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Abstract Integrating noble metal nanostructures, specifically silver nanoparticles, into sensor designs has proven to enhance sensor performance across key metrics, including response time, stability, and sensitivity. However, a critical gap remains in understanding the unique contributions of various synthesis parameters on these enhancements. This study addresses this gap by examining how factors such as temperature, growth time, and choice of capping agents influence nanostructure shape and size, optimizing sensor performance for diverse conditions. Using silver nitrate and sodium borohydride, silver seed particles were created, followed by controlled growth in a solution containing additional silver ions. The size and morphology of the resulting nanostructures were regulated to achieve optimal properties for biochemical sensing in wireless sensor networks. Results demonstrated that embedding these nanostructures in Polyvinyl Alcohol (PVA) matrices led to superior stability, maintaining 93% effectiveness over 30 days compared to 70% in Polyethylene Glycol (PEG). Performance metrics revealed significant improvements: reduced response times (1.2 ms vs. 1.5 ms at zero analyte concentration) and faster responses at higher analyte levels (0.2 ms). These outcomes confirm that higher synthesis temperatures and precise shape control contribute to larger, more stable nanostructures.The enhanced stability and responsiveness underscore the potential of noble metal nanostructures for scalable and durable sensor applications, offering a significant advancement over current methods.

Abstract A mask identification and social distance monitoring system using Unmanned Aerial Vehicles (UAV) in the outdoors has been proposed for a health establishment. The above approach performed surveillance of the surrounding area using cameras installed in UAVs and internet of things technologies, and the captured images seem useful for tracking the entire environment. However, innate images from unmanned aerial vehicles show an adaptable visual effect in an uncontrolled environment, making face-mask detection and recognition harder. The UAV picture first had to be converted to grayscale, then its contrast was amplified. Image contrast was improved using Optimum Wavelet-Based Masking and the Enhanced Cuckoo Methodology (ECM). According to the contrast-enhanced image, Gabor-Transform (GT) and Stroke Width Transform (SWT) methods are used to derive attributes that help categorise mask-wearers and non-mask-wearers. Using the retrieved attributes, a Weighted Naive Bayes Classification (WNBC) detected masks in the images. Additionally, a deep neural network-based, the faster Region-Based Convolutional Neural Networks (R-CNN) algorithm combined with Adaptive Galactic Swarm Optimization (AGSO) is being used to identify appropriate and incorrect face mask wear in images, as well as to monitor social distancing among individuals in crowded areas. When the system recognises unmasked individuals, it sends their information to the doctor and the nearby police station. One unmanned aerial vehicle’s automated system alert people via speakers, ensuring social spacing. The problem involves a large percentage of appropriate and incorrect face mask wear using data from GitHub and Kaggle, including a training repository of 16,000 images and a testing data set of 12,751 images. To enhance the performance of the model’s learning, the methodology of 10-fold cross-validation will be used. Precision, recall, F1-score, and speed are then measured to determine the efficacy of the suggested approach.

Abstract The traditional methods used in big data, like cluster creation and query-based data extraction, fail to yield accurate results on massive networks. To address such issues, the proposed approach involves using the Hadoop Distributed File System (HDFS) for data processing, the map-reduce programming paradigm for data processing, and query optimization techniques to quickly and effectively extract accurate outcomes from a variety of options with a high processing capacity. The methodology proposed in this work makes use of Gaussian Mixture Model (GMM) for data clustering and the Salp Swarm Algorithm (SSA) for optimization. The security of preprocessed data stored on networked clusters with interconnections has been ensured by SHA algorithms. Finally, incorporating into consideration the important parameters, evaluation findings for the experimental performance of the model in the indicated methodology are produced. For this work, the estimated range of input file sizes is 60–100 MB. The processing of 100 MB of input files yielded an accuracy of 96% and results for specificity and sensitivity of 90% and 93%, respectively. The outcomes have been compared with well-known methods like fuzzy C-means and K-means approaches, and the results show that the proposed method effectively distributes accurate data processing to cluster nodes with low latency. Moreover, it uses the least amount of memory resources possible when operating on functional CPUs. As a result, the proposed approach outperforms existing techniques.

