Miracle-dz/UniFAR-FTUs huggingface dataset:
The North West Shelf (NWS), a Digital Petroleum Ecosystem (PDE) in a Big Data Scale

The North West Shelf (NWS) and its associated petroleum systems have varied geographies, geomorphologies and complex geological environments. In spite of the ongoing exploration activities in many sedimentary basins, the appraisal and field development campaigns are challenging. Besides, interpreting the connectivity between petroleum systems is challenging. The heterogeneity and multidimensionality of multi-stacked reservoirs associated with multiple oil and gas fields complicate the data integration process. Volumes and varieties of data existing in these basins are in different scales, sizes and formats, demanding new storage and retrieval methods, emphasizing both data integration and data structuring. Since the data are in terabyte size; the multiple dimensions and domains need to be brought in a single repository, we take advantage of Big Data tools and technologies. In this context, we aim at articulating the digital petroleum ecosystems and petroleum database management systems, with new data modelling, data warehousing and mining, visualization and interpretation artefacts. This approach facilitates the data management not only for individual basins but groups of basins of the NWS. Warehoused cuboid metadata can explore the connections providing new insights in the data interpretation and knowledge of new prospective areas. The multidimensional warehousing repository that supported by cloud computing, data analytics and virtualization features, provide new opportunities for delivering quality and just-in-time online ecosystem services. Other goals are deducing an integrated unified metadata model and characterizing the connectivity among the basins of the NWS and associated oil & gas fields. The study supports the features of PDE and its knowledge management.
Finite Difference Formulation for Prediction of Water Pollution

Water is an important component of the earth. Human being and living organisms are demand for the quality of water. Human activity is one of the causes of the water pollution. The pollution happened give bad effect to the physical and characteristic of water contents. It is not practical to monitor all aspects of water flow and transport distribution. So, in order to help people to access to the polluted area, a prediction of water pollution concentration must be modelled. This study proposed a one-dimensional advection diffusion equation for predicting the water pollution concentration transport. The numerical modelling will be produced in order to predict the transportation of water pollution concentration. In order to approximate the advection diffusion equation, the implicit Crank Nicolson is used. For the purpose of the simulation, the boundary condition and initial condition, the spatial steps and time steps as well as the approximations of the advection diffusion equation have been encoded. The results of one dimensional advection diffusion equation have successfully been used to predict the transportation of water pollution concentration by manipulating the velocity and diffusion parameters.
Prevalence of Hymenolepis nana Infections in Abu-Ghraib City /Baghdad/Iraq

INTRODUCTION : Intestinal parasitic infestation represents a considerable medical and public health problem in the developing countries and up to 10% of the population of the developing world is infected with intestinal worms (1,2) . Hymenolepis nana, a dwarf tapeworm, is the most common human tapeworm infection, with an estimated 50 to 75 million carriers worldwide. This tapeworm is endemic in Asia, Africa, and southern and eastern Europe, and its life cycle involves humans or rodents as the definitive host and arthropods as the intermediate host (3) . Humans and rodents are infected when they ingest cysticercoid-infected arthropods or embryonated eggs from contaminated food, water, or hands. Upon ingestion, eggs hatch and release a 6-hooked larva called the oncosphere
Ecological Connectivity of Kimberley Marine Communities

This project was funded (commissioned) by the Western Australian Marine Science Institution as part of the WAMSI Kimberley Marine Research Program, a $30M program with seed funding of $12M provided by State government as part of the Kimberley Science and Conservation Strategy. The Program has been made possible through co-investment from the WAMSI Joint Venture partners and further enabled by data and information provided by Woodside Energy Ltd.
Causal Relationship Between Branched‐Chain Amino Acids and Hypertension: A Mendelian Randomization Study

