220 VAC household via grid-connected inverter. Therefore, the proposed study showed the effectiveness of the phase-shift ZVS full-bridge power converter with high-frequency transformer. This power converter can control the operation of the desired voltage system and has a small sizing of power converter system, low switching loss, and high system efficiency.

Biowaste generated in the process of Oxytree cultivation and logging represents a potential source of energy. Torrefaction (a.k.a. low-temperature pyrolysis) is one of the methods proposed for the valorization of woody biomass. Still, energy is required for the torrefaction process during which the raw biomass becomes biochar with fuel properties similar to lignite coal. In this work, models describing the influence of torrefaction temperature and residence time on the resulting fuel properties (mass and energy yields, energy densification ratio, organic matter and ash content, combustible parts, lower and higher heating values, CHONS content, H:C and O:C ratios) were proposed according to the Akaike criterion. The degree of the models’ parameters matching the raw data expressed as the determination coefficient (R²) ranged from 0.52 to 0.92. Each model parameter was statistically significant (p<0.05). Estimations of the value and quantity of the produced biochar from 1 Mg of biomass residues were made based on two models and a set of simple assumptions. The value of torrefied biochar (€123.4·Mg^-1) was estimated based on the price of commercially available coal fuel and its lower heating value (LHV) for biomass moisture content of 50%, torrefaction for 20 min at 200 °C. This research could be useful to inform techno-economic analyses and decision-making process pertaining to the valorization of pruned biomass residues.

The present paper aims to increase knowlodge of the methods of resistance estimating of concrete in situ by means of non-destructive tests used to integrate the quantitative results from cylindrical specimens (core). The results of experimental investigations carried out on concrete conglomerate samples of a school building are shown. The experimental campaign then will be presented like a case study, conducted on a series of concrete beams and pillars of an existing building. The distructive tests on cores were conducted at the Civil Structures Laboratory of the Engineering Department of the University of Campania "Luigi Vanvitelli". The expression obtained through the calibration procedure of the values of non-destructive tests with those provided by the core drills allowed to estimate the average values of the compressive strength of the concrete. It is highlighted how this result was achieved with a very limited core number provided that they are extracted in selected points and that there was a proportionality link with the resistances obtained from non distructive tests.

Project complexity is usually considered as one of main causes of cost overruns, resulting in poor performance and thus project failure. However, empirical studies focused on evaluating its effects on project cost remain lacking. Given this circumstance, this study attempts to develop the relationships between project cost and the multidimensional project complexity elements. We establish complexity as a multidimensional factor including the task, organization, market, legal, and environment complexities. This study uses an empirical evidence-based structural model to account for the relationships between project cost and project complexity. By doing so, a quantitative assessment of multi-dimensional project complexity has been developed. The findings suggest that task and organization complexities have direct effects on project cost, while market, legal and external environment complexities have indirect effects on project cost. The practical contribution is that the findings can improve the understanding of which dimension of complexity significantly influence project cost, and the need to focus efforts on strategically addressing that complexities.

Porosity in plasma sprayed coatings is vital for most engineering applications. It is either advantageous or disadvantageous depending on the functionality of the coating and the immediate working environment. Consequently, the formation mechanisms and development of porosity has been extensively explored to find out modes of controlling porosity in plasma sprayed coatings. In this work, a comprehensive review of porosity on plasma sprayed coatings is established. The formation and development of porosity on plasma sprayed coatings are governed by set spraying parameters. Optimized set spraying parameters have been used to achieve the most favorable coatings with minimum defects. Even with the optimized set spraying parameters, defects like porosity still occur. Here, we discuss other ways that can be used to control porosity in plasma sprayed coating with emphasis to atmospheric plasma sprayed chromium oxide coatings. Techniques like multi-layer coatings, nano-structured coatings, doping with rare earth elements, laser surface re-melting and a combination of the above methods have been suggested in adjusting porosity. The influences of porosity on properties of plasma sprayed coatings and the measurement methods of porosity have also been reviewed.

Modern gas turbines firing temperatures (1500-2000K) are well beyond the maximum allowable blade material temperatures. Continuous safe operation is made possible by cooling the HP turbine first stages -nozzle vanes and rotor blades- with a portion of the compressor discharge air, a practice that induces a penalty on the cycle thermal efficiency. Therefore, a current issue is to investigate the real advantage, technical and economical, of raising maximum temperatures much further beyond current values. In this paper, process simulations of a gas turbine are performed to assess HP turbine first-stage cooling effects on cycle performance. A new simplified and properly streamlined model is proposed for the non-adiabatic expansion of the hot gas mixed with the cooling air within the blade passage, which allows for a comparison of several cycle configurations at different TIT (turbine inlet temperature) and max (total turbine expansion ratio) with a realistically acceptable degree of approximation.. The calculations suggest that, at a given max, the TIT can be increased in order to reach higher cycle efficiency up to a limit imposed by the required amount and temperature of the cooling air. Beyond this limit, no significant gains in thermal efficiency are obtained by adopting higher max and/or increasing the TIT, so that it is convenient in terms of cycle performance to design at lower rather than higher max. The small penalty on cycle efficiency is compensated by lower plant cost. The results of our model agree with those of some previous much more complex and computationally expensive studies, so that the novelty of this paper lies in the original method adopted on which the proposed model is based, and in the fast, accurate and low resource intensity of the corresponding numerical procedure: all advantages that can be crucial for industry needs. The presented analysis is purely thermodynamic, with no investigation on the effects of the different configurations on plant costs, so that future work addressing a thermo-economic analysis of the air-cooled gas turbine power plant is the next logical step.

Over the last two decades, residential buildings have accounted for nearly 50 percent of total energy use in New Zealand. In order to reduce household energy use, the factors that influence energy use should be continuously monitored and managed. Building researchers and professionals have made efforts to investigate the factors that affect energy use. However, few have concentrated on the association between household energy use and the cost of residential buildings. This study examined the correlation between household energy use and residential building cost. Analysis of the correlation between energy use data and residential building cost indicated that residential building cost in the construction phase and energy use in the operation stage were significantly correlated. These findings suggest that correct monitoring of building costs can help to identify trends in energy use. Therefore, this study proposes a time series model for forecasting residential building costs of five categories of residential building (one-story house, two-story house, townhouse, apartment, retirement village) in New Zealand. The primary contribution of this paper is the identification of the close correlation between household energy use and residential building costs and provide a new area for optimize energy management.

Concrete beams built from individual segments connected to each other by special mechanism referred as segmental beam. The aim of this research is to exhibit a new technique for fabrication of small scale segmental beams from wedge shape unreinforced concrete segments and pultruded carbon fiber reinforced polymers (CFRP) in laminate form. Eight segmental beams including two parameters are tested experimentally. The first parameter is the area of CFRP and second is the adhesive material used to bond segments of the beams. The test shows that segmental beams have a higher ratio of CFRP area undergoes less deflection and sustain higher ultimate loading value of 38.4%. Moreover, the test of beams with concrete segments adhered by epoxy resin sustain a higher load than segments adhered by cementitious materials about 11.87%. Theoretically, segmental beams were analyzed by modified American Concrete Institute ACI 440.2R-17 report with slight modifications. The analysis results in an overestimation of flexural strength of segmental beams when compared with experimental outcomes.

Considering the manufacturing of automotive components, there exists a dilemma around the substitution of traditional Cast Iron (CI) with lighter metals. Nowadays, aluminium alloys, being lighter compared to traditional materials, are considered as a more environmentally friendly solution. However, the energy required for the extraction of the primary materials and manufacturing of components is usually not taken into account in this debate. In this study, an extensive literature review has been performed to estimate the overall energy required for the manufacturing of an engine cylinder block using (a) cast iron and (b) aluminium alloys. Moreover, data from over 100 automotive companies, ranging from mining companies to consultancy firms, have been collected in order to support the soundness of this investigation. The environmental impact of the manufacturing of engine blocks made of these materials is presented with respect to the energy burden; the “cradle-to-grave approach” has been implemented to take into account the energy input of each stage of the component lifecycle starting from the resource extraction and reaching to the end-of-life processing stage. Our results indicate that although aluminium components contribute towards reduced fuel consumption during their use phase, the vehicle distance needed to be covered in order to compensate for the up-front energy consumption related to the primary material production and manufacturing phases is very high. Thus, the substitution of traditional materials with lightweight ones in the automotive industry should be very thoughtfully evaluated.

In this study, the decision-making process management of forest tending in the forestry business is decentralized, and forest tending decision-making activities at different points in time are integrated by decision makers at different geographical locations. The decision-making process was analyzed and optimized from a system perspective. Based on the optimized decision-making process, a forest tending business group decision support system (FTGDSS) was established. We first reviewed and discussed the characteristics and development of the forest tending business and forestry decision support system. Business Process Modeling Notation was used to draw a current state flow chart of the forest tending business, to identify and discover important decision points in the process of tending decision-making. We also analyzed the content and attributes of each decision point, and described the system structure, functional framework, knowledge base structure, and reasoning algorithm of FTGDSS in detail. Finally, FTGDSS was evaluated from the two dimensions of the technology adoption model. FTGDSS integrates different levels of time-space decision-making activities, historical tending data, business plans, decision-makers' management tendencies into the decision-making process and automatically extracts decision-making data from the forest business process management enterprise resource planning system (Smartforest) that improves the ease of use of the decision support system (DSS). It also improves the quality of forest tending decisions, and enables the DSS to better support multi-target management strategies.

Selective laser melting is an emerging Additive Manufacturing (AM) technology for metals. Intricate three-dimensional parts can be generated from the powder bed by selectively melting the desired location of the powders. The process is repeated for each layer until the part is built. The necessary heat is provided by a laser. Temperature magnitude and history during SLM directly determine the molten pool dimensions, thermal stress, residual stress, balling effect, and dimensional accuracy. Laser-matter interaction is a crucial physical phenomenon in the SLM process. In this paper, five different heat source models are introduced to predict the three-dimensional temperature field analytically. These models are known as steady state moving point heat source, transient moving point heat source, semi-elliptical moving heat source, double elliptical moving heat source, and uniform moving heat source. The analytical temperature model for all of the heat source models are solved using three-dimensional differential equation of heat conduction with different approaches. The Steady state and transient moving heat source are solved using separation of variables approach. However, the rest of models are solved by employing the Green’s functions. Due to the high magnitude of the temperature in the presence of the laser, the temperature gradient is usually high which has a substantial impact on thermal material properties. Consequently, the temperature field is predicted by considering the temperature sensitivity thermal material properties. Moreover, due to the repeated heating and cooling, the part usually undergoes several melting and solidification cycles, this physical phenomenon is considered by modifying the heat capacity using latent heat of melting. Furthermore, the multi-layer aspect of metal AM process is considered by incorporating the temperature history from the previous layer since the interaction of the layers have an impact on heat transfer mechanisms. The proposed temperature field models based on different heat source approaches are validated using experimental measurement of melt pool geometry from independent experimentations. The detailed explanation of the comparison of models is also provided. Moreover, the effect of process parameters on the balling effect is also discussed.

