Miracle-dz/UniFAR-FTUs huggingface dataset:
Probabilistic Reconstruction of Internal Exposure for Nuclear Power Plant Workers Using Air Concentration Measurements

According to 10 CFR Part 20, “Standards for Protection Against Radiation,” air surveys, whole-body counting, bioassays or a combination of these measurements can be utilized for the purpose of assessing an internal dose in order to determine compliance with occupational dose equivalent limits. Studies show that air sampling together with biological monitoring can provide consistent dose estimates over an extended period of time for groups of employees (Britcher and Strong, 1994). Air sampling alone or with limited additional data provided by whole body counting and/or bioassays has often been relied on in dose calculations. The utility of air sampling for internal dose reconstruction is addressed in this paper. We attempt to reconstruct an internal dose due to inhalation of beta+gammaand alpha-emitting radionuclides by a contractual electrician, Mr. X, who worked in restricted areas of a LWR nuclear power plant. Missing exposure records, as well as high uncertainty and variability in measured air concentrations taken at the places he worked, makes the use of probabilistic methods desirable. Committed Effective Dose Equivalent (CEDE) was calculated for Mr. X using exposure-to-dose conversion factors for inhalation (EPA, 1988), as suggested by the Nuclear Regulatory Commission (NRC) Regulatory Guide 8.34 (1992). The high variability of the measured air concentrations resulted in large ranges of variation in the internal doses reconstructed for Mr. X. Our analysis suggests that data generated by general air surveys results in a broad range of internal doses and reduction of the uncertainty in reconstructed internal dose can be achieved by using personal air sampling and/or individual bioassays and whole-body counting.
Recent Development Applications of Nanotechnology in Cancer A Literature Review

Oral health could be maintained by application of this technology in prevention, diagnosis and treatment. Oral cancer is a debilitating disease, and numerous research activities are being pursued worldwide to combat this deleterious process. Nanotechnology is very diverse field that has revolutionized the industry and is setting new trends in the management of oral cancer. Hence, we performed a PubMed search on nanotechnology in oral cancer and found 211 articles related to this search. We have reviewed the reported literature to the best of our abilities and summarized the various aspects of nanotechnology, its role in diagnosis nanodiagnostics and treatment of oral cancer nanotherapeutics in this article. Nanotechnology for oral cancer could be a use full tool for detection and drug therapy. By Possessing unprecedented potential for early stage detection, treatment of oral cancer, nanoparticles have been demonstrated over the last decade. In this review, we will summarize the current state-of-the-art of nanoparticles in biomedical applications such as detection, diagnosis and targeting over the oral cancer. Gold nanospheres, nanorods, nanoshells, nanocages, and surface enhanced nanoparticles will be discussed in detail regarding their uses in vivo imaging, cancer therapy, and drug delivery. The key feature of nanoparticlebased drug agents to target over ligands, therapeutic drugs, and other functionalities can all be integrated to allow for targeted molecular imaging and molecular therapy of oral cancer. The future of the nanotechnology for oral cancer looks brighter than ever yet many hurdles remain to be conquered. A multifunctional platform based on gold nanoparticles, with multiple receptor targeting, multimodality imaging, and multiple therapeutic entities, holds the promise for a ―magic gold bullet‖ against oral cancer. Keywords— Diagnosis, nanotechnology, oral cancer, therapy
Climatic Zoning Methodology Based On Data-Driven Approach

Climatic zoning is largely used by building energy efficiency applications. However, in some cases, climatic zoning is performed by oversimplified methodologies. Further study in climatic variables and building simulation could increase the robustness of climatic zoning methodologies. The aim of this study was to develop a data-driven climatic zoning methodology. The methodology is composed of four main steps. The initial steps were focused on weather data and building simulation, followed by a combined data analysis and a clustering process. Climatic zones validation was performed by cluster quality measures and building simulation results. The proposed methodology was applied in a simplified case study to demonstrate each step. Sensible and latent thermal loads as performance indicators demonstrated the differences in energy needs for each climatic zone.
Realizing Source Routed Multicast Using Mellanox’s Programmable Hardware Switches

