Chronology of Fabry-Perot interferometer fiber-optic sensors and their applications: a review.

Optical fibers have been involved in the area of sensing applications for more than four decades. Moreover, interferometric optical fiber sensors have attracted broad interest for their prospective applications in sensing temperature, refractive index, strain measurement, pressure, acoustic wave, vibration, magnetic field, and voltage. During this time, numerous types of interferometers have been developed such as Fabry-Perot, Michelson, Mach-Zehnder, Sagnac Fiber, and Common-path interferometers. Fabry-Perot interferometer (FPI) fiber-optic sensors have been extensively investigated for their exceedingly effective, simple fabrication as well as low cost aspects. In this study, a wide variety of FPI sensors are reviewed in terms of fabrication methods, principle of operation and their sensing applications. The chronology of the development of FPI sensors and their implementation in various applications are discussed.

A machine learning approach for vocal fold segmentation and disorder classification based on ensemble method.

In the healthcare domain, the essential task is to understand and classify diseases affecting the vocal folds (VFs). The accurate identification of VF disease is the key issue in this domain. Integrating VF segmentation and disease classification into a single system is challenging but important for precise diagnostics. Our study addresses this challenge by combining VF illness categorization and VF segmentation into a single integrated system. We utilized two effective ensemble machine learning methods: ensemble EfficientNetV2L-LGBM and ensemble UNet-BiGRU. We utilized the EfficientNetV2L-LGBM model for classification, achieving a training accuracy of 98.88%, validation accuracy of 97.73%, and test accuracy of 97.88%. These exceptional outcomes highlight the system's ability to classify different VF illnesses precisely. In addition, we utilized the UNet-BiGRU model for segmentation, which attained a training accuracy of 92.55%, a validation accuracy of 89.87%, and a significant test accuracy of 91.47%. In the segmentation task, we examined some methods to improve our ability to divide data into segments, resulting in a testing accuracy score of 91.99% and an Intersection over Union (IOU) of 87.46%. These measures demonstrate skill of the model in accurately defining and separating VF. Our system's classification and segmentation results confirm its capacity to effectively identify and segment VF disorders, representing a significant advancement in enhancing diagnostic accuracy and healthcare in this specialized field. This study emphasizes the potential of machine learning to transform the medical field's capacity to categorize VF and segment VF, providing clinicians with a vital instrument to mitigate the profound impact of the condition. Implementing this innovative approach is expected to enhance medical procedures and provide a sense of optimism to those globally affected by VF disease.

BDSL 49: A comprehensive dataset of Bangla sign language.

Language is a method by which individuals express their thoughts. Each language has its own alphabet and numbers. Oral and written communication are both effective means of human interaction. However, each language has a sign language equivalent. Hearing-impaired and/or nonverbal individuals communicate through sign language. BDSL is the abbreviation for the Bangla sign language. The dataset contains images of hand signs in Bangla. The collection comprises 49 individual sign language images of the Bengali alphabet. BDSL49 is a set of 29,490 images with 49 labels. During data collection, images of fourteen distinct adults, each with a unique appearance and context, were captured. During data preparation, numerous strategies have been utilized to reduce noise. This dataset is available for free to researchers. Using techniques such as machine learning, computer vision, and deep learning, they are able to develop automated systems. Moreover, two models were applied to this dataset. The first is for detection, and the second is for identification.

Health problems and utilization of health services among Forcibly Displaced Myanmar Nationals in Bangladesh.

BACKGROUND: Access to and utilization of health services have remained major challenges for people living in low- and middle-income countries, especially for those living in impaired public health environment such as refugee camps and temporary settlements. This study presents health problems and utilization of health services among Forcibly Displaced Myanmar Nationals (FDMNs) living in the southern part of Bangladesh. METHODS: A mixed-method (quantitative and qualitative) approach was used. Altogether 999 household surveys were conducted among the FDMNs living in makeshift/temporary settlements and host communities. We used a grounded theory approach involving in-depth interviews (IDIs), focus group discussions (FGDs), and key informant interviews (KIIs) including 24 IDIs, 10 FGDs, and 9 KIIs. The quantitative data were analysed with STATA. RESULTS: The common health problems among the women were pregnancy and childbirth-related complications and violence against women. Among the children, fever, diarrhoea, common cold and malaria were frequently observed health problems. Poor general health, HIV/AIDS, insecurity, discrimination, and lack of employment opportunity were common problems for men. Further, 61.2% women received two or more antenatal care (ANC) visits during their last pregnancy, while 28.9% did not receive any ANC visit. The majority of the last births took place at home (85.2%) assisted by traditional birth attendants (78.9%), a third (29.3%) of whom suffered pregnancy- and childbirth-related complications. The clinics run by the non-governmental organizations (NGOs) (76.9%) and private health facilities (86.0%) were the most accessible places for seeking healthcare for the FDMNs living in the makeshift settlements. All participants heard about HIV/AIDS. 78.0% of them were unaware about the means of HIV transmission, and family planning methods were poorly used (45.2%). CONCLUSIONS: Overall, the health of FDMNs living in the southern part of Bangladesh is poor and they have inadequate access to and utilization of health services to address the health problems and associated factors. Existing essential health and nutrition support programs need to be culturally appropriate and adopt an integrated approach to encourage men's participation to improve utilization of health and family planning services, address issues of gender inequity, gender-based violence, and improve women empowerment and overall health outcomes.