An increasing number of aircraft is equipped with wing tip devices, which either are installed by the aircraft manufacturer at the production line or are retrofitted after the delivery of the aircraft to its operator. Installation of wing tip devices has not been a popular choice for regional turboprop aircraft and the novelty of the current study is to investigate the feasibility of retrofitting the British Aerospace (BAe) Jetstream 31 with an appropriate wing tip device (or winglet) to increase its cruise range performance, taking also into account the aerodynamic and structural impact of the implementation. To the best of the knowledge of the authors, no previous study exists which has attempted to assess the winglet retrofit of an existing aircraft type of similar size and operating profile. The optimal winglet design achieved a 2.38% increase of the maximum range by reducing the total drag by 1.19% at a mass penalty of 3.25%, as compared with the baseline aircraft configuration. Other designs were found to be more effective in reducing the total drag, but the structural reinforcement required for their implementation outweighed the achieved performance improvements. Since successful winglet retrofit programs for typical short to medium range narrow body aircraft report even more than 3% of block fuel improvements, undertaking the project of installing an optimal winglet design to the BAe Jetstream 31, should also consider a Direct Operating Cost (DOC) assessment on top of the aerodynamic and structural aspects of the retrofit.

Existing methods for detecting magnetic leakage signals from damaged wire ropes require axial saturation magnetisation, and the accuracy of the detection equipment depends on the saturation degree of magnetisation. Moreover, the expected magnetisation effect is usually difficult to achieve owing to the special characteristics of the rope structure, diameter, and operating environment of the wire rope. Consequently, in addition to other issues, the detection accuracy and versatility of the equipment are low. In this study, a method based on spatial multidimensional orthogonal array loop magnetisation for detecting mine wire rope defects is proposed. Firstly, a new sensor excitation structure model is developed. Then a method of radiant magnetic flux of permanent magnet array is analysed, and the influence law of the number of permanent radiant magnetic blocks on the magnetisation effect is studied. In addition, a method of wire rope detection based on the principle of clustering is investigated, according to which the influence law of the shape, structure, and size of the polyurethane device on the effect of magnetisation is discussed. Finally, through laboratory experiments, the test model and proposed method are verified. The results show that the magnetisation effect is better and more cost-effective when the number of permanent magnet radiation magnetic block is n=8, and the proposed detection method can effectively distinguish damaged wire rope joints. Furthermore, the proposed equipment achieved signal-to-noise ratio of the signals, improving the reliability of damage detection.

Boiler efficiency is called to some extent of total thermal energy which can be recovered from the fuel. Boiler efficiency losses are due to four major factors: the dry gas flux, the latent heat of steam in the flue gas, the combustion loss or the loss of unburned fuel, radiation and convection losses. In this research, the thermal behavior of boilers in gas refinery facilities is studied and their efficiency and their losses are calculated. The main part of this research is comprised of analyzing the effect of various parameters on efficiency such as excess air, fuel moisture, air humidity, fuel and air temperature, the temperature of combustion gases, and thermal value of the fuel. Based on the obtained results, it is possible to analyze and make recommendations for optimizing boilers in the gas refinery complex using response-surface method (RSM).

Renewable energy sources (RES) will play a crucial role in future sustainable energy systems. In scenarios analyzing future energy system designs, a detailed spatial and temporal representation of renewable-based electricity generation is essential. For this, sufficiently representative weather data are required. Most analyses performed in this context use the historical data of either one specific reference year or an aggregation of multiple years. In contrast, this study analyzes the impact of different weather years based on historical weather data from 1980 through 2015 in accordance with the design of an exemplary future energy system. This exemplary energy system consists of on- and offshore wind energy for power-to-hydrogen via electrolysis, including hydrogen pipeline transport for most southwestern European countries. The assumed hydrogen demand for transportation needs represents a hypothetical future market penetration for fuel cell-electric vehicles of 75%. An optimization framework is used in order to evaluate the resulting system design with the objective function of minimizing the total annual cost (TAC) of the system. For each historical weather year, the applied optimization model determines the required capacities and operation of wind power plants, electrolyzers, storage technologies and hydrogen pipelines to meet the assumed future hydrogen demand in a highly spatially- and temporally-detailed manner, as well as the TAC of the system. Following that, the results of every individual year are compared in terms of installed capacities, overall electricity generation and connection to the transmission network, as well as the cost of these components within each region. The results reveal how sensitive the final design of the exemplary system is to the choice of the weather year. For example, the TAC of the system changes by up to 20% across two consecutive weather years. Furthermore, significant variation in the optimization results regarding installed capacities per region with respect to the choice of weather years can be observed.

This paper presents findings of a study on continuous feed-in management and continuous synthetic inertia contribution with wind turbines. A realistic case study, based on real measurements, is outlined. A wind turbine feeds into a weak feeder, such that its power has to be adapted to the permissible loading of this feeder. At the same time the wind turbine is to provide inertia to the grid by applying the previously published variable inertia constant controller. It is discussed that optimal grid utilisation and simultaneous inertia contribution are mandatory for the frequency control in power systems that are heavily penetrated with renewable energies. The study shows that continuous feed-in management can be combined well with continuous inertia provision. There are hardly any negative consequences for the wind turbine. The benefits for the grid are convincing, both in terms of increased system utilisation and in terms of provided inertia. It is concluded that wind turbines can enhance angular stability in a power system to a larger extent than conventional power plants.

In a thin-volume photobioreactor where a concentrated suspension of microalgae is circulated throughout the established spatial irradiance gradient, microalgal cells experience a time-variable irradiance. Deploying this feature is the most convenient way of obtaining the so-called “flashing light” effect, improving biomass production in high irradiance. This work investigates the light flashing features of sloping wavy photobioreactors, a recently proposed type, by introducing and validating a Computational Fluid Dynamics model. Two characteristic flow zones (straight top-bottom stream and local recirculation stream), both effective toward light flashing, have been found and characterised: a recirculation-induced frequency of 3.7 Hz and straight flow-induced frequency of 5.6 Hz were estimated. If the channel slope is increased, the recirculation area becomes less stable while the recirculation frequency is nearly constant with flow rate. The validated CFD model is a mighty tool that could be reliably used to further increase the flashing frequency by optimising the design, the dimensions, the installation and the operational parameters of the sloping wavy photobioreactor.

This research describes numerical methods to analyze the absolute transport demand of cyclists and then to quantify the road network weaknesses of a city with the aim to identify infrastructure improvements in favor of cyclists. The methods are based on a combination of bicycle counts and map-matched GPS traces and are demonstrated with the city of Bologna, Italy: the dataset is based on approximately 27,500 GPS traces from cyclists, recorded over a period of one month on a volunteer basis using a smartphone application. A first method estimates absolute, city-wide bicycle flows, by scaling map-matched bicycle flows of the entire network to manual and instrumental bicycle counts of the main bikeways of the city. As there is a good correlation between the two sources of flow data, the absolute bike-flows on the entire network have been correctly estimated. A second method describes a novel link-deviation index, which quantifies for each network edge the total deviation generated for cyclists in terms of extra distances traveled with respect to the shortest possible route. The deviations are accepted by cyclists either to avoid unpleasant road attributes along the shortest route or to experience more favorable road attributes along the chosen route. The link deviation index indicates the planner which road links are contributing most to the total deviation of all cyclists – in this way, repelling and attracting road attributes for cyclists can be identified. This is why the deviation index is of practical help to prioritize bike infrastructure construction on individual road network links.

This paper proposes a novel deterministic methodology for estimating the optimal sampling frequency (SF) of water quality monitoring systems. The proposed methodology is based on employing two-dimensional contaminant transport simulation models to determine the minimum SF considering all the potential changes in the boundary conditions of a water body. A two-dimensional contaminant transport simulation model (RMA4) was implemented to estimate the distribution patterns of the total dissolved solids (TDS) within the Al-Hammar Marsh in the southern part of Iraq for 30 cases of potential boundary conditions. Using geographical information system (GIS) tools, a spatiotemporal analysis approach was applied to the results of the RMA4 model to determine the minimum SF of the monitoring stations with an accuracy level of detectable change in TDS concentration (ALC) of 5%, 10% and 15%. The proposed methodology specified a minimum and maximum SF for each monitoring station (MS) that ranged between 12 and 33 times per year, respectively. Additionally, increasing the ALC to 10% and 15% increase the minimum SF for some MSs by approximately 18% and 21%, respectively. However, the proposed methodology includes all the potential values and cases of boundary conditions, which increases the certainty of monitoring the system and the efficiency of the SF schedule. Moreover, the proposed methodology can be effectively applied to all types of surface water resources.

New developments in broadcast technology, such as the ad hoc wireless network and the detection devices that work, enable daily connections to the Internet, commonly referred to as the Internet of Things (IoT). IoT saw this as a support for the development of creative and contextual services and applications. The service can respond to the user's environment and preferences. Finite state machines are recently making waves when the issue of self-controlling systems come into the picture of designs. This research focuses on the design of a fault tolerant system that controls random failures impressed on the network. The method comprises of diodes, Resistors, Optocouplers, ESP8266 WI-FI Module Webpage with button, and two Arduinos which in effect will determine iteratively who to handle a failure at hand modelled as when the button on the webpage is pressed. In this work, a Heartbeat Detection and Monitoring System will be made using Arduino that detect the electrical state using the Pulse Sensor and show the readings in Beats Per Minute on the LED connected a microcontroller. It sends the texts to the server using the Wi-Fi module ESP8266 to signals can be monitored from the monitoring zone via the internet.   This is reflected by the declaration of a master state by either of two Arduinos whenever the push button is pressed as observed on the codes. The result obtained from the research shows apparently, the state of the fault tolerance system none power deny of heartbeat condition connected to the LED.

Currently the majority of studies on vision-based measurement has been conducted under ideal environments so that an adequate measurement performance and accuracy is ensured. However, vision-based systems may face some adverse influencing factors such as illumination change and fog interference, which can affect the measurement accuracy. This paper develops a robust vision-based displacement measurement method which can handle the two common and important adverse factors given above and achieve sensitivity at the subpixel level. The proposed method leverages the advantage of high-resolution imaging incorporating spatial and temporal context aspects. To validate the feasibility, stability and robustness of the proposed method, a series of experiments was conducted on a two-span three-lane bridge in the laboratory. The illumination change and fog interference are simulated experimentally in the laboratory. The results of the proposed method are compared to conventional displacement sensor data and current vision-based method results. It is demonstrated that the proposed method gives better measurement results than the current ones under illumination change and fog interference.

