Logical Observation Identifiers Names and Codes (LOINC®) Applied to Microbiology: A National Laboratory Mapping Experience in Taiwan

Background and Objective: Logical Observation Identifiers Names and Codes (LOINC) is a universal standard for identifying laboratory tests and clinical observations. It facilitates a smooth information exchange between hospitals, locally and internationally. Although it offers immense benefits for patient care, LOINC coding is complex, resource-intensive, and requires substantial domain expertise. Our objective was to provide training and evaluate the performance of LOINC mapping of 20 pathogens from 53 hospitals participating in the National Notifiable Disease Surveillance System (NNDSS). Methods: Complete mapping codes for 20 pathogens (nine bacteria and 11 viruses) were requested from all participating hospitals to review between January 2014 and December 2016. Participating hospitals mapped those pathogens to LOINC terminology, utilizing the Regenstrief LOINC mapping assistant (RELMA) and reported to the NNDSS, beginning in January 2014. The mapping problems were identified by expert panels that classified frequently asked questionnaires (FAQs) into seven LOINC categories. Finally, proper and meaningful suggestions were provided based on the error pattern in the FAQs. A general meeting was organized if the error pattern proved to be difficult to resolve. If the experts did not conclude the local issue’s error pattern, a request was sent to the LOINC committee for resolution. Results: A total of 53 hospitals participated in our study. Of these, 26 (49.05%) used homegrown and 27 (50.95%) used outsourced LOINC mapping. Hospitals who participated in 2015 had a greater improvement in LOINC mapping than those of 2016 (26.5% vs. 3.9%). Most FAQs were related to notification principles (47%), LOINC system (42%), and LOINC property (26%) in 2014, 2015, and 2016, respectively. Conclusions: The findings of our study show that multiple stage approaches improved LOINC mapping by up to 26.5%.

Perfect t-codes in Cayley graphs of groups

A perfect [Formula: see text]-code in a graph [Formula: see text] is a subset [Formula: see text] of [Formula: see text] such that every vertex of [Formula: see text] is at a distance not more than [Formula: see text], to exactly one vertex of [Formula: see text]. In this paper, we present a new family of perfect [Formula: see text]-codes in Cayley graphs of groups. We proposed the role of the subgroups of a group to create perfect [Formula: see text]-codes by restricting the elements of the left transversal of the subgroups in the given group. Also, we introduce a new decoding algorithm for the all of perfect [Formula: see text]-codes in Cayley graphs. These codes are able to correct every [Formula: see text]-error pattern.

Hardware Decoding Accelerator of (73, 37, 13) QR Code for Power Line Carrier in UPIoT

The proposal of the ubiquitous power Internet of Things (UPIoT) has increased the demand for communication coverage and data collection of smart grid; the quantity and quality of communication networks are facing greater challenges. This brief applies (73, 37, 13) quadratic residue (QR) codes to power line carrier technology to improve the quality of local data communication in UPIoT. In order to improve the decoding performance of the QR codes, an induction method for the error pattern is proposed, which can divide the originally coupled error pattern into six parts and reuse the same module for decoding. This method greatly reduces the resource requirements, so that (73, 37, 13) QR code can be implemented on FPGA hardware. Notably, the hardware architecture is a modular framework, which can fit into an FPGA with different sizes. As an example (73, 37, 13), QR code is implemented on Intel Arria10 FPGA; the experimental result shows that the maximum decoding frequency of this architecture is 21.7 M Hz, which achieves 4121x speedup compared to CPU. Moreover, the proposed architecture benefits from high flexibility, such as modular design and decoding framework in the form of the pipeline which can be seen as an alternative scheme for decoding long-length QR codes.

Voice and Emphasis in Arabic Coronal Stops: Evidence for Phonological Compensation

The current study investigates multiple acoustic cues–voice onset time (VOT), spectral center of gravity (SCG) of burst, pitch (F0), and frequencies of the first (F1) and second (F2) formants at vowel onset—associated with phonological contrasts of voicing and emphasis in production of Arabic coronal stops. The analysis of the acoustic data collected from eight native speakers of the Qatari dialect showed that the three stops form three distinct modes on the VOT scale: [d] is (pre)voiced, voiceless [t] is aspirated, and emphatic [ṭ] is voiceless unaspirated. The contrast is also maintained in spectral cues. Each cue influences production of coronal stops while their relevance to phonological contrasts varies. VOT was most relevant for voicing, but F2 was mostly associated with emphasis. The perception experiment revealed that listeners were able to categorize ambiguous tokens correctly and compensate for phonological contrasts. The listeners’ results were used to evaluate three categorization models to predict the intended category of a coronal stop: a model with unweighted and unadjusted cues, a model with weighted cues compensating for phonetic context, and a model with weighted cues compensating for the voicing and emphasis contrasts. The findings suggest that the model with phonological compensation performed most similar to human listeners both in terms of accuracy rate and error pattern.