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Low resolution remote sensing object detection with fine grained enhancement and swin transformer

Abstract Object detection in remote sensing images is a highly complex and challenging task. Remote sensing images typically suffer from issues such as small target sizes and densely distributed targets. Existing object detection algorithms often underperform in such scenarios due to their limited capability in handling fine-grained details and multi-scale objects. To address the persistent challenges in remote sensing image object detection, this study introduces a novel detection framework comprising three key innovations. First, we propose the Fine-grained Enhanced Downsampling Network (FEDNet) as the feature extraction backbone, specifically designed to preserve critical target information during downsampling through enhanced fine-grained feature representation. Second, we develop the Swin Transformer-based Progressive Aggregation Network (STPANet), which integrates Swin Transformer Blocks into the C3CST module to achieve superior multi-scale feature fusion while simultaneously capturing global contextual information and local spatial details. Finally, we incorporate the Shape-IoU loss function to optimize bounding box regression, significantly improving small target detection accuracy while maintaining computational efficiency. Experimental results demonstrate that the proposed method achieves outstanding performance on the DOTA and DIOR datasets, with mean average precision (mAP@50) scores of 69.9% and 85.5%, respectively. These results highlight its superior detection performance under low-resolution conditions.

New insights on femoral head trabecular bone directionality in elderly humans using a micro-CT-based analysis

Quantitative MRI relaxometry in brain tumor needle biopsies: Multimodal comparison with tissue fluorescence, radiology, and neuropathology

Background Quantitative MRI (qMRI) relaxometry holds potential for brain tumor identification beyond contrast enhancement on conventional images. However, clinical implementation is limited by long acquisition times, changing conditions between imaging and surgery, and lack of correlation with standard techniques. Purpose To extend a methodology for multimodal data analysis to relaxometry data. To integrate relaxometry into the burr hole needle biopsy procedure with optical guidance, setup a workflow for multimodal data processing and analysis, and apply the methodology in a clinical setting. Methods Multi-dimensional multi-echo relaxometry data (2x6 min) was acquired in addition to the clinical imaging protocol. Relaxation rate and proton density maps, as well as their differences were calculated before (R1, R2) and after gadolinium contrast-agent administration (R1Gd, R2Gd). Radiological volumes of interest (VOIs: tumor, edema, white matter, and biopsy) were defined on clinical images. Rate distribution changes were analyzed on three levels: the biopsied volume, along the needle trajectory (4x4x4 mm3 volumes), and VOIs. Increased R1Gd and R2Gd were compared to indications from 5-aminolevulinic acid-induced fluorescence and detailed neuropathological evaluation. Results Neuropathological analysis confirmed seven glioblastoma, one lymphoma, and one non-tumorous diagnosis. Increased R1Gd was found in all biopsied volumes, although tumorous volumes presented larger R1Gd increase (3–9 times) compared to volumes dominated by necrotic or non-tumorous tissue. Along the trajectory, increased R1Gd and R2Gd were not tumor-specific, however, the greatest R1Gd shifts were found in or adjacent to radiologically defined tumorous tissue. Increased relaxation rates corresponded to 82% and 45% (R1Gd: φ = 0.35, R2Gd: φ = 0.27) of fluorescence peaks. In the radiological VOIs, increased R1Gd and R2Gd were found in tumorous tissue, a slight right shift in edematous tissue, and negligible changes in white matter. Conclusion Combined analysis suggests increased R1Gd together with fluorescence peaks as a marker for tumor tissue. The presented multimodal approach provides a workflow toward clinical translation of relaxometry.

