Miracle-dz/UniFAR-FTUs huggingface dataset:
The 3 km Norwegian reanalysis (NORA3) – a validation of offshore wind resources in the North Sea and the Norwegian Sea

Abstract. We validate a new high-resolution (3 km) numerical mesoscale weather simulation for offshore wind power purposes for the time period 2004–2016 for the North Sea and the Norwegian Sea. The 3 km Norwegian reanalysis (NORA3) is a dynamically downscaled data set, forced with state-of-the-art atmospheric reanalysis as boundary conditions. We conduct an in-depth validation of the simulated wind climatology towards the observed wind climatology to determine whether NORA3 can serve as a wind resource data set in the planning phase of future offshore wind power installations. We place special emphasis on evaluating offshore wind-power-related metrics and the impact of simulated wind speed deviations on the estimated wind power and the related variability. We conclude that the NORA3 data are well suited for wind power estimates but give slightly conservative estimates of the offshore wind metrics. In other words, wind speeds in NORA3 are typically 5 % (0.5 m s−1) lower than observed wind speeds, giving an underestimation of offshore wind power of 10 %–20 % (equivalent to an underestimation of 3 percentage points in the capacity factor) for a selected turbine type and hub height. The model is biased towards lower wind power estimates due to overestimation of the wind speed events below typical wind speed limits of rated wind power (u<11–13 m s−1) and underestimation of high-wind-speed events (u>11–13 m s−1). The hourly wind speed and wind power variability are slightly underestimated in NORA3. However, the number of hours with zero power production caused by the wind conditions (around 12 % of the time) is well captured, while the duration of each of these events is slightly overestimated, leading to 25-year return values for zero-power duration being too high for the majority of the sites. The model performs well in capturing spatial co-variability in hourly wind power production, with only small deviations in the spatial correlation coefficients among the sites. We estimate the observation-based decorrelation length to be 425.3 km, whereas the model-based length is 19 % longer.
An adaptive PID neuron network controller for congestion control

We propose a congestion controller based on the Proportional-Integral-Differential Neuron Network (PIDNN). As existing controllers, our controller employs the queue size in bottleneck link router as a congestion indicator to trigger packet dropping. The target queue length and the feedback, actual queue length, act as the controller's two input signals. The packet dropping probability is computed by PIDNN controller with its simple embedded algorithm in term of the predefined state function and output function. Thus, the dropping probability decides to drop or to accept an incoming packet so that the queue length is kept at (or near) the target level. This controller's performance is examined under various network configurations, and compared to proposed congestion algorithms, including PI and RED. Our simulation results show that, with comparable simple implementation, this scheme has short response time, better robustness, and more adaptability, especially under highly dynamic network and heavy traffic load.
A multiobjective fuzzy model for selecting and planning a project portfolio in a public organisation

Our purpose in this paper is to assist decision-makers in the task of selecting project portfolios to satisfy their requirements and guarantee profitable growth. This task usually involves decision-makers having to face multiple objectives and constraints, as well as the uncertainty associated with certain parameters of the problem. Therefore, in this paper, we propose a multiobjective programming model, which includes uncertainty through the use of fuzzy parameters and allows us to represent information not fully known by the decision-makers. The resulting model has been applied to a project portfolio selection process in a public organisation.
Local Plastic Strain Measurement by EBSD

Work has been carried out recently, which demonstrates misorientation measurements recorded by using electron backscatter diffraction (EBSD) enables one to undertake local post mortem plastic strain quantification once the degree of misorientation is calibrated against plastic strain. The present paper builds on this work and investigates the possibility of determining strain in individual grains. Due to the anisotropy of crystalline grains, polycrystalline material deform inhomogeneously on a microstructural level. In this study, the local strain induced in a pure copper specimen during tensile loading measured using EBSD was compared to in-situ strain measurements using optical microscopy imaging in conjunction with image correlation technique. By applying an averaging procedure for improving the accuracy of the measured EBSD data, the distribution of the misorientation within grains was quantified, and, as one would expect, it tended to be highest near the grain boundaries.
Test results of F/A-18 autoland trials for aircraft carrier operations

Raytheon and the US Navy conducted aircraft carrier precision approach trials using the F/A-18 as the test platform. These trials are part of the Navy Joint Precision and Landing System (JPALS) effort to demonstrate Global Positioning System (GPS) technology for aircraft carrier precision approach. The team achieved the historic milestone of the first fully coupled approach and landing to the ground in an F/A-18 using a GPS-based navigation solution. These trials used a Raytheon Navigation and Landing System developed test bed, Rockwell-Collins 24-channel Enhanced Miniature Airborne GPS Receiver (EMAGR), and US Navy aircraft integration. This paper shows data and analysis that measures and/or models accuracy of the test bed and GPS measurements. This is done in two phases. The first phase demonstrates the performance of the existing test bed as performed real-time using ship and airborne platforms. The second phase shows post processed analysis of measurement data and a proposed relative navigation solution. Because the ship environment is unique to the precision landing technology, special considerations must be made to monitor integrity and to model continuity. Considerations include ship flexure, RF interference, ship blockage, pseudorange multipath, carrier phase multipath, and ship dynamics. The test and analysis results show that GPS technology provides the quality needed to perform relative precision approaches in an aircraft carrier environment.
Fault Tolerant Control Using Gaussian Processes and Model Predictive Control

Abstract Essential ingredients for fault-tolerant control are the ability to represent system behaviour following the occurrence of a fault, and the ability to exploit this representation for deciding control actions. Gaussian processes seem to be very promising candidates for the first of these, and model predictive control has a proven capability for the second. We therefore propose to use the two together to obtain fault-tolerant control functionality. Our proposal is illustrated by several reasonably realistic examples drawn from flight control.
A Bayesian Graph Embedding Model for Link-Based Classification Problems

In recent years, the analysis of human interaction data has led to the rapid development of graph embedding methods. Topological information is typically interpreted into embedded vectors or convolution kernels for link-based classification problems. This paper introduces a Bayesian graph embedding model for such problems, integrating network reconstruction, link prediction, and behavior prediction into a unified framework. Unlike the existing graph embedding methods, this model does not embed the topology of nodes or links into a low-dimensional space but sorts the probabilities of upcoming links and fuses the information of node topology and data domain via sorting. The new model integrates supervised transaction predictors with unsupervised link prediction models, summarizing local and global topological information. The experimental results on a financial trading dataset and a retweet network dataset demonstrate that the proposed feature fusion model outperforms the tested benchmarked machine learning algorithms in precision, recall, and F1-measure. The proposed learning structure has a fundamental methodological contribution and can be extended and applied to various link-based classification problems in different fields.
Subzero Nonfreezing Hypothermia with Insect Antifreeze Protein Dramatically Improves Survival Rate of Mammalian Cells

Cells for therapeutic use are often preserved at +4 °C, and the storage period is generally limited to 2–3 days. Here, we report that the survival rate (%) of mammalian cells is improved to 10–20 days when they are preserved with a subzero supercooled solution containing the antifreeze protein (AFP), for which an ability to stabilize both supercooled water and cell membrane integrity has been postulated. We chose adherent rat insulinoma (RIN-5F) cells as the preservation target, which were immersed into −5 °C-, −2 °C-, or +4 °C-chilled “unfrozen” solution of Euro-Collins or University of Washington (UW) containing the AFP sample obtained from insect or fish. Our results show that the survival rate of the cells preserved with the solution containing insect AFP was always higher than that of the fish AFP solution. A combination of the −5 °C-supercooling and insect AFP gave the best preservation result, namely, UW solution containing insect AFP kept 53% of the cells alive, even after 20 days of preservation at −5 °C. The insect AFP locates highly organized ice-like waters on its molecular surface. Such waters may bind to semiclathrate waters constructing both embryonic ice crystals and a membrane–water interface in the supercooled solution, thereby protecting the cells from damage due to chilling.
Simulation Analysis of MEMS Based Capacitive Differential Pressure Sensor for Aircraft Application

In this paper, simulation solution for Microelectromechanical systems (MEMS) based capacitive differential pressure sensor for aircraft altimeter is proposed. The principle of proposed MEMS capacitive differential pressure sensor design was explained. Analysis for the measurement of center deflection and capacitive sensitivity square diaphragm membrane was done. Simulation on deflection and capacitive sensitivity was carried out for the range of pressure from 100mbar to1100mbar. Gold, diaphragm membrane was used in this analysis. Analysis result shows, linear variation on center deflection and capacitance variation which is more suitable for this application.
A new species of Prosyntexis Sharkey 1990 (Hymenoptera: Sepulcidae) from Crato Formation confirmed by geometric morphometric analysis

Examination of new fossil specimens of Prosyntexis from the Lower Cretaceous Crato Formation of Brazil reveled a new species namely Prosyntexis sennlaubi sp. nov. To ensure the validity of the new taxon we examined previously described and figured specimens but we also figured an additional specimen of Prosyntexis gouleti Sharkey, 1990. The new species can be differentiated from the other Prosyntexis species of the Crato formation by its larger size but also in having the cell 2R1 small, the cell 2M small and short, the cell 3R1 narrow and the vein a directed toward wing apex. We performed a Geometric Morphometric Analysis (GMA) to estimate the variation in the forewing venation of the two species from the Crato Formation and ensure our placement.
Multiscale modeling of phase changes during femtosecond laser metal interaction

A multiscale model is developed to study to the femtosecond laser single pulse and pulse train processing of the metal films. In our model, molecular dynamics simulation combined with the improved two-temperature model is employed in the ablation area and the improved two-temperature model is applied in heat-affected zone. This paper extends the improved two-temperature model to describe higher laser fluences processing by introducing the phase change. The phase change mechanisms of the non-equilibrium thermal melting and vaporization are both analyzed, which has a strong impact on the lattice temperature evaluation. The model can simulate phase change process of gold with higher accuracy. It is demonstrated that the pulse train could improve the fabrication accuracy, repeatability, and controllability.
The Improved Personnel Location Algorithm Based on RSSI

In the process of that based on the RSSI received signal strength indicator technique, resulting in the positioning accuracy is so low, since the simple RSSI, multipath, diffraction and non line of sight and other factors. In order to achieve higher accuracy node localization in wireless sensor, the paper is proposed based on the probability of recycling triangle centroid location algorithm in the RSSI technique,The probability of the cycle to handle triangle centroid localization algorithm. Through the Matlab simulation, compared with the traditional triangle centroid localization algorithm, the error is significantly reduced and positioning accuracy improved when the anchor point number exceeds a certain number.
Optimum piezoelectric patch positioning: A strain energy–based finite element approach

For the design of smart composite structures, the placement of piezoelectric patch transducers is a very important issue. For sensing and passive vibration damping applications, the electric charge and the generalized electromechanical coupling coefficient are important parameters. This article presents an approximative but efficient method for the calculation of the generalized coupling coefficient using finite element simulation results. A smart composite turbomachine blade serves as a test structure for the new method. The influence of different laminate integration layers, positions, rotation angles, patch size variations and performance benefits of increased allowable strain is tested with the new procedure. The efficient nature of the method allows for parametric placement studies or even exhaustive search to find the global positioning optimum for the structure under investigation. The proposed method is compared to state-of-the-art procedures for the calculation of the generalized electromechanical coupling factor. The 1:5 scale models of full carbon fabric/epoxy blades were manufactured with structurally integrated piezo modules. Two different piezo placement positions inside the blade were investigated experimentally. The coupling coefficient was determined and compared to the calculated values.
Enhancing Border Security through Public-Private Partnerships in South Africa

This study sought to explore the opportunities in forming effective partnerships between the public sector and the private space technology sector to address border security challenges in South Africa using space technology. The study focused on border security authorities and the space technology sector in South Africa. To achieve the above objective, a qualitative research methodology was used to ascertain the possibilities and opportunities of adopting Public-Private Partnerships (PPPs) to enhance border security in South Africa through the adoption of space technology. Findings indicate that there is a need for effective PPPs for the purpose of enhancing border security through the application of space technology in South Africa. The findings further highlighted the massive benefits of partnership but also highlighted the possible challenges of engaging in PPPs. This study revealed that it is reasonably expected for the South African border security authorities to collaborate with the private space technology sector for the purpose of developing and providing effective space technology for border security.
On-pathway multivalency of a broadly neutralizing evolution drug candidate

A promising approach to prevent antibiotic resistance is design of multivalent antibiotics that constrain bacterial evolvability by blocking multiple escape routes. Previously we discovered that an ―evolution drug‖ lead designed to broadly neutralize bacterial dihydrofolate reductase (DHFR) and its trimethoprim escape variants is multivalent in E. coli cells. In this work we identified the second target of this new lead. We analysed antibiotic induced global metabolic perturbations and deployed metabolic-network analysis and structural similarity search of the putative targets and narrowed down the search to a few enzymes. Further, combining in vivo and in vitro analyses, we identified HPPK (folK), an essential protein in bacterial folate-metabolism, as the non-DHFR target of the lead. We observed that compounds with interaction specificity against WT do not develop multivalency against related proteins. This multivalent-design strategy is effective in blocking the emergence of drug-resistant phenotypes.
Keggin-type heteropoly acid H3PW12O40 supported on magnetic amine-grafted Graphene oxide: A new magnetically separable adsorbent for rapid and selective removal of cationic organic pollutants in aqueous solutions

* Corresponding Author Email: farhadi.s@lu.ac. ir How to cite this article Farhadi S. , Maleki M. , Nirumand L. , Moradifard F. Keggin-type heteropoly acid H3PW12O40 supported on magnetic aminegrafted Graphene oxide: A new magnetically separable adsorbent for rapid and selective removal of cationic organic pollutants in aqueous solutions. Int. J. Nano Dimens. , 2022; 13(1): 40-53. Abstract In the present study, phosphotungstic acid decorated on magnetic amine-grafted graphene oxide (GOAmine/Fe3O4/H3PW12O40; GOA/Fe3O4/HPW) was prepared and evaluated as a new adsorbent for removal and separation of organic dyes from water. The obtained nanocomposite was fully characterized by means of XRD, FT-IR, Raman, VSM, SEM, EDX, AFM and BET surface area analysis. The results demonstrated the successful loading of HPW (~34 wt.%) on the surface of magnetic aminosilanized graphene oxide. The adsorption ability of the nanocomposite was tested towards cationic methylene blue (MB) and anionic methyl orange (MO) dyes. The nanocomposite exhibited very good adsorption performance for cationic organic pollutants; it could absorb approximately 100% of methylene blue (MB) from an aqueous solution within only 5 min. The removal rate of GOA/Fe3O4/HPW nanocomposite was greater than that of pure GOA, Fe3O4 and HPW. Further study revealed that the GOA/Fe3O4/HPW exhibited a fast adsorption rate and selective adsorption ability towards the cationic dyes from dyes mixtures. The effect of adsorbent dosage, initial concentration of dye and pH were studied in detail. Due to the existence of the Fe3O4, the GOA/Fe3O4/HPW nanocomposite could be magnetically separated from the reaction mixture and reused without any change in structure. This research could provide a new easy platform for wastewater purification.
Semantic Detection of Targeted Attacks Using DOC2VEC Embedding

—The targeted attack is one of the social engineering attacks. The detection of this type of attack is considered a challenge as it depends on semantic extraction of the intent of the attacker. However, previous research has primarily relies on the Natural Language Processing or Word Embedding techniques that lack the context of the attacker's text message. Based on Sentence Embedding and machine learning approaches, this paper introduces a model for semantic detection of targeted attacks. This model has the advantage of encoding relevant information, which helps to improve the performance of the multi-class classification process. Messages will be categorized based on the type of security rule that the attacker has violated. The suggested model was tested using a dialogue dataset taken from phone calls, which was manually categorized into four categories. The text is pre-processed using natural language processing techniques, and the semantic features are extracted as Sentence Embedding vectors that are augmented with security policy sentences. Machine Learning algorithms are applied to classify text messages. The experimental results show that sentence embeddings with doc2vec achieved high prediction accuracy 96.8%. So, it outperformed the method applied to the same dialog dataset.
A Robust Image Object Recognition Method Using RCNN and Big Data

This paper presents a novel approach to object detection using deep convolutional neural networks and Big Data. The aim is to build an accurate, fast and reliable object detection system, which is a vital element of an autonomous robotic vision platform; it is a key element for object estimation and automated event based systems. Recent work in deep neural networks has led to the development of a state-of-the-art object detector termed Faster Region-based CNN (Mask R-CNN). This model, through transfer learning, for the task of object detection using imagery obtained from two modalities: color (RGB) and Near-Infrared (NIR). Early and late fusion methods are explored for combining the multi-modal (RGB and NIR) information. This leads to a novel multi-modal masked R-CNN model, which achieves state-of-the-art results compared to prior work with the F1 score, which takes into account both precision and recall performances improving from 0.781 to 0.812 for the detection of solid metal objects. In addition to improved accuracy, this approach is also much quicker to deploy for new objects, as it requires bounding box annotation rather than pixel-level annotation (annotating bounding boxes is approximately an order of magnitude quicker to perform). The model is retrained to perform the detection of four types of metal objects, with the entire process taking four hours to annotate and train the new model per solid block. A key benefit of Deep Learning is the analysis and learning of massive amounts of unsupervised data, making it a valuable tool for Big Data Analytics where raw data is largely unlabeled and un-categorized. In the present study, we explore how Deep Learning can be utilized for addressing some important problems in Big Data Analytics, including extracting complex patterns from massive volumes of data, semantic indexing, data tagging, fast information retrieval, and simplifying discriminative tasks.
Quadruple Memory cell design with Complete protection against SEU and DNU

--This paper presents novel quadruple cross-coupled memory cell designs, namely Proposed cell (BQCCM10T cell), with complete protection against single event upsets and double-node upsets. The proposed cell BQCCM10T consists of four cross-coupled input-split inverters and a buffer.Memory cell circuits are more sensitive to external radiation phenomena that are likely to cause the occurrence of soft errors SEU & DNU. Therefore, the tolerance of the circuit to the soft errors is a stern requirement in memory cell circuit designs. Since the traditional error-tolerant methods result in signiﬁcant cost fines in terms of power, area, and performance, the development of low-cost hardened designs for storage cells(such as latches and memories)is of increasing importance. The Proposed cell achieves complete SEU and DNU tolerance through feedback and buffering mechanisms among its internal nodes.The proposed BQCCM10T cell has high robustness and high Read Access time and Write Access Time. With the help of T-Spice, simulation results of several SRAM cells and the BQCCM10T cell are compared.Hence the proposed cell BQCCM10T is used for highly terrestrial reliable low voltage applications.
KEPCO's national control center with an advanced energy management system

The advanced energy management system of the Korea Electric Power Corporation (KEPCO) is described. Emphasis is on the power application software, computer system configuration, data acquisition subsystem, data communications, man-machine interfaces, security-constrained economic dispatch, and simulators for factory acceptance testing. Its functional overview, high system performance, and actual operating experience are also described. >