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In this paper, the influence of the variation in transformer oil temperature on the accuracy of the all-acoustic non-iterative method for partial discharge location in a power transformer is researched. The research can improve power transformers’ testing and monitoring, particularly given the large transformer oil temperature variations during real-time monitoring. The research is based on quantifying the contribution of oil temperature to the standard combined measurement uncertainty of the non-iterative algorithm by using analytical, statistical, and Monte Carlo methods. The contribution can be quantified and controlled. The contribution varied significantly with different mutual placements of partial discharge and acoustic sensors. The correlation between the contribution and the mean distance between partial discharge and acoustic sensors was observed. Based on these findings, the procedure to quantify and control the contribution in practice was proposed. The procedure considers the specificity of the method’s mathematical model (the assumption that the oil temperature is constant), the non-iterative algorithm’s nonlinearity, and the large variations in transformer oil temperature. Existing studies did not consider the significant effect of the oil temperature on the combined measurement uncertainty of partial discharge location influenced by those phenomena. The research is limited to partial discharge located in the transformer oil.

A π-phase-shifted fiber Bragg grating (π-FBG) shows high sensitivity to the ultrasonic (US) wave as compared to the conventional FBG due to the strong slow-light phenomenon at the resonance peak. However, its sensitivity is limited by the interrogation schemes. A combination of π-FBG and unbalanced fiber Mach– Zehnder interferometer (F-MZI) are theoretically analyzed and optimized for the highly sensitive acoustic sensor. The coupled-mode theory (CMT) and transfer matrix method (TMM) are used to establish the numerical modelling of π-FBG. For the optimized grating parameters of π-FBG, the proposed sensing system shows the high strain sensitivity of 1:2 108=", the highest dynamic strain resolution of 4:1f"= p Hz, and the highest wavelength shift resolution of 4:9 10��9 pm. Further, the proposed sensing system strongly supports both time andwavelength division multiplexing techniques. Therefore, the proposed sensing system shows extreme importance in single as well as quasi-distributed US acoustic wave sensing networks.

Manufacturing companies operate in an environment characterized as increasingly volatile, uncertain, complex and ambiguous. At the same time, their customer orientation makes it increasingly important to ensure high delivery reliability. Manufacturing sites within a supply network must therefore be resilient against events from the supply network. This requires deeper integration between the supply network and manufacturing control. Therefore, this article presents a concept to connect supply network and manufacturing more closely by integrating events from the supply network into manufacturing control’s decisions. In addition to the requirements, the concept describes the structure of the system as a control loop, a reinforcement learning-based controlling element as the central decision-making component, and the integration into the existing production IT landscape of a company as well as with latest internet of things (IoT) devices and cyber-physical systems. The benefits of the concept were elaborated in expert workshops. In summary, this approach enables an effective and efficient response to events from the supply network through smarter manufacturing control, and thus more resilient manufacturing.

Energy scenarios represent a prominent tool to support energy system transitions towards sustainability. In order to better fulfil this role, two elements are widely missing in previous work on designing, analyzing, and using scenarios: First, a more systematic integration of social and socio-technical characteristics of energy systems in scenario design, and, second, a method to apply an accordingly enhanced set of indicators in scenario assessment. In this article, an integrative scenario assessment methodology is introduced that combines these two requirements. It consists of: (i) A model-based scenario analysis using techno-economic and ecological indicators; (ii) a non-model-based analysis using socio-technical indicators; (iii) an assessment of scenario performances with respect to pre-determined indicator targets; (iv) a normalization method to make the two types of results (model-based and non-model-based) comparable; (v) an approach to classify results to facilitate structured interpretation. The combination of these elements represents the added-value of this methodology. It is illustrated for selected indicators, and exemplary results are presented. Methodological challenges and remaining questions, e.g., regarding the analysis of non-model-based indicators, resource requirements, or the robustness of the methodology are pointed out and discussed. We consider this integrative methodology being a substantial improvement of previous scenario assessment methodologies., Helmholtz-Gemeinschaft

In the field of food packaging, the addition of exfoliated layered silicates in polymers has been established to improve the polymers&rsquo, gas barrier properties. Using these polymers as coatings to protect smart textiles from oxidation and corrosion while maintaining their textile properties should significantly extend their lifetime and promote their market penetration. The aim of this study was to print new polymer dispersions containing layered silicates to protect screen-printed conductive structures, and to test the resulting samples. For this, appropriate printing parameters were determined by statistical design of experiments. According to these results, conductive structures were printed and protected with the selected coating. The abrasion resistance and the continuity of the protective layer of the printed samples were then measured. A continuous protective coating of approximately 70&ndash, 80 &micro, m thickness was applied on a conductive structure. The printed samples showed a very high resistance to abrasion (unchanged by 85,000 abrasion cycles) while remaining flexible and presenting a lower water vapor permeability (&lt, 2.5 g/m&sup2, d) than the coatings commonly used in the textile field.

Due to the toxicity of cadmium (Cd) and the scarcity of telluride (Te), CdTe-based photovoltaic modules have been under discussion during the last few years. In particular, the stability of CdTe in aqueous solutions is under debate. Here we show that the stability of CdTe depends not only on the pH of water-based solutions but also on size and surface treatment of CdTe particles. We compare milled module pieces with CdTe powders of different particle size. The leaching of CdTe is conditioned by the outdiffusion of Cd and Te at the interface between CdTe particles and the aqueous solution. The smaller the particle size, the faster the leaching. Therefore, milled module pieces decompose faster than CdTe powders with relatively large grains. We observe a dependence on time t according to t0.43. The room temperature diffusion coefficients are calculated as DCd ≈ 3 × 10-17 cm2/s for Cd, and DTe ≈ 1.5 × 10−17 cm2/s for Te in pH4. The chemical instability in aqueous solutions follows thermodynamic considerations. The solution behavior of Cd and Te depends on the pH value and the redox potential of the aqueous solutions. Chemical treatments such as those used in solar cell production modify the surface of the CdTe particles and their leaching behavior., Bundesministerium für Wirtschaft und Technologie

This study identifies unstable and soluble layers in commercial photovoltaic modules during 1.5 year long-term leaching. Our experiments cover modules from all major photovoltaic technologies containing solar cells from crystalline silicon (c-Si), amorphous silicon (a-Si), cadmium telluride (CdTe), and copper indium gallium diselenide (CIGS). These technologies cover more than 99.9% of the world market. We cut out module pieces of 5 &times, 5 cm2 in size from these modules and leached them in water-based solutions with pH 4, pH 7, and pH 11, in order to simulate different environmental conditions. Unstable layers open penetration paths for water-based solutions, finally, the leaching results in delamination. In CdTe containing module pieces, the CdTe itself and the back contact are unstable and highly soluble. In CIGS containing module pieces, all of the module layers are more or less soluble. In the case of c-Si module pieces, the cells&rsquo, aluminum back contact is unstable. Module pieces from a-Si technology also show a soluble back contact. Long-term leaching leads to delamination in all kinds of module pieces, delamination depends strongly on the pH value of the solutions. For low pH-values, the time dependent leaching is well described by an exponential saturation behavior and a leaching time constant. The time constant depends on the pH, as well as on accelerating conditions such as increased temperature and/or agitation. Our long-term experiments clearly demonstrate that it is possible to leach out all, or at least a large amount, of the (toxic) elements from the photovoltaic modules. It is therefore not sufficient to carry out experiments just over 24 h and to conclude on the stability and environmental impact of photovoltaic modules.

