Browse Articles

Integrating ICT in education: A scoping review of pre-service teachers’ ICT beliefs

This scoping review aims to map the current understanding of pre-service teachers’ beliefs about ICT, identify critical research gaps, and provide actionable insights for teacher education policy and practice. A comprehensive search of seven electronic databases yielded 1366 studies, of which 277 were included. The review identified seven key themes, including a predominant focus on general ICT beliefs and the limited exploration of cutting-edge technologies or regional variations. Quantitative methods dominated the research landscape, often employing standardized instruments like the Technology Attitude Scale. The findings highlight the need for greater emphasis on underrepresented regions, innovative technologies, and qualitative methodologies to deepen contextual understanding. This review offers a foundational resource for stakeholders in teacher education and sets the stage for future research to bridge identified gaps, ultimately enhancing the integration of ICT in teacher training and classroom practice.

Provenancing ancient materials with lead isotopes: overlap uncovered

Analyzing and forecasting under-5 mortality trends in Bangladesh using machine learning techniques

Background Under-5 mortality remains a critical social indicator of a country’s development and economic sustainability, particularly in developing nations like Bangladesh. This study employs machine learning models, including Linear Regression, Ridge Regression, Lasso Regression, Bayesian Ridge, Decision Tree, Gradient Boosting, XGBoost, and CatBoost, to forecast future trends in under-5 mortality. By leveraging these models, the study aims to provide actionable insights for policymakers and health professionals to address persistent challenges. Methods Data from the 1993–94 to 2017–18 Bangladesh Demographic and Health Survey (BDHS) was analyzed using advanced machine learning algorithms. Key metrics, including Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), R-squared, and Mean Absolute Percentage Error (MAPE), were employed to evaluate model performance. Additionally, k-fold cross-validation was conducted to ensure robust model evaluation. Results This study confirms a significant decline in under-5 mortality in Bangladesh over the study period, with machine learning models providing accurate predictions of future trends. Among the models, Linear Regression emerged as the most accurate, achieving the lowest MAE (4.05), RMSE (4.56), and MAPE (6.64%), along with the highest R-squared value (0.98). Projections indicate further reductions in under-5 mortality to 29.87 per 1,000 live births by 2030 and 26.21 by 2035. Conclusions From 1994 to 2018, under-5 mortality in Bangladesh decreased by 76.72%. While the Linear Regression model demonstrated exceptional accuracy in forecasting trends, long-term predictions should be interpreted cautiously due to inherent uncertainties in socio-economic conditions. The forecasted rates fall short of the Sustainable Development Goal (SDG) target of 25 deaths per 1,000 live births by 2030, underscoring the need for intensified interventions in healthcare access and maternal health to achieve this target.

Optoelectric coordinated modulation of resistive switching behavior in perovskite based synaptic device

Abstract Triple cation halide perovskite (TCP) stands out as a superior photoelectric material, with a broader absorption range, higher absorption efficiency, and improved environmental stability. Due to its excellent synaptic plasticity, TCP facilitates advanced neural morphological operations like light-assisted learning. Here, a modifying layer of polythiophene (P3HT) was incorporated onto the TCP thin film to enhance the resistive switching (RS) characteristics of the synaptic device, which exhibits excellent stability (103 endurance cycles and > 103 s retention time) and low energy consumption (~ 6.3 pJ for electrical stimulus and ~ 6 pJ for optical stimulus). Additionally, the synaptic properties of the perovskite / P3HT heterojunction synaptic device were explored under optoelectric coordinated modulation, encompassing Long-Term Potentiation (LTP), Long-Term Depression (LTD), frequency-dependent plasticity (SRDP) and voltage-dependent plasticity (SVDP). By leveraging the linear characteristics of synaptic plasticity, arithmetic operations, Pavlovian conditioned reflex and vision recognition are achieved. The recognition accuracies of 89.8% / 88.1% (electric synapse) are enhanced to 92.4% / 92.2% after the introduction of optoelectronic cooperative stimulation on the 8 × 8 and 28 × 28 modified national institute of standards and technology (MNIST) handwritten digit datasets. This study holds significant implications for guiding the optoelectronic co-regulation of perovskite synaptic devices in the field of synaptic electronics.

