Miracle-dz/UniFAR-FTUs huggingface dataset:
Supply Chain and Cost Evaluation for Laser Powder Bed Fusion

Additive Manufacturing offers new possibilities in designing and producing products. Thus, several companies implement such technologies. Especially in the beginning of the AM implementation and for small lot sizes, an external supply chain is preferred in industry, but only marginally investigated in literature. Hence, this paper develops three aspects for the AM supply chain evaluation: At first, a literature review about supply chain and costing literature is conducted. Secondly, five scenarios for the internal production (make) and four scenarios of external supply (buy) are proposed. As a third aspect, a benchmark study, which is based on 140 quotes from AM suppliers, provides insights in external supply costs for LPBF manufactured parts.
Model Parameter Analysis of Cracked Photovoltaic Module under Outdoor Conditions

In the recent decade, the intelligent maintenance of the PV systems is a new trend in this area. In order to facilitate the further fault prognosis of the PV module, in this paper, the model parameters of the single diode model for a glass-cracked PV module with cracked cells, which cannot be visually inspected, are extracted based on the outdoor measured current-voltage (I-V) curves. The cracks of the solar cells are also observed from the electroluminescence images. The comparison results of the I-V curves from a referenced normal PV module and the glass-cracked PV module indicate that the photocurrent and the equivalent shunt resistance of the cracked solar cells in the fault PV module decreases significantly. The extracted parameters can quantitatively describe the failure degree of the cracked solar cells. The extracted parameter can also be used to further predict the degradation trend of the fault PV module and realize fault prognosis of PV modules.
An Interpretable Deep Learning Framework for Health Monitoring Systems: A Case Study of Eye State Detection using EEG Signals

Effective monitoring and early detection of deterioration in patients play an essential role in healthcare. This includes minimizing the number of emergency encounters, reducing the length of hospitalization stay, re-admission rates of the patients, and etc. Cutting-edge methods in artificial intelligence (AI) have the ability to significantly improve outcomes. However, the struggle to interpret these black box models presents a serious problem to the healthcare industry. When selecting a model, the decision to sacrifice accuracy for interpretability must be made. In this paper, we propose an interpretable framework with the ability of real-time prediction. To demonstrate the predictive power of the framework, a case study on eye state detection using electroencephalogram (EEG) signals was employed to investigate how a deep neural network (DNN) model makes a prediction, and how that prediction can be interpreted. The promising results can be used to employ more advanced models in healthcare solutions without any concern of sacrificing the interpretation.
Adaptive Resilient Operation of Cooperative Grid-Forming Converters Under Cyber Attacks

This paper proposes an attack-resilient adaptive controller to ensure synchronization of multiple cooperative grid-forming converters (GfCs) under cyber attacks. As the existing cooperative control philosophies do not consider the impact of intrusion into the sensors, communication links with an open-end vulnerability in the control layer, this scenario may lead to asynchrony or even cause shutdown of multiple GfCs in a cascaded manner by unnecessarily triggering the protection schemes. Hence, these vulnerabilities in the control layer have firstly been assessed in this paper. Further, an adaptive resilient control update is designed to ensure synchronization despite the presence of these attacks. Finally, the efficacy of the proposed control strategy for multiple GfCs is evaluated under simulated conditions.
Techno-Economic Assessment of PV System for self-consumption Case Study of Malta

High penetration of PVs within the grid system has initiated policies and regulations to enable and encourage self-consumption PVs as a viable solution for mitigating adverse effects of high pv-penetration on grid as also for reducing energy cost to the prosumer. This solution comes with an additional cost of energy storage such as battery. This paper presents an economic assessment of grid-connected PV systems for self-consumption. Assuming values for performance ratio of PV system, insolation level, inflation, interest rates and incentive grants, the levelized cost of electricity (LCOE) is evaluated for the case study in Malta. The influence for incentives proposed by the Maltese Government is analysed.
AccessNet: A Three Layered Visual Based Access Authentication System for Restricted Zones

Contactless authentication has become a trending topic these days, especially in this Corona pandemic situation, where contactless access systems are precautionary steps. The two most important contactless authentication systems right now are facial recognition and gait recognition. With having a possibility of spook attacks on facial recognition, gait patterns have gained popularity especially in the high-secured zones. Gait biometrics is considered as the next generation of biometrics which can enable identification of subjects even through a certain distance, avoiding any surface-contact. However, complex challenges gait patterns are mainly the view angles and distances for environmental setup and crucial gait features for algorithm setup. Hence, the authors are extending their previous work which is identifying ‘specific targets’ based on their gait patterns in Defence applications. We combined facial recognition designed with our anti-spoofing algorithm and gait patterns which gave on an average 92% accuracy irrespective of their background, motion, and angle.
Clustering Based Optimization and Automation of Utility Scale Solar Site Design