Monitoring caesarean births using the Robson ten group classification system: A cross-sectional survey of private for-profit facilities in urban Bangladesh.

BACKGROUND: Globally, Caesarean section (CS) rates are mounting and currently exceed the safe upper limit of 15%. Monitoring CS rates using clinical indications and obstetric sub-group analysis could confirm that women in need have been served. In Bangladesh, the reported CS rate was 31% in 2016, and almost twice that rate in urban settings. Delivering in the private healthcare sector was a strong determinant. This study uses Robson Ten Group Classification System (TGCS) to report CS rates in urban Bangladesh. The clinical causes and determining factors for CS births have also been examined. METHODS: This record linkage cross-sectional survey was undertaken in 34 urban for-profit private hospitals having CS facilities during the period June to August 2015. Data were supplied by inpatient case records and operation theatre registers. Descriptive analyses were performed to calculate the relative size of each group; the group-specific CS rate, and group contribution to total CS and overall CS rate. CS indications were grouped into eleven categories using ICD 10 codes. Binary logistic regression was performed to explore the determinants of CS. RESULTS: Out of 1307 births, delivery by CS occurred in 1077 (82%). Three obstetric groups contributed the most to overall CS rate: previous CS (24%), preterm (23%) and term elective groups (22%). The major clinical indications for CS were previous CS (35%), prolonged and obstructed labor (15%), fetal distress (11%) and amniotic fluid disorder (11%). Multiple gestation, non-cephalic presentation, previous bad obstetric history were positive predictors while oxytocin used for labour induction and increased parity were negative predictors of CS. CONCLUSIONS: As the first ever study in urban private for-profit health facilities in Bangladesh, this study usefully identifies the burden of CS and where to intervene. Engagement of multiple stakeholders including the private sector is crucial in planning effective strategies for safe reduction of CS.

Association between socioeconomic status and prevalence of non-communicable diseases risk factors and comorbidities in Bangladesh: findings from a nationwide cross-sectional survey.

OBJECTIVES: This study aimed to examine the prevalence and distribution in the comorbidity of non-communicable diseases (NCDs) among the adult population in Bangladesh by measures of socioeconomic status (SES). DESIGN: This was a cross-sectional study. SETTING: This study used Bangladesh Demographic and Health Survey 2011 data. PARTICIPANTS: Total 8763 individuals aged ≥35 years were included. PRIMARY AND SECONDARY OUTCOME MEASURES: The primary outcome measures were diabetes mellitus (DM), hypertension (HTN) and overweight/obesity. The study further assesses factors (in particular SES) associated with these comorbidities (DM, HTN and overweight/obesity). RESULTS: Of 8763 adults, 12% had DM, 27% HTN and 22% were overweight/obese (body mass index ≥23 kg/m2). Just over 1% of the sample had all three conditions, 3% had both DM and HTN, 3% DM and overweight/obesity and 7% HTN and overweight/obesity. DM, HTN and overweight/obesity were more prevalent those who had higher education, were non-manual workers, were in the richer to richest SES and lived in urban settings. Individuals in higher SES groups were also more likely to suffer from comorbidities. In the multivariable analysis, it was found that individual belonging to the richest wealth quintile had the highest odds of having HTN (adjusted OR (AOR) 1.49, 95% CI 1.29 to 1.72), DM (AOR 1.63, 95% CI 1.25 to 2.14) and overweight/obesity (AOR 4.3, 95% CI 3.32 to 5.57). CONCLUSIONS: In contrast to more affluent countries, individuals with NCDs risk factors and comorbidities are more common in higher SES individuals. Public health approaches must consider this social patterning in tackling NCDs in the country.