Finding a way for mitigating climate change is one of the main challenges of our generation. Sorption-enhanced gasification (SEG) is a process by which syngas as an important intermediate for the synthesis of e.g., dimethyl ether (DME), bio-synthetic natural gas (SNG) and Fischer-Tropsch (FT) products or hydrogen can be produced by using biomass as feedstock. It can, therefore, contribute to a replacement for fossil fuels to reduce greenhouse gas (GHG) emissions. SEG is an indirect gasification process that is operated in a dual-fluidized bed (DFB) reactor. By the use of a CO2-active sorbent as bed material, CO2 that is produced during gasification is directly captured. The resulting enhancement of the water-gas shift reaction enables the production of a syngas with high hydrogen content and adjustable H2/CO/CO2-ratio. Tests were conducted in a 200 kW DFB pilot-scale facility under industrially relevant conditions to analyze the influence of gasification temperature, steam to carbon (S/C) ratio and weight hourly space velocity (WHSV) on the syngas production, using wood pellets as feedstock and limestone as bed material. Results revealed a strong dependency of the syngas composition on the gasification temperature in terms of permanent gases, light hydrocarbons and tars. Also, S/C ratio and WHSV are parameters that can contribute to adjusting the syngas properties in such a way that it is optimized for a specific downstream synthesis process., H2020 European Research Council

Building Information Modeling (BIM) and Virtual reality (VR) have attracted growing attention within the architecture, engineering, and construction (AEC) industry in recent years. Integration of BIM and VR technology can develop workflow efficiency through enhanced common understanding and prepare students in architecture and engineering programs to become leaders of the AEC industry. However, the current shortage of AEC professionals trained in BIM and VR is still a barrier to collaborative working practice in this industry. This paper reviews previous work on the BIM, VR, and BIM-into-VR in AEC education/training to bridge this gap. It also presents an advanced framework to clarify creating and using the BIM model into VR workflow in the AEC industry through the integrated definition function (IDEF0) model. The authors further evaluated the BIM-into-VR applications in literature and real-life by surveying students’ learning performance in terms of eight characteristics relevant to the VR environment and students’ performance within two projects, one involving the “NASA Mars Habitat Project” and the other involving the “Norris Center Project” at Northwestern University. The results confirmed that BIM-into-VR usability and efficiency in improving students’ main learning performance characteristics: Learnability, Interoperability, Visualization, Real-world, Interaction, Creativity, Motivation, and Comfort. This study addresses the advantages of using BIM-into-VR in AEC programs. It also offers suggestions to AEC educators and students in implementing BIM-into-VR in different courses and creating a roadmap for their future as professionals in the AEC industry.

The last decade has witnessed tremendous efforts to shape the Internet of things (IoT) platforms to be well suited for healthcare applications. These platforms are comprised of a network of wireless sensors to monitor several physical and physiological quantities. For instance, long-term monitoring of brain activities using wearable electroencephalogram (EEG) sensors is widely exploited in the clinical diagnosis of epileptic seizures and sleeping disorders. However, the deployment of such platforms is challenged by the high power consumption and system complexity. Energy efficiency can be achieved by exploring efficient compression techniques such as compressive sensing (CS). CS is an emerging theory that enables a compressed acquisition using well-designed sensing matrices. Moreover, system complexity can be optimized by using hardware friendly structured sensing matrices. This paper quantifies the performance of a CS-based multichannel EEG monitoring. In addition, the paper exploits the joint sparsity of multichannel EEG using subspace pursuit (SP) algorithm as well as a designed sparsifying basis in order to improve the reconstruction quality. Furthermore, the paper proposes a modification to the SP algorithm based on an adaptive selection approach to further improve the performance in terms of reconstruction quality, execution time, and the robustness of the recovery process. Other information Published in: Wireless Communications and Mobile Computing License: http://creativecommons.org/licenses/by/4.0 See article on publisher's website: http://dx.doi.org/10.1155/2017/9823684

Material from the electric arc furnace smelting of municipal solid waste incineration (MSWI) bottom ash was easily converted into highly porous glass-ceramics by a combination of inorganic gel casting and sinter-crystallization at 1000 &deg, C. In particular, the gelation of aqueous suspensions of fine glass powders, transformed into &ldquo, green&rdquo, foams by intensive mechanical stirring, occurred with a limited addition of alkali activator (1 M NaOH). The products coupled the stabilization of pollutants with good mechanical properties (e.g., compressive strength approaching 4 MPa). Interestingly, they could be used also as raw material for new glass-ceramic foams, obtained by the same gel casting and sintering method, with no degradation of chemical stability. Limitations in the crushing strength, derived from the limited viscous flow densification of semi-crystalline powders, were overcome by mixing powders from recycled foams with 30 wt% soda-lime glass. The new products finally featured an even higher strength-to-density ratio than the foams from the first cycle.

Classification of electroencephalography (EEG) is the most useful diagnostic and monitoring procedure for epilepsy study. A reliable algorithm that can be easily implemented is the key to this procedure. In this paper a novel signal feature extraction method based on dynamic principal component analysis and nonoverlapping moving window is proposed. Along with this new technique, two detection methods based on extracted sparse features are applied to deal with signal classification. The obtained results demonstrated that our proposed methodologies are able to differentiate EEGs from controls and interictal for epilepsy diagnosis and to separate EEGs from interictal and ictal for seizure detection. Our approach yields high classification accuracy for both single-channel short-term EEGs and multichannel long-term EEGs. The classification performance of the method is also compared with other state-of-the-art techniques on the same datasets and the effect of signal variability on the presented methods is also studied.

