Background: Hepatitis B virus (HBV) is highly infectious and poses significant global health challenges. Healthcare workers (HCWs) are particularly at risk of HBV infection due to occupational exposure through contact with infected blood or body fluids. To mitigate this risk, hepatitis B vaccination is recommended for all healthcare workers and is administered in three doses at 0, 1, and 6 months, aiming to reduce exposure and provide immunity against the virus. However, the effectiveness of the HBV vaccine remains uncertain in the study area.
Objective: This study aimed to evaluate hepatitis B vaccine efficacy and factors affecting vaccine effectiveness among healthcare workers in all Hospitals of East Gojjam Zone Amhara Region, North West Ethiopia
Materials and Methods: A hospital-based cross-sectional study was conducted among 422 healthcare workers from March 25, 2024, to November 30, 2024. Simple random sampling technique was used to recruit the study participants. Total sample size was allocated proportionally to each hospital. Socio-demographic and clinical data were collected using a pretested structured questionnaire. Moreover, venous blood samples (4–5 ml) were collected from the study participants, and serum samples were analyzed to measure and detect the concentrations of anti-hepatitis B surface antigen (anti-HBs), hepatitis B surface antigen (HBsAg), and anti-hepatitis B core antibody (anti-HBc) using a sandwich enzyme-linked immunosorbent assay (ELISA). Additionally, hepatitis B virus DNA (HBV DNA) was assessed using real-time polymerase chain reaction (qPCR). Data were analyzed using SPSS software version 25. Logistic regression model with 95% CI was used to show the statistical association. P value < 0.05 was considered as statistically significant.
Results: The overall sero-prevalence of HBsAg among all study participants was 23/422 (5.5%) (95% CI: 3.0-8.0). In multivariable logistic regression, blood transfusion (AOR: 16.5, 95% CI: 1.53, 29.24) (P<0.011) and dental extraction (AOR: 13.5, 95% CI: 3.99, 45.57) (P<0.000) were significantly associated with HBsAg positivity. In addition, the overall sero-reactivity of anti-HBc was 30/422 (7.1%) (95% CI: 5.0-10.0). Injectable medication (AOR: 4.3, 95% CI: 1.73, 10.72) (P<0.002) was significant determinant for anti-HBc sero-positivity. Furthermore, of fully vaccinated healthcare workers, 346/422 (82.0%) (95% CI: 78.0- 86.0 %) of them were sero-protected (had anti-HBs titer ≥ 10 mIU/ml). The paradoxical sero-positivity of HBsAg and anti-HBs among fully vaccinated healthcare workers was 16(4.6%). Moreover, among HBsAg-positive individuals, HBV DNA was detected in 17 (73.9%) of them.
Conclusions and recommendations: The study identified intermediate HBV seroprevalence among vaccinated healthcare workers, indicating suboptimal vaccine efficacy. These findings emphasize the necessity of monitoring post-vaccination anti-HBs antibody levels to address gaps in protection.
Keywords: Hepatitis B vaccine effectiveness, Healthcare workers, Hospitals

This study suggests an enhanced version of the adaptive fuzzy fast terminal synergetic controller(AF-FTSC) for controlling the uncertain DC/DC buck converter based on the synergetic theory ofcontrol (STC) and newly developed terminal attractor technique (TAT). The benefits of the proposedSC algorithm involve the features of finite-time convergence, unaffected by parameter variations, andchattering-free phenomenon. A type-1 fuzzy logic system (T1-FLS) make the considered controllermore robust and is utilized to estimate the undefined converter nonlinear dynamics without resortingto the usual linearization and simplifications of the converter model. Taking a switching DC-DC buckconverter as a demonstration, the suggested AF-FTSC is thoroughly analyzed and executed on adSPACE ds1103 controller board. The outcomes of the experiment confirm the competence andapplicability of the suggested regulator.Keywords: Synergetic control, Fuzzy logic system, Fast terminal method, Finite-time convergence, DC/DCbuck converter

