Miracle-dz/UniFAR-FTUs huggingface dataset:
MEC Empowered Internet of Vehicles for Smart City Optimisations

With the growth of data driven services, vehicles are moving one step beyond their connected nature by becoming an intelligent part of the Internet of Things ecosystem. The concept of Internet of Vehicles (IoV) stems from this development describing the network of humans, vehicles and things as a means of achieving an intelligent urban infrastructure. With the next-generation communication technologies promise of providing ultra low latencies, one of the major obstacles in achieving highly-performing smart city is the distribution of computing power where the vehicles computing power is no longer sufficient for the increasing multimedia based services and the cloud introducing unacceptable latency for real time scenarios. As multi-access edge computing (MEC) can be employed to overcome these issues by co-locating additional computing power with the communication access points, the main goal of this paper is to investigate the performances of this complex MEC empowered IoV ecosystem to support the development of smart cities. Our analysis shows that large scale urban scenarios can be created to simulate all relevant aspects of the ecosystem as a whole, but more work is needed in optimising the underlying technologies and their performances.
Efficient 3‐D Large‐Scale Forward Modeling and Inversion of Gravitational Fields in Spherical Coordinates With Application to Lunar Mascons

An efficient forward modeling algorithm for calculation of gravitational fields in spherical coordinates is developed for 3‐D large‐scale gravity inversion problems. The 3‐D Gauss‐Legendre quadrature (GLQ) is used to calculate the gravitational fields of mass distributions discretized into tesseroids. Equivalence relations in the kernel matrix of the forward modeling are exploited to decrease storage and computation time. The numerical tests demonstrate that the computation time of the proposed algorithm is reduced by approximately 2 orders of magnitude, and the memory requirement is reduced by N′λ times compared with the traditional GLQ method, where N′λ is the number of the model elements in the longitudinal direction. These significant improvements in computational efficiency and storage make it possible to calculate and store the dense Jacobian matrix in 3‐D large‐scale gravity inversions. The equivalence relations can be applied to the Taylor series method or combined with the adaptive discretization to ensure high accuracy. To further illustrate the capability of the algorithm, we present a regional synthetic example. The inverted results show density distributions consistent with the actual model. The computation took about 6.3 hr and 0.88 GB of memory compared with about a dozen days and 245.86 GB for the traditional 3‐D GLQ method. Finally, the proposed algorithm is applied to the gravity field derived from the latest lunar gravity model GL1500E. Three‐dimensional density distributions of the Imbrium and Serenitatis basins are obtained, and high‐density bodies are found at the depths 10–60 km, likely indicating a significant uplift of the high‐density mantle beneath the two mascon basins.
Improving Co-integration Trading Rule Profitability with Forecasts from an Artificial Neural Network

— Co-integration models the long-term, equilibrium relationship of two or more related financial variables. Even if co-integration is found, in the short run, there may be deviations from the long run equilibrium relationship. The aim of this work is to forecast these deviations using neural networks and create a trading strategy based on them. A case study is used: co-integration residuals from Australian Bank Bill futures are forecast and traded using various exogenous input variables combined with neural networks. The choice of the optimal exogenous input variables chosen for each neural network, undertaken in previous work [1], is validated by comparing the forecasts and corresponding profitability of each, using a trading strategy.
Tailoring second harmonic emission by ZnO nanostructures: enhancement of directionality

Tailoring nonlinear optical properties at the nanoscale is a hot topic in nowadays nanophotonics, promising for applications spanning from sensing to ultrafast optical communications. Here we present a numerical approach of designing a simple semiconductor nanostructure able to tailor second harmonic emission in the near- and far-field. We start from linear simulations of ZnO nanospheres, which reveal multipolar nature of the scattering. Next, we show how the same nanospheres, excited at 800 nm, manipulate the directivity of the emitted second harmonic. We observe that the nanospheres which exhibit Kerker condition at 400 nm, emit the second harmonic field in the forward direction. We further investigate how the asymmetry (ellipsoid geometry) tailors the second harmonic directivity. We finally introduce geometry with low chiro-optical response, and observe that the second harmonic far-field depends on the handedness of the light exciting the nanostructure at 800 nm.
Advanced Reactor Safeguards Use Machine Learning to Improve Burnup Measurement in Pebble Bed Reactors