We study the energy-efficient power allocation techniques for OFDM-based cognitive radio (CR) networks, where a CR transmitter is communicating with CR receivers on a channel borrowed from licensed primary users (PUs). Due to non-orthogonality of the transmitted signals in the adjacent bands, both the PU and the cognitive secondary user (SU) cause mutual-interference. We assume that the statistical channel state information between the cognitive transmitter and the primary receiver is known. The secondary transmitter maintains a specified statistical mutual-interference limits for all the PUs communicating in the adjacent channels. Our goal is to allocate subcarrier power for the SU so that the energy efficiency metric is optimized as well as the mutual-interference on all the active PU bands are below specified bounds. We show that the green power loading problem is a fractional programming problem. We use Charnes-Cooper transformation technique to obtain an equivalent concave optimization problem for what the solution can be readily obtained. We also propose iterative Dinkelbach method using parametric objective function for the fractional program. Numerical results are given to show the effect of different interference parameters, rate and power thresholds, and number of PUs.

The lack of a standard test method for evaluating the resistance of pervious concrete to cycles of freezing and thawing in the presence of deicing salts is the motive behind this study. Different sample size and geometry, cycle duration, and level of submersion in brine solutions were investigated to achieve an optimized test method. The optimized test method was able to produce different levels of damage when different types of deicing salts were used. The optimized duration of one cycle was found to be 24 h with twelve hours of freezing at −18 °C and twelve hours of thawing at +21 °C, with the bottom 10 mm of the sample submerged in the brine solution. Cylinder samples with a diameter of 100 mm and height of 150 mm were used and found to produce similar results to 150 mm-cubes. Based on the obtained results a mass loss of 3%–5% is proposed as a failure criterion of cylindrical samples. For the materials and within the cycles of freezing/thawing investigated here, the deicers that caused the most damage were NaCl, CaCl 2 and urea, followed by MgCl 2 , potassium acetate, sodium acetate and calcium-magnesium acetate. More testing is needed to validate the effects of different deicers under long term exposures and different temperature ranges.

A novel method for the detection of short pulses of gas at very low concentrations, the differential surface reduction (DSR), is presented. DSR is related to the temperature pulsed reduction (TPR) method. In a high temperature phase, e.g., at 400 ∘C, the surface of a metal oxide semiconductor gas sensor (MOS) is oxidized in air and then cooled abruptly down to, e.g., 100 ∘C, conserving the large excess of negative surface charge. In this state reactions of reducing gases with surface oxygen are strongly favored, which increases the sensitivity. Due to the large energy barrier between metal oxide grains caused by the excess surface charge, a highly precise electrical measurement at very low conductance (down to 10−11 S) is a prerequisite for this method. Moreover, the electrical measurement must be very fast to allow a good resolution of retention times. Applying the method to a doped SnO2 detector, gas pulses down to a dosage of 1 ppb times seconds can be detected. The gas transport inside the detector is simulated using the finite element method (FEM) to optimize the gas transport and to keep response and recovery time as short as possible. With this approach, we have demonstrated a detection limit for ethanol of below 47 fg.

“Industrie 4.0” or the Industrial Internet of Things (IIoT) are two terms for the current (r)evolution seen in industrial automation and control. Everything is getting smarter and data generated at all levels of the production process are used to improve product quality, flexibility, and productivity. This would not be possible without smart sensors, which generate the data and allow further functionality from self-monitoring and self-configuration to condition monitoring of complex processes. In analogy to Industry 4.0, the development of sensors has undergone distinctive stages culminating in today's smart sensors or “Sensor 4.0”. This paper briefly reviews the development of sensor technology over the last 2 centuries, highlights some of the potential that can be achieved with smart sensors and data evaluation, and discusses success requirements for future developments. In addition to magnetic sensor technologies which allow self-test and self-calibration and can contribute to many applications due to their wide spectrum of measured quantities, the paper discusses condition monitoring as a primary paradigm for introducing smart sensors and data analysis in manufacturing processes based on two projects performed in our group.

This paper proposes a design and implementation of an axial type magnetic gear (MG) based on the composition of the magnetic arrangement. We report a quantitative comparison of two MG topologies with rectangular magnets arranged in series and parallel. Increased magnetic flux is done through magnetic circuit analysis and electrical circuit approach. Testing is done by using the rotation of a DC motor drive from 300–2600 rpm with a DC generator under load conditions. Measurement of load current and generator output power for both axial MG topologies are taken and analyzed. The results showed that the performance of an axial MG with the rectangular magnetic arrangement in parallel is better than that of a series arrangement. Based on the measured loading current of the two axial MG topologies, at generator rotation between 300–1300 rpm with 100, 200, and 300 Ω resistance loads show the same load current. Conversely, after a DC generator rotation approaches ½ of the maximum rotation (1300–2600 rpm) there is a significant increase in load current fluctuations. That is, with an increase of load currents occurring, the parallel magnetic topology shows an increase in load torque due to an increase of magnetic flux in the gear train magnets of the MG.

Future energy systems must be clean, efficient and profitable. Previous cases have shown that incorporating local production into district heating networks is often unprofitable due to high temperature levels. This article suggests a new way of determining areas in existing networks suitable for low temperature district heating, which makes investments by real estate owners into local production profitable. A simulation of district heating network is used together with an investment optimization model to analyse the spatial potential of local production. The results show that some local production was profitable at current temperature levels, but it could be increased significantly if supply temperatures are decreased. This was shown to decrease losses and emissions of the system. Spatial optimization models are needed to increase knowledge on the effects of local production and lower network temperatures in district heating networks. Furthermore, new pricing models for customers have to be developed.

The researchers are applying different methods to increase the performance of nanocomposites. There are some techniques that will used to increase the properties of epoxy resins for example attachment of functional groups to the carbon nanotubes, better dispersal techniques to disperse the nanofillers into the polymer matrices and so on. In this research, we made a comparative study on epoxy with different weight ratio of MWCNTs. Based on observation and experiment, developing an experimental relation between changes in different types of properties. For this purpose, a combination of epoxy resin (Araldite® LY 5052) and hardeners (Aradur® HY 5052) was dispersed into the matrix, and then the effect of this epoxy based matrix was investigated after dispersing p-MWCNTs and f-MWCNTs.

Detailed demand profiles for the residential sector are an important prerequisite for the improvement of building energy efficiency and the development of polygeneration systems. Therefore, the aim of this paper is to provide a generic pattern for each energy demand required for a single-family dwelling. The data of electricity, domestic hot water and freshwater were obtained through literature reported methods of demand profile estimation, whereas heating and cooling have been estimated by TRNSYS software simulation. All methods provide energy consumption profiles with 1-hour time step along 1-year period. Daily, weekly and yearly results are presented. The total dwelling consumption amounts to 3,866 kWh/y of electricity, 941 kWh/y of heating, 1,450 kWh/y of cooling, 41 m3/y or 2,090 kWh/y of domestic hot water and 110 m3/y of freshwater. Primary energy saving can achieve up to 13,917 kWh/y in case of renewable energy use and a higher comfort level is felt by the users during summer.