Background This study aimed to investigate the causal relationships between branched‐chain amino acids (BCAAs) and the risks of hypertension via meta‐analysis and Mendelian randomization analysis. Methods and Results A meta‐analysis of 32 845 subjects was conducted to evaluate the relationships between BCAAs and hypertension. In Mendelian randomization analysis, independent single‐nucleotide polymorphisms associated with BCAAs at the genome‐wide significance level were selected as the instrumental variables. Meanwhile, the summary‐level data for essential hypertension and secondary hypertension end points were obtained from the FinnGen study. As suggested by the meta‐analysis results, elevated BCAA levels were associated with a higher risk of hypertension (isoleucine: summary odds ratio, 1.26 [95% CI, 1.08–1.47]; leucine: summary odds ratio, 1.28 [95% CI, 1.07–1.52]; valine: summary odds ratio, 1.32 [95% CI, 1.12–1.57]). Moreover, the inverse variance‐weighted method demonstrated that an elevated circulating isoleucine level might be the causal risk factor for essential hypertension but not secondary hypertension (essential hypertension: odds ratio, 1.22 [95% CI, 1.12–1.34]; secondary hypertension: odds ratio, 0.96 [95% CI, 0.54–1.68]). Conclusions The increased levels of 3 BCAAs positively correlated with an increased risk of hypertension. Particularly, elevated isoleucine level is a causal risk factor for essential hypertension. Increased levels of leucine and valine also tend to increase the risk of essential hypertension, but further verification is still warranted.
Introduction to target trial emulation in rehabilitation: a systematic approach to emulate a randomized controlled trial using observational data

Rehabilitation providers and policymakers need valid evidence to make informed decisions about the healthcare needs of the population. Whenever possible, these decisions should be informed by randomized controlled trials (RCTs). However, there are circumstances when evidence needs to be generated rapidly, or when RCTs are not ethical or feasible. These situations apply to studying the effects of complex interventions, including rehabilitation as defined by Cochrane Rehabilitation. Therefore, we explore using the target trial emulation framework by Hernán and colleagues to obtain valid estimates of the causal effects of rehabilitation when RCTs cannot be conducted. Target trial emulation is a framework guiding the design and analysis of non-randomized comparative effectiveness studies using observational data, by emulating a hypothetical RCT. In the context of rehabilitation, we outline steps for applying the target trial emulation framework using real world data, highlighting methodological considerations, limitations, potential mitigating strategies, and causal inference and counterfactual theory as foundational principles to estimating causal effects. Overall, we aim to strengthen methodological approaches used to estimate causal effects of rehabilitation when RCTs cannot be conducted.
Paired single-B-cell transcriptomics and receptor sequencing reveal activation states and clonal signatures that characterize B cells in acute myeloid leukemia

Background Acute myeloid leukemia (AML) is associated with a dismal prognosis. Immune checkpoint blockade (ICB) to induce antitumor activity in AML patients has yielded mixed results. Despite the pivotal role of B cells in antitumor immunity, a comprehensive assessment of B lymphocytes within AML’s immunological microenvironment along with their interaction with ICB remains rather constrained. Methods We performed an extensive analysis that involved paired single-cell RNA and B-cell receptor (BCR) sequencing on 52 bone marrow aspirate samples. These samples included 6 from healthy bone marrow donors (normal), 24 from newly diagnosed AML patients (NewlyDx), and 22 from 8 relapsed or refractory AML patients (RelRef), who underwent assessment both before and after azacitidine/nivolumab treatment. Results We delineated nine distinct subtypes of B cell lineage in the bone marrow. AML patients exhibited reduced nascent B cell subgroups but increased differentiated B cells compared with healthy controls. The limited diversity of BCR profiles and extensive somatic hypermutation indicated antigen-driven affinity maturation within the tumor microenvironment of RelRef patients. We established a strong connection between the activation or stress status of naïve and memory B cells, as indicated by AP-1 activity, and their differentiation state. Remarkably, atypical memory B cells functioned as specialized antigen-presenting cells closely interacting with AML malignant cells, correlating with AML stemness and worse clinical outcomes. In the AML microenvironment, plasma cells demonstrated advanced differentiation and heightened activity. Notably, the clinical response to ICB was associated with B cell clonal expansion and plasma cell function. Conclusions Our findings establish a comprehensive framework for profiling the phenotypic diversity of the B cell lineage in AML patients, while also assessing the implications of immunotherapy. This will serve as a valuable guide for future inquiries into AML treatment strategies.
Central giant-cell granuloma in a patient with neurofibromatosis type 1: 7 years of follow-up

