A customised fetal growth and birthweight standard for Qatar: a population-based cohort study.

OBJECTIVES: Customized birthweight centiles have improved the detection of small for gestational age (SGA) and large for gestational age (LGA) babies compared to existing population standards. This study used perinatal registry data to derive coefficients for developing customized growth charts for Qatar. METHODS: The PEARL registry data on women delivering in Qatar (2017-2018) was used to develop a multivariable linear regression model predicting optimal birthweight. Physiological variables included gestational age, maternal height, weight, ethnicity, parity, and sex of the baby. Pathological variables such as hypertension, preexisting and gestational diabetes and smoking were calculated and excluded to derive the optimal weight at term. RESULTS: The regression model found a term optimal birthweight of 3,235 g for a Qatari nationality mother with median height (159 cm), booking weight (72 kg), parity (1) and gestation at birth (276 days) at the end of an uncomplicated pregnancy. Constitutional coefficients significantly affecting birthweight were gestational age, height, weight, and parity. The main pathological factors were preexisting diabetes (increase by +175.7 g) and smoking (decrease by -190.9 g). The SGA and LGA rates in the entire cohort after applying the population-specific customized centiles were 11.1 and 12.2 %, respectively (contrasting with the Hadlock standard: SGA-26.3 % and LGA-1.8 %, and Fenton standard: SGA-12.9 % and LGA-4.0 %). CONCLUSIONS: Constitutional and pathological variations in fetal growth and birthweight apply in the maternity population in Qatar and have been quantified to allow the generation of customised charts for better identification of pregnancies with abnormal growth. Currently in-use population standards may misdiagnose many SGA and LGA babies.

Catalytic innovations: Improving wastewater treatment and hydrogen generation technologies.

The effective reduction of hazardous organic pollutants in wastewater is a pressing global concern, necessitating the development of advanced treatment technologies. Pollutants such as nitrophenols and dyes, which pose significant risks to both human and aquatic health, making their reduction particularly crucial. Despite the existence of various methods to eliminate these pollutants, they are not without limitations. The utilization of nanomaterials as catalysts for chemical reduction exhibits a promising alternative owing to their distinguished catalytic activity and substantial surface area. For catalytically reducing the pollutants NaBH4 has been utilized as a useful source for it because it reduces the pollutants quiet efficiently and it also releases hydrogen gas as well which can be used as a source of energy. This paper provides a comprehensive review of recent research on different types of nanomaterials that function as catalysts to reduce organic pollutants and also generating hydrogen from NaBH4 methanolysis while also evaluating the positive and negative aspects of nanocatalyst. Additionally, this paper examines the features effecting the process and the mechanism of catalysis. The comparison of different catalysts is based on size of catalyst, reaction time, rate of reaction, hydrogen generation rate, activation energy, and durability. The information obtained from this paper can be used to steer the development of new catalysts for reducing organic pollutants and generation hydrogen by NaBH4 methanolysis.

Impact of bariatric surgery on maternal gestational weight gain and pregnancy outcomes in women with obesity: A population-based cohort study from Qatar.