Broadening the spectrum of conflict and coexistence: A case study example of human-wolf interactions in British Columbia, Canada

Coexistence has seen an explosive rise within conservation social science scholarship. While this represents an exciting shift in the field, many academics are still skeptical. Some scholars have expressed concerns around the omission of “conflict”, naïveté, and impracticality associated with coexistence literature. In this paper, we aim to demonstrate that critiques of coexistence often stem from reductionism and decontextualization, process inefficiencies and/or inequities, failure to address and prioritize human well-being as a goal, and a lack of tools to foster open, collaborative dialogue. We draw on a case study of human-wolf interactions in the Pacific Rim National Park Reserve Region, British Columbia, Canada, to illustrate how coexistence efforts can, and should, prioritize “conflict”, be attentive to the real challenges of sharing spaces with wildlife, and encourage collaborative, inclusive processes that work toward tangible, actionable outcomes. We conducted 32 semi-structured interviews with residents from diverse backgrounds and levels of experience with wolves in the region. From these interviews, we articulated novel, co-developed, contextual definitions of human-wolf conflict and coexistence in the region. We then developed a collaborative tool for visualizing behavioral and cognitive elements of human-wildlife interactions through open and inclusive dialogue, using real examples from these research interviews. The research findings highlight three main principles: (1) that conflict and coexistence are contextual and should be understood as such, (2) that coexistence requires collaborative processes that pay attention to equity and inclusivity, and (3) that there are frameworks or tools that can help facilitate discussions toward practical outcomes of coexistence projects. We believe that this paper helps to disambiguate coexistence and reinforce that coexistence requires focused attention to the well-being of people as much as wildlife.

The relationship between oxLDL, sLOX-1, PCSK9 and carotid intima-media thickness in patients with prediabetes and type 2 diabetes

Optimized transmission of multi-path low-latency routing for electricity internet of things based on SDN task distribution

With the continuous development of 5G network, how to further improve the routing and transmission efficiency of electric power IoT has become a popular research at present. Facing the current problems of high data transmission delay, low efficiency, and large task volume in the electric power IoT, this research combines software-defined networking to design a multi-path low-latency routing and transmission model under the concept of task allocation. First, the grid data communication network model and network slicing technology in 5G power IoT are introduced. On this basis, considering the data transmission in the core network in the power IoT, a multi-path low-latency routing optimization transmission model based on software-defined network task allocation is designed by combining the software-defined network controller and task allocation concept. The results indicated that the average delay of the designed model is only 15.78ms when the transmission task size is 10KB and 23.38ms when the transmission task size is 50KB. In addition, the designed model was able to achieve a throughput of 298bps in the local area network and the lowest jitter and packet loss in the wide area network, which are 0.13ms and 0.001%. It can be concluded that the constructed multi-path low-latency routing and transmission model can not only provide theoretical guidance for the optimization of data transmission in the power IoT, but also lay the foundation for the in-depth application and development of software-defined networking in the power IoT and other fields.

Synergizing GIS and genetic algorithms to enhance road management and fund allocation with a comprehensive case study approach

Experimental study on in-situ simulation of rainfall-induced soil erosion in forest lands converted to cash crop areas in Dabie Mountains

