Browse Articles

Spatial and multi-level analysis of factors associated with long-acting reversible modern contraceptive use among married women in Ethiopia

Background Despite the increasing global use of modern contraceptives, the utilization of Long-Acting Reversible Contraception (LARC) remains low in Sub-Saharan Africa, particularly in Ethiopia, where less than 3% of women use LARC. This low usage is associated with high rates of maternal and under-five morbidity and mortality, contrasting with the global average utilization rate of 15%. Previous studies have found a significant association between marital status and LARC usage, but there is a lack of understanding regarding other individual and community-level factors that may influence this utilization. Additionally, many of the previous studies were either localized or had limited sample sizes, failing to account for spatial variations and clustering effects. In light of these gaps, this study aims to investigate the spatial and multilevel factors from nationally collected data to identify the individual and community-level factors associated with LARC usage among married women in Ethiopia. Methods The study employed a cross-sectional design, utilizing secondary analysis of data from the 2019 Ethiopian Mini Demographic and Health Survey (EMDHS). The sample included 5,743 married women; selected using a two-stage stratified sampling technique. Data analysis was conducted with the verification of basic assumptions for multilevel logistic regression. The study utilized spatial and multilevel mixed effect model analysis. To demonstrate the strength and direction of associations, Adjusted Odds Ratios (AOR) with 95% confidence intervals were used. Result The study findings revealed significant associations with LARC usage in Ethiopia based on individual-level and community-level variables. Individual-level variables: Older age group was negatively associated with LARC use, (AOR = 0.28, 95%CI = 0.12, 0.64), higher education level was positively associated with LARC use (AOR = 2.99, 95%CI = 1.82, 4.92). Community-level variables: Communities with high female educational status (AOR = 1.75, 95%CI = 1.10, 2.76), communities with higher wealth were also positively associated with LARC use (AOR = 1.92, 95%CI = 1.15, 3.20). Living in Somali region was negatively associated with LARC use, indicating that women residing in this region had a significantly lower likelihood of using LARC (AOR = 0.03, 95%CI = 0.00, 0.33). Overall, these results highlight the influence of both individual-level and community-level factors on LARC utilization among married women in Ethiopia. Conclusions Age, educational attainment, community-level female education, community-level wealth, and region were identified as significant predictors of Long-Acting Reversible Contraceptive (LARC) use. Based on these findings, it is recommended that policies be implemented to enhance women’s education across various levels of the population. Special attention should be directed towards the most economically disadvantaged population, and efforts should focus on strengthening the health-seeking behaviors of the people. To improve contraceptive rates, regions with low utilization should be targeted for the expansion of tailored services that align with the lifestyles of their populations.

Enhancing intrusion detection in wireless sensor networks using a Tabu search based optimized random forest

Abstract Intrusion detection in Wireless Sensor Networks (WSNs) is an emerging area of research, given their extensive use in sensitive fields like military surveillance, healthcare, environmental monitoring, and smart cities. However, WSNs face several security challenges due to their limited computational capabilities and energy constraints. Their deployment in open, unattended environments makes them especially vulnerable to threats like eavesdropping, interference, and jamming. To address this problem, Random Forest (RF) is a popular machine learning model. The RF model can be tweaked because of its multiple hyperparameters. Tuning these parameters manually is tedious, as the combinations will be exponential. This work presents an enhanced intrusion detection approach by integrating Tabu Search (TS) optimization with a RF classifier. As a result, TS will help RF automatically search optimal hyperparameters and improve the generalization ability. This work integrates the pros of TS with RF. The model was tested on three different datasets, i.e., (a) the WSN-DS dataset, (b) CICIDS 2017, and (c) the CIC-IoT 2023 dataset, which shows better results on different metrics like precision, recall, F1-score, Cohen’s Kappa, and ROC AUC. Detection of Blackhole and Gray Hole attacks also improved, demonstrating the effectiveness of combining metaheuristic optimization with ensemble learning for stronger WSN security.