We propose an iterative scheme for feature-based positioning using a new weighted dissimilarity measure with the goal of reducing the impact of large errors among the measured or modeled features. The weights are computed from the location-dependent standard deviations of the features and stored as part of the reference fingerprint map (RFM). Spatial filtering and kernel smoothing of the kinematically collected raw data allow efficiently estimating the standard deviations during RFM generation. In the positioning stage, the weights control the contribution of each feature to the dissimilarity measure, which in turn quantifies the difference between the set of online measured features and the fingerprints stored in the RFM. Features with little variability contribute more to the estimated position than features with high variability. Iterations are necessary because the variability depends on the location, and the location is initially unknown when estimating the position. Using real WiFi signal strength data from extended test measurements with ground truth in an office building, we show that the standard deviations of these features vary considerably within the region of interest and are neither simple functions of the signal strength nor of the distances from the corresponding access points. This is the motivation to include the empirical standard deviations in the RFM. We then analyze the deviations of the estimated positions with and without the location-dependent weighting. In the present example the maximum radial positioning error from ground truth are reduced by 40% comparing to kNN without the weighted dissimilarity measure.

Floods are one of the most wide-spread, frequent, and devastating natural disasters that continue to increase in frequency and intensity. Remote sensing, specifically synthetic aperture radar (SAR), has been widely used to detect surface water inundation to provide retrospective and near-real time (NRT) information due to its high-spatial resolution, self-illumination, and low atmospheric attenuation. However, the efficacy of flood inundation mapping with SAR is susceptible to reflections and scattering from a variety of factors including dense vegetation and urban areas. In this study, the topographic dataset height above nearest drainage (HAND) was investigated as a potential supplement to Sentinel-1A C-Band SAR along with supervised machine learning to improve the detection of inundation in heterogeneous areas. Three machine learning classifiers were trained on two sets of features SAR only (VV & VH) and VV, VH & HAND to map inundated areas. Three study sites along the Neuse River in North Carolina, USA during the record flood of Hurricane Matthew in October 2016 were selected. The binary classification analysis (inundated as positive vs. non-inundated as negative) revealed significant improvements when incorporating HAND in several metrics including classification accuracy (ACC) (+37.1%), true positive rate (TPR) (+51.2%), and negative predictive value (NPV) (+23.7%), A marginal improvement of +1.4% was seen for positive predictive value (PPV), but true negative rate (TNR) fell -15.1%. By incorporating HAND, a significant number of areas with high SAR backscatter but low HAND values were detected as inundated which increased true positives. This in turn also increased the false positives detected but to a lesser extent as evident in the metrics. This study demonstrates that HAND could be considered a valuable feature to enhance SAR flood inundation mapping especially in areas with heterogeneous land covers with dense vegetation that interfere with SAR.

Research on early stages of corrosion of steel bars, together with the formation and development of cracks induced in the surrounding concrete and caused by chloride penetration, is relevant in improving the durability of reinforced concrete structures. This paper uses integration of the analytical models examined in the published literature, combined with experimental research in corrosion induced at the concrete/steel interface, in estimating the time-to-crack initiation of reinforced concrete subjected to corrosion. This work studies the influence of the porous network and electric current density on the cracking process at early ages. The experimental campaign was performed by using an accelerated corrosion test on a conventional concrete (CC) and a concrete with silica fume (SFC) by submitting them to a current density of 50μA/cm² and 100μA/cm². Examination performed by scanning electron microscopy (SEM) and energy-dispersive X-ray spectroscopy (EDS) provided both qualitative and quantitative information on the penetration of the rust layer in the surrounding concrete porous network. Strain gauges were used to measure corrosion-induced deformations between steel and concrete matrices, as well as the formation of corrosion-induced cracks. A good correlation between the rate of penetration of the rust products in the surrounding pores  and the delay of the cracking pressure in concrete was observed from the experimental results. This phenomenon is incorporated into the analytical model by using a reduction factor, which mainly depends on the pore size of the concrete. The crack width obtained exhibited a significant dependency on electric current density at the beginning of the test, depending mainly on the pore size of the concrete later.

This study aims to investigate the hydration and strength characteristics of green cementitious mortars with ultrahigh-volume limestone-calcined clay as well as two kinds of Class F fly ash. Using the ASTM C311 strength activity index test method, the effect of different pozzolan replacement levels of cement (0%, 20%, 50%, and 80%, by weight) were investigated. Compressive strength at 3, 7, 14, 28 and 90 days under standard curing was recorded, and hydration heat of the 20% and 80% replacement mixes was studied using iso-thermal calorimetry. It was observed that the effectiveness of the pozzolan in mortars depends on particle size distribution, glassy or amorphous nature, surface area and replacement level. The sum of all these effects can be captured by the strength activity test only if the standard recommended 20% pozzolan mix is substituted with the actual mix composition. The results in this study provide insights into the mix design and applications of ultrahigh-volume pozzolanic cementitious materials specifically made with limestone-calcined clay, and promote greener cement and concrete in construction industry.

In this study, the multi-relaxation-time lattice Boltzmann method is applied to investigate the oscillatory instability of lid-driven flows in two-dimensional semi-elliptical cavities with different vertical to horizontal aspect ratios K in the range of 1.0--3.0. The program implemented in this study is parallelized using CUDA (compute unified device architecture), a parallel computing platform, and computations are carried out on NVIDIA Tesla K40c GPU. To carry out precise calculations, the CUDA algorithm is extensively investigated, and its parallel efficiency indicates that the maximum speedup is 47.6 times faster. Furthermore, the steady--oscillatory Reynolds numbers are predicted by implementing the CUDA-based programs. The amplitude coefficient is defined to quantify the time-dependent oscillation of the velocity magnitude at the monitoring point. The simulation results indicate that the transition Reynolds numbers correlate negatively with the aspect ratio of the semi-elliptical cavity, and are smaller than those of the rectangular cavity at the same aspect ratio. In addition, the detailed vortex structures of the semi-elliptical cavity within a single period are also investigated when the Reynolds number is larger than the steady--oscillatory value to determine the effects of periodic oscillation of the velocity magnitude.

Adequate knowledge about the development and operation of the components of water systems is of high importance in order to optimize them. For this reason, forecasting of future events becomes greatly significant due to making the appropriate decision. Moreover, operational river management severely depends on accurate and reliable flow forecasts. In this regard, current study inspects the accuracy of support vector regression (SVR), and SVR regulated with fruit fly optimization algorithm (FOASVR) and M5 model tree (M5), in river flow forecasting. Monthly data of river flow in two stations of the Lake Urmia Basin (Vaniar and Babarud stations on the Aji Chay and the Barandouz Rivers) were utilized in the current research. Additionally, the influence of periodicity (π) on the forecasting enactment was examined. To assess the performance of mentioned models, different statistical meters were implemented, including root mean squared error (RMSE), mean absolute error (MAE), correlation coefficient (R), and Bayesian information criterion (BIC). Results showed that the FOASVR with RMSE (4.36 and 6.33 m3/s), MAE (2.40 and 3.71 m3/s) and R (0.82 and 0.81) values had the best performances in forecasting river flows in Babarud and Vaniar stations, respectively. Also, regarding BIC parameters, Q_t-1 and π were selected as parsimonious inputs for predicting river flow one month ahead. Overall findings indicated that, although both FOASVR and M5 predicted the river flows in suitable accordance with observed river flows, the performance of FOASVR was moderately better than the M5 and periodicity noticeably increased the performances of the models; consequently, FOASVR can be suggested as the accurate method for forecasting river flows.

The purpose of this paper is to investigate the effect of a varying moment on the rotation angle of a large tethered satellite that is orbiting a planet. Two different types of orbits were investigated: a simple circular orbit and an elliptical orbit. Cases with zero and non-zero initial angular rotation velocity were investigated as well. This investigation will assist satellite docking missions. The large rigid tethered satellite is a futuristic concept, and this investigation is meant to assist possible docking missions to the satellite. To simplify the problem, the rotation is constrained to the orbital plane.

The advances mobile communications has seen in recent years has rendered the radio spectrum a limited and, hence, an expensive resource. Therefore, technologies that support unlicensed access to spectrum are needed. Therefore, the adoption of novel modulation schemes becomes of utmost importance to obtain better spectral-localization and reduce the OOBE (\textit{Out of Band Emission}) inherent to OFDM (\textit{Orthogonal Frequency Division Multiplexing}) and, consequently, mitigating the interference between secondary (\textit{unlicensed}) and primary users. In this scenario, we assess the gain in the bit error probability using f-OFDM (\textit{filtered-OFDM}) in MIMO systems, both used in the 5G RANGE project.

Greenhouse Gases (GHGs) emission has considerable impact on global warming and climate change. Since energy systems and their features noticeably influence on the amount of GHGs emission, it can be modeled based on the specifications of energy sources utilized by the countries. In addition, economic activity is another factor which should be considered in GHG emission modeling. In this work, Artificial Neural Network (ANN) is used for estimating carbon dioxide emission, as one of the most abundant GHGs, on the basis of shares of various energy sources used as primary energy supply and GDP as an indicator for economic activities. Five countries including Iran, Kuwait, Qatar, Saudi Arabia and United Arab Emirates (UAE) are considered as case studies. Comparing between the estimated data by the achieved model and actual quantities showed acceptable precision of the ANN model for prediction of carbon dioxide emission. The average absolute relative error and the R-squared values of the GMDH model are approximately 2.28% and 0.9998, respectively. The obtained values for the mentioned statistical criteria show the precision of the model in forecasting the emission of Co2.

A method for infrared and cameras sensor fusion, applied to indoor positioning in intelligent spaces, is proposed in this work. The fused position is obtained with a maximum likelihood estimator from infrared and camera independent observations. Specific models are proposed for variance propagation from infrared and camera observations (phase shifts and image respectively) to their respective position estimates and to the final fused estimation. Model simulations are compared with real measurements in a setup designed to validate the system. The difference between theoretical prediction and real measurements  is between 0.4 cm (fusion) and  2.5 cm (camera), within a 95% confidence margin. The positioning precision is in the cm level (sub-cm level can be achieved at most tested positions) in a 4x3 m locating cell with 5 infrared detectors on the ceiling and one single camera, at distances from target up to 5 m and 7 m respectively. Due to the low cost system design and the results observed, the system is expected to be feasible and scalable to large real spaces.

A conventional 2D numerical model is improved by incorporating three submodels to consider different effects of secondary flow and a module for cohesive sediment transport. The model is applied to a meandering reach of Yangtze River to investigate secondary flow effects on cohesive sediment deposition, and a preferable submodel is selected based on the flow simulation results. Sediment simulation results indicate that the improved model predictions are in better agreement with the measurements in planar distribution of deposition as the increased sediment deposits caused by secondary current on the convex bank have been well predicted. Secondary flow effects on predicted amount of deposition become more obvious during the period when the sediment load is low and velocity redistribution induced by the bed topography is evident. Such effects vary with the settling velocity and critical shear stress for deposition of cohesive sediment. The bed topography effects can be reflected by the secondary flow submodels.