The machining of lightweight composite materials is a challenging process in the field of materials and manufacturing. The composites have materials with distinct phases of hard fibre and soft matrix, inducing the cutting tool edge to face varying cutting pressure. In this paper, alumina based ceramic cutting tools in different combinations [(i) Al2O3 + Ti [C,N] and (ii) Al2O3 + SiCw] are used to machine a glass fibre rod at different process conditions. From the experimentation, the performance of the tool is observed to measure the flank wear at individual process conditions. The tool wear found to be rapid and failure occurred with increase in cutting velocity. On other hand, it was found that the tool life decreases with increase cutting velocity and the performance of cutting tools is similar in both cases. Wear morphology was found to be smooth and regular on machining fibre material. A modified Adam – Gene algorithm (AGA) was used with a constraint, to find the optimal machining process condition. It is found that the Al2O3 + Ti [C,N] cutting tool has a better tool life with minimal production cost for an optimal process condition of 250 m/min and 0.1 mm/rev. Economically, the Al2O3 + Ti [C,N] cutting tool has lower production cost, however, Al2O3 + SiCw cutting tool has better performance.

Interlaminar stitching has proven to be an effective through-thickness reinforcement technique for laminated composites but a major drawback is the negative effect it has on the in-plane tensile properties. This study aims to optimise stitching for composites to reduce the associated negative effects by introducing a new stitch type to composite laminates. The double-thread chain-stitch (ISO-401) has shown great utility in the textile industry but has not yet been applied as through-thickness reinforcement to composites. In this paper, tensile properties of glass fibre composite laminates stitched with ISO-401 double-thread chain stitch have been examined, as stitching reduces in-plane strength while improving damage tolerance. Three variations of the ISO-401 geometry were employed by varying the stitch junction position and each position was applied at two stitch densities by varying the stitch pitch at 3 mm and 4 mm. The composites were manufactured using E-glass 2 × 2 twill fabric and a Bisphenol-A type epoxy resin, stitching was performed with core-spun tex 24 polyester thread. The experimental data demonstrated the influence of ISO-401 junction position and stitch density on the composite tensile properties. Furthermore, adjustment of the junction position was found to affect the size of stitch-induced resin pockets and severity of in-plane fibre misalignment.

Wastewater treatment and post-treatment drying techniques play a crucial role on the fertilizer value of biosolids. The aim of this study was to investigate: the nitrogen fertilizer value of biosolids from selected biosolid treatment and post treatment dewatering techniques (a) and the effect of drying depth and time on the nitrogen content of biosolid (b). To achieve the first aim, laboratory nitrogen mineralization study was conducted using thermally-hydrolysed-biosolid, activated-biosolid, and anaerobically-digested-biosolids. To achieve the second aim five drying depths were tested (5, 10, 15, 20 and 25 cm). Nitrogen release per tonne biosolid applied was highest for activated (24 kg), and lowest for anaerobically-digested-biosolid (6 kg). Highest total nitrogen was reported for 10 cm drying depth (3.63%) (4 weeks) and lowest for 25 cm (2.64%) (8 weeks). Selection of appropriate biosolid treatment and dewatering technique is key to improve fertilizer value of biosolids.

To accelerate deep decarbonisation in the energy sector, the discipline of economics should focus on identifying feasible instead of optimal policies. To do so, economic analysis should include four features: complexity (a), non-economic aspects (b), uncertainty (c) and stakeholders (d). The aim of this paper is to show that qualitative scenario analysis represents a promising alternative to conventional optimisation approaches and meets these requirements. This paper develops qualitative scenarios for the case study of gas infrastructure modifications with hydrogen and carbon capture and storage technologies in Germany. In the results, the six socio-economic qualitative scenarios are described in more detail. A comparison between the case study and a conventional approach reveals three limitations of the latter and highlights the value of qualitative scenario development. The authors distil the advantages of qualitative scenario analysis and discuss challenges and chances, that go beyond the case study. In conclusion, developing socio-economic scenarios has a large potential to improve economic policy assessment. It also allows to catch up with the rethinking of energy research taking place in other disciplines.

Single point burnishing of low strength alloy with cylindrical shape and small diameter may lead to bending and failure of sample. Thus, there is limitation applying sufficient pressure to the sample that may lead to inappropriate quality characteristics or long time machining due to further pass number. Using hydrostatic pressure through series of face to face roller can be used as potential method for processing of specimens in aforementioned categories. In the present investigation an attempt was made for processing of AA6061-T6 samples which were turned to 8 mm diameter of by multi-roller rotary burnishing process. Experiments were designed and conducted to correlate factors viz burnishing depth, number of rollers, spindle speed and feed rate to surface roughness, surface residual stress and hardness. Analysis of variances have been carried out to check adequacy of developed empirical models and to identify factors with great importance on quality characteristics. Finally, an optimum processing condition was defined by response surface methodology and fatigue life and surface integrity factors of optimum condition were compared with as turned material. The obtained results revealed that feed rate, roller number and spindle speed are factors with great influence on surface roughness, residual stress and hardness respectively. It is also find out that low cycle fatigue life of the sample at optimum level are improved more than three times compared to as turned sample. The outcome of this research can be useful for processing of small size products that need to be surface treated to enhance their properties.

The absorber section is a critical component in solar thermal collectors and is responsible for converting electromagnetic radiation into sensible thermal energy. The optical properties of the surface of the absorber dictates the effectiveness of this conversion. In this work, a manufacturing process is designed to provide bulk surface treatment for solar absorber plates in order to enhance their radiative properties. The process utilizes a novel approach of graphene synthesis and simultaneous deposition via high pressure blasting of graphite. The results show enhanced spectral, thermal and electrochemical performance of the absorber due to the turbostratic nature of graphene adhesion to its surface, especially when three blasting passes are used. The absorptivity of the absorber exhibited a 7% increase when three passes were applied on its surface. Using graphene has also enhanced the corrosion resistance of the absorber plate. This manufacturing system will provide a facile yet effective treatment of absorbers with various degrees of automation. Applied as a final layer, the process can be retrofitted to existing manufacturing facilities with minimum overhead costs.

In processing of metallic material by ultrasonic nanocrystalline surface modification (UNSM), obtaining high quality sample with desired surface property needs a careful control on processing factors. In the present investigation, an attempt was made to find optimal setting of UNSM factors viz static pressure, vibration amplitude, spindle speed, feed rate and ball size on surface integrity factors such as surface roughness and hardness. Number of 32 experiments were conducted using small fraction central composite design to correlate input-output relationship. Grey relational analysis and desirability function was also applied to experimental data to identify a setting corresponds to maximum hardness and minimum surface roughness. The optimum samples were also subjected to fatigue tests as well the microscopic observations to find how much improvement occurs as a result of optimization with respect to as turned sample. It is found from both approaches that the setting of 0.03 MPa static pressure, 10 μm vibration amplitude, 100 rpm spindle speed, 0.08mm/rev feed rate and 8 mm strike size is an optimal parameter setting that results in surface roughness of 0.356 μm and hardness of 155 HV. The sample machined by optimum condition result in fatigue life cycles improvement more than 2.5 times in different levels of applied stress. The improvement can be attributed to combined effect of smoothed surface topography, fine microstructure, hardened surface layers and exerting compressive residual stress.

