Search Results (38)

Background/Objectives: Family self-care (FSC) is increasingly recognized as a vital aspect of caregiving in pediatric chronic conditions. However, its development in families of children with intellectual disabilities (IDs) remains underexplored. This study aimed to examine how families construct and sustain FSC, and to identify factors that shape its development across four domains: physical, cognitive, psychosocial, and behavioral. Methods: A qualitative study was conducted using an abductive approach, combining inductive thematic analysis with a deductively applied theoretical framework. Semi-structured interviews were carried out with nine families of children with ID in southern Portugal. The children ranged in age from 4 to 15 years, and the parents were aged between 29 and 53 years. The data was analyzed using Bardin’s content analysis, supported by NVivo software, and organized according to the FSC framework. This study followed COREQ guidelines. Results: The families described a range of self-care strategies, including environmental adaptations, experiential learning, emotional regulation, and long-term planning. These practices were shaped by contextual factors such as access to healthcare, relationships with professionals, emotional support networks, and socioeconomic conditions. Four emergent conclusions illustrate how structural and relational dynamics influence FSC in daily caregiving. Conclusions: FSC is a dynamic, multidimensional process shaped by lived experience, family interactions, and systemic support. The findings support inclusive, family-centered care models and inform clinical practice, training, and policy in pediatric IDs. Full article

This paper presents platforms as a solution to rethink how we build, addressing the pressing paradox between meeting growing housing demands. The construction sector has not fully grasped the advantages of platforms beyond standardisation and efficiency. In contrast, other sectors have begun acknowledging that platforms can capture increased value through interactions among firms within a networked ecosystem. Learning from other sectors, this paper investigates platforms in the construction context, aiming to define, classify, and assess their impact on the construction value chain. The research approach was abductive, involving a cross-sectoral review of 190 platforms across 16 Australian and New Zealand Standard Industrial Classification (ANZSIC) industries and semi-structured interviews with stakeholder groups of the construction value chain in Australia. The findings categorise platforms as physical, digital, or hybrid, highlighting their potential to move value-added activities upstream, facilitate collaboration, and foster innovation through data-driven insights. The paper’s novelty lies in the exhaustive cross-sectoral review, the classification of platforms in the construction context, and the proposition of a platform approach as a versatile framework tailored to diverse needs and circumstances that offers a fresh perspective on sustainable building practices. The practical contribution of this study lies in offering guidelines for industry practitioners aiming to develop or refine a platform-based approach tailored to the construction context. Full article

The pursuit of Artificial General Intelligence (AGI) demands AI systems that not only perceive but also reason in a human-like manner. While symbolic systems pioneered early breakthroughs in logic-based reasoning, such as MYCIN and DENDRAL, they suffered from brittleness and poor scalability. Conversely, modern deep learning architectures have achieved remarkable success in perception tasks, yet continue to fall short in interpretable and structured reasoning. This dichotomy has motivated growing interest in Neural–Symbolic AI, a paradigm that integrates symbolic logic with neural computation to unify reasoning and learning. This survey provides a comprehensive and technically grounded overview of AI reasoning in the deep learning era, with a particular focus on Neural–Symbolic AI. Beyond a historical narrative, we introduce a formal definition of AI reasoning and propose a novel three-dimensional taxonomy that organizes reasoning paradigms by representation form, task structure, and application context. We then systematically review recent advances—including Differentiable Logic Programming, abductive learning, program induction, logic-aware Transformers, and LLM-based symbolic planning—highlighting their technical mechanisms, capabilities, and limitations. In contrast to prior surveys, this work bridges symbolic logic, neural computation, and emergent generative reasoning, offering a unified framework to understand and compare diverse approaches. We conclude by identifying key open challenges such as symbolic–continuous alignment, dynamic rule learning, and unified architectures, and we aim to provide a conceptual foundation for future developments in general-purpose reasoning systems. Full article