Nitride and carbide are the second phases which play an important role in the performance of bearing steel, and their precipitation behavior is complicated. In this study, TiN-MC_x precipitations in GCr15 bearing steels were obtained by non-aqueous electrolysis, and their precipitation mechanisms were studied. TiN is the effective heterogeneous nucleation site for Fe₇C₃ and Fe₃C, therefore, MC_x can precipitate on the surface of TiN easily, its chemistry component consists of M₃C and M₇C₃ (M = Fe, Cr, Mn) and Cr₃C₂. TiN-MC_x with high TiN volume fraction, TiN forms in early stage of solidification, and MC_x precipitates on TiN surface after TiN engulfed by the solidification advancing front. TiN-MC_x with low TiN volume fraction, TiN and MC_x form in late stage of solidification, TiN can not grow sufficiently and is covered by a large number of precipitated MC_x particles.

Electric vehicles, being able to reduce pollutant and greenhouse gas emissions and shift the economy away from oil products, can play a major role in the transition towards low-carbon energy systems. However, the related increase in electricity demand inevitably affects the strategic planning of the overall energy system as well as the definition of the optimal power generation mix. With this respect, the impact of electric vehicles may vary significantly depending on the composition of both total primary energy supply and electricity generation. In this study, Italy and Germany are compared to highlight how a similarity in their renewable shares not necessarily leads to a CO₂ emissions reduction. Different energy scenarios are simulated with the help of EnergyPLAN software assuming a progressive increase in renewable energy sources capacity and electric vehicles penetration. Results show that, for the German case, the additional electricity required leads to a reduction in CO₂ emissions only if renewable capacity increases significantly, whereas the Italian energy system benefits from transport electrification even at low renewable capacity. Smart charging strategies are also found to foster renewable integration; however, power curtailments are still significant at high renewable capacity in the absence of large-scale energy storage systems.

Adequate knowledge about the development and operation of the components of water systems is of high importance in order to optimize them. For this reason, forecasting of future events becomes greatly significant due to making the appropriate decision. Moreover, operational river management severely depends on accurate and reliable flow forecasts. In this regard, current study inspects the accuracy of support vector regression (SVR), and SVR regulated with fruit fly optimization algorithm (FOASVR) and M5 model tree (M5), in river flow forecasting. Monthly data of river flow in two stations of the Lake Urmia Basin (Vaniar and Babarud stations on the Aji Chay and the Barandouz Rivers) were utilized in the current research. Additionally, the influence of periodicity (π) on the forecasting enactment was examined. To assess the performance of mentioned models, different statistical meters were implemented, including root mean squared error (RMSE), mean absolute error (MAE), correlation coefficient (R), and Bayesian information criterion (BIC). Results showed that the FOASVR with RMSE (4.36 and 6.33 m3/s), MAE (2.40 and 3.71 m3/s) and R (0.82 and 0.81) values had the best performances in forecasting river flows in Babarud and Vaniar stations, respectively. Also, regarding BIC parameters, Qt-1 and π were selected as parsimonious inputs for predicting river flow one month ahead. Overall findings indicated that, although both FOASVR and M5 predicted the river flows in suitable accordance with observed river flows, the performance of FOASVR was moderately better than the M5 and periodicity noticeably increased the performances of the models; consequently, FOASVR can be suggested as the accurate method for forecasting river flows.

This study presents a new form of velocity distribution in laminar liquid flow in rectangular microchannels using the eigenfunction expansion technique. Darcy friction factor and Poiseuille number are also obtained analytically. Due to the symmetry of the solutions, the effects of changing the aspect ratio from 0 to ∞ are also discussed. Using finite element method (FEM), the obtained analytical results are further compared with the 3D numerical simulations for the rectangular microchannels with different range of aspect ratio and pressure gradient, and excellent agreements were found. These findings provide additional insights in interpreting the results of the pressure-driven flows in finite aspect ratio microchannels, in which very precise comparison with the macroscale theory is crucial.

The availability of rail infrastructure resources is a major driver of rail operations performance.  To evaluate the impact of infrastructure provision, network simulation models can be used to accurately represent train traffic behavior in a wide range of scenarios. However, performing this task can result in a problem of high combinatorial nature as the number of factors and their associated levels increase. This requires more sophisticated techniques such as experimental design formulations or optimization modeling in order to yield satisfactory results. Yet the research in network simulation models for rail systems has hitherto been limited to simple what-if analysis, made up from few factors that cannot represent the whole spectrum of interventions. This is especially critical in closed-loop rail systems where trains are subject to various interferences. Local improvements can be misleading as the queues are merely transferred within the network. Considering this, we propose a hybrid simulation-optimization model to aid the strategic decision of minimizing supplementary capital costs in a heavy-haul Brazilian railroad under construction. As soon as the railroad is completed, investments in both loading and unloading rail terminals will be necessary. First, we developed a representative and flexible model capable of dealing with complex relations between variable infrastructure provision and the resulting operational performance. Then, we simulated this system to prove that the current set of proposed infrastructure resources cannot meet the transportation demands. Afterwards, we demonstrate that local improvements can be delusive as the queues are shifted from loading to unloading operations, reciprocally. Then, we solve an optimization model to define the minimal supplementary investment in order to meet the commercial goals of mining companies. This is done by choosing the best trackage configuration, equipment quantity and capacity and fleet sizing in 3 different production scenarios. The best values of the objective function were found by improving both loading and unloading equipment and increasing the number of trains.

The advances mobile communications has seen in recent years has rendered the radio spectrum a limited and, hence, an expensive resource. Therefore, technologies that support unlicensed access to spectrum are needed. Therefore, the adoption of novel modulation schemes becomes of utmost importance to obtain better spectral-localization and reduce the OOBE (\textit{Out of Band Emission}) inherent to OFDM (\textit{Orthogonal Frequency Division Multiplexing}) and, consequently, mitigating the interference between secondary (\textit{unlicensed}) and primary users. In this scenario, we assess the gain in the bit error probability using f-OFDM (\textit{filtered-OFDM}) in MIMO systems, both used in the 5G RANGE project.

Concrete-like materials have been widely used in structures and constructions, those concrete structures might be subjected to intensive impact loads during the service. In order to investigate the dynamic tensile behaviors of the concrete and cement mortar, a 50 mm split Hopkinson pressure bar was applied for the dynamic tensile experiments with Brazilian disc specimens. The evaluation of dynamic tensile stress, dynamic tensile strength and failure pattern of concrete and cement mortar specimens were investigated under quasi-static and dynamic loads, which the strain rate range from 10-5 s-1 to 20 s-1. The high-speed camera technique was conducted to record the dynamic failure progress of the specimens. Based on the experimental results from seven different strain rates, it is found that both the dynamic tensile strength of concrete and cement mortar specimens present a strong strain rate effect, which increases with increasing strain rate obviously. Furthermore, three typical failure patterns of the concrete and cement mortar specimen were summarized. The type III failure model of specimen was confirmed by the digital image correlation at the high strain rate state, which the crack initiation starts from the contact point between the bar and specimen rather than the specimen center. At last, the semi-empirical relationship was established between dynamic tensile strength and strain rate. The dynamic increase factor for tensile strength (DIFt) of the concrete specimen shows more sensitive to strain rate than that of the cement mortar ones. In addition, the strain rate effect and its limitation in the dynamic split with Brazilian disc experiments were discussed in detail.

Nitride and carbide are the second phases which play an important role in the performance of bearing steel, and their precipitation behavior is complicated. In this study, TiN-MC_x precipitations in GCr15 bearing steels were obtained by non-aqueous electrolysis, and their precipitation mechanisms were studied. TiN is the effective heterogeneous nucleation site for Fe₇C₃ and Fe₃C, therefore, MC_x can precipitate on the surface of TiN easily, its chemistry component consists of M₃C and M₇C₃ (M = Fe, Cr, Mn) and Cr₃C₂. TiN-MC_x with high TiN volume fraction, TiN forms in early stage of solidification, and MC_x precipitates on TiN surface after TiN engulfed by the solidification advancing front. TiN-MC_x with low TiN volume fraction, TiN and MC_x form in late stage of solidification, TiN can not grow sufficiently and is covered by a large number of precipitated MC_x particles.

This paper presents a control algorithm for soft-switching series LC converters. The conventional voltage-to-voltage controller is split into a master and a slave controller. The master controller implements constant-current-constant-voltage (CCCV) control, required for demanding applications, i.e. lithium battery charging or laboratory power supplies. It defines the set-current for the open-loop current slave controller, which generates the PWM parameters. The power supply achieves fast large-signal responses, e.g. from 5 V to 24 V, where 95% of the target value is reached in less than 400 µs. The design is evaluated extensively in simulation and on a prototype. A consensus between simulation and measurement is achieved.

Analysis of Fatigue Strain, Fatigue Modulus and Fatigue Damage for the modeling of concrete plays a vital role in the evolution material behaviour which is heterogeneous and anisotropic in nature. In this paper, the Level-S nonlinear fatigue strain curve, fatigue modulus curve, residual strain curve of concrete in compression, tension, flexure and torsional fatigue loading were proposed using strain life approach. The parameters such as physical meaning, the ranges, and the impact on the shape of the curve were discussed. Then, the evolution model of fatigue modulus was established based on the fatigue strain evolution model, fatigue modulus evaluation model, residual strain evaluation model, secondary strain evaluation model. The hypothesis of fatigue modulus is inversely related with the fatigue strain amplitude. The fatigue evolution of concrete damages the bond between material grains, changed the orientation of structure of molecules and affects the elastic properties resulting in the reduction of material stiffness and modulus by utilizing strain life analysis, regarding stiffness degradation and inelastic deformation by formation of microcracking, macro cracking, cracking which is heterogeneous and anisotropic in nature . This paper presents the Fatigue Strain Life Model and analyses of fatigue strain, fatigue modulus and damage parameters of concrete which is capable of predicting stiffness degradation, inelastic deformation, strength reduction under fatigue loading. Hence, the obtainable results were compared with experimental results for the validation of the proposed model.

This paper proposes a novel micro-grid structure, which can operate fully-autonomously with inherent seamless switching. It can operate independently in both grid-connected and islanded mode as a self-governed entity without relying on the utility grid. An AC/DC/AC converter is employed as the interface between the micro-grid and the utility grid, which enables the two entities to have different voltages in grid-connected mode. Seamless switching between operation modes can be achieved naturally. The micro-grid is regulated to exchange predefined amount of power with the utility grid in grid-connected mode. This will benefit the power dispatching algorithm of the power system. The predefined power is estimated based on power forecasting of local renewable generations and loads with consideration of the Sate of Charge (SOC) of the battery, and is updated and broadcasted every certain period. A small scale AC micro-grid with a rotating generator, battery storage and solar arrays etc. is built for investigation. Matlab/Simulink results are provided to validate the robustness and flexibility of proposed micro-grid and its operation strategy.

