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This paper is an effort to establish the financial sector in developing countries to promote financial literacy of their customers. This could have access to finance and savings, which in turn support livelihoods, economic growth, sound financial systems, and participate in the economy. The main objectives of this paper is to enhance a bank's visibility in the communities it serves, empowering consumers to take action to improve their financial well-being, and enable consumers to make better-informed choices in the financial market is the ultimate goal. Financial literacy is critical for promoting access to finance by creating incentives and environments that promote desired financial behaviours such as saving, budgeting, or using credit wisely. Having access to savings products or insurance can greatly affect consumers' financial future, and are also likely to represent a higher share of income and thus, have a significant impact on well-being.

This study assessing the effect of home-to-school distance on student's dropout rate in Adi-Keyih sub-zone, Southern administrative region, Eritrea. In the current study, correlational method is used to test the significance of home-to-school distance on dropout rate of students. The population of the study embraces all 24 schools in Adi-Keyih sub-zone and their 15,457 students. Out of the total students there were 1215 dropout students (7.9 %) and all of them have been included in the study. For comparative and inferential purposes, the same number of non-dropout students (1215) were selected using systematic random sampling methods and the sample of non-dropout students from each school is proportional to the active student population in each school. This approach yielded a total of 2430 students, which is 15.7 % of the total population. Data were collected from student's personal files by conducting field visits to each school and analysed using simple Chi-Square and logistic model. The finding of logistic regression analyses show that home-to-school distance has a direct effect on dropout rate: as home-to-school distance increases, the likelihood for a dropping out also increases. The relationship is statistically significant at P < 0.10. The study clearly demonstrates that home-to-school distance affect the dropout rate in Adi-Keyih sub-zone, but this result could not be generalized across the whole country as it requires a bigger and more detailed study.

Fetal electrocardiogram (fECG) monitoring is crucial for assessing fetal condition during pregnancy. However, current fECG extraction algorithms are not suitable for wearable devices due to their high computational cost and multi-channel signal requirement. The paper introduces a novel and efficient algorithm called Adaptive Improved Permutation Entropy (AIPE), which can extract fetal QRS from a single-channel abdominal ECG (aECG). The proposed algorithm is robust and computationally efficient, making it a reliable and effective solution for wearable devices. To evaluate the performance of the proposed algorithm, we utilized our clinical data obtained from a pilot study with 10 subjects, each recording lasting 20 min. Additionally, data from the PhysioNet 2013 Challenge bank with labeled QRS complex annotations were simulated. The proposed methodology demonstrates an average positive predictive value ( + P ) of 91.0227%, sensitivity (Se) of 90.4726%, and F1 score of 90.6525% from the PhysioNet 2013 Challenge bank, outperforming other methods. The results suggest that AIPE could enable continuous home-based monitoring of unborn babies, even when mothers are not engaging in any hard physical activities., (© 2024. Australasian College of Physical Scientists and Engineers in Medicine.)

Monitoring of electrocardiogram (ECG) provides vital information as well as any cardiovascular anomalies. Recent advances in the technology of wearable electronics have enabled compact devices to acquire personal physiological signals in the home setting; however, signals are usually contaminated with high level noise. Thus, an efficient ECG filtering scheme is a dire need. In this paper, a novel method using Ensemble Kalman Filter (EnKF) is developed for denoising ECG signals. We also intensively explore various filtering algorithms, including Savitzky-Golay (SG) filter, Ensemble Empirical mode decomposition (EEMD), Normalized Least-Mean-Square (NLMS), Recursive least squares (RLS) filter, Total variation denoising (TVD), Wavelet and extended Kalman filter (EKF) for comparison. Data from the MIT-BIH Noise Stress Test database were used. The proposed methodology shows the average signal to noise ratio (SNR) of 10.96, the Percentage Root Difference of 150.45, and the correlation coefficient of 0.959 from the modified MIT-BIH database with added motion artifacts.

Fetal electrocardiogram (fECG) assessment is essential throughout pregnancy to monitor the wellbeing and development of the fetus, and to possibly diagnose potential congenital heart defects. Due to the high noise incorporated in the abdominal ECG (aECG) signals, the extraction of fECG has been challenging. And it is even a lot more difficult for fECG extraction if only one channel of aECG is provided, i.e., in a compact patch device. In this paper, we propose a novel algorithm based on the Ensemble Kalman filter (EnKF) for non-invasive fECG extraction from a single-channel aECG signal. To assess the performance of the proposed algorithm, we used our own clinical data, obtained from a pilot study with 10 subjects each of 20 min recording, and data from the PhysioNet 2013 Challenge bank with labeled QRS complex annotations. The proposed methodology shows the average positive predictive value (PPV) of 97.59%, sensitivity (SE) of 96.91%, and F1-score of 97.25% from the PhysioNet 2013 Challenge bank. Our results also indicate that the proposed algorithm is reliable and effective, and it outperforms the recently proposed extended Kalman filter (EKF) based algorithm.

We develop a novel wearable fetal electrocardiogram (fECG) monitoring system consisting of an abdominal patch that communicates with a smart device. The system has two main components: the fetal patch and the monitoring app. The fetal patch has electronics and recording electrodes fabricated on a hybrid flexible-rigid platform while the Android app is developed for a wide range of applications. The patch collects the abdominal ECG (aECG) signals that are sent to the smart device app via secure Bluetooth Low Energy (BLE) communication. The app software connects to a cloud server where processing and extraction algorithms are executed for real-time fECG extraction and fetal heartrate (fHR) calculation from the collected raw data. We have validated the algorithms and real-time data recordings on pregnant subjects yielding promising results. Our system has the potential to transform the currently used fetal monitoring system to an effective distanced and telematernity care.