Soil erosion is a pervasive global challenge and a significant ecological and environmental concern in China. Its occurrence frequently triggers ecological crises, including soil degradation and water contamination. It is of great scientific and practical significance to study the factors influencing the mechanism of soil erosion occurrence. Economic development in the Dabie Mountains of China has necessitated the conversion of vast tracts of forest land into economic crops, notably tea gardens and orchards, thereby disrupting soil structure and precipitating large-scale soil erosion. Rainfall serves as the primary catalyst for soil erosion in this region. Therefore, this study was designed to reveal the evolution characteristics of rainfall-induced slope erosion and the key influencing factors in the forest land converted to cash crop area in Dabie Mountains. It focused on a tea plantation slope of the Dabie Mountains, employing four rainfall scenarios, i.e. light rain, moderate rain, heavy rain, and heavy rain following drought, to conduct in-situ simulation experiments, mirroring the prevalent rainfall patterns in the study region. Monitoring stations for soil moisture content, slope runoff, and soil erosion were strategically positioned at varying depths across experimental plots with vegetation cover percentages of 20%, 40%, and 60%. Mathematical methods of descriptive statistics were used to analyze the monitored runoff, soil erosion and soil water content data, and to study the characteristics of their changes and response relationships. The findings underscore that rainfall prompts a swift surge in surface soil moisture, destabilizing the soil surface and culminating in slope erosion; thus, the rate of change in surface soil moisture content emerges as a pivotal indicator for predicting slope soil erosion. Furthermore, within the bounds of rainfall infiltration, preceding drought conditions followed by intense rainfall exacerbate soil erosion accumulation, highlighting the significance of initial soil moisture content as a critical factor. Lastly, for the economic crop cultivation zones in the Dabie Mountains, achieving a vegetation cover of 40% or more can significantly enhance soil water retention capacity and the overall soil and water conservation efficacy.

3,5,6,7,8,3’,4’- Heptamethoxyflavonoid inhibits TGF-β1-induced epithelial–mesenchymal transition by regulating oxidative stress and autophagy through MEK/ERK/PI3K/AKT/mTOR signaling pathway

Immunoinformatic strategy for developing multi-epitope subunit vaccine against Helicobacter pylori

Helicobacter pylori is a gram-negative bacterium that persistently infects the human stomach, leading to peptic ulcers, gastritis, and an increased risk of gastric cancer. The extremophilic characteristics of this bacterium make it resistant to current drug treatments, and there are no licensed vaccines available against H. pylori. Computational approaches offer a viable alternative for designing antigenic, stable, and safe vaccines to control infections caused by this pathogen. In this study, we employed an immunoinformatic strategy to design a set of candidate multi-epitope subunit vaccines by combining the most potent B and T cell epitopes from three targeted antigenic proteins (BabA, CagA, and VacA). Out of the 12 hypothetical vaccines generated, two (HP_VaX_V1 and HP_VaX_V2) were found to be strongly immunogenic, non-allergenic, and structurally stable. The proposed vaccine candidates were evaluated based on population coverage, molecular docking, immune simulations, codon adaptation, secondary mRNA structure, and in silico cloning. The vaccine candidates exhibited antigenic scores of 1.19 and 1.01, with 93.5% and 90.4% of the most rama-favored regions, respectively. HP_VaX_V1 and HP_VaX_V2 exhibited the strongest binding affinity towards TLR-7 and TLR-8, as determined by molecular docking simulations (ΔG = −20.3 and −20.9, respectively). Afterward, multi-scale normal mode analysis simulation revealed the structural flexibility and stability of vaccine candidates. Additionally, immune simulations showed elevated levels of cell-mediated immunity, while repeated exposure simulations indicated rapid antigen clearance. Finally, in silico cloning was performed using the expression vector pET28a (+) with optimized restriction sites to develop a viable strategy for large-scale production of the chosen vaccine constructs. These analyses suggest that the proposed vaccines may elicit potent immune responses against H. pylori, but laboratory validation is needed to verify their safety and immunogenicity.

Biological control of Schistocerca gregaria and Locusta migratoria migratorioides using Entomopathogenic bacteria

Abstract This study investigated the potential of indigenous entomopathogenic bacterial (EPB) strains from Egypt to control the two most prevalent locust species, Schistocerca gregaria (Forsskål) (Orthoptera: Acrididae), and Locusta migratoria migratorioides (Reiche & Fairmaire) (Orthoptera: Acrididae). To assess the efficacy of the EPB strains, experiments were conducted in the laboratory, semi field, and field. The results showed that Xenorhabdus nematophila (Thomas et Poinar) BA2 (Enterobacterales: Morganellaceae) and Photorhabdus luminescens (Thomas et Poinar) EGAP3 (Enterobacterales: Morganellaceae) were the most effective strains against S. gregaria and L. migratoria migratorioides in laboratory settings. Under semi-field conditions, X. nematophila BA2 recorded nymphal mortality rates of 89.31% and 85.00% against the 2nd and 5th nymph instars of S. gregaria, respectively, and P. luminescens EGAP3 showed nymphal mortality rates of 88.00% and 80.00% against the 2nd and 5th nymph instars of S. gregaria, respectively. In field trials, X. nematophila BA2 exhibited the highest nymphal mortality rate of 88.70% at 7 days after spraying. Overall, the findings of this study suggest that X. nematophila BA2 and P. luminescens EGAP3 are promising candidates for environment-friendly, safe locust pest management. Further research is needed to explore and develop these bacteria for commercial use in agriculture.