Integration of wind energy into the grid faces a great challenge regarding power quality. The International Electrotechnical Commission (IEC)~61400-21 standard defines the electrical characteristics that need to be assessed in a Wind Turbine (WT), as well as the procedure to measure the disturbances produced by the WT. One of the parameters to be assessed are voltage fluctuations or flicker. To estimate the flicker emission of a Wind Power Plant (WPP), the standard establishes that a quadratic exponent should be used in the summation of the flicker emission of each WT. This exponent was selected based on studies carried out in WPPs with type I and II WTs. Advances in wind turbines technology have reduced their flicker emission, mainly thaks to the implementation of power electronics for the partial or total management of the power injected into the grid. This work is based on measurements from a WPP with 16 type III WTs. The flicker emission of a single WT and of the WPP were calculated. Low flicker emission values at the Point of Common Coupling (PCC) of the WPP were obtained. The flicker estimation at the PCC, based on the measurement from a single WT, was analyzed using different exponents. The results show that a cubic summation performs better than the quadratic one in the estimation of the flicker emission of a WPP with type III WTs.

The Brazilian economy, the rural in particular, was until the end of the 19th century based on slave labour. In this research, it was intended to obtain, through a review of historical descriptive studies, a detailed picture of the occupational hygiene conditions related to the slaves’ work and their interactions with climatic and environmental conditions. The search was done in the following databases: Science Direct, Scopus, Web of Science, Criminal Justice, Ebsco, Business Source Supplement, as well as original historical documents. Descriptive studies, without the restriction of language, were selected that involved the rural work of slaves in colonial and imperial Brazil. Working environmental conditions have been evaluated: environmental and occupational hygiene conditions to which the captive workers were exposed, as well as their accommodation and clothing. The analysed studies registered the existence of accommodation and similar dresses all over Brazil, regardless of the region's climate. In addition to these accommodation conditions, slaves were still exposed in a similar way to physical, chemical and biological agents throughout the country. Finally, it was also possible to identify a clear similarity with the occupational exposure conditions of the modern slaves in the 21^st century.

The dynamic resistance is an important parameter in flux pumping. It is simple to calculate in slab geometry but more complex in tapes. In this paper another simple geometry is analysed which is a wire loop. To do this it is essential to make clear what is meant by the term 'voltage', which is ambiguous when there are alternating magnetic fields present. The term can be used if it refers to the EMF along a specified path. However this means that results are dependent on how the voltage leads are attached. If the n value of the V-I characteristic is large simple analytic expressions can be obtained. This will be referred to as the 'Bean' model solution. A more exact approach uses Faraday's law to derive a differential equation which is solved and compared with the Bean model. The dynamic resistance of the loop can be used to drive a flux pump of the type proposed by Geng and Coombs [4] and again simple expressions can be obtained for the performance. If the load is of high inductance L2 and the loop has an inductance L1 then the load current increases exponentially with a characteristic number of cycles L2/L1. The final current can be close to the critical value.

In this paper, the elemental construct of an I-shaped fin cooling a heat generating volume is studied through the entropy production perspective. Using the constructal design method framework with entropy production minimization as an objective leads to different optimal shape ratio, different from the ones obtained using the excess of temperature. Moreover, the optimal shape ratio evolution is not necessary the same for local entropy production minimization (excess of entropy production) and global entropy production (entropy produced in the whole area). Finally, an analysis on a possible dimensionless number is introduced for the study of constructal plates.

The cooperation between humans and robots is becoming increasingly important in our society. Consequently, there is a growing interest in the development of models that can enhance the interaction between humans and robots. A key challenge in the Human-Robot Interaction (HRI) field is to provide robots with cognitive and affective capabilities, developing architectures that let them establish empathetic relationships with users. Several models have been proposed in recent years to solve this open-challenge. This work provides a survey of the most relevant attempts/works. In details, it offers an overview of the architectures present in literature focusing on three specific aspects of HRI: the development of adaptive behavioural models, the design of cognitive architectures, and the ability to establish empathy with the user. The research was conducted within two databases: Scopus and Web of Science. Accurate exclusion criteria were applied to screen the 1007 articles found (at the end 30 articles were selected). For each work, an evaluation of the model is made. Pros and cons of each work are detailed by analysing the aspects that can be improved so that an enjoyable interaction between robots and users can be established.

Recent works in mechanical fatigue consider that a threshold of entropy exists, the fracture fatigue entropy. The determination of this quantity is usually done considering empirical models for the mechanical power estimation. In this paper, we experimentally observe the existence of a threshold of entropy and exergy in low cycle fatigue for a flat Al-2024 specimen avoiding the use of a model, solely measuring the heat generated during a fatigue test. Results are then compared considering various hypotheses (1D heat dissipation with convection and radiation considered as heat sources, and, heat transfer from a fin with convection and radiation as boundary conditions) to an empirical mechanical model known in the literature and deviations between them are discussed.

The Gleeble-1500D thermal simulation test machine was used to conduct the isothermal compression test on 21-4N at the strain rate ( ) of 0.01-10s-1, the deformation temperature (T) of 1273-1453K and the maximum deformation is 0.916. The data of the stress-strain ( - )were obtained. Based on the - data, the Johnson-Cook (J-C), modified J-C, Arrhenius and Back-Propagation Artificial Neural Network (BP-ANN) models were established. The accuracy of four models were verified, analyzed and compared. The results show that J-C model has a higher accuracy only under reference deformation conditions. When the deformation condition changes greatly, the accuracy of J-C model is significantly reduced. The coupling effect of T and of modified J-C model is considered, and the prediction accuracy is greatly improved The Arrhenius model introduces Zener-Hollomon (Z) to represent the coupling effect of T and , it has a fairly high prediction accuracy. And it can predict flow stress ( ) accurately at different conditions. The accuracy of BP-ANN model is the highest, but its learning rate is low, the learning and memory are unstable. It has no memory for the weights and thresholds of the completed training. So, there are certain limitations of it in use. Finally, a FEM of the isothermal compression experiment for four models were established, and the distribution of the equivalent stress field, equivalent strain field and temperature field with the deformation degree of 60% were obtained.

One way to increase overall natural gas engine efficiency is to transform exhaust waste heat into useful energy by means of a bottoming cycle. Organic Rankine cycle (ORC) is a promising technology to convert medium and low grade waste heat into mechanical power and electricity. This paper presents an energy and exergy analysis of three ORC-Waste heat recovery configurations by using an intermediate thermal oil circuit: Simple ORC (SORC), ORC with Recuperator (RORC) and ORC with Double Pressure (DORC), and Cyclohexane, Toluene and Acetone have been proposed as working fluids. An energy and exergy thermodynamic model is proposed to evaluate each configuration performance, while available exhaust thermal energy variation under different engine loads was determined through an experimentally validated mathematical model. Additionally, the effect of evaportating pressure on net power output , absolute thermal efficiency increase, absolute specific fuel consumption decrease, overall energy conversion efficiency, and component exergy destruction is also investigated. Results evidence an improvement in operational performance for heat recovery through RORC with Toluene at an evaporation pressure of 3.4 MPa, achieving 146.25 kW of net power output, 11.58% of overall conversion efficiency, 28.4% of ORC thermal efficiency, and an specific fuel consumption reduction of 7.67% at a 1482 rpm engine speed, a 120.2 L/min natural gas Flow, 1.784 lambda, and 1758.77 kW mechanical engine power.

In the fashion field, the use of electroplated small metal parts such as studs, clips and buckles is widespread. The plate is often made of precious metal, such as gold or platinum. Due to the high cost of these materials, it is strategically relevant and of primary importance for manufacturers to avoid any waste by depositing only the strictly necessary amount of material. To this aim, Companies need to be aware of the overall number of items to be electroplated so that it is possible to properly set the parameters driving the galvanic process. Accordingly, the present paper describes a Machine Vision-based method able to automatically count small metal parts arranged on a galvanic frame. The devised method relies on the definition of a proper acquisition system and on the development of image processing-based routines. Such a system is then implemented on a counting machine is meant to be adopted in the galvanic industrial practice to properly define a suitable set or working parameters (such as current, voltage and deposition time) for the electroplating machine and, thereby, to assure the desired plate thickness from one side and to avoid material waste on the other.

Although many devices have recently been proposed for pressure regulation and energy harvesting in water distribution and transport networks, very few applications are still documented in the scientific literature. A new in-line Banki turbine with positive outflow pressure and a mobile regulating flap, named PRS, was installed and tested in a real water transport network for pressure and discharge regulation. The PRS turbine was directly connected to a 55 kW asynchronous generator with variable rotational velocity, coupled to an inverter. The start-up tests showed how automatic adjustment of the flap position and the impeller velocity variation are able to change the characteristic curve of the PRS according to the flow delivered by the water manager or to the pressure set-point assigned downstream or upstream of the system, still keeping good efficiency values in hydropower production.

A number of experimental and numerical studies point out that incorporating a rotating cylinder can superiorly enhance the aerofoil performance, especially for higher velocity ratios. Yet, there have been less or no studies exploring the effects of lower velocity ratio at a higher Reynolds number. In the present study, we investigated the effects of Moving Surface Boundary-layer Control (MSBC) at lower velocity ratios (i.e. cylinder tangential velocity to free stream velocity) and higher Reynolds number on a symmetric aerofoil (e.g. NACA 0021) and an asymmetric aerofoil (e.g. NACA 23018). In particular, the aerodynamic performance with and without rotating cylinder at the leading edge of the NACA 0021 and NACA 23018 aerofoil was studied on the wind tunnel installed at Aerodynamics Laboratory. The aerofoil section was tested in the low subsonic wind tunnel, and the lift coefficient and the drag coefficient were studied for different angles of attack. The experiments were conducted for two Reynolds numbers: 200000 and 250000 corresponding to two free stream velocities: 20 m/s and 25 m/s, respectively, for six different angle of attacks (-5°, 0°, 5°, 10°, 15° and 20°). This study demonstrates that the incorporation of a leading edge rotating cylinder results in an increase of lift coefficient at lower angle of attacks (maximum around 33%) and delay in stall angle (from 10° to 15°) relative to the aerofoil without rotating cylinder.

Designing the future energy supply in accordance with ambitious climate change mitigation goals is a challenging issue. Common tools for planning and calculating future investments in renewable and sustainable technologies are often linear energy system models based on cost optimisation. However, input data and the underlying assumptions of future developments are subject to uncertainties that negatively affect the robustness of results. This paper introduces a quadratic programming approach to modifying linear, bottom-up energy system optimisation models in order to take cost uncertainties into account. This is accomplished by implementing specific investment costs as a function of the installed capacity of each technology. In contrast to established approaches like stochastic programming or Monte Carlo Simulation, the computation time of the quadratic programming approach is only slightly higher than that of linear programming. The model’s outcomes were found to show a wider range as well as a more robust allocation of the considered technologies than the linear model equivalent.