BACKGROUND: Bariatric surgery is performed in obese women of reproductive age to help achieve a healthy prepregnancy weight to reduce the complications associated with obesity in pregnancy. However, these procedures can impact maternal nutrition and gestational weight gain (GWG). This study evaluates the maternal and neonatal outcomes in women with prepregnancy bariatric surgery and determines the impact on GWG. METHODS: This study included 24 weeks gestation or more pregnancies, with a maternal BMI at delivery of 30 kg/m2 or more. It was categorized into two groups based on whether they had prepregnancy bariatric surgery (exposed) or not (unexposed). The outcomes included gestational diabetes (GDM), gestational hypertension (GHT), mode of delivery, preterm birth (PTB), GWG, birthweight (BW) and customized BW centiles, low birthweight (LBW), congenital anomalies, and admission to the neonatal intensive unit (NICU). Categorization was also done based on the adequacy of GWG (low, adequate, and excess). RESULTS: A total of 8,323 women were included in the study, 194 of whom had prepregnancy bariatric surgery. After adjusting for confounders, the exposed group had a mean GWG 1.33 kg higher than the unexposed group (95% CI 0.55-2.13, p = 0.001). The exposed group had higher odds of PTB (aOR 1.78, 95% CI 1.16-2.74, p = 0.008), CD (aOR 6.52, 95% CI 4.28-9.93, p < 0.001), LBW in term babies (aOR 2.60, 95% CI 1.34-5.03, p = 0.005), congenital anomalies (aOR 2.64, 95% CI 1.21-5.77, p = 0.015), low APGAR score (aOR 3.75, 95% CI 1.12-12.5, p = 0.032) and 80.4g lesser birthweight (95% CI -153.0, -5.8; p = 0.034). More women in the low GWG category had LBW babies (28.6% versus 6.7% in the high GWG group, p = 0.033), lowest mean BW and median BW centiles (2775 grams versus 3289 grams in the high GWG group, p = 0.004 and 57.5% versus 74.5% in the high GWG group, p = 0.040, respectively). CONCLUSION: The findings of this study highlight differences in perinatal outcomes such as preterm birth, low birth weight, congenital anomalies, cesarean deliveries, and gestational weight gain between post-bariatric women and controls. These insights can help inform the planning and provision of appropriate maternity care to enhance patient safety and outcomes. The results of this study can also guide the counseling of reproductive age-group women who are planning to undergo bariatric surgery.

Maternal and neonatal outcomes associated with multiple repeat cesarean deliveries: A registry-based study from Qatar.

BACKGROUND: Cesarean delivery (CD) is associated with increased maternal and neonatal morbidity compared to vaginal delivery, particularly in cases classified as emergency procedures or when there are multiple CDs. This retrospective cohort study aims to examine the incidence of maternal and neonatal complications in women with multiple CDs. METHODS: This study used data from a national perinatal database obtained from a single tertiary maternity care hospital. Women who delivered a singleton live birth after 24 weeks of gestation by CD were stratified into five groups based on the number of CDs, with the last group having five or more CDs. The women were divided into those with five or more CDs (Group 5) versus those with fewer than five (Groups 1 to 4). The maternal outcomes included intra-operative surgical complications, blood loss, and intensive care unit (ICU) admission. The neonatal outcomes included preterm birth, neonatal ICU (NICU) admission, respiratory distress syndrome (RDS), and perinatal death. RESULTS: Of the 6,316 women in the study, 2,608 (41.3%) had a primary CD. 30.3%, 17.5%, and 7.3% of the cohort had their second, third, and fourth CDs, respectively. Women undergoing the 5th CD and above formed the remaining 3.5% (227). Women in Group 5 had the highest risk of suffering a surgical complication (3.1%, p = 0.015) and postpartum hemorrhage (7.5%, p = 0.010). 24% of babies in Group 5 were born preterm (p < 0.001). They also had a 3.5 times higher risk of having a surgical complication (RR = 3.5, 95% CI 1.6-7.6, p = 0.002), a 1.8 times higher risk of developing postpartum hemorrhage (RR = 1.8, 95% CI 1.1-2.9, p = 0.014), a 1.7 times higher risk of delivering between 32-37 weeks of gestation (RR = 1.7, 95% CI 1.3-2.2, p < 0.001), a higher risk of the baby getting admitted to NICU (RR = 1.3, 95% CI 1.0-1.6, p = 0.038), and developing RDS (RR = 1.5, 95% CI 1.2-2.0, p = 0.002) compared to Groups 1-4. The risks of neonatal outcomes such as NICU admission (RR 2.9, 95% CI 2.1-4.0) and RDS (RR 3.5, 95% CI 2.3-5.5) were much higher in elective CDs performed at term compared to preterm births (p < 0.001 for both). CONCLUSION: Maternal morbidity significantly increases with the increasing number of CD. The increased risk of RDS and NICU admissions in the neonate with multiple CDs reflects lower gestational age and birthweight in these groups-consideration of preoperative steroids for lung maturation in these women to reduce neonatal morbidity warrants further discussion.

