Search Results (139)

The Sustainable Development Goals (SDGs), launched by the United Nations in 2015, outline 17 interconnected objectives designed to promote human well-being and sustainable development worldwide. Education is recognized by the United Nations as a key factor in promoting sustainable development. To cultivate students with both global perspectives and local engagement, it is essential to integrate sustainability education into elementary curricula. Accordingly, this study aimed to enhance elementary school students’ understanding of the SDGs by designing a structured instructional activity and developing a hybrid digital-physical board game. The game was implemented as a supplementary review tool to traditional classroom teaching, leveraging the motivational and knowledge-retention benefits of physical board games while incorporating digital features to support learning process monitoring. To address the limitations of conventional review approaches—such as reduced student engagement and increased cognitive load—the instructional model incorporated the board game during review sessions following formal instruction. This was intended to maintain student attention and reduce unnecessary cognitive effort, thereby supporting learning in sustainability-related content. A quasi-experimental design was employed to evaluate the effectiveness of the instructional intervention and the board game system, focusing on three outcome variables: learning motivation, cognitive load, and learning achievement. The results indicated that students in the game-based Sustainable Development Goals group achieved significantly higher delayed posttest scores (M = 72.91, SD = 15.17) than the traditional review group (M = 61.30, SD = 22.82; p < 0.05). In addition, they reported significantly higher learning motivation (M = 4.40, SD = 0.64) compared to the traditional group (M = 3.99, SD = 0.69; p < 0.05) and lower cognitive load (M = 1.84, SD = 1.39) compared to the traditional group (M = 2.66, SD = 1.30; p < 0.05), suggesting that the proposed approach effectively supported student learning in sustainability education at the elementary level. Full article

The present work intends to assess the impact of trochoidal toolpath rounding radius loop adjustments on surface roughness, nose radius wear, and resultant cutting force during end milling of AISI D3 steel. Twenty experimental trials have been performed utilizing a face-centered central composite design through a response surface approach. Artificial Neural Network (ANN) models were built to forecast outcomes, utilizing four distinct learning algorithms: the Batch Back Propagation Algorithm (BBP), Quick Propagation Algorithm (QP), Incremental Back Propagation Algorithm (IBP), and Levenberg–Marquardt Back Propagation Algorithm (LMBP). The efficacy of these models was evaluated using RMSE, revealing that the LMBP model yielded the lowest RMSE for surface roughness (Ra), nose radius wear, and resultant cutting force, hence demonstrating superior predictive capability within the trained dataset. Additionally, a Genetic Algorithm (GA) was employed to ascertain the optimal machining settings, revealing that the ideal parameters include a cutting speed of 85 m/min, a feed rate of 0.07 mm/tooth, and a rounding radius of 7 mm. Moreover, the detachment of the coating layer resulted in alterations to the tooltip cutting edge on the machined surface as the circular loop distance increased. The initial arc radius fluctuated by 33.82% owing to tooltip defects that alter the edge micro-geometry of machining. The measured and expected values of the surface roughness, resultant cutting force, and nose radius wear exhibited discrepancies of 6.49%, 4.26%, and 4.1%, respectively. The morphologies of the machined surfaces exhibited scratches along with laces, and side flow markings. The back surface of the chip structure appears rough and jagged due to the shearing action. Full article