The study of the distribution and health risk assessment is meaningful to provide basic data for environmental management．To investigate the pollution of potential toxic organics and their health risk to human beings, water samples were collected at 7 sites of main surface water of Beijing during wet and dry seasons respectively. The targeted 92 organics were detected, including phthalates (PAEs), volatile organic compounds (VOCs), polycyclic aromatic hydrocarbons (PAHs) and phenols. The results showed that: there were 56 organics detected out, and the number ratios of detected compounds to the total compounds of the same kind increased as the following: VOCs, phthalates, phenols, PAHs. 8 VOCs were detected in wet season, and 3 in dry season. The concentration of 2,2-Dichloropropane was highest as 10.62ug/L, while the concentrations of other VOCs were below 5ug/L; There were 11 phthalates detected during dry season. The content of Bis(2-methoxyethyl) phthalate was highest as 188.47ng/L; 17 phenols were found in samples during wet season, and the highest concentration was 1244.73ng/L for 4-nitrophenol; PAHs could be detected in all samples, and the detected compounds and the corresponding average concentrations were higher in wet seasons than those in dry seasons, which indicated that non-point pollution was possibly the main pollution source. The health risk assessment of the detected 56 pollutants by using a model from US EPA showed that, the risk caused by the four kinds of toxic organics in this study was in the acceptable ranges.

In this research paper we describe the development phase of a low-cost LoRa IoT solution applied to a kindergarten school with three years results. A set of sensors solution was developed in a LoRa communication board, battery powered, providing a simplified setup process. These sensors were used in order to measure temperature, humidity, luminosity, air quality and presence. Also, energy monitor solutions were integrated. The acquired data is transmitted and analysed for knowledge extraction, identifying savings and other related KPIs. From data, automatic saving actions were performed towards heating and cooling systems, lighting and a set of if-then actions were developed for automatic cost-saving actions, based on infrared signals to heating/cooling systems using some procedure of external command devices. This approach avoids the usage of proprietary vendor solutions in a flexible approach that can easily be deployed to any building facility. This is an important achievement since most of the building consumption is based on heating and cooling systems. In a three years test of the solution, the total energy consumption savings surpassed 20%

Artificial groundwater recharge is commonly used for drinking water supply although the resulting water quality is highly dependent on the raw water quality, and in many cases, pre-treatment is required. Such pre-treatment improves the drinking water quality, although how and to what extent pre-treatment affects the subsequent pond infiltration process is still unknown. Here we evaluate the impact of two different pre-treatment methods of water from a eutrophic, temperate lake. An artificial recharge pond was divided into two parts, where one received raw water from a lake only filtered through a 500 µm pore size drum filter, while the other part received pre-treated lake water using chemical flocculation with polyaluminium chloride (PACl), combined with sand filtration (contact filtration). Changes in water quality were assessed at different stages in the two treatment processes. We show that contact filtration reduced phosphorus with 96 %. Moreover, the total organic carbon (TOC) reduction was improved from 55 % to 70 %, corresponding to an average reduction from 3.5 mg/L to 2.4 mg/L In addition, the pre- treatment in the artificial recharge pond reduced the cyanobacteria blooms and reduced the microcystin level. However, there were no sigificant differences in microcystin levels in the groundwater, i.e. the artifical recharge infiltration pond was effective for microcystin removal even without contact filtration. Hence, in a broader drinking water management perspective, the presented method is promising to reduce the levels of cyanobacterial toxins, as well as nutrients and TOC, which are all predicted to increase in a future climate change perspective.

The combination of new-generation information technology and manufacturing technology has resulted in major and profound impact on future development paradigm of manufacturing. It is challenging for existing methods to conduct a multidimensional trend exploration related to machine tool domain, which is the basis of virtually everything in manufacturing. In this paper, we proposed an integrating approach framework combined topic models, bibliometric, trend analysis and patent analysis to mine insightful information about future development from multi-source data related to machine tool, such as papers, grants, patents and news. Specifically, papers and grants provided two different perspectives to explore the current focuses and future trends in machine tool research. Furthermore, the future technology development of machine tool was investigated through patents analysis. Finally, news related to machine tool industry in recent years was analyzed to analyze future machine tool business mode. The integration of the above various analytical methods and the multi-dimensional mining of literatures enabled the analysis of the future development of machine tool domain systematically from multi-perspectives which include research, technology development and industry. The conclusions obtained in this paper is beneficial to different communities of machine tool in terms of determining the research directions for researchers, identifying industry opportunities for corporations and developing reasonable industry policy for policy makers.

Based on the topology of wide area protection system (WAPS), after studying the reliability of hardware system and information flow in the WAPS and establishing the reliability assessment model, the multi-service reliability analysis method with multi monitoring and protection tasks in WAPS was proposed. In the model, the impact of network quality of service (QoS) such as information flow loss and delay, is studied. On the base of the model, the multi-service reliability evaluation method is employed to analyze the reliability of a WAPS of IEEE14 node power system, and the key nodes of the WAPS is given, which provides a basis for improving the reliability of the WAPS.

We studied the effect of the structure parameters of engine annular cooling fan with outer ring on the aerodynamic performance by means of experiments and model simulation in fluent^®. Firstly, based on the experiment, a computational model is developed to calculate and analyze the aerodynamic performance of the tested annular fan. The model is validated by comparing the test results with the calculated data. Besides, the aerodynamic performance differences between two types of fans (common fan without outer ring and annular fan with outer ring) are discussed. Based on the computational model, the relation between aerodynamic performance and the outer ring structure parameters are investigated. The results show that the relative parameter on the axial direction has great influence on the aerodynamic performance; while the effect of radial relative parameter is minor. In addition, the outer ring with arc chamfer structure in the downstream side can improve its static pressure efficiency effectively.

Electricity demand prediction is vital for energy production management and proper exploitation of the present resources. Recently, several novel machine learning (ML) models have been employed for electricity demand prediction to estimate the future prospects of the energy requirements. The main objective of this study is to review the various ML models applied for electricity demand prediction. Through a novel search and taxonomy, the most relevant original research articles in the field are identified and further classified according to the ML modeling technique, perdition type, and the application area. A comprehensive review of the literature identifies the major ML models, their applications and a discussion on the evaluation of their performance. This paper further makes a discussion on the trend and the performance of the ML models. As the result, this research reports an outstanding rise in the accuracy, robustness, precision and the generalization ability of the prediction models using the hybrid and ensemble ML algorithms.

A number of experimental and numerical studies point out that incorporating a rotating cylinder can superiorly enhance the aerofoil performance, especially for higher velocity ratios. Yet, there have been less or no studies exploring the effects of lower velocity ratio at a higher Reynolds number. In the present study, we investigated the effects of Moving Surface Boundary-layer Control (MSBC) at lower velocity ratios (i.e. cylinder tangential velocity to free stream velocity) and higher Reynolds number on a symmetric aerofoil (e.g. NACA 0021) and an asymmetric aerofoil (e.g. NACA 23018). In particular, the aerodynamic performance with and without rotating cylinder at the leading edge of the NACA 0021 and NACA 23018 aerofoil was studied on the wind tunnel installed at Aerodynamics Laboratory. The aerofoil section was tested in the low subsonic wind tunnel, and the lift coefficient and the drag coefficient were studied for different angles of attack. The experiments were conducted for two Reynolds numbers: 200000 and 250000 corresponding to two free stream velocities: 20 m/s and 25 m/s, respectively, for six different angle of attacks (-5°, 0°, 5°, 10°, 15° and 20°). This study demonstrates that the incorporation of a leading edge rotating cylinder results in an increase of lift coefficient at lower angle of attacks (maximum around 33%) and delay in stall angle (from 10° to 15°) relative to the aerofoil without rotating cylinder.

The beautiful and stable posture is essential for ballet. Especially the pirouette which dancers balance themselves on their one leg is one of the most impressive postures. During the pirouette motion, loads from the rotation and the body weight are concentrated to the one leg. Proper ballet shoes can reduce the muscle burden and improve the stability. Four types of shoes; bare feet, running shoes, pointe shoes, and dance sneakers are analyzed. The motion capture system and SIMM are used for the analysis of muscle burden and stability during pirouette. The major 6 muscles in the leg (beceps femoris long head, beceps femoris short head, soleus, rectus femoris, gastrocnemius medialis, and gastrocnemius lateralis) are analyzed, and the effect of rotating velocity is considered. Dance sneakers are outstanding for improving the stability and lessening the muscle burdens in all conditions for beginners. That comes from the design feature of the divided shoe soles and protecting the ankle of the axis leg. With the results and analysis, the direction for design optimization of ballet shoes is suggested. Consequently, this research is about the verification of sports equipment using computer simulation with the musculoskeletal model from a scientific viewpoint of biomechanics.

A crucial role is played by personal biomedical data when it comes to maintaining proficient access to health records by patients as well as health professionals. However, it is difficult to get a unified view pertaining to health data that have been scattered across various health center/hospital sections. To be specific, health records are distributed across many places and cannot be found integrated easily. In recent years, blockchain is regarded as a promising explanation that helps to achieve individual biomedical information sharing in a secured way along with privacy preservation, because of its benefit of immutability. This research work put forwards a blockchain-based managing scheme that helps to establish interpretation improvements pertaining to electronic biomedical systems. In this scheme, two blockchain were employed to construct the base of it, where the second blockchain algorithm is used to generate a secure sequence for the hash key that generated in first blockchain algorithm. The adaptively feature enable the algorithm to use multiple data types and combine between various biomedical images and text records as well. All the data, including keywords, digital records as well as the identity of patients are private key encrypted along with keyword searching capability so as to maintain data privacy preservation, access control and protected search. The obtained results which show the low latency (less than 750 ms) at 400 requests / second indicate the ability to use it within several health care units such as hospitals and clinics.

Wasted Green tuff powder produced by cutting Towada stone is recycled as environmental cleaning material. The optimum temperature for green tuff powder calcination to reduce the hydroxyl radical produced in hydrogen peroxide decomposition with ultraviolet light (UV) and no light. The green tuff calcined at 800 °C shows the large decomposition rate of hydrogen peroxide with no UV light when measured by using ESR. With UV light, the optimum temperature for calcinating the green tuff powder in order to reduce hydroxyl radical is also 800 °C. Next, the powder calcined at 800 °C is used to produce the tile by compression and heating, and then the formaldehyde adsorption rate was measured. The green tuff powder calcined at 800 °C showed a high adsorption rate, similar to that of the activated carbon. The tiles formed at 40 MPa and heated st 1100 °C were the strongest and also adsorbed formaldehyde. The adsorbed formaldehyde on the green tuff tile and powder might have a possibility to decompose by photocatalytic.