Application effect of short-term traffic flow prediction method based on CNNBLSTM algorithm

Reduced forecast efficiency and accuracy are the result of traditional traffic flow prediction algorithms’ inability to adequately capture the spatiotemporal characteristics and dynamic changes of traffic flow. To address this problem, this study proposes a short-term traffic flow prediction method based on an improved convolutional neural network and a bidirectional long short-term memory algorithm. The method firstly identifies, repairs and decomposes the abnormal traffic flow data by smoothing the estimation threshold and adaptive noise integration empirical modal decomposition method to improve the data quality and stability. The suggested model is then supplemented with the enhanced Adam and Lookahead algorithms in an effort to increase the model’s prediction accuracy and rate of convergence. The outcomes indicated that the method showed faster convergence and lower loss values during both training and validation. The training loss decreased from 0.0250 to 0.0021, and the validation loss decreased from 0.0010 to 0.0008. Compared with the traditional convolutional neural network with bidirectional long short-term memory algorithm, the training loss decreased by 42.86% The suggested algorithm outperformed the current advanced algorithms in terms of prediction precision, with an average absolute percentage error of 0.233 and a root mean square error of 23.87. The findings display that the study’s suggested algorithm can effectively and precisely forecast the short-term traffic flow, which serves as a solid foundation for planning and traffic management decisions.

Adaptation of finnish diabetes risk score for screening undiagnosed diabetes and hyperglycemia in Chinese adults

Objective China has the largest population with diabetes globally, with over half of the cases going undiagnosed, highlighting the need for improved screening efforts. This study aimed to adapt the Finnish Diabetes Risk Score (FINDRSC), a widely used tool for assessing diabetes risk without relying on clinical indicators, for screening undiagnosed hyperglycemia and diabetes among Chinese adults. Methods Data from the China Health and Nutrition Survey (CHNS), collected in the 2009 wave, were utilized as the training data (n = 7277), and data from the Guangzhou Nutrition and Health Study (GNHS, n = 2970), conducted in the years 2011–2014, were used for validation. Diabetes was defined as fasting plasma glucose (FPG) ≥ 7.0 mmol/L and/or glycated hemoglobin A1c (HbA1c) ≥ 6.5%. Hyperglycemia was defined as FPG ≥ 5.6 mmol/L and/or HbA1c ≥ 5.7%. Predictors in the original FINDRISC model were adjusted according to local standards and guidelines to develop the Modified Chinese screening model (ModChinese). Coefficients and scores of the ModChinese model were estimated using logistic regression. Area under the receiver operating characteristic curve (AUC) was calculated to evaluate model performance. Results The prevalence of undiagnosed diabetes and prediabetes was 8.6% and 40.1% in CHNS, and 3.1% and 27.9% in GNHS, respectively. The ModChinese demonstrated superior performance compared to the original FINDRISC, with higher AUC values for detecting both diabetes (0.707 vs. 0.681, p = 0.001) and hyperglycemia (0.680 vs. 0.661, p < 0.001) in the CHNS. Similar improvements were observed in the GNHS, where the ModChinese achieved AUC values of 0.663 for diabetes and 0.606 for hyperglycemia, compared to FINDRISC’s 0.622 and 0.593, respectively. Compared with the original FINDRISC, the ModChinese model showed improved sensitivity and specificity for screening undiagnosed diabetes and enhanced sensitivity for hyperglycemia screening in both training and validation datasets. Conclusion The ModChinese model is a simple and effective screening tool for identifying undiagnosed diabetes and hyperglycemia in Chinese adults.

Optimization design of internal space layout of three-bedroom residential apartment based on IGA and DE algorithm

To solve the problems of insufficient global optimization ability and easy loss of population diversity in building interior layout design, this study proposes a novel layout optimization model integrating interactive genetic algorithm and improved differential evolutionary algorithm to improve the global optimization ability and maintain population diversity in building layout design. The model characterizes room functions and spatial locations through binary coding, and uses dynamic fitness function and backtracking strategy to improve space utilization and functional fitness. In the experiments, optimization metrics such as kinematic optimization rate (calculated based on the shortest path and connectivity between functional areas), space utilization rate (calculated by the ratio of room area to total usable space), and functional fitness (based on the weighted sum of users’ subjective evaluations and functional matches) all perform well. Quantitatively, it is found that the model achieves 94.76% in terms of motion optimization rate, the highest space utilization rate is 96.6%, functional fitness is 9.4, and user satisfaction is close to 94.21%. The optimization results show that the proposed method has significant advantages in improving space utilization and meeting personalized design needs. However, despite the good optimization results, the method still faces the problem of improving the optimization ability under high-dimensional space and complex constraints. This study provides an efficient solution for intelligent building layout design and has certain practical value.