Abstract Globally, the prevalence of multidrug-resistant tuberculosis (MDR-TB) has been increasing recently. This is a major public health concern, as MDR-TB is more difficult to treat and has poorer outcomes compared to drug-sensitive tuberculosis. The main objective of the study was to identify risk factors for recurrent multidrug-resistant tuberculosis, at Alert Specialized Hospital, Addis Ababa, by using different parametric shared frailty models. From January 2016 to December 2021, a retrospective study was conducted on MDR-TB patients at Alert Specialized Hospital in Addis Ababa. The data for the study were collected from the medical records of MDR-TB patients at the hospital during this time period. Gamma and inverse-Gaussian shared frailty models were used to analyze the dataset, with the exponential, Weibull, and lognormal distributions included as baseline hazard functions. The data were analyzed using R statistical software. The median recurrence time of the patients was 12 months, and 149 (34.3%) had recurrences. The clustering effect was statistically significant for multiple drug-resistant tuberculosis patients’ recurrence. According to the Weibull-Inverse-Gaussian model, factors that reduced time to MDR-TB recurrence included lower weight (ɸ = 0.944), smoking (ɸ = 0.045), alcohol use (ɸ = 0.631), hemoptysis (ɸ = 0.041), pneumonia (ɸ = 0.564), previous anti-TB treatment (ɸ = 0.106), rural residence (ɸ = 0.163), and chronic diseases like diabetes (ɸ = 0.442) were associated with faster recurrence. While, higher education (ɸ = 3.525) and age (ɸ = 1.021) extended time to recurrence. For weight increment, smokers and alcohol users, clinical complications of hemoptysis and pneumonia, patients with pulmonary disease who had a history of previous anti-TB treatment, and being rural residents are prognostic factors. There was a significant clustering effect at the Alert Specialized Hospital in Addis Ababa, Ethiopia. The Weibull-Inverse Gaussian Shared Frailty Model was chosen as the best model for predicting the time to recurrence of MDR-TB.

Abstract Background Agriculture is Ethiopia’s principal economic activity, employing the vast majority of the population, however, smallholder farmers in the country face long-standing challenges, including low levels of production, low income, and less credit use. This study aimed to analyze the impact of access to credit on crop productivity and income among small-scale farmers in the study area. Methods We conducted a cross-sectional study from January 1st to 30th, 2022. Primary data were collected from purposely selected seven (7) kebele of the district based on potential users of agricultural credit. Then the impact was investigated by applying the propensity score matching technique. A Probit regression model was used to determine factors associated with credit use. The significance of the variables was decided at a 5% level of significance. Results In our study, a total sample of 347 smallholder farmers were randomly selected, out of which 42.9% were credit-users while 59.1% were non-users. Regarding sources of credit use, Vision Fund was the most commonly used source of credit with 67.8% of users, followed by the Omo Microfinance institution with 22.1%. Sex of the farmer, age of the farmer, existence collateral, farm size, attitude towards risk, extension service, membership in credit institutions, road access, and number of total livestock units in tropical livestock units (TLU) had significant effects on credit use at a 5% level of significance. Moreover, the estimates showed that utilizing credit resulted in a rise in crop productivity in current market value and total yearly income by 19,302.44 and 18,009.63 respectively. Conclusion Our study suggested that credit use had a considerable impact on crop productivity and total annual income. As a result, the study recommended that the government’s rural finance policy agenda should prioritize opening different branches near farmers, improving credit usability, and providing more training programs to smallholder farmers to improve their skills and help reduce the problems that impede their use of agricultural credit.

