Miracle-dz/UniFAR-FTUs huggingface dataset:
Development of a Geant4‐based independent patient dose validation system with an elaborate multileaf collimator simulation model

Abstract Despite the improvements in the dose calculation models of the commercial treatment planning systems (TPS), their ability to accurately predict patient dose is still limited. One of the limitations is caused by the simplified model of the multileaf collimator (MLC). The aim of this study was to develop a Monte Carlo (MC) method‐based independent patient dose validation system with an elaborate MLC model for more accurate dose evaluation. Varian Clinac 2300 IX was simulated using Geant4 toolkits, after which MC commissioning with measurements was performed to validate the simulation model. A DICOM‐RT interface was developed to obtain the beam delivery conditions including the hundreds of MLC motions. Finally, the TPS dose distributions were compared with the MC dose distributions for water phantom cases and a patient case. Our results show that the TPS overestimated the absolute abutting leakage dose in the closed MLC field, with about 20% more of the maximum dose than that of the MC calculation. For water phantom cases, the dose distributions inside the target region were almost identical with the dose difference of less than 2%, while the dose near the edge of the target shows difference about 10% between Geant4 and TPS due to geometrical differences in MLC model. For the patient analysis, the Geant4 and TPS doses of all organs were matched well within 1.4% of the prescribed dose. However, for organs located in areas with high ratio of leaf pairs with distances less than 10 mm leaf pair (LP (<10mm)), the maximum dose of TPS was overestimated by about 3% of the prescribed dose. These dose comparison results demonstrate that our system for calculating the patient dose is quite accurate. Furthermore, if the MLC sequences in treatment plan have a large ratio of LP (short), more than 3% dose difference in normal tissue could be seen.
Genotypic resistance testing of HCV – is there a clinical need?

Persistent infections with the hepatitis C virus (HCV) pose a profound global public health burden. In the past 5 years treatment of chronic hepatitis C has dramatically changed. Novel direct-acting antivirals (DAAs) specifically inhibiting viral enzymes or factors that are essential for the viral replication cycle have been developed and licensed for hepatitis C therapy. These novel drugs target the viral NS3/4A protease, the NS5B RNA-dependent RNA-polymerase or the replication factor NS5A. Combinations of DAAs against these targets are highly efficacious achieving virus elimination in the majority of treated patients. In countries where affordable, this rapid clinical development virtually replaced earlier interferon (IFN)-α based therapy that had been in use as standard of care for the last 25 years. With the approval of DAAs for the treatment of chronic hepatitis C the question emerged whether resistance-associated substitutions (RASs) might be of clinical relevance. Here, we discuss the available evidence for the possible benefit of resistance genotyping prior to therapy to optimize treatment of chronic hepatitis C.
The Role of Motor Activity in Insight Problem Solving (the Case of the Nine-Dot Problem)

Attempts to estimate the contribution made by motor activity to insight problem solving is hindered by a lack of detailed description of motor behavior. The goal of this study was to develop and put to the test a novel method for studying the dynamics of insight problem solving based on a quantitative analysis of ongoing motor activity. As a proper problem model, we chose the nine-dot problem (Maier, 1930), in which solvers had to draw a sequence of connected line segments. Instead of using the traditional pen-and-paper way of solving the nine-dot problem we asked participants to use their index finger to draw line segments on the surface of a tablet computer. We are arguing that successful studying of the role of motor activity during problem solving requires the distinction between its instrumental and functional role. We considered the functional role on the motor activity as closely related to the on-line mode of motor planning. The goal of Experiment 1 was to explore the potential power of the method and, at the same time, to assay the patterns of motor activity related to on-line and off-line modes of motor planning. Experiments 2 and 3 were designed to uncover the potential impact of preliminary motor training on the motor output of successful and unsuccessful problem solvers. In these experiments, we tested hypotheses on how preliminary motor training, which presumably played a functional role in Experiment 2 and an instrumental role in Experiment 3, affects the motor activity of a problem solver and hence their effectiveness in solving the problem. The three experiments showed consistent results. They suggest that successful solving of the nine-dot problem relies upon the functional role of motor activity and requires both off-line and on-line modes of motor planning, with the latter helping to overcome the perceptual constraints imposed by a spatial arrangement of the nine dots. The method that we applied allows for systematic comparison between successful and unsuccessful problem solvers based on the quantitative parameters of their motor activity. Through it, we found new specific patterns of motor activity that differentiate successful and unsuccessful solvers.
Characteristics and Deposit Stratigraphy of Submarine-Erupted Silicic Ash, Havre Volcano, Kermadec Arc, New Zealand

