Miracle-dz/UniFAR-FTUs huggingface dataset:
Lamport Certificateless Signcryption Deep Neural Networks for Data Aggregation Security in WSN

Confidentiality and data integrity are essential paradigms in data aggregation owing to the various cyberattacks in wireless sensor networks (WSNs). This study proposes a novel technique named Lamport certificateless signcryption-based shift-invariant connectionist artificial deep neural networks (LCS-SICADNN) by using artificial deep neural networks to develop the data aggregation security model. This model utilises the input layer with several sensor nodes, four hidden layers to overcome different attacks (data injection, compromised node, Sybil and black hole attacks) and the output layer to analyse the given input. The Lamport one-time certificateless signcryption technique involving three different processes (key generation, signcryption and unsigncryption) is adopted to achieve secure data transmission between the sender and receiver. Firstly, a one-way function is executed to generate the public and private keys for each sensor node in the WSN. Secondly, digital signature generation and encryption are both performed. The sender node, which handles the signature generation and data encryption, forwards the data to the aggregator node. Then, the receiver verifies the data by using the sender’s public key during data decryption. Thus, data aggregation security can be guaranteed. Finally, the authorised node aggregates the data with much higher data confidentiality and privacy. Performance analysis is conducted by simulating the proposed LCS-SICADNN and conventional models. Results of comparison indicate that the LCS-SICADNN can improve data aggregation security with higher throughput and lesser delay, packet drop and overhead compared with the conventional methods.
Potential field modelling and U–Pb geochronology reveal the pluton emplacement dynamics of the Lower Devonian Tarnagulla Granodiorite, southeast Australia

Abstract The Tarnagulla Granodiorite in the western Lachlan Fold Belt is poorly exposed; however, magnetic data reveal its distinct U shape (in plan view) and patterns characterised by multiple nested cusp-shaped anomalies of contrasting magnetic intensity. We interpret this magnetic response to reflect incremental assembly of the pluton above a persistent magma point source that migrated laterally. Here, we use potential field data, geophysical modelling and high-precision zircon U–Pb dating to constrain the shape of the intrusion at depth and deduce the migration path of the magmatism epicentre. Potential field profile modelling across three traverses, constrained by petrophysics, resolves the subsurface geometry of the Tarnagulla Granodiorite. In cross-section, the intrusion is funnel-shaped, with a lower boundary that varies from ∼2 km depth in the north to ∼6 km depth in the southwest, reaching a maximum depth of ∼8 km, which may coincide with a magma feeder zone. New precise U–Pb geochronology provides ages that confirm the interpreted magnetic phases were emplaced sequentially along a clockwise intrusion path that progressed stepwise from the north to the southwest between 406.62 ± 0.27 Ma and 399.3 ± 1.1 Ma. We attribute migration of the epicentre of magmatism within the Tarnagulla Granodiorite to syn-intrusion (Early Devonian) reactivations of the underlying Tarnagulla Fault, with relative displacements between the fault hanging wall (which hosts the pluton) and footwall (which we interpret to have hosted a persistent point-source magma feeder zone during pluton emplacement). Our model explains the intrusion’s U-shaped magnetic response and reveals critical information about the paleo-stress and paleo-strain conditions during emplacement, characterised by three main displacement vectors and fault slip magnitudes. The geophysical characteristics, path of emplacement and mineralisation distribution all suggest that progressive fractional crystallisation contributed to the formation of an enriched final pulse of magma that resulted in Mo, Cu and W mineralisation hosted in the Moliagul Granodiorite. KEY POINTS High-precision U–Pb zircon geochronology reveal that the Tarnagulla Granodiorite was emplaced along a clockwise pathway from the northeast to the southwest between 406.62 ± 0.27 Ma and 399.3 ± 1.1 Ma. Active faulting during pluton emplacement is the suggested explanation for the Tarnagulla Granodiorites’ U-shaped magnetic response. Fractional crystallisation throughout pluton emplacement produced the varying internal geophysical response of the pluton as well as the enrichment of Mo and W at Mt Moliagul.
Automated Generation of Training Dataset for Crack Detection in Nuclear Power Plant Components

Inspection for crack-like features in nuclear power plant components is vital to maintain safe continued operation. The traditional manual-based inspection can discover such features but could be time-consuming and repetitive. Deep learning technique such as convolutional neural network (CNN) offers improved efficiency by automating the inspection of cracks in images and videos. However, a significant overhead of the CNN implementation is the preparation of the large training dataset for training the classification system. The traditional manual-based labelling process costs intensive labour and could become prone to inconsistent labelling standard to annotate cropped patches from inspection videos. As a result, the CNN system may learn irrelevant features for decision-making. This paper presents an automated labelling technique to efficiently generate crack training dataset with consistent labelling standard. The proposed labelling technique is based on binary masks and can generate sufficient labelled patches with customised resolution. A showcase study of automated patch labelling of crack-like features in superheater tube plate upper radius is provided. The result shows that the proposed labelling technique is suitable for the fast creation of training dataset to build autonomous crack detection systems. The only overhead is the generation of binary crack masks which weights much less than the manual labelling burden.
Fungal Pathogenesis-Related Cell Wall Biogenesis, with Emphasis on the Maize Anthracnose Fungus Colletotrichum graminicola