Burnup measurement is an important step in material control and accountancy (MC&A) at nuclear reactors. Comparing to conventional reactors, e.g., light-water reactors (LWRs) that use large fuel assemblies, pebble bed reactors (PBRs) post unique challenges in burnup measurement. First, each day there are a few hundred up to one thousand fuel pebbles ejected from the reactor core, requiring high throughput in the burnup measurement. Second, the ejected pebbles are extremely radioactive, and if the burnup measurement cannot take such hot sources, cooling the pebbles will result in a holdup area that complicates the operation of the reactors because of the potential safety and security concerns. Previous research has shown that height of certain photopeaks in gamma spectra, such as 134 Cs, 137 Cs, 154 Eu, etc., can be used independently or in combination to infer or predict the level of burnup in the fuel. However, our research revealed that the linear regression method requires high-quality gamma spectra to give reasonably good results, hence long pebble cooling time and long data acquisition time, making this method impractical to PBRs. In this project, we are developing machine learning (ML) method to interpret gamma-ray spectra and predict the burnup values of the pebbles. ML has achieved widespread success and adoption across a few domains that require pattern recognition and analysis in varied data types. In this work, three proven ML approaches – multilayer perceptron’s (MLPs), convolutional neural networks, and transformers – are applied to the task of predicting fuel burnup from measured gamma spectra, and a dataset of simulated spectra is compiled for training and validation of the ML models. This report summarizes the research activities and the main achievements in this work in FY2021.
International Journal of Information Management

Recent years have seen a huge increase in the digital data generated by companies, projects and individuals. This has led to signiﬁcant challenges in visualizing and using large, diverse collections of digital information. Indeed the speed and accuracy with which these large datasets can be effectively mined for information that is relevant and valuable can have a signiﬁcant effect on company performance. This research investigates the feasibility of using visual representations for the searching and browsing of large, complex, multimedia data sets. This paper introduces the SIZL ( Searching for Information in a Zoom Landscape ) system, which was developed to enable the authors to effectively test whether a 2.5D graphical representation of a multimedia data landscape produces quantiﬁable improvements in a user’s ability to assess its contents. The usability of this visualization system was analyzed using experiments and a combination of quantitative and qualitative data collection methods. The paper presents these results and discusses potential industrial applications as well as future work that will improve the SIZL data visualization method. © 2015
in Computer

--In the internet searching for a specific topic it contains some type of events and activities . The topic contains more documents in the search engine about a specific type of topic, topic anatomy is define summarizes and associates the core parts of a topic temporally so that readers can understand the content easily. The existing topic anatomy model, called TSCAN, evolves the major themes of a topic from the eigenvectors of a temporal block association matrix. Then, the similarity of events of the themes and their summaries are extracted by examining the constitution of the eigenvectors. Finally, the events which are extracted are associated through their temporal closeness and context similarity to form an evolution graph of the topic. Experiments found on the official TDT4 corpus demonstrate that the generated temporal summaries present the storylines of topics in a comprehensible form. The proposed framework contains ranking for a specific topics and contents, by this process it give usefulness of topic anatomy.
DESIGNING AND BUILDING A JAPANESE CONTROLLED LANGUAGE FOR THE AUTOMOTIVE DOMAIN Toward the development of a writing assistant tool

In this paper, we propose a controlled language for authoring technical documents and report the status of its development, while maintaining a specific focus on the Japanese automotive domain. To reduce writing variations, our controlled language not only defines approved and unapproved lexical ele­ ments but also prescribes their preferred location in a sentence. It consists of components of a) case frames, b) case elements, c) adverbial modifiers, d) sentence­ending functions, and e) connectives, which have been developed based on the thorough analyses of a large­scale text corpus of automobile repair manuals. We also present our prototype of a writing assistant tool that implements word substitution and reorder­ ing functions, incorporating the constructed controlled language.
Residual stress effects on the fracture toughness behaviour of a narrow-gap austenitic stainless steel pipe weld

Automated narrow-gap girth-butt welds are replacing conventional welding methods to join sections of austenitic stainless steel pipe in the primary circuit of Pressurised Water Reactors, to reduce manufacturing costs and improve quality. To ensure the safe operation of these systems, reliable structural integrity assessments have to be undertaken, requiring the mechanical properties of welded joints to be characterised alongside the weld residual stress magnitude and distribution.This research project characterised, for the first time, the weld residual stress field and the tensile and ductile fracture toughness properties of a 33 mm thick narrow-gap 304L stainless steel pipe weld. The residual stress was characterised using two complementary approaches: deep hole drilling and neutron diffraction. A novel neutron diffraction scanning technique was developed to characterise the residual stress field, without cutting an access window into the component, leaving the original weld residual stress field undisturbed. A modified deep hole drilling technique was developed to characterise the residual stress retained in fracture mechanics specimens extracted from the pipe weld in two orientations. The modified technique was shown to measure the original weld residual stress field more accurately than through conventional deep hole drilling. Residual stresses, exceeding 50% of the weld material proof strength, were retained in axially-orientated fracture mechanics specimens.Tensile tests showed that the weld was approximately 60% overmatched. It was demonstrated that neither retained residual stress, nor specimen orientation, had a discernible effect on the measured fracture toughness of the weld material. In less ductile materials, however, the level of retained residual stress may unduly influence the measurement of fracture toughness. At initiation, the fracture toughness properties of both the parent and weld materials were far in excess of the measuring capacity of the largest fracture mechanics specimens that could be machined from the weld.The influence of residual stress and fracture toughness on the performance of narrow-gap welded pipework was investigated. Full elastic-plastic finite element analyses were used to model the pipe weld, containing a postulated defect under combined primary and secondary loading. The results, applied within the framework of an R6 structural integrity assessment, compared different plasticity interaction parameters on the prediction of failure load; the conventional ? -parameter approach was compared with the recently developed, more advanced, g-parameter. It was shown that the g-parameter significantly reduced the conservatism of the ? -parameter approach. However, for this pipe weld, plastic collapse was predicted to precede failure by ductile initiation, suggesting that a plastic collapse solution may be an appropriate failure criterion to use in structural integrity assessments of similar component and defect combinations.
Does Total Biopsy Core Length In Prostate Fusion Biopsy Affect The Diagnosis Of Clinically Significant Prostate Cancer?