EDFR has developed a series of methods for quickly drafting a set of utility scale photovoltaic plant layouts and choosing an optimized plant design from that set. The automated drafting methodology utilizes a standard clustering technique and a novel cluster equalizing post-processing algorithm to solve a problem in existing automated drafting software, which is that existing techniques cannot assign dc power in the form of trackers to inverters without human intervention. This step is crucial to create end to end automation of a PV plant layout. Without it, it is impossible to accurately determine the layout of dc wiring and associated electrical equipment. The work nearly eliminates the need for developer drafting of utility scale photovoltaic plant layouts and provides a foundation for reducing levelized cost of energy by allowing EDFR to select the most financially optimal project design without investing large amounts of time creating the feasibility space under which optimization can occur. (Abstract)
Time-Series-Based Clustering for Failure Analysis in Hardware-in-the-Loop Setups: An Automotive Case Study

Testing is an important cost driver in development projects. Especially in the automotive industry, immense efforts are spent to carry out validation facing increasingly complex systems. Hardware-in-the-Loop test benches are essential elements for (functional) validation. Naturally, failures commonly occur, whose analysis is challenging, time-consuming and oftentimes performed manually, making the diagnosis process one decisive cost-driving factor. By experience, many failures happen due to few underlying faults. We discuss our lessons learned when performing similarity-based clustering to identify representative tests for each fault for system-level testing where test execution times are high and the complexity of the system-under-test and also the test setup leads to complicated failure conditions. Results from an industrial automotive case study–a drive train system dataset consisting of 57 test runs–show that utilizing our general, project-agnostic approach can effectively reduce failure analysis time even with a limited set of data points.
In Situ STM Study of Chloride Adsorption on Cu(110) Electrode in Hydrochloric Acid Aqueous Solution

The adsorption of chloride on Cu(110) single-crystal surface has been investigated in hydrochloric acid aqueous solution by using in situ scanning tunneling microscopy (STM). A (1 × 1) structure of Cu(110) substrate lattice was visualized at the potentials negative than −450 mV vs SCE, where chloride anions were desorbed. In the potential range from −400 mV to −150 mV vs SCE, two main complex adlayers with 3-fold and 4-fold periodicity along [110] direction of the substrate were observed. These structures with long-range periodicity are attributed to ordered chloride adlayers on unreconstructed Cu(110)-(1 × 1) structure. The different corrugation heights and periodic modulations along [110] direction observed in STM images indicate that chloride anions are located at nonequivalent binding sites. The average measured lattice constants for the two adlayers have the same size as the substrate lattice along [001] direction and have 4 and 5 times the lattice spacing of the underlying copper substrate along [.
Performance Assessment of Bioremediation and Natural Attenuation

Bioremediation and monitored natural attenuation are among the most cost-effective approaches to manage soil and groundwater contamination by hazardous organic pollutants. However, these remediation alternatives are not universally applicable and may be marginally effective for recalcitrant pollutants if the necessary microbial catabolic capacity is not present or expressed. Thus, regulatory and public approval of bioremediation and natural attenuation requires documentation of the efficacy of microbial degradation of the target pollutants. Performance assessment generally consists of three components: documented contaminant mass loss, geochemical fingerprints associated with biodegradation, and microcosm studies that show direct evidence of biodegradation. More recently, new molecular and isotope fractionation techniques have emerged to complement existing technologies for the forensic analysis and the demonstration of bioremediation and natural attenuation. This critical review examines the current state-of-art in performance assessment methods and discusses future research directions.
Active and Passive Control of a Dual Rotor Wind Turbine Generator for DC Grids

This paper proposes a vector and a linear control strategy for wind turbines connecting to a DC collector grid. Two conversion schemes are considered for the wind turbine, (i) an active scheme where a dual rotor generator (DRG) is connected to a voltage source converter (VSC), and (ii) a passive scheme where a DRG is connected to a passive uncontrolled rectifier. The vector controller is implemented on VSC and DRG where the turbine rotor speed and output power are controlled via a VSC, and the wind turbine generator (WTG) rectified output DC voltage is controlled via a DRG DC filed current. A load-angle control and analytical modeling is discussed for the vector controller and evaluated via simulation results. In the passive scheme where the VSC is replaced with an uncontrolled rectifier the control is entirely shifted to DRG where a linear controller is implemented that controls the turbine rotor speed, power and voltage. A comparison of the vector and linear control is presented, and simulation results verify the effectiveness of the control strategy in steady-state and when turbine transitions between different wind velocities.
A Smart Building Energy Management and Pricing System with Renewable Energy Resources