The genus Colletotrichum harbors many plant pathogenic species, several of which cause significant yield losses in the field and post harvest. Typically, in order to infect their host plants, spores germinate, differentiate a pressurized infection cell, and display a hemibiotrophic lifestyle after plant invasion. Several factors required for virulence or pathogenicity have been identified in different Colletotrichum species, and adaptation of cell wall biogenesis to distinct stages of pathogenesis has been identified as a major pre-requisite for the establishment of a compatible parasitic fungus–plant interaction. Here, we highlight aspects of fungal cell wall biogenesis during plant infection, with emphasis on the maize leaf anthracnose and stalk rot fungus, Colletotrichum graminicola.
The Political Discourse of the Church of Greece during the Crisis: An Empirical Approach

This article presents the analysis of an empirical study that explored the political discourse of the Church of Greece (CoG) during a specific period of the Greek financial crisis, that is, from January 2015 to January 2019. The analysis is based on official texts of the CoG, reports in two daily newspapers, and the results of a quantitative content analysis. Through our analysis, we explore three main questions. The first focuses on the types of issues (‘agenda’) raised by the main representatives of the CoG; the second focuses on the framing used concerning these issues; and the third focuses on how the agenda and frames interact with regard to the CoG’s active (discursive) role as an ‘established church’ and/or a ‘public religion’. Our findings indicated that the CoG intervened in a considerable number of issues not strictly related to religion or the Greek financial crisis. Moreover, the main framing devices of the CoG’s discourse varied from identity to conflict frames, while its discourse reflected its self-understanding as both an established church and a public religion.
Techno-economic and Environmental Assessments of Replacing Conventional Fossil Fuels: Oil Sands Industry Case Studies

Conventional fossil fuels are widely used, however there are growing concerns about the security of their supply, volatility in their prices and the environmental impacts of their extraction and use. The objective of this research is to investigate the potential for replacing conventional fuels in various applications, focusing on the Alberta oil sands industry. Such investigations require systems-level approaches able to handle multiple criteria, uncertainty, and the views of multiple stakeholders. To address this need, the following are developed: life cycle assessment (LCA) and life cycle costing models of polygeneration systems; a life cycle-based framework for multisectoral resource use decisions; and a method combining LCA and real options analyses to yield environmental and financial insights into projects. These tools are applied to options for utilizing oil sands outputs, both the petroleum resource (bitumen) and by-products of its processing (e.g., asphaltenes, coke), within the oil sands industry and across other sectors. For oil sands on-site use, multiple fuels are assessed for the polygeneration of electricity, steam and hydrogen, in terms of life cycle environmental and financial impacts; asphaltenes gasification with carbon capture and storage (CCS) is the most promising option, able to reduce greenhouse gas (GHG) emissions to 25% of those of current natural gas-based systems. Coke management options are assessed with the life cycle-based framework; the most promising options are iii identified as: Electricity generation in China through integrated gasification combined cycle; and, hydrogen production in Alberta, either for sale or use by the oil sands industry. Without CCS, these options have amortized project values ranging from $21 to $160/t coke. The application of the combined LCA and real options analyses method finds that uncertainty in natural gas and potential carbon prices over time significantly impacts decisions on coke management; the formulated decision tree identifies increases of 29% and 11% in the financial and GHG emissions performance, respectively, of the overall coke management project compared to pursuing the decision identified by the life cycle-based framework. While promising options for replacing conventional fossil fuels are identified through systems-level analyses, there are trade-offs to be made among the financial, risk and environmental criteria.
Resemblance-Ranking Peptide Library to Screen for Binders to Antibodies on a Peptidomic Scale

A novel resemblance-ranking peptide library with 160,000 10-meric peptides was designed to search for selective binders to antibodies. The resemblance-ranking principle enabled the selection of sequences that are most similar to the human peptidome. The library was synthesized with ultra-high-density peptide arrays. As proof of principle, screens for selective binders were performed for the therapeutic anti-CD20 antibody rituximab. Several features in the amino acid composition of antibody-binding peptides were identified. The selective affinity of rituximab increased with an increase in the number of hydrophobic amino acids in a peptide, mainly tryptophan and phenylalanine, while a total charge of the peptide remained relatively small. Peptides with a higher affinity exhibited a lower sum helix propensity. For the 30 strongest peptide binders, a substitutional analysis was performed to determine dissociation constants and the invariant amino acids for binding to rituximab. The strongest selective peptides had a dissociation constant in the hundreds of the nano-molar range. The substitutional analysis revealed a specific hydrophobic epitope for rituximab. To show that conformational binders can, in principle, be detected in array format, cyclic peptide substitutions that are similar to the target of rituximab were investigated. Since the specific binders selected via the resemblance-ranking peptide library were based on the hydrophobic interactions that are widespread in the world of biomolecules, the library can be used to screen for potential linear epitopes that may provide information about the cross-reactivity of antibodies.
Proposing New Technique for Designing AC Convertor

