Browse Articles

Stigma, discrimination and associated determinants among people living with HIV/AIDS accessing Anti-Retroviral Therapy in Ikeja, Lagos state, Nigeria

Abstract Stigmatization and discrimination toward people living with HIV/AIDS (PLWHA) continue to be significant hurdles to successful treatment and social integration, especially in Nigeria. These barriers have a negative impact on mental health, deter disclosure, and reduce access to healthcare. The purpose of this study was to determine the prevalence, types, and causes of stigma and discrimination among PLWHA receiving antiretroviral therapy (ART) in Ikeja, Lagos State, Nigeria. A cross-sectional study of 400 PLWHA on ART was done at three randomly chosen treatment centers. Structured questionnaires and the Berger HIV Stigma Scale were used to collect information about individualized stigma, disclosure concerns, negative self-image, and public attitudes. The statistical study used descriptive statistics, chi-square tests, and logistic regression, with a significance level of p  < 0.05. Overall, 37.7% of individuals reported stigma in hospital settings, 41.5% received negative attitudes from family or friends, and 48.7% felt ashamed or condemned because of their situation. Fear of revelation was widespread, with 65% refusing to declare their status. Stigma had a major impact on mental health (64.3%) and reduced ART access for 39.7% of responders. Younger age (20–39 years), poor income, HIV-positive partners, and disclosure of status were all significant predictors of stigmatization. Discrimination was highly related to occupation, gender, and socioeconomic status. Stigma and discrimination continue to be pervasive in Ikeja, limiting PLWHA access to healthcare and negatively impacting their psychological well-being. To establish a more inclusive and supportive environment, it is necessary to enhance anti-discrimination regulations, train healthcare providers, educate the public, and implement socioeconomic empowerment activities.

Investigation of the cutting effects on high-temperature granite based on cerchar abrasivity test

3D scan-based classification of Chinese young female hand morphology

Effects of parasitic capacitance on switching transients and thermal performance in a single-phase SiC power MOSFET inverter

FalsEye: proactive detection of false data injection attacks in smart grids using IceCube-optimised ensemble learning

Abstract False Data Injection Attacks (FDIAs) represent a significant cybersecurity threat to smart grids (SGs), compromising both system stability and operational reliability. Conventional detection approaches frequently prove inadequate, largely due to challenges such as data imbalance and suboptimal model parameterisation. To overcome these limitations, this study proposes a proactive detection framework that integrates ensemble learning, adaptive oversampling, and a novel metaheuristic optimization algorithm, termed FalsEye. At the core of the proposed framework is a Voting Classifier ensemble, which strategically combines heterogeneous base learners, including ExtraTrees, CatBoost, and LightGBM. The performance of this ensemble is further enhanced through the IceCube Optimization (IO) algorithm, a physics-inspired metaheuristic technique employed to fine-tune the hyperparameters of the individual base models. Additionally, the framework incorporates adaptive oversampling using the Adaptive Synthetic method to effectively mitigate class imbalance within the dataset, thereby improving the detection rate of minority FDIA instances. Experimental results demonstrate that the IO Voting Classifier achieves superior F1-scores and exhibits a more balanced precision–recall trade-off compared to conventional ensemble approaches. The optimized framework attains an accuracy of 99%, with a precision of 92%, a recall of 98%, and an F1-score of 95%, marking a substantial improvement over traditional methods. These findings highlight the considerable potential of combining metaheuristic optimization with ensemble learning to develop robust and cyber-resilient SG infrastructures.

Development of hesperidin loaded lipid-chitosan nanoparticles: physicochemical characterization, molecular docking and ex vivo study

Characterizing persistent Post-COVID-19 vaccination symptoms using MedDRA system organ class and preferred term classifications

Bifurcation analysis and exploration of new optical soliton solutions in parabolic law medium with weak non-local nonlinearity

Improving workplace safety at EOT crane operating area through behavioral-based safety approach: a case study analysis

Attitudes of medical and life sciences university students and postdoctoral fellows toward AI chatbots in education: an international cross-sectional survey

