Search Results (882)

Traumatic fractures are common in small animal emergency care, yet subtle fracture lines may be difficult to identify accurately using routine three-dimensional reconstruction workflows, particularly when access to specialized software is limited. This study describes the use of the open-source platform Three-Dimensional Slicer for computed tomography-based reconstruction and three-dimensional printing in a small dog with cranial trauma, with emphasis on documenting a practical and reproducible workflow through voxel resampling. Imaging data were imported into the software, bone structures were segmented using a rapid workflow, voxel spacing was resampled for smoother surface visualization by volume resampling, and the reconstructed model was processed for physical printing. Digital models of different resolutions were generated within minutes, and a life-size skull model was successfully fabricated using fused deposition modeling in less than three hours at a material cost of under one United States dollar. The enhanced model provided an intuitive representation of fracture morphology and spatial relationships compared with routine reconstruction alone. These findings demonstrate that open-source software combined with low-cost printing can provide a rapid, affordable, and user-friendly approach for practical skeletal reconstruction in small animals, with practical value for fracture assessment, preoperative planning, and broader use in resource-limited veterinary settings. Full article

The high dropout rate in massive open online courses (MOOCs) continues to limit their potential in promoting inclusive and sustainable learning. Although many prediction models have been used to identify potential dropouts, most studies view dropout as a static classification problem and fail to clearly reveal the dynamic trajectory of learner participation over time. Therefore, this study introduces a phased analysis perspective, treating MOOC dropout as a process that continuously evolves at different stages. On the basis of the KDDCUP2015 dataset, we constructed behavioral characteristics at three time points: the first week, the third week, and the fifth week. By combining robust feature analysis and interpretable models, we systematically examined the changing patterns of dropout modes. The results revealed significant differences across the different stages. In the early stage of the course, dropout was related mainly to the unstable interaction behaviors of learners, such as restricted access to resources and irregular participation rhythms. In the middle and late stages, task-oriented behaviors, especially those related to video-based learning activities, gradually became key factors. Notably, high-frequency video participation does not always reduce the risk of dropout; when video activity is high but the overall interaction rate is low, it is more likely to indicate an increase in the risk of dropout. These results indicate that the combination of behaviors is more crucial than mere activity levels. By revealing the changing characteristics of behaviors at different stages, this study helps support the design of more practical early warning methods. Full article

Background: Confident chemical annotation in nontarget small-molecule mass spectrometry critically depends on the availability of high-quality tandem mass spectral (MS²) reference libraries. While community efforts have driven significant expansion of open-access repositories, technical challenges in assembling standardized, metadata-rich records continue to limit broader participation, underscoring the need for improved computational tools to assist contributors. Methods: To promote the creation and sharing of standardized reference MS² spectral records, we have developed Librarian, a free, open-access web application designed for rapid and scalable assembly of high-resolution MS² libraries. Librarian integrates automated retrieval and harmonization of chemical identifiers and metadata from PubChem, compound mixture design for high-resolution mass spectrometry (HRMS) acquisition, and assembly of curated MS² spectra into repository-ready records compatible with public spectral databases. Results: Through a simple in-browser interface, Librarian offers a flexible end-to-end workflow compatible with popular open-source pre-processing tools to lower technical barriers and facilitate broader community participation in library development. As a demonstration, we used Librarian to create and deposit a spectral library comprising over 1500 new MS² records into MassBank, which was further applied in retrospective analysis of environmental datasets. Conclusions: Librarian streamlines the creation of standardized, metadata-rich and repository-ready MS² reference records. Addressing a key bottleneck in community spectral library development and sharing, Librarian supports the continued growth of open-access resources for metabolomics, exposomics, and environmental mass spectrometry. The Librarian web application is publicly accessible via the SciLifeLab Serve platform. Full article