Repercussions faced by health professionals who have experienced the phenomenon of the second victims due to incidents related to patient safety: A scoping review protocol

Introduction: Patient safety is a relevant, timely and globally significant topic. In hospital settings, despite ongoing discussions and advances in patient safety, adverse events continue to occur, impacting not only patients but also healthcare professionals. Health professionals involved in adverse events are considered second victims. This justifies the growing and pertinent interest in the phenomenon of second victim, as this experience affects the competencies and skills of health professionals while influencing their personal livesand general health. Objective: To map and characterize the repercussions experienced by health professionals who have experienced the phenomenon of second victims due to incidents or adverse events related to patient safety in health services. Method: We will conduct a scoping review following the Joanna Briggs Institute’s methodology for scoping reviews, in alignment with the guidelines of the Preferred Reporting Items for Systematic Reviews and Meta-Analyses for Scoping Reviews (PRISMA-ScR) extension. The databases to be searched include PUBMED, SCOPUS, CINAHL, Web of Science (WOS) and LILACS, while the gray literature will be retrieved from the Brazilian Digital Library of Theses and Dissertations (BDTD) and PROQUEST. The identified studies will be compiled and uploaded to EndNote (version X9.3.3), where duplicates will be removed. Two independent reviewers will import the studies into the Rayyan QCR® for organization and selection, with disagreements resolved by a third party. The selection of studies will be based on the population, the concept investigated and the context. The data will be extracted through a pre-established form. Results: The findings will be analyzed in a descriptive and narrative way, aligning the data with the objective of the study and the research question. The data will are presented in tables, diagrams, and graphs containing information from the extraction tool.

A protocol for the evaluation of the PneumoWave biosensor in supported accommodation settings: A study on feasibility and acceptability (RESCU-2)

Background People who overdose on opioids when they are alone or unmonitored are at heightened risk of death as other people do not know they should provide an emergency response. Wearable technology provides an opportunity to continuously measure respiratory function and ultimately send an alert if respiratory depression occurs. Objective This study evaluates the feasibility and acceptability of PneumoWave DC in UK homeless hostels or supported accommodation settings (equivalent to Housing First in the USA) for individuals at high risk of opioid overdose. The PneumoWave system consists of a wearable biosensor that is affixed to the chest and records chest motion and which, in future, could potentially provide early detection of respiratory depression and trigger overdose response. Methods RESCU-2 is a non-randomised, observational trial conducted in supported accommodation facilities across the UK. 50 participants who currently use opioids and live in homeless hostels in England and Scotland will wear the PneumoWave biosensor for varying periods to collect data over 2,000 participant-days. The biosensor will be linked via Bluetooth to a hub for continuous respiratory data collection. Self-reported drug use during the trial will be measured using drug diaries. Quantitative acceptability data will be measured using structured satisfaction surveys, while qualitative acceptability data will be obtained from interviews and focus groups with both residents and staff. Statistical analysis will include descriptive evaluation of feasibility outcomes, while qualitative data will undergo thematic analysis. The primary objectives of the study are: 1) feasibility of the study protocol within the hostel setting; 2) acceptability and usability of the device among people who use opioids and live in hostels; 3) acceptability of the device among staff who work in hostels and respond to overdose events. Primary outcomes are recruitment, total hours of usable data collected and successful recording of key outcome measures, among others. Trial registration: ISRCTN12060022. Results & Significance Findings will inform the feasibility of future integration of chest biosensor technology into hostel settings, assessing participant adherence, usability, and acceptability among people who use substances and staff. Insights gained will support the design of future trials and further development of remote monitoring technologies for overdose prevention and response strategies.

Fair curve designing by Said-Ball curve

Fair curves are visually alluring curves and are free of unnecessary design features. A new curve designing method is introduced using the tangential continuous rational cubic Said-Ball curve. Fair curves are achieved by controlling its length and variation in curvature. It has enough degrees of freedom (control points and free parameters). A family of curves can be obtained for different choices for the values of free parameters. The control points are fixed by employing the G1 continuity conditions at the end points of the RCSBC. The optimal values of the remaining free parameters that are the weights of the underlying curve are obtained by constructing the optimization problems with stretch energy and curvature variation energy as the objective functionals. Different numerical examples are constructed to consolidate the effectiveness of the presented methods. Finally, two applications of the proposed techniques is also presented in the end.