Objective: The aim of our study was to determine the optimal total core length for multiparametric magnetic resonance imaging (MpMRI)-guided prostate fusion biopsy (PFB) that would standardize clinical practice and maximize the detection rate of clinically significant prostate cancer (csPC) while reducing procedural complications, overdiagnosis, and cost. Materials and Methods: A total of 212 patients with at least 1 lesion with Prostate Imaging Reporting and Data System score of ≥3 were evaluated retrospectively. Biopsy core lengths from the lesions were summed, and total core lengths (TCL) were calculated. Multiple logistic regression analyses were conducted for PIRADS 3, 4, and 5 lesions and the effects of TCL on the detection of csPC were analyzed separately. Results: A total of 365 lesions were detected by MPMRI in 212 men included in the study. The mean TCL was 43.66 ± 12.91 mm in PIRADS 3 lesions, 47.71 ± 11.78 mm in PIRADS 4 lesions, and 60.36 ± 14.64 mm in PIRADS 5 lesions. As TCL increased, the probability of detecting both csPC (1.26-fold per unit increase in TCL) and clinically insignificant prostate cancer (cIPC)(1.25-fold per unit increase in TCL) increased for PIRADS 3 lesions at a statistically significant level,while for PIRADS 4 lesions, only the probability of detecting csPC (1.13-fold for each unit increase in TCL) increased. Conclusion: This study has shown that the frequency of PCa detection rises with increasing TCL. For PI-RADS 3, both csPC and cIPC rise with increasing TCL, whereas for PI-RADS 4 only the frequency of csPC rises.
Profiling the Resource Usage of OLTP Database Queries

This technical report contains eight final project reports contributed by ten participants in "Hot Topics in Database Systems," a CMU advanced graduate course offered by Professor Anastassia Ailamaki in Fall 2002. The course covers advanced research issues in modern database system design through paper presentations and discussion. In Fall 2002, topics included query optimization, data stream and adaptive query processing, continuous queries, self-tuning database systems, interaction between the database software and the underlying hardware, distributed and peer-to-peer DBMS, and XML applications. The participating students studied and evaluated the cutting-edge research papers from each topic, and addressed the related issues in in-class discussions. Inspired by the course material, the students proposed and carried out a total of eight projects. The projects included innovative research and implementation and the students worked in teams of two or by themselves. The project reports were carefully evaluated by the students using a conference program committee-style blind-review process. The resulting camera-ready papers are available in this technical report. Several of these reports (as noted in their first page) have resulted in conference submissions. The projects were presented using posters and demos during a half-day HotDB workshop that was held at Carnegie Mellon on December 10, 2002.
STRUCTURAL OPTIMIZATION OF GO-KART CHASSIS BY GEOMETRICAL MODIFICATIONS BASED ON MODAL ANALYSIS

A chassis houses various key components such as the power-train, suspension system and the bodywork of an automobile. Thus, the chassis must be robust enough to absorb the static and dynamic forces generated by these components to assure safety and superior rideability of an automobile. In this work, efforts were made to improve the static and dynamic characteristic of a Go-kart chassis by using the modal analysis. The results of the modal analysis were carefully examined and the members are geometrically modified on the basis of excessive deformation. At the termination of the of this analysis, it was observed that geometrical modification has not only improved the dynamic characteristic by 17.5% but also shown improvement in the stiffness and crashworthiness of the chassis.
Thermophysical properties of selected wear-resistant alloys

Thermophysical properties of 13 selected wear-resistant materials, including specific heat, thermal conductivity, thermal diffusivity, and thermal expansion (instantaneous, mean, and linear) are provided. The Center for Information and Numerical Data Analysis and Synthesis (CINDAS) at Purdue University supplied properties data.
Sensitivity of midnineteenth century tropospheric ozone to atmospheric chemistry‐vegetation interactions