Particle Image Velocimetry (PIV) has recently evolved from a costly, specialized technique into an accessible method thanks to affordable hardware and open-source software. This work introduces a time calibration validation method tailored for low-cost or Do-It-Yourself (DIY) PIV systems. By utilizing inexpensive components such as light-dependent resistors (LDRs), basic resistors, and data acquisition devices or microcontrollers, the study enables accurate timing analysis of light pulses from synchronized lasers or LEDs. Experimental data obtained in real time using a National Instruments USB-6003 DAQ device confirm the system’s ability to detect light pulses with high temporal resolution. Through voltage signal interpretation, the synchronization accuracy of light sources is validated across different sampling rates. Moreover, the study demonstrates how the internal frequency settings of PIVlab, an open-source PIV software package, can be customized to enhance acquisition flexibility. Timing deviations of up to 20% were identified across selected default frequency settings. The proposed method ensures that low-cost systems maintain sufficient accuracy for phase-sensitive flow measurements, such as oscillatory flow or wave action, contributing to the broader adoption of PIV in resource-limited environments. It presents a low-cost method for validating timing accuracy in PIV systems, employs widely available components and is adaptable to multiple platforms, and enables precise synchronization checks critical for flow visualization. Full article

Diagnostic imaging is central to clinical decision-making across many care pathways, yet the expertise needed to use these images well is unevenly distributed across health systems, with workforce limitations identified as a major barrier to equitable access, particularly in low- and middle-income countries. Digital education has emerged as one response to this gap, offering scalability, asynchronous and just-in-time access, and the cost-efficiency required for global deployment. This paper examines the digital education portfolio of the International Atomic Energy Agency’s Nuclear Medicine and Diagnostic Imaging Section, hosted mainly on the open-access Human Health Campus, which in 2025 recorded approximately 45,800 active users and 150,000 views across 159 countries. The portfolio combines structured e-learning courses, interactive webinars, virtual conference access through the Livestream programme, and a broader repository of publications, teaching cases, and reference resources, supported by an internal e-learning framework and learning management system infrastructure. Partnerships with international scientific societies further extend the reach of expert knowledge and professional exchange. The paper argues that these initiatives are best understood not as content delivery alone but as a coordinated strategy to support diagnostic quality at the level of the practising physician, extending access to expertise and strengthening the conditions for better practice, while remaining a complement to, rather than a substitute for, supervised clinical training. Full article

Disability Enterprises have the potential to address employment barriers that people with an intellectual disability experience as they move into open employment. Disability Enterprises are able to facilitate this transition through strategic organisational design, but it is unknown the extent to which organisations are following what could be considered best practice. Utilising a survey and interview approach, we aimed to understand how well organisations align with the ‘WISE-Ability’ model and the ongoing challenges they face in supporting open employment pathways for people with a disability working at the enterprise. Staff (94) from Disability Enterprises completed a survey where they rated their own organisation against a number of criteria related to organisational design and operation related to transitioning supported employees to open employment. After completing the survey, organisational staff (19) participated in a semi-structured interview. Disability Enterprises provide training and life skills development options with the end goal of employment transition. Training is adapted to the needs of individuals and there is flexibility in the pace of learning and rostering of shifts. Disability Enterprises develop industry-specific work skills and independent life skills. Pathways to employment are offered in most cases; however, there is variation in the success and scale of employment pathways. Organisations continue to face challenges that exist in the disability service system and open labour market. Organisations are confident that they are able to offer a culture of support and respect, choice and variety of employment options, busy and quiet spaces, and areas for rest and accessible workspaces where individuals feel empowered and safe to try new tasks. Many organisations developed relationships with external stakeholders and employers to facilitate financial sustainability and employment pathways. However, organisations face challenges in the following areas: resourcing pathways to employment and offering certified training options for people working in a Disability Enterprise. Employment pathways were often carved out on a case-by-case basis relying on significant staff support and after-hour work. Full article