In today’s digital economy, long-term business success increasingly depends on both financial resources and digital capabilities. However, limited research explores how these two factors jointly drive sustainable performance in SMEs. This study investigates how access to finance influences sustainability outcomes among SMEs, with digital agility as a mediator and Industry 5.0 as a moderator. Based on cross-sectional data collected from 383 Hungarian manufacturing SMEs in late 2024, we apply PLS-SEM and Machine Learning (ML) techniques to validate our model. The results show that access to finance significantly influences digital agility and SMEs’ sustainability. Digital agility significantly mediates between access to finance and SMEs’ sustainability. Industry 5.0 further strengthens the relationships between access to finance and both SMEs’ sustainability and digital agility. ML identified digital agility as the key factor of SMEs’ sustainability. This study contributes to the Resource-Based View and Triple Bottom Line views by synergizing digital agility and human-centered Industry 5.0. Theoretically, it also supports methodological innovation in showing that the combined usage of PLS-SEM and ML can produce stronger and more fine-grained conclusions on complex sustainability dynamics. The findings are practically relevant guidance for SMEs, policymakers, and banks intending to enable digitally facilitated sustainable growth. To the scientific community, this study bridges a critical void by linking finance, technology, and sustainability within an innovative framework. Socially, it highlights how SMEs’ financial and digital capabilities can be strengthened not only to drive economic performance but also to support environmental sustainability and social well-being—resulting in inclusive and sustainable growth for emerging economies. Full article

Eye illness detection is important, yet it can be difficult and error-prone. In order to effectively and promptly diagnose eye problems, doctors must use cutting-edge technologies. The goal of this research paper is to develop a sophisticated model that will help physicians detect different eye conditions early on. These conditions include age-related macular degeneration (AMD), diabetic retinopathy, cataracts, myopia, and glaucoma. Common eye conditions include cataracts, which cloud the lens and cause blurred vision, and glaucoma, which can cause vision loss due to damage to the optic nerve. The two conditions that could cause blindness if treatment is not received are age-related macular degeneration (AMD) and diabetic retinopathy, a side effect of diabetes that destroys the blood vessels in the retina. Problems include myopic macular degeneration, glaucoma, and retinal detachment—severe types of nearsightedness that are typically defined as having a refractive error of –5 diopters or higher—are also more likely to occur in people with high myopia. We intend to apply a user-friendly approach that will allow for faster and more efficient examinations. Our research attempts to streamline the eye examination procedure, making it simpler and more accessible than traditional hospital approaches. Our goal is to use deep learning and machine learning to develop an extremely accurate model that can assess medical images, such as eye retinal scans. This was accomplished by using a huge dataset to train the machine learning and deep learning model, as well as sophisticated image processing techniques to assist the algorithm in identifying patterns of various eye illnesses. Following training, we discovered that the CNN, VggNet, MobileNet, and hybrid Deep Learning models outperformed the SVM and Random Forest machine learning models in terms of accuracy, achieving above 98%. Therefore, our model could assist physicians in enhancing patient outcomes, raising survival rates, and creating more effective treatment plans for patients with these illnesses. Full article

The importance of mathematical problem solving (MPS) has been widely recognized. While there has been significant progress in developing and studying interventions to support teaching and learning MPS for students with disabilities, the research on how to accurately and effectively assess the impact of those interventions has lagged, leaving a gap in understanding whether interventions are truly achieving their intended outcomes. The purpose of this mixed-method study was to explore how a dynamic assessment (DA) approach can be used in the context of an evidence-based MPS intervention, Enhanced Anchored Instruction, as an alternative means of assessing the MPS of students with disabilities. Our findings suggest that DA is an adequate assessment tool and can provide additional information for teachers to better understand the MPS strengths and challenges of students with disabilities such as MPS ownership transition. Study limitations, considerations for future research, and implications for practice are discussed, emphasizing the importance of rigorous evaluation of the DA approach to improve teaching and learning MPS for students with disabilities. Full article