Realization of an optical nanostructure 4×1 multiplexer based on metal-insulator-metal plasmonic waveguides.

The optical multiplexer was created at a nanoscale plasmonic structure utilizing the finite element method (FEM) with COMSOL version 5.5 software to enable maximum light confinement, high-speed optical systems, and a tiny structure. The metal-insulator-metal technology at a nanoscale dimension is used for creating the 4×1 multiplexer. In this design, the transmission threshold (T t h r e s h o l d ) is selected to be 100% for separating between logic "1" and logic "0" at a 1310 nm operating wavelength. The modulation depth (MD), contrast ratio (CR), and insertion loss (IL) characteristics were explained to evaluate the performance of the multiplexer. The CR has 3.48 dB, the MD offers an ideal performance with 95.28 %, and the IL has 3.31 dB.

Enhancing System Performance through Objective Feature Scoring of Multiple Persons' Breathing Using Non-Contact RF Approach.

Breathing monitoring is an efficient way of human health sensing and predicting numerous diseases. Various contact and non-contact-based methods are discussed in the literature for breathing monitoring. Radio frequency (RF)-based breathing monitoring has recently gained enormous popularity among non-contact methods. This method eliminates privacy concerns and the need for users to carry a device. In addition, such methods can reduce stress on healthcare facilities by providing intelligent digital health technologies. These intelligent digital technologies utilize a machine learning (ML)-based system for classifying breathing abnormalities. Despite advances in ML-based systems, the increasing dimensionality of data poses a significant challenge, as unrelated features can significantly impact the developed system's performance. Optimal feature scoring may appear to be a viable solution to this problem, as it has the potential to improve system performance significantly. Initially, in this study, software-defined radio (SDR) and RF sensing techniques were used to develop a breathing monitoring system. Minute variations in wireless channel state information (CSI) due to breathing movement were used to detect breathing abnormalities in breathing patterns. Furthermore, ML algorithms intelligently classified breathing abnormalities in single and multiple-person scenarios. The results were validated by referencing a wearable sensor. Finally, optimal feature scoring was used to improve the developed system's performance in terms of accuracy, training time, and prediction speed. The results showed that optimal feature scoring can help achieve maximum accuracy of up to 93.8% and 91.7% for single-person and multi-person scenarios, respectively.

Mapping of tide-dominated Hooghly estuary water quality parameters using Sentinel-3 OLCI time-series data.

The study explores the spatio-temporal variation of water quality parameters in the Hooghly estuary, which is considered an ecologically-stressed shallow estuary and a major distributary for the Ganges River. The estimated parameters are chlorophyll-a, total suspended matter (TSM), and chromophoric dissolved organic matter (CDOM). The Sentinel-3 OLCI remote sensing imageries were analyzed for the duration of October 2018 to February 2019. We observed that the water quality of the Hooghly estuaries is comparatively low-oxygenated, mesotrophic, and phosphate-limited. Ongoing channel dredging for maintaining shipping channel depth keeps the TSM in the estuary at an elevated level, with the highest amount of TSM observed during March of 2019 (41.59g m-3) at station A, upstream point. Since the pre-monsoon season, TSM data shows a decreasing trend towards the mouth of the estuary. Chl-a concentration is higher during pre-monsoon than monsoon and post-monsoon periods, with the highest value observed in April at 1.09 mg m-3 in station D during the pre-monsoon period. The CDOM concentration was high in the middle section (January-February) and gradually decreased towards the estuary's head and mouth. The highest CDOM was found in February at locations C and D during the pre-monsoon period. Every station shows a significant correlation among CDOM, TSM, and Chl-a measured parameters. Based on our satellite data analysis, it is recommended that SNAP C2RCC be regionally used for TSM, Chl-a, and CDOM for water quality product retrieval and in various algorithms for the Hooghly estuary monitoring.

