Miracle-dz/UniFAR-FTUs huggingface dataset:
Degradation of a wind-turbine drive-train under turbulent conditions: effect of the control law

This work compares the simulated degradation produced at the drive-train of a given wind turbine when it is functioning at optimal and sub-optimal operating points. The simulation considers different classes of wind conditions and supposes the use of the same Maximum Power Point Tracking (MPPT) algorithm but calibrated with different feedback gains. The dissipation at the drive-train is modeled using contact mechanics principles, and it is intended for modeling the mechanical fatigue due to changes in wind speed and turbulence. The paper presents the proposed model for the drive-train degradation and the obtained power curves for a simulated variable-speed 2MW (100m rotor diameter) wind turbine, with fixed gear box and horizontal-axis.
Hilbert Marginal Spectrum for Failure Mode Diagnosis of Rotating Machines

Mechanical bearings are a core component for rotating machines. These critical elements suffer from degradation which can be gradual or abrupt which typically results in premature failure modes occurring. In recent years, bearing faults have been reported to be the cause of up to 75% of low voltage motor/generator breakdowns and up to 41% of all rotating machine failures. Abrupt equipment failure in many critical applications such as aircraft, automotive and energy converters is often extremely costly, untimely and catastrophic, as well presenting serious health and safety implications. The paper investigates a unique approach for bearing fault mode diagnosis through incorporating novel Hilbert Marginal Spectrum derived features that are utilised in Machine Learning (ML) classification algorithms, specifically that of Support Vector Machines (SVM) and k-Nearest Neighbour (k-NN). The techniques and methods proposed are tested and validated on real vibration signals achieving 93.8% classification accuracy.
Selection of Pavement for Highway Rehabilitation Based on Life-Cycle Cost Analysis

Life-cycle cost analysis (LCCA) for highway projects is an analytical technique that uses economic principles to evaluate long-term alternative investment options, especially for comparing the value of alternative pavement structures and strategies. Recently, the California Department of Transportation (Caltrans) mandated LCCA implementation to evaluate the cost-effectiveness of pavement design alternatives for highway projects in the state. An LCCA approach was utilized for validation of the pavement design on the I-710 Long Beach rehabilitation project with three pavement types: innovative (long-life) asphalt concrete pavement (ACP), standard-life ACP, and long-life portland cement concrete pavement (PCCP). The LCCA followed the Caltrans procedure and incorporated information filed by the project team. The software tools Construction Analysis for Pavement Rehabilitation Strategies (CA4PRS) and RealCost were used for quantitative estimates of construction schedule, work zone user cost, and agency cost for initial and future maintenance and rehabilitation activities. Conclusions from the LCCA supported use of the innovative ACP alternative, the one actually implemented in the I-710 Long Beach project (Phase 1), since the innovative ACP alternative had the lowest life-cycle costs over the 60-year analysis period. For example, the life-cycle agency cost for the innovative ACP alternative ($33.2 million) was about $7.9 million more cost-effective than that of the standard-life ACP alternative ($41.1 million) and about $17.2 million less expensive than the long-life PCCP alternative ($50.4 million). Utilization of the proposed computer tool–aided LCCA procedure would contribute substantial economic benefits to nationwide highway projects, especially rehabilitation and reconstruction.
Distribution system power flow analysis-a rigid approach

This approach is oriented toward applications in three phase distribution system operational analysis rather than planning analysis. The solution method is the optimally ordered triangular factorization Y/sub BUS/ method (implicit Z/sub BUS/ Gauss method) which not only takes advantage of the sparsity of system equations but also has very good convergence characteristics on distribution problems. Detailed component models are needed for all system components in the simulation. Utilizing the phase frame representation for all network elements, a program called Generalized Distribution Analysis Systems, with a number of features and capabilities not found in existing packages, has been developed for large-scale distribution system simulations. The system being analyzed can be balanced or unbalanced and can be a radial, network, or mixed-type distribution system. Furthermore, because the individual phase representation is employed for both system and component models, the system can comprise single, double, and three-phase systems simultaneously. >
Numerous Patterns of Inductor Currents in DCM of Coupled SIDO Boost Converter