This paper presents the tropology of direct conversion of AC-AC with step down frequency operation. Due to, the higher order harmonics AC conversion always arises difficulty and complex calculation, which effect the output response. Precise controlling and harmonics reduction is much easier using frequency control strategy, in this regards cycloconverter has drawn higher attention in terms of frequency conversion. In this paper, an approach for designing of single phase and three phase cycloconverter is reported. The gating sequence has been designed for desired frequency conversion. Again the performance of the designed converters is evaluated using Matlab/Simulink software for different types of loads. The rotor speed of Induction motor (IM) is observed here as a dynamic AC load . The complete description of the models for the frequency conversion is provided and simulation results for resistive, inductive and motor validate the developed strategy.
A Comparative Study on Machine Learning Algorithms for Smart Manufacturing: Tool Wear Prediction Using Random Forests

Manufacturers have faced an increasing need for the development of predictive models that predict mechanical failures and the remaining useful life (RUL) of manufacturing systems or components. Classical model-based or physics-based prognostics often require an in-depth physical understanding of the system of interest to develop closedform mathematical models. However, prior knowledge of system behavior is not always available, especially for complex manufacturing systems and processes. To complement model-based prognostics, data-driven methods have been increasingly applied to machinery prognostics and maintenance management, transforming legacy manufacturing systems into smart manufacturing systems with artificial intelligence. While previous research has demonstrated the effectiveness of data-driven methods, most of these prognostic methods are based on classical machine learning techniques, such as artificial neural networks (ANNs) and support vector regression (SVR). With the rapid advancement in artificial intelligence, various machine learning algorithms have been developed and widely applied in many engineering fields. The objective of this research is to introduce a random forests (RFs)-based prognostic method for tool wear prediction as well as compare the performance of RFs with feed-forward back propagation (FFBP) ANNs and SVR. Specifically, the performance of FFBP ANNs, SVR, and RFs are compared using an experimental data collected from 315 milling tests. Experimental results have shown that RFs can generate more accurate predictions than FFBP ANNs with a single hidden layer and SVR. [DOI: 10.1115/1.4036350]
LIFBI WORKING PAPERS

A substantial number of studies suggests a strong relationship between education and aspects of political participation and interest. Only a small body of literature, however, addresses whether these patterns represent causal effects. We add to this research and re-examine the question in the German context. For identification, we exploit an exogenous increase in lower secondary compulsory schooling between 1949 and 1969 in former West Germany, and use data from the National Educational Panel Study (NEPS) to identify individuals’ educational biographies more precisely than prior research. Our results reinforce findings from Siedler (2010): multiple regression analyses first indicate a positive, statistically significant correlation between schooling and our measures of political activities. IV estimates, however, are all trivial, for both compliers and the full sample, indicating that the reform did not stimulate long-term changes in political participation and interest.
Time Based Hybrid Secured Access Control System for Cloud Data

: The cloud-based data outsourcing paradigm is a two-edged sword. On the one hand, it relieves data owners of technical administration responsibilities and makes it easier for them to share their data with their intended consumers. On the other side, it raises additional concerns about privacy and security. Several works have been presented to offer fine-grained data access control in order to guarantee data confidentiality against the honest-but-curious cloud service provider. To support fine-grained data access control, a number of projects have been proposed. To date, no solution has been able to handle both fine-grained access control and sensitive data publication related to time factor. To do so the project titled Time-based hybrid secured access control system for cloud data has been proposed which uses Ciphertext-policy attribute-based encryption (CP-ABE) along with Residue Number System (RNS) encryption technique for data confidentiality, security and integrity of data owner data. Also, considering the factor of time every time when the user requests for the file a new secret code is generated and give to user to decrypt the file received from data owner. The proposed system works properly with no issues.
Comparisons between high power fiber systems in the presence of radiation induced photodarkening

Performance of high power fiber lasers and amplifiers with different pump sources are evaluated in the presence of radiation induced photodarkening in post-irradiated active fibers. Evolutions of output power and thermal mode instability threshold under different radiation doses are examined and analyzed. Severe degradation in both output power and thermal mode instability threshold is recorded for high power fiber systems with their active fibers exposed to radiations. Comparisons show that, amplifiers present a relatively better performance than lasers in adverse environments. Besides, 976 nm pump source is more favorable for high power laser sources in radioactive applications, even though 915 nm pump ensures a much lower temperature profile inside the active fiber.
Science Goals and Mission Concept for a Landed Investigation of Mercury