Renewable Energy Resources (RES) has become mature enough to be integrated into commercial buildings. Even though this integration brings many opportunities; one must decide how to price electrical energy in case of a peer-to-peer (P2P) mechanism is enabled, and utilize the RES in an efficient way, e.g. avoiding grid interaction. This paper proposes a virtual aggregator (VA) for a building that has a shared RES system, thus enabling an internal trading environment for its users. The algorithm also considers load shifting capabilities of such devices, and fair pricing of the excess energy. The load shifting process takes advantage of the results of P2P trade as well. First, the energy distribution between P2P trade participants is handled. Subsequently, home appliances with shifting capabilities are taken into account and priced accordingly. Finally, the system performance is evaluated considering load shifting scenarios and different VA parameters.
Discovering Communities with SGNS Modelling-based Network connections and Text communications Clustering

By the community discovery, the microblogging services facilitate diverse applications such as viral marketing, disaster management, customized programs, and many more. However, the sparseness and heterogeneity of user networks and text content make it difficult to group users with a similar interest. In this paper, we present a novel method to discover user communities with common interests. The proposed method utilizes both text content and interaction network information where network information is modeled using the concept of Skip-Gram with Negative Sampling for Non-negative Matrix Factorization. Empirical analysis using several real-world Twitter datasets shows that the proposed method is able to produce accurate user communities as compared to the state-of-the-art community discovery and clustering methods.
Minimax Optimal Stochastic Strategy (MOSS) For Neighbor Discovery and Selection In Millimeter Wave D2D Networks

The millimeter-waves (mmWaves) features promote its soon employment in device to device (D2D) communications. In D2D, neighbor discovery and selection (NDS) problem is a critical one due to the tradeoff between exploring more devices for the best choice and the expected beamforming training (BT) overhead. In this paper, mmWave D2D neighbor discovery and selection (NDS) problem is modeled as a budget-constrained multiarmed bandit (MAB). Specifically, an energy constrained minimax optimal stochastic strategy (E-MOSS) algorithm is proposed, which reflects the real network scenario by counting the remaining battery levels of the neighboring devices. Simulation results prove the efficiency of the proposed E-MOSS algorithm over the traditional NDS arrangements regards network lifetime, convergence rate, energy performance, and average throughput. Index Terms—MOSS, mmWave, D2D, Multiarmed Bandit (MAB).
Markov chains and Petri nets for software rejuvenation systems

In this article, we introduce the book chapter “Markov Chains and Petri Nets” in the “Handbook of Software Aging and Rejuvenation” [1]. The chapter reviews the literature of Markov and Petri net models developed for analyzing software rejuvenation systems. Besides the summary of the chapter, we also discuss the recent advances in this research field and some future research directions.
Study of electrical transport properties in perovskite solar cells based on MAPI grown by physical vapor deposition

This work seeks to contribute to development of new photovoltaic materials to be used in low cost solar cells and low environmental impact. Special emphasis was placed on the study of electric transport properties in perovskite solar cells with structure FTO/ZnO/MAPI/P3HT/Au, using impedance spectroscopy technique. MAPI (CH3NH3PbI3) thin films were deposited by sequential evaporation and its thickness was varied between 300 and 600 nm. Through simulations of impedance curves, it was possible to model the electrical behavior of the cells, where simulated circuit elements values were found: $R_{0},R_{S1}, R_{S2}, c_{g}$ and $C_{S}$. From the above, the resistance at low frequencies $R_{S1}$ has higher values than the component $R_{S2}$ at high frequencies (HF) and since these have an inverse relationship with the recombination current $J_{rec,s}$ we can say that photocurrent losses by recombination are greater at HF.
Modelling and Simulation of Microgrid in Grid-Connected Mode and Islanded Mode

This paper presents the modelling and simulation of an 80kW AC microgrid network in MATLAB/Simulink environment. The network comprises a 50 kW photovoltaic system, a 10 kW fuel cell system, and a 20 kW battery energy storage system (BESS). The model is simulated under four operating conditions: (i) grid-connected mode, (ii) islanded mode (iii) islanded mode with excess power generation from Distributed Energy Resources (DERs) and (iv) islanded mode with limited power generation from DERs. Simulation results of the microgrid during various operating conditions were observed to investigate the charging and discharging characteristics of the battery and the total harmonic distortion of the power network during grid-connected mode and islanded modes.
Multimodal Prediction of Public Trust in Politicians from Speech and Text

This study investigates whether public trust in politicians can be automatically predicted from their speech or twitter messages, and integration of these two communication modalities into a multimodal prediction procedure. A database of speech samples and twitter messages representing ten USA political figures was generated and labeled based on the outcomes of a publicly available online ranker. Two trust labels were created, i.e., the low-trust (five politicians) and the high-trust (five politicians). The database was first used to test unimodal prediction based either on speech or text, and then it was applied to validate a proposed multimodal approach. The unimodal classification was achieved by training two separate multilayer perceptron neural network (MLP-NN) models, one for speech and one for text. Whereas the proposed multimodal approach concatenated prediction vectors resulting from individual modalities to train a third multimodal decision-making MLP-NN. The text-based prediction achieved the F1score of 83%, the speech-based - 89%, and the combined speech and text-based prediction resulted in the F1-score of 92.5%.
Effects of Automated Vehicles on Traffic Flow With Different Levels of Automation