The coupled inductor single input dual output (CI-SIDO) converters have applications in portable electronic devices, electric vehicles, hybrid energy systems, renewable energy systems, etc. The coupled inductors present in the converter helps to reduce the physical size of the converter and also helps to reduce the ripples in input and inductor currents. The CI-SIDO converters are generally operated in continuous conduction mode (CCM) because of the load-independent input-output voltage relations and control. However, once the converter is designed and used, loads of the converter may reduce, leading to the discontinuous conduction mode (DCM). Therefore, the DCM analysis is essential when the converter is used in practical applications. This paper presents the DCM operation of the CI-SIDO boost converter. The DCM operations depend on the inductor current waveforms. As illustrated in the literature, the CI-SIDO boost converter has numerous inductor current waveforms depending on the voltages and coupled inductor parameters. As the analysis of each waveform in DCM is very tedious, this paper presents the DCM operation when the two inductor current slopes are opposite to each other. With these slope conditions, it is found the CI-SIDO boost converter has eight different DCM waveform possibilities. Each waveform is analyzed separately, and the output-input voltage relations are found for each case. The analysis is verified by simulations in Matlab/Simulink.
A Novel Diagnosis Method of Proton Exchange Membrane Fuel Cells Based on Multi-Grained Cascade Forest and Principal Component Analysis

Fuel cell diagnosis is very important to ensure the reliability of its operation and application. The data-driven method is concerned for its simplicity and accuracy. This paper proposes a fuel cell fault diagnosis method based on multi-Grained Cascade Forest (gcForest) and principal component analysis (PCA). This method uses PCA to reduce the dimensionality of the fault data and extract appropriate features. Based on relatively simplified features, the classification algorithm of gcForest is used to diagnose the fault status of the fuel cell. Through experimental analysis, this proposed method can quickly identify the three health states of membrane drying, hydrogen leakage, and normal state. The diagnostic accuracy of this method is 99.39%, and the diagnosis period is 0.372s. Therefore, the method proposed in this paper is suitable for online fault identification of proton exchange membrane fuel cell systems with large data samples and multi-dimensional data.
Implementation and Performance Analysis for Energy Harvesting-based Wireless Sensor Networks

A wireless sensor network (WSN) depends on energy to operate and considerable efforts have been devoted to extend its operational lifetime. Energy harvesting (EH) has emerged as one promising approach to prolong the lifetime of a WSN without interrupting its operations. In an EH-WSN, sensor nodes typically have batteries being charged with energy harvested from ambient environments such as wind, solar, water mills etc. However, uncertainties remain about when and how much energy can be harvested over time from the environments and about traffic fluctuation at each node. Unfortunately, existing works on EH-WSN are limited in evaluating the impacts of these factors and the number of the nodes in an EH-WSN on its key performance metrics. This paper, therefore, presents a general method and a practical implementation of a node capable of harvesting solar energy in an EH-WSN. The paper then evaluates the performance of the EH-WSN. Explicit mathematical expressions define models for network operational time, throughput and reliability of the EH-WSN and simulations are used to validate the models. Influential factors are energy arrival rate, traffic rate at each node and the number of nodes in an EH-WSN. The results document that the analytical models and simulations correspond.
A Model Predictive Scheme for Autonomous Vehicles Cybersecurity

Nowadays, many vehicles are connected to the Internet for providing enhanced or additional functionalities to drivers. However, the existing vehicle communication protocols are not designed for cybersecurity. As a result, Autonomous Vehicles (AVs) are exposed to hacker attacks which could jeopardize the safety of passengers. In this paper, a novel Informative Model Predictive Scheme (I-MPS) against Replay Attacks (RAs) and Denial of Service (DoS) attacks for AVs is proposed. The effectiveness of the scheme is shown by numerical simulations of an autonomous vehicles during an overtaking and is compared against a typical architecture not implementing the I-MPS.
Driving Range Extension of Electric City Buses using Opportunity Wireless Charging

