Description of stomatopod larvae from the Arabian Sea

The Northern Arabian Sea Ecological and Environmental Research (NASEER) Programme cruise I, January, 1992 and other collections from Manora Channel (Karachi) were examined. Six stages i.e. IV, VII, VIII, IX, X and XI of stomatopod larvae are recognized. Day samples talen in 1994 (January to December) from Manora Channel revealed the presence of only three specimens of stage IV. They all belong to the family Squillidae. Each stage is measured, described and illustrated. None of the stages could be correlated to their adults

Implementation of the Pulmonary Tuberculosis Program at Sasi Health Center, Kefamenanu City District, North Central Timor Regency

Pelaksanaan program tuberkulosis paru telah berjalan dengan baik namun masih terdapat peningkatan penemuan kasus dari 48 pada 2020 menjadi 149 di tahun 2021. Tujuan penelitian untuk mengetahui implementasi program tuberkulosis paru di Puskesmas Sasi. Penelitian dengan pendekatan kualitatif, jenis penelitian studi kasus, bersifat deskriptif. Informan dipilih dengan teknik purposive sampling. Hasil penelitian menunjukkan Input, ketersediaan tenaga kesehatan untuk TB Paru hanya satu orang, terdapat beban kerja rangkap untuk petugas. Dana program TB Paru berasal dari BOK, DAK dan bantuan non pemerintah (Global Fund), terdapat dua sumber dana pada program yang sama yaitu pemantauan kontak serumah, penjaringan TB dan sarana prasarana memiliki kualitas baik. Proses, meliputi persiapan, analisis situasi, perumusan masalah, dan penyusunan rencana usulan kegiatan telah berjalan dengan baik namun pada penyusunan rencana pelaksanaan kegiatan belum efektif karena masih terdapat pendoubelan dana pada program yang sama. Output, capaian penemuan kasus TB Paru di Puskesmas Sasi mencapai 15%, keberhasilan pengobatan TB Paru 14,8% dan capaian pengobatan lengkap 39,5% yang masih jauh dari target nasional. Disarankan untuk Dinas Kesehatan Kabupaten TTU perlu melakukan pengawasan terhadap anggaran puskesmas dalam pelaksanaan program TB Paru. &nbsp

Multilingual advertising in the linguistic landscape of Seoul

This study examines commercial signs in arguably the two most visited tourism districts in Seoul, namely Myeongdong and Insadong. It focuses on beauty and food businesses and analyzes featured languages and their content and roles in signage. This article argues that business types, specialized marketing focus, and intended sales pitch influence business owners’ linguistic choices. The findings of the study suggest that the beauty industry relies heavily on English in general, but the power of K‐Beauty popularized by ‘Hallyu’ (The Korean Wave) beyond Korea inevitably invites linguistic accommodation in the form of using Chinese and Japanese. In general, the business category of beauty features a more prevalent use of English than the gastronomic business in this study. Moreover, as an area specializing in traditions and cultural heritage, Insadong shows more signs exclusively in Korean than in Myeongdong.Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/151302/1/weng12427_am.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/151302/2/weng12427.pd

Learning from mistakes: An empirical study of elicitation interviews performed by novices

© 2018 IEEE. [Context] Interviews are the most widely used elicitation technique in requirements engineering. However, conducting effective requirements elicitation interviews is challenging, due to the combination of technical and soft skills that requirements analysts often acquire after a long period of professional practice. Empirical evidence about training the novices on conducting effective requirements elicitation interviews is scarce. [Objectives] We present a list of most common mistakes that novices make in requirements elicitation interviews. The objective is to assist the educators in teaching interviewing skills to student analysts. [Re-search Method] We conducted an empirical study involving role-playing and authentic assessment with 110 students, teamed up in 28 groups, to conduct interviews with a customer. One re-searcher made observation notes during the interview while two researchers reviewed the recordings. We qualitatively analyzed the data to identify the themes and classify the mistakes. [Results and conclusion] We identified 34 unique mistakes classified into 7 high level themes. We also give examples of the mistakes made by the novices in each theme, to assist the educationists and trainers. Our research design is a novel combination of well-known pedagogical approaches described in sufficient details to make it re-peatable for future requirements engineering education and training research

New record of Phrosina semilunata Risso, 1822 (Crustacea: Amphipoda: Phrosinidae) from Sindh territorial waters (Northern Arabian Sea)

Hyperiids are pelagic, mostly oceanic amphipods; few species are found in coastal waters. Phrosina semilunata Risso, 1822 belongs to the family Phrosinidae. The species P. semilunata has been previously reported from the Arabian Sea by Barnard (1937) and also from its southern part by Pillai (1966).The description of the monotypic genus Phrosina given by Bowman and Gruner (1973) is based only on female. Since in our collection specimens of both sexes are present, the male description would be the first one for this phrosinid population, it being important from the point of view of sexual dimorphism in head appendages found in the species. The specimens are housed in the MRC (cat No.AMPH-10)

Convolutional network based animal recognition using YOLO and darknet.

In general, the manual detection of animals with their names is a very tedious task. To overcome this challenge, this research work has developed a YOLOV3 model to identify the animal present in the image given by user. The algorithm used in YOLOV3 model is darknet, which has a pre-trained dataset. The overall performance of the model is based on different training images and testing images of the dataset. The main goal of this research work is to build an animal recognition methodology using YOLOV3 model. The image of animal will be given as input, then it will display the name of the animal as output by using YOLOV3 model. The detection is done by using a pre-trained coco dataset from darknet

AutoFB: Automating Fetal Biometry Estimation from Standard Ultrasound Planes

During pregnancy, ultrasound examination in the second trimester can assess fetal size according to standardized charts. To achieve a reproducible and accurate measurement, a sonographer needs to identify three standard 2D planes of the fetal anatomy (head, abdomen, femur) and manually mark the key anatomical landmarks on the image for accurate biometry and fetal weight estimation. This can be a time-consuming operator-dependent task, especially for a trainee sonographer. Computer-assisted techniques can help in automating the fetal biometry computation process. In this paper, we present a unified automated framework for estimating all measurements needed for the fetal weight assessment. The proposed framework semantically segments the key fetal anatomies using state-of-the-art segmentation models, followed by region fitting and scale recovery for the biometry estimation. We present an ablation study of segmentation algorithms to show their robustness through 4-fold cross-validation on a dataset of 349 ultrasound standard plane images from 42 pregnancies. Moreover, we show that the network with the best segmentation performance tends to be more accurate for biometry estimation. Furthermore, we demonstrate that the error between clinically measured and predicted fetal biometry is lower than the permissible error during routine clinical measurements

AutoFB: Automating Fetal Biometry Estimation from Standard Ultrasound Planes

During pregnancy, ultrasound examination in the second trimester can assess fetal size according to standardized charts. To achieve a reproducible and accurate measurement, a sonographer needs to identify three standard 2D planes of the fetal anatomy (head, abdomen, femur) and manually mark the key anatomical landmarks on the image for accurate biometry and fetal weight estimation. This can be a time-consuming operator-dependent task, especially for a trainee sonographer. Computer-assisted techniques can help in automating the fetal biometry computation process. In this paper, we present a unified automated framework for estimating all measurements needed for the fetal weight assessment. The proposed framework semantically segments the key fetal anatomies using state-of-the-art segmentation models, followed by region fitting and scale recovery for the biometry estimation. We present an ablation study of segmentation algorithms to show their robustness through 4-fold cross-validation on a dataset of 349 ultrasound standard plane images from 42 pregnancies. Moreover, we show that the network with the best segmentation performance tends to be more accurate for biometry estimation. Furthermore, we demonstrate that the error between clinically measured and predicted fetal biometry is lower than the permissible error during routine clinical measurements

BiometryNet: Landmark-based Fetal Biometry Estimation from Standard Ultrasound Planes

Fetal growth assessment from ultrasound is based on a few biometric measurements that are performed manually and assessed relative to the expected gestational age. Reliable biometry estimation depends on the precise detection of landmarks in standard ultrasound planes. Manual annotation can be time-consuming and operator dependent task, and may results in high measurements variability. Existing methods for automatic fetal biometry rely on initial automatic fetal structure segmentation followed by geometric landmark detection. However, segmentation annotations are time-consuming and may be inaccurate, and landmark detection requires developing measurement-specific geometric methods. This paper describes BiometryNet, an end-to-end landmark regression framework for fetal biometry estimation that overcomes these limitations. It includes a novel Dynamic Orientation Determination (DOD) method for enforcing measurement-specific orientation consistency during network training. DOD reduces variabilities in network training, increases landmark localization accuracy, thus yields accurate and robust biometric measurements. To validate our method, we assembled a dataset of 3,398 ultrasound images from 1,829 subjects acquired in three clinical sites with seven different ultrasound devices. Comparison and cross-validation of three different biometric measurements on two independent datasets shows that BiometryNet is robust and yields accurate measurements whose errors are lower than the clinically permissible errors, outperforming other existing automated biometry estimation methods. Code is available at https://github.com/netanellavisdris/fetalbiometry