Abstract Background Skilled health care is essential for the mother's and newborn's health and well-being during pregnancy, labor, and the postpartum period. This study aimed to analyze women's knowledge, attitudes, and practices while requesting competent assistance for maternity healthcare in Gesha District, Southwest Region of Ethiopia. Methods A community-based cross-sectional study design was conducted from September 20, 2021 to October 19, 2021. A total of 424 mothers participated in this study and a systematic sampling technique was used to select the respondents. The data were collected using a pretested and structured questionnaire. Statistical software SPSS-20 and R-4.1.2 were used to enter and analyze the data respectively. The factors associated with the prevalence of Knowledge, Attitude, and Practice in seeking Skilled Maternal Healthcare Services were identified using descriptive analysis and a binary logistic regression model. Results This study result revealed that the overall proportions of good knowledge, positive attitude, and good practice in seeking skilled maternal health care services were 39.15%, 37.5%, and 34.67% respectively. Estimated odds of having knowledge, attitude, and practice were as follows: for having age between 20–24 years at first pregnancy 1.859, 1.86, and 1.799; having a plan for pregnancy 2.74,2.315 and 2.579; mothers attended elementary education 2.337, 2.565 and 3.312; having maternal age 20–24 years 4.336,4.989 and 5.870; maternal age 25–29 years 2.917, 3.794 and 4.017; maternal age 35–49 years 2.837, 2.991 and 3.412; having husbands who had attended elementary education level 2.736, 2.542 and 2.134; secondary and above education 3.464, 3.360 and 2.508; rich mothers 2.261, 1.995 and 2.452; having antenatal care 4 times and above 2.606, 2.570, 2.682; having transportation access 1.921, 1.956 and 2.404; having media access 1.979, 2.171 and 2.715 respectively. The odds of having attitude and practice respectively were as follows: married 1.762, and 2.208; having medium wealth index 1.933 and 2.424. The odds of having previous pregnancy complications was 2.147 which significantly affect the practice of seeking skilled maternal care assistance. Conclusions This study discovered that the study participants' knowledge, attitude, and practice of skilled maternal health care are low. Associated factors included age at first pregnancy, planned pregnancy, maternal education level, husband’s education level, maternal age, antenatal care service visits, transport access, and access to media were found to significantly affect the knowledge, attitude, and practice of the respondents in seeking skilled maternal care assistance in common. The household wealth index was also associated with attitude. Pregnancy complications, current marital status, and household wealth index also significantly affect the practice of seeking skilled maternal care assistance. As a result of the findings, initiatives to increase women's knowledge, attitudes, and use of expert maternal health services in the research area are needed for women residing in rural areas.

Abstract Nowadays, Tuberculosis remains the major cause of HIV-associated mortality, which accounts for 1 out of every 5 HIV-related mortality worldwide. This study aimed to determine the survival rate and predictors of mortality among TB/HIV co-infected patients. An institution-based retrospective cohort study was undertaken on adult TB/HIV co-infected individuals between 1st February 2014 and 30th January 2022 at Mettu Karl Referral Hospital. A Cox regression model was used to identify predictors of survival time to death among TB/HIV co-infected patients. This study comprised 402 TB and HIV co-infected adult patients. Among these, 84 (20.9%) died, and 318 (79.1%) were censored. The study subjects have been followed up for 6920 person-months with an overall median survival time of 17.6 months. The overall incidence rate was 12.1 per 1000 person months [95% CI: 9.77–14.98]. The results of a multivariable Cox regression analysis showed that being at an older age, urban residence, WHO clinical stage II & IV, CD4 count of ≥ 200 cells/mm3, bedridden functional status, using INH, and using CPT were associated with the survival time of TB and HIV co-infected patients at a significance level of alpha = 0.05. This retrospective study found that high mortality of TB/HIV co-infected patients occurred in the earlier months of treatment initiation. Close monitoring of patients with low CD4, who do not utilize CPT, who are in advanced WHO stages, and who have poor functional levels can help them improve their health and live longer.