Evaluation of a pilot family planning educational seminar and subsequent attitudes towards family planning among Muslim communities in Tanzania

Evidence has demonstrated that uncertainty about compatibility with religious beliefs and limited health knowledge hinder uptake of family planning (FP), even among women who would like to prevent or delay childbearing. Empowering women and men to choose the number and timing of children is a global goal and enhances both maternal and child health. Building on data demonstrating the effectiveness of religious leaders in Tanzania to provide public health information in communities, the aim of this study was to understand whether and how an educational seminar about FP that was provided to Tanzanian Muslim religious leaders could be an effective means by which education about FP could reach members of their communities. This study employed a mixed-methods approach to pilot-test a one-day educational seminar about social, medical, and theological aspects of FP. The seminar was provided to Muslim religious leaders from two mosques in northwest Tanzania in April 2022. Six weeks after the seminar, the same religious leaders were invited to evaluate the seminar both by a quantitative survey assessing acceptability, appropriateness, and feasibility, and in in-depth interviews. Interviews explored participants’ knowledge and perceptions of FP, views of its permissibility in Islam, and actions that they had taken since attending the seminar. Demographic and survey data was analyzed using R software. Thematic analysis using de-identified transcripts was performed using NVivo (Version 12). In June 2022, 48 Muslim religious leaders (26 women; 22 men) completed the quantitative survey and in-depth interviews. Participants rated the seminar as highly acceptable, appropriate, and feasible, with mean scores above 4.5 out of 5 for every statement. Participants viewed the seminar as enlightening and expressed that it improved their knowledge about FP and enabled them to consider FP from both medical and Islamic faith-based viewpoints. Others described having taught their communities about FP and described the positive impact the seminar had for enhancing couple communication and enabling FP uptake for those desiring to use it. Almost all participants recommended that the seminars return to their community more frequently and indicated the importance of allowing discussion time for men and women separately during part of the seminar. Muslim religious leaders reported feeling equipped by an educational seminar to teach about FP to their communities. These data highlight the high potential of trusted religious leaders to build knowledge about FP, which could address women’s current unsatisfied demand for FP and promote maternal and child health in Muslim communities in Tanzania.

Analysis of graphene coatings on various metallic/oxide crystal/composite material substrates using machine learning for enhanced solar thermal energy conversion

Direct detection and identification of viruses in saliva using a SpecID ionization modified mass spectrometer

The COVID-19 (SARS-CoV-2) pandemic has led to a significant mortality globally and persistent health challenges in many survivors. Early accurate diagnosis, surveillance, identification of cohorts, and prophylaxis are considered essential measures to reduce the spread of infectious viral pathogens such as SARS-CoV-2. A reliable, fast, high-throughput screening method that can detect viral particles and identify the pathogenic virus in infected individuals could help to reduce the spread of the next viral threat through quick knowledge and implementation of appropriate prevention strategies. Since respiratory viruses are typically present in nasal and oral secretions, saliva is a good target for testing for viral infections. Saliva testing has slowly gained popularity in the diagnostics based on biomarkers and other constituents ranging from organic compounds ( e . g ., food additives), peptides, and even microorganisms. Polymerase chain reaction (PCR) remains the gold standard for sensitive detection of SARS-CoV-2 infection in biological samples. However, while PCR testing for COVID is sensitive and widely used by hospitals, the method has a false-negative rate of 15–20% and is kit-based necessitating the development of alternative methods of detection that provide higher accuracy. This paper describes the use of a SpecID Mass Spectrometer that can detect the presence of viral particles in saliva at very low levels (<500 virions/0.5 ml). The main goal of this study was to demonstrate that our previously developed, portable, mass spectrometry based method, SpecID, could also be sued for detecting viruses in saliva, including but not limited to SARS-CoV-2; the SpecID method has the potential to provide a reliable solution that overcomes some of the challenges with molecular testing like PCR.