Bearing Fault Diagnosis Using Lightweight and Robust One-Dimensional Convolution Neural Network in the Frequency Domain.

The massive environmental noise interference and insufficient effective sample degradation data of the intelligent fault diagnosis performance methods pose an extremely concerning issue. Realising the challenge of developing a facile and straightforward model that resolves these problems, this study proposed the One-Dimensional Convolutional Neural Network (1D-CNN) based on frequency-domain signal processing. The Fast Fourier Transform (FFT) analysis is initially utilised to transform the signals from the time domain to the frequency domain; the data was represented using a phasor notation, which separates magnitude and phase and then fed to the 1D-CNN. Subsequently, the model is trained with White Gaussian Noise (WGN) to improve its robustness and resilience to noise. Based on the findings, the proposed model successfully achieved 100% classification accuracy from clean signals and simultaneously achieved considerable robustness to noise and exceptional domain adaptation ability. The diagnosis accuracy retained up to 97.37%, which was higher than the accuracy of the CNN without training under noisy conditions at only 43.75%. Furthermore, the model achieved an accuracy of up to 98.1% under different working conditions, which was superior to other reported models. In addition, the proposed model outperformed the state-of-art methods as the Signal-to-Noise Ratio (SNR) was lowered to -10 dB achieving 97.37% accuracy. In short, the proposed 1D-CNN model is a promising effective rolling bearing fault diagnosis.

Contactless Small-Scale Movement Monitoring System Using Software Defined Radio for Early Diagnosis of COVID-19.

The exponential growth of the novel coronavirus disease (N-COVID-19) has affected millions of people already and it is obvious that this crisis is global. This situation has enforced scientific researchers to gather their efforts to contain the virus. In this pandemic situation, health monitoring and human movements are getting significant consideration in the field of healthcare and as a result, it has emerged as a key area of interest in recent times. This requires a contactless sensing platform for detection of COVID-19 symptoms along with containment of virus spread by limiting and monitoring human movements. In this paper, a platform is proposed for the detection of COVID-19 symptoms like irregular breathing and coughing in addition to monitoring human movements using Software Defined Radio (SDR) technology. This platform uses Channel Frequency Response (CFR) to record the minute changes in Orthogonal Frequency Division Multiplexing (OFDM) subcarriers due to any human motion over the wireless channel. In this initial research, the capabilities of the platform are analyzed by detecting hand movement, coughing, and breathing. This platform faithfully captures normal, slow, and fast breathing at a rate of 20, 10, and 28 breaths per minute respectively using different methods such as zero-cross detection, peak detection, and Fourier transformation. The results show that all three methods successfully record breathing rate. The proposed platform is portable, flexible, and has multifunctional capabilities. This platform can be exploited for other human body movements and health abnormalities by further classification using artificial intelligence.

Tsunami inundation maps for the northwest of Peninsular Malaysia and demarcation of affected electrical assets.

The massive destruction and loss caused by the 2004 Sumatra-Andaman tsunami were attributed to the lack of knowledge on tsunami and low regional detection and communication systems for early warning in that region. This study aimed to identify locations at risk of impending tsunami from Andaman Sea for the safety of community and proper development planning at the coastal areas by providing an updated and revised inundation maps. The last study on this area was conducted several years ago which open the possibility to new findings. Generated by tsunami simulation models, the maps illustrate the extent and level of inundation to which the coastal community and infrastructure would be subjected. As a result of coastal changes and availability of better topographic data, the existing inundation maps for the coastal areas of northwest Peninsular Malaysia at risk to impending tsunami from the Andaman Sea are revised. This paper documented the computational setup leading to the generation of the revised inundation maps. The tsunami simulation model TUNA was used to simulate the generation, propagation, and subsequent run-up and inundation of tsunamis triggered by earthquakes of moment magnitudes (Mw) 8.5, 9.0, and 9.25 along the Sunda Trench. From the simulations, it was found that at Mw 9.25, Balik Pulau, Pulau Pinang would be subjected to inundation of as far as 3.47 km with 5.40-m-deep inundation at the highest section.