Elastoplastic analytical solutions for a shallow-buried circular tunnel in a semi-infinite medium

In order to explore the distribution pattern of the plastic zone around a shallow tunnel, the elastoplastic analytical solutions for a shallow-buried circular tunnel in a semi-infinite medium were proposed in this study to calculate the plastic zone radius and stresses around a shallow-buried circular tunnel. Based on the Hoek-Brown yield criterion, the solutions were derived by using the bipolar coordinate system method under the influence of the geotechnical gravity. The rationality of the proposed calculation method was verified by comparing with the existing calculation methods and numerical simulation methods, and the proposed method was further used to analyze the influence of the mechanical parameters of the rock and soil mass and uniform ground load on the plastic zone radius and stresses around a shallow-buried circular tunnel. The results show that the theoretical calculation results obtained by the proposed method fit better with the numerical simulation results, and the maximum and average errors between the two are 1.63% and 1.35%, respectively, so the calculation accuracy of the proposed method is proven to be high. The quality and strength of the geotechnical medium and the uniform ground load are both the main influencing factors of the plastic zone radius and stresses around a shallow-buried circular tunnel, and the plastic zone radius is negatively correlated with the quality and strength of the geotechnical medium, and the stresses are also negatively correlated with the quality and strength of the geotechnical medium, but are positively correlated with the uniform ground load. Therefore, the mechanical parameters of the rock and soil mass and uniform ground load should be controlled strictly to ensure the safety of shallow tunnels under construction.

Historical trends in histological composition and cause specific mortality of small intestine tumors based on SEER database analysis

Abstract Small intestine tumors, though rare, have shown a concerning increase in incidence and mortality in recent years. This study aimed to investigate the historical trends in histological composition and causes of death among patients with small intestine tumors using the SEER database. A total of 18,234 patients diagnosed with primary small intestine cancer from 1992 to 2018 were included in this study. Demographic details, tumor characteristics, treatment modalities, and outcomes were collected. Cause-specific mortality was analyzed using Fine and Gray regression, with non-small intestine-specific deaths considered as competing risks. Small intestine-specific deaths were the leading cause of mortality, with adenocarcinoma and carcinoid tumors being the most common histological types. Heart disease emerged as a significant cause of death following diagnosis, surpassing small intestine-specific deaths after 5–6 years. The study revealed variations in mortality causes across histological subtypes and identified risk factors for small intestine-specific mortality, including age, tumor site, grade, and treatment modalities. The findings highlight the substantial impact of heart disease on the long-term survival of patients with small intestine tumors. This underscores the potential benefits of adopting comprehensive management strategies that integrate oncological and cardiological care to improve survival rates and quality of life. A multidisciplinary approach in oncocardiology may help address the complex needs of these patients and optimize clinical outcomes.

How valuable are the questions and answers generated by large language models in oral and maxillofacial surgery?

Introduction In this study, we aim to evaluate the ability of large language models (LLM) to generate questions and answers in oral and maxillofacial surgery. Methods ChatGPT4, ChatGPT4o, and Claude3-Opus were evaluated in this study. Each LLM was instructed to generate 50 questions about oral and maxillofacial surgery. Three LLMs were asked to answer the generated 150 questions. Results All 150 questions generated by the three LLMs were related to oral and maxillofacial surgery. Each model exhibited a correct answer rate of over 90%. None of the three models were able to answer correctly all the questions they generated themselves. The correct answer rate was 97.0% for questions with figures, significantly higher than the 88.9% rate for questions without figures. The analysis of problem-solving by the three LLMs showed that each model generally inferred answers with high accuracy, and there were few logical errors that could be considered controversial. Additionally, all three scored above 88% for the fidelity of their explanations. Conclusion This study demonstrates that while LLMs like ChatGPT4, ChatGPT4o, and Claude3-Opus exhibit robust capabilities in generating and solving oral and maxillofacial surgery questions, their performance is not without limitations. None of the models were able to answer correctly all the questions they generated themselves, highlighting persistent challenges such as AI hallucinations and contextual understanding gaps. The results also emphasize the importance of multimodal inputs, as questions with annotated images achieved higher accuracy rates compared to text-only prompts. Despite these shortcomings, the LLMs showed significant promise in problem-solving, logical consistency, and response fidelity, particularly in structured or numerical contexts.

Author Correction: Naringin attenuates angiotensin II induced cardiac hypertrophy by inhibiting carbonic anhydrase II

Asymptotic expansions as control variates for deep solvers to fully-coupled forward-backward stochastic differential equations

Coupled forward-backward stochastic differential equations (FBSDEs) are closely related to financially important issues such as optimal investment. However, it is well known that obtaining solutions is challenging, even when employing numerical methods. In this paper, we propose new methods that combine an algorithm recently developed for coupled FBSDEs and an asymptotic expansion approach to those FBSDEs as control variates for learning of the neural networks. The proposed method is demonstrated to perform better than the original algorithm in numerical examples, including one with a financial implication. The results show that the proposed method exhibits not only faster convergence but also greater stability in computation.