Neurofibromatosis type 1 (NF1) is an autosomally dominant tumor suppressor syndrome and multisystem disease. Central giant-cell granulomas (CGCGs) can be seen in patients with NF1. A 21-year-old female was diagnosed with two CGCGs, one in the mandible and then one in the maxilla, in a 7-year period. Increased incidence of CGCGs in NF1 patients was thought to be caused by an underlying susceptibility to developing CGCG-like lesions in qualitatively abnormal bone, such as fibrous dysplasia. However, germline and somatic truncating second-hit mutations in the NF1 gene have been detected in NF1 patients with CGCGs, validating that they are NF1-associated lesions. Oral manifestations in patients with NF1 are very common. Knowledge of these manifestations and the genetic link between NF1 and CGCGs will enhance early detection and enable optimal patient care.
Comparison of Different Osteobiologics in Terms of Imaging Modalities and Time Frames for Fusion Assessment in Anterior Cervical Discectomy and Fusion: A Systematic Review

Study Design Systematic review. Objectives The study’s primary objective was to determine how osteobiologic choice affects fusion rates in patients undergoing anterior cervical discectomy and fusion (ACDF). The study’s secondary objectives were to 1) determine the optimal timing of fusion assessment following ACDF and 2) determine if osteobiologic type affects the timing and optimal modality of fusion assessment. Methods A systematic search of PubMed/MEDLINE was conducted for literature published from 2000 through October 2020 comparing anterior fusion in the cervical spine with various osteobiologics. Both comparative studies and case series of ≥10 patients were included. Results A total of 74 studies met the inclusion criteria. Seventeen studies evaluated the efficacy of autograft on fusion outcomes, and 23 studies assessed the efficacy of allograft on fusion outcomes. 3 studies evaluated the efficacy of demineralized bone matrix, and seven assessed the efficacy of rhBMP-2 on fusion outcomes. Other limited studies evaluated the efficacy of ceramics and bioactive glasses on fusion outcomes, and 4 assessed the efficacy of stem cell products. Most studies utilized dynamic radiographs for the assessment of fusion. Overall, there was a general lack of supportive data to determine the optimal timing of fusion assessment meaningfully or if osteobiologic type influenced fusion timing. Conclusions Achieving fusion following ACDF appears to remain an intricate interplay between host biology and various surgical factors, including the selection of osteobiologics. While alternative osteobiologics to autograft exist and may produce acceptable fusion rates, limitations in study methodology prevent any definitive conclusions from existing literature.
The relationship between substance use and self-reported aspects of social functioning in patients with a psychotic disorder

Background In patients with a psychotic disorder, rates of substance use (tobacco, cannabis, and alcohol) are higher compared to the general population. However, little is known about associations between substance use and self-reported aspects of social functioning in patients with a psychotic disorder. Methods In this cross-sectional study of 281 community-dwelling patients with a psychotic disorder, linear regression models were used to assess associations between substance use (tobacco, cannabis, or alcohol) and self-reported aspects of social functioning (perceived social support, stigmatization, social participation, or loneliness) adjusting for confounders (age, gender, and severity of psychopathology). Results Compared to nonsmokers, both intermediate and heavy smokers reported lower scores on loneliness (E = −0.580, SE = 0.258, p = 0.025 and E = −0.547, SE = 0,272, p = 0.046, respectively). Daily cannabis users reported less social participation deficits than non-cannabis users (E = −0.348, SE = 0.145, p = 0.017). Problematic alcohol use was associated with more perceived social support compared to non-alcohol use (E = 3.152, SE = 1.102, p = 0.005). Polysubstance users reported less loneliness compared to no users (E = −0.569, SE = 0.287, p = 0.049). Conclusions Substance use in patients with psychosis is associated with more favorable scores on various self-reported aspects of social functioning.
Empirical Modeling of the Plasmasphere Dynamics Using Neural Networks

