Patch antennas are compact, less complex, planar structures and therefore, widely used in small satellite missions for telecommand, data link, and intersatellite link, particularly in S- band and X- band. Improved performance of these patch antennas in terms of gain and compactness will di-rectly affect the communication efficiency of small satellite missions. Especially the coming IoT (Internet of Things) constellations require high gain and efficient antenna arrays. An optimization of single patch antenna elements is an important cornerstone for the missions. Therefore, the ef-fects of various antenna enhancement techniques, such as slotted ground plane, resistor and ca-pacitor integration, parasitic patch elements, are analyzed. These techniques were applied on a rectangular patch antenna with parameter variation to identify the optimal performances with respect to bandwidth, operating frequency, gain, polarization, and power flow. Finally, the techniques were combined to obtain an optimized antenna in terms of gain and compactness. The results were compared to a slotted reference antenna. For the scenario of a 2.4 GHz patch antenna, a gain optimization of 27 % (from 7.09 to 8.14 dBi) or size reduction of 52 % (from 96.04 to 46.2 cm²) could be achieved. Overall, our study revealed an effective way to increase the patch antenna performance, which can directly contribute to more efficient communication links and design of antenna arrays.

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.

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%.

It is essential to mitigate gaseous emissions that result from poultry and livestock production to increase industry sustainability. Odorous volatile organic compounds (VOCs), ammonia (NH3), hydrogen sulfide (H2S), and greenhouse gases (GHGs) have detrimental effects on the quality of life in rural communities, the environment, and climate. This study's objective was to evaluate the photocatalytic UV treatment of gaseous emissions of odor, odorous VOCs, NH3, and other gases (GHGs, O3 – sometimes considered as by-products of UV treatment) from stored swine manure on a pilot-scale. The manure emissions were treated in fast-moving air using a mobile lab equipped with UV-A and UV-C lights and TiO2-based photocatalyst. Treated gas airflow (0.25 to 0.76 m3/s) simulates output from a small ventilation fan in a barn. Through controlling the light intensity and airflow, UV dose was tested for techno-economic analyses. The treatment effectiveness depended on the UV dose and wavelength. Under UV-A (367 nm) photocatalysis, the percent reduction of targeted gases was up to i) 63% of odor, ii) 51%, 51%, 53%, 67%, and 32% of acetic acid, propanoic acid, butanoic acid, p-cresol, and indole, respectively, iii) 14% of nitrous oxide (N2O), iv) 100% of O3, and 26% generation of CO2. Under UV-C (185+254 nm) photocatalysis, the percent reductions of target gases were up to i) 54% and 47% for p-cresol and indole, respectively, ii) 25% of N2O, iii) 71% of CH4, and 46% & 139% generation of CO2 & O3, respectively. The results proved that the UV technology was sufficiently effective in treating odorous gases, and the mobile lab was ready for farm-scale trials. The UV technology can be considered for the scaled-up treatment of emissions and air quality improvement inside livestock barns.

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.

Supply chain agility and resilience are key factors for the success of manufacturing companies in their attempt to respond to dynamic changes. Circular economy, the need for optimized material flows, ad-hoc responses and personalization are some of the trends that require supply chains to become “cognitive”, i.e. able to predict trends and flexible enough in dynamic environments, ensuring optimized operational performance. Digital Twins (DTs) is a promising technology, and a lot of work is done on the factory level. In this paper, the concept of Cognitive Digital Twins (CDTs) and how they can be deployed in connected and agile supply chains is elaborated. The need for CDTs in the supply chain as well as the main CDT enablers and how they can be deployed under an operational model in agile networks is described. Emphasis is given on the modelling, cognition and governance aspects as well as on how a supply chain can be configured as a network of connected CDTs. Finally, a deployment methodology of the developed model into an example of a circular supply chain is proposed.

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.

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.