Analysis of foamed glass structure under thermal shocks and their impact on local breaking strength

Survival status and predictors of early neonatal mortality among neonates admitted to neonatal intensive care units in Addis Ababa Public Hospitals, Ethiopia: A retrospective cohort study

Background The first week after birth is the high-risk time for neonatal death. Early neonatal mortality is still a major global health problem, particularly in sub-Saharan African nations like Ethiopia. Most neonatal deaths (about 75%) occur within the first seven days of life. Still, there is limited evidence on survival status and predictors that may determine when a neonate dies in the early days of life in the study area. Objective The study aimed to assess survival status and predictors of early neonatal mortality for neonates admitted to neonatal intensive care units in the study area, 2023. Methods A retrospective cohort study was conducted in four randomly selected public hospitals from January 1–2022 to January 1–2023 on 375 randomly selected neonatal data charts. The data were collected through document review and checklists using conventional random selection approaches. The collected data was analyzed by SPSS version 26.0. The Kaplan-Meier survival curve with a log-rank test was applied to compare the survival rates between groups. Bivariable and multivariate Cox proportional hazard regression analyses were conducted to identify the predictors of early neonatal mortality. The assumptions of the model were verified. Results A total of 375 eligible neonates were studied and the incidence rate of 37.1 deaths per 1000 live births was found (95% CI: 25.5, 40.9), with 75 of them (20%) died. Premature birth [AHR: 4 (95% CI: 1.234, 5.80)], pre-eclampsia [AHR: 2.4 (95% CI: 1.16, 4.98)], fifth-minute Apgar score [AHR: 3.93 (95% CI: 1.76, 8.77)], not initiating exclusive breastfeeding [AHR: 3.69 (95% CI: 1.14, 9.02)], and low birth weight [AHR: 2.01 (95% CI; 1.28, 3.43) were found to be the independent predictors of early neonatal mortality. Conclusion and recommendation High early neonatal mortality is observed in the study area. Gestational age, pre-eclampsia, 5th min Apgar score, not initiating exclusive breastfeeding, and low birth weight were independent predictors of early neonatal death. We recommend that proactive care for a neonate with a low Apgar score, who is not breastfeeding, is premature, has a low birth weight, and is born from a mother who has pregnancy-induced hypertension is very important.

Study on the scale dependence of the spatial distribution pattern of ecosystem service value - a case study of Gangu County, China

Music and sedation relieve intraoperative stress: A randomized controlled trial

Music is useful for relieving anxiety; however, its impact on autonomic nervous system activity and circulatory dynamics during tooth extraction surgery under intravenous sedation is unclear. We conducted a prospective randomized controlled trial to compare the effects of music listening on circulatory dynamics and autonomic nervous system activity during the extraction of impacted mandibular third molars under intravenous sedation. We hypothesized that music listening combined with intravenous sedation would enhance stress reduction. In this randomized controlled trial, 34 women aged 20–40 years were assigned to a music or control group. Patients in the music group listened to music during tooth extraction under intravenous sedation. Autonomic nervous system activity, heart rate, and systolic blood pressure were assessed in both groups. Statistical significance was set at p < 0.05. Systolic blood pressure and autonomic nervous system activity were not significantly different between the two groups. However, the heart rate during the separation of the tooth crown, extraction, and suturing was significantly lower in the music group than in the control group (p < 0.05). Within-group systolic blood pressure comparisons showed significant differences during treatments between the groups. Intraoperative music listening may enhance the stress-reducing effects of intravenous sedation, as auditory intervention attenuates blood pressure variability and increments in heart rate even when the level of consciousness is reduced by sedation. The combination of intravenous sedation and music listening may be useful for reducing stress during dental treatment. Trial registration UMIN Clinical Trials Registry, UMIN 000054970

Scalable and robust machine learning framework for HIV classification using clinical and laboratory data

Factors associated with meeting homosexual partners at fixed offline locations among MSM recruited through the internet: A cross-sectional survey

Objective This study aimed to investigate the demographic characteristics of Men who have Sex with Men (MSM) recruited online and identify the factorsassociated with meeting homosexual partners at fixed offline locations. Methods Univariate and multivariate logistic regression analyses were used to examine the factors that influenced their meeting up with homosexual partners at fixed offline locations. Results A total of 604 MSM were included, with 133 participants (22.02%) meeting homosexual partners at fixed offline locations. Multivariate logistic regression analysis showed that participantswho were willing to engage in commercial sex, engage in behaviors such as alcohol consumption, drug use, or aphrodisiac use during homosexual activities, and hadused HIVPre-Exposure Prophylaxis (PrEP) in the last six months were more likely to meet homosexual partners at fixed offline locations. Conclusions The proportion of participants meeting homosexual partners at fixed offline locations was relatively high, emphasizing the need to increase education and awareness among MSM to reduce relatedbehaviors during homosexual activities. Further promotion of the proper use of PrEP and avoidance of commercial sex are essential for lowering the risk of HIV infection among this population.