Natural fiber-reinforced polymer composites are the most widely used materials and preferable interms of biodegradability, cost production, recyclability, and low density. The main aim of thisstudy is to conduct an experimental investigation on tensile strength and impact strength ofpalmyra palm leaf stalk fiber (PLSF) and sisal fiber reinforced polymer hybrid composite. Thecomposite material was fabricated using hand lay-up techniques. The working parameters aremass fraction ratio of PLSF/sisal fiber and volume fiber fraction with the matrix. Tensile strengthand impact energy resistance tests were experimentally conducted according to the ASTM stan-dard dimensions. The results revealed that the addition of sisal fiber to PLSF enhanced the tensilestrength by 12.850 %, 26.540 %, and 30.630 % respectively compared to pure Palmyra palm leafstalk fiber reinforced composite (PPFRC). Whereas, the addition of PLSF to sisal fiber improvedthe impact of energy by 20.980 %, 13.610 %, and 11.880 % compared to pure sisal fiber rein-forced composite (PSFRC). The tensile strength with 20 % fiber volume fraction is improved by53.996 % and 12.188 % compared to 10 % and 15 % of fiber respectively. The impact strengthwas also enhanced by 24.931 % and 10.030 % compared to 10 % and 15 % of volume fiberfraction respectively. The tensile strength and impact energy of the treated fiber compositeincreased by 62.243 % and 22.478 % respectively compared to the untreated hybrid Palmyrapalm leaf stalk and sisal hybrid fiber reinforced composite (UHPSFRC). Generally, the HPSFRC-2(Palmyra palm leaf stalk/sisal fiber) (P/S ratio 50/50 % ratio with 20/80 % ratio of fiber/matricpercentage reinforced polymer hybrid composite) has good tensile strength and impact energy.Therefore, the mechanical property of the (Palm/Sisal) hybrid composite can be used for themanufacturing of the automotive interior parts like door panel, dash board, seat back, andautomotive roof.
Keywords: Handy lay-up, Hybrid fiber, Mechanical properties. unsaturated polyester resin

This study aims at exploring the potentials and drawbacks of utilizing social media to promote tourism industry in East Gojjam Zone. The study will be geographically limited to East Gojjam Zone and will not include media platforms other than social media. The research will bring significance to the culture and tourism offices, agencies that work on tourism industry and an input for the government organizations in policy formulation as well as for future researchers. Qualitative research approach with descriptive design will be employed. The population of this study is social media pages of Culture and Tourism Offices of all the woredas found in East Gojjam Zone and of the Zones’ Culture and Tourism Department. The sample will be purposive sampling of all social media pages of the woredas and the zone. Focus group discussion and indepth interview will be conducted in eight selected woredas and zonal culture and tourism office. East Gojjam Zone has 19 woredas including Debre Markos town. The data will be gathered using content analysis of the social media posts, focus group discussion and in-depth interview. In addition, data will also be collected from reports, and publications like newsletters and articles.To collect the data official support letter will be submitted to the Culture and Tourism Department of East Gojjam Zone. The authenticity of social media pages of the woreda tourism offices will be confirmed with the concerned bodies at the zonal level. The collected data using online content, in-depth interview and focus group discussion will be triangulated for data analysis. The researchers expect to find out social media usage patterns and preferences in tourism promotion, social media promotion strategies and implementation, social media regulation and governance in tourism promotion. Atotal budget of 201, 722 ETB will be allocated to conduct the whole study from April to September, 2024.

Evidence shows that financial inclusion plays a key role in driving economic growth and social development by strengthen-ing the financial system and reducing poverty and income inequality. However, its impact on the financial performance ofbanks remains inconclusive. This paper explores the relationship between financial inclusion and the financial performanceof commercial banks in Ethiopia, using a sample of 16 banks. We analyse 10 years of data (2013–2022) collected manuallyfrom the National Bank of Ethiopia (NBE) and the annual reports of commercial banks. A two-step system GeneralizedMethod of Moments (GMM) is employed, alongside other linear panel data model estimators. The findings reveal that in-creased financial inclusion has a significant positive impact on the financial performance (ROA and ROE) of commercialbanks in Ethiopia. The GMM estimation result also shows that bank performance indicators (ROA and ROE) are positivelyassociated with their past realizations. Regarding bank-specific control variables, the cost-efficiency ratio has a significantnegative impact on bank profitability. The study recommends that banks improve accessibility by expanding branch net-works and ATMs and by offering innovative financial products to enhance profitability.Keywords: financial inclusion, GMM, bank performance, Ethiopia