Submarine eruptions dominate volcanism on Earth, but few are observed or even identified. Knowledge of how they operate is largely based on inference from ancient deposits, lagging by a decade or more our understanding of subaerial eruptions. In 2012, the largest wholly deep-subaqueous silicic eruption with any observational record occurred 700–1220 m below sea level at Havre volcano, Kermadec Arc, New Zealand. Pre- and post-eruption shipboard bathymetry surveys, acquisition by autonomous underwater vehicle of meter-scale-resolution bathymetry, and sampling by remote-operated vehicle revealed 14 seafloor lavas and three major seafloor clastic deposits. Here we analyze one of these clastic deposits, an Ash with Lapilli (AL) unit, which drapes the Havre caldera, and interpret the fragmentation and dispersal processes that produced it. Seafloor images of the unit reveal multiple subunits, all ash-dominated. Sampling destroyed layering in all but two samples, but by combining seafloor imagery with granulometry and componentry, we were able to determine the subunits’ stratigraphy and spatial extents throughout the study area. Five subunits are distinguished; from the base these are Subunit 1, Subunit 2a, Subunit 3, Subunit 4 (comprising the coeval Subunit 4 west and Subunit 4 east), and Subunit 2b. The stratigraphic relationships of the four AL unit subunits to other seafloor products of the 2012 Havre eruption, coupled with the wealth of remote-operated vehicle observations and detailed AUV bathymetry, allow us to infer the overall order of events through the eruption. Ash formed by explosive fragmentation of a glassy vesicular magma and was dispersed by a buoyant thermal plume and dilute density currents from which Subunits 1 and 2 were deposited. Following a time break (days/weeks? ), effusion of lava along the southern caldera rim led to additional ash generation; first by syn-extrusive ash venting, quenching, brecciation, and comminution (S3 and S4e) and then by gravitational collapse of a dome (S4w). Slow deposition of extremely fine ash sustained S2 deposition across the times of S3 and S4 emplacement, so that S2 ash was the last deposited. These thin ash deposits hold information critical for interpretation of the overall eruption, even though they are small in volume and bathymetrically unimpressive. Ash deposits formed during other submarine eruptions are similarly likely to offer new perspectives on associated lavas and coarse pumice beds, both modern and ancient, and on the eruptions that formed them. Submarine ash is widely dispersed prior to deposition, and tuff is likely to be the first product of eruption identified in reconnaissance exploration; it is the start of the trail to vent hydrothermal systems and associated mineralized deposits of submarine volcanoes, as well as a sensitive indicator of submarine eruptive processes.
A University and School Working in Partnership to Develop Professional Practice Knowledge for Teaching

Abstract The article reports on the University of Johannesburg’s efforts at offering a practice-based teacher education program in partnership with a university-affiliated school (a ‘teaching school’) with a view to developing inquiry-oriented novice teachers. The research aimed at exploring what it takes to develop a ‘learningplace’ (Conway, Murphy and Rutherford 2014) conducive to student teachers’ development of professional practice knowledge and the role of mentoring in this regard. A two-phase process of generating data was used. In the first phase the published research on the university-affiliated school was analyzed, using qualitative content analysis to look for patterns across the historical unfolding of the findings of these papers. In phase two a questionnaire was administered involving final year student teachers in the teacher education program. The research shows that a teaching school, if integrated into the program design and delivery, provides a rich practice learning site for student teachers. In addition, mentoring in cognitive apprenticeship mode could indeed be a powerful contributor to student teachers’ professional development. However, the overall programme design is decisive. The preparation of teachers with strong professional practice knowledge requires attention to four interrelated aspects of program design, namely program identity, organizational structures, curriculum and teacher education pedagogy. Addressing these aspects in an integrated manner would be difficult without working closely with one or more partnership schools.
Time Synchronization for Wireless Sensors Using Low-Cost GPS Module and Arduino