Abstract: Nonlinear spectrum distortions are caused by the peculiarities of the operation of charge-coupled device elements (CCD), in which the signal exposition time (TINT) is one of the significant parameters. A change of TINT on a CCD leads to a nonlinear distortion of the resulting spectrum. Nonlinear distortion of the spectrum, in its turn, leads to errors in determining the central wavelength of Fiber Bragg Gratings (FBG) and spectrally sensitive sensors, which, in general, negatively affects the accuracy of measuring systems. The paper proposes an algorithm for correcting nonlinear distortions of the spectrum obtained on a spectrum analyzer using CCD as a receiver. It is shown that preliminary calibration of the optical spectrum analyzer with subsequent mathematical processing of the signal makes it possible to make corrections in the resulting spectrum, thereby leveling the errors caused by measurements at different TINT.

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.

Additive manufacturing (AM) is an emerged layer-by-layer manufacturing process. However, its broad adoption is still hindered by limited material options, different fabrication defects, and inconsistent part quality. Material extrusion (ME) is one of the most widely used AM technologies, and, hence, is adopted in this research. Low-cost metal ME is a new and AM technology used to fabricate metal composite parts using sintered metal infused filament material. Since the involved materials and process are relatively new, there is a need to investigate the dimensional accuracy of ME fabricated metal parts for real-world applications. Each step of the manufacturing process, from the material extrusion to sintering, might significantly affect the dimensional accuracy. This research provides a comprehensive analysis of dimensional changes of metal samples fabricated by the ME and sintering process, using statistical and machine learning algorithms. Machine learning (ML) methods can be used to assist researchers in sophisticated pre-manufacturing planning and product quality assessment and control. This study compares linear regression to neural networks in assessing and predicting the dimensional changes of ME made components after 3D printing and sintering process. The prediction outcomes using a neural network performed the best with the highest accuracy as compared to regression. The findings of this study can help researchers and engineers to predict the dimensional variations and optimize the printing and sintering process parameters to obtain high quality metal parts fabricated by the low-cost ME process.

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.

The widespread deployment of social media has helped researchers access an enormous amount of data in various domains, including the pandemic caused by the COVID-19 spread. This study presents a heuristic approach to classify Commercial Instagram Posts (CIPs) and explores how the businesses around the Holy Shrine – a sacred complex in Mashhad, Iran, surrounded by numerous shopping centers – were impacted by the pandemic. Two datasets of Instagram posts (one gathered data from March 14th to April 10th, 2020, when Holy Shrine and nearby shops were closed, and one extracted data from the same period in 2019), two word embedding models – aimed at vectorizing associated caption of each post, and two neural networks – multi-layer perceptron and convolutional neural network – were employed to classify CIPs in 2019. Among the scenarios defined for the 2019 CIPs classification, the results revealed that the combination of MLP and CBoW achieved the best performance, which was then used for the 2020 CIPs classification. It is found out that the fraction of CIPs to total Instagram posts has increased from 5.58% in 2019 to 8.08% in 2020, meaning that business owners were using Instagram to increase their sales and continue their commercial activities to compensate for the closure of their stores during the pandemic. Moreover, the portion of non-commercial Instagram posts (NCIPs) in total posts has decreased from 94.42% in 2019 to 91.92% in 2020, implying the fact that since the Holy Shrine was closed, Mashhad citizens and tourists could not visit it and take photos to post on their Instagram accounts.

We report world record high data transmission over standard optical fiber from a single optical source. We achieve a line rate of 44.2 Terabits per second (Tb/s) employing only the C-band at 1550nm, resulting in a spectral efficiency of 10.4 bits/s/Hz. We use a new and powerful class of micro-comb called soliton crystals that exhibit robust operation and stable generation as well as a high intrinsic efficiency that, together with an extremely low spacing of 48.9 GHz enables a very high coherent data modulation format of 64 QAM. We achieve error free transmission across 75 km of standard optical fiber in the lab and over a field trial with a metropolitan optical fiber network. This work demonstrates the ability of optical micro-combs to exceed other approaches in performance for the most demanding practical optical communications applications.

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.