Chickpea is a self-pollinated, diploid, and annual plant (2x = 2n = 16). After peas and beans, it is the most important legumein the world. Reduced chickpea production and productivity have been significantly influenced by the lack of improvedand adaptable genotypes, poor management practices, biotic factors such as disease and pests, and abiotic factors includingfluctuating rainfall and temperature. New chickpea genotypes introduced in Northern Ethiopia lack adaptability, stability,and performance evaluation, resulting in crop losses for farmers due to their susceptibility to the new and variable environment. To address these challenges, recently released high-yielding genotypes, alongside a standard check, were evaluatedfor adaptability, performance, and yield stability over 2 years (2022/2023 and 2023/2024) in three districts (Shebel, Awabel,and, Jabitenan) of Northern Ethiopia across six multienvironment field trials each employing a randomized complete blockdesign with three replications. SAS 9.4 and R software were used, showing significant differences in crop phenological stages,growth, and yield parameters across years and locations for test traits. Genotype, location, and year interactions significantlyinfluenced all Kabuli chickpea genotypes. The highest combined mean grain yield was obtained from genotype “Arerti”(2.42 t ha-1) followed by “Yelbie” (2.18 t ha-1), which explained their best performance among the tested genotypes. Analysis ofvariance revealed significant interactions and differences between genotypes and environments, with 15.6% of the variationin grain yield attributed to environmental factors, 6.4% to genotype differences, and 11.4% to genotype-by-environment interactions. The Genotype and Genotype × Environment biplot and Additive Main Effect and Multiplicative Interaction analysisidentified stable genotypes, representative environments, and interesting genotype–environment interactions. GenotypesArerti, Chefe, and Yelbie were identified as stable based on Genotype and Genotype × Environment biplot and Additive MainEffect and Multiplicative Interaction analysis. Environments Shebel and Jabitenan were identified as representative amongall environments considered.

Background: Digital health data hubs contribute significantly to finding the right solutions to health problems, which forms the basis for achieving sustainable development goals. However, in Ethiopia, the health system has been coming to one central hub for all data, there is limited evidence of health professionals' intentions to use these systems. Understanding their intentions is crucial, as this can significantly improve the advancement of digital health in healthcare organizations. This study assessed health professionals' intention to use digital health data hubs in hospitals in East Gojjam, northwest Ethiopia, in 2024.
Methods: A cross-sectional study design was used to conduct the study. Eleven hospitals were included in the study area. Using an a priori structural equation modeling sample size calculator, the total sample size was 616. Stratified proportional allocation sampling was performed. The study participants were selected using a systematic sample. Structural equation modeling (SEM) was used for the analysis. Because it is a more powerful multivariate technique for testing and evaluating multivariate causal relationships. The assumptions of SEM-like normality, average variance extracted (AVE), composite reliability (CR), Cronbach's alpha, confirmatory factor analysis (CFA), and model specifications were checked using Amos and Stata version 16.
Results: This study was conducted with a sample size of 616 healthcare professionals; 591 (95.94%) responded to the survey. The results showed that 57.69% (n = 341) of the healthcare professionals intended to use the digital health data hub. Further analysis showed that perceived usefulness (PU: β = 0.576, p = 0.000), perceived trust (PT: β = 0.116, p = 0.022), and attitude (β = 0.143, p = 0.043) significantly and positively influenced health professionals' intention to use digital health data hubs.
Conclusion: Overall, the findings showed that 42.31% of health professionals have low intention to use digital health data hubs. These shall be needed to improve their intentions to use digital health data hubs through targeted interventions. Therefore, focusing on critical factors, such as perceived usefulness, trust, and attitude are crucial factors to reinforce their intention to use the system. Additionally, overcoming implementation challenges and building trust is critical to the successful integration and use of digital health data hubs.