Time synchronisation for wireless sensors is important for a proper interpretation of measurements, particularly for acceleration measurements to estimate mode-shapes correctly. This paper presents a new time synchronisation method working independently on each node without exchanging time-sync packets among nodes. This stand-along operation can make field measurement campaigns very time-efficient without a need of constructing and validating the wireless sensor network. The proposed method firstly time-stamps measurements using the accurate time-source from a GPS module on each node, and secondly re-samples the time-stamped data to get time-synchronised data. The time-stamping method proposed in the study utilises Pulse-Per-Second (PPS) signals and NMEA (National Marine Electronics Association) sentences generated by a low-cost GPS module, and the internal timer/counter unit of Arduino. Error analysis on the proposed time-stamping method was carried out and derived an analytical expression for the maximum variance of time-stamping error of the proposed method. Four experiments have been carried out to observe 1) the long-term operational stability of the GPS module, 2) the accuracy of the PPS signals, 3) the accuracy of the proposed time-stamping method, and 4) the validity of the proposed time-synchronisation method for output-only modal analysis on a laboratory floor structure. The GPS module was found to operate or to resume operating stably for the entire test period of seven days even with the limited field of view to the sky. The relative time errors of two PPS signals from four GPS modules were found to be within +/-400 nsec. The time-stamping error measured by two identical time-stamping Arduinos for common trigger signals was found to have a standard deviation of 40.8 nsec, which agreed well with the maximum value of 42.0 nsec predicted by the error analysis. From the output-only modal analysis, the estimated modal parameters were found to agree well with that from the wired acceleration sensors. The phase angle of the cross spectral density of the two wireless accelerations showed that there was no apparent time-synchronisation error observable. These observations indicated a successful operation of the proposed time-synchronisation method.
Efficient Phrase Table pruning for Hindi to English machine translation through syntactic and marker-based filtering and hybrid similarity measurement

Abstract For a given training corpus of parallel sentences, the quality of the output produced by a translation system relies heavily on the underlying similarity measurement criteria. A phrase-based machine translation system derives its output through a generative process using a Phrase Table comprising source and target language phrases. As a consequence, the more effective the Phrase Table is, in terms of its size and the output that may be derived out of it, the better is the expected outcome of the underlying translation system. However, finding the most similar phrase(s) from a given training corpus that can help generate a good quality translation poses a serious challenge. In practice, often there are many parallel phrase entries in a Phrase Table that are either redundant, or do not contribute to the translation results effectively. Identifying these candidate entries and removing them from the Phrase Table will not only reduce the size of the Phrase Table, but should also help in improving the processing speed for generating the translations. The present paper develops a scheme based on syntactic structure and the marker hypothesis (Green 1979, The necessity of syntax markers: two experiments with artificial languages, Journal of Verbal Learning and Behavior) for reducing the size of a Phrase Table, without compromising much on the translation quality of the output, by retaining the non-redundant and meaningful parallel phrases only. The proposed scheme is complemented with an appropriate similarity measurement scheme to achieve maximum efficiency in terms of BLEU scores. Although designed for Hindi to English machine translation, the overall approach is quite general, and is expected to be easily adaptable for other language pairs as well.
Non-Markovian Evolution: a Quantum Walk Perspective

Quantum non-Markovianity of a quantum noisy channel is typically identified with information backflow or, more generally, with departure of the intermediate map from complete positivity. But here, we also indicate certain non-Markovian channels that cannot be witnessed by the CP-divisibility criterion. In complex systems, non-Markovianity becomes more involved on account of subsystem dynamics. Here we study various facets of non-Markovian evolution, in the context of coined quantum walks, with particular stress on disambiguating the internal vs. environmental contributions to non-Markovian backflow. For the above problem of disambiguation, we present a general power-spectral technique based on a distinguishability measure such as trace-distance or correlation measure such as mutual information. We also study various facets of quantum correlations in the transition from quantum to classical random walks, under the considered non-Markovian noise models. The potential for the application of this analysis to the quantum statistical dynamics of complex systems is indicated.
Extractive multi-document summarization based on textual entailment and sentence compression via knapsack problem

Abstract By increasing the amount of data in computer networks, searching and finding suitable information will be harder for users. One of the most widespread forms of information on such networks are textual documents. So exploring these documents to get information about their content is difficult and sometimes impossible. Multi-document text summarization systems are an aid to producing a summary with a fixed and predefined length, while covering the maximum content of the input documents. This paper presents a novel method for multi-document extractive summarization based on textual entailment relations and sentence compression via formulating the problem as a knapsack problem. In this approach, sentences of documents are ranked according to the extended Tf-Idf method, then entailment scores of selected sentences are computed. Through these scores, the final score of each sentence is calculated. Finally, by decreasing the lengths of sentences via sentence compression, the problem has been solved by greedy and dynamic Programming approaches to the knapsack problem. Experiments on standard summarization datasets and evaluating the results based on the Rouge system show that the suggested method, according to the best of our knowledge, has increased F-measure of query-based summarization systems by two per cent and F-measure of general summarization systems by five per cent.
Framework and modelling of inclusive manufacturing system

ABSTRACT The world has seen tremendous changes in manufacturing, since its first industrial revolution to the current era of Industry 4.0. In the past few years, numerous advanced manufacturing models have been introduced by researchers and the same was implemented by companies. Similarly, in this domain, a new concept of inclusive manufacturing (IM) has been coined by Inclusive Manufacturing Forum for inclusive growth of manufacturing sector. In this work, a conceptual model of inclusive manufacturing system (IMS) is proposed along with a framework and flow chart. The IM is a composition of various policies and standards for resolving societal issues, environmental degradation and economic difficulties along with the integration of advanced manufacturing models and emerging technologies, like information and communication technologies, cyber-physical system, Internet of Things and Semantic Web. The vision of the proposed concept is to achieve the target of minimum time to market, high quality, low cost, better service, clean environment, flexible and precise information sharing. In this work, IMS has been conceptualised, along with visualisation of its practical implication by considering numerous emerging and supportive technologies, for achieving the goal of better collaboration among micro, small and medium enterprises.
[Transvaginal cholecystectomy in animal model: first series in Hungary].

INTRODUCTION The Natural Orifice Transluminal Endoscopic Surgery (NOTES) is the newest trend in minimally invasive surgery. Based on clinical experiences, transvaginal cholecystectomy causes less pain and operative stress, requires shorter hospitalization and allows patients to return quicker to normal activity. MATERIALS AND METHODS A transvaginal cholecystectomy was carried out using hybrid technique in animal model first time in Hungary. A 5 mm umbilical trocar was used for preparation of cystic artery and duct, clip application and gallbladder dissection. A transvaginally inserted 10 mm trocar was used for laparoscopic camera to follow the procedure. Gallbladder was fixed and secured with a special curved instrument inserted also transvaginally during the procedure. At the end of procedure the gallbladder was removed transvaginally. RESULTS Six transvaginal cholecystectomies was performed on pigs. The mean time of operations was 78 min (40-145 minutes). During the operations and the follow up period (3 months) no complications and mortality was detected. CONCLUSIONS According to our experiences both procedures can be safely carried out on animal model, but further refinement of devices is necessary.
A note on the rainbow cycle cover problem

Given an edge‐colored graph G, a cycle with all its edges with different colors is called a rainbow cycle. The rainbow cycle cover (RCC) problem consists of finding the minimum number of disjoint rainbow cycles covering G. We present an integer linear programming model for the RCC problem and a reduction process for decreasing the dimensions of the graph, resulting in a more efficient method that was able to find new optimal solutions for instances that were unsolved.
Identifying signs of syntactic complexity for rule-based sentence simplification

Abstract This article presents a new method to automatically simplify English sentences. The approach is designed to reduce the number of compound clauses and nominally bound relative clauses in input sentences. The article provides an overview of a corpus annotated with information about various explicit signs of syntactic complexity and describes the two major components of a sentence simplification method that works by exploiting information on the signs occurring in the sentences of a text. The first component is a sign tagger which automatically classifies signs in accordance with the annotation scheme used to annotate the corpus. The second component is an iterative rule-based sentence transformation tool. Exploiting the sign tagger in conjunction with other NLP components, the sentence transformation tool automatically rewrites long sentences containing compound clauses and nominally bound relative clauses as sequences of shorter single-clause sentences. Evaluation of the different components reveals acceptable performance in rewriting sentences containing compound clauses but less accuracy when rewriting sentences containing nominally bound relative clauses. A detailed error analysis revealed that the major sources of error include inaccurate sign tagging, the relatively limited coverage of the rules used to rewrite sentences, and an inability to discriminate between various subtypes of clause coordination. Despite this, the system performed well in comparison with two baselines. This finding was reinforced by automatic estimations of the readability of system output and by surveys of readers’ opinions about the accuracy, accessibility, and meaning of this output.
Best Trade-Off Point Method for Efficient Resource Provisioning in Spark

Considering the recent exponential growth in the amount of information processed in Big Data, the high energy consumed by data processing engines in datacenters has become a major issue, underlining the need for efficient resource allocation for more energy-efficient computing. We previously proposed the Best Trade-off Point (BToP) method, which provides a general approach and techniques based on an algorithm with mathematical formulas to find the best trade-off point on an elbow curve of performance vs. resources for efficient resource provisioning in Hadoop MapReduce. The BToP method is expected to work for any application or system which relies on a trade-off elbow curve, non-inverted or inverted, for making good decisions. In this paper, we apply the BToP method to the emerging cluster computing framework, Apache Spark, and show that its performance and energy consumption are better than Spark with its built-in dynamic resource allocation enabled. Our Spark-Bench tests confirm the effectiveness of using the BToP method with Spark to determine the optimal number of executors for any workload in production environments where job profiling for behavioral replication will lead to the most efficient resource provisioning.