Electric city buses usually possess a large battery pack and demand overnight charging at the depot. As a result, it offers a high capital cost, longer charging times, and limits the passenger capacity. Opportunity charging of electric vehicles (EVs) during brief stops is considered to be one of possible solutions to conquer these problems. This paper presents a prospective battery sizing methodology and charger evaluation of electric city buses enabled by wireless charging at frequent stops to achieve driving range extension while minimizing battery size. The quasi-instantaneous power connection can be possible using wireless power transfer (WPT) charging technology. Battery size reduction and range extension of electric city buses employing opportunity wireless charging is illustrated for various WPT charger power ratings under Federal test procedure (FTP) drive cycle and New European driving cycles (NEDC).
ESD wrist strap-based EDA sensor cum ESD strap integrity monitor

Workers on Electronic Manufacturing Service (EMS) assembly lines are exposed to a high degree of occupational stress and fatigue. There is a need for assistive technologies to limit worker stress/fatigue. Electro Dermal Activity (EDA) (Skin Conductance) is a known indicator of a worker’s stress/fatigue level. We propose a novel Electrostatic Discharge (ESD) wrist strap-based EDA sensor. Our sensor seamlessly integrates with the dual-wire grounded ESD wrist strap that EMS operators are mandated to wear. Our approach for EDA measurement is non-intrusive, considering that the operators are accustomed to continuous usage of ESD wrist straps. Besides EDA measurement, our front-end circuit also monitors the integrity of the ESD wrist strap and alerts the wearer in the event of an intermittent or open ground. We present the circuit design and stress-test-based experimental results.
Torque Ripple Minimization Control Strategy in Synchronous Reluctance Machines

Torque smoothness is an essential requirement for high-performance motor drive applications. Synchronous reluctance machines have high torque ripple due to non-linear magnetic path coupled with saturation of the rotor core segments during operation. This paper proposes an active torque ripple minimization technique for Synchronous reluctance machine using current injection. The proposed method is aimed to compensate for both cogging and torque ripple generated by the machine. The method also optimizes for the minimum magnitude of ripple injection for active cancellation. The effectiveness of the proposed method is evaluated with both simulation and experimental results.
Wave propagation in sandwich structures with resonators and periodic cores

Wave propagation in sandwich structures containing periodic cores and local resonators are studied. The effect on wave attenuation is emphasized. It is found that within certain frequency bands wave is forbidden from propagating through the structure with periodic cores or with resonators. It is also found that for the structure consisting of two different core materials periodically placed along the beam length, the location and range of the stop band can be tailored by the physical properties of the core. Also, with fixed core properties, the lattice constant becomes an important factor in controlling the location of the stop band. Moreover, the coupling of the two mechanisms can effectively enlarge the width of the stop band and enhance wave attenuation. It is evident that resonators are easier to use for filtering disturbances with low frequencies.
CLONING BY SOMATIC CELL NUCLEAR TRANSFER Generation of genetically engineered livestock using somatic cell nuclear transfer