Concerning the demand for upscaling in severe plastic deformation (SPD) processes, in this research, a novel method is presented, during which a billet with a rectangular cross-section is enforced under a cross-sectional shear strain, and its cross-section is changed to the parallelogram. Then the parallelogram is again enforced under another cross-sectional shear strain, and its shape is changed back to the original rectangular shape. Due to the form of the deformation and the manner in which cross-section changes, the method is entitled “Simple Shear Forging” (SSF). A new apparatus was designed and manufactured to impose plastic strain on the billet in a plane strain condition, while a hydrostatic pressure is applied, and without any change in the cross-sectional area, which ensures the repeatability of the process. In order to study the process parameters, most notably friction and pressure, simulation of the process was carried out using the commercial finite element code ABAQUS/Explicit, and the resultant strain distributions were investigated. The presented method is applied to Al 1050 billets, and the Vickers microhardness test was carried out for different numbers of applied passes. The results indicate that the SSF method is capable of introducing high strain values and grain refinement. Unlike most of the previous methods, it is not necessary to remove and reinsert the billet after each pass, which consequently enhances the productivity of the process significantly.

Skygardens are increasingly being seen as design interventions for improving the social, economic and environmental values of a building. Additionally, the integration of vegetation within the building space can have health benefits for occupants, occupants’ satisfaction while improving the air quality. However, the aerodynamic response of skygarden vegetation must be understood in detail to assist designers in the selection and arrangement of species for creating a conducive environment for occupants. The current study analyses nine different vegetation configurations within a high-rise skygarden to determine the aero-thermal performance. Computational fluid dynamics simulations were carried out using the standard k-Ɛ method, where the vegetation was modelled as a porous zone with cooling capacity. The results indicate that vegetation can attenuate high wind speeds within comfort levels at occupants’ height, as well as reduce temperatures by over 1 °C in the wake of the vegetation. Whereas the performance improves with higher number of trees, but when considering limited vegetation space, the relative performance of a single-row configuration is better than a double row of trees along the skygarden edge.

Turning operation is one of the primary machining operations and enhanced machining performance is obtained by its process parameters. In turning process, heat is generated and it must be removed from the machining zone. Now a days, hydrocarbon oil based cutting fluid is used and it generates environmental issues as well as operator health issues. Recently, textured cutting tool inserts are attempted towards sustainable machining. In this work, turning process is carried out on aluminium-metal matrix composite (Al-MMC) with micro hole textured cutting tool inserts. Micro hole texturing involves various parameters such as hole diameter, hole depth and pitch between the holes. The main aim of this work is to analyze the effect of micro hole design parameters on machining performance. The input parameters include diameter of the micro hole, pitch between hole and hole and depth. Process outcomes include surface roughness, tool wear and power consumption. The experiments are performed based on L9 Taguchi orthogonal array. The result revealed that micro hole textured tools with solid lubricant embedded cutting inserts and its design parameters influence machining performance. Texture with solid lubricant could act as alternative to hydrocarbon oil based cutting fluid and also chip breaker.

Round bars made of Inconel 718 are widely used in aerospace industries due to their excellent properties of working at harsh environment. However, machinability of this alloy by conventional turning process results in excessive tool wear and poor surface integrity. The former increases the production cost and latter reduces the fatigue life. In the present paper, interrupted cutting of Inconel 718 round bars was introduced as an effective way for enhancing the life time. Turn-milling as newly emerging cutting technology was utilized here to machine cylindrical samples of Inconel 718. Effect of process factors such as tool rotational speed, workpiece rotational speed, feed rate and eccentricity was analyzed on surface roughness and residual stress. The obtained results showed that the feed rate is most influential parameter that determines value of surface roughness and residual stress and. Furthermore, by considering production rate as a constraint, it was logically discussed that a setting of 1000 rpm cutter speed, 300 rpm work rotational speed, 0.12 mm/rev feed rate and 0.2 mm eccentricity can guarantee maximum production rate as well minimum surface roughness and tensile residual stress.

With rapid urbanization and the requirements for new buildings and infrastructure, cement production is expected to increase in China. Meanwhile, China’s energy consumption and carbon emissions associated with cement industry are expected to increase, as cement production is one of the most energy intensive industries. Introducing new and more pertinent technologies with better management, as indicated by government goals and measures, can improve energy efficiency and reduce carbon emission. However, many challenges and uncertainties in the cement industry have been addressed without considering several interconnected processes among sectors including industry, energy, construction, and services, which lead to little improvements in the industry. Nexus approaches are recognized as effective methods to simultaneously examine interactions among multiple sectors with various elements. This research intends to provide insights for future energy-cement-carbon emission nexus, through integrated assessment capabilities, related to China’s new urbanization planning toward 2050. Several challenges and uncertainties, including different trends in population growth, structural transformation of economic driving forces, and new but stricter policy targets for resource use efficiency and environment production, are put into multiple scenarios. The results of our analysis provide perspectives on future direction to underscore the growing interconnection among energy, cement and carbon emission with clear and more specific roadmap for promoting integrated planning, management and governance.

Limited strength and ductile joints for bonding γ-TiAl intermetallics are one of the main concerns on γ-TiAl applications in the aerospace industry. In this study, a new ductile brazing filler with TiZrCuNi system was developed by an established model for calculating the electrons distribution of the unit cell for the filler. Three kinds of fillers with fixed amount of Cu and Ni as 25 wt.%, but different Zr level (25, 18, 10 wt.%) were designed. It was found that the filler diffusion was affected by the filler composition, which was insufficient with high Zr content up to 37.5 wt.%. And the defects such as the voids and the cracks were observed with medium Zr level as 25 wt.% and brazing temperature as 1000 °C. The sound joints by Zr as 18 wt.% and 10 wt.% filler at 950 °C and 980 °C respectively were achieved with tensile strength as 520 MPa–570 MPa at room temperature. This tensile strength was remained as 495 MPa in average at 600 °C. The strong joints were attributed to the fine, randomly oriented Ti3Al grains and evenly distributed elements at the interfaces.