We present the first hardware implementation of Elmo [7], a recently proposed source-routed multicast technology that leverages programmable switches. Elmo addresses the longstanding controland data-plane scalability issues in IP multicast. These scalability bottlenecks have made it impossible to adopt IP multicast at the scale of today’s public clouds, despite the demand and pervasive use of point-tomultipoint communication patterns in modern cloud applications (e.g., streaming telemetry, replicated state machines, and distributed machine learning [7]). Elmo overcomes the controland data-plane scalability limitations of IP multicast by using source-routing and exploiting the unique characteristics of data-center networks (like symmetric topologies and virtual switches). In Elmo, virtual switches, like PISCES [6] or OVS [5], encode multicast trees inside packets using an efficient and compressed header encoding—for a given data-center network—that can be interpreted at line rate by programmable hardware switches. By using source-routing, Elmo significantly reduces the usage of limited-size hardware group tables, the primary data-plane scalability bottleneck. Elmo absorbs the processing and update overhead of physical switches by configuring multicast groups at the virtual switches, which overcomes the control-plane scalability bottleneck. Central to Elmo’s approach is the encoding of the output port list at every hop of a multicast tree using a bitmap, in a data-center network. This design aligns with how existing switching ASICs handle multicast; for example, using group tables, wherein a group ID (e.g., destination IP address) is converted into an output port bitmap, which indicates the set of egress ports for a packet. Elmo’s feasibility hinges on hardware switches being able to obtain this bitmap directly from a packet header, and bypassing the pipeline table lookup. While feasible, this idea of an output-port-bitmap primitive was not demonstrated using hardware switches in the Elmo paper [7], because existing switching ASICs [2] do not have a programmable multicast pipeline. In this work, we leverage Mellanox’s Spectrum-2 hybrid programmable switching ASIC [1] to support Elmo. The hybrid switch retains its legacy forwarding pipeline (bridging and routing), while allowing specific programmable logic to be inserted at certain locations (Figure 1). Fixed Parser Flex. Parser Ingress Ports Elmo Pipeline Logic if (UP.valid) egress_ports=UP.bitmap elif (DP.valid) egress_ports=DP.bitmap else . Egress Ports
A new MATLAB Simulink Toolbox for Dynamic Building Simulation with B.I.M. and Hardware in the Loop compatibility

A new tool carnotUIBK was developed for dynamic building simulations within MATLAB. It consists of two parts: the first part is a MATLAB based object-oriented building model framework for handling data within building simulation matters, including import (of gbXML (B.I.M.), Excel and PHPP), export, data preparation and data analysis. The second is a simulation framework within Simulink, which provides the simulation engine. One of the main features of the new model is that it is tailored for hardware in the loop applications, controller development and indoor air quality simulations in terms of multi-zone modelling.
Synthetic Data Generation for Economists

As more tech companies engage in rigorous economic analyses, we are confronted with a data problem: in-house papers cannot be replicated due to use of sensitive, proprietary, or private data. Readers are left to assume that the obscured true data (e.g., internal Google information) indeed produced the results given, or they must seek out comparable public-facing data (e.g., Google Trends) that yield similar results. One way to ameliorate this reproducibility issue is to have researchers release synthetic datasets based on their true data; this allows external parties to replicate an internal researcher's methodology. In this brief overview, we explore synthetic data generation at a high level for economic analyses.
Glyph-Based Visualization of Affective States

Decades of research in psychology on the formal measurement of emotions led to the concept of affective states. Visualizing the measured affective state can be useful in education, as it allows teachers to adapt lessons based on the affective state of students. In the entertainment industry, game mechanics can be adapted based on the boredom and frustration levels of a player. Visualizing the affective state can also increase emotional self-awareness of the user whose state is being measured, which can have an impact on well-being. However, graphical user interfaces seldom visualize the user’s affective state, but rather focus on the purely objective interaction between the system and the user. This paper proposes two graphical user interface widgets that visualize the user’s affective state, ensuring a compact and unobtrusive visualization. In a user study with 644 participants, the widgets were evaluated in relation to a baseline widget and were tested on intuitiveness and understandability. Particularly in terms of understandability, the baseline was outperformed by our two
Sentence Boundary Detection in Legal Texts Grading: Option 3

fThis report contains results from an effort to detect sentence boundaries in complex legal text (that do not conform to standard English syntax) using Transformer architecture based neural nets. Segmenting sentences in legal decisions are very challenging for existing algorithms as legal text do not conform to standard linguistic sentence structures. This project did not utilize any additional features for ﬂagging punctuation and other syntactic elements in the text. Even without the use of additional features, the project demonstrated superior results over baseline studies that utilized such features. The system achieved a 97% F1-score and 96% and 97% for Precision and Recall for detecting sentence boundaries in legal text, the highest achieved so far. Visualization of attention heads are also presented to aid the understanding of the focus of attention mechanism in processing text that do not conform to standard sentence structures.
Landslide Susceptibility Zonation Using Bivariate Models, Around Tehri Reservoir, Uttarakhand, India