Mercury holds valuable clues to the distribution of elements at the birth of the solar system and how planets form and evolve in close proximity to their host stars. This Mercury Lander mission concept returns in situ measurements that address fundamental science questions raised by the MErcury Surface, Space ENvironment, GEochemistry, and Ranging (MESSENGER) mission’s pioneering exploration of Mercury. Such measurements are needed to understand Mercury's unique mineralogy and geochemistry, characterize the proportionally massive core's structure, measure the planet's active and ancient magnetic fields at the surface, investigate the processes that alter the surface and produce the exosphere, and provide ground truth for remote data sets. The mission concept achieves one full Mercury year (∼88 Earth days) of surface operations with an 11-instrument, high-heritage payload delivered to a landing site within Mercury's widely distributed low-reflectance material, and it addresses science goals encompassing geochemistry, geophysics, the Mercury space environment, and geology. The spacecraft launches in 2035, and the four-stage flight system uses a solar electric propulsion cruise stage to reach Mercury in 2045. Landing is at dusk to meet thermal requirements, permitting ∼30 hr of sunlight for initial observations. The radioisotope-powered lander continues operations through the Mercury night. Direct-to-Earth communication is possible for the initial 3 weeks of landed operations, drops out for 6 weeks, and resumes for the final month. Thermal conditions exceed lander operating temperatures shortly after sunrise, ending operations. Approximately 11 GB of data are returned to Earth. The cost estimate demonstrates that a Mercury Lander mission is feasible and compelling as a New Frontiers–class mission.
Ocean Remote Sensing Using Ambient Noise

Abstract : Our long-term scientific goal is to understand the basic physics of low-frequency sound propagation in the ocean, and the effects of environmental variability on signal stability and coherence. We seek to understand the fundamental limits to signal processing imposed by ocean variability to enable advanced signal processing techniques, including matched field processing and other adaptive array processing methods. Work on this project has focused on noise interferometry the process by which an approximation to the transient Greens function G(xAxB, t) between locations xA and xB is estimated by cross-correlating records of ambient noise measured at xA and xB. In that context, our objective is to investigate and identify the limitations of noise interferometry for remote sensing applications in a variety of ocean environments.
Emerging Polymer Electrolyte Based On Methacrylate for Dye Sensitized Solar Cell

This paper highlights on the application of polymer electrolytes membrane as an ionic conductor in DSSC. Polymer electrolyte is chosen for use in DSSCs to overcome the problem of leaking and sealing, flammability issues, shape flexibility and electrochemical stability faced by liquid electrolyte. In addition, the presence of liquid electrolytes posed challenges in integration of large area modules, difficulty in implementing tandem architectures, possible desorption and photodegradation of the attached dyes in DSSC, corrosion of counter electrode and photodegradation of some components which lead to lower lifetimes, performance and practical usage of the photovoltaic cells. Keyword: polymerization; methacrylate compounds; polymer electrolyte; photovoltaic cells 1. Background and development
AReviewofRailwayTrackLaboratoryTestswithVariousScales for Better Decision-Making about More Efficient Apparatus Using TOPSIS Analysis

Improving the performance of railways depends on increasing their efficiency and the speed of locomotives, which is provided by improving the infrastructure of railways. ,erefore, in various studies, researchers seek to modify ballast materials, sleepers and rails, or to study various geometric and environmental conditions in order to reduce the existing weaknesses of railways. ,erefore, in order to evaluate the above conditions, various tests should be performed on railways. Some of the tests take place in the field when the train is passing. However, these tests have various limitations and endanger the safety of researchers and are associated with difficulties. ,erefore, many tests are performed using various apparatuses on a laboratory scale. In this study, by reviewing different apparatuses used by researchers to simulate loading on the railway, they were compared and by examining different criteria such as loading type, different loading directions, scale of apparatuses, static and dynamic simulated loads and other environmental and geometric conditions, the best apparatuses on a laboratory scale are recommended for simulating vertical, longitudinal and lateral loads and measuring noise and vibration. By the use of TOPSIS analysis on 16 introduced apparatuses from reviewing different studies, it was found that the scaled railway track model had the first rank among different apparatuses and by modifying the weaknesses of this apparatus, it can be turned into the most comprehensive laboratory apparatus to simulate the loading of railway tracks with a scale of 1 : 5. Among the introduced apparatuses, wheel on roller test rig and roller rig had the second and third ranks, respectively, the apparatuses of which usually have a scale of 1 : 5 and deal with the interaction between the rail and the wheel and the impact of the vehicle on the railway track.