Positron emission tomography directed re-irradiation using volumetric modulated arc technique in recurrent head and neck squamous cell carcinoma

Intelligent penetration testing method for power internet of things systems combining ontology knowledge and reinforcement learning

With the application of new-generation information technologies such as big data, artificial intelligence, and the energy Internet in Power Internet of Things (IoT) systems, a large number of IoT terminals, acquisition terminals, and transmission devices have achieved integrated interconnection and comprehensive information interaction. However, this transformation also brings new challenges: the security risk of intrusions into power IoT systems has significantly increased, making the assurance of power system information security a research hotspot. Penetration testing, as an essential means of information security protection, is critical for identifying and fixing security vulnerabilities. Given the complexity of power IoT systems and the limitations of traditional manual testing methods, this paper proposes an automated penetration testing method that combines prior knowledge with deep reinforcement learning. It aims to intelligently explore optimal attack paths under conditions where the system state is unknown. By constructing an ontology knowledge model to fully utilize prior knowledge and introducing an attention mechanism to address the issue of varying state spaces, the efficiency of penetration testing can be improved. Experimental results show that the proposed method effectively optimizes path decision-making for penetration testing, providing support for the security protection of power IoT systems.

The effect of different carbon materials’ addition on the biomethane production from food waste

Abstract Anaerobic digestion (AD) is a useful process that could be utilized for food waste (FW) management. Previous studies have shown that carbon materials (CMs) could be an important additive for increasing biomethane yield. However, why CMs improve AD is still uncertain. A significant body of research has been dedicated to investigating the impact of CMs supplementation on biogas production. However, this article specifically emphasizes examining this effect concerning the specific surface area and the functional groups (e.g. hydroxyl groups, carbonyl groups, or unsaturated carbon structures) present on the surface of CMs, produced by torrefaction—TP (240 °C/60 min), pyrolysis—BC (600 °C/60 min), and hydrothermal carbonization—HC (240 °C/60 min/6–10 Bar) processes. The analyses showed that the size of the specific surface area of the CMs (TP—7.72 m2 g−1, BC- 115.00 m2 g−1, HC—5.46 m2 g−1), does not correspond to the production of biomethane. The highest biomethane potential was found for CMs with the lowest SSA, precisely TP and HC, equal to 407 and 394 mL gVS−1, which was about 13 and 9% higher than production from FW as a sole source of carbon, respectively. The FTIR analysis confirmed the abundance of different organic functional groups on the surface of TP and HC, which could contribute to improved AD performance. These organic residuals, as thermal degradation products, could be an additional source of carbon for microorganisms. The addition of BC, with the highest SSA, decreased the first-order biomethane rate constant k by 16.4% in comparison to food waste without CMs, which could be related to the presence of harmful, more complex organic compounds on the surface of biochar.

T-RippleGNN: Predicting traffic flow through ripple propagation with attentive graph neural networks

Recently, accurate traffic flow prediction has become a significant part of intelligent transportation systems, which can not only satisfy citizens’ travel need and life satisfaction, but also benefit urban traffic management and control. However, traffic forecasting remains highly challenging because of its complexity in both topology structure and time transformation. Inspired by the propagation idea of graph convolutional networks, we propose ripple-propagation-based attentive graph neural networks for traffic flow prediction (T-RippleGNN). Firstly, we adopt Ripple propagation to capture the topology structure of the traffic spatial model. Then, a GRU-based model is used to explore the traffic model through the timeline. Lastly, those two factors are combined and attention scores are assigned to differentiate their influences on the traffic flow prediction. Furthermore, we evaluate our approach with three real-world traffic datasets. The results show that our approach reduces the prediction errors by approximately 2.24%-62,93% compared with state-of-the-art baselines, and the effectiveness of T-RippleGNN in traffic forecasting is demonstrated.