Economic growth and suicide rates: Differential accumulated effects

Economic growth has a protective effect against suicide, but the nature of this association remains unclear. This ecological study explored the relationship between economic growth and suicide rates across countries within a specific timeframe. Data on age-standardized suicide rates and gross domestic product per capita (GDPpc) from 198 countries between 1991 and 2021 were obtained from the Global Burden of Disease Study and the World Bank. Using a two-way fixed-effects model and the compound annual growth rate, the association between age- and sex-adjusted suicide rates and GDPpc changes in preceding years was analyzed. GDPpc growth and lower suicide rates were significantly correlated, with a stronger correlation over longer periods, and similar associations were observed in upper-middle, lower-middle, and low-income countries. The opposite correlation was found between increased suicide rates and short-term average GDPpc growth in high-income countries, with economic growth being associated with increased suicide rates in these countries. In low- and lower-middle-income countries, increased suicide rates were associated with long-term economic stagnation. Socioenvironmental stress related to economic changes should be considered when implementing suicide prevention policies.

Evidence of play behavior in captive California two-spot octopuses, Octopus bimaculoides

Play is considered to be an essential part of development that supports learning, memory, and the development of flexible behavioral strategies. It may also serve as an informative factor in assessing an animal’s welfare state and in improving care and husbandry practices. An increasing number of non-mammalian species have been discovered to engage in play behavior, including several cephalopod species. Here, we characterized play behavior in wild-caught, laboratory-housed California Two-Spot Octopuses, Octopus bimaculoides , a species with growing relevance as a model in biomedical research, with the goal of establishing a behavioral repertoire and encouraging further research into the behavior and welfare of this species.

Understanding the determinants of treated bed net use in Ethiopia: A machine learning classification approach using PMA Ethiopia 2023 survey data

Introduction Malaria remains a significant public health challenge in Ethiopia, with over 7.3 million cases and 1,157 deaths reported between January 1 and October 20, 2024. Despite extensive distribution campaigns, 35% of insecticide-treated nets (ITNs) remain underutilized, hindering malaria control efforts. Traditional statistical approaches have identified socioeconomic and demographic factors as predictors of ITN use, but often fail to capture complex, nonlinear interactions. This study applies machine learning to identify non-apparent factors of ITN utilization and investigates its performance in prediction as compared to traditional logistic regression. Methods This study applied ML models, including Random Forest, XGBoost, and Gradient Boosting, to predict ITN utilization using the 2023 Performance Monitoring for Action (PMA) Ethiopia dataset, a nationally representative survey of 9,763 households. The dataset included 18 variables: region, household size, wealth quintile, and housing conditions. Model performance was evaluated using accuracy, precision, recall, F1-score, and AUC-ROC. The values of SHAP (Shapley Additive Explanations) were used to interpret feature importance and interaction effects. Results Random Forest and XGBoost outperformed traditional logistic regression, achieving AUC scores of 0.89(0.91 after optimization) and 0.88, respectively. Key determinants of ITN utilization included geographic region, household size, wealth quintile, and maternal education. Nonlinear interactions, such as the moderating effect of maternal education on income-related barriers, were identified. Regional disparities were evident, with Amhara and Oromia showing higher ITN Utilization compared to urban areas like Harari and Dire Dawa. Middle-income households exhibited the highest ITN usage (23.7%), challenging the assumption of linear wealth gradients. Conclusion This study demonstrates the superiority of machine learning (ML) models in capturing complex, nonlinear determinants of ITN utilization, providing actionable insights for targeted malaria prevention strategies. Findings underscore the need for region-specific interventions, integration of ITN distribution with educational and economic empowerment programs, and synergies with environmental health improvements. The study highlights the potential of ML to enhance precision in public health in resource-limited settings, contributing to Ethiopia’s National Malaria Elimination Roadmap and global malaria eradication efforts.