The safe disposal of sharps, particularly acupuncture and dry needling needles, remains a challenge in clinical and therapeutic environments, where inadequate management increases the risk of occupational injuries and infections. Commercial needle disposal devices are often costly, non-portable, and closed-source, limiting their adoption in small clinics and low-resource contexts. This work presents the design, construction, and validation of an open-source electric needle incinerator developed as a low-cost, safe, and reproducible alternative for biomedical waste management. The device was designed using accessible materials, 3D-printed components, and standard electronic parts, ensuring ease of replication. Detailed build and operating instructions are provided, to facilitate reproduction and future development of the system. Validation tests confirmed that the prototype incinerates individual needles in 3–5 s, processing typical sessions of 5–20 needles without performance degradation. Safety was ensured through thermal insulation, protective casing, and compliance with international standards. The fabrication cost of approximately 199 USD represents a reduction of over 65% compared to commercial devices priced at 600–1500 USD. By openly releasing the design, this contribution supports the hardware community with a replicable solution that enhances occupational safety, reduces costs, and fosters innovation in therapeutic and educational contexts. Full article

This review examines the relationship among foreign direct investment (FDI), trade openness, and economic growth, with emphasis on the channels through which external integration influences development outcomes. The literature generally suggests that FDI can raise growth through capital accumulation, technology transfer, productivity gains, and stronger linkages with domestic firms, while trade openness can promote growth by expanding market access, increasing competition, and improving resource allocation. However, the evidence is not uniform: some studies report that trade openness is the main driver of growth, while others find that FDI has a stronger effect, or that both variables matter only under favorable macroeconomic, institutional, and financial conditions. This review synthesizes theoretical arguments and empirical findings, identifies major transmission mechanisms and conditional factors, and highlights the policy environment needed for FDI and trade liberalization to translate into sustained economic growth. Full article

Background: Cataract diagnosis and management pose a significant global health challenge, contributing to 17 million cases of blindness and over 83 million cases of vision impairment worldwide in 2020. This issue is particularly acute in regions lacking adequate ophthalmological services, where a shortage of eye care clinicians and specialized equipment like slit-lamp cameras leads to late diagnoses. To address this accessibility gap, we developed a computer-assisted cataract grading system using smartphone-acquired external eye photographs. This approach utilizes image processing and deep learning on a standard, hardware-free smartphone, offering a low-cost and portable alternative to traditional equipment. Methods: The study introduces a new advanced algorithm to stratify cataract severity into three distinct stages: normal, pre-mature, and mature. The methodology was developed using a combined dataset of 799 images sourced from the Cataract v01 Computer Vision Project and the Indian Institute of Technology, Delhi. A key step is isolating the iris and lens using a region of interest (ROI) extraction procedure powered by the open-source MediaPipe framework. Key to the algorithm’s efficacy is the use of transfer learning, adapting four customized ResNet architectures (ResNet-18, ResNet-34, ResNet-50, and ResNet-101) to address medical image analysis intricacies. These models were fine-tuned with specific modifications, including dropout layers and the Adam optimizer, for analyzing the digital periocular images. Results: Evaluation of the models shows varied performance across the various architectures when classifying cataract stages. While the simpler ResNet-18 model exhibited the lowest performance, the deeper models showed significant improvement. The ResNet-50 architecture achieved the highest accuracy of 94%. This model also demonstrated excellent precision (94%), recall (95%), and an F1-score of 95% in multi-class classification, outperforming the other tested models. Its depth enables precise cataract classification, positioning it as a robust and reliable tool for potential medical diagnostic deployment. Conclusions: Deep learning-based analysis of smartphone-acquired external eye images demonstrated feasibility for cataract detection in this study. This method could be a scalable and easy-to-use addition to screening, especially in places where resources are limited. Further work is needed to expand the dataset and to validate the algorithm against established clinical grading systems before broader clinical implementation. Full article