Real-world data of dacomitinib as first-line treatment for patients with EGFR-mutant non-small-cell lung cancer

Characterization of gene expression profiles in Alzheimer’s disease and osteoarthritis: A bioinformatics study

Background Alzheimer’s disease (AD) and Osteoarthritis (OA) have been shown to have a close association in previous studies, but the pathogenesis of both diseases are unclear. This study explores the potential common molecular mechanisms between AD and OA through bioinformatics analysis, providing new insights for clinical treatment strategies. Methods The AD and OA-related datasets were downloaded from the gene expression database GEO. The datasets were analyzed to obtain differentially expressed gene (DEG) datasets for OA and AD, respectively. The intersection of these DEGs was analyzed to identify common DEGs (Co-DEGs). Subsequently, the Co-DEGs were enriched, and a protein-protein interaction network was constructed to identify core genes. The expression of these genes was validated in a separate dataset, and their diagnostic value for the diseases was analyzed. In addition, the core genes were analyzed using gene set enrichment analysis and single-gene genome variation analysis. Results Analysis of DEGs on gene chips from OA and AD patients revealed significant changes in gene expression patterns. Notably, EFEMP2 and TSPO, genes associated with inflammatory responses, showed lower expression levels in both AD and OA patients, suggesting a downregulation in the pathological backgrounds of these diseases. Additionally, GABARAPL1, which is crucial for the maturation of autophagosomes, was found to be upregulated in both conditions. These findings suggest the potential of these genes as diagnostic biomarkers and potential therapeutic targets. However, to confirm the effectiveness of these genes as therapeutic targets, more in-depth mechanistic studies are needed in the future, particularly to explore the feasibility and specific mechanisms of combating disease progression by regulating the expression of these genes. Conclusions This study suggests that AD and OA shares common molecular mechanisms. The identification of EFEMP2, GABARAPL1, and TSPO as key target genes highlights potential common factors in both diseases. Further investigation into these findings could lead to new candidate targets and treatment directions for AD and OA, offering promising avenues for developing more effective and targeted therapeutic interventions.

Numerical investigation of water droplet collision dynamics on moving surfaces

Association between wasting and inadequate breastfeeding practices among infants under six months in SNNPR and Somali regions of Ethiopia: A multilevel cross-sectional study

Background Wasting is a severe threat to children’s survival and development. Attaining optimal breastfeeding practices for infants under six months of age remains a significant challenge in low-income countries. This study assessed the association between wasting and breastfeeding practices among infants under six months of age in the SNNPR and Somali regions of Ethiopia. Methods The study used data from a large feasibility study conducted in the SNNPR and Somali regions of Ethiopia, from August-September 2021. This study involved 895 infants under six months of age with their mothers. The Poisson regression model with robust variance estimation was used to produce adjusted prevalence ratios (APRs) with 95% confidence intervals (CIs). Results The prevalence of wasting was 16.5% (95% CI: 14.2, 19.2) among infants under six months of age. Non-exclusive breastfeeding (APR = 1.50; 95% CI:1.02, 2.21), delayed initiation of breastfeeding (APR = 1.52; CI:1.00, 2.30), being male infants (APR = 1.50; 95% CI:1.09, 2.07), and mothers who attained primary level (APR = 0.62; 95% CI: 0.40, 0.95) or secondary level education (APR = 0.30; 95% CI: 0.09, 0.99) were independently associated with wasting in the multivariable analysis. Conclusion This study indicates a high prevalence of wasting among infants under six months of age. Non-exclusive breastfeeding and delayed initiation of breastfeeding were the modifiable factors significantly linked to infant wasting. Strengthening breastfeeding promotion and support may help reduce wasting in infants under six months.

A hybrid machine learning model for intrusion detection in wireless sensor networks leveraging data balancing and dimensionality reduction