We present the PINE (Plasma density in the Inner magnetosphere Neural network‐based Empirical) model ‐ a new empirical model for reconstructing the global dynamics of the cold plasma density distribution based only on solar wind data and geomagnetic indices. Utilizing the density database obtained using the NURD (Neural‐network‐based Upper hybrid Resonance Determination) algorithm for the period of 1 October 2012 to 1 July 2016, in conjunction with solar wind data and geomagnetic indices, we develop a neural network model that is capable of globally reconstructing the dynamics of the cold plasma density distribution for 2≤L≤6 and all local times. We validate and test the model by measuring its performance on independent data sets withheld from the training set and by comparing the model‐predicted global evolution with global images of He+ distribution in the Earth's plasmasphere from the IMAGE Extreme UltraViolet (EUV) instrument. We identify the parameters that best quantify the plasmasphere dynamics by training and comparing multiple neural networks with different combinations of input parameters (geomagnetic indices, solar wind data, and different durations of their time history). The optimal model is based on the 96 h time history of Kp, AE, SYM‐H, and F10.7 indices. The model successfully reproduces erosion of the plasmasphere on the nightside and plume formation and evolution. We demonstrate results of both local and global plasma density reconstruction. This study illustrates how global dynamics can be reconstructed from local in situ observations by using machine learning techniques.
Recurrent neural network-enhanced HMM speech recognition systems

In this paper, we show how speech recognition systems can be improved, using an adaptive model transition penalty term in the Viterbi decoding process. This term is calculated using the phone-micsegmentationofthespeechsignal, whereabi-directionalrecur-rent neural network is used to segment the speech into phonemes. No higher level lexical knowledge (phoneme sequence) is used in the segmentation process. The method is compared to an existing technique, on the state-of-the-art speech recognition system, HTK. It is shown that our technique results in signiﬁcantly better phoneme recognition accuracy.
Generalization of the Bargmann-Wigner approach to constructing relativistic fields

We review the method for constructing local relativistic fields corresponding to the Bargmann-Wigner wave functions that describe the unitary irreducible representations of the $4D$ Poincar\'{e} group. The method is based on the use of the generalized Wigner operator connecting the wave functions of induced representations and local relativistic fields. Applications of this operator for constructing massive local relativistic fields as well as massless helicity local fields and massless local infinite spin fields are considered.
IEEE TRANSACTIONS ON SYSTEMS , MAN , AND CYBERNETICS — PART A : SYSTEMS AND HUMANS

Robotic systems range from teleoperated (where the human operator is in full control of all aspects of the robot’s behavior) to fully autonomous (where no human intervention takes place). The word “telerobotic” describes robotic systems which, although guided by a human, have a degree of autonomous behavior. This paper examines the tradeoff between the increasing design and implementation effort necessary as the system moves through the continuum from teleoperated to autonomous and the amount of human intervention required. A case study of a human “shepherd” interacting with a robotic “sheepdog” which directs a robotic “sheep” is used.
Remote Measurements of Ship Emissions