Genetic engineering (GE) of livestock initially has been accomplished primarily using pronuclear microinjection into zygotes (1985–1996). The applications of the technology were limited due to low integration efficiency, aberrant transgene expression resulting from random integration and the presence of genetic mosaicism in transgenic founder animals. Despite enormous efforts to established embryonic stem cells (ESCs) for domestic species, the ESC GE technology does not exist for livestock. Development of somatic cell nuclear transfer (SCNT) has bypassed the need in livestock ESCs and revolutionized the field of livestock transgenesis by offering the first cell-based platform for precise genetic manipulation in farm animals. For nearly two decades since the birth of Dolly (1996–2013), SCNT was the only method used for the generation of knockout and knockin livestock. Arrival of CRISPRS/Cas9 system, a new generation of gene-editing technology, gave us an ability to introduce precise genome modifications easily and efficiently. This technological advancement accelerated production of GE livestock by SCNT and reinstated zygote micromanipulation as an important GE approach. The primary advantage of the SCNT technology is the ability to confirm in vitro that the desired genetic modification is present in the somatic cells prior to animal production. The edited cells could also be tested for potential off-target mutations. Additionally, this method eliminates the risk of genetic mosaicism frequently observed following zygote micromanipulation. Despite its low efficiency, SCNT is a well-established procedure in numerous laboratories around the world and will continue to play an important role in the GE livestock field.
Aetiology, prophylaxis and management of preeclampsia

Although preeclampsia affects approximately 3%–8% of pregnancies worldwide and is a major contributor to maternal and neonatal mortality and morbidity, the aetiology of preeclampsia is still not fully understood. This review presents the current knowledge on the aetiology of preeclampsia, with a special emphasis on risk factors and their role, and describes recommendations for the prevention and treatment of preeclampsia.
Development of railway contracting for the national passenger rail services in the Netherlands

This article mainly gives an overview of the policy developments in the Dutch railway sector over the past decade. The sector has come a long way and the outlook now looks positive. Customer satisfaction and performance have improved and the number of people that travel by train is rising. The development of a more long-term policy window for the sector seems to have brought rest and stability and room for continuous growth of passenger numbers. The combination of a policy making and negotiation regulatory style seems to have worked out very well on the performance of the sector. In a second section the paper also considers a theoretical framework on regulation schemes and develops a first tentative attempt to apply these schemes on an analysis of the Dutch railway sector by giving some statements for discussion.
Chemical–protein relation extraction in PubMed abstracts using BERT and neural networks

—Automatically extracting relations from scientific literature is a major task in biomedical text mining that is helpful in database curation. Particularly, the automatic extraction of chemical–protein interactions is relevant for accelerating drug discovery. This work describes the participation of the BIT.UA team from the University of Aveiro: we use PubMedBERT to create embedding representations for candidate pairs which are then forwarded through a neural network classifier. Our best system achieved a 0.7114 micro-averaged F1-score which is above the official mean by 9 percentage points.
Improving Tagging Consistency and Entity Coverage for Chemical Identification in Full-text Articles

This paper is a technical report on our system submitted to the chemical identification task of the BioCreative VII Track 2 challenge. The main feature of this challenge is that the data consists of full-text articles, while current datasets usually consist of only titles and abstracts. To effectively address the problem, we aim to improve tagging consistency and entity coverage using various methods such as majority voting within the same articles for named entity recognition (NER) and a hybrid approach that combines a dictionary and a neural model for normalization. In the experiments on the NLM-Chem dataset, we show that our methods improve models' performance, particularly in terms of recall. Finally, in the official evaluation of the challenge, our system was ranked 1st in NER by significantly outperforming the baseline model and more than 80 submissions from 16 teams.
Biotic and abiotic filters in translocation experiments with the three threatened plant species Gentiana cruciata L., Gentiana germanica Willd. and Myricaria germanica (L) Desv.-Master Thesis

3 Introduction . 3 Material & Methods . 5 Gentiana . 5 Study species and study sites . 5 Study design . 6 Measurements . 7 Statistical analysis . 7 Myricaria . 8 Study species and site . 8 Study design . 8 Measurements . 9 Statistical analysis . 10 Results . 10 Gentiana . 10 Myricaria . 12 Discussion . 14 Gentiana . 14 Myricaria . 15 Conclusion . 16 Acknowledgements . 17 References . 17 Appendix . 22
NeeDrop: Self-supervised Shape Representation from Sparse Point Clouds using Needle Dropping

