The Roles of Consonant, Rime, and Tone in Mandarin Spoken Word Recognition: An Eye-Tracking Study

This study investigated the relative role of sub-syllabic components (initial consonant, rime, and tone) in spoken word recognition of Mandarin Chinese using an eye-tracking experiment with a visual world paradigm. Native Mandarin speakers (all born and grew up in Beijing) were presented with four pictures and an auditory stimulus. They were required to click the picture according to the sound stimulus they heard, and their eye movements were tracked during this process. For a target word (e.g., tang2 “candy”), nine conditions of competitors were constructed in terms of the amount of their phonological overlap with the target: consonant competitor (e.g., ti1 “ladder”), rime competitor (e.g., lang4 “wave”), tone competitor (e.g., niu2 “cow”), consonant plus rime competitor (e.g., tang1”soup”), consonant plus tone competitor (e.g., tou2 “head”), rime plus tone competitor (e.g., yang2 “sheep”), cohort competitor (e.g., ta3 “tower”), cohort plus tone competitor (e.g., tao2 “peach”), and baseline competitor (e.g., xue3 “snow”). A growth curve analysis was conducted with the fixation to competitors, targets, and distractors, and the results showed that (1) competitors with consonant or rime overlap can be adequately activated, while tone overlap plays a weaker role since additional tonal information can strengthen the competitive effect only when it was added to a candidate that already bears much phonological similarity with the target. (2) Mandarin words are processed in an incremental way in the time course of word recognition since different partially overlapping competitors could be activated immediately; (3) like the pattern found in English, both cohort and rime competitors were activated to compete for lexical activation, but these two competitors were not temporally distinctive and mainly differed in the size of their competitive effects. Generally, the gradation of activation based on the phonological similarity between target and candidates found in this study was in line with the continuous mapping models and may reflect a strategy of native speakers shaped by the informative characteristics of the interaction among different sub-syllabic components.

RESEARCH OF A LATERAL REAMER BIT FOR DIGGING PLANTING HOLE IN ROCKY ABANDONED MINE AREA

In the abandoned mine area with Karst landform in China, soils are few and thin but rocks are common, traditional planting hole diggers are unequal to work in rocks for vegetation restoration. A reamer bit with variable lateral drilling radius was designed based on the PDC (polycrystalline diamond compact) bit technology and metamorphic mechanism. Two lateral camber blades with PDC teeth were installed inside the bit body, a screw mechanism was employed as the actuation and a spatial double triangle mechanism was taken for the transmission. The curve of the camber blade was specially defined thus the reaming load was decentralized to 85.7% teeth on the blade. The kinematics of the lateral reamer bit was analysed, the mapping models from the actuation to the reaming radius and speed were established. Concrete samples were reamed indoors from 240mm to 407mm in diameter, the reaming cutting load and time length were measured and analysed. The lateral reamer bit was approved with the experiment results, this study provided equipment support for digging the planting hole in rocky abandoned mine areas and also expanded the PDC bit application.

A New Mapping Function for Spaceborne TEC Conversion Based on the Plasmaspheric Scale Height

The mapping function is crucial for the conversion of slant total electron content (TEC) to vertical TEC for low Earth orbit (LEO) satellite-based observations. Instead of collapsing the ionosphere into one single shell in commonly used mapping models, we defined a new mapping function assuming the vertical ionospheric distribution as an exponential profiler with one simple parameter: the plasmaspheric scale height in the zenith direction of LEO satellites. The scale height obtained by an empirical model introduces spatial and temporal variances into the mapping function. The performance of the new method is compared with the mapping function F&K by simulating experiments based on the global core plasma model (GCPM), and it is discussed along with the latitude, seasons, local time, as well as solar activity conditions and varying LEO orbit altitudes. The assessment indicates that the new mapping function has a comparable or better performance than the F&K mapping model, especially on the TEC conversion of low elevation angles.

Higher-order tensor decomposition based scalp-to-intracranial EEG projection for detection of interictal epileptiform discharges

Objective. Interictal epileptiform discharges (IEDs) occur between two seizures onsets. IEDs are mainly captured by intracranial recordings and are often invisible over the scalp. This study proposes a model based on tensor factorization to map the time-frequency (TF) features of scalp EEG (sEEG) to the TF features of intracranial EEG (iEEG) in order to detect IEDs from over the scalp with high sensitivity. Approach. Continuous wavelet transform is employed to extract the TF features. Time, frequency, and channel modes of IED segments from iEEG recordings are concatenated into a four-way tensor. Tucker and CANDECOMP/PARAFAC decomposition techniques are employed to decompose the tensor into temporal, spectral, spatial, and segmental factors. Finally, TF features of both IED and non-IED segments from scalp recordings are projected onto the temporal components for classification. Main results. The model performance is obtained in two different approaches: within- and between-subject classification approaches. Our proposed method is compared with four other methods, namely a tensor-based spatial component analysis method, TF-based method, linear regression mapping model, and asymmetric-symmetric autoencoder mapping model followed by convolutional neural networks. Our proposed method outperforms all these methods in both within- and between-subject classification approaches by respectively achieving 84.2% and 72.6% accuracy values. Significance. The findings show that mapping sEEG to iEEG improves the performance of the scalp-based IED detection model. Furthermore, the tensor-based mapping model outperforms the autoencoder- and regression-based mapping models.