Extravascular lung water (EVLW) is a basic symptom of congestive heart failure and other conditions. Computed tomography (CT) is standard to assess EVLW, but it requires ionizing radiation and radiology facilities. Lung ultrasound reverberation artifacts called B-lines have been used to assess EVLW. However, B-line artifact analysis relies on visual interpretation and subjects to inter-observer variability. We developed lung ultrasound surface wave elastography (LUSWE) to measure lung surface wave speed. This research aims to develop LUSWE to measure the change of lung surface wave speed due to lung water in an ex vivo swine lung model. The surface wave speeds of a fresh ex vivo swine lung were measured at four frequencies of 100 Hz, 200 Hz, 300 Hz, and 400 Hz. An amount of water was then filled into the lung through its trachea. Ultrasound imaging was used to guide the water filling until significant changes were visible on the imaging. The lung surface wave speeds were measured. An additional 120 ml of water was then filled into the lung. The lung surface wave speeds were then measured again. The results demonstrated that the lung surface wave speed decreased with respect to water content.

Este artículo aborda el problema del desconocimiento por parte de la ciencia de cómo perciben el riesgo laboral los trabajadores de la construcción. Específicamente, se da respuesta a las siguientes cuestiones de investigación: ¿cuál es el riesgo que perciben los trabajadores de la construcción?; ¿qué aspectos del riesgo influyen de forma significativa en la formación del riesgo percibido global?; ¿existen variables sociodemográficas que ayuden a entender la percepción del riesgo de los trabajadores de la construcción?, y en caso afirmativo, ¿cuáles son estas variables y de qué forma influyen? En definitiva, se examina el perfil del riesgo percibido, su relación con la demora de las consecuencias y la influencia de las variables socio-demográficas. Los participantes rellenaron un cuestionario en presencia del encuestador. El cuestionario se basaba en el paradigma psicométrico, y se componía de: (a) nueve cuestiones, donde cada una de ellas exploraba un atributo o dimensión del riesgo en una escala Likert de 7 puntos, (b) una pregunta global de la percepción del riesgo, y (c) preguntas sociodemográficas acerca de ellos mismos. La muestra se consiguió en Granada (España). Como resultado se obtuvo un perfil del riesgo percibido para el trabajador de la construcción. Las respuestas a cada atributo siempre estuvieron por encima de la línea neutral (puntuaciones superiores a cuatro). El perfil muestra que el atributo con mayor puntuación fue el atributo relacionado con la demora de las consecuencias en la exposición al riesgo, una dimensión que puede estar relacionada con la ergonomía y la higiene. Esto es un resultado novedoso ya que tradicionalmente esta dimensión ha obtenido una puntuación menor en la percepción que el trabajador tiene frente a la inmediatez de los efectos. Una regresión lineal muestra que el atributo global del riesgo puede ser explicado por la demora de las consecuencias (R 2 =0.29). Finalmente, mediante un análisis de la varianza (ANOVA) y varios análisis T se exploró la relación entre esta dimensión y las variables sociodemográficas de la muestra. En conclusión, la demora de las consecuencias es la dimensión del riesgo que los trabajadores perciben más crítica en sus tareas diarias. Además, esta dimensión del riesgo es determinante en la creación de una alta percepción del riesgo global. La paternidad, una categoría laboral superior y la formación son las únicas variables sociodemográficas que tienen impacto en esta dimensión de la percepción del riesgo. Por consecuente, hay una relación directa entre estas dos variables.

Many anthropological researches require identification and measurement of craniometric and cephalometric landmarks which provide valuable information about the shape of a head. This information is necessary for morphometric analysis, face approximation, craniafacial identification etc. Traditional techniques use special anthropological tools to perform required measurements, identification of landmarks usually being made by an expert-anthropologist. Modern techniques of optical 3D measurements such as photogrammetry, computer tomography, laser 3D scanning provide new possibilities for acquiring accurate 2D and 3D data of high resolution, thus creating new conditions for anthropological data analysis. Traditional anthropological manual point measurements can be substituted by analysis of accurate textured 3D models, which allow to retrieve more information about studied object and easily to share data for independent analysis. The paper presents the deep learning technique for anthropological landmarks identification and accurate 3D measurements. Photogrammetric methods and their practical implementation in the automatic system for accurate digital 3D reconstruction of anthropological objects are described.

This paper proposes a new hybrid method for automatic detection and recognition of the presence/absence of a protective mask on human's face. It combines visual features extracted using Convolutional Neural Network (CNN) with image histograms that convey information about pixel intensity. Several pre-trained models for building feature extraction systems using a CNN and several types of image histograms are considered in this paper. We test our approach on the Medical Mask Dataset and perform cross-corpus analysis on two other databases named Masked Faces (MAFA) and Real-World Masked Face Dataset (RMFD). We demonstrate that the proposed hybrid method increases the Unweighted Average Recalls (UARs) of recognition of the presence/absence of a protective mask on human's face in comparison with traditional CNNs on the MAFA and RMFD databases by 0.96% and 1.32%, respectively. The proposed method can be generalized and used for other tasks of biometry, computer vision, machine learning and automatic face recognition.

The active development of neural network technologies and optoelectronic systems has led to the introduction of computer vision technologies in various fields of science and technology. Deep learning made it possible to solve complex problems that a person had not been able to solve before. The use of multi-spectral optical systems has significantly expanded the field of application of video systems. Tasks such as image recognition, object re-identification, video surveillance require high accuracy, speed and reliability. These qualities are provided by algorithms based on deep convolutional neural networks. However, they require to have large databases of multi-spectral images of various objects to achieve state-of-the-art results. While large and various databases of color images of different objects are widely available in public domain, then similar databases of thermal images are either not available, or they represent a small number of types of objects. The quality of three-dimensional modeling for the thermal imaging spectral range remains at an insufficient level for solving a number of important tasks, which require high precision and reliability. The realistic synthesis of thermal images is especially important due to the complexity and high cost of obtaining real data. This paper is focused on the development of a method for synthesizing thermal imaging images based on generative adversarial neural networks. We developed an algorithm for a multi-spectral image-to-image translation. We have changed to the original GAN architecture and converted the loss function. We presented a new learning approach. For this, we prepared a special training dataset including about 2000 image tensors. The evaluation of the results obtained showed that the proposed method can be used to expand the available databases of thermal images.

In this paper, a new technique for real-time object detection and tracking is presented. This technique is based on the geometrized histograms method (GHM) for segmenting and describing color images (frames of video sequences) and on the facilities for global image analysis provided by this method. Basic elements of the technique that make it possible to solve image understanding problems almost without using the pixel arrays of images are introduced and discussed.A real-time parallel software implementation of the developed technique is briefly discussed. This technique is applied to solving problems of road scene analysis. The application to finding small contrast objects in images, like traffic signals and signal zones of vehicles is given. The developed technique is applied also to detecting other vehicles in the frame. The results of processing different frame of videos of road scenes are presented and discussed.

Today, possibilities of artificial intelligence allow us to see the emergence of autonomous cars. However, there are still many problems in this area at present. Often, such vehicles are “too slow to think”, are not able to reliably process data from video cameras in the event of reflections, glare, and there are also questions about the safety of such driving in difficult weather conditions or in heavy traffic. At the same time, the human factor plays a major role in accidents of driven vehicles. Many accidents involve driver fatigue, distraction, or even falling asleep. At the same time, it is potentially possible to monitor the state of a person behind the wheel by a video sequence received from a camera installed in the car's interior and registering the driver's face in video sequence. In this paper, the existing databases of images of faces and eyes are considered, and an algorithm is presented that detects the state of closed eyes based on Haar detectors and convolutional neural networks.

Image matting often requires advanced image processing, especially in conditions, when small details such as hair are present in the image. In this article the hybrid method for human image matting based on convolutional neural network and principal curvatures is proposed. The U-Net based neural network is used to predict a rough foreground segmentation mask. Then the obtained foreground mask is refined by principal curvatures method to process the elongated hair-like structures. Test results show that the proposed method can improve the coarse human segmentation.

In the era of data-driven machine learning algorithms, data represents a new oil. The application of machine learning algorithms shows they need large heterogeneous datasets that crucially are correctly labeled. However, data collection and its labeling are time-consuming and labor-intensive processes. A particular task we solve using machine learning is related to the segmentation of medical devices in echocardiographic images during minimally invasive surgery. However, the lack of data motivated us to develop an algorithm generating synthetic samples based on real datasets. The concept of this algorithm is to place a medical device (catheter) in an empty cavity of an anatomical structure, for example, in a heart chamber, and then transform it. To create random transformations of the catheter, the algorithm uses a coordinate system that uniquely identifies each point regardless of the bend and the shape of the object. It is proposed to take a cylindrical coordinate system as a basis, modifying it by replacing the Z-axis with a spline along which the h-coordinate is measured. Having used the proposed algorithm, we generated new images with the catheter inserted into different heart cavities while varying its location and shape. Afterward, we compared the results of deep neural networks trained on the datasets comprised of real and synthetic data. The network trained on both real and synthetic datasets performed more accurate segmentation than the model trained only on real data. For instance, modified U-net trained on combined datasets performed segmentation with the Dice similarity coefficient of 92.6±2.2%, while the same model trained only on real samples achieved the level of 86.5±3.6%. Using a synthetic dataset allowed decreasing the accuracy spread and improving the generalization of the model. It is worth noting that the proposed algorithm allows reducing subjectivity, minimizing the labeling routine, increasing the number of samples, and improving the heterogeneity.

In medical image processing, image fusion is the process of combining complementary information from different (multimodality) images to obtain a fused image, which plays a vital role in further analysis and treatment planning. The main idea of this paper is to improve the image content by fusing computer tomography (CT) and magnetic resonance (MR) images. We propose here the new algorithm based on the probabilistic gamma-normal model with structure-transferring properties. Firstly, we select the areas with the highest pixel intensity on original CT and MR images. In parallel with this, the structures of original images are distinguished using the probabilistic gamma-normal model. The weighted-fusion image can be obtained based on detected objects and structure. Finally, we smooth the weighted-fusion image using the structure-transferring filter and combine the smoothed image with the weighted-fusion image for obtaining the resulting image. The key point here is that we do not need to re-allocate the structure, which leads to the reduction of computation time. The proposed method gives the best result in terms of the spatial frequency metric and lower computation time than other image fusion methods.

Over the past few years closer cooperation can be observed in various aspects of digital techniques in such disciplines as dentistry and anthropology. And in most cases that consists in imaging and image processing which results in obtaining 3D reconstructions. And indeed, they can significantly improve research and practice. Thus, depending on imaging technique and application, they can support CAD/CAM technology or precisely reconstruct morphology of invisible structures. However the currently presented study refers to technical aspects of shade and texture mapping, which is more aimed to obtain more realistic 3D reconstructions of palaeoanthropological material. Colour or shade matching has become an integral part of dental practice. It can be carried in a traditional manner though matching the tooth with conventional shade-guides, or, which is in line with the subjects of our study, by means of spectrophotometry. And the main procedures of shade detection have been performed by SpectroShade (MHT). Necessary attention has been paid to conditions influencing shade detection process with respect to the studied material teeth taken from the Bronze Age findings. Reconstructive techniques have traditionally been a scientific and practical part of palaeoanthropological research which is directed at appearance reconstruction. Though the leading part of this branch has been always aimed at analysis of skull morphology. In our time of rapidly developing digital techniques reconstructions have become to a large extent a matter of improvements of imaging and image processing techniques. Even though this doesnt directly refer to soft tissue reconstruction, it undoubtedly applies to dental reconstructions. And the current study presents improvements in reconstruction through combining imaging with shade and texture mapping on 3D models of teeth.

Visual information plays a key role in automatic speech recognition (ASR) when audio is corrupted by background noise, or even inaccessible. Speech recognition using visual information is called lip-reading. The initial idea of visual speech recognition comes from humans’ experience: we are able to recognize spoken words from the observation of a speaker's face without or with limited access to the sound part of the voice. Based on the conducted experimental evaluations as well as on analysis of the research field we propose a novel task-oriented approach towards practical lip-reading system implementation. Its main purpose is to be some kind of a roadmap for researchers who need to build a reliable visual speech recognition system for their task. In a rough approximation, we can divide the task of lip-reading into two parts, depending on the complexity of the problem. First, if we need to recognize isolated words, numbers or small phrases (e.g. Telephone numbers with a strict grammar or keywords). Or second, if we need to recognize continuous speech (phrases or sentences). All these stages disclosed in detail in this paper. Based on the proposed approach we implemented from scratch automatic visual speech recognition systems of three different architectures: GMM-CHMM, DNN-HMM and purely End-to-end. A description of the methodology, tools, step-by-step development and all necessary parameters are disclosed in detail in current paper. It is worth noting that for the Russian speech recognition, such systems were created for the first time.

Mobile devices provide a huge amount of multimedia information sending to the members of social groups every day. Sometimes it is required to authorize the sending information using the limited computational resources of smartphones, tablets or laptops. The hardest problem is with smartphones, which have the limited daily energy and battery life. There are two scenarios for using mobile watermarking techniques. The first scenario is to implement the embedding and extraction schemes using proxy server. In this case, the watermarking scheme does not differ from conventional techniques, including the advanced ones based on adaptive paradigms, deep learning, multi-level protection, and so on. The main issue is to hide the embedding and extracting information from the proxy server. The second scenario is to provide a pseudo-optimized algorithm respect to robustness, imperceptibility and capacity using limited mobile resources. In this paper, we develop the second approach as a light version of adaptive image and video watermarking schemes. We propose a simple approach for creating a patch-based set for watermark embedding using texture estimates in still images and texture/motion estimates in frames that are highly likely to be I-frames in MPEG notation. We embed one or more watermarks using relevant large-sized patches according to two main criteria: high texturing in still images and high texturing/non-significant motion in videos. The experimental results confirm the robustness of our approach with minimal computational costs.