Groundwater quality assessment in urban and peri-urban environments is often constrained by incomplete monitoring records, irregular sampling frequencies, and heterogeneous environmental datasets. The primary objective of this study is to predict the Water Quality Index (WQI) in the Attica River Basin, Greece, using advanced machine learning (ML) techniques. A groundwater quality dataset comprising 958 observations from 80 monitoring stations was analyzed using six physicochemical parameters, namely electrical conductivity, ammonium, nitrate, nitrite, chloride, and sulphate. Three modeling approaches, namely TabNet (with Winsorization), SVM, and Gradient Boosting Machines (GBM), were implemented to estimate groundwater quality conditions. To address the challenge of missing data, Multiple Imputation by Chained Equations (MICE) with Predictive Mean Matching (PMM) was implemented and systematically compared against conventional imputation approaches, including smoothed averages, interpolation, and forward-fill methods. The novelty of this study lies in the integration of open-access groundwater chemistry data, advanced multivariate imputation (MICE-PMM), and attention-based deep learning (TabNet) for groundwater quality prediction in a Mediterranean peri-urban area under data-scarce conditions. Using a multi-year groundwater monitoring dataset, the results indicate that the integrated MICE-PMM and TabNet framework achieved the highest predictive performance, with R² = 0.91, NSE = 0.91, RMSE = 52.21, and MAE = 25.68. Feature importance and sensitivity analyses identified nitrate as the dominant driver of WQI variability, highlighting the strong influence of anthropogenic nutrient loading on groundwater quality. Overall, the proposed framework provides a transferable, data-driven approach for groundwater quality prediction, environmental monitoring, and groundwater resource management in urban and peri-urban aquifer systems characterized by incomplete environmental datasets. Full article

Open innovation has become a central framework for explaining how firms access, integrate, and exploit knowledge beyond organizational boundaries. However, the conditions shaping its adoption by small- and medium-sized enterprises remain insufficiently understood, particularly in Latin American contexts. This study examines the determinants of open innovation adoption in Colombian SMEs and develops an analytical model that integrates six explanatory dimensions: external partnership and cooperation, government support, rules and regulatory factors, market and customer factors, organizational and human resource factors, and technological factors. Empirically, the study combines an exploratory qualitative phase, based on semi-structured interviews with SME managers in Bogotá, D.C., with a quantitative phase using survey data from 319 SMEs operating in ISIC 6201 and 6202. The hypotheses were tested using partial least squares structural equation modeling. The results show that technological factors have the strongest direct association with open innovation adoption, followed by government support and external partnership and cooperation. Market and customer factors, as well as organizational and human resource factors, also exert positive and significant effects, whereas rules and regulatory factors do not show a significant direct effect. Additional analyses indicate that organizational and human resource factors partially mediate the relationship between technological factors and open innovation adoption, while a complementary moderation test does not support an interaction-based effect. These findings suggest that open innovation adoption in SMEs is technologically enabled, partially translated through organizational and human resource capabilities, and shaped by a configuration of relational, institutional, market-based, and internal conditions rather than by any single determinant. Full article

Converging millimetre-wave (mmWave) radio access with passive optical network (PON) fronthaul under the Open RAN (O-RAN) architecture promises unprecedented capacity for beyond-5G and 6G systems. Yet today, dynamic bandwidth allocation (DBA) in the PON and physical resource block (PRB) scheduling in the mmWave RAN operate independently, a critical design flaw that causes severe latency accumulation, resource fragmentation, and consistent failure to meet the divergent quality-of-service requirements of network slices. This paper breaks that deadlock by introducing the first slice-aware, computationally efficient orchestration framework that jointly optimises DBA and PRB allocation in a converged mmWave-PON O-RAN. We formulate the problem as a constrained Markov decision process (CMDP) with explicit latency, reliability, and throughput constraints for URLLC, eMBB, and mMTC slices. The core technical advance is a reward-shaped proximal policy optimisation (RS-PPO) algorithm whose potential-based shaping function directly penalises DBA–PRB misalignment and dense feedback on queue build-up, accelerating learning without compromising optimality. To make this work in near-real time on the O-RAN RIC, we embed three complementary efficiency engines: graph convolutional network (GCN) state abstraction, action masking, and prioritised N-step replay. Extensive 3GPP-compliant simulations show that RS-PPO slashes URLLC end-to-end latency by 37% (from 1.38 ms to 0.87 ms), boosts PRB utilisation by 28% (from 68% to 87%), and delivers 99.999% reliability, all while converging 45% faster and cutting inference time by 45% (to just 2.3 ms). The result is a sub-5 ms control cycle, compatible with O-RAN specifications and deployable as an xApp on the near-RT RIC. Our framework closes a long-standing coordination gap left unresolved by prior art, enabling true slice-aware convergence between the optical and wireless domains. Full article