Abstract Artificial intelligence chatbots (AICs) are advanced systems capable of generating and processing human-like text, and are being increasingly integrated in various fields, including education. Despite their potential to significantly impact learning, little is known about university students’ and postdoctoral fellows’ (US&PD) views on AICs in educational settings. This study investigated the familiarity, perceptions, and factors influencing adoption of AICs by US&PDs in the life and medical sciences. We conducted a cross-sectional online survey. Recruitment involved two approaches: (1) using R script on PubMeD metadata to extract contact details of corresponding authors with recent MEDLINE-indexed publications, and (2) collecting publicly listed contact information of program administrators from the top 50 global, English-speaking universities, as ranked by the Quacquarelli Symonds (QS) University World Rankings. Both authors and administrators were contacted and requested to forward the survey to US&PDs. The survey was administered via SurveyMonkey from February 2 to March 18, 2024, with two reminder emails sent between February 14 and 26, 2024. A total of 1209 responses from 73 countries were analyzed. Most respondents identified as female (62.07%) and were enrolled in doctoral (40.48%) or master’s programs (17.55%). Over 63% were familiar with AICs, with ChatGPT being the most used (60.3%). While many recognized the educational value of AICs, concerns about reliability and integration into academia persisted. Calls for more training and institutional support were common. The study underscores the potential and challenges of AICs in education. While enthusiasm exists, significant concerns remain about their implementation, requiring targeted training and policy development.

Habituation and sensitization learning in adult solitary ascidians

Navigation activities in an organized colorectal cancer screening program improve follow-up colonoscopy completion

Abstract Patient navigation is a promising intervention that could improve follow-up of abnormal fecal immunochemical test (FIT) results in colorectal cancer (CRC) screening. We describe changes in navigation activities in an organized screening program aimed to improve follow-up colonoscopy completion. Between 2022 and 2023, we decreased the time between an abnormal FIT result and contact from the program’s patient navigator from 3 months to 1 month and provided the patient navigator with direct access to schedule colonoscopies at two endoscopy sites. We conducted a pre-post analysis that examined the proportion of patients with abnormal FIT results who completed a colonoscopy, were referred for a colonoscopy, time to referral, time to colonoscopy, and colonoscopy outcomes including CRC diagnoses. Our analysis included 368 patients with abnormal FIT results: 175 in 2022 and 193 in 2023. After changes to navigation activities, colonoscopy completion within 1 year increased by 22.9% points (42.9% to 65.8%; p  < 0.001). In 2022, the median time (interquartile range; IQR) to colonoscopy was 103.5 (IQR 60.2-161.5) days. In 2023, the median time to colonoscopy was 99.0 (IQR 52.0-150.0) days. Differences in the proportion of patients referred to colonoscopy, time to referral, and time to colonoscopy were not statistically significant. Patient navigation beginning within 1 month of an abnormal FIT result and granting a patient navigator direct access to the endoscopy scheduling template increased 1-year colonoscopy completion. Understanding navigation activities in CRC screening programs could inform broader adoption of practices that are associated with increased follow-up colonoscopy completion.

Tracing the water–beef safety nexus: assessing water quality’s role in beef contamination from slaughterhouse to plate, in Southwest Ethiopia

Enhancing urban vehicular communication and safety through HMM-OCR

Abstract Vehicular Ad Hoc Networks (VANETs) work in urban environments where the topology changes due to high mobility, limited communication and dense traffic conditions. These factors lead to increase in end-to-end delay, energy consumption, and significant packet loss such challenges highlight the need for robust and adaptive routing mechanisms that can maintain reliable communication under dynamic and dense traffic scenarios. To overcome these issues, this study proposes a Hybrid Meta-Heuristic and Machine Learning-based Optimised Cluster-Based Routing (HMM-OCR) aimed at enhancing communication reliability and routing efficiency in urban VANETs. The proposed method integrates Modified Golden Eagle Optimisation (MGEO) for energy efficient clustering and Improved Jackal Optimisation (IJO) for optimal cluster head selection. Additionally, a Multivariable Output Neural Network (MONN) is employed to ensure efficient data forwarding and path establishment. Simulation results obtained using NS2 shows that HMM-OCR outperforms across key performance metrics. Specifically, HMM-OCR enhances throughput by 4.83–8.55%, reduces packet drop by 4.53–22.24%, improves packet delivery by 3.78–21.9%, reduces delay by 0.56–2.24 s, energy consumption by 20.38–62.43%, and routing overhead by 5.87–22.87%. These results clearly demonstrate that HMM-OCR method which significantly, enhances communication efficiency and reliability in urban environments, making it suitable for intelligent transportation systems.