Background: Down syndrome (DS) is one of the most prevalent chromosomal abnormalities affecting global healthcare. Recent advances in artificial intelligence (AI) and machine learning (ML) have enhanced DS diagnostic accuracy. However, there is a lack of thorough evaluations analyzing the overall impact and effectiveness of AI-based DS diagnostic approaches. Objectives: This review intends to identify methodologies and technologies used in AI-driven DS diagnostics. It evaluates the performance of AI models in terms of standard evaluation metrics, highlighting their strengths and limitations. Methodology: In order to ensure transparency and rigor, the authors followed the preferred reporting items for systematic reviews and meta-analyses (PRISMA) guidelines. They extracted 1175 articles from major academic databases. By leveraging inclusion and exclusion criteria, a final set of 25 articles was selected. Outcomes: The findings revealed significant advancements in AI-powered DS diagnostics across diverse data modalities. The modalities, including facial images, ultrasound scans, and genetic data, demonstrated strong potential for early DS diagnosis. Despite these advancements, this review outlined the limitations of AI approaches. Small and imbalanced datasets reduce the generalizability of the AI models. The authors present actionable strategies to enhance the clinical adoptions of these models. Full article

The role of the school principal has garnered international significance. When it comes to student learning outcomes, the effectiveness of the principal has been recognized as being more important than the effectiveness of a single teacher. Studies also highlight the role school leadership plays in fostering equity and social justice practices in schools and communities. Yet only a small body of research exists on how to prepare leaders to lead for equity. In this paper, we will describe, analyze, and reflect on the components of one school leadership preparation program’s (SLLP’s) candidate assessment system (CAS), which guided the selection of equity-centered leadership candidates for a cohort program in a university–district partnership. We applied a qualitative content analysis to the documents we utilized to select aspiring equity-centered leaders through the program’s redesigned CAS. Our findings revealed that the content and process of the program’s CAS was aligned with many tenets of equity-centered leadership, specifically in CAS documents and in the involvement of a broad group of constituencies and partners during the design and implementation of CAS. We also uncovered a new finding outside of our framework: an emphasis on instructional leadership in our CAS documents as a key component of leading for equity. This study is likely to inform other SLPPs intending to select leadership candidates who will have the capacity to lead for equity. Full article

Small enterprises are essential in supporting economic growth, particularly in emerging countries. Due to their constrained resources and capacities, many small businesses in developing nations encounter intricate obstacles when trying to enter the global market rapidly. The study is intended to develop a theoretical framework that can reveal the essential and integrated resources, and capabilities in the internationalization process. Instead of literature investigations, multiple case studies were adopted to explore the process of achieving the international success of Indonesia’s export-oriented small enterprises. In-depth interviews with twelve small enterprises across the culinary, fashion, and craft sectors were conducted to collect qualitative data. A content analysis followed the input–process–output–outcome structure as the basis for developing the robust framework. Based on the Resource-Based View (RBV), this study reveals the synergistic role of production, networks, marketing, learning, and legal capabilities in creating competitive advantages that support business continuity and sustainability. Research findings reveal that successful internationalization is not achieved by a single capability but through an integrated bundle of capabilities that can serve market needs. This study contributes to the literature by offering a comprehensive framework that maps the input–process–output–outcome structure of the internationalization process and offers practical insights for policymakers and practitioners aiming to enhance SME competitiveness. The results underscore the importance of capability development and government support in facilitating SME global expansion. Ultimately, this study provides a basis for further investigation into the dynamic capabilities that SMEs need to thrive in international markets. Full article

Using version control tools is an indispensable skill for engineers in the software industry. This study introduces a gamification approach together with a serious game called the Git Education Game (GEG) to teach Git concepts and usage, intending to improve students’ motivation and learning performance compared to traditional lectures. An experiment was designed with two classes of the same course to compare the effect of GEG. A post-test was designed to verify whether the game could help students achieve better learning outcomes and higher motivation. The results show that our approach had a positive effect on students’ motivation, so the experimental group had a higher pass rate than the control group for most items in the post-test. Based on this study’s results, we emphasize the impact of interactive learning environments in software engineering education. Full article