This contribution addresses dry sliding friction of a “Non-Asbestos Organic” friction material (pad) rubbed against a pearlitic grey cast iron disc. This configuration is commonly used in brakes in US, Europe and Asia. The initial stage of friction is of particular interest when addressing “creep-groan” phenomena occurring in passenger and sport utility vehicles with automatic transmissions. The custom built Universal Friction Tester allowing the variation of sliding speed, test temperature, normal load, stiffness and humidity of the system was used in this experiment in combination with surface analysis by polarized light microscopy, scanning electron microscopy and stylus profilometry. The obtained results indicate that the “stick” phase in the so-called “stick-slip” phenomenon does not really exist. The surfaces of pad and disc are in continuous relative movement. The complexity of surfaces does not allow for a definitive description of exact mechanisms responsible for the detected changes of friction forces. The observed friction process can be rather described as a stretching with possible localized relaxations causing the jerky behavior in the stretching phase, followed by slip between two materials in contact. Understanding of factors contributing to the stretching at different scale levels of friction system is necessary for development of proper models. An accurate friction model should also incorporate the vibrational element introduced by phenomena occurring at the friction surfaces. The absence of heterogeneous regions on the friction surface and “evenly distributed friction level” can help when mitigating creep-groan in the investigated brake system. Understanding of factors on different scales of friction is also required when developing the improved brakes.

As construction costs continue to rise and adequate amounts of funding continues to be a challenge, the allocation of resources is of critical importance when it comes to the maintenance and rehabilitation (M&R) of highway infrastructure. A Life Cycle Assessment (LCA) methodology is presented here that integrates realistic traffic conditions in the operational phase to compare M&R scenarios over the analysis period of a 26-km stretch of Interstate-495. Pavement International Roughness Index (IRI) were determined using AASHTO’s PavementME System. Meanwhile, vehicle fuel consumption and emission factors were calculated using a combination of Google Maps®, the U.S. EPA’s Motor Vehicle Emission Simulator, the SHRP2 Naturalistic Driving Study, and MassDOT’s Transportation Data Management System. The evaluation of pavement performance with realistic traffic conditions, varying M&R strategies and material characteristics was quantified in terms of Life Cycle Cost (LCC), Global Warming Potential (GWP) and Cumulative Energy Demand (CED) for both agencies and users. The inclusion of realistic traffic conditions into the use phase of the LCA resulted in a 6.4% increase in CED and GWP when compared to baseline conditions simulated for a week long operation duration. Results from this study show that optimization of M&R type, material selection and timing may lead to a 2.72% decrease in operations cost and 47.6% decrease in construction/maintenance costs.

Cognitive deterioration caused by illness or aging often occurs before symptoms arise, and their timely diagnosis is crucial to reducing its medical, personal, and societal impacts. Brain-Computer Interfaces (BCIs) stimulate and analyze key cerebral rhythms, enabling reliable cognitive assessment that can accelerate diagnosis. The BCI system presented analyzes Steady-State Visually Evoked Potentials (SSVEPs) elicited in subjects of varying age to detect cognitive aging, predict its magnitude, and identify its relationship with SSVEP features (band power and frequency detection accuracy), which were hypothesized to indicate cognitive decline due to aging. The BCI system was tested with subjects of varying age to assess its ability to detect aging-induced cognitive deterioration. Rectangular stimuli flickering at theta, alpha, and beta frequencies were presented to subjects, and frontal and occipital EEG responses were recorded. These were processed to calculate detection accuracy for each subject and calculate SSVEP band power. A neural network was trained using the features to predict cognitive age. The results showed potential cognitive deterioration through age-related variations in SSVEP features. Frequency detection accuracy declined after age group 20–40 and band power, throughout all age groups. SSVEPs generated at theta and alpha frequencies, especially 7.5 Hz, were the best indicators of cognitive deterioration. Here, frequency detection accuracy consistently declined after age group 20-40 from an average of 96.64% to 69.23%.  The presented system can be used as an effective diagnosis tool for age related cognitive decline.

Mercury content in the output gas from boilers was predicted using an Adaptive Neuro-Fuzzy Inference System (ANFIS). Input parameters were selected from coal characteristics and the operational configuration of boilers. The ANFIS approach is capable of developing a nonlinear model to represent the dependency of flue gas mercury content into the specifications of coal and also the boiler type. In this study, operational information from 82 power plants has been gathered and employed to educate and examine the proposed ANFIS model. Resulted values from the model were compared to the collected data and it indicates that the model possesses an extraordinary level of precision with a correlation coefficient of unity. The MARE% for training and testing parts were 0.003266 and 0.013272, respectively. Furthermore, relative errors between acquired data and predicted values were between -0.25% and 0.1% which confirm the accuracy of PSO-ANFIS model.

Scour depth around bridge piers plays a vital role in the safety and stability of the bridges. Existing methods to predict scour depth are mainly based on regression models or black box models in which the first one lacks enough accuracy while the later one does not provide a clear mathematical expression to easily employ it for other situations or cases. Therefore, this paper aims to develop new equations using particle swarm optimization as a metaheuristic approach to predict scour depth around bridge piers. To improve the efficiency of the proposed model, individual equations are derived for laboratory and field data. Moreover, sensitivity analysis is conducted to achieve the most effective parameters in the estimation of scour depth for both experimental and filed data sets. Comparing the results of the proposed model with those of existing regression-based equations reveal the superiority of the proposed method in terms of accuracy and uncertainty. Moreover, the ratio of pier width to flow depth and ratio of d50 (mean particle diameter) to flow depth for the laboratory and field data were recognized as the most effective parameters, respectively. The derived equations can be used as a suitable proxy to estimate scour depth in both experimental and prototype scales.

The complexity of Mixed-Integer Linear Programs (MILPs) increases with the number of nodes in energy system models. An increasing complexity constitutes a high computational load that can limit the scale of the energy system model. Especially in microgrid optimisation problems with multiple buildings and energy systems with a number of rival supply, distribution and storage technologies, methods are sought to reduce this complexity. In this paper, we present a new 2-Level Approach to MILP energy system models that determine the system design through a combination of continuous and discrete decisions. On the first level, data reduction methods are used to determine the discrete design decisions in a simplified solution space. Those decisions are then fixed, and on the second level the full dataset is used to extract the exact scaling of the chosen technologies. The performance of the new 2-Level Approach is evaluated for a case study of an urban energy system with six buildings and an island system based on a high share of renewable energy technologies. The results of the studies show a high accuracy with respect to the total annual costs, chosen system structure, installed capacities and peak load with the 2-Level Approach compared to the results of a single level optimization. The computational load is thereby reduced by more than one order of magnitude, while a significantly higher accuracy is reached in comparison to the common time series aggregation approach.

In the Brazilian Amazon, rural settlements are increasingly isolated by large-scale production farms, jeopardizing their sustainability and the good living of family farmers. Works were carried out in settlements to measure sustainability. However, the majority does not consider the participation and the collectively of those involved. In this way, we propose to evaluate, in a collective and participatory way, the sustainability and good living of the SDP São Paulo Rural Settlement, of the northern Amazon of Mato Grosso. We used the didactic-pedagogical method Circle of Sustainability, developed from five points: 1st - circle of investigation of generating themes; 2nd - circle of the history of the subject world; 3rd - circle of diagnosis of rural settlements; 4th - circle of exchange of knowledge; and 5th - circle of sustainable perceptions and narratives. The historical, socioeconomic and cultural characterization of the settlement allowed us to understand how sustainability and good living are being built in the settlement history process. Sustainability and good living are dialectical processes, are under construction, in movement.

This study surveyed the variation in Perfluorinated Compounds (PFCs) concentration entering urban wastewater treatment plants and proposed an optimal PFCS treatment method. The PFCS concentration in influent was shown to be affected by the types of industries and operating rate. The concentration of PFCs in the wastewater treatment effluent was slightly lower than that of influent. Thus, PFCs were considered to have barely been removed by the existing biological treatments. The pilot test result showed that about 10% of PFCs were removed by coagulation and precipitation, and the ozone and chlorine test also showed that few PFCs were removed regardless of the amount of injection. The activated carbon adsorption test showed that the removal was significantly increased by the empty bed contact time, with about a 60% removal in five minutes and over a 90% removal in 15 minutes. Therefore it is determined that a more stable and higher PFCs removal would result from the continuous oxidation processes such as ozone and adsorption processes such as activated carbon rather than a single biological treatment. 

Exoskeleton robots are a rising technology in industrial contexts to assist humans in onerous applications. Mechanical and control design solutions are intensively investigated to achieve a high performance human-robot collaboration (e.g., transparency, ergonomics, safety, etc.). However, the most of the investigated solutions involve high-cost hardware, complex design solutions and standard actuation. In the presented work, an industrial exoskeleton for lifting and transportation of heavy parts is proposed. A low-cost mechanical design solution is proposed, exploiting compliant actuation at the shoulder joint to increase safety and transparency in human-robot cooperation. A hierarchic model-based controller is then proposed (including the modeling of the compliant actuator) to actively assist the human while executing the task. An inner optimal controller is proposed for trajectory tracking, while an outer fuzzy logic controller is proposed to online deform the task trajectory on the basis of the human’s intention of motion. A gain scheduler is also designed to calculate the optimal control gains on the basis of the performed trajectory. Simulations have been performed in order to validate the performance of the proposed device, showing promising results. The prototype is under realization.

Longitudinal dispersion coefficient (LDC) plays an essential role in modeling the transport of pollution and sediment in the natural rivers. As a result of transportation processes, the concentration of pollution changes along the river. Different studies have been conducted to provide simple equations for estimating LDC. In this study, Support Vector Regression (SVR), Gaussian Process Regression (GPR), M5P and Random Forest (RF) examined to predict the LDC in the natural streams. The hydraulic and geometric features of different rivers gathered for developing the mentioned models for LDC estimation and various statistical criteria were utilized to scrutinize of the models. Furthermore, the Taylor chart was used to evaluate the models and achieved results showed that among machine learning models, M5P displayed the superior performance with CC of 0.823, SI of 0.812, NS of 0.577 and WI of 0.879. As well, S-D model with CC of 0.795 SI of 0.827, NS of 0.558 and WI of 0.890 had more precise results than other empirical models. The results indicates that the developed M5P model with simple formulations was superior to other machine learning models and empirical models and therefore, it can be used as a proper tool for estimating LDC in natural rivers.

Although different assessments and evaluations of Dual-Miller cycle performed, specified output power and thermal performance associated with engine determined. Besides, multi objective optimization of thermal efficiency, Ecological Coefficient of performance ( ) and Ecological function ( ) by the mean of NSGA-II technique and thermodynamic analysis performed. The Pareto optimal frontier obtaining the best optimum solution is chosen by fuzzy Bellman-Zadeh, LINMAP, and TOPSIS decision-making techniques. Based on the results, performances of dual-Miller cycles and their optimization are improved.