The combustion of sulphur-containing fossil fuels gives rise to emissions of sulphur dioxide. Nitrogen oxides are also emitted when fuels are burned. The International Maritime Organization (IMO) has ratified regulations regarding Sulphur Fuel Content (SFC) and NOx emissions from the shipping sector. There is economic incentive to ignore legislation and run with cheaper residual fuel that contains high concentrations of sulphur. In open waters, there is no effective method of controlling whether ships are running on low or high sulphur fuel. A surveillance system, denoted Identification of Gross Polluting Ships (IGPS), has been developed and used with the objective of enforcing the use of low sulphur fuel and NOx abatement equipment according to the new conventions within the IMO. The system is foremost an airborne system consisting of an optical remote measuring system for total emission measurements of SO2 or NO2 from ships. When the emission values reach a certain threshold, an aircraft flies into the ship’s exhaust and takes in situ measurements, denoted the sniffer system, from which SFC and NOx g/kg fuel measurements are obtained. The uncertainty for the optical measurements is estimated to be approximately 45% with optical and wind as the largest uncertainty sources. Estimates of the overall uncertainty for the sniffer measurements, derived during a campaign in Rotterdam, corresponds to 14% for SO2 (S%) and 33% for NO (g/kg fuel). For the emission factors of NOx versus axial power, g/kWh, the uncertainty is 37%. It should be noted that the NO uncertainty was unusually high due to instrument problems. Measurement campaigns have been carried out in the Baltic and North Seas using a CASA-212 aeroplane and a Dauphin helicopter. Optical measurements show the possibility to detect SO2 and NO2 emission from ships. Many of the ocean going ships had a total emission of 60 kg/h SO2. The sniffer measurements showed good agreement with the current IMO regulations for the majority of ships. When the strictest IMO regulation of 0.1% SFC is introduced, ships using fuels with 0.5-4.5% SFC will be easily detected with the current uncertainties.
Technology Competence of Business Managers : A Definition and Research Model

Please do not quote or cite without authors’ permission. Comments are welcome. Many thanks are due to Yolande Chan of Queen's University for participating in the development of this work. Thanks also to Michelle Brown, V. Sambamurthy and Robert W. Zmud for their comments on our original models. This work has been supported by an operating grant from the Social Sciences and Humanities Research Council of Canada.
Early Detection and Forecasting of Research Trends Conference Item

Identifying and forecasting research trends is of critical importance for a variety of stakeholders, including researchers, academic publishers, institutional funding bodies, companies operating in the innovation space and others. Currently, this task is performed either by domain experts, with the assistance of tools for exploring research data, or by automatic approaches. The constant increase of research data makes the second solution more appropriate, however automatic methods suffer from a number of limitations. For instance, they are unable to detect emerging but yet unlabelled research areas (e.g., Semantic Web before 2000). Furthermore, they usually quantify the popularity of a topic simply in terms of the number of related publications or authors for each year; hence they can provide good forecasts only on trends which have existed for at least 3-4 years. This doctoral work aims at solving these limitations by providing a novel approach for the early detection and forecasting of research trends that will take advantage of the rich variety of semantic relationships between research entities (e.g., authors, workshops, communities) and of social media data (e.g., tweets, blogs).
ExLing 2020

Multi-domain learning allows for joint feature detection to promote the performance on a learning task. The shared feature space, however, has limited capacity and should include only the most discriminative task-independent features that are useful for all the tasks. To this end, we proposed a global-local task uncertainty measure to monitor the usefulness of features for all tasks, increasing their effectiveness and generalizability while disentangling them from task-specific features that are not helpful for other tasks. Besides, this measure can utilize unlabeled domain data, tapping the vast reserves of unlabeled data to have even better features. An experiment on a multi-domain text classification shows that the proposed method consistently improves the baseline’s performance and improves the knowledge transfer of learned features to unseen data.
Projected Response of Low‐Level Convergence and Associated Precipitation to Greenhouse Warming

The parameterization of convection in climate models is a large source of uncertainty in projecting future precipitation changes. Here an objective method to identify organized low‐level convergence lines has been used to better understand how atmospheric convection is organized and projected to change, as low‐level convergence plays an important role in the processes leading to precipitation. The frequency and strength of convergence lines over both ocean and land in current climate simulations is too low compared to reanalysis data. Projections show a further reduction in the frequency and strength of convergence lines over the midlatitudes. In the tropics, the largest changes in frequency are generally associated with shifts in major low‐latitude convergence zones, consistent with changes in the precipitation. Further, examining convergence lines when in the presence or absence of precipitation results in large spatial contrasts, providing a better understanding of regional changes in terms of thermodynamic and dynamic effects.