There has been recently a growing interest for implicit shape representations. Contrary to explicit representations, they have no resolution limitations and they easily deal with a wide variety of surface topologies. To learn these implicit representations, current approaches rely on a certain level of shape supervision (e.g., inside/outside information or distance-to-shape knowledge), or at least require a dense point cloud (to approximate well enough the distance-to-shape). In contrast, we introduce NeeDrop, an self-supervised method for learning shape representations from possibly extremely sparse point clouds. Like in Buffon’s needle problem, we “drop” (sample) needles on the point cloud and consider that, statistically, close to the surface, the needle end points lie on opposite sides of the surface. No shape knowledge is required and the point cloud can be highly sparse, e.g., as lidar point clouds acquired by vehicles. Previous self-supervised shape representation approaches fail to produce good-quality results on this kind of data. We obtain quantitative results on par with existing supervised approaches on shape reconstruction datasets and show promising qualitative results on hard autonomous driving datasets such as KITTI.
Scene-based pedestrian safety performance model in mixed traffic situation

Compared to vehicle-only collisions, traffic collisions with pedestrians are studied less because of insufficient data. However, with the development of image processing technology, a growing number of road user behavioural analyses have been conducted using video data. This study tries to extract road users' movements from video data in order to analyse the conflict between pedestrian and vehicle and to evaluate pedestrian safety performance during conflicts. The time difference to collision (TDTC) parameter is used to fit the safety analysis on pedestrian-involved conflicts. Scene-based analysis, which evaluates the safety performance of traffic intersections and segments where several pedestrian-vehicle conflicts may happen together, is conducted using 91 groups of scene data. The parameters most related to pedestrian safety are located using a sensitivity test, a quantitative definition of pedestrian-vehicle conflict is then defined, and a scene-based pedestrian safety performance evaluation model is built. The model can correctly detect nearly 94.4% of possibly dangerous traffic scenes. Other kinds of mixed traffic scenes can also be studied based on this research.
Integrating Kano’s Model into Web- community Service Quality

This study uses Kano’s two-way quality model to categorize web-community service quality dimensions and their elements and understand the demands of users. The results show that, in terms of overall samples, among the 29 service quality elements, 6 are attractive quality, 5 are one-dimensional quality, 11 are must-be quality, and 7 are indifferent quality. In the six service quality dimensions, there is 1 attractive quality, 2 one-dimensional quality, and 3 must- be quality. The satisfaction increment index and dissatisfaction decrement index of service quality of the web-community, based on the CQM (1993) quality index, are also calculated in this study. Through these two indexes, we can find the increase of satisfaction and the decrease of dissatisfaction in the analysis of improvement on certain quality elements, and use the results as a reference for future improvements on services provided to the web-community. Future research can incorporate Kano’s model with a Quality Function Deployment (QFD) to investigate the demands of the web-community, their significance and to prioritize the service items.
Misinformation on Misinformation: Conceptual and Methodological Challenges

Alarmist narratives about online misinformation continue to gain traction despite evidence that its prevalence and impact are overstated. Drawing on research examining the use of big data in social science and reception studies, we identify six misconceptions about misinformation and highlight the conceptual and methodological challenges they raise. The first set of misconceptions concerns the prevalence and circulation of misinformation. First, scientists focus on social media because it is methodologically convenient, but misinformation is not just a social media problem. Second, the internet is not rife with misinformation or news, but with memes and entertaining content. Third, falsehoods do not spread faster than the truth; how we define (mis)information influences our results and their practical implications. The second set of misconceptions concerns the impact and the reception of misinformation. Fourth, people do not believe everything they see on the internet: the sheer volume of engagement should not be conflated with belief. Fifth, people are more likely to be uninformed than misinformed; surveys overestimate misperceptions and say little about the causal influence of misinformation. Sixth, the influence of misinformation on people’s behavior is overblown as misinformation often “preaches to the choir. ” To appropriately understand and fight misinformation, future research needs to address these challenges.