This study examines multi-lexical data sources, utilizing an extracted dataset from an open-source corpus and the Global Terrorism Datasets (GTDs), to predict lexical patterns that are directly linked to terrorism. This is essential as specific patterns within a textual context can facilitate the identification of terrorism-related content. The research methodology focuses on generating a corpus from various published works and extracting texts pertinent to “terrorism”. Afterwards, we extract additional lexical contexts of GTDs that directly relate to terrorism. The integration of multi-lexical data sources generates lexical patterns linked to terrorism. Machine learning models were used to train the dataset. We conducted two primary experiments and analyzed the results. The analysis of data obtained from open sources reveals that while the Extra Trees model achieved the highest accuracy at 94.31%, the XGBoost model demonstrated superior overall performance with a higher recall (81.32%) and F1-Score (83.06%) after tuning, indicating a better balance between sensitivity and precision. Similarly, on the GTD dataset, XGBoost consistently outperformed other models in recall and the F1-score, making it a more suitable candidate for tasks where minimizing false negatives is critical. This implies that we can establish a specific co-occurrence and context within the terrorism dataset from multiple lexical data sources in effectively identifying certain multi-lexical patterns such as “Suicide Attack/Casualty”, “Civilians/Victims”, and “Hostage Taking/Abduction” across various applications or contexts. This will facilitate the development of a framework for understanding the lexical patterns associated with terrorism. Full article

Background and Objectives: Shoulder pain, mainly involving rotator cuff tears, is a common type of musculoskeletal pain that significantly impairs quality of life. Arthroscopic rotator cuff repair has become the gold standard for treating symptomatic, full-thickness rotator cuff tears. Double-row repair techniques are widely used because of their superior fixation and healing results. However, fewer implants may reduce treatment costs and raise questions about the impact on clinical outcomes and re-tear rates. This study compares the functional outcomes and re-tear rates of two transosseous-like double-row repair techniques: one anchor and one push lock (Group 1), and two anchors and two push locks (Group 2). Materials and Methods: A prospective, randomized, single-blind study was conducted on 53 patients undergoing arthroscopic repair for crescent-shaped rotator cuff tears (3–5 cm). Before surgery and 24 months after surgery, patients were evaluated for shoulder function using Constant–Murley scores and shoulder abduction angles. MRI was used to assess re-tear rates. Results: Both groups showed significant postoperative improvement in Constant scores (Group 1: 84.1; Group 2: 84.0; p > 0.05). Re-tear rates were slightly higher in Group 1 (23.1%) than in Group 2 (18.5%), but this was not statistically significant (p > 0.05). Shoulder abduction angles improved similarly between groups, with no significant difference in outcome. Despite higher costs and longer operative times, the two-anchor technique provided more stable fixation, but its functional outcomes were comparable to the single-anchor method. Conclusions: Using fewer implants in a double-row repair provides comparable functional outcomes and re-tear rates, and offers surgeons a cost-effective alternative, especially at the beginning of their learning curve. However, the two-anchor technique may be more beneficial in cases requiring improved mechanical stability. These findings provide valuable information to balance cost and effectiveness in rotator cuff repair. Full article

Despite the plethora of digital and technological advances made in the construction industry over the past three decades, at its core, the sector remains human-centric. Consequently, this research investigates the core soft skills employed on public linear infrastructure (PLI) projects (during the construction phase) that are digitally enabled and concludes with the development of a decision support tool for PLI project team management. A mixed philosophical stance is implemented using interpretivism, postpositivism and grounded theory together with abductive reasoning to examine subject matter experts’ perceptions of the phenomena under investigation. Textual analysis is then utilised to formulate a decision support tool as a theoretical construct. The research findings demonstrate that communication, leadership and creativity/curiosity are the three main soft skills required of PLI projects. Furthermore, the key elements of a decision support tool—namely, trackable and measurable data, clear objectives and success criteria, and an easy-to-understand visual format—were identified. Such knowledge provides a strong base for building an emotionally intelligent project team. This research constitutes the first attempt to understand the essential soft skills required on PLI projects and, premised upon this, generate a decision support tool for project management in teams that helps to augment project performance through workforce investment via a learning organisation. Full article