Development and characterization of hydroxyapatite and multiwall carbon nanotubes reinforced polypropylene biocomposites

Abstract Two groups of Polypropylene/hydroxyapatite nanocomposites (PP/nHA) were fabricated by melt blending of PP with (5, 10, and 20 wt%) of hydroxyapatite nanoparticles nHA with two different sizes (~ 40 and 90 nm). The nanocomposites were prepared without and with the addition of 0.3 wt% of multiwall carbon nanotubes (MWCNTs). The effect of HA size and content as well as the addition of MWCNTs on the structure, thermal and mechanical properties of the prepared composites was investigated. All the samples were immersed in simulated body fluid (SBF) to compare their ability to precipitate HA from SBF. Scanning electron microscope (SEM), X-ray diffraction analysis (XRD), thermogravimetric analysis (TGA), and tensile strength tests were used for the investigation. XRD patterns reveal that incorporating nHA particles and/or MWCNT into the polymer matrix does not cause any structural change to the PP crystalline lattice. SEM shows a significant improvement in the dispersion of nHA particles along PP matrix with a smoother surface morphology by the addition of MWCNTs. Based on the thermal studies, both nHA particles and MWCNTS were known to enhance the thermal stability of the prepared composites. The tensile strength of PP increased by 20% for PP-5% (90 nm) HA-MWCNTs composites and 44% for PP-5% (40 nm) HA-MWCNTs composites. Furthermore, in vitro studies reveal the apatite-forming ability on the surface of the prepared composites especially those prepared using MWCNTs.

Treatment outcome and associated factors of patients with atrial fibrillation in adult emergency rooms of selected hospitals of Addis Ababa, Ethiopia: A one-year cross-sectional study

Background Atrial fibrillation is the most common sustained cardiac rhythm disorder with substantial mortality and morbidity from stroke, thromboembolism, heart failure, and impaired quality of life. This study aimed to investigate the emergency presentation, treatment outcome and associated risk factors of atrial fibrillation patients admitted to three tertiary hospitals in Addis Ababa. Methods A cross-sectional study was conducted in the adult emergency room of three tertiary hospitals in Addis Ababa. All atrial fibrillation patients admitted to these hospitals from August 15, 2021, to August 15, 2022, fulfilling inclusion criteria were included in the study. Data was collected by reviewing medical records in the form of a structured data abstraction form and analyzed by SPSS. Result A total of 133 chart records were reviewed and the mean (±SD) age of study participants was 54.82 ± 20.49 years and ranged between 18–85 years of age. The majority of the patients, 47 (35.3%) were aged between 18–40 years. Nearly more than half of the patients 69 (51.9%) were females and two third [91 (68.4%)] of the patients were from Addis Ababa. Patients of the age group 40–59 with atrial fibrillation have an 82% lower chance of developing stroke than those in the age group >60 years and patients with underlying cardiovascular diseases have seven times higher odds of having a stroke compared to those without underlying cardiac diseases. Male patients with atrial fibrillation had 2.15 times higher odds of staying for > 24 hours at the emergency department compared to female patients. The odds of a > 24-hour stay in the ER significantly increased by 2.7 times as the patient became unstable compared to stable patients. Conclusion Males and those with unstable atrial fibrillation have a higher chance of staying more than 24 hours in the emergency room. According to the study, patients who were more than 60 years of age with underlying cardiovascular diseases had a higher likelihood of developing ischemic stroke.

Wastewater sequencing from a rural community enables identification of widespread adaptive mutations in a SARS-CoV-2 alpha variant

Abstract Central Michigan University (CMU) participated in a state-wide wastewater monitoring program starting in 2021. One rural site consistently produced higher concentrations of SARS-CoV-2 genome copies. Samples from this site were sequenced retrospectively and exclusively contained a derivative of Alpha variant lineage B.1.1.7 that shed from the same site for 20–28 months. Complete reconstruction of each SARS-CoV-2 open reading frame (ORF) and alignment to an early B.1.1.7 clinical isolate identified novel mutations that were selected in non-structural (nsp1, nsp2, nsp3, nsp4, nsp5/3CLpro, nsp6, RdRp, nsp15, nsp16, ORF3a, ORF6, ORF7a, and ORF7b) and structural genes (Spike, M, and N). These were rare mutations that have not accumulated in clinical samples worldwide. Mutational analysis revealed divergence from the reference Alpha variant lineage sequence over time. We present each of the mutations on available structural models and discuss the potential role of these mutations during a chronic infection. This study further supports that small wastewater treatment plants can enhance resolution of rare events and facilitate reconstruction of viral genomes due to the relative lack of contaminating sequences and identifies mutations that may be associated with chronic infections.