Background: Depressive symptom is the most widely reported mental health consequence of natural or man-made disasters and traumatic events. Research on depressive symptoms in low-income nations is still scarce, although it can be a public health burden in post-conflict situations. Therefore, the primary objective of this study was to identify the prevalence and contributing factors of depressive symptoms among people of south Wollo zones following liberation from TPLF-led army invasions. Methods: A community-based cross-sectional study was conducted on South Wollo zone residents after the liberation of invasions of the TPLF-led force, from May 1st to June 1st, 2022. A self-administered questionnaire was used to collect data from residents chosen using a simple random sampling technique. This study used both descriptive and inferential analysis. To investigate the relationship between response and predictor variables, the chi-squared test of association was performed. The logistic regression was performed to identify predictors of depressive symptoms among residents. Results: The overall prevalence of depressive symptoms among residents was 51.0% [95% C.I: 48.01, 53.99]. Being female [AOR = 1.428, 95% C.I: 1.044–1.955], being an alcohol consumer [AOR = 2.051, 95% C.I: 1.349–3.119], chewing Khat [AOR = 4.617, 95% C.I: 2.873–7.418], history of mental illness [AOR = 4.316, 95% C.I: 1.263–14.751], destruction of personal property [AOR = 2.909, 95% C.I: 2.028–4.175], lacked basic needs such as food and water [AOR = 2.738, 95% C.I: 1.922–3.900], and illness without medical care [AOR = 2.369, 95% C.I: 1.684–3.331] were all found to be statistically significant at the 5% level of significance in a multivariable binary logistic regression. Conclusions: The main finding of this study discovered that residents had a higher prevalence of depressive symptoms following liberation from TPLF-led army invasions. Being an alcoholic, chewing Khat, having a previous history of mental illness, destruction of personal property, lack of food or water, and illness without medical care were associated with an increased risk of developing depressive symptoms. Interventions based on influencing factors should be performed to ensure residents' mental health.

BackgroundInsomnia is a prevalent sleep disorder that affects people all over the world. Creating suitable interventions will require a better understanding of the magnitude and determinants of insomnia. This study aimed to assess the prevalence and associated factors of insomnia symptoms among residents of Mettu town during the pandemic lockdown.MethodologyA community-based cross-sectional study was conducted among residents of Mettu town from October 1st to October 15th, 2020. Residents who lived in Mettu town at least for six months were included. To determine the prevalence and determinants of insomnia symptoms, both descriptive and inferential analyses were used. The chi-squared test of association and logistic regression was used to identify predictors of insomnia symptoms among residents of Mettu town. We used SPSS version 25 for all statistical analyses.Principal findingsThe prevalence of depressive symptoms among residents of Mettu town was 52.6%. According to results of multivariable binary logistic regression, being female [AOR = 3.677, 95%CI: 2.124-6.365], being aged between 19 and 40 [AOR = 13.261, 95%CI: 6.953-25.291], being aged above 41 [AOR = 2.627, 95%CI: 1.120-6.159], smoking [AOR = 15.539, 95%CI: 7.961-30.329], satisfaction with information available [AOR = 0.310, 95%CI: 0.168-0.570], fear Corona Virus Disease 2019 (COVID-19), [AOR = 2.171, 95%CI: 1.262-3.733], feeling alienated from others [AOR = 3.288, 95%CI: 1.897-5.699], having somatic symptoms [AOR = 2.298, 95% CI: 1.360-3.884], having depressive symptoms [AOR = 1.841, 95% CI: 1.073-3.160], and experiencing psychological distress [AOR = 1.962, 95% CI: 1.173-3.281] were significantly associated with insomnia symptoms.ConclusionIn this study, the prevalence of insomnia symptoms was found to be high among residents of Mettu town. Being female, being aged between 19 and 40, being aged above 41 years, smoking, fear of Corona Virus Disease 2019, feeling alienated from others, having somatic symptoms, having depressive symptoms, and experiencing psychological distress were all associated with an increased risk of developing insomnia symptoms while being satisfied with the information available decreased the risk of insomnia symptoms among residents of Mettu town. Interventions should be put in place to promote healthy sleep among residents of Mettu town.

Background. Stroke is a life-threatening condition that occurs due to impaired blood flow to brain tissues. Every year, about 15 million people worldwide suffer from a stroke, with five million of them suffering from some form of permanent physical disability. Globally, stroke is the second-leading cause of death following ischemic heart disease. It is a public health burden for both developed and developing nations, including Ethiopia. Objectives. This study is aimed at estimating the time to death among stroke patients at Jimma University Medical Center, Southwest Ethiopia. Methods. A facility-based retrospective cohort study was conducted among 432 patients. The data were collected from stroke patients under follow-up at Jimma University Medical Center from January 1, 2016, to January 30, 2019. A log-rank test was used to compare the survival experiences of different categories of patients. The Cox proportional hazard model and the accelerated failure time model were used to analyze the survival analysis of stroke patients using R software. An Akaike’s information criterion was used to compare the fitted models. Results. Of the 432 stroke patients followed, 223 (51.6%) experienced the event of death. The median time to death among the patients was 15 days. According to the results of the Weibull accelerated failure time model, the age of patients, atrial fibrillation, alcohol consumption, types of stroke diagnosed, hypertension, and diabetes mellitus were found to be the significant prognostic factors that contribute to shorter survival times among stroke patients. Conclusion. The Weibull accelerated failure time model better described the time to death of the stroke patients’ data set than other distributions used in this study. Patients’ age, atrial fibrillation, alcohol consumption, being diagnosed with hemorrhagic types of stroke, having hypertension, and having diabetes mellitus were found to be factors shortening survival time to death for stroke patients. Hence, healthcare professionals need to thoroughly follow the patients who pass risk factors. Moreover, patients need to be educated about lifestyle modifications.

Background: Generalized anxiety disorder is characterized by excessive and uncontrollable worry about a variety of events. It is critical to ensure a pregnant mother's mental health in order to reduce pregnancy and birth-related problems. The major goal of current study was to identify the factors associated with generalized anxiety disorder among mothers attending perinatal services in the study area during COVID-19 using ordinal logistic regression. Methods: The institution-based cross-sectional study was conducted from July 10th, 2020 to August 10th, 2020 at Kembata Tembaro zone, Southern Ethiopia. The current study included 423 mothers. The GAD-7 scale was used to assess the anxiety level among mothers. An Ordered logit model was used to identify the determinants of GAD. Brant test of the parallel line was utilized to check proportionality assumption. The statistical significance was determined using an adjusted proportional odd ratio with a 95%CI, and a p-value

Background: The maternal mortality rate in poor nations remains unacceptably high. The purpose of this study was to identify factors associated with institutional delivery usage. Methods: The data came from the Ethiopian mini demographic and health survey, which was conducted in 2019. This study comprised 3978 women of reproductive age who had given birth within the previous 5 years. To uncover significantly linked parameters associated with institutional delivery, we used a multilevel logistic regression model. Statistical significance was declared at p

Background. Socioeconomic status (SES) refers to an individual’s or group’s social position or class, which is often determined by a combination of education, income, and occupation. Knowing factors that affect the SES of the society might help to take action and improve their economy. In addition, using an ordinal logistic regression model for ordered SES outcomes will yield suitable results and conclusions. This study aimed to utilize an ordinal logistic regression model to find the factors associated with SES for households in Tepi town, Southwest Ethiopia. Methods. The community-based cross-sectional study was carried out in Tepi town, southwest Ethiopia, with data collected from 382 households using a simple random sample technique. The ordinal logistic regression models were evaluated and contrasted for proper accounting of ordinal form. In addition, to come up with a better model, we compared fitted ordinal logistic models with the likelihood-ratio test and AIC criteria. We performed data analysis using STATA version 16. Results. Of all 382 household heads, 170 (45.5%), 120 (31.4%), and 92 (24.1%) were at low, medium, and high SES of households, respectively. According to the result of the multivariable, partial proportional odds model (PPOM), age, education level, family size, and the saving habit were significantly associated with the SES of households at a 5% level of significance. Conclusions. According to the findings of this study, ordinal regression may be a better option in the event of the ordinal form of the outcome. Furthermore, PPOM may be a preferable option if any of the covariates violate the proportionality requirement. Based on the result of this study, the most likely associated indicators with the SES of families in Tepi town, southwest Ethiopia, were family size, age, saving habit, and education level. It is recommended that action should be taken to improve the SES of households.