Implicit voice learning through discrimination outperforms explicit listen-and-memorize tasks

Abstract Voice learning primarily occurs implicitly in everyday situations—as an incidental by-product of other activities such as participating in conversation or listening to voices in the media. Most research on voice learning is conducted in laboratory settings, where participants are explicitly instructed to attend to and memorize voices for later recognition. Yet, the impact of task awareness (awareness regarding the goal of voice learning) on voice recognition performance remains poorly understood. To address this gap, we conducted a study comparing two voice-learning modalities: explicit learning, instructing participants to listen to and memorize voices for later recognition, and implicit learning, based on exposure during a voice discrimination task, without awareness of a subsequent recognition test. After both exposure phases, participants completed a surreptitious old–new voice recognition task. To further examine whether task awareness is modulated by voice load (number of voice identities introduced in the experiment), we implemented both a simple and a challenging version of the experiment. We found that, irrespective of voice load, implicit learning through participation in a discrimination task, resulted in higher recognition performance than explicit listen-and-memorize training. These findings suggest that highly demanding explicit listen-and-memorize tasks may benefit from incorporating ecologically valid familiarization paradigms, such as voice discrimination. We discuss the implications of our findings in relation to previous empirical research and their relevance for forensic applications.

Miller–Ross-functions coefficients and kernel-based CT-scan enhancement technique

Precise peak width estimation for solving key challenges in biosignal and spectral analysis

Biomechanical evaluation of X-ray permeable CF/PEEK composite versus conventional titanium alloy for tibial external fixation plates: a finite element analysis

Acid leaching process of an ultramafic mine tailing for indirect CO2 mineralization

Abstract The extraction of divalent metals from ultramafic mine tailings for indirect CO₂ mineralization is a promising strategy for large-scale carbon sequestration. This study investigates the acid leaching process of an ultramafic nickel tailing, rich in magnesium silicates, using two different acids: hydrochloric acid and citric acid. The aim was to evaluate the leaching efficiency of key metals, including magnesium, calcium, and iron, under varying operational conditions. These conditions included acid concentration (0.5 to 2 mol/L), solid-to-liquid (S/L) ratio (0.01 to 0.5 g/mL), temperature (25 to 65 °C), and multi-stage leaching. The experimental results show that both acids are effective in leaching the target metals, but with differing efficiencies. HCl, being a strong acid, exhibited higher leaching efficiency, particularly for Mg and Fe, due to its complete dissociation and greater ability to break down the mineral structure. Citric acid, a weaker organic acid, demonstrated more moderate leaching efficiency, especially for Mg, with a relatively lower impact from increased acid concentrations. The efficiency of metal extraction in both systems decreased with higher S/L ratios, likely due to reduced surface area for leaching, and the formation of more viscous slurries that hindered the movement of the leachant. Temperature had a distinct effect on the two acid systems. In the HCl system, increasing temperature resulted in a decrease in leaching efficiency for some elements like manganese, nickel, and sulfur, likely due to precipitation or changes in the solubility of certain metal species. In contrast, in the citric acid system, elevated temperatures improved the leaching efficiency for elements such as Mg, Fe, and Mn, enhancing the complexation and solubility of metals. However, temperature had minimal effect on other elements that do not readily form stable complexes with citric acid. The findings highlight that both HCl and citric acid can be effectively used for the acid leaching step in indirect CO₂ mineralization, with HCl providing higher extraction efficiencies for faster leaching and broader applicability to various metals, while citric acid offers a more environmentally friendly, less corrosive alternative. The results provide valuable insights into optimizing the leaching conditions to improve the overall efficiency of indirect mineral carbonation, which could contribute to more sustainable mining practices and enhanced CO₂ sequestration technologies.

Evaluating repellence properties of a catnip essential oil-based mosquito repellent using the human landing catch method in Eastern Uganda

Abstract Mosquitoes (Diperta: Culidae) act as vectors for several diseases, including malaria, dengue fever and yellow fever. Mosquito repellents represent one of the primary measures used to reduce the risk of these diseases in humans by reducing mosquito landing and biting events. Nepetalactone, a natural insect repellent primarily found in the essential oil of catnip (Lamiaceae; Nepeta cataria ), has established mosquito repellence properties, but has not been widely exploited as a mosquito repellent in malaria-endemic regions such as Sub-Saharan Africa. Here, we evaluated the potential of a locally produced lotion containing catnip essential oil (comprising > 92% nepetalactone) for use as a mosquito repellent in Eastern Uganda. Using the human landing catch method in field trials, we analysed the effectiveness of a lotion containing 2% or 6% catnip oil at repelling mosquitoes compared to a lotion lacking catnip essential oil as a negative control and to a commercial repellent containing 15% DEET as a positive control. We found that lotions containing both concentrations of catnip essential oil were highly effective at preventing mosquito landing, with 6% catnip oil performing as well as 15% DEET. Our findings suggest that nepetalactone could be used as a natural, locally sourced and effective alternative to synthetic commercial mosquito repellents, thereby representing a viable import substitution option for protection against mosquito-borne diseases.