Glycine soja, the ancestor of cultivated soybean [Glycine max (L.) Merr.], is an important genetic resource for soybean improvement and a National Grade II Key Protected Wild Annual Plant in China. Understanding its intra- and interspecific interactions in natural communities is critical for effective conservation, yet these dynamics remain poorly characterized in field settings. This study aims to characterize these interactions within herbaceous communities, providing insights to optimize the management of G. soja populations and conservation reserves. We surveyed twenty natural G. soja communities and revealed the following: (1) G. soja exhibited stronger intraspecific than interspecific competition. Spatial patterning among morphotypes and their proportional displacement provided direct evidence of intraspecific interactions within G. soja populations. (2) The annual associated plant group exhibited coexistence mechanisms similar to G. soja, characterized by stronger intragroup competition relative to the perennial group, which conversely displayed stronger intergroup competition. (3) A significant negative correlation existed between perennials and annuals. Perennials posed a greater threat to G. soja than annuals via distinct threat mechanisms: while annuals suppressed G. soja primarily through proportional dominance in species number, perennials reduced G. soja density by leveraging G. soja’s tendency toward stronger intraspecific competition. (4) G. soja exhibited intraspecific niche differentiation among morphotypes defined by functional traits (leaf shape, leaf size, and plant height), where morphological similarity correlated with niche overlap. Extreme morphotypes followed a bimodal pattern, with intermediate forms acting as ecological buffers, thereby enhancing adaptation to heterogeneous environments. This study yields important implications for effective in situ conservation, requiring the mediation of the trade-off between intra- and interspecific competition. Optimal strategies should either maintain moderately open communities accessible to humans and grazing animals, thereby allowing residual associated plants to mitigate excessive intraspecific competition in G. soja while reducing intense interspecific competition, or employ artificial interventions in closed nature reserves to prevent excessive intra- and interspecific competitive exclusion of G. soja. Full article

Many South African schools face educational resource shortages that hinder effective teaching and learning. This study investigates a community-driven Open Educational Resources (OER) initiative implemented within a teacher education programme at a South African university, where Bachelor of Education (BEd) students (n = 400) from first to fourth year across the Vanderbijlpark and Potchefstroom campuses were engaged in designing sustainable Learning and Teaching Support Materials (LTSM) from recyclable materials. A purposively selected qualitative subsample (n = 53) participated in the study. Framed within an interpretivist and qualitative phenomenological design, data were collected through structured written reflections capturing participants’ experiences of the project. The findings show that creating OER from recyclable materials strengthened resourcefulness, collaboration, and awareness of educational inequality, while also encouraging more accessible and context-responsive teaching practices. The study contributes to understanding how Environmental, Social, and Governance (ESG)-aligned operational innovation can foster self-directed learning and sustainable teacher education in under-resourced contexts. Full article

Background: Accurate child development assessment is crucial, particularly in developing countries where access to validated tools remains limited. Many assessment tools are adapted for local contexts, but their psychometric properties require evaluation. Objective: This systematic review examines the reliability, validity, and overall psychometric properties of new and adapted child development assessment tools used in developing countries. The focus on these settings stems from the need to assess tools that are culturally appropriate, feasible, and accurate in resource-constrained environments, where early identification of developmental delays can significantly impact long-term child outcomes. Methods: Descriptive and meta-analyses were conducted to synthesize findings from eligible studies. Psychometric properties such as internal consistency, inter-rater reliability, construct validity, sensitivity and specificity were assessed. This review is registered on Open Science Framework (OSF) doi:10.17605/OSF.IO/GU28K. Results: The findings indicate that although some adapted tools demonstrate strong reliability and validity, others exhibit inconsistencies, highlighting challenges in adaptation. The meta-analysis provided pooled estimates of key psychometric properties with a net sensitivity and specificity of 0.859 and 0.805, respectively, illustrating the validity of these local tools but also variability in performance across different tools. Conclusion: The results emphasize the need for rigorous validation processes to ensure that adapted tools maintain their psychometric integrity. Future research should focus on refining these measures to improve their applicability in diverse cultural and socioeconomic settings. Full article