In computer vision, recognizing plant pictures has emerged as a multidisciplinary area of interest. In the last several years, much research has been conducted to determine the type of plant in each image automatically. The challenges in identifying the medicinal plants are due to the changes in the effects of image light, stance, and orientation. Further, it is difficult to identify the medicinal plants due to factors like variations in leaf shape with age and changing leaf color in response to varying weather conditions. The proposed work uses machine learning techniques and deep neural networks to choose appropriate leaf features to determine if the leaf is a medicinal or non-medicinal plant. This study presents a neural network design based on PSR-LeafNet (PSR-LN). PSR-LeafNet is a single network that combines the P-Net, S-Net, and R-Net, all intended for leaf feature extraction using the minimum redundancy maximum relevance (MRMR) approach. The PSR-LN helps obtain the shape features, color features, venation of the leaf, and textural features. A support vector machine (SVM) is applied to the output achieved from the PSR network, which helps classify the name of the plant. The model design is named PSR-LN-SVM. The advantage of the designed model is that it suits more considerable dataset processing and provides better results than traditional neural network models. The methodology utilized in the work achieves an accuracy of 97.12% for the MalayaKew dataset, 98.10% for the IMP dataset, and 95.88% for the Flavia dataset. The proposed models surpass all the existing models, having an improvement in accuracy. These outcomes demonstrate that the suggested method is successful in accurately recognizing the leaves of medicinal plants, paving the way for more advanced uses in plant taxonomy and medicine. Full article

With the increasing adoption of robotic surgery in clinical practice, institutions intending to adopt this technology should understand the learning curve in order to develop strategies to help its surgeons and operating theater teams overcome it in a safe manner without compromising on patient care. Various statistical methods exist for the analysis of learning curves, of which a cumulative sum (CUSUM) analysis is more commonly described in the literature. Variables used for analysis can be classified into measures of the surgical process (e.g., operative time and pathological quality) and measures of patient outcome (e.g., postoperative complications). Heterogeneity exists in how performance thresholds are defined during the interpretation of learning curves. Factors that influence the learning curve include prior surgical experience in colorectal surgery, being in a mature robotic surgical unit, case mix and case complexity, robotic surgical simulation, spending time as a bedside first assistant, and being in a structured training program with proctorship. Full article

Background: Teaching style has a significant influence on students’ learning outcomes. This study focused on identifying teaching profiles in Physical Education characterized by high directiveness, using structure and control behaviors that impact students’ outcomes, basic psychological needs (BPN), and engagement. It was based on the circumplex model and self-determination theory (SDT) and intended to explore how these styles affect students’ motivation and engagement. Methods: A cluster-based methodological design was applied, evaluating teachers through self-reports. Adapted measures of structure and control were used to classify teachers into four distinct profiles within the educational context of Physical Education. Results: The study identified three teaching profiles: ‘high structure–low control’, ‘high structure–high control’, ‘low structure–low control’, and ‘low structure–high control’. The ‘high structure–low control’ profile showed the best results in autonomous and controlled motivation, with greater behavioral engagement among students. In contrast, the ‘high structure–high control’ profile was associated with higher levels of demotivation. Conclusions: Teaching styles of structure and control can combine in various ways among Physical Education teachers, significantly influencing student motivation, satisfaction of basic psychological needs, and engagement. It is recommended that teachers adopt behaviors that support structure without becoming controlling to enhance student learning and participation in classes. Full article

Digital platforms are increasingly prevalent among young students in K-12 education, offering significant opportunities but also raising concerns about their effects on self-assessment and academic performance. This study investigates the effectiveness of Kahoot! compared to traditional instructional methods in enhancing mathematics achievement and its impact on multiple screen addiction (MSA) among Greek students aged 9 to 12 during a STEM summer camp. A quasi-experimental design was employed with a purposefully selected sample of one hundred and ten (n = 110) students, who were non-randomly divided into two groups: (a) an experimental group of fifty-five students (n = 55) who engaged with Kahoot! (using dynamic visual aids and interactive content) and (b) a control group of fifty-five students (n = 55) who received traditional instruction (using digital textbooks and PowerPoint slides with multimedia content) on laptops and tablets. The findings revealed a statistically significant difference in MSA scores, with the experimental group exhibiting lower MSA scores compared to their counterparts, indicating a positive impact on reducing screen addiction levels. While Kahoot! led to lower MSA levels, it significantly improved overall mathematical achievement, with a substantial effect size, suggesting a strong positive impact on learning outcomes. The current study highlights the importance of aligning educational tools with the intended outcomes and recommends further research to explore the broader impact of gamified learning on student engagement, screen addiction, and learning outcomes. Full article