Computational thinking skills among high school students have become a global concern, especially in the context of the ever-evolving digital education era. However, the attention given by teachers to this skill during mathematics instruction has not been a priority. This study aims to evaluate and explore the impact of project-based learning (PBL) integrated with flipped classroom on high school students’ computational thinking skills in mathematics. The research design employed a mixed-method approach with a quasi-experimental, nonequivalent pre-test post-test control group design. The experimental group (46 students) and control group (45 students) were selected through simple random sampling from 12th-grade science students. Data were collected through tests, questionnaires, and in-depth interviews, using instruments such as computational thinking skills assessment questions, questionnaires, and interview protocols. Quantitative data analysis was performed using SPSS Version 26 for t-tests and ANOVA, while qualitative analysis was conducted using ATLAS.ti with an abductive-inductive and thematic approach. The findings indicate that PBL integrated with flipped classrooms significantly improved students’ decomposition, pattern recognition, and abstraction skills. The implementation of PBL, integrated with a flipped classroom, created an interactive learning environment, fostering active engagement and enhancing students’ understanding and skills in solving mathematical concepts. Although there was an improvement in algorithmic thinking skills, some students still faced difficulties in developing systematic solutions. The results of this study suggest that further research could explore other methodologies, such as grounded theory and case studies integrated with e-learning, and emphasize visual analysis methods, such as using photo elicitation to explore thinking skills. Full article

An accurate assessment of thumb range of motion is crucial for diagnosing musculoskeletal conditions, evaluating functional impairments, and planning effective rehabilitation strategies. In this study, we aimed to enhance the accuracy of estimating thumb range of motion using a combination of MediaPipe, which is an AI-based posture estimation library, and machine learning methods, taking the values obtained using angle sensors to be the true values. Radial abduction, palmar abduction, and pronation angles were estimated using MediaPipe based on coordinates detected from videos of 18 healthy participants (nine males and nine females with an age range of 30–49 years) selected to reflect a balanced distribution of height and other physical characteristics. A conical thumb movement model was constructed, and parameters were generated based on the coordinate data. Five machine learning models were evaluated, with LightGBM achieving the highest accuracy across all metrics. Specifically, for radial abduction, palmar abduction, and supination, the root mean square error (RMSE), mean absolute error (MAE), coefficient of determination (R²), and correlation coefficient were 4.67°, 3.41°, 0.94, and 0.97; 4.63°, 3.41°, 0.95, and 0.98; and 5.69°, 4.17°, 0.88, and 0.94, respectively. These results demonstrate that when estimating thumb range of motion, the AI model trained using angle sensor data and LightGBM achieved accuracy that was high and comparable to that of prior methods involving the use of MediaPipe and a protractor. Full article

Background/Objectives: Preoperative muscle atrophy leads to persistent gait abnormalities in patients undergoing total hip arthroplasty (THA). Efficient motor learning of the gluteus medius is crucial for their recovery. In this study, a single-joint hybrid assistive limb (HAL) was developed to assist hip abduction. We aimed to evaluate the muscle activity and safety of this device during hip abduction in healthy adults. Methods: Ten healthy adults (five males and five females; mean age, 40.7 years) with no hip disorders performed one set of 30 repetitions of side-lying hip abduction under three conditions: without HAL (pre-HAL), with HAL, and without HAL (post-HAL). Muscle activities of the gluteus medius, gluteus maximus, tensor fasciae latae, rectus femoris, and biceps femoris (expressed as percentage of maximum voluntary contraction [%MVC]); vital signs; hip visual analog scale (VAS); and hip abduction and flexion angles were assessed. The mean values were compared among the conditions. Results: The %MVC of the gluteus medius significantly increased from 52% (pre-HAL) to 75.4% (HAL) and then decreased slightly to 61.6% (post-HAL). No other muscle groups showed significant changes. Vital signs and hip VAS scores showed no significant variation. Although no significant differences were found in the hip abduction and flexion angles, a reduction in the hip flexion angle was observed in the HAL and post-HAL conditions. Conclusions: The hip abduction HAL effectively and safely enhanced gluteus medius activity. Reduction in the hip flexion angle during HAL and post-HAL suggests the possibility of appropriate abduction movements and motor learning effects. Full article

