The paper presents the measurement campaign carried out on a real-world motorway stretch of Hungary with the participation of both industrial and academic partners from Austria and Hungary. The measurement included vehicle based as well as infrastructure based sensor data. The obtained results will be extremely useful for future automotive R&D activities due to the available ground truth for static and dynamic content. The aim of the measurement campaign was twofold. On the one hand, road geometry was mapped with high precision in order to build Ultra High Definition (UHD) map of the test road. On the other hand, the vehicles - equipped with differential Global Navigation Satellite Systems (GNSS) for ground truth localization - carried out special test scenarios while collecting detailed data using different sensors. All test runs were recorded by both vehicles and infrastructure. As a complementary task, the available 5G network was monitored and tested. The paper also showcases application examples based on the measurement campaign data, in which the added value of having access to the ground truth labeling and the created UHD map of the motorway section becomes apparent. In order to present our work transparently, a part of the measured data have been shared openly such that interested automotive as well as academic parties may use it for their own purposes.

Energy transitions are reshaping the global energy system. Such shift has taken the power system to become a critical infrastructure for achieving economic development of every nation in the planet, therefore, guaranteeing its security is crucial, not only for energy purposes but as a part of a national security strategy. This paper presents a multi-dimensional index developed to assess energy security of electrical systems in the long term. This tool, named Power System Security Index (PSIx), and which has been previously used for the evaluation of a country in two different time frames, is applied to evaluate the member countries of the Latin American Energy Organization, located within the Latin America and the Caribbean region, in order to measure their performance on energy security. Mixed results were obtained from the analysis, with clear top performers in the region such as Argentina, while there are others with broad areas of opportunity, as it is the case of Haiti.

Focal vibration therapy can provide neurophysiological benefits. Unfortunately, standardized protocols are non-existent. Previous research presents a wide range of protocols with a wide range of effectiveness. This paper is part of a broader effort to identify effective, standardized protocols for focal vibration therapy. The vibration characteristics of four commercially available focal vibration devices that have been used for research and clinically were measured. An accelerometer was used for the measurements. Frequency and peak-to-peak amplitude were measured. Measurements were made when the devices were free and then again when they were strapped to the human body. Vibration frequency ranged from 120 to 225 Hz. Free vibration amplitude ranged from 2.0 to 7.9 g’s (peak-to-peak). When the devices were strapped to the body (constrained), vibration amplitude decreased by up to 65.7%. These results identify effective ranges of focal vibration frequency and amplitude. They illustrate the importance of identifying vibration environment, free or constrained, when quoting vibration characteristics. Finally, the inconsistency of multi-actuator devices is discussed. These results will guide protocol development for focal vibration and potentially better focal vibration devices.

Worldwide, millions of kilometres of sewers are constructed from concrete pipes. Unfortunately, concrete sewers are susceptible to corrosion from biogenic hydrogen sulphide, and though they may pass visual inspection, their ability to hold together under load may be degraded. This paper presents the design of a teleoperated robot with a protractible probe, that allows an operator to apply a localised load to selected points within a concrete sewer pipe. We report findings from laboratory and field trials of our prototype, with initial results suggesting that this approach has the potential to contribute useful information to sewer maintenance planning.

System-of-systems (SoS) approach is often used for simulating disruptions to business and infrastructure system networks allowing for integration of several models into one simulation. However, the integration is frequently challenging as each system is designed individually with different characteristics, such as time granularity. Understanding the impact of time granularity on propagation of disruptions between businesses and infrastructure systems and finding the appropriate granularity for the SoS simulation remain as major challenges. To tackle these, we explore how time granularity, recovery time, and disruption size affect the propagation of disruptions between constituent systems of an SoS simulation. To address this issue, we developed a High Level Architecture (HLA) simulation of 3 networks and performed a series of simulation experiments. Our results revealed that time granularity and especially recovery time have huge impact on propagation of disruptions. Consequently, we developed a model for selecting an appropriate time granularity for an SoS simulation based on expected recovery time. Our simulation experiments show that time granularity should be less than 1.13 of expected recovery time. We identified some areas for future research centered around extending the experimental factors space.

Scintillators play a key role in the detection chain of several applications which rely on the use of ionizing radiation, and it is often mandatory to extract and detect the generated scintillation light as efficiently as possible. In positron emission tomography (PET), for example, both energy resolution and coincidence resolving time, two of the key detection parameters, depend strongly on the total amount of light which reaches the photodetector surface as well as its spatial and temporal distribution. Typical inorganic scintillators do however feature a high index of refraction, which impacts light extraction efficiency in a negative way. Furthermore, several applications such as preclinical PET rely on pixelated scintillators with small pitch. In this case, applying reflectors on the crystal pixel surface, as done conventionally, can have a dramatic impact of the packing fraction and thus the overall system sensitivity. This paper presents a study on light extraction techniques, as well as combinations thereof, for two of the most used inorganic scintillators (LYSO and BGO). Novel approaches, employing distributed Bragg reflectors (DBRs), metal coatings, and a modified Photonic Crystal (PhC) structure, are described in detail and compared with commonly used techniques. The nanostructure of the PhC is surrounded by a hybrid organic/inorganic silica sol-gel buffer layer which ensures robustness while maintaining its performance unchanged. We observed in particular a maximum light gain of about 41% on light extraction and 21% on energy resolution for BGO, a scintillator which has gained interest in the recent past due to its prompt Cherenkov component and lower cost.

Refinery planning optimization is a challenging problem as regards handling the nonconvex bilinearity mainly due to pooling operations in processes such as crude oil distillation and product blending. This work investigates the performance of several representative piecewise-linear (or piecewise-affine) relaxation schemes (referred to as McCormick, bm, nf5, nf6t, and de (which is a new approach proposed based on eigenvector decomposition) that mainly give rise to mixed-integer optimization programs to convexify a bilinear term using predetermined univariate partitioning for instances of uniform and non-uniform partition sizes. Computational results show that applying these schemes give improved relaxation tightness than only applying convex and concave envelopes as estimators. Uniform partition sizes typically perform better in terms of relaxation solution quality and convergence behavior. It is also seen that there is a limit on the number of partitions that contributes to relaxation tightness, which does not necessarily correspond to a larger number of partitions, while a direct relation between relaxation size and tightness does not always hold for non-uniform partition sizes.

Research abounds in the literature on kinetic analyses using thermogravimetric (TG) runs. Many of these studies use approximations of integral or derivative forms of the kinetic law and all of them use programmed temperature, not the actual temperature measured by thermocouples close to the sample. In addition, it is common to conduct a single run in order to perform the calculation. Nevertheless, many authors consider that numerical methods should be used to analyse the kinetics of decomposition. In such cases, the actual temperature is used and, generally, several runs are fitted using the same kinetic parameters, giving robustness to the results. In the present work, a numerical integration procedure was discussed and applied to different examples. We focused on materials presenting a single decomposition curve as well as other materials with more complex processes. Different examples were explored, and the methodology was applied to a number of wastes such as coffee husks, polystyrene and polyethylene.

Monitoring volatile organic compounds (VOCs) places a crucial role in environmental pollutants control and indoor air quality. In this study, a metal-oxide (MOx) sensor detector (uRAD A3 mobile air quality monitor) was employed to delineate the composition of air containing three common VOCs (ethanol, acetone and hexane) under various concentrations. Experiments with a single component and double components were conducted to investigate how the solvents interact with the metal oxide sensor. The experimental results revealed that the affinity between VOC and sensor was in the following order: acetone > ethanol > n-hexane. A mathematical model was developed, based on the experimental findings and data analysis, to convert the output resistance value of the sensor into concentration values, which in turn can be used to calculate a VOC-based air quality index. Empirical equations were established based on inferences of vapor composition versus resistance trends, and on an approach of using original and diluted air samples to generate two sets of resistance data per sample. The calibration of numerous model parameters allowed matching simulated curves to measured data. As such, the predictive mathematical model enabled quantifying not only the total concentration of sensed VOCs, but also estimating the VOC composition. This first attempt to obtain semi-quantitative data from a single MOx sensor is aimed at expanding the capability of mobile air pollutants monitoring devices.

The paper is focused on numerical modeling of multi-strand cable lines placed in free air. Modeling is carried out within the framework of the so-called multi-physics approach using commercial software. The paper describes in detail the steps undertaken to develop realistic, reliable numerical models of power engineering cables, taking into account their geometries and heat exchange conditions. The results might be of interest to the designers of multi-strand cable systems.

The end of 2019 marked a bushfire crisis for Australia that affected more than 100000km2 of land and destroyed more than 2000 houses. Here, we propose a method of in-orbit bushfire detection with high efficiency to prevent a repetition of this disaster. An LEO satellite constellation is first developed through NSGA-II (Nondominated Sorting Genetic Algorithm II), optimising for coverage over Australia. Then edge computing is adopted to run a bushfire detection algorithm using several constellation satellites as edge nodes to reduce fire detection time. A geostationary satellite is used for inter-satellite communications, such that an image taken by a satellite can be distributed among several satellites for processing. The geostationary satellite also maintains a constant link to the ground, so that a bushfire detection can be reported back without any significant delay. Overall, this system is able to detect fires that span more than 5m in length, and can make detections in 1.39s per image processed. This is faster than any currently available bushfire detection method.

Building roofs are sources of unwanted heat for buildings situated in zones with a warm climate. Thus, reflective coatings have emerged as an alternative to reject a significant fraction of solar energy received by roofs. In this research, the thermal behavior of concrete slab-type roofs with traditional and solar reflective coatings was simulated using a computational tool. Weather data from four cities in Mexico with a warm climate were used as boundary conditions. This tool is an in-house code based on the Finite Volume Method developed by the author to perform building components simulations. The code was validated with experimental data from previous work. A series of comparative simulations were developed, taking a gray roof as a control case. The results showed that for the roof without thermal insulation (single roof), the solar reflective coatings reduced the exterior surface between 11 and 16∘C. Consequently, the single roofs’ daily heat gain was reduced by a factor ranging between 41 and 54%. On the other hand, for the insulated roof, the reflective coatings reduced the exterior surface temperature between 17 and 21∘C. At the same time, the daily heat gain of composite roofs was reduced between 37 and 56%.

The paper deals with the problems of direct kinematics related to a 2T6R robot, the direct kinematics of the plane mechanism of the robot. The direct kinematics of this proposed new robot help to study the movement, including the dynamic one, to determine the forces in the mechanism and to model the possible trajectories of the final effector point, thus determining the robot's working space and some of its multiple possible uses. Two totally different methods were used to verify the calculation relations, the results obtained by both methods being practically identical. The first method used is an original trigonometric method, and the second is an original geometrically analytical method.

South Asian countries experience frequent drought incidents recently, and due to this reason, many scientific studies were carried to explore the drought in South Asia. In this context, we review scientific studies related to drought in South Asia. The study initially identifies the importance of drought-related studies and discusses drought types for South Asian regions. The representative examples of drought events, severity, frequency, and duration in South Asian countries are identified. The Standardized Precipitation Index (SPI) was mostly adopted in South Asian countries to quantify and monitor droughts. Nevertheless, the absence of drought quantification studies in Bhutan and Maldives is of great concern. Future studies to generate a combined drought severity map for the South Asian region are required. Moreover, the drought prediction and projection in the regions is rarely studied. Further, the teleconnection between drought and large-scale atmospheric circulations in the South Asian area has not been discussed in detail in the most scientific literature. Therefore, as a take-home message, there is an urgent need for scientific studies related to drought quantification for some regions in South Asia, prediction and projection of drought for an individual country (or as a region), and drought teleconnection to atmospheric circulation.

Systems thinking is a valuable skill that may be required for an individual to be promoted in the business arena to managerial or leading positions. Thus, assessing systems thinking skills is an essential transaction for decision-makers in the organization as a preceding step to the promotion decision. One of the well-known and validated tools for this task is a questionnaire. However, because some of the questions invade the employee / candidate’s privacy, the answer may be biased. In this paper, we consider this potential bias, a phenomenon that is becoming more and more significant as privacy concerns and awareness continuously increase in the modern digital world. We propose a formal methodology to optimize the questionnaire based on the privacy sensitivity of each question, thereby providing a more reliable assessment. We conducted an empirical study (n=142), and showed that a systems skills questionnaire can be enhanced. This research has a significant contribution to improving the systems skills assessment process in particular, and also lays the foundations for improving the evaluation of other skills / traits.

Optical artificial neural networks (ONNs) have significant potential for ultra-high computing speed and energy efficiency. We report a novel approach to ONNs that uses integrated Kerr optical micro-combs. This approach is programmable and scalable and is capable of reaching ultra-high speeds. We demonstrate the basic building block ONNs — a single neuron perceptron — by mapping synapses onto 49 wavelengths to achieve an operating speed of 11.9 x 10⁹ operations per second, or Giga-OPS, at 8 bits per operation, which equates to 95.2 gigabits/s (Gbps). We test the perceptron on handwritten-digit recognition and cancer-cell detection — achieving over 90% and 85% accuracy, respectively. By scaling the perceptron to a deep learning network using off-the-shelf telecom technology we can achieve high throughput operation for matrix multiplication for real-time massive data processing.

This study presents of the microsphere-based fiber-optic sensor with the ZnO ALD coating thickness of 100 nm and 200 nm for temperature measurements. Metrological properties of the sensor were investigated over the temperature range of 100°C to 300°C, with a 10°C step. The interferometric signal is used to control whether the microstructure is intact. Spectrum shift of a reflected signal is used to conclude changes in measured parameter for the sensor with a 100 nm coating, while the reflected signal intensity is an indicator during measurements executed by a sensor with a 200 nm coating. With changing temperature, the peak position or intensity of a reflected signal also changes. The R2 coefficient of the presented sensors indicates a linear fit of over 0.99 to the obtained data. The sensitivity of the sensors, investigated in this study, equals 103.5 nW/°C and 0.019 nm/°C for ZnO thickness of 200 nm and 100 nm, respectively.

A theoretical model to translate the evolution over time, in early stages, of growth and accumulation of biofilm bacterial mass is introduced. The model implies the solution of a system of differential-difference master equations. The application of an algorithm like Miller´s tree term recurrence, already known for Bessel functions of first kind, allows an exact calculation of the solutions of such equations, for a wide range of parameters values and time. For biofilm model a five term recurrence is deduced and applied in a backwards computation. A suitable normalisation condition completes the reach of the solution.

This study aims to explore the possibility of estimating a multitude of kinematic and dynamic quantities using subject-specific musculoskeletal models in real-time. The framework was designed to operate with marker-based and inertial measurement units enabling extensions far beyond dedicated motion capture laboratories. We present the technical details for calculating the kinematics, generalized forces, muscle forces, joint reaction loads, and predicting ground reaction wrenches during walking. Emphasis was given to reduce computational latency while maintaining accuracy as compared to the offline counterpart. Notably, we highlight the influence of adequate filtering and differentiation under noisy conditions and its importance for consequent dynamic calculations. Real-time estimates of the joint moments, muscle forces, and reaction loads closely resemble OpenSim's offline analyses. Model-based estimation of ground reaction wrenches demonstrates that even a small error can negatively affect other estimated quantities. An application of the developed system is demonstrated in the context of rehabilitation and gait retraining. We expect that such a system will find numerous applications in laboratory settings and outdoor conditions with the advent of predicting or sensing environment interactions. Therefore, we hope that this open-source framework will be a significant milestone for solving this grand challenge.

Several neuroimaging findings by using different modalities (e.g., fMRI and PET) have suggested that social anxiety disorder (SAD) is correlated with alterations in regional or network-level brain function. However, these modalities do not quantify the fast dynamic connectivity of causal information networks due to their poor temporal resolution. In this study, SAD-related changes in brain connections within the default mode network (DMN) was investigated using Electroencephalogram (EEG). Partial directed coherence (PDC) was used to assess the causal influences of DMN regions on each other and indicate the changes in the DMN effective network related to SAD severity. The EEG data were collected from 88 subjects (control, mild, moderate, severe) and used to estimate the effective connectivity between DMN regions at different frequency bands. Among the healthy control (HC) and the three considered levels of severity of SAD, the results indicated a higher level of causal interactions for the mild and moderate SAD groups than for the severe and HC groups. Between the control and the severe SAD groups, the results indicated a higher level of causal connections for the control throughout all the DMN regions. We found significant increases in the mean PDC in the delta and alpha bands between the SAD groups. Among the DMN regions, the precuneus exhibited a higher level of causal influence than other regions. Therefore, it was suggested to be a major source hub that contributes to the mental exploration and emotional content of SAD. In contrast to the severe group, the HC exhibited higher resting-state connectivity at the mesial prefrontal cortex (mPFC), providing evidence for mPFC dysfunction in the severe SAD group. Furthermore, the total Social Interaction Anxiety Scale (SIAS) was positively correlated with the mean values of the PDC of the severe SAD group and negatively correlated with those of the HC group. The reported results may facilitate greater comprehension of the underlying potential SAD neural biomarkers and can be used to characterize possible targets for further medication.

The study takes into account different classes of tunnel boring machines (TBMs), with the aim of identifying correlation models which are meant to estimate, at a preliminary design phase, the construction time of a tunnel and to evaluate the mechanical and operational parameters of the TBMs, starting from the knowledge of the tunnel length and/or the excavation diameter. To achieve this goal, first of all a database was created, thanks to the collection of the most meaningful technical parameters from a large number of tunnels; afterward, it was statistically analysed through Microsoft Excel. In a first phase, forecasting models were identified for the three types of machines investigated, separately for compact rocks (open TBM) and fractured rocks (single and double shield TBM). Then, the mechanical parameters collected through the database were analysed, with the aim of obtaining models that take into account, in addition to the type of TBM, the geological aspect, and the type of rock characterising the rock mass. Finally, the validation of the study was proposed in a real case, represented by the Moncenisio base tunnel, a work included in the new Turin–Lyon connection line. The estimated values were compared with the real ones, in order to verify the accuracy of the experimental models identified.

High-penetration of Distributed Energy Resources (DER) in low voltage distribution grids, mainly photovoltaics (PV), might lead to overvoltage in the point of common coupling. Volt-VAr is one of the common control functions for DER power converters used to enhance the stability and the reliability of the voltage in the distribution system and, thus, fulfilling the network operator requirements. In this study, a centralized algorithm will provide local Volt-VAr control parameters to each PV inverter, based on the electrical grid characteristics where each equipment is installed. Since accurate information of grid characteristics is typically not available, the parametrization of the electrical grid is done using power meter data in DER location and a voltage sensitivity matrix. The algorithm has different optimization modes to both minimize voltage deviation and line current. In order to validate the effectiveness of the algorithm and its deployment in real infrastructure, it has been tested in an experimental setup with PV emulators in a set of 5-day tests. Volt-VAr control algorithm successfully adapted its parameters based on grid topology and PV inverter characteristics, achieving a voltage reduction up to 25% of the allowed voltage deviation.

Legacy risks from infrastructures and industrial installations often reveal themselves when a potential for failure has been discovered much later than at the stage of the design and construction of a structure. In which case, there might already be a problem with the legacy installation, or even a crisis, without having had an accident. When the hazard cannot be taken away, the question arises as to how much effort, if any, should be spent on improving the situation. The usefulness of the three archetypical approaches to this problem: setting a standard, the As Low As Reasonably Practicable approach and a case-by-case discourse approach are discussed for their applicability for these legacy risks. Although it would be desirable to retrofit legacy risks to previously set legal requirements as is the case when acceptability limits are set in law or demonstration of ALARP is demanded, it may be impossible to reduce the residual risk to an otherwise acceptable level without taking away or replacing the infrastructure, which is not acceptable either. Therefor in conclusion the only available solution to persistent legacy risk problems seems to be to have a thorough discussion with all relevant stakeholders until an agreement is in some way found.

We report on the design, characterization and validation of a spherical irradiation system for inactivating SARS-CoV-2, based on UV-C 275 nm LEDs. The system is designed to maximize ir-radiation intensity and uniformity, and can be used for irradiating a volume of 18 liters. To this aim: (i) several commercially available LEDs have been acquired and analyzed; (ii) a complete optical study has been carried out in order to optimize the efficacy of the system. (iii) The re-sulting prototype has been characterized optically, and tested for the inactivation of SARS-CoV-2 for different exposure times, doses and surface types. Results indicate that a 99.9% inactivation can be reached after 1 minute of treatment with a dose of 83.1 J/m2.

In MBSE there is yet no converged terminology. The term ’system model’ is used in different contexts in literature. In this study we elaborated the definitions and usages of the term ’system model’, to find a common definition. 104 publications have been analyzed in depth for their usage and definition as well as their meta-data e.g., the publication year and publication background to find some common patterns. While the term is gaining more interest in recent years it is used in a broad range of contexts for both analytical and synthetic use cases. Based on this three categories of system models have been defined and integrated into a more precise definition.

Many Caribbean island nations have historically been heavily dependent on imported fossil fuels for both power and transportation, while at the same time being at an enhanced risk from the impacts of climate change, although their emissions represent a very tiny fraction of the global total responsible for climate change. Small island developing states (SIDS) are among the leaders in advocating for the ambitious 1.5°C Paris Agreement target and the transition to 100% sustainable, renewable energy systems. In this work we present three central results. First, we show through GIS mapping of all Caribbean islands the potential for near-coastal deep-water as a resource for Ocean Thermal Energy Conversion (OTEC) and couple these results with an estimate of the countries for which OTEC would be most advantageous due to a lack of other dispatchable renewable power options. Second, hourly data have been utilized to explicitly show the trade-offs between battery storage needs and dispatchable renewable sources such as OTEC in 100% renewable electricity systems, both in technological and economic terms. Finally, the utility of near-shore, open-cycle OTEC with accompanying desalination is shown to enable a higher penetration of renewable energy and lead to lower system levelized costs than those of a conventional fossil fuel system.

Potholes on roads pose a major threat to motorists and autonomous vehicles. Driving over a pothole has the potential to cause serious damage to a vehicle, which in turn may result in fatal accidents. Currently, many pothole detection methods exist. However, these methods do not utilize deep learning techniques to detect a pothole in real-time, determine the location thereof and display its location on a map. The success of determining an effective pothole detection method, which includes the aforementioned deep learning techniques, is dependent on acquiring a large amount of data, including images of potholes. Once adequate data had been gathered, the images were processed and annotated. The next step was to determine which deep learning algorithms could be utilized. Three different models, including Faster R-CNN, SSD and YOLOv3 were trained on the custom dataset containing images of potholes to determine which network produces the best results for real-time detection. It was revealed that YOLOv3 produced the most accurate results and performed the best in real-time, with an average detection time of only 0.836s per image. The final results revealed that a real-time pothole detection system, integrated with a cloud and maps service, can be created to allow drivers to avoid potholes.

Turbulent flow is the first and fundamental physical phenomena to evaluate when optimising cost and reducing emissions from an Anode Baking Furnace (ABF). Gas flow patterns, velocity field, pressure drop, shear stress, and turbulent dissipation rate variables are the main operational parameters to be optimised, considering a specific geometry. Computational Fluid Dynamics (CFD) allows simulating physical phenomena using numerical methods with computer resources. In particular, the finite element method is one of the most used methods to solve the flow equations. This method requires a discretisation of the geometry of the ABF, called mesh. Hence, mesh is the main input to the finite element method. A suitable mesh for applying a discretisation method determines whether the problem can be simulated or not. Generating an appropriate mesh remains a challenge to perform accurate simulations. In this work, a comparison between meshes generated using two mesh generation tools is presented. Results of different study cases are included.

Convolutional neural networks (CNNs), inspired by biological visual cortex systems, are a powerful category of artificial neural networks that can extract the hierarchical features of raw data to greatly reduce the network parametric complexity and enhance the predicting accuracy. They are of significant interest for machine learning tasks such as computer vision, speech recognition, playing board games and medical diagnosis [1-7]. Optical neural networks offer the promise of dramatically accelerating computing speed to overcome the inherent bandwidth bottleneck of electronics. Here, we demonstrate a universal optical vector convolutional accelerator operating beyond 10 Tera-OPS (TOPS - operations per second), generating convolutions of images of 250,000 pixels with 8-bit resolution for 10 kernels simultaneously — enough for facial image recognition. We then use the same hardware to sequentially form a deep optical CNN with ten output neurons, achieving successful recognition of full 10 digits with 900 pixel handwritten digit images with 88% accuracy. Our results are based on simultaneously interleaving temporal, wavelength and spatial dimensions enabled by an integrated microcomb source. This approach is scalable and trainable to much more complex networks for demanding applications such as unmanned vehicle and real-time video recognition.

This paper aims to provide an overall review of degradation, damage evolution and fatigue models in the literature of various engineering materials, mostly metals, and composites.

The transportation system has evolved into a complex cyber-physical system with the introduction of wireless communication and the emergence of connected travelers and connected automated vehicles. Such applications create an urgent need to develop high-fidelity transportation modeling tools that capture the mutual interaction of the communication and transportation systems. This paper addresses this need by developing a high-fidelity, large-scale dynamic and integrated traffic and direct cellullar vehicle-to-vehicle and vehicle-to-infrastructure (collectively known as V2X) modeling tool. The unique contributions of this work are (1) we developed a scalable analytical communication model that captures packet movement at the millisecond level; (2) we coupled the communication and traffic simulation models in real-time to develop a fully integrated dynamic connected vehicle modeling tool; and (3) we developed scalable approaches that adjust the frequency of model coupling depending on the number of concurrent vehicles in the network. The proposed scalable modeling framework is demonstrated by running on the Los Angeles downtown network considering the morning peak hour traffic demand (145,000 vehicles), running faster than real-time on a regular personal computer (1.5 hours to run 1.86 hours of simulation time). Spatiotemporal estimates of packet delivery ratios for downtown Los Angeles are presented. This novel modeling framework provides a breakthrough in the development of urgently needed tools for large-scale testing of Direct C-V2X enabled applications.

Samarinda’s flooding issue is threatening future city development. As the most populated city in Kalimantan, Samarinda (the municipality of East Borneo) plays a role-model in disaster management for a neighboring city. This paper introduces current flood disaster handling in this city. History of disaster management in Indonesia is started from the earlier of Indonesian independence. Year 2008, after hit by severe Tsunami in Aceh and its surrounding, Government of Republic of Indonesia form special agency to manage disaster specifically, namely National Board for Disaster Management (in Bahasa called: Badan Penanggulangan Bencana Nasional [BNPB]) and follows by the regional and local government to form similar agency in provincial and local scale (including Samarinda), called Regional Board for Disaster Management (in Bahasa: Badan Penanggulangan Bencana Daerah[BPBD]) which is formed in 2009 and 2011, respectively. The aim of this paper is to explain flood management in Samarinda where is flood hazard increase gradually and need to be a priority. Descriptive analysis is used in this study including secondary data and interviewed stakeholders. Finally, the finding of study obtains found five constraints related to Samarinda’s flood management including administrative and policy, social, economic, environment and technical and knowledge constraint. This study also promotes several schemes of non-structural approach to enlarge alternative perspective in flood management.

Microcombs provide a potential compact and efficient light source for multi-Terabit-per-second optical superchannels. However, as the bandwidth of these multi-wavelength light sources is increased, this can result in low per-line power. Optical amplifiers can be used to overcome power limitations, but the accompanying spontaneous optical noise can degrade performance in optical systems. To overcome this issue, we propose wideband noise reduction for comb lines using a high-Q microring resonator, whose resonances align with comb lines. When applying the proposed distillation to a superchannel system with 18 Gbaud, 64-QAM sub-channels in a > 10 Tb/s optical superchannel, we find that noise-corrupted comb lines can reduce the optical signal-to-noise ratio required for the comb by ~ 9 dB when used as optical carriers at the transmitter side, and by ~ 12 dB when used as a local oscillator at the receiver side.

This paper proposes a model to evaluate business projects to get into an incubator, allowing to rank them in order of selection priority. The model combines the Momentum method to build prospective scenarios and the Multiple Criteria Decision Making (MCDM) method TOPSIS-2N to rank the alternatives. Six business projects were evaluated to be incubated. The Momentum method made it possible for us to create an initial core of criteria for the evaluation of incubation projects. The TOPSIS-2N method supported the decision to choose the company to be incubated by ranking the alternatives in order of relevance. Our evaluation model has improved the existing models used by incubators. This model can be used and / or adapted by any incubator to evaluate the business projects to be incubated. The set of criteria for the evaluation of incubation projects is original and the use of prospective scenarios with a MCDM method to evaluate companies to be incubated does not exist in the literature.

Liquid marbles are defined as hydrophilic liquid droplets that are coated with hydrophobic powdered materials. Till now, the behaviour of liquid marbles has been studied for triphasic systems comprising of the constituent hydrophilic phase, the hydrophobic coating and ambient air. In this article, we report the dynamics of aqueous droplets of varying pH (i.e. acidic, neutral and basic, respectively) moving under the influence of gravity in commonly available mustard oil. We find that the said dynamics could be divided into four parts: (i) formation of hanging aqueous droplets from the top surface of oil, (ii) oblate spheroid droplets moving at constant velocity due to viscous drag, (iii) distant repulsive interactions between two droplets due to “reverse Cheerios effect” and (iv) final impact between the two droplets explained by viscoelastic sliding friction over a compliant surface. This work would be of great interest to researchers working in the domain of interfacial phenomena like oil exploration, biomedical engineering, food technology and towards the realization of droplet-based microfluidic computational platforms for “more than Moore’s” paradigm in the domain of unconventional computation.

The research results presented in the article were carried out during the realization of the project, the aim of which is to develop a method of drying cellulose insulation in power transformers with the use of synthetic ester. This method uses a very high water absorption of the ester. During the drying of transformers, the ester is systematically contaminated with mineral oil, which gradually loses its ability to absorb water. Information on the oil concentration in the mixture is needed in two cases: at the stage of making a decision on the treatment of the mixture and during its treatment. The article presents the results of investigations of two methods: 1) based on the measurement of the mixture density, and 2) based on the measurement of the capacitance of the capacitor immersed in the mixture. The conducted research shows that the method of measuring the density of the mixture gives an uncertainty of 2.6 p. %, while the method of measuring the capacitance of a capacitor gives an uncertainty of 2.2 p. %. A significant advantage of the method of measuring the capacitance is the possibility of using it online to control the ester treatment process.

Karakoram highway (K.K.H.) the only road link between two countries China and Pakistan. This road network is essential for two countries due to its strategic location and socioeconomic. The highway is more vulnerable due to landslide disasters, especially in rain and snow melting seasons, and different kinds of mass movement activities have occurred along K.K.H., such as rockfall, debris flow, and snow avalanche. The slope stability problems are widespread along with Karakorum (K.K.H.) between Besham city and the Dasu area because of the high seismic zone, rainfall, snow melting, and complex geology slope geometry, week, and adverse discontinuities sets. The detailed fieldwork was done along the Karakorum highway to minimize the risk of slope stability and for planning purposes in Besham to Dasu area and selected nine road-cut slopes. However, in these nine selected roadcut slopes, three slopes were already failed, four slopes are partially stable, and two slopes were stable. Both kinematic and empirical approaches are applied on all these nine road cut slopes and their discontinuities. The kinematic result has shown that all kinds of mode failure such as Toppling, Planar, and Wedge failure mode occurred in these slopes. The RMRb result has shown that all discounters lie in between fair to good rock. Both discrete and continuous (S.M.R.) results show that all discontinuity sets lie between the unstable, partially stable, and stable conditions.

The utilization of eco-fonts for office printing is a sustainable, “green” printing concept, which has obvious economic benefits. As a result, it has a significant effect on environmental sustainability. This practice's fundamental problem is the decreased quality of text printed using eco-fonts compared to those printed with regular fonts. The aim of this research is eco-font efficiency estimation, i.e. determination of toner usage reduction level of inkjet-printed documents typed with this font type, as well as estimation of the extent humans perceive differences between text printed with eco-font and the one printed by its “non-eco“ equivalent. Combining the instrumental measuring method and digital image analysis, it was found that this simple principle (eco-font utilization) enables substantial toner usage reduction for an inkjet printing system. At the same time, a visual test showed that the visual experience of text printed using eco-font was sufficient. In addition, awareness of the benefits that eco-font utilization brings, change users’ attitude towards eco-font quality. The concept of removing the black pixel from this commonly used Thai font has a great potential for the sustainability printing process, and this simple solution could be applied to other languages as part of the GIT campaign.

In this study, a simple and direct numerical scheme is proposed for the similarity solution of Smoluchowski coagulation equation based on Taylor series expansion method of moment. The results show that the present method is convergence with high accuracy by comparison with previous work in the literatures.

Concrete, as one of the essential construction materials is responsible for a vast amount of emissions. Using recycled materials and gray water can considerably contribute to the sustainability aspect of concrete production. Thus, finding a proper replacement for fresh water, in the production of concrete, is significant. The usage of industrial wastewater, instead of water in the concrete can is considered in this paper. In this study, 450 concrete samples are produced with different amounts of wastewater. The mechanical parameters such as slump, compressive strength, water absorption, tensile strength, electrical resistivity, rapid freezing, half-cell potential, and appearance are investigated. The results showed that the usage of industrial wastewater does not significantly change the main characteristics of concrete. Although, increasing the concentration of the wastewater can decrease durability and strength features nonlinearly.

Online food delivery service (OFDS) has been widely utilized during the new normal of COVID-19 pandemic especially in a developing country such as Indonesia. The purpose of this study was to determine factors influencing customer satisfaction & loyalty in OFDS during the new normal of COVID-19 pandemic in Indonesia by utilizing an extended Theory of Planned Behavior (TPB) approach. 253 respondents voluntarily participated and answered 65 questions. Structural equation modeling (SEM) indicated that Hedonic Motivation (HM) was found to have the highest effect on customer satisfaction, followed by Price (P), Information Quality (IQ), and Promotion (PRO). Interestingly, this study found out that usability factors such as Navigational Design (ND) and Perceived Ease of Use (PEOU) were not significant to customer satisfaction and loyalty in OFDS during the new normal of COVID-19. This study can be the theoretical foundation that could be very beneficial for OFDS investors, IT engineers, and even academicians. Finally, this study can be applied and extended to determine factors influencing customer satisfaction and loyalty in OFDS during the new normal of COVID-19 in other countries.

This work aims to investigate systematically the influence of process temperature and biomass-to-water ration on the chemical composition of aqueous and gaseous phases and mass production of chemical by hydrothermal processing of Açaí (Euterpe Oleraceae, Mart.) seeds. The hydrothermal carbonization carried out at 175, 200, 225, and 250 °C, 2 °C/min, biomass-to-water ratio of 1:10, and at 250 °C, 2 °C/min, and biomass-to-water ratios of 1:10, 1:15, and 1:20, in technical scale. The chemical composition of aqueous phase determined by GC and HPLC and the volumetric composition of gaseous phase by using an infrared gas analyzer. For constant biomass-to-water ratio of 1:10, the yields of solid, liquid, and gaseous phases varied between 53.39 and 37.01% (wt.), 46.61 and 59.19% (wt.), and 0.00 and 3.80% (wt.), respectively. The yield of solids shows a smooth exponential decay with temperature, while that of liquid and gaseous phases a smooth growth. By variation of biomass-to-water ratios, the yields of solid, liquid, and gaseous reaction products varied between 53.39 and 32.09% (wt.), 46.61 and 67.28% (wt.), and 0.00 and 0.634% (wt.), respectively. The yield of solids decreases exponentially with increasing water-to-biomass ratio and that of liquid phase increases in a sigmoid fashion. For constant biomass-to-water ratio, the concentrations of Furfural and HMF decrease drastically with temperature, reaching a minimum at 250 °C, while that of phenols increases. In addition, the concentrations of CH3COOH and total carboxylic acids increase, reaching a maximum at 250 °C. For constant process temperature, the concentrations of aromatics vary smoothly with the temperature. The concentrations of furfural, HMF, and cathecol decrease with temperature, while that of phenols increases. The concentrations of CH3COOH and total carboxylic acids decrease exponentially with temperature. Finally, for the experiments with varying water-to-biomass ratios, the productions of chemicals (furfural, HMF, phenols, cathecol, and acetic acid) in the aqueous phase is highly dependent on the biomass-to-water ratio.

Currently, the clinical impact of cell therapy after a myocardial infarction (MI) is limited by low cell engraftment due to significant cell death, including apoptosis, in an infarcted, inflammatory, poor angiogenic environment, low cell retention and secondary migration. Cells interact with their environment through integrin mechanoreceptors that control their survival/apoptosis/differentiation/migration/proliferation. Optimizing these interactions may be a way of improving outcomes. The association of free cells with a 3D-scaffold may be a way to target their integrins. Collagen is the most abundant structural component of the extracellular matrix (ECM) and the best contractility levels are achieved with cellular preparations containing collagen, fibrin, or Matrigel (i.e. tumor extract). In the interactions between cells and ECM, 3 main proteins are recognised: collagen, laminin and RGD (Arg-Gly-Asp) peptide. The RGD plays a key role in heart development, after MI, and on cardiac cells. Cardiomyocytes secrete their own laminin on collagen. The collagen has a non-functional cryptic RGD and is thus suboptimal for interactions with associated cells. The use of a collagen functionalized with RGD may help to improve collagen biofunctionality. It may help in the delivery of paracrine cells, whether or not they are contractile, and in assisting tissue engineering a safe contractile tissue.

In this research, users’ perception towards Universal Mobility in old core cities of India has been critically analyzed. Despite Universal Design guidelines from the United Nations and Union Government of India, old cities in India are seldom having Universal Mobility, in effect endangering the lifestyle of senior citizens and differently-abled people. The core of Kolkata Municipal Corporation in Kolkata, India has been considered as a case example for this research. This research has considered three types of data sets for analysis. First, the authors interviewed 310 respondents from the Indian design fraternity, with the objective of understanding their opinion on the concept of Universal Design. In the next investigative study of 125 respondents from different wards of Kolkata Municipal Corporation, the purpose was to comprehend people’s perception about walkability and mobility in an old Indian city. In the last visual survey of a stretch in Central Kolkata, the focus was identifying the hindrance in Universal Mobility in an old city core of Indian origin. Significant dissatisfaction was found in walkability amongst all user groups; which is linked to poor infrastructural conditions. Furthermore, accessing public transportation is difficult due to improper waiting facilities. However, the design fraternity in India suggests the need of separate accessibility guidelines for old and new cities in India. The design fraternity also recommends a customized rating system for accessing Universal Design. The result of this study indicates a need of recognizing the difficulty in imparting Universal Mobility in old core cities in India. This information can be used for preparing an Access Audit Checklist through Architectural Planning, which is the first step in proposing a framework for Universal Mobility in old core cities in India.

An 18.8–33.9-GHz, 2.26-mW current-reuse (CR) injection-locked frequency divider (ILFD) for radar sensor applications is presented in this paper. A fourth-order resonator is designed using a transformer with a distributed inductor for wideband operating of the ILFD. The CR core is employed to reduce the power consumption compared to conventional cross-coupled pair ILFDs. The targeted input center frequency is 24 GHz for radar application. The self-oscillated frequency of the proposed CR-ILFD is 14.08 GHz. The input frequency locking range is from 18.8 to 33.8 GHz (57%) at an injection power of 0 dBm without a capacitor bank or varactors. The proposed CR-ILFD consumes 2.26 mW of power from a 1-V supply voltage. The entire die size is 0.75 mm ´ 0.45 mm. This CR-ILFD is implemented in a 65-nm CMOS technology.

Beer fermentation is typically monitored by periodic sampling and off-line analysis. In-line sensors would remove the need for time-consuming manual operation and provide real-time evaluation of the fermenting media. This work uses a low-cost ultrasonic sensor combined with machine learning to predict the alcohol concentration during beer fermentation. The highest accuracy model (R2=0.952, MAE=0.265, MSE=0.136) used a transmission-based ultrasonic sensing technique along with the measured temperature. However, the second most accurate model (R2=0.948, MAE=0.283, MSE=0.146) used a reflection-based technique without the temperature. Both the reflection-based technique and the omission of the temperature data are novel to this research and demonstrate the potential for a non-invasive sensor to monitor beer fermentation.

As air traffic demand grows, robust, data-driven anomaly detection methods are required to ensure that aviation systems become safer and more efficient. The terminal airspace is identified as the most critical airspace for both individual flight-level and system-level safety and efficiency. As such, developing data-driven anomaly detection methods to analyze terminal airspace operations is paramount. With the expansion of ADS-B technology, open-source flight tracking data has become more readily available to enable larger-scale analyses of aircraft operations. This paper makes a distinction between spatial metrics in ADS-B trajectory data and energy metrics derived from ADS-B trajectory data. Motivated by the limited number of approaches that simultaneously consider both spatial and energy metrics, this paper introduces the concepts of spatial anomalies and energy anomalies. In particular, it proposes a novel, unified framework for detection of spatial and energy anomalies in ADS-B trajectory data (and associated derived metrics). The framework consists of three main parts - a data processing procedure, a spatial anomaly detection method, and an energy anomaly detection method. The framework is demonstrated utilizing four months of ADS-B trajectory data associated with arrivals at San Francisco International Airport, and the relationship between the spatial and energy anomalies in this terminal airspace is explored. The results that stem from the implementation of this framework indicate that if an aircraft is spatially not conforming to an identified set of air traffic flows representing standard spatial operations, then this aircraft is more likely to experience non-conformance to standard operations in its energy metrics. Aviation operators, such as air traffic controllers, may benefit from this observation, as it may factor into decision-making in instances where there is the potential to instruct an aircraft to spatially deviate from standard operations. Additionally, this research revealed underlying differences between trajectories that are spatially nominal yet energy-anomalous and those trajectories that are spatially anomalous and energy-anomalous. Focusing solely on energy anomaly detection does not provide insight into potential spatial-related decisions that may have been made to result in off-nominal energy behavior.

The preservation of the authenticity of a building artefact is a responsible practice. On the other side, the need to save the building artefact from the natural and anthropic degradation, to ensure the structural reliability to the different actions, to define an efficient maintenance program are big challenges, that involves the cooperation of several professionals, responsible use of innovative techniques and materials that are nowadays available. This paper focuses on a specific design approach for the rehabilitation works of ancient constructions in archaeological sites. The proposed conceptual design approach implies different steps that allow the optimization of the design at an increasing level of knowledge on the existing structures and their materials. The design procedure on historical constructions generally includes the following steps: the collection of data, the structural identification, hazard, and vulnerability analysis, damage and risk analysis, a cost-benefit analysis, so only at the end of the process, the final design is achieved. In the archaeological area, some important design aspects cannot be defined before the execution work phase, since some elements could be revealed and identified during work execution, as a consequence, the final design has been often optimized after all this information has been acquired. A studied case in the archaeological site of Pompeii is herein presented to prove the efficiency of the proposed approach.

Flood routing in flood forecasting issue, calculation the height of flood bands, determining the river boundaries, and estimation of protective facilities for flood –exposed building is applicable. In many cases, due to the lack of measuring stations, the status of the upstream flood generating hydrograph is not known. The purpose of this study is to present an integrated method comprising of an optimization model and a hydrodynamic numerical model for flood modeling to determine the upstream hydrograph using the provided hydrograph at the downstream measuring station of a river. The routing procedure consists of three steps: (1) generating a hypothetical upstream hydrograph using genetic algorithm method; (2) hydrodynamic modeling using a numerical simulation model for flood routing according to the hypothetical hydrograph which is generated in the first step; (3) compare the calculated and observed hydrograph in downstream by using a fitness function. This recommended procedure was named as Reverse Flood Routing Method (RFRM) and was then applied to Karun River, the largest river in Iran. Comparing the generated upstream hydrograph by the RFRM model with the corresponding measured hydrograph at Ahvaz hydrometric station, as an ungauged river location, shows the high accuracy of the recommended model in this study.

Online food delivery service (OFDS) has been widely utilized during the new normal of COVID-19 pandemic especially in a developing country such as Indonesia. The purpose of this study was to determine factors influencing customer satisfaction & loyalty in OFDS during the new normal of COVID-19 pandemic in Indonesia by utilizing an extended Theory of Planned Behavior (TPB) approach. 253 respondents voluntarily participated and answered 65 questions. Structural equation modeling (SEM) indicated that Hedonic Motivation (HM) was found to have the highest effect on customer satisfaction, followed by Price (P), Information Quality (IQ), and Promotion (PRO). Interestingly, this study found out that usability factors such as Navigational Design (ND) and Perceived Ease of Use (PEOU) were not significant to customer satisfaction and loyalty in OFDS during the new normal of COVID-19. This study can be the theoretical foundation that could be very beneficial for OFDS investors, IT engineers, and even academicians. Finally, this study can be applied and extended to determine factors influencing customer satisfaction and loyalty in OFDS during the new normal of COVID-19 in other countries.

The city of Lisbon, has any other capital of a European country, has a large number of issues while managing the waste and recycling containers spread throughout the city. This document presents the results of a study promoted by the Lisbon City Council for trialing LPWAN technology on the waste management vertical under the Lisbon Smart City initiative. Current waste management is done using GSM sensors, and the aim is to use LPWAN to reduce the costs, improve range and reduce provisioning times when changing the communications provider. After an initial study, LoRa was selected as the LPWAN of choice for the trials. The study is composed of multiple use cases at different distances, types of recycling waste containers, placements (underground and surface) and different kinds of waste level measurement LoRa sensors, deployed in order to assess the impact of the different use cases on the LoRa sensor usage. The results shown that the underground waste containers present the most difficult challenge, where the container itself imposes attenuation levels of 26dB on the link budget. The results promoted the deployment of a city wide LoRa network available to all departments inside the Lisbon City Council, and considering the network capacity the network, the network is also available to citizens to be used freely.

This study presents established methods, along with new algorithmic developments, to automate the point cloud processing within the Field Information Modeling (FIM)™ framework. More specifically, given an n-D designed information model, and the point cloud’s spatial uncertainty, the problem of automatic assignment of the point clouds to their corresponding elements within the designed model is considered. The methods addressed two classes of field conditions, namely, (i) negligible construction errors; and (ii) existence of construction errors. The emphasis was given to describing and defining the assumptions in each method and shed light on some of their potentials and limitations in practical settings. Considering the shortcomings of current point cloud processing frameworks, three new and generic algorithms were developed to help solve the point cloud to model assignment in field conditions with both negligible, and existence (or speculation) of construction errors. The effectiveness of the new methods was demonstrated in real-world point clouds, acquired from construction projects, with promising results.

3D-printed aluminium alloy fabrications made by selective laser melting (SLM) offer a promising route for the production of small series of custom-designed heat exchangers with complex geometry and shape and miniature size. Alloy composition and printing parameters need to be optimized to mitigate fabrication defects (pores and microcracks) and enhance the part performance. The deformation response needs to be studied with adequate characterization techniques at relevant dimensional scale capturing the peculiarities of micro-mechanical behavior relevant to the particular article and specimen dimensions. Purposefully designed Al-Si-Mg 3D-printable RS-333 alloy was investigated with a number of microscopy techniques including in situ mechanical testing with a Deben Microtest 1 kN stage integrated and synchronized with Tescan Vega3 SEM to acquire high resolution image datasets for Digital Image Correlation (DIC) analysis. Dog bone specimens were 3D-printed in different orientation of gauge zone cross-section with respect to the fast laser beam scanning and growth directions. This corresponds to varying local conditions of metal solidification and cooling. Specimens show variation in mechanical properties, namely, Young’s modulus (65…78 GPa), yield stress (80–150 MPa), ultimate tensile strength (115–225 MPa) and elongation at break (0,75–1,4 %). Furthermore, the failure localization and character was altered with the change of gauge cross-section orientation. DIC analysis allowed correct strain evaluation that overcame the load frame compliance effect and helped to identify the unevenness of deformation distribution (plasticity waves) that ultimately resulted in exceptionally high strain localization near the ultimate failure crack position.

Purpose: Prostate cancer remains the 2nd leading cancer killer of men, yet it is also a disease with a high rate of overtreatment. Diffusion weighted imaging (DWI) has shown promise as a reliable, grade-sensitive imaging method, but it is limited by low image quality. Currently, DWI image quality is directly related to low gradient ampli-tudes, since weak gradients must be compensated with long echo times. Methods: We propose a new type of MRl accessory, an "inside-out" and nonlinear gradient, whose sole purpose is to deliver diffusion encoding to a region of interest. Performance was simulated in OPERA and the resulting fields were used to simulate DWI with two compartment and kurtosis models. Experiments with a nonlinear head gradient prove the accuracy of DWI and ADC maps diffusion encoded with nonlinear gradients. Results: Simulations validated thermal and mechanical safety while showing a 5 to 10-fold increase in gradient strength over prostate. With these strengths, lesion CNR in ADC maps approximately doubled for a range of anatomical positions. Proof-of-principle experiments show that spatially varying b-values can be corrected for accurate DWI and ADC. Conclusions: Dedicated nonlinear diffusion encoding hardware could improve prostate DWI.

This paper contains a concise overview of the deployment of scattered solar power plants in Poland, mainly from the perspective of their communication networks, and how the recent development of the Polish 4G networks has a very positive impact for the performance of the whole monitoring system (production control and video-surveillance), with a special emphasis on video-analytics, due to its higher bandwidth demand. All the information will be shown from the point of view of the solar photovoltaics developer I+D Energías, and therefore it constitutes a real user’s experience.

In large infrastructures such as dams, which have a relatively high economic value, ensuring the proper operation of the associated hydraulic facilities in different operating conditions is of utmost importance. To ensure the correct and successful operation of the dam's hydraulic equipment and prevent possible damages, including gates and downstream tunnel, to build laboratory models and perform some tests are essential (the advancement of the smart sensors based on artificial intelligence is essential). One of the causes of damage to dam bottom outlets is cavitation in downstream and between the gates, which can impact on dam facilities, and air aeration can be a solution to improve it. In the present study, six dams in different provinces in Iran has been chosen to evaluate the air entrainment in the downstream tunnel experimentally. Three artificial neural networks (ANN) based machine learning (ML) algorithms are used to model and predict the air aeration in the bottom outlet. The proposed models are trained with genetic algorithms (GA), particle swarm optimization (PSO), i.e., ANN-GA, ANN-PSO, and ANFIS-PSO. Two hydrodynamic variables, namely volume rate and opening percentage of the gate, are used as inputs into all bottom outlet models. The results showed that the most optimal model is ANFIS-PSO to predict the dependent value compared with ANN-GA and ANN-PSO. The importance of the volume rate and opening percentage of the dams' gate parameters is more effective for suitable air aeration.

This work opens a new pathway to fabricate high dimensional stability Invar36 aerospace devices with Binder Jetting technology, for applications where temperature fluctuations directly interfere in the correct performance of high sensibility systems. Since full density part fabrication is one of the main ongoing challenges for Binder Jetting, the leading objective of this work is to study and optimise the main process parameters to increase the final density of Invar36 printed parts. Microstructural analysis and obtained density and CTE values, confirmed the feasibility to fabricate Invar36 parts.

In many advanced industrial applications, the thickness is a critical index, especially for metallic coatings. However, the variance of lift-off spacing between sensors and test pieces affects the measured voltage or impedance, which leads to unreliable results from the sensor. Massive research works have been proposed to address the lift-off issue, but few of them applies to the thickness measurement of planar metallic films with finite-size circular (disk) geometry. Previously, a peak-frequency feature from the swept-frequency inductance was used to compensate the measurement error caused by lift-offs, which was based on the slow-changing rate of impedance phase term in the Dodd-Deeds formulas. However, the phase of measured impedance is nearly invariant merely on a limited range of sample thicknesses and working frequencies. Besides, the frequency sweeping is time-consuming, where a recalibration is needed for different sensor setups applied to the online real-time measurement. In this paper, a single-frequency algorithm has been proposed, which is embedded in the measurement instrument for the online real-time retrieval of thickness. Owing to the single-frequency measurement strategy, the proposed method does not need to recalibrate for different sensor setups. The thickness retrieval is based on a triple-coil sensor (with one transmitter and two receivers). The thickness of metallic disk foils is retrieved from the measured electrical resistance of two transmitter-receiver sensing pairs. Experiments on materials of different electrical conductivities (from direct current), thicknesses and planar sizes (radii) have been carried out to verify the proposed method. The error for the thickness retrieval of conductive disk foils is controlled within 5 % for lift-offs up to 5 mm.

Modeling the post-peak behaviour of brittle materials like concrete is still a challenge from the point of view of computational mechanics, due to the strong nonlinearities arising in the material behaviour during softening and the complexity of the yield criterion that may describe their deformation capacity in generic triaxial stress states. A numerical model for plain concrete in compression is formulated within the framework of the coupled elasto-plastic-damage theory. The aim is to simulate via the Finite Element (FE) method the stress-strain behaviour of concrete at the meso-scale, where local confinement effects generally characterize the cement paste under the action of the surrounding aggregates. The mechanical characterization of the components are accomplished through a specific experimental campaign. With the subsequent validation study, it is shown that a few calibration parameters give a good prediction of load strength and deformation capacity coming from real uniaxial compression tests.

New York State Department of Transportation designates potentially hazardous conditions on bridges as flags. From 1982 until 2006 the flags issued for the bridges owned by New York City underwent all phases typical of crises, including a gradual increase, an exponential expansion, an extended peak, a gradual decline, and a convergence to a higher but manageable level. The attempts to forecast the flag pattern as it was developing are reviewed for possible relevance to management of the transportation infrastructure and in general.

Blockchain is the consensus-based technology to resolve the conflicts in Byzantine environments. Vehicles validate the messages received from the neighboring vehicles using Gradient Boosting Technique (GBT). Based on the validation results the message source vehicle generates the ratings that are to be uploaded to Road Side Units (RSUs) through that trust offset value can be calculated. All RSUs maintain the trust blockchain and each RSU tries to add their blocks to trust blockchain. We proposed a blockchain-based trust management model for VANET based on Tendermint. It eliminates the problem of malicious nodes entering the network and will also overcome the problem of power consumption. Simulation results also show that the proposed system is 7.8% and 15.6% effective and efficient in terms of Packet Delivery Ratio (PDR) and End-to-End Delay (EED) respectively to collect the trusted data between the vehicles.

This study aims to explore the possibility of estimating a multitude of kinematic and dynamic quantities using subject-specific musculoskeletal models in real-time. The framework was designed to operate with marker-based and inertial measurement units enabling extensions far beyond dedicated motion capture laboratories. We present the technical details for calculating the kinematics, generalized forces, muscle forces, joint reaction loads, and predicting ground reaction wrenches during walking. Emphasis was given to reduce computational latency while maintaining accuracy as compared to the offline counterpart. Notably, we highlight the influence of adequate filtering and differentiation under noisy conditions and its importance for consequent dynamic calculations. Real-time estimates of the joint moments, muscle forces, and reaction loads closely resemble OpenSim's offline analyses. Model-based estimation of ground reaction wrenches demonstrates that even a small error can negatively affect other estimated quantities. An application of the developed system is demonstrated in the context of rehabilitation and gait retraining. We expect that such a system will find numerous applications in laboratory settings and outdoor conditions with the advent of predicting or sensing environment interactions. Therefore, we hope that this open-source framework will be a significant milestone for solving this grand challenge.

As 180-degree meanders are observed in abundance in nature, a meandering channel with two consecutive 180-degree bends was designed and constructed to investigate bed topography variations. These two 180-degree mild bends are located between two upstream and downstream straight paths. In this study, different mean velocity to critical velocity ratios have been tested at the upstream straight path to determine the meander's incipient motion. To this end, bed topography variations along the meander and the downstream straight path were addressed for different mean velocity to critical velocity ratios. In addition, the upstream bend's effect on the downstream bend has been investigated. Results indicated that the maximum scour depth at the downstream bend has increased as a result of changing the mean velocity to critical velocity ratio from 0.8 to 0.84, 0.86, 0.89, 0.92, 0.95, and 0.98 by respectively 1.5, 2.5, 5, 10, 12, and 26 times. Moreover, increasing the ratio increased the maximum sedimentary height by 3, 10, 23, 48, 49, and 56 times. The upstream bend's incipient motion was observed for the mean velocity to critical velocity ratio of 0.89, while the downstream bend was equal to 0.78.

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

The bio-oil obtained by pyrolysis of Açaí (Euterpe oleracea Mart.) seeds at 450 ºC, 1.0 atmosphere, in technical scale, submitted to fractional distillation to produce biofuels-like fractions. The distillation of bio-oil carried out in a laboratory distillation column (Vigreux) of 30 cm. The physical-chemistry properties (density, kinematic viscosity, acid value and refractive index) determined by official methods. The chemical functions present in distillation fractions determined by FT-IR and the chemical composition by GC-MS. The distillation of bio-oil yielded gasoline, light kerosene, and kerosene-like fuel fractions of 16.16, 19.56, and 41.89% (wt.), respectively. All the physical-chemistry properties (density, kinematic viscosity, acid value and refractive index) increase with boiling temperature. The gasoline-like fraction is composed by 64.0% (area.) hydrocarbons and 36.0% (area.) oxygenates, while light kerosene-like fraction by 66.67% (area.) hydrocarbons and 33.33% (area.) oxygenates, and kerosene-like fraction by 19.87% (area.) hydrocarbons and 81.13% (area.) oxygenates.

In this paper we designed system for smart university building based on artificial intelligence (AI) and internet of things (IOT). Our idea can be summarized in smart security system that has different sensors to detect the surrounding environment of the class room in campus which keep everyone and everything on campus safer. By using (IOT), (AI) technologies and applications and by using microcontroller programming we can make the university building safer, secure and more energy saves.

(1) Background: Sensing of critical events or flow signatures in nature often presents itself as a coupled interaction between a fluid and arrays of slender flexible beams, such a wind-hairs or whiskers. It is hypothesized that important information is gained in highly noisy environments by the inter-correlation within the array. (2) Methods: The present study uses a model sea lion head with artificial whiskers in the form of slender beams (optical fibres), which are subjected to a mean flow with overlaid turbulent structures generated in the wake of a cylinder. Motion tracking of the array of fibres is used to analyse the correlation of the bending deformations of pairs of fibres. (3) Results: Cross-correlation of the bending signal from tandem pairs of whiskers proves that the detection of vortices and their passage along the animals head is possible even in noisy environments. The underlying pattern, during passage of a vortex core, is a jerk-like response of the whiskers, which can be found at later arrival-times in similar form in the downstream whisker's response. (4) Conclusion: Coherent vortical structures can be detected from cross-correlation of pairs of cantilever-beam like sensors even in highly turbulent flows. Such vortices carry important information within the environment, e.g. the underlying convection velocity. More importantly in nature, these vortices are characteristic elementary signals left by prey and predators. The present work can help to further develop flow, or critical event, sensory systems which can overcome high noise levels due to the proposed correlation principle.

Direct yaw moment control (DYC) is an effective way to alter the behaviour of electric cars with independent drives. Controlling the torque applied to each wheel can improve the handling performance of a vehicle making it safer andfaster on a race track. The state-of-the-art literature covers the comparison of various controllers (PID, LPV, LQR, SMC, etc.) using ISO manoeuvres. However, more advanced comparison on important characteristics of the controllers performance is missed, such as the robustness of the controllers under changes in the vehicle model, steering behaviour, use of the friction circle and, ultimately, lap time on a track. In this study, we have compared the controllers according to some of the aforementioned parameters on a modelled race car. Interestingly, best lap times are not provided by perfect neutral or close-to-neutral behaviour of the vehicle, but rather by allowing certain deviations from the target yaw rate. In addition, a modified PID controller showed that its performance is comparable to other more complex control techniques such as MPC.

The simplest elasticity model of foundation underlying a slender beam under flexure was conceived by Winkler, requiring local proportionality between soil reactions and beam deflection. Such an approach leads to well-posed elastostatic and elastodynamic problems, but, as highlighted by Wieghardt, it provides elastic responses which are not technically significant for a wide variety of engineering applications. Thus, Winkler's model was replaced by Wieghardt himself by assuming that the beam deflection is the convolution integral between soil reaction field and an averaging kernel. Due to conflict between constitutive and kinematic compatibility requirements, the corresponding elastic problem of an inflected beam resting on Wieghardt foundation results to be ill-posed. Modifications of the original Wieghardt model were proposed by introducing fictitious boundary concentrated forces of constitutive type, which are physically questionable, being significantly influenced on prescribed kinematic boundary conditions. Inherent difficulties and issues are overcome in the present research using a displacement-driven nonlocal integral strategy got by swapping input and output fields involved in Wieghardt's original formulation. That is, nonlocal soil reaction fields are output of integral convolutions of beam deflection fields with an averaging kernel. Equipping the displacement-driven nonlocal integral law with the bi-exponential averaging kernel, an equivalent nonlocal differential problem, supplemented with non-standard constitutive boundary conditions involving nonlocal soil reactions, is established. As a key implication, the integro-differential equations governing the elastostatic problem of an inflected elastic slender beam resting on displacement-driven nonlocal integral foundation are replaced with much simpler differential equations supplemented with kinematic, static and new constitutive boundary conditions. The proposed nonlocal approach is illustrated by examining and analytically solving exemplar problems of structural engineering. Benchmark solutions for numerical analyses are also detected.

The article deals with aspects of identifying industrial products in motion based on their color. An automated robotic workplace with conveyor belt, robot and industry color sensor is created for this purpose. Measured data are processed in a database and then statistically evaluated in form of standard uncertainties of type A and B, in order to obtain combined standard uncertainties results. Based on the acquired data, control charts of RGB color components for identified products are created. Influence of product speed on the measuring process identification and process stability is monitored. In case of identification uncertainty i.e. measured values are outside the limits of control charts, the K-nearest neighbor machine learning algorithm is used. This algorithm, based on the Euclidean distances to the classified value, estimates its most accurate iteration. This results into the comprehensive system for identification of product moving on conveyor belt, where based on the data collection and statistical analysis using machine learning, industry usage reliability is demonstrated.

The feasibility of hydroelectric plants depends on a variety of factors: water resource regime, geographical, geological and environmental context, available technology, construction cost and economic value of produced energy. Choices about the building or renewal of hydroelectric plants should be based on the forecast of the future trend of these factors at least during the project life of the system. Focusing on the economic value of the produced energy this paper examines its influence on the feasibility of hydroelectric plants. Analysis, referred to Italian case, were based on three different phases: i) the economic sustainability of small-scale hydroelectric plants under a minimum price guaranteed to the hydroelectric operator; ii) the estimate of the incentives to reach the thresholds of "acceptability" and "bankability" of the investment; iii) the analysis of results obtained in the previous phases using a model of the evolution of the electricity price in the period 2014-2100.

Use of porous titanium (Ti) and Ti alloys in orthopedic implants or in light structures requires processing routes that could generate an as-much-as possible control in the pores amount, shape, size and connectivity. In this work, a colloidal approach to the processing of Ti porous structures by the inside foaming of a porogen into a gelled high solid content aqueous suspension of Ti powders, is presented. The prepared slurries contained different amounts of Methyl Cellulose (MC) as gelling agent (8, 10 and 12 g/L) and ammonium bicarbonate (BA) as porogen (15, 20, 25 wt. %). The gel-casted samples were heated at mild temperatures ranging (60, 70 and 80 °C) to promote the gelation and produce, at the same time, the porosity by the thermal decomposition of the ammonium bicarbonate. Different structures are obtained depending on the combination of the study variables

The potential of the Additive Manufacturing technologies is impeded by the surface finish obtained on the as-manufactured material. Therefore, the influence of various surface treatments, commonly applied to space hardware, on the mechanical properties of three selected metallic alloys (SS316L, AlSi10Mg, Ti6Al4V) prepared by using Selective Laser Melting (SLM) and Electron Beam Melting (EBM) additive manufacturing processes have been investigated. Within this study, SLM using EOS M400 and EOS M280 equipment and in addition EBM using an ARCAM Q20 machine have been applied for sample manufacturing. A half-automated shot-peening process followed by a chemical and/or electrochemical polishing or Hirtisation® process has been applied in order to obtain lower surface roughness compared to their as-received states. Special emphasize has been taken on their tensile, fatigue, and fracture toughness properties. In addition, their stress corrosion cracking (SCC) behaviour including microstructural analysis using HR-SEM have been investigated.

Metal-supported fuel cells (MSCs) offer potential material cost and robustness advantage over anode supported cells (ASCs). Because of the very good thermal shock stability of such MSCs, these can be heated up very quickly. However the challenge co-firing of metal substrate and electrolyte stays still unsolved. The production of the core component, the tape casted metal substrate, with defined shrinkage resulting from modification of metal powders is presented in the paper. This approach leads to an adjustment of the shrinkage mismatch during sintering, between metal and ceramic components, and adjustment of target specifications like porosity, green density and layer thickness.

The present study examines the effect of SiO2 nano-additives on the performance and emission characteristics of a diesel engine fuelled with soybean biodiesel. Soybean biofuel was prepared using the transesterification process. Nano-additives characterisations were done using different tests such as FESEM, XRD, EDS, etc., to study the morphology of nano-additives. For proper blending of nano-additives with biodiesel, the ultrasonication process was used. Surfactant was used for the stabilisation of nano-additives. After making all the combinations of nano fuel blends, physicochemical properties were measured as per ASTM standards. Performance and emissions readings were taken at different load conditions. It was found that with the addition of SiO2 nano-additives, brake thermal efficiency (BTE) and brake specific fuel consumption (BSFC) was increased by 3.48-6.39% and 5.81-9.88%, respectively. Significant reduction of CO, CO2, NOx, and smoke emissions were also observed compared to baseline fule due to better combustion efficiency with the use of SiO2 nano-additive.

The connection of two phenomena - non-conservative friction forces and dissipation-induced instability can lead to many interesting engineering problems. The paper studies general material-dependent damping influence on dynamical instability of disc brake systems leading to brake squeal. The effect of general damping is demonstrated on a minimal and complex model of a disc brake. A complex system including material-dependent damping is defined in the commercial finite element software. The finite element model validated by experimental data on the brake-disc test bench is used to compute the influence of a pad and a disc damping variations on system stability by complex eigenvalue analysis. Analyzes show a significant sensitivity of the experimentally verified unstable mode of the system to the ratio of the damping between the disc and the friction material components.

Tree pruning is applied to reduce conflict with infrastructure, buildings, and any other human activity. However, heavy pruning may result in diminished tree crown capacity for sugar production and exposure to fungi infection risk that lead to decrease of tree stability or vigour. In this paper, we analyse the effect of heavy pruning of landscape road-side trees on their photosynthetic performance reflected in the photosystem II function relative to neighbouring unpruned trees. Four-year circumference increments and tree crown growth were studied by terrestrial imaging. Tree vitality (Roloff’s classification) and risk (Visual Tree Assessment) were evaluated. Over-pruned trees showed intensified photosynthetic efficiency in the growing season following pruning. After four years, pruned trees rebuilt their crowns, however not all of them. Results obtained from biometric, vitality, and risk assessment showed high differentiation in pruned tree crown recovery. Our results reveal that despite the intensified physiological efforts of trees to recover from wounding effects, severe pruning evokes the dieback occurrence and higher risk of failure in mature trees.

In the production of green parts from powder, there is unavoidable slight deviation in the die filling, even when high-quality powders are used. The quantity of powder in the die varies and thus affects the weight of the compact. This filling variation results in variation of the pressing force, and thus influences the part geometry. The development of the DORST Netshape® System was conceived as an autonomous manufacturing system in order to compensate for these effects. Based on the Dorst Industry 4.0 innovations for part weight measuring immediately after pressing in combination with a laser dimension measuring system, this technology package attempts to reach enhanced precision and consistency in production. The paper presents results from various trials that show the capability of this new system, designed to improve the quality of pressed parts.

This paper presents the results of the investigation of composite sinters W-TiB2 which were used as an electrode in the process of electro-spark deposition (ESD) and the examination of the deposited layers. The scope of the study includes detailed characteristics of powder mixtures, composite sinters made using the spark plasma sintering method (SPS) and layers deposited in the electro-spark process. The ESD process, using the W+30 vol.% TiB2 electrode, was carried out using an automated device. The substrates were made of copper and aluminium. The topography analysis of the surfaces of the composite layer and the evaluation of their wear resistance properties are also presented. The analysis of the results of research showed the possibility to obtain, using the SPS method, composite materials of good quality, which can be used as electrodes in the ESD process. The obtained layers had increased wear resistance in relation to the substrate material.

This paper presents a framework for the motion planning and control of redundant manipulators with the added task of collision avoidance. The algorithms that were previously studied and tested by the authors for planar cases are here extended to full mobility redundant manipulators operating in a three-dimensional workspace. The control strategy consists of a combination of off-line path planning algorithms with on-line motion control. The path planning algorithm is used to generate trajectories able to avoid fixed obstacles, detected before the robot starts to move; it is based on the potential fields method combined with a smoothing interpolation that exploits Bézier curves. The on-line motion control is designed to compensate for the motion of the obstacles and to avoid collisions along the kinematic chain of the manipulator; it is realized by means of a velocity control law based on the null space method for redundancy control. A term of the control law takes into account the speed of the obstacles as well as their position. In order to test the algorithms, a set of simulations are presented: the robot KUKA LBR iiwa is controlled in different cases, where fixed or dynamic obstacles interfere with its motion. Simulations are also used to estimate the required computational effort in order to verify the transferability to a real system.

The building sector is the largest consumer of energy and there are still major scientific challenges in this field. The façade, being the interface between the exterior and interior space, plays a key role in the energy efficiency of a building. In this context, this paper focuses on a ventilated bioclimatic wall for NZEB zero energy buildings. The objective of this study is to investigate an experimental set-up based on a Hot Box allowing characterizing the thermal performances of the ventilated wall. A specific ventilated prototype and an original thermal metrology has been developed. This paper presents the ventilated prototype, the experimental set-up and experimental results on the thermal performances of the ventilated wall. The influence of the air space thickness and the air flow rate on the thermal performances of the ventilated wall is studied.

The aim of this paper is to examine the beneficial impact of feedback information in the dynamics of production-inventory control systems. Two production-inventory control system models are analyzed: APIOBPCS and 2APIOBPCS models. The simulation-based experiment designs were conducted by using the state-space equations of the two models. The bullwhip effect as measured by the variance ratio between the order rate and the consumption rate, and inventory responsiveness as measured by the Integral of Absolute Error between the actual and the target levels of inventory, are two metrics used to evaluate the performance of the production-inventory control system in response to a random customer demand. To ensure that both models work under optimal performance, multi-objective particle swarm optimization (MOPSO) is employed to address the problem of tuning the controller’s parameters. The simulation results show the 2APIOBPCS model outperforms the APIOBPCS model at achieving the desired bullwhip effect and being able to provide better inventory responsiveness. The improvement in the inventory responsiveness becomes more significant when the system operates under mismatched lead time and/or an initial condition.

Intermetallic alloys like e.g. Iron-Aluminides, Titanium-Aluminides or Molybdenum- Silizides are prospective materials for high-temperature applications. For additive manufacturing (AM) intermetallic structural materials are particularly challenging due to their high melting points, oxygen susceptibility and low temperature brittleness. The feasibility of manufacturing intermetallic Mo-Si-B alloys with the laser additive manufacturing process of direct energy deposition (DED) is demonstrated and recent results in characterizing rapidly solidified material with respect to correlations between process, composition and microstructures are presented. The possibility to dope the material with Yttrium oxide (Y2O3) for dispersion is successfully demonstrated. Current challenges, e.g. homogenous distribution of alloying elements and applicability are addressed.

The purpose of this paper is to study the sliding mode control as a Ricci flow process in the context of a three-story building structure subjected to seismic waves. The stability conditions result from two Lyapunov- functions, the first associated with slipping in a finite period of time, and the second with convergence of trajectories to the desired state. Simulation results show that the Ricci flow control leads to the minimization of the displacements of the floors. 3D Ricci solitons projection via a semi-conformal mapping to a surface is also studied.

The flowability of a powder is a characteristic dependent on both the particle ensemble's physical properties as well as the testing equipment and conditions. In powder bed additive manufacturing processes (SLM, EBM), flowability can be used to predict the quality of the deposited powder layer. However, how representative flowability is for the powder bed layer quality is not always clear.In this work, we compared several steel powders, as well as different testing methods. Each powder had an individual combination of composition or particle size distribution. Furthermore, the flowability testing equipment and methods were selected according to standards (Tap density, Hall flow) or referring to the published literature (angle of repose, FT4 Powder Rheometer). After measuring the flowability of different samples for each testing method, we created a database to evaluate a first correlation between the different testing methods. Finally, with a Powder Rheometer, we investigated for one sample some flow properties as function of different particle size distributions.

Methods for cost calculation of Laser-based Additive Manufacturing (LAM) have evolved over the last two decades. Starting from a rapid prototyping manufacturing method, cost calculation over time included more than single part production for small series or mixed batches. New machine developments nowadays aim at large scale production and introduce modular machine designs that give LAM factory operators more decision freedom on machine configurations and expansibility. To leverage this new possibilities, LAM costing methods must be adapted to calculate and visualize the economic consequences of different module configurations.In this work a modelling architecture of L-PBF machines using virtual modules is presented. Using principles of the industry 4.0 communication standard OPC UA, a virtual machine is set up, parametrized and connected to the AM digital process chain. Two different views on the cost information are used, optimized for different use cases.First, the product view allows detailed analysis of cost allocation to parts in mixed batches and the layered geometry of the part. Second, the machine view tracks utilization of all machine modules, divided into the states idle, active, off and maintenance. The theoretical Overall Equipment Effectiveness (OEE) is calculated on a module basis, enabling economic analysis and optimization of module composition as well as module improvements and new modular machine constellations. This allows focusing research and development on most valuable improvements. By calculating different scenarios like spare part production, mass production or rapid prototyping, the part portfolio to be manufactured can be considered.

We present progress in fast, high-resolution imaging, material classification, and fault detection using hyperspectral X-ray measurements. Classical X-ray CT approaches rely on data from many projection angles, resulting in long acquisition and reconstruction times. Additionally, conventional CT cannot distinguish between materials with similar densities. However, in additive manufacturing, the majority of materials used are known a priori. This knowledge allows to vastly reduce the data collected and increase the accuracy of fault detection. In this context, we propose an imaging method for non-destructive testing of materials based on the combination of spectral X-ray CT and discrete tomography. We explore the use of spectral X-ray attenuation models and measurements to recover the characteristic functions of materials in heterogeneous media with piece-wise uniform composition. We show by means of numerical simulation that using spectral measurements from a small number of angles, our approach can alleviate the typical deterioration of spatial resolution and the appearance of streaking artifacts.

Infrared (IR) thermography is a powerful tool to measure temperature with a high space and time resolution. A particular interesting application of this technology is in the field of catalysis, where new insights to highly dynamic surface reactions are possible. This paper presents guidelines for the development of a reactor cell that can aid in the efficient exploitation of infrared thermography for the investigation of catalytic and other surface reactions. Firstly, the necessary properties of the catalytic reactor are described. In fact, special equipment must be developed to ensure the realization of true operando conditions in the IR thermography experiments. Here, we provide the guidelines to assemble a chemical reactor with an IR transmitting window through which the reaction can be studied with the infrared camera. Secondly, we analyze the requirements towards the catalytic system to be directly observable by IR thermography. This includes the need for a catalyst that provides a sufficiently high heat production (or absorption) rate. We present selected examples of catalytic reactions that can be monitored by IR thermography and showing the potential of the technology in revealing transient and steady state chemical phenomena.

The decarbonization of the electrical energy sector is in progress for contrasting the climate changes, with a relevant increase of the Renewable Energy Sources (RES) power plants, mostly in Dispersed Generation (DG). The adequacy and the security of power systems, with a huge penetration of RES in DG is possible with a suitable integration of energy storage. In fact, energy storages are able to provide many different services for long-term adequacy and real time security. In this framework the present paper deals with a Thermal Energy Storage (TES) proposed for power system services. The technology presented is made up of modules containing a bed of fluidizable solid particles, which can store thermal energy from waste heat, process heat and/or from electricity. Stored thermal energy can be released, e.g. as superheated steam, for thermal uses or converted into electricity, by means of steam turbines. Some possible applications are then reported explaining advantages and limits.

In this paper, we investigate the aerothermal performance of active clearance control (ACC) methods that use impingement as a means of enhancing heat transfer. We describe a numerical approach to compare the aerothermal performance of two circumferential impingement manifold supply designs that vary in the number of entry points to the manifold channel. For a 180°-sector, the first design has a single entry point, while the second has two. Both the single-entry and multiple-entry systems analysed in this paper are idealised version of ACC systems in current production engines. Aerothermal performance is quantitatively assessed on the basis of the HTC distribution, driving temperature difference for heat transfer between the jet and casing wall, and total pressure loss within the HPT ACC system. We conclude key advantages and disadvantages of each system based on the impact on engine efficiency, response time, ease of optimisation and implications for weight, cost and complexity of the design.

While L-PBF offers advantages in terms of geometrical freedom or lightweight construction, its use is often limited by economic constraints or currently achievable part costs. With an anticipated increase of L-PBF machine productivity during the next years, an increase of the share of material costs and the share of non-productive time for powder layer application process is to be expected. This results in a demand for less expensive powder materials and advanced processing strategies for the short-and medium-term advancement of L-PBF. As one possible approach, the processing of gas- and water atomized stainless steel (316L) powders with different morphology and particle size distribution as well as their impact on L-PBF productivity is investigated. The actual powder applicability in L-PBF systems and the L-PBF processability determines the minimum necessary powder quality. The main focus of the presented work is put on the interaction between powder quality, powder layer properties, part quality and cost-effectiveness of the L-PBF process. To this end, the influence of the gas and water-atomized powder properties (particle size and morphology) during the powder layer application process at highest possible powder recoater speeds and the resulting powder bed properties (powder layer density, powder bed density, powder demixing) and part properties (part density and tensile strength) is investigated.

The purpose of this study is to analyse the feasibility of using waste from granite gang saws (GCW-GS) to manufacture ultra-high performance, steel-fibre reinforced concrete (UHPFRC). These machines cut granite blocks by abrasion using a steel blade and slurry containing fine steel grit. The waste generated by gang saws (GCW-GS) contains up to 15% Fe2O3 and up to 5% CaO. This is the main difference from the waste produced by diamond saws (GCW-D). Consequently, the second objective of this study is to compare the results of the waste obtained with gang saws with that from diamond saws, in order to determine the influence of iron and calcium oxides. The waste from cutting granite with gang saws was used in different percentages to replace micronized quartz powder of natural origin in the manufacture of UHPRFC. All the test specimens were analysed to determine their compressive strength, elasticity modulus, flexural strength and indirect tensile strength. The final conclusion is that wastes from both gang saws and diamond saws can be used to manufacture UHPFRC with an improvement in the mechanical properties up to a 35% replacement. The results for GCW-GS are better, mainly due to the pozzolanic effect of the iron dioxide. For higher percentage replacements the mechanical properties are close to the control concrete with small decreases.

Selective laser melting (SLM) is a commonly used laser powder bed technique where the final properties are influenced by many different powder related properties, such as particle size distribution, chemical composition and flowability. In applications where high hardness, wear resistance, strength and good heat properties are required, high speed steels (HSS) are widely used today. HSS has high carbon content and are therefore considered as unweldable. The rapidly growing implementation of AM technologies has led to a growing range of new applications and demands for new alloys and properties. The interest in being able to manufacture HSS by SLM without cracking is therefore increasing. In SLM, it is possible to preheat the base plate to a few hundred degrees Celsius which has been used for HSS and proved successful due to reduced thermal gradients. In this study, the properties of SLM produced high speed steel PEARL Micro®2012 with a carbon content of 0.61 wt.-% have been investigated and compared to those of a forged and rolled PM-HIP counterpart ASP®2012.

Quality assurance has been one of the major challenges in laser-based additive manufacturing (AM) processes. This study proposes a novel process modeling methodology for layer-wise in-situ quality monitoring based on image series analysis. An image-based autoregressive (AR) model has been proposed based on the image registration function between consecutively observed thermal images. Image registration is used to extract melt pool location and orientation change between consecutive images, which contains sensing stability information. Subsequently, a Gaussian process model is used to characterize the spatial correlation within the error matrix. Finally, the extracted features from the aforementioned processes are jointly used for layer-wise quality monitoring. A case study of a thin wall fabrication by a Directed Laser Deposition (DLD) process is used to demonstrate the effectiveness of the proposed methodology.

The fast and flexible characteristics of badminton determine the necessity of its lunge, and fast lunge will have an important impact on the results of the game in actual combat. In lunge evaluation, the contact time to the ground is a key indicator. This article selects two middle-aged male badminton amateurs with similar body shape and age， but different skill levels as the research objects to carry out comparative experiments. The microseismic equipment is used to record the floor vibration which is caused by the running of badminton amateurs in sports. The microseismic signals of lunge are processed and analyzed by MATLAB software. And we evaluate the pro and cons of various time to touch the ground from amateurs with different skill levels. The research found that 1. Microseismic technology can detect the contact time to the ground. 2. High-level badminton amateurs have obvious advantages in the footwork contact time. Microseismic technology has a certain feasibility as a tool for evaluating the footwork contact time of badminton.

Accurate monthly runoff estimation is fundamental in water resources management, planning, and development, resulting in preventing and reducing water-related problems, such as flooding and drought. This article evaluates the performance of the monthly hydrological rainfall-runoff model, GR2M model, in Thailand's southern basins. The GR2M model requires only two parameters, and no prior research has been reported on its application in this region. The 37 runoff stations, which are distributively located in three sub-watersheds of Thailand's southern region, namely; Thale Sap Songkhla, Peninsular-East Coast, and Peninsular-West Coast, were selected as study cases. The available monthly hydrological data of runoff, rainfall, air temperature from the Royal Irrigation Department (RID) and the Thai Meteorological Department (TMD) were collected and analyzed. Thornthwaite method was utilized for the determination of evapotranspiration. The model's performance was conducted using three statistical indices: Nash-Sutcliffe Efficiency (NSE), Correlation Coefficient (r), and Overall Index (OPI). The model's calibration results for 37 runoff stations gave the average of NSE, r, and OPI of 0.637, 0.825, and 0.757, and those values for verification of 0.465, 0.750, and 0.639, respectively. It indicated a model's acceptable performance and could apply the GR2M model for determining monthly runoff variation in this region. The spatial distribution of X1 and X2 values was conducted by using IDW method. It was susceptible to the X1 value and X2 value of approximately more than 0.90 gave the higher model's performance.

Access to reliable, clean, modern cooking enhances life chances. One option is photovoltaic cooking systems. Accurate solar data is needed to ascertain to what extent these can satisfy the needs of local people. This paper investigates how to choose the most accurate satellite derived solar irradiance database for use in Africa. This is necessary because there is a general shortage of ground measurements for Africa. The solar data is needed to model the output of solar cooking systems, for instance, a solar panel, battery and electric pressure cooker. Four easily accessible satellite databases are validated against ground measurements using a range of statistical tests. Results demonstrate the impact of the mathematical measure used and the phenomenon of balancing errors. Fitting of the satellite model to appropriate climate zone and/or nearby measurements improves accuracy, as does higher spatial and temporal resolution of input parameters. That said, all the four databases reviewed were found to be suitable for simulating PV yield in East Africa.

One of the challenges facing the industrial adoption of additively manufactured parts is the surface roughness on the as-built part. The surface roughness of parts is frequently characterized by metrics specified by international standards organizations. However, these standards list many surface metrics that can make it unclear which to use to best describe the surface. In this work, the ability of the various surface metrics to successfully classify the as-built and post-processed surfaces is studied using linear classification models. Laser polishing via remelting and manual grinding are the post-processing techniques used to smooth the as-built surface. The ability of the linear classifier to successfully categorize the various surfaces is demonstrated, and the various surface metrics are ranked according to the strength of their individual ability to classify the surfaces. This work promotes the method as a potential way to autonomously classify as-built and laser polished surfaces.

Additive Manufacturing (AM) simplifies the fabrication of complex geometries. Its scope has rapidly expanded from the fabrication of pre-production visualization models to the manufacturing of end use parts driving the need for better part quality assurance in the additively manufactured parts. Machine learning (ML) is one of the promising techniques that can be used to achieve this goal. Current research in this field includes the use of supervised and unsupervised ML algorithms for quality control and prediction of mechanical properties of AM parts. This paper explores the applications of supervised learning algorithms - Support Vector Machines and Random Forests. Support vector machines provide high accuracy in classifying the data and is used to decide whether the final parts have the desired properties. Random Forests consist of an ensemble of decision trees capable of both classification and regression. This paper reviews the implementation of both algorithms and analyzes the research carried out on their applications in AM.

Layer-wise 3D surface morphology information is critical for the quality monitoring and control of additive manufacturing (AM) processes. However, most of the existing 3D scan technologies are either contact or time consuming, which are not capable of obtaining the 3D surface morphology data in a real-time manner during the process. Therefore, the objective of this study is to achieve real-time 3D surface data acquisition in AM, which is achieved by a supervised deep learning-based image analysis approach. The key idea of this proposed method is to capture the correlation between 2D image and 3D point cloud, and then quantify this relationship by using a deep learning algorithm, namely, convolutional neural network (CNN). To validate the effectiveness and efficiency of the proposed method, both simulation and real-world case studies were performed. The results demonstrate that this method has strong potential to be applied for real-time surface morphology measurement in AM, as well as other advanced manufacturing processes.