Targeting interferon-stimulated gene of 20 kDa protein (Isg20) inhibits ribosome biogenesis to ameliorate the progression of renal fibrosis

Chronic kidney disease (CKD) is a global health issue that significantly threatens human health, with its incidence increasing annually. Renal fibrosis is characterized by the progressive loss of kidney function, leading to significant morbidity and mortality. Although ribosome biogenesis has been reported to be increased in several kidney diseases, its role in renal fibrosis remains unclear. This study investigates the role of the interferon-stimulated gene of 20 kDa protein (Isg20), an RNA exonuclease involved in several stages of ribosome biogenesis, in the progression of renal fibrosis. Bioinformatics analysis of Gene Expression Omnibus (GEO) datasets identified upregulation of ribosome biogenesis-related genes and Isg20 expression in renal fibrosis samples. Using the unilateral ureteral obstruction (UUO)-induced renal fibrosis mouse model, we confirmed elevated Isg20 expression, promoted renal fibrosis, and increased ribosome biogenesis. Knockdown of Isg20 significantly reduced ribosome biogenesis, ameliorated kidney damage, inhibited pro-inflammatory cytokines levels and renal fibrotic changes, and decreased endoplasmic reticulum stress and cell apoptosis. Our findings suggest that Isg20 exacerbates renal fibrosis by promoting ribosome biogenesis, ER stress and cell apoptosis highlighting a potential therapeutic target for renal fibrosis treatment.

Comparison of muscle activity distribution in the biceps brachii using monopolar and bipolar multichannel surface electromyography

The detailed distribution of muscle activity within the biceps brachii (BB) muscle during isometric contraction as contraction intensity increases is not yet fully understood using multichannel surface electromyography (EMG). This study aimed to elucidate the distribution of muscle activity within the BB muscle during isometric contractions using monopolar and bipolar recording methods with multichannel surface EMG. The participants were 23 healthy young adults who performed a gradually increasing load task involving right elbow flexion for 10 seconds. The results showed that muscle activity was higher in the proximal medial area from the middle region with the monopolar recording method, and higher in the central medial area with the bipolar recording method as contraction intensity increased. These results with the monopolar recording method were consistent with previous reports regarding the increased shortening rate of the BB muscle from the middle to the proximal area and the migration of the innervation zone. Therefore, the monopolar recording method of multichannel surface EMG is considered more suitable for clarifying the detailed distribution of muscle activity within the same muscle.

Estimation of compressive strength of ultra-high performance lightweight concrete (UHPLC) using neural network

High strength and lightweight are key trends in concrete development. Achieving a balance between these properties to produce high structural efficiency (strength-to-weight ratio) concrete is challenging due to the complex relationship between compressive strength and material components. In this study, two artificial neural network (ANN) models-the BP and Elman networks were used to predict the compressive strength of ultra-high-performance lightweight concrete (UHPLC), based on a robust database of 115 test datasets from previous studies. The investigated parameters included the cement grade (Grade 42.5 and Grade 52.5), cement content (352 kg/m3-938 kg/m3), silica fume content (0 kg/m3-350 kg/m3), fly ash content (0 kg/m3-220 kg/m3), microsphere content (0 kg/m3-624 kg/m3), lightweight sand types (pottery sand, expanded perlite sand, and expanded shale lightweight sand), lightweight sand content (0 kg/m3-769 kg/m3), sand type (quartz sand, river sand), sand content (0 kg/m3-1314 kg/m3), water (90 kg/m3-395 kg/m3), water reduce (0 kg/m3-42.8 kg/m3), steel fiber content (0 kg/m3-234 kg/m3). Correlation analysis and sensitive analysis indicated that lightweight sand content and sand content had the most significant effects on UHPLC compressive strength, followed by water content. Conversely, fly ash content and lightweight sand type had minimal impact. The developed ANN models for UHPLC compressive strength demonstrated high predictive accuracy for both training and testing datasets, which the RMSE of BP network and Elman network were 0.226 and 0.160, respectively, while R2 of both two developed models were more than 0.98. Additionally, UHPLC exhibited a higher compressive strength-to-density ratio than high-strength concrete, ultra-high-performance concrete, and even Q235 steel. Three strategies were proposed for creating ultra-high-performance lightweight composites: optimizing packing density and lowering the water-binder ratio, along with careful selection of lightweight aggregates.