Evaluating the usability and acceptability of a geographical information system (GIS) prototype to visualise socio-economic and public health data

Background Understanding the impact of socio-economic inequality on health outcomes is arguably more relevant than ever before given the global repercussions of Covid-19. With limited resources, innovative methods to track disease, population needs, and current health and social service provision are essential. To best make use of currently available data, there is an increasing reliance on technology. One approach of interest is the implementation and integration of mapping software. This research aimed to determine the usability and acceptability of a methodology for mapping public health data using GIS technology. Methods Prototype multi-layered interactive maps were created demonstrating relationships between socio-economic and health data (vaccination and admission rates). A semi-structured interview schedule was developed, including a validated tool known as the System Usability Scale (SUS), which assessed the usability of the mapping model with five stakeholder (SH) groups. Fifteen interviews were conducted across the 5 SH and analysed using content analysis. A Kruskal-Wallis H test was performed to determine any statistically significant difference for the SUS scores across SH. The acceptability of the model was not affected by the individual use of smart technology among SHs. Results The mean score from the SUS for the prototype mapping models was 83.17 out of 100, indicating good usability. There was no statistically significant difference in the usability of the maps among SH (p = 0.094). Three major themes emerged with respective sub-themes from the interviews including: (1) Barriers to current use of data (2) Design strengths and improvements (3) Multiple benefits and usability of the mapping model. Conclusion Irrespective of variations in demographics or use of smart technology amongst interviewees, there was no significant difference in the usability of the model across the stakeholder groups. The average SUS score for a new system is 68. A score of 83.17 was calculated, indicative of a “good” system, as falling within the top 10% of scores. This study has provided a potential digital model for mapping public health data. Furthermore, it demonstrated the need for such a digital solution, as well as its usability and future utilisation avenues among SH.

Representing and Validating Cultural Heritage Knowledge Graphs in CIDOC-CRM Ontology

In order to unify access to multiple heterogeneous sources of cultural heritage data, many datasets were mapped to the CIDOC-CRM ontology. CIDOC-CRM provides a formal structure and definitions for most cultural heritage concepts and their relationships. The COURAGE project includes historic data concerning people, organizations, cultural heritage collections, and collection items covering the period between 1950 and 1990. Therefore, CIDOC-CRM seemed the optimal choice for describing COURAGE entities, improving knowledge sharing, and facilitating the COURAGE dataset unification with other datasets. This paper introduces the results of translating the COURAGE dataset to CIDOC-CRM semantically. This mapping was implemented automatically according to predefined mapping rules. Several SPARQL queries were applied to validate the migration process manually. In addition, multiple SHACL shapes were conducted to validate the data and mapping models.

Beyond prediction: methods for interpreting complex models of soil variation

Understanding the spatial variation of soil properties is central to many sub-disciplines of soil science. Commonly in soil mapping studies, a soil map is constructed through prediction by a statistical or non-statistical model calibrated with measured values of the soil property and environmental covariates of which maps are available. In recent years, the field has gradually shifted attention towards more complex statistical and algorithmic tools from the field of machine learning. These models are particularly useful for their predictive capabilities and are often more accurate than classical models, but they lack interpretability and their functioning cannot be readily visualized. There is a need to understand how these these models can be used for purposes other than making accurate prediction and whether it is possible to extract information on the relationships among variables found by the models. In this paper we describe and evaluate a set of methods for the interpretation of complex models of soil variation. An overview is presented of how model-independent methods can serve the purpose of interpreting and visualizing different aspects of the model. We illustrate the methods with the interpretation of two mapping models in a case study mapping topsoil organic carbon in France. We reveal the importance of each driver of soil variation, their interaction, as well as the functional form of the association between environmental covariate and the soil property. Interpretation is also conducted locally for an area and two spatial locations with distinct land use and climate. We show that in all cases important insights can be obtained, both into the overall model functioning and into the decision made by the model for a prediction at a location. This underpins the importance of going beyond accurate prediction in soil mapping studies. Interpretation of mapping models reveal how the predictions are made and can help us formulating hypotheses on the underlying soil processes and mechanisms driving soil variation.

METAMDB: METABOLIC ATOM MAPPING DATABASE

MetAMDB (https://metamdb.tu-bs.de/) is an open source metabolic atom mapping database, providing atom mappings for around 75000 metabolic reactions. Each atom mapping can be inspected and downloaded either as a RXN file or as a graphic in SVG format. In addition, MetAMDB offers the possibility of automatically creating atom mapping models based on user-specified metabolic networks. These models can be of any size (small to genome scale) and can subsequently be used in standard 13C metabolic flux analysis software.