Uttarakhand, the 27th state of India, is highly susceptible to landslides probably owing to its 86% area in Himalayan terrain. In recent times, however, the landslide incidents have increased leaps and bounds mainly due to unprecedented human interventions in the form of settlements, farming, road construction, myriad of hydroelectricity projects. One such case study is done in the current study around the Tehri Dam reservoir, Uttarakhand, India. Landslide causative factors such as slope, aspect, lithology, geology and geomorphology are derived using remote sensing techniques. Thereafter, two methods, Information Value (IV) and weight of evidence (WofE) model were applied and the output was reclassified into five zones viz. very low, low, moderate, high and very high. The validation of these models was performed using area under curve (AUC) analysis, which shows the accuracy of WofE model was 83% while that of IV model was 81%. Both WofE and IV susceptibility map shows 1.95% area under very high susceptibility zone which mostly covers the area bordering the reservoir hence implementing reservoir rim to be most prone for landslides.
Super-BPD: Super Boundary-to-Pixel Direction for Fast Image Segmentation

Image segmentation is a fundamental vision task and still remains a crucial step for many applications. In this paper, we propose a fast image segmentation method based on a novel super boundary-to-pixel direction (super-BPD) and a customized segmentation algorithm with super-BPD. Precisely, we define BPD on each pixel as a two-dimensional unit vector pointing from its nearest boundary to the pixel. In the BPD, nearby pixels from different regions have opposite directions departing from each other, and nearby pixels in the same region have directions pointing to the other or each other (i.e., around medial points). We make use of such property to partition image into super-BPDs, which are novel informative superpixels with robust direction similarity for fast grouping into segmentation regions. Extensive experimental results on BSDS500 and Pascal Context demonstrate the accuracy and efficiency of the proposed super-BPD in segmenting images. Specifically, we achieve comparable or superior performance with MCG while running at ~25fps vs 0.07fps. Super-BPD also exhibits a noteworthy transferability to unseen scenes.
Chatter: Exploring Classiﬁcation of Malware based on the Order of Events ∗

Using runtime execution artifacts to identify malware and its families is an established technique in the security domain. Several papers in the literature relied on explicit features derived from network, ﬁle system, or registry interaction. While effective, the collection and analysis of these ﬁne-granularity data points makes the technique quite computationally expensive. Moreover, the signatures and heuristics this analysis produces are often easily circum-vented by subsequent malware authors. To this end, we propose C HATTER , a system that is concerned only with the order in which high-level system events take place. Individual events are mapped onto an alphabet and execution traces are captured via terse con-catenations of those letters. Then, leveraging an analyst labeled corpus of malware, n -gram document classiﬁcation techniques are applied to produce a classiﬁer predicting malware family. This paper describes that technique and its proof-of-concept evaluation. This proof-of-concept concentrates only on network ordering and three malware families are highlighted. We show the technique achieves roughly 80% accuracy in isolation and makes non-trivial performance improvements when integrated with a baseline classi-ﬁer of non-ordered features (of roughly 95%).
Examining Peer-to-Peer and Patient-Provider Interactions on a Social Media Community Facilitating Ask the Doctor Services

Ask the Doctor (AtD) services provide patients the opportunity to seek medical advice using online platforms. While these services represent a new mode of healthcare delivery, study of these online health communities and how they are used is limited. In particular, it is unknown if these platforms replicate existing barriers and biases in traditional healthcare delivery across demographic groups. We present an analysis of AskDocs, a subreddit that functions as a public AtD platform on social media. We examine the demographics of users, the health topics discussed, if biases present in offline healthcare settings exist on this platform, and how empathy is expressed in interactions between users and physicians. Our findings suggest a number of implications to enhance and support peer-to-peer and patient-provider interactions on online platforms.
Twenty years of building simulation trends: Text mining and topic modeling of the Bldg-sim email list archive

The recent growth of the building performance research community has been in parallel with the oneb-uilding.org Bldg-sim email list. This list was formed in 1999 and steadily grew into a major venue for building simulation community announcements, discussions, and questions and answers (Q&A). This paper presents an analysis of the Bldg-sim email archive to determine how traﬃc has grown over the years and what general trends have come and gone in this particular user community. We use a text mining approach to ﬁnd the most prominent topics over the years and create trend metrics from the frequency of the most common words from those topics. The re-sults illustrate the relative rise and fall of various software tools, organizations, and simulation topics from the portion of the simulation community who used the email list. Visualizations showing the trends are presented and discussed. The paper also discusses the generalizability of results as it should be noted that the users of this email list are primarily English-speaking simulation practitioners from North America. All data and code used in this analysis are available in a reproducible GitHub repository.
TALLINN UNIVERSITY OF TECHNOLOGY School of Information Technologies Tanel Kossas 164119IAPB OPTIMAL LENGTH OF FINE MOTOR TESTS FOR DIAGNOSING PARKINSON’S DISEASE

Among the most employed diagnostic tests for Parkinson’s disease (PD) are the pen-and-paper versions of Luria’s alternating series tests, in which the patient is asked to draw a series of two alternating shapes, repeating for 5-7 times. In recent times, the same test has also been used in digitised form, using a digital pen and a smart device to measure precise information during the drawing process. When graduating the use of tools analysing digitised alternating series tests from prototypes to use in medical diagnosis, however, it is important for the lengths of the tests to be as short as possible – yet also still provide a valuable reference for diagnosis – in order to maximise their usability in the diagnostic tool. Despite this, to the best knowledge of the author, no research on the optimal length of the aforementioned digitised tests is available. The main aim of the thesis was to determine the optimal length of six variations of Luria’s alternating series tests, i.e. determine the shortest length of a test that at the same time does not decrease the classification power between PD patients and healthy controls by a significant amount. The thesis found that the length of a test could be reduced by up to 70% of the initial test length for half of the test types chosen, and by at least 30% in all the cases. This means that depending on the test type, only 1-4 repetitions are required for maintaining the same classification power, in comparison to the 5.5 repetitions that were used in most of the tests in the the test data. In addition, for facilitating diagnostic use and further related research, an application was developed for displaying detailed results of a given test drawn by the patient and data on the classification power of the test type. This thesis is written in English and is 68 pages long, including 7 chapters, 31 figures and 6 tables.
How to perform an efficient learning process for a combined POD and PGD soil urban thermal model?

A parametric soil model has been developed to improve the computational time of microclimate simulation tools. It combines the use of two methods: the Proper Generalized Decomposition and the Proper Orthogonal Decomposition. Offline, a learning process is required to build the model, before its use online. A methodology to select a short and representative learning process needs to be developed. The k-means clustering method is used to build a training climate made of 24 days representative of a full climate. The offline computation costs are reduced by 94.4% for an error of 0.8%.
Molecular Investigation of Paramecium bursaria Endosymbiotic Algae: the First Records of Symbiotic Micractinium reisseri from Kamchatka

Paramecium bursaria is a symbiotic ciliate species which cells contain hundreds of algae enclosed in perialgal vacuoles. The aim of the present study was to identify endosymbiotic algal strains of P. bursaria and to define the geographical distribution of the identified species. We analyzed symbiotic strains of P. bursaria originating from distant geographical locations and housed at the Culture Collection of Ciliates and their Symbionts (CCCS) at St. Petersburg University. Based on the obtained results, we identified these strains as Micractinium reisseri, Chlorella vulgaris, and Chlorella variabilis. We did not confirm the occurrence of a division into American and European groups and we guess that this division is only contractual and corresponds to the amount of introns in the 18S rDNA, and that there is no strong correlation with the geographical location. We have demonstrated that the range of M. reisseri is greater than previously supposed. We identified algae strains originating from Southern Europe (Serbia), Western Asia, and from the Far East (Kamchatka) as M. reisseri. Moreover, we identified two strains originating from Europe as C. variabilis, which also contradicts the predetermes about a division into American and European groups.
Measuring Impact and Consumption of Local News in a Changing Environment

In this project, we aim to identify "impactful" (award-winning) articles from the NewsBank corpus, a dataset of over two million publications from local newspapers and journals across the United States, collected over the past two decades. We leveraged baseline metadata within the articles retrieved using a dedicated XML scraper, alongside a meticulous process of text data analysis for synthetic feature extraction. The ﬁrst step of this binary classiﬁcation task was to retrieve features that quantify the prevalence of anecdotal data, quantitative analysis, logistical records, and the extent of further investigations and discourse on the topic, as well as to to detect major entities and topics, using a subset of the corpus of articles. After this, the next step was to extract further synthetic features to utilize a Word2Vec clustering methodology obtain categorical groups as features for each article, followed by the use of a pre-trained BERT model to extract major named entities within each article. As expected, the addition of these synthetic features worked successfully for the purposes of building a more effective classiﬁcation model for the purposes of identifying "impactful" (award-winning) articles.
Design Optimisation of Façade-Integrated Photobioreactors Using CFD Simulation

Following the success of the BIQ Haus in 2013, improved photobioreactors are being developed for façade integration in buildings. The photobioreactor façade panels, in which microalgae biomass is cultivated, also act as solar collectors and generate energy for building occupants. Algae growth in the reactors is positively influenced by high velocities which transport microalgae cells to the reactor surface more frequently, allowing them to absorb more sunlight and grow more efficiently. This paper outlines CFD flow simulations of the photobioreactors conducted to investigate the effects of different reactor geometries on flow velocities and improve the efficiency of next generation reactor panels.