Plant tissue type and mineral contents shape endophytic bacterial communities in the Sisrè berry plant [Synsepalum dulcificum (Schumach & Thonn.) Daniell] in Benin

Diverse endophytic bacteria inhabit distinct tissues of a given species and are essential for plant growth and resilience to various stresses. Little information is available on bacterial endophytes associated with Synsepalum dulcificum, an opportunity fruit crop with high economic and medicinal values. Using Illumina sequencing of the bacterial 16S rRNA gene, the diversity and structure of the endophytic bacterial community in the roots and leaves of S. dulcificum were determined, considering 29 accessions from three distinct phenotypes located either in home gardens or on farms in Benin. 2,468 Operational Taxonomic Units (OTUs) were recorded in the leaf and root endosphere of S. dulcificum, affiliated with 20 bacterial phyla, 49 classes, 125 orders, 217 families and 365 genera. Actinomycetota, Pseudomonadota and Chloroflexota were the most abundant phyla in the roots. In comparison, Pseudomonadota stood out as almost the unique phylum in the leaves, suggesting a significant decrease in diversity from roots to leaves. Significant correlations (p < 0.05) were observed between the relative abundance of the endophytic bacterial taxa and the mineral contents in the leaves, roots, and soil. While bacterial communities depended highly on accession, plant phenotype and habitat discriminated them in roots and leaves, respectively. Metagenome function prediction indicated that S. dulcificum harbors bacteria with the potential to metabolize carbohydrates and amino acids, as well as synthesize secondary metabolites and antimicrobial compounds beneficial for plant growth and adaptation to environmental stresses. These findings open room for exploiting endophytic diversity to enhance the growth and sustainable production of S. dulcificum.

From traits to puffs: The interplay of personality, pandemic stress, and smoking behaviors

Smoking, a leading cause of chronic diseases, is often used to cope with stress, which has been heightened by the pandemic due to health and economic concerns. Studies have shown that the Big Five personality traits are linked to smoking behavior, suggesting that different personality traits influence nicotine use in varying ways. However, there remains a significant gap in understanding how individuals with different personalities respond to nicotine use under stress. This study aims to investigate how nicotine dependence changes for different Big Five personalities under the pandemic stress and whether other stress-related factors influence nicotine dependence during COVID-19. This cross-sectional study collect data from randomly selected adults aged 18−30 in the US. The Big Five Personality Model assessed personality traits, and nicotine dependence was measured with the Hooked-on Nicotine Checklist. Stress was evaluated using the Perceived Stress Scale, while demographics and other pandemic-related stressors were gathered through structured questions. Correlation and multiple logistic regression models were used for data analysis. The main findings showed that both before (r = −.25, p < .001) and during (r = −.19, p < .001) the pandemic, agreeableness was significantly negatively associated with nicotine dependence, indicating that higher agreeableness was linked to lower nicotine dependence. Similarly, conscientiousness was negatively correlated with nicotine dependence both before (r = −.123, p < .001) and during COVID-19 (r = −.19, p < .001). Although no direct association was found between perceived stress, personality traits, and smoking behavior, the analysis identified that external stressors played a moderating role. These findings emphasize the importance of understanding how different personality traits influence young people’s dependence on nicotine under stress. The outcome can guide the design of targeted nicotine withdrawal interventions and inform effective public health strategies.