Monitoring of fetal electrocardiogram (fECG) would provide useful information about fetal wellbeing as well as any abnormal development during pregnancy. Recent advances in flexible electronics and wearable technologies have enabled compact devices to acquire personal physiological signals in the home setting, including those of expectant mothers. However, the high noise level in the daily life renders long-entrenched challenges to extract fECG from the combined fetal/maternal ECG signal recorded in the abdominal area of the mother. Thus, an efficient fECG extraction scheme is a dire need. In this work, we intensively explored various extraction algorithms, including template subtraction (TS), independent component analysis (ICA), and extended Kalman filter (EKF) using the data from the PhysioNet 2013 Challenge. Furthermore, the modified data with Gaussian and motion noise added, mimicking a practical scenario, were utilized to examine the performance of algorithms. Finally, we combined different algorithms together, yielding promising results, with the best performance in the F1 score of 92.61% achieved by an algorithm combining ICA and TS. With the data modified by adding different types of noise, the combination of ICA-TS-ICA showed the highest F1 score of 85.4%. It should be noted that these combined approaches required higher computational complexity, including execution time and allocated memory compared with other methods. Owing to comprehensive examination through various evaluation metrics in different extraction algorithms, this study provides insights into the implementation and operation of state-of-the-art fetal and maternal monitoring systems in the era of mobile health., Competing Interests: Conflicts of Interest: The authors declare no conflict of interest.

Though the incidence of gout in general population is less than 5% (globally), a study conducted in Eritrea among patients with multidrug resistant tuberculosis (MDR-TB) revealed a higher incidence (25%). Other similar studies conducted elsewhere, however, did not report gout as an adverse effect. It is unknown why a higher incidence of gout is being reported in Eritrea compared to similar studies from other countries. The objective of this study is therefore to explore risk factors for the increased risk of incident gout among MDR-TB patients in Eritrea. A case-control study was conducted in Merhano MDR-TB National Referral Hospital in Eritrea. All MDR-TB patients diagnosed with gout between June 2011 and June 2018 were considered as cases. Controls matched by age, sex, and cumulative drug exposure time were selected from the same study population (1:1 ratio). A total of 84 MDR-TB patients, 42 cases and 42 controls, were included in this study. No patient from the control group has comorbidities, while six patients from the case group have diabetes ( χ 2 = 6.46, df=1, p=0.026). Patients having tachycardia (OR=3.26, 95% CI=1.28, 8.27), alopecia (OR=3.11, 95% CI=1.00, 9.67), and gastrointestinal upset (OR=3.17, 95% CI=1.26, 7.96) as adverse effects and being on prolonged use of propranolol (OR=3.26, 95% CI=1.28, 8.27) were found to be more likely to develop incident gout compared to their controls. In conclusion, MDR-TB patients with diabetes mellitus, tachycardia, alopecia, and gastrointestinal upset and on prolonged use of propranolol tablet had increased risk of incident gout.

Electrocardiogram (ECG) monitoring of the fetus during pregnancy, before and during labor, can provide crucial information for the assessment of fetal well-being and development, as well as labor progress. An out-of-clinics fetal ECG monitoring system may pave the way for instant diagnosis, suggesting immediate intervention, which could help reduce the fetal mortality rate. In this paper, we present an unobtrusive fetal maternal ECG monitoring system which can operate in the home setting. The acquisition of the mother's abdominal ECG is done using the non-contact electrode approach. The extraction of the fetal ECG from the combined fetal/maternal ECG signal is investigated using both Fast Independent Component Analysis (FastICA) and RobustICA algorithms. An accelerometer is integrated for motion artifact detection which would help reduce interferences due to movement. The device also is connected to a cloud server, allowing doctors to access the data in real time.

For efficient and accurate estimation of the location of objects, a network of sensors can be used to detect and track targets in a distributed manner. In nonlinear and/or non-Gaussian dynamic models, distributed particle filtering methods are commonly applied to develop target tracking algorithms. An important consideration in developing a distributed particle filtering algorithm in wireless sensor networks is reducing the size of data exchanged among the sensors because of power and bandwidth constraints. In this paper, we propose a distributed particle filtering algorithm with the objective of reducing the overhead data that is communicated among the sensors. In our algorithm, the sensors exchange information to collaboratively compute the global likelihood function that encompasses the contribution of the measurements towards building the global posterior density of the unknown location parameters. Each sensor, using its own measurement, computes its local likelihood function and approximates it using a Gaussian function. The sensors then propagate only the mean and the covariance of their approximated likelihood functions to other sensors, reducing the communication overhead. The global likelihood function is computed collaboratively from the parameters of the local likelihood functions using an average consensus filter or a forward-backward propagation information exchange strategy., Competing Interests: The author declares no conflict of interest.

We report on a late Oligocene proboscidean species from Eritrea, dated to 26.8 +/- 1.5 Mya. This "missing link" between early elephantiformes and Elephantimorpha is the oldest known nongomphothere proboscidean to probably display horizontal tooth displacement, typical of elephants [Elephantimorpha consists of Mammutida (mastodons) and Elephantida, and Elephantida includes gomphotheres, stegodons, and elephants]. Together with the newly discovered late Oligocene gomphotheres from Chilga, Ethiopia, the Eritrean taxon points to the importance of East Africa as a major area for the knowledge of the early evolution of Elephantimorpha before the faunal exchange between Eurasia and Africa.