A survey of 3D printing technology applied to paper microfluidics

This critical review describes efforts to apply 3D printing technology to the advancement of paper microfluidic device development.

Owner's Perception of Seizure Detection Devices in Idiopathic Epileptic Dogs

Accurate knowledge of seizure frequency is key to optimising treatment. New methods for detecting epileptic seizures are currently investigated in humans, which rely on changes in biomarkers, also called seizure detection devices. Critical to device development, is understanding user needs and requirements. No information on this subject has been published in veterinary medicine. Many dog health collars are currently on the market, but none has proved to be a promising seizure detector. An online survey was created and consisted of 27 open, closed, and scaled questions divided over two parts: part one focused on general questions related to signalment and seizure semiology, the second part focused specifically on the use of seizure detection devices. Two hundred and thirty-one participants caring for a dog with idiopathic epilepsy, were included in the study. Open questions were coded using descriptive coding by two of the authors independently. Data was analysed using descriptive statistics and binary logistic regression. Our results showed that the unpredictability of seizures plays a major part in the management of canine epilepsy and dog owners have a strong desire to know when a seizure occurs. Nearly all dog owners made changes in their daily life, mainly focusing on intensifying supervision. Owners believed seizure detection devices would improve their dog's seizure management, including a better accuracy of seizure frequency and the ability to administer emergency drugs more readily. Owners that were already keeping track of their dog's seizures were 4.2 times more likely to show confidence in using seizure detection devices to manage their pet's seizures, highlighting the need for better monitoring systems. Our results show that there is a receptive market for wearable technology as a new management strategy in canine epilepsy and this topic should be further explored.

Analisis Bibliometrik Mobile Mathematics Learning

Penelitian ini memaparkan tinjauan kritis mengenai Mobile Mathematics Learning berdasarkan analisis bibliografi atas 195 artikel yang dipublikasikan dalam jurnal internasional berdasarkan database scopus selama kurun waktu 1997– 2021. Tujuan penelitian ini untuk menjawab kekosongan dengan memberikan analisis bibliometrik ekstensif dari literatur yang berkaitan dengan istilah ini untuk menjawab pertanyaan-pertanyaan berikut: (1) Bagaimana artikel Mobile Mathematics Learning diklasifikasikan?, (2) Bagaimana trend penelitian Mobile Mathematics Learning? Topik penelitian apa yang menjadi subjek lebih banyak publikasi?, (3) Apa topik Mobile Mathematics Learning masa depan yang memberikan kesempatan untuk penelitian lebih lanjut?. Analisis bibliometrik menggunakan lima langkah meliputi mendefinisikan istilah pencarian yang sesuai, hasil pencarian awal, penyempurnaan hasil pencarian, penyusunan data statistika awal, dan analisis data. Hasil penelitian memperoleh 195 makalah dengan hasil awal sebanyak 1606 kutipan (66.92 kutipan / tahun dan 8.24 kutipan/makalah). Pemurnian hasil menyisakan 60 artikel (penurunan 69,2%); data mengenai kutipan juga berubah, dengan 566 kutipan (menurun 64,8%), 40.43 kutipan/tahun (menurun 39,6%), dan 9.43 kutipan/makalah (meningkat 14,4%). Temuan ini menunjukkan bahwa jurnal Q1 dan Q2 tidak memberikan pengaruh yang signifikan terhadap kutipan dibandingkan jurnal lainnya. Penelitian ini menunjukkan informasi untuk masa yang akan datang dalam bidang Mobile Mathematics Learning, serta merangkum dan mendukung temuan penting dari tinjauan tersebut.Secara keseluruhan, konsep Mobile Mathematics Learning masih perlu ditingkatkan dalam penelitian-penelitian yang akan datang. Penelitian selanjutnya dapat mengambil topik tentang context, effect, mobile device, development, STEM Education dan mobile technology

AI/ML Models to Aid in the Diagnosis of COVID-19 Illness from Forced Cough Vocalizations: Good Machine Learning Practice and Good Clinical Practices from Concept to Consumer for AI/ML Software Devices

From a comprehensive and systematic search of the relevant literature on signal data signature (SDS)-based artificial intelligence/machine learning (AI/ML) systems designed to aid in the diagnosis of COVID-19 illness, we identified the highest quality articles with statistically significant data sets for a head-to-head comparison to our own model in development. Further comparisons were made to the recently released "Good Machine Learning Practice (GMLP) for Medical Device Development: Guiding Principles" and, in conclusions, we proposed supplemental principles aimed at bringing AI/ML technologies in closer alignment GMLP and Good Clinical Practices (GCP).