In today’s digital age, innovative artificial intelligence (AI) methodologies, notably machine learning (ML) approaches, are increasingly favored for their superior accuracy in anticipating the characteristics of cementitious composites compared to typical regression models. The main focus of current research work is to improve knowledge regarding application of one of the new ML techniques, i.e., gene expression programming (GEP), to anticipate the ultra-high-performance concrete (UHPC) properties, such as flowability, flexural strength (FS), compressive strength (CS), and porosity. In addition, the process of training a model that predicts the intended outcome values when the associated inputs are provided generates the graphical user interface (GUI). Moreover, the reported ML models that have been created for the aforementioned UHPC characteristics are simple and have limited input parameters. Therefore, the purpose of this study is to predict the UHPC characteristics while taking into account a wide range of input factors (i.e., 21) and use a GUI to assess how these parameters affect the UHPC properties. This input parameters includes the diameter of steel and polystyrene fibers (µm and mm), the length of the fibers (mm), the maximum size of the aggregate particles (mm), the type of cement, its strength class, and its compressive strength (MPa) type, the contents of steel and polystyrene fibers (%), and the amount of water (kg/m³). In addition, it includes fly ash, silica fume, slag, nano-silica, quartz powder, limestone powder, sand, coarse aggregates, and super-plasticizers, with all measurements in kg/m³. The outcomes of the current research reveal that the GEP technique is successful in accurately predicting UHPC characteristics. The obtained R², i.e., determination coefficients, from the GEP model are 0.94, 0.95, 0.93, and 0.94 for UHPC flowability, CS, FS, and porosity, respectively. Thus, this research utilizes GEP and GUI to accurately forecast the characteristics of UHPC and to comprehend the influence of its input factors, simplifying the procedure and offering valuable instruments for the practical application of the model’s capabilities within the domain of civil engineering. Full article

Hepatic steatosis, characterized by excess fat in the liver, is the main reason for discarding livers intended for transplantation due to its association with increased postoperative complications. The current gold standard for evaluating hepatic steatosis is liver biopsy, which, despite its accuracy, is invasive, costly, slow, and not always feasible during liver procurement. Consequently, surgeons often rely on subjective visual assessments based on the liver’s colour and texture, which are prone to errors and heavily depend on the surgeon’s experience. The aim of this study was to develop and validate a simple, rapid, and accurate method for detecting steatosis in donor livers to improve the decision-making process during liver procurement. We developed LiverColor, a co-designed software platform that integrates image analysis and machine learning to classify a liver graft into valid or non-valid according to its steatosis level. We utilized an in-house dataset of 192 cases to develop and validate the classification models. Colour and texture features were extracted from liver photographs, and graft classification was performed using supervised machine learning techniques (random forests and support vector machine). The performance of the algorithm was compared against biopsy results and surgeons’ classifications. Usability was also assessed in simulated and real clinical settings using the Mobile Health App Usability Questionnaire. The predictive models demonstrated an area under the receiver operating characteristic curve of 0.82, with an accuracy of 85%, significantly surpassing the accuracy of visual inspections by surgeons. Experienced surgeons rated the platform positively, appreciating not only the hepatic steatosis assessment but also the dashboarding functionalities for summarising and displaying procurement-related data. The results indicate that image analysis coupled with machine learning can effectively and safely identify valid livers during procurement. LiverColor has the potential to enhance the accuracy and efficiency of liver assessments, reducing the reliance on subjective visual inspections and improving transplantation outcomes. Full article