Introduction: Promoting healthy lifestyle behaviors during pregnancy is a crucial health promotion strategy that could reduce pregnancy-related complications that may harm women and their fetuses. However, very few studies have assessed the prevalence of health-promoting lifestyle behaviors among pregnant women in Ethiopia. This study aimed to evaluate the extent and associated factors of health-promoting lifestyle behaviors among pregnant women in public health institutions in Debre Markos, northwest Ethiopia.
Methods: An institutional-based cross-sectional study was conducted among 275 pregnant women who were recruited using a systematic random sampling technique from 19 April to 19 May 2021. A face-to-face interview-administered questionnaire was used to collect the data. The data were analyzed using SPSS version 25. Multivariable binary logistic regression was used to identify the factors associated with the outcome variable. adjusted odds ratio (AOR), with a 95% confidence interval (CI) were used to measure the strength of the associations at a p-value

Accurate regulation of the liquid level in a quadruple tank system (QTS) is not easy and imposes higherrequirements on control strategies, so the design of controllers in these systems is challenging dueto the difficulty of dynamic analysis of its nonlinear characteristics and parametric uncertainties.To overcome these problems in liquid level regulation and increase the robustness to the pumpcoefficients, this article proposes and investigates the use of an optimal hybrid fractional-ordertype-2 fuzzy-PID (OH-FO-T2F-PID) regulator using a combination of two bio-inspired evolutionaryoptimizers, namely augmented grey wolf optimizer and cuckoo search optimizer, which gives rise tothe new hybrid A-GWOCS algorithm. This control mechanism was chosen to facilitate the convergenceof the water liquids in the two tanks as quickly as possible to the corresponding required values. Inaddition, a collaborative optimization technique with several objectives is used to adjust the regulatorparameters. The capability and efficiency of the suggested regulator is first investigated throughcomputer simulation results and then confirmed by real-time control experimental results on the QTSbased on dSPACE 1104 computation engine. The findings showed that the suggested OH-FO-T2F-PIDregulator significantly outperformed both the optimized ADRC and the OH-FO-T1F-PID regulators.Specifically, it reduced the rising time by 17.02% and 95.21%, respectively, and the settling time by25.13% and 74.28%. Additionally, the designed OH-FO-T2F-PID regulator successfully eliminatedthe steady-state error and overshoot, enabling precise regulation of the QTS, and maintenance theliquid level at the desired set point under a wide range of working situations. The robustness of therecommended regulator is also studied by considering − 50% disturbance in the QTS parameters, andthe findings showed that the OH-FO-T2F-PID regulator is less susceptible to variations in parameters.Keywords: Quadruple tank system (QTS), Optimal hybrid fractional order type 2 fuzzy PID regulator,Hybrid A-GWOCSO algorithm, Multi-objective optimization, dSPACE 1104 computation engine

Hate speech has been disseminated more frequently on social media sites like Facebook in recent years. OnFacebook, hate speech can proliferate through text, image, or video. We suggested a deep learning approach toidentify offensive memes posted on Facebook in case of Amharic language’. The research process commenced bymanually gathering memes posted by Facebook users. Next came textual data extraction, annotation, pre-processing, splitting, feature extraction, model development and assessment Amharic OCRs were employed toextract textual data. Character normalization, stop word removal, and unnecessary character removal make upthe text-preprocessing step. Using Stratified KFold the textual dataset is split into the train set (80 %), thevalidation set (10 %) and the test set (10 %). Vectors are created from the preprocessed texts using the Bog ofwords (BOW), TFIDF and word embeddings. Following that, the vectors are fed into Machine learning algo-rithms: NB, DT, RF, KNN, LSVM and LR, and deep learning models that are based on Dense, BiGRU, and BiLSTMalgorithms. The model with the optimal parameters is chosen after numerous experiments. With an accuracy rateof 94 %, the BiLSTM + Dense model, the suggested technique identified nasty meme posts on Facebook written inAmharic.
Keywords: Deep learning, BILSTM, BIGRU, Amharic language hate speech