One of the most important subjects of hydraulic engineering is the reliable estimation of the transverse distribution in rectangular channel of bed and wall shear stresses. This study makes use of the Tsallis entropy, Genetic Programming (GP) and (ANFIS) methods to assess the shear stress distribution (SSD) in rectangular channel. To evaluate the results of the Tsallis entropy, GP and ANFIS models, laboratory observations were used in which shear stress was measured using an optimized Preston tube. This is then used to measure the SSD in various aspect ratios in the rectangular channel. To investigate the shear stress percentage, 10 data series with a total of 112 different data for were used. The results of the sensitivity analysis show that the most influential parameter for the SSD in smooth rectangular channel is the dimensionless parameter B/H, Where the transverse co-ordinate is B, and the flow depth is H. With the parameters (b/B), (B/H) for the bed and (z/H), (B/H) for the wall as inputs, the modeling of the GP was better than the other one. Based on the analysis, it can be concluded that the use of GP and ANFIS algorithms is more effective in estimating shear stress in smooth rectangular channels than the Tsallis entropy-based equations.

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 market for autonomous vehicles (AV) is expected to experience significant growth over the coming decades and to revolutionize the future of transportation and mobility. The AV is a vehicle that is capable of perceiving its environment and perform driving tasks safely and efficiently with little or no human intervention and is anticipated to eventually replace conventional vehicles. Self-driving vehicles employ various sensors to sense and perceive their surroundings and, also rely on advances in 5G communication technology to achieve this objective. Sensors are fundamental to the perception of surroundings and the development of sensor technologies associated with AVs has advanced at a significant pace in recent years. Despite remarkable advancements, sensors can still fail to operate as required, due to for example, hardware defects, noise and environment conditions. Hence, it is not desirable to rely on a single sensor for any autonomous driving task. The practical approaches shown in recent research is to incorporate multiple, complementary sensors to overcome the shortcomings of individual sensors operating independently. This article reviews the technical performance and capabilities of sensors applicable to autonomous vehicles, mainly focusing on vision cameras, LiDAR and Radar sensors. The review also considers the compatibility of sensors with various software systems enabling the multi-sensor fusion approach for obstacle detection. This review article concludes by highlighting some of the challenges and possible future research directions.

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.

This paper presents a detailed description of three methodologies to electrify the rural households of small loads with reliable and economical method using solar energy as input source of power. It is observed that high DC voltage is better and economical solution rather than AC based solar systems and low voltage DC systems. In this paper, three methodologies were analysed to select the right combination of portable converters with low prices, which may further enhance the energy access and affordability to common people. The proposed three methodologies were tested through action research in collaboration with a team of scientists and technical experts of National Institute of Solar Energy. The comparative analysis is carried out on three different converters, various kind of house loads and types of power supply i.e. AC/DC. The performance, quality and economic evaluation has been carried out with the help of MATLAB SIMULINK and other measuring instruments specific for solar photovoltaic systems. The derived results were analysed and inferences made for use in village household loads and their income groups. Further, a three-step analytical framework on village-level power energy efficient power at affordable power preposition was presented. This study will help in the designing as well as in the performance and economic evaluation of converters based on solar photovoltaic systems of different technologies.

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.

Interleaved power factor correction (PFC) is widely used circuit topology due to good efficiency and power density for single-switch boost PFC. As the differential mode (DM) electromagnetic interference (EMI) noise magnitude depends upon the input current ripple, this research details a comprehensive study of DM EMI filter design for interleaved boost PFC with the aim of minimizing the component size. It is also demonstrated that the different numbers of interleaved stages and switching frequency influence the filter attenuation requirement and, thus, the EMI filter size. First, an analytical model is derived on the basis of the Norton equivalent circuit model for the differential mode noises of interleaved boost PFC within the frequency range of 9-500 kHz. The derived model can help identify the optimal phase shifting among the interleaved boost converters in order to minimize the considered differential mode noises at the filter design frequency. So, a novel phase-shift method is developed to get a minimized attenuation required by a filter in Band B. Further, a volume optimization of the required DM filter was introduced based on the calculated filter attenuation and volumetric component parameters. Based on the obtained results, unconventional and conventional phase shifts have demonstrated a good performance in decreasing the EMI filter volume in Band B and Band A, respectively. A 2-kW interleaved PFC case study is presented to verify the theoretical analyses and the impact of phase-shifting on EMI filter size.