The spatial and dynamic impact of air pollution on public health: Evidence from China 2000–2021

China’s rapid economic growth and improving quality of life have led to severe air pollution, primarily due to the country’s development model. This pollution not only raises public health risks but also shortens life expectancy, drawing significant attention from both the public and the government. This study focuses on 31 provincial-level regions within China, utilizing data collected annually from 2000 to 2021. It begins by examining the spatial relationships between air pollution and public health, then delves into how air pollution and various influencing factors affect public health outcomes. Lastly, the research investigates how these effects vary across different regional contexts. The findings show a clear connection between the medical visits for diagnosis and treatment and the levels of air pollution across different provinces. The spatial econometric model reveals that PM2.5 levels, industrial SO2 emissions, and smoke and dust emissions from industries all significantly increase medical visits for diagnosis and treatment. A 1% rise in PM2.5, SO2, or industrial smoke and dust emissions leads to increases of 0.2884%, 0.0563%, and 0.1365%, respectively, in medical visits. This suggests that air pollution contributes to a decline in public health. The impact of air pollution on public health shows considerable variation across different regions, including the eastern, central, and western parts of the country. The results of this study offer fresh insights into how air pollution affects public health, providing important guidance for policies aimed at improving air quality and protecting the health of citizens.

Impact of self-directed learning strategy, an innovative method in nursing undergraduates: Study protocol for a randomized controlled trial

Background In today’s fast-paced healthcare environment, self-directed learning is essential for healthcare professionals to stay updated and provide optimal care. Game based learning has a potential of motivating students’ engagement and creating fascinating self-directed learning environment for favorable outcome. This randomized controlled trial aims to investigate the impact of game-based learning as an innovative self-directed learning strategy compared to conventional self-directed learning strategy on knowledge acquisition, and self-directed learning abilities among nursing undergraduates based on Self-directed learning Instrument (SDLI) score. Methods This quantitative, randomized controlled trial will enroll and randomize 140 undergraduate nursing students in the experimental and control group using stratified random sampling and follow them for 12 weeks. Self-directed learning (SDL) orientation session will be conducted prior to the randomization for all the participants. After randomization, experimental group will undergo SDL with game-based learning and control group will undergo SDL with conventional learning for four weeks. Follow up sessions will be conducted once a week for a period of 4 weeks. Evaluation of impact of intervention will be assessed at four time points: preintervention, immediately postintervention, 4 weeks postintervention and 12 weeks postintervention using structured knowledge questionnaire, Self-Directed Learning Instrument (SDLI) and Cognitive, Affective and Psychomotor (CAP) perceived learning scale. ANOVA will be used to compare the variations in self-directed learning ability, perceived learning competency, and knowledge between two groups. Regression analysis will be conducted to explore the correlation between various independent variables and the dependent variables. Paired t-test will be used to analyze the variation between pre-test and post-test results, considering a p-value less than 0.05 to be statistically significant. Discussion This trial aims to determine whether game-based learning as an innovative self -directed learning strategy is superior to conventional self-directed learning strategy in improving knowledge level and self-directed learning abilities of nursing undergraduates. This trial is approved by the Datta Megha Institute of Higher Education & Research, Institutional ethics committee (DMIHER(DU)/IEC/2023/141C). We plan to disseminate study results in peer-reviewed journals and international conferences. Trial registration Trial Registered Prospectively [CTRI/2024/01/061599].

From chisel to inscription: affordable protocols for the digital documentation of stone carving techniques. An experimental archaeology and traceological approach applied to epigraphy

This study investigates technological traces in stone epigraphs to reconstruct the methods, tools, and gestures used by artisans. It aims to analyze the techniques behind these inscriptions, highlighting skills, challenges, and interactions between craftspeople and stone, as well as territorial differences. Experimental archaeology enables the creation of a reference collection of replicated inscriptions, providing a comparative framework for technological trace analysis. By integrating experimental archaeology with traceological analysis, this research introduces a novel methodology for epigraphic studies through qualitative and quantitative approaches. A key contribution is the use of micro-photogrammetry as a non-invasive, non-destructive documentation technique, particularly valuable for fieldwork. This method enables high-resolution, meso-scale recording of inscriptions, even on immobile surfaces, common in epigraphic studies. Both qualitative and quantitative analyses are applied to interpret technological traces, including fatigue and abrasive wear. These traces reveal information on the direction, depth, and angle of engraving, shedding light on artisans’ techniques and challenges. Quantitative methods refine the analysis by providing precise insights into the engraved surface topography and roughness. Moreover, slope analysis clarifies tool orientation and movement, enabling the visualization of trace profiles and validating qualitative observations.