Future cyber-physical systems may extend over broad geographical areas, like cities or regions, thus requiring the deployment of large real-time networks. A strategy to guarantee predictable communication over such networks is to synthesize an offline time-triggered communication schedule. However, this synthesis problem is computationally hard (NP-complete), and existing approaches do not scale satisfactorily to the required network sizes. This article presents a segmented offline synthesis method which substantially reduces this limitation, being able to generate time-triggered schedules for large hybrid (wired and wireless) networks. We also present a series of algorithms and optimizations that increase the performance and compactness of the obtained schedules while solving some of the problems inherent to segmented approaches. We evaluate our approach on a set of realistic large-size multi-hop networks, significantly larger than those considered in the existing literature. The results show that our segmentation reduces the synthesis time up to two orders of magnitude.

A common reactor type in the chemical and process industry is the fixed-bed reactor. Accurate modeling can be achieved with particle-resolved Computational Fluid Dynamic (CFD) simulations. However, the underlying bed morphology plays a paramount role. Synthetic bed-generation methods are much more flexible and faster than image-based approaches. In this study, we look critically at the two different bed generation methods: Discrete Element Method (DEM) (in the commercial software STAR-CCM+) and the rigid-body model (in the open-source software Blender). The two approaches are compared in terms of synthetically generated beds with experimental data of overall and radial porosity, particle orientation, as well as radial velocities. Both models show accurate agreement for the porosity. However, only Blender shows similar particle orientation than the experimental results. The main drawback of the DEM is the long calculation time and the shape approximation with composite particles.  

The combination of artificial intelligence algorithms and numerical methods has recently become popular in the prediction of macroscopic and microscopic hydrodynamics parameters of bubble column reactors. The multi inputs and outputs machine learning can cover small phase interactions or large fluid behavior in industrial domains. This numerical combination can develop the smart multiphase bubble column reactor with the ability of low-cost computational time. It can also decrease case studies for the optimization process when big data is appropriately used during learning. There are still many model parameters that need to be optimized for a very accurate artificial algorithm, including data processing and initialization, the combination of inputs and outputs, number of inputs and model tuning parameters. For this study, we aim to train four inputs big data during learning process by an adaptive neuro-fuzzy inference system or adaptive-network-based fuzzy inference system  (ANFIS) method, and we consider the superficial gas velocity as one of the input variables, while for the first time, one of the computational fluid dynamics (CFD) outputs named gas velocity is used as an output of the artificial algorithm. The results show that the increasing number of input variables improves the intelligence of the ANFIS method up to , and the number of rules during learning process has a significant effect on the accuracy of this type of modeling. The results also show that propper selection of model parameters results in more accuracy in prediction of the flow characteristics in the column structure.

The energy efficiency of ice hockey arenas is a central concern for the administrations, as these buildings are well known to consume a large amount of energy. Since they are composite, complex systems, solutions to such a problem can be approached from many different areas, from managerial to technological to more strictly physical. In this paper we consider heat transfer processes in an ice hockey hall, during operating conditions, with a bottom-up approach based upon on-site measurements. Detailed heat flux, relative humidity and temperature data for the ice pad and the indoor air are used for a heat balance calculation in the steady-state regime, which quantifies the impact of each single heat source. We then solve the heat conduction equation for the ice pad in transient regime, and obtain a generic analytical formula for the temperature profile that can be used in practical applications. We then apply this formula to the resurfacing process for validation, and find good agreement with an analogous numerical solution. Since it is given with implicit initial condition and boundary conditions, it can be used not only in ice hockey halls, but in a large variety of engineering applications.

HybridAC/DC microgrids(HMG) are emerging as an attracting method for integrating the AC/DC distributed energy resources(DERs) with the features of high-performance and low-cost. In the isolated hybrid AC/DC microgrid (IHMG), the key problem is how to balance the power variation and regulate the voltage and frequency. Various energy storage systems (ESS)and interlinking converter (IC) technologies are viable for this application. The present study proposes a novel unified power flow model to evaluate and compare the abilities of the ESS with different connection topologies and ICs with different control approaches to maintain the voltage and frequency stability of the IHMG. In order to investigate the performance of the proposed scheme, five operation modes of the IHMG are defined and explained. The classification is based on the connection topologies and control modes of the ESS/IC in the IHMG. Then, a set of generic PF equations are derived. Moreover, three binary matrices are applied in the construction of the unified power equations. These matrices are used for describing the running state of the IHMG. Finally, in order to verify the proposed scheme, it is applied to several case studies of the IHMG. The operation characteristics of multi-DC subgrids IHMG in different modes, particularly when an external disturbance occurs, are investigated.

Tissue engineering has the potential for repairing large bone defects, which impose a heavy financial burden on the public health. However, difficulties with O₂ delivery to the cells residing in the interior of tissue engineering scaffolds make it challenging to grow artificial tissues of clinically-relevant sizes. This study uses image-based simulation in order to provide insight into how to better optimize the scaffold manufacturing parameters, and the culturing conditions, in order to resolve the O₂ bottleneck. To do this, high resolution 3D X-ray images of two common scaffold types (salt leached foam and non-woven fiber mesh) are fed into a Lattice Boltzmann Method fluid dynamics and reactive Lagrangian Scalar Tracking mass transfer solvers. The obtained findings indicate that the scaffolds should have maximal surface area-to-solid volume ratios, for higher chances of the molecular collisions with the cells. Furthermore, the cell culture media should be flown through the scaffold pores as fast as practically possible (without detaching or killing the cells). Finally, we have provided a parametric sweep that maps how the molecular transport within the scaffolds is affected by variations in rates of O₂ consumption by the cells. Ultimately, the results of this study are expected to benefit the computer-assisted design of tissue engineering scaffolds and culturing experiments.

In this study, an optimization of the first blade in new test rig presented. Blade tuning is conducted using 3D geometrical parameters. Sweep and dihedral play an essential role in this study. Compressor characteristics and blades vibrational behavior are the main objective of the evaluation. Here, the attachments are designed to isolate blade dynamics from Disk. So, the Vibrational behaviors of the one's blade are tuned based on the self-excited and forced vibration phenomenon. Using a semi analytical MATLAB code instability conditions are satisfied. The code takes advantages of whitehead and force response theory to predict classically and stall flutter speeds. Beside, Forced vibrations instability is controlled using a theory presented by Campbell. Aerodynamics of new blade geometry determined using multistage simulations Computational fluid dynamics (CFD) software. Numerical results show increasing performance near the surge line and working interval along with increasing mass flow.

Pipelines are widely used for transportation of hydrocarbon fluids over millions of miles over the world. The structures of the pipelines are designed to withstand several environmental loading conditions to ensure safe and reliable distribution from point of production to the shore or distributions deport. However, leaks in pipeline networks are one of the major causes of innumerable losses in pipeline operators and nature. Incidents of pipeline failure can result in serious ecological disasters, human casualties and financial loss. In order to avoid such menace and maintain safe and reliable pipeline infrastructure, substantial research efforts have been devoted to implementing pipeline leak detection and localisation using different approaches. This paper discusses on pipelines leakage detection technologies and summarises the state-of-the-art achievements. Different leakage detection and localisation in pipeline systems are reviewed and their strengths and weaknesses are highlighted. Comparative performance analysis is performed to provide a guide in determining which leak detection method is appropriate for particular operating settings. In addition, research gaps and open issues for development of reliable pipeline leakage detection systems are discussed.

In order to perceive the behavior presented by the multiphase chemical reactors,  ant colony optimization algorithm was combined with CFD data. This intelligent algorithm creates a probabilistic technique for computing flow and it can predict various levels of three-dimensional bubble column reactor. This artificial ant algorithms is mimicking the real ant behavior. We found that this method can anticipate the flow characteristics in the reactor using almost 30 % of whole data in the domain. Following discovering the suitable parameters, the method is used for predicting the points not being simulated with CFD, which represent mesh refinement of Ant colony method. In addition, it is possible to anticipate the bubble-column reactors in the absence of numerical results or training of exact values of evaluated data. The major benefits include reduced computational costs and time savings.

Recently, generative adversarial networks (GANs) can be used to generate the photo-realistic image from a low-dimension random noise. It is very dangerous that the synthesized or generated image is used on inappropriate contents in social media network. In order to successfully detect such fake image, an effective and efficient image forgery detector is desired. However, conventional image forgery detectors are failed to recognize the synthesized or generated images by using GAN-based generator since they are all generated but manipulation from the source. Therefore, we propose a deep learning-based approach to detect the fake image by combining the contrastive loss. First, several state-of-the-art GANs will be collected to generate the fake-real image pairs. Then, the contrastive will be used on the proposed common fake feature network (CFFN)to learn the discriminative feature between the fake image and real image (i.e., paired information). Finally, a smaller network will be concatenated to the CFFN to determine whether the feature of the input image is fake or real. Experimental results demonstrated that the proposed method significantly outperforms other state-of-the-art fake image detectors.

Bioimpedance measurements are used increasingly in health applications because bioelectric parameters have been associated with anatomical and physiological properties, thus enabling to distinguish medical conditions. For bone fracture diagnostics, nevertheless, there is no established non-invasive method. Ex vivo studies and In vivo bioimpedance procedures, both invasive and non-invasive, on mammalians long bones are associated with promising results. In this work, out of a total of 568 papers, we reviewd 59 articles that mention long bone integrity by electric properties, be it Bioimpedance Analysis, Electrical Impedance Spectroscopy or Electrical Impedance Tomography. The papers are described in three sections, “Ex vivo measurements”, “In vivo invasive measurements” and “In vivo non-invasive measurements”. This review allows to establish the basics to planning the development of new technology to detect bone fracture via bioimpedance measurements.

Poly(hydroxyalkanoates) (PHAs) are currently considered competent candidates to replace traditional plastics in several market sectors. However, commercial PHA grades exhibit unsatisfactory smell that can negatively affect the quality of the final product. The cause of this typical rancid odour is attributed to oxidized cell membrane glycolipids, coming from Gram negative production strains, which remain frequently attached to PHAs granules after extraction. The aim of this research is the development of customised PHA nano-biocomposites for industrial applications containing organo-modified nanoclays with high adsorption properties able to capture volatile compounds responsible of the displeasing fragrance in PHAs. To this end, a methodology for the detection and identification of the key volatiles released due to oxidative degradation of PHAs has been established using a headspace solid-phase microextraction technique. We report the development of nine nano-biocomposite materials based on three types of commercial PHA matrices loaded with three species of nanoclays which represent a different polar behaviour. It has been demonstrated that although the reached outcome effect depends on the volatile nature, natural sepiolite (T2) might result in the most versatile candidate for all PHA matrices selected.