Lattice and code cryptography can replace existing schemes such as Elliptic Curve Cryptography because of their resistance to quantum computers. In support of public key infrastructures, the distribution, validation and storage of the cryptographic keys is then more complex to handle longer keys. This paper describes practical ways to generate keys from physical unclonable functions, for both lattice and code based cryptography. Handshakes between client devices containing the PUFs and a server are used to select sets of addressable positions in the PUFs, from which streams of bits called seeds are generated on demand. The public and private cryptographic key pairs are computed from these seeds together with additional streams of random numbers. The method allows the server to independently validate the public key generated by the PUF, and act as a certificate authority in the network. Technologies such as High performance computing, and graphic processing units can further enhance security by preventing attackers to make this independent validation when only equipped with less powerful computers.

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.

Traditional education, particularly at a university level isn’t necessarily very engaging1 or strong at building teamwork skills. Educational escape rooms are a recent game based learning2 approach which combines team based problem solving with a story-line and cryptic clues. In this3 paper we apply the concept of educational escape rooms to the telecommunications engineering4 classroombycreatingaseriesoftwoseparatescenarios,eachcontainingthreepuzzles. Ourevaluation5 is based on beta tester survey results which suggest that this will be an engaging and challenging6 tool for teaching telecommunications engineering. Although educational escape rooms are rapidly7 being deployed in education, these are the first educational escape rooms that specifically addresses8 the field of telecommunications engineering.

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.

In this research work, an adaptive scheme for the coordinated protection of AC Microgrids using directional overcurrent (DOCR) relays is presented. Protection of AC MGs is a complex and challenging issue due to the dynamic nature of the network including, a) its capability to reconfigure the modes of operation ranging from grid-connected to the islanded mode, c) bidirectional-power flow capability, and c) integration of intermittent renewable energy resources with real-time variations in the resource availability. Consequently, the fault current contributions may largely vary depending upon the incident conditions on the network. Conventional protection schemes, generally designed for radial networks, and unidirectional power flow from the source end to the load may either mal-operate or exhibit very poor performance, if not adapted according to the dynamic conditions of the network. To address this issue, a communication-based adaptive protection scheme capable to adapt its settings according to the generation resource availability and network configuration is presented in this work. The proposed scheme consists of an intelligent central protection unit (ICPU) capable to update the settings and communicate it to the individual relays based on the pre-calculated offline settings. The directional overcurrent relays employed in the scheme use two-stage settings, i.e. definite time and inverse definite minimum time characteristics for the effective coordination among the downstream and upstream relays. An adaptive algorithm for ICPU operation is presented and a case study is implemented for a modified IEEE 9-bus system using DigSilent Power factory. The results for various scenarios including, a) grid-connected mode of operation, b) islanded mode of operation, and c) variable distributed generation mode are obtained and compared to the static scheme, which validates the effectiveness of the proposed scheme.

A micro Sagnac interferometer integration is proposed for electron cloud distributed sensors. The Sagnac interferometer consists of four microring probes integrated into a Sagnac loop. Each of the microring probes is embedded with the silver bars to form the plasmonic wave oscillation. At the center microrings, electrons are trapped and oscillated by the whispering gallery modes (WGMs), where the plasmonic antennas are established and applied for wireless fidelity (WiFi) and light fidelity (LiFi) transmissions for distributed sensors. The antenna gains are 2.59dB, 0.93dB, 1.75dB, and 1.16dB respectively for the four antennas formed at the center microrings. The polarized light of 1.50µm wavelength is fed into the interferometer input, which is polarized randomly into upstream and downstream directions. The polarization components can be obtained by the space-time modulation control. By controlling the electron cloud spin orientation, the space-time projection can be applied, and the ultra-high measurement resolution can be obtained in terms of fast switching time (change in phase). In manipulation, the applied stimuli are substituted by the change in input source power. The light input power variation causes a change in electron cloud density. Similarly, when the electron cloud is excited by the microscopic medium, which can be employed as the microscopic sensors. The WGM sensors have sensitivities of 1.35µm-2, 0.90µm-2, 0.97µm-2 and, 0.81µm-2, respectively. The WGMs behave as a four-point probe for the electron cloud distributed sensors, where the electron cloud sensitivities of 2.31 prads-1mm3 (electrons)-1, 2.27prads-1mm3 (electrons)-1, 2.22 prads-1mm3(electrons)-1, 2.38prads-1mm3(electrons)-1 are respectively obtained.