The presented research considers the problems of studying the cluster structure of multidimensional data volumes. This paper presents the results of numerical experiments on the study of data volumes consisting of frequencies of joint use of words from different parts of speech, for instance “noun + verb” or “adjective + noun”. The volumes of data are obtained from samples from text collections in Russian. The aim of the research is to analyze the cluster structure of the studied volume and semantic proximity of words in clusters and subclusters. The hypothesis was used that words with similar meaning should occur in approximately the same context. In this regard, in the space of features, they will be at a relatively close distance from each other, while differing words will be at a more distant distance from each other. Research is carried out using elastic maps, which are effective tools for visual analysis of multidimensional data. The construction of elastic maps and their extensions in the space of the first three principal components makes it possible to determine the cluster structure of the studied multidimensional data volumes. Such analysis can be useful in the tasks of confronting negative verbal influences such as fake news, hidden propaganda, involvement in sects, verbal manipulation, etc. Also this approach can be applied to text collections having medical origin.

Object Re-Identification (ReID) is the task of matching a given object in the new environment with its image captured in a different environment. The input for a ReID method includes two sets of images. The probe set includes one or more images of the object that must be identified in the new environment. The gallery set includes images that may contain the object from the probe image. The ReID task’s complexity arises from the differences in the object appearance in the probe and gallery sets. Such difference may originate from changes in illumination or viewpoint locations for multiple cameras that capture images in the probe and gallery sets. This paper focuses on developing a deep learning ThermalReID framework for cross-modality object ReID in thermal images. Our framework aims to provide continuous object detection and re-identification while monitoring a region from a UAV. Given an input probe image captured in the visible range, our ThermalReID framework detects objects in a thermal image and performs the ReID. We evaluate our ThermalReID framework and modern baselines using various metrics. We use the IoU and mAP metrics for the object detection task. We use the cumulative matching characteristic (CMC) curves and normalized area-under-curve (nAUC) for the ReID task. The evaluation demonstrated encouraging results and proved that our ThermalReID framework outperforms existing baselines in the ReID accuracy. Furthermore, we demonstrated that the fusion of the semantic data with the input thermal gallery image increases the object detection and localization scores. We developed the ThermalReID framework for cross-modality object re-identification. We evaluated our framework and two modern baselines on the task of object ReID for four object classes. Our framework successfully performs object ReID in the thermal gallery image from the color probe image. The evaluation using real and synthetic data demonstrated that our ThermalReID framework increases the ReID accuracy compared to modern ReID baselines.

Floods and flash floods are complex events, depending on weather dynamics, basin physiographical characteristics, land use cover and water management. For this reason, the prediction of such events usually deals with very accurate model tuning and validation, which is usually site-specific and based on climatological data, such as discharge time series or flood databases. In this work, we developed and tested two hydrological-stress indices for flood detection in the Italian Central Apennine District: a heterogeneous geographical area, characterized by complex topography and medium-to-small catchment extension. The proposed indices are threshold-based and developed considering operational requirements of National Civil Protection Department end-users. They are calibrated and tested through the application of signal theory, in order to overcome data scarcity over ungauged areas, as well as incomplete discharge time series. The validation has been carried out on a case study basis, using flood reports from various sources of information, as well as hydrometric-level time series, which represent the actual hydrological quantity monitored by Civil Protection operators. Obtained results show that the overall accuracy of flood prediction is greater than 0.8, with false alarm rates below 0.5 and the probability of detection ranging from 0.51 to 0.80. Moreover, the different nature of the proposed indices suggests their application in a complementary way, as the index based on drained precipitation appears to be more sensitive to rapid flood propagation in small tributaries, while the discharge-based index is particularly responsive to main-channel dynamics.

Lowland karst aquifers can generate unique wetland ecosystems which are caused by groundwater fluctuations that result in extensive groundwater–surface water interactions (i.e. flooding). However, the complex hydrogeological attributes of these systems, linked to extremely fast aquifer recharge processes and flow through well-connected conduit networks, often present difficulty in predicting how they will respond to changing climatological conditions. This study investigates the predicted impacts of climate change on a lowland karst catchment by using a semi-distributed pipe network model of the karst aquifer populated with output from the high spatial resolution (4 km) Consortium for Small-scale Modelling Climate Lokalmodell (COSMO-CLM) regional climate model simulations for Ireland. An ensemble of projections for the future Irish climate were generated by downscaling from five different global climate models (GCMs), each based on four Representative Concentration Pathways (RCPs; RCP2.6, RCP4.5, RCP6.0 and RCP8.5) to account for the uncertainty in the estimation of future global emissions of greenhouse gases. The one-dimensional hydraulic/hydrologic karst model incorporates urban drainage software to simulate open channel and pressurised flow within the conduits, with flooding on the land surface represented by storage nodes with the same stage volume properties of the physical turlough basins. The lowland karst limestone catchment is located on the west coast of Ireland and is characterised by a well-developed conduit-dominated karst aquifer which discharges to the sea via intertidal and submarine springs. Annual above ground flooding associated with this complex karst system has led to the development of unique wetland ecosystems in the form of ephemeral lakes known as turloughs; however, extreme flooding of these features causes widespread damage and disruption in the catchment. This analysis has shown that mean, 95th and 99th percentile flood levels are expected to increase by significant proportions for all future emission scenarios. The frequency of events currently considered to be extreme is predicted to increase, indicating that more significant groundwater flooding events seem likely to become far more common. The depth and duration of flooding is of extreme importance, both from an ecological perspective in terms of wetland species distribution and for extreme flooding in terms of the disruption to homes, transport links and agricultural land inundated by flood waters. The seasonality of annual flooding is also predicted to shift later in the flooding season, which could have consequences in terms of ecology and land use in the catchment. The investigation of increasing mean sea levels, however, showed that anticipated rises would have very little impact on groundwater flooding due to the marginal impact on ebb tide outflow volumes. Overall, this study highlights the relative vulnerability of lowland karst systems to future changing climate conditions, mainly due to the extremely fast recharge which can occur in such systems. The study presents a novel and highly effective methodology for studying the impact of climate change in lowland karst systems by coupling karst hydrogeological models with the output from high-resolution climate simulations.