We use an Earth System model (HadGEM2‐ES) to investigate the sensitivity of midnineteenth century tropospheric ozone to vegetation distribution and atmospheric chemistry‐vegetation interaction processes. We conduct model experiments to isolate the response of midnineteenth century tropospheric ozone to vegetation cover changes between the 1860s and present day and to CO2‐induced changes in isoprene emissions and dry deposition over the same period. Changes in vegetation distribution and CO2 suppression of isoprene emissions between midnineteenth century and present day lead to decreases in global isoprene emissions of 19% and 21%, respectively. This results in increases in surface ozone over the continents of up to 2 ppbv and of 2–6 ppbv in the tropical upper troposphere. The effects of CO2 increases on suppression of isoprene emissions and suppression of dry deposition to vegetation are small compared with the effects of vegetation cover change. Accounting for present‐day climate in addition to present‐day vegetation cover and atmospheric CO2 concentrations leads to increases in surface ozone concentrations of up to 5 ppbv over the entire northern hemisphere (NH) and of up to 8 ppbv in the NH free troposphere, compared with a midnineteenth century control simulation. Ozone changes are dominated by the following: (1) the role of isoprene as an ozone sink in the low NOx midnineteenth century atmosphere and (2) the redistribution of NOx to remote regions and the free troposphere via PAN (peroxyacetyl nitrate) formed from isoprene oxidation. We estimate a tropospheric ozone radiative forcing of 0.264 W m−2 and a sensitivity in ozone radiative forcing to midnineteenth century to present‐day vegetation cover change of −0.012 W m−2.
Feel What You Don’t Hear: A New Framework for Non-Aural Music Experiences

Just as the way a performer is moved differs even among audiences who have the same impression of the performance (Oode et al. 2009), the sensations and experiences felt by the performers themselves and the audiences’ experiences also differ. The purpose of this research is to create a new listening experience by analyzing and extracting the performer’s introspection of rests, groove, and rhythm, and physically presenting it to the audience. Although these elements are important in shaping music, they are not always directly expressed as auditory sounds. Our hypothesis is that this introspection, such as a sense of rhythm and groove, is latent and observable in physiological states such as breathing and heartbeat. By sensing and presenting them to the audience, music appreciation that includes introspection could become possible. In other words, by sensing and presenting introspection to the audience, the music listening experience itself can be redesigned to include a physicality that is closer to the performer’s experience of the music, rather than being passive in an auditory sense (Morisawa 2014). In this study, preliminary experiments were conducted on the extraction of the performer’s introspection, and a device was designed to present it to the audience.
Habit Formation and Behavior Change

Within psychology, the term habit refers to a process whereby contexts prompt action automatically, through activation of mental context–action associations learned through prior performances. Habitual behavior is regulated by an impulsive process, and so can be elicited with minimal cognitive effort, awareness, control, or intention. When an initially goal-directed behavior becomes habitual, action initiation transfers from conscious motivational processes to context-cued impulse-driven mechanisms. Regulation of action becomes detached from motivational or volitional control. Upon encountering the associated context, the urge to enact the habitual behavior is spontaneously triggered and alternative behavioral responses become less cognitively accessible. By virtue of its cue-dependent automatic nature, theory proposes that habit strength will predict the likelihood of enactment of habitual behavior, and that strong habitual tendencies will tend to dominate over motivational tendencies. Support for these effects has been found for many health-related behaviors, such as healthy eating, physical activity, and medication adherence. This has stimulated interest in habit formation as a behavior change mechanism: It has been argued that adding habit formation components into behavior change interventions should shield new behaviors against motivational lapses, making them more sustainable in the long-term. Interventions based on the habit-formation model differ from non-habit-based interventions in that they include elements that promote reliable context-dependent repetition of the target behavior, with the aim of establishing learned context–action associations that manifest in automatically cued behavioral responses. Interventions may also seek to harness these processes to displace an existing “bad” habit with a “good” habit. Research around the application of habit formation to health behavior change interventions is reviewed, drawn from two sources: extant theory and evidence regarding how habit forms, and previous interventions that have used habit formation principles and techniques to change behavior. Behavior change techniques that may facilitate movement through discrete phases in the habit formation trajectory are highlighted, and techniques that have been used in previous interventions are explored based on a habit formation framework. Although these interventions have mostly shown promising effects on behavior, the unique impact on behavior of habit-focused components and the longevity of such effects are not yet known. As an intervention strategy, habit formation has been shown to be acceptable to intervention recipients, who report that through repetition, behaviors gradually become routinized. Whether habit formation interventions truly offer a route to long-lasting behavior change, however, remains unclear.