Highly automated vehicles are regarded as the next revolution of the transport system. Automated vehicles include a spectrum from vehicles with driver assistance systems through to highly automated vehicles. These vehicles will only gradually appear in the overall vehicle fleet. Their impact as part of future traffic is of reference value for transport decision-makers. The present paper starts from assumptions for the shares of vehicles with different levels of automation in 2030 and 2050 (representing the near and far-distant future) and compares the effects of these automated vehicles on traffic flow using microscopic traffic simulations. The simulated vehicles include non-assisted vehicles, semi-automated vehicles with driver assistance systems, and fully autonomous vehicles. To obtain a more realistic result, a traffic scenario of the city of Duisburg is used in this thesis. With the support of the city administration, existing data of the origin/target matrix, detector data including induction loops, and cameras were available. Thus, the data of the origin/target matrix are used to generate the real traffic scenario and the detector data to investigate the accuracy of the generated traffic. The result shows that automated vehicles would have a positive impact on traffic, a proportion of automated vehicles can reduce the average travel time. For areas with different traffic conditions, the degree of impact of automated vehicles can be very different.
XPS characterization of the corrosion film formed on the electroless nickel deposit prepared using different stabilizers in NaCl solution

X‐ray photoelectron spectroscopy (XPS) was used to examine the corrosion films formed on electroless nickel (EN) deposits prepared in an EN solution containing two different types and concentrations of bath stabilizers in Ar‐purged and oxygenated neutral 5% NaCl solution. Two distinctive corrosion films were observed after long periods of immersion. An enrichment of elemental phosphorus compared to nickel was observed on the surface of the EN deposit produced with no stabilizer and with maleic acid (MA). By comparison, a high degree of surface oxidation was observed on the EN deposit prepared with thiourea. Trace impurities of sulfur in the EN deposit prepared with thiourea appear to result in enhanced corrosion and, in turn, lead to the build‐up of corrosion products on the EN surface. By contrast, a P‐enriched chemical passivation layer was formed on the EN deposits prepared either without any stabilizer or with MA present. Copyright © 2007 Crown in the right of Canada, and John Wiley & Sons, Ltd.
Scattering performance analysis of reflectance photometer for quantifying gold labeled test strips

Gold labeled immunochromatography assay is widely used in many fields. Quantitative test can be realized by using a reflectance photometer. However, theoretical analysis of the strip and the photometer has seldom been reported. In this paper, the microstructure of immunochromatographic strip labeled by nanogold particles is analyzed with scanning electron microscope (SEM). Based on the SEM images of the strip, Mie's scattering theory is used to investigate the scattering behaviors of particles in the strip, and Lambert cosine law is applied to analyze the diffuse reflection of porous strip. Besides, a reflectance photometer for gold labeled test strip has been developed for fast quantification of immunochromatography assay in our group. Theoretical model is achieved by introducing the parameter of the developed reflectance photometer. The calculated scattering signal distribution is well consistent with that measured by the reflectance photometer.
Role of endocytic proteins in mechanotransduction and impact on autosomal dominant centronuclear myopathy

Clathrin and dynamin 2 (DNM2), two key proteins of intracellular membrane trafficking, are co-expressed at specialized adhesion and force transmitting sites of muscle fibers called costameres. These assemblies link the plasma membrane to the extracellular matrix and to the contractile units of muscle. Importantly, mutations in their components cause several distinct myopathies. At the plasma membrane, clathrin forms large flat lattices interacting with costameric cytoskeleton. Clathrin depletion leads to defective costamere formation and induces an impairment of contractile properties. In addition, it has been shown that DNM2 mutations cause autosomal dominant centronuclear myopathy (CNM). In this project, I set out to investigate the interaction between clathrin plaques and the surrounding cytoskeleton with a particular emphasis on DNM2 contribution. I show that actin filaments surrounding mechanically sensitive clathrin plaques anchor a three-dimensional web of muscle-specific intermediate filaments and sequestrate YAP/TAZ, two nucleocytoplasmic shuttling proteins involved in muscle cell proliferation and differentiation. Importantly, my work demonstrates costameric defects in vivo in an heterozygous knock-in mouse model harboring the most frequent CNM mutation. By virtue of shaping both clathrin lattices and branched actin filaments, and by forming a complex with TAZ, DNM2 takes center stage as a central regulator of YAP/TAZ-mediated mechanotransduction and intermediate filament organization. This role may be the Achilles’ heel of several tissues and its dysfunction may lead to other diseases where the fine coupling between adhesion and force transduction is perturbed.