This paper presents preliminary operational performance results of a pilot grid-connected photovoltaic (PV) system designed and installed on the rooftop of the Ministry of Petroleum, Energy and Mining headquarter in Nouakchott (latitude of 18.1°N and the longitude of 16.0°W), Mauritania. The aim is for the government to demonstrate the relevance of using solar energy and to encourage the uptake of solar PV technology for commercial and residential building applications in Mauritania.. In this study, the grid-connected PV system has a peak power of 48 kW and the performance monitoring was carried out during one year, with a system that allow to measure DC power, inverter and system conversion efficiency, energy generated by the PV arrays, solar radiation in the inclination plane of panels, ambient temperature and module temperature. During this period, the PV plant was found to supply 65,668 kWh to the grid. The final yield ranged from 3.91 to 5.09 kWh/kWp/day. The performance ratio was found to vary from 69.69% to 89.35% and the annual capacity factor was found to be 19%. Finally, performance parameters were compared with other PV plants installed in the same region of Northern Africa. The outcome of this work is deemed important in assisting accurate PV system design and decision-making.

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.

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.

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.

Smart city infrastructure has a significant impact on improving the quality of humans life. However, a substantial increase in the urban population from the last few years is posing challenges related to resource management, safety, and security. In order to ensure safe mobility and security in the smart city environment, this paper proposes a novel Artificial Intelligence (AI) based approach empowering the authorities to better visualize the threats and to help them identify the highly-reported crime zones yielding greater predictability of crime hot-spots in a smart city. To this end, it first investigates the Hierarchical Density-Based Spatial Clustering of Applications with Noise (HDBSCAN) to detect the hot-spots that have a higher risk of crimes to be committed. Second, for crime prediction, Seasonal Auto-Regressive Integrated Moving Average (SARIMA) exploited in each dense crime region to predict the number of crimes in the future with spatial and temporal information. The proposed HDBSCAN and SARIMA based crime prediction model is evaluated on ten years of crime data (2008-2017) for New York City (NYC). The accuracy of the model is measured by considering different time period scenarios i.e. (a) year-wise, i.e., for each year and (b) for the whole period of ten years, using an 80:20 ratio where 80\% data was used for training and 20\% data was used for testing. The proposed approach outperforms with an average Mean Absolute Error (MAE) of 11.47.

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.

Mexico is currently facing important water management challenges. Cities in the country are facing water scarcity and at the same time, they struggle with floods during the raining season. The water sensitive urban design (WSUD) approach has proved to be helpful in tackling urban water challenges such as floods and water scarcity and it is being implemented in cities around the world. The WSUD approach highlights the role of both the water cycle and the water utilities systems, when transitioning towards a water sensitive stage. Therefore, the objective of this research is to analyse the current situation of the water cycle and the water utility (SIAPA). To do so, we have selected the Metropolitan Area of Guadalajara (MAG) and proposes a case study approach. Within our case of study, we answer two questions: 1) What are the causes of water scarcity and flooding in the MAG? and 2) What are the proposals to solve these problems under a WSUD approach? By answering these questions, we identified that the water management in the MAG corresponds to a single purpose infrastructure. This type of management does not contribute to solve the problems of water scarcity and floods. The water supply policy is based only on the construction of large dams disregarding the storage and use of rainwater, and reuse of greywater, and water-conservation devices. In order to transition towards a water sensitive stage, a WSUD approach that includes multi-purpose infrastructure should be considered. Such as green roofs, swales, rainwater gardens, infiltration trenches, etc.

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.

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.

Fatigue remains a challenge especially for high end metal AM parts and materials. From a design perspective, the fatigue limit of an AM solution can be improved upon by optimizing the manufacturing process for a certain material and part. In addition, improved accuracy of design methodologies aids in capturing the features critical for fatigue and quantifies their significance to desired part lifetime as well as providing a basis for their avoidance. In current work we present an overall concept merging thermomechanical process and powder bed solidification modeling to micromechanical analysis of fatigue of the resulting material microstructure. Material features critical to fatigue, particularly surface roughness, internal defects such as porosity and cracks and on the other hand inclusions, can be assessed directly on the basis of AM part microstructure with respect to the resulting fatigue limit. Case analyses consist of maraging steel and nickel alloys. The overall scheme provides a basis for optimization of metal AM solutions against fatigue and multiscale modeling founded basis for fatigue design.

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.

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.

Laser powder bed fusion (L-PBF) is a type of additive manufacturing technology that processes metal powders into a component. Support structures are an essential part of the L- PBF process as they transfer the laser-induced heat during and shortly after the process to the substrate, sustaining positional accuracy of downward facing surfaces of the component. Since the use of support structures is inevitable, optimized designs for them are crucial in realizing more sustainable production process. In a serial production setup, reducing the lead time and cost of a non-value-added process step like support structure removal is of significance when improving the overall business case and competitiveness.The goal of this study was to verify the applicability of lattice-based support structures for L-PBF. To achieve this, different lattice types as support structures were designed. They were tested, compared and verified for a Siemens gas turbine component. The results showed that the generated lattice-based support structures could be suitable for L-PBF. The supports had to be designed appropriately such that they could preserve the geometry of the part. Furthermore, they had to have a short fabrication time and to be removable easily, preferably without machining or sawing.

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.

In this paper a novel and flexible solution for fault prediction based on data collected from Supervisory Control and Data Acquisition (SCADA) system is presented. Generic fault/status prediction is offered by means of a data driven approach based on a self-organizing map (SOM)and the definition of an original Key Performance Indicator (KPI). The model has been assessed on a park of three photovoltaic (PV) plants with installed capacity up to 10 MW, and on more than sixty inverter modules of three different technology brands. The results indicate that the proposed method is effective in predicting incipient generic faults in average up to 7 days in advance with true positives rate up to 95%. The model is easily deployable for on-line monitoring of anomalies on new PV plants and technologies, requiring only the availability of historical SCADA data, fault taxonomy and inverter electrical datasheet.

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.

Laser powder bed fusion (LPBF) is an additive manufacturing process employed in many industries, for example for aerospace, automotive and medical applications. In these sectors, mainly nickel-, aluminum- and titanium-based alloys are used. In contrast, the mechanical engineering industry is interested in more wear-resistant steel alloys with higher hardness, both of which can be achieved with a higher carbon content, like in high-speed steels. Since these steels are susceptible to cracking, preheating needs to be applied during processing by LPBF. In a previous study, we applied a base plate preheating temperature of 500 °C for HS6-5-3-8 with 1.3 % carbon content. We were able to manufacture dense (p > 99.9 %) and crack-free parts from HS6-5-3-8 with a hardness > 62 HRC (as built) by LPBF. In this study, we investigate the influence of preheating temperatures up to 600 °C on hardness and microstructure dependent on part height for HS6-5-3-8. The microstructure was studied by light optical microscopy (LOM), scanning electron microscopy (SEM) and electron backscatter diffraction (EBSD). The analysis of hardness and microstructure at different part heights is necessary because state-of-the-art preheating systems induce heat only into the base plate. Consequently, parts are subjected to temperature gradients and different heat treatment effects depending on part height during the LPBF process.

The emergence of Artificial Intelligence (AI) driven Keyword Spotting (KWS) technologies has revolutionized human to machine interaction. Yet, the challenge of end-to-end energy efficiency, memory footprint and system complexity of current Neural Network (NN) powered AI-KWS pipelines has remained ever present. This paper evaluates KWS utilizing a learning automata powered machine learning algorithm called the Tsetlin Machine (TM). Through significant reduction in parameter requirements and choosing logic over arithmetic based processing, the TM offers new opportunities for low-power KWS while maintaining high learning efficacy. In this paper we explore a TM based keyword spotting (KWS) pipeline to demonstrate low complexity with faster rate of convergence compared to NNs. Further, we investigate the scalability with increasing keywords and explore the potential for enabling low-power on-chip KWS.