Noah's Path

Abstract Background HVL in diagnostic X-ray machine is an important property that is used to define the penetrating ability of an X-ray beam and hence can be used to determine the component of beam hardness. Purpose The purpose of the study was to assess the effect of HVL on the dose output of diagnostic X-ray machine and to determine the different corrective actions for the cases which did not comply the established dose limits. Material and Methods The study utilized the findings of the radiation safety inspections conducted between 2021–2022 by Atomic Energy Council to investigate different Half value layer (HVL) ranges for diagnostic X-ray machines. A number of 64 X-ray machines were selected for the study based on a set criterion. Results A total of 26 X-ray machines failed the HVL test though produced different descriptions of measured dose output. Fifteen (15) X-ray machines produced doses within the permissible range of 0.025 mGy/mAs – 0.080 mGy/mAs, eight (08) X-ray machines produced doses below the lower limit of the dose range of 0.025 mGy/mAs, while three (03) X-ray machines produce doses above the upper limit of the dose range of 0.080 mGy/mAs. The other 38 X-ray machines passed the HVL test but failed the dose output test. These were classified in different HVL groups that exceeded the recommended regulatory limit, that is, moderate (5), high (19), very high (10) and extreme high (4) X-ray machines. The corrective action made was either adding or removing filter plates to enhance or minimize the filtration for machines that failed the HVL test and the ones that passed HVL test but failed the dose output test respectively. However, this should only be after a conclusive investigation of checking the accuracy of tube current (mA), timer (s) and tube potential (kVp) parameters. Conclusion Therefore, the corrective action for HVL should not be done in isolation from other machine dose contributing parameters like tube current potential parameters and exposure time. The regulatory body recommends that HVL tests should be part of the acceptance and commissioning tests for the new machines and done routinely for the machines in use as specified in the quality control program for each facility.

A pair of authors set out to humanize the enigmatic pharaoh 100 years after his tomb's discovery.

Source code summarization aims at generating concise and clear natural language descriptions for programming languages. Well-written code summaries are beneficial for programmers to participate in the software development and maintenance process. To learn the semantic representations of source code, recent efforts focus on incorporating the syntax structure of code into neural networks such as Transformer. Such Transformer-based approaches can better capture the long-range dependencies than other neural networks including Recurrent Neural Networks (RNNs), however, most of them do not consider the structural relative correlations between tokens, e.g., relative positions in Abstract Syntax Trees (ASTs), which is beneficial for code semantics learning. To model the structural dependency, we propose a StruCtural RelatIve Position guided Transformer, named SCRIPT. SCRIPT first obtains the structural relative positions between tokens via parsing the ASTs of source code, and then passes them into two types of Transformer encoders. One Transformer directly adjusts the input according to the structural relative distance; and the other Transformer encodes the structural relative positions during computing the self-attention scores. Finally, we stack these two types of Transformer encoders to learn representations of source code. Experimental results show that the proposed SCRIPT outperforms the state-of-the-art methods by at least 1.6%, 1.4% and 2.8% with respect to BLEU, ROUGE-L and METEOR on benchmark datasets, respectively. We further show that how the proposed SCRIPT captures the structural relative dependencies.

The Fact Extraction and VERification Over Unstructured and Structured information (FEVEROUS) shared task, asks participating systems to determine whether human-authored claims are Supported or Refuted based on evidence retrieved from Wikipedia (or NotEnoughInfo if the claim cannot be verified). Compared to the FEVER 2018 shared task, the main challenge is the addition of structured data (tables and lists) as a source of evidence. The claims in the FEVEROUS dataset can be verified using only structured evidence, only unstructured evidence, or a mixture of both. Submissions are evaluated using the FEVEROUS score that combines label accuracy and evidence retrieval. The shared task received 13 entries, six of which were able to beat the baseline system. The winning team was ``Bust a move!'', achieving a FEVEROUS score of 27% (+9% compared to the baseline). In this paper we describe the shared task, present the full results and highlight commonalities and innovations among the participating systems.

The localization system, which outputs vehicle position, velocity, and attitude, is one of the fundamental components in the autonomous driving vehicle. The global pose is not only used for the planning and control system, but also an important reference for the cloud source-based HD Map building and updating. The accuracy, availability, and reliability are key requirements for the localization system to ensure that the whole system runs smoothly and efficiently.IMU/Vehicle extrinsic calibration is one of the primary jobs that should be addressed. Due to the observability issue, the IMU/vehicle relative roll cannot be calibrated by the traditional maneuver-based calibration method. In this paper, we solve this issue with the proposed Multiple Orientation-based Vehicle/IMU Extrinsic Calibration (MOVIE-Cali) method, which is evaluated by Monte Carlo simulations and experiments.When the vehicle is cornering or making a U-turn, the sideslip of the tires will have negative influence on the localization system which uses Non-Holonomic Constraints (NHC)/Wheel speed sensor measurement in the model. We derive a sideslip angle model and propose an online slip parameter calibration and compensation method to improve the localization accuracy. The performance of proposed method has been evaluated by the vehicle tests.

Recently, some studies have utilized the Markov Decision Process for diversifying (MDP-DIV) the search results in information retrieval. Though promising performances can be delivered, MDP-DIV suffers from a very slow convergence, which hinders its usability in real applications. In this paper, we aim to promote the performance of MDP-DIV by speeding up the convergence rate without much accuracy sacrifice. The slow convergence is incurred by two main reasons: the large action space and data scarcity. On the one hand, the sequential decision making at each position needs to evaluate the query-document relevance for all the candidate set, which results in a huge searching space for MDP; on the other hand, due to the data scarcity, the agent has to proceed more "trial and error" interactions with the environment. To tackle this problem, we propose MDP-DIV-kNN and MDP-DIV-NTN methods. The MDP-DIV-kNN method adopts a $k$ nearest neighbor strategy, i.e., discarding the $k$ nearest neighbors of the recently-selected action (document), to reduce the diversification searching space. The MDP-DIV-NTN employs a pre-trained diversification neural tensor network (NTN-DIV) as the evaluation model, and combines the results with MDP to produce the final ranking solution. The experiment results demonstrate that the two proposed methods indeed accelerate the convergence rate of the MDP-DIV, which is 3x faster, while the accuracies produced barely degrade, or even are better.

Two cases of renal klossiellosis were diagnosed by histopathology in pet sugar gliders (Petaurus breviceps). In both cases, parasites were associated with tubular dilation and mild interstitial nephritis. Rare schizonts were seen in the proximal convoluted renal tubular epithelium, whereas all other life cycle stages were found within distal convoluted tubule cells or the urinary space of the structures distal to the loop of Henle. Conventional optical and transmission electron microscopies were used to assess the life stages of the parasite. The morphologic characteristics and measurements observed differ from those of previously described species of Klossiella infecting marsupial hosts, and the name Klossiella dulcis n. sp. is hereby proposed. This is the first report of a Klossiella sp. infection in Petaurus breviceps .