Human Pose Estimation (HPE) is a computer vision application that utilizes deep learning techniques to precisely locate Key Joint Points (KJPs), enabling the accurate description of a person’s pose. HPE models can be extended to facilitate Range of Motion (ROM) assessment by leveraging patient photographs. This study aims to evaluate and compare the performance of HPE models for assessing upper limbs ROM. A physiotherapist evaluated the degrees of ROM in shoulders (flexion, extension, and abduction) and elbows (flexion and extension) for fifty-two participants using both Universal Goniometer (UG) and five HPE models. Participants were instructed to repeat each movement three times to obtain measurements with the UG, then positioned while photos were captured using the NLMeasurer mobile application. The paired t-test, bias, and error measures were employed to evaluate the difference and agreement between measurement methods. Results indicated that the MoveNet Thunder INT16 model exhibited superior performance. Root Mean Square Errors obtained through this model were <10° in 8 of 10 analyzed movements. HPE models demonstrated better performance in shoulder flexion and abduction movements while exhibiting unsatisfactory performance in elbow flexion. Challenges such as image perspective distortion, environmental lighting conditions, images in monocular view, and complications in the pose may influence the models’ performance. Nevertheless, HPE models show promise in identifying KJPs and facilitating ROM measurements, potentially enhancing convenience and efficiency in assessments. However, their current accuracy for this application is unsatisfactory, highlighting the need for caution when considering automated upper limb ROM measurement with them. The implementation of these models in clinical practice does not diminish the crucial role of examiners in carefully inspecting images and making adjustments to ensure measurement reliability. Full article

The Internet of Things (IoT) is gaining increasing attention in healthcare due to its potential to enable continuous monitoring of patients, both at home and in controlled medical environments. In this paper, we explore the integration of IoT with human-robotics in the context of motor rehabilitation for groups of patients performing moderate physical routines, focused on balance, stretching, and posture. Specifically, we propose the I-TROPHYTS framework, which introduces a step-change in motor rehabilitation by advancing towards more sustainable medical services and personalized diagnostics. Our framework leverages wearable sensors to monitor patients’ vital signs and edge computing to detect and estimate motor routines. In addition, it incorporates a humanoid robot that mimics the actions of a physiotherapist, adapting motor routines in real-time based on the patient’s condition. All data from physiotherapy sessions are modeled using an ontology, enabling automatic reasoning and planning of robot actions. In this paper, we present the architecture of the proposed framework, which spans four layers, and discuss its enabling components. Furthermore, we detail the current deployment of the IoT system for patient monitoring and automatic identification of motor routines via Machine Learning techniques. Our experimental results, collected from a group of volunteers performing balance and stretching exercises, demonstrate that we can achieve nearly 100% accuracy in distinguishing between shoulder abduction and shoulder flexion, using Inertial Measurement Unit data from wearable IoT devices placed on the wrist and elbow of the test subjects. Full article

This study has designed an easy-to-wear parallel continuum robot-based hand rehabilitation system that supports and enhances the finger’s flexion, extension, abduction, and adduction movements. The primary novelty of the proposed system lies in its ability to focus therapeutic exercises on a single joint, a feature not commonly found in existing rehabilitation robots. A kinematic model of the system was developed, and to perform both kinematic and dynamic analyses, a multibody model was constructed in the MATLAB Simulink environment. Joint angle control was implemented using a nominal controller, and to account for individual uncertainties in joint dynamics, a neuroadaptive controller was integrated with the nominal controller. This approach aims for the neural network architecture to learn these uncertainties during control iterations and incorporate them into the control, resulting in a robust controller. Thus, a model reference control approach was proposed for active and passive rehabilitation processes. The system model was tested in a simulation environment, and then all tests were repeated in the physical system. The simulation and real system results include the real system’s open-loop responses, nominal controller responses for each joint, responses, and the results for active, passive, and assistive control modes. Full article

This article contributes new knowledge on collaboration within and between Change Laboratory sessions and how it offers the potential to support the zone of proximal development and the expansive learning of participants. The Change Laboratory is a formative intervention method that helps participants transform their practices. This qualitative study examines how six educators participating in a Change Laboratory intervention dealt with problems that arise when implementing digital artifacts in teaching mathematics. We collected data through participant observation between sessions and video recordings during sessions. We conducted the data analysis abductively, using the seven expansive learning actions as an analytical framework. The findings highlight how collaboration between the researcher and the participants, both during and between Change Laboratory sessions, helped support the participants’ zone of proximal development and expansive learning. This close collaboration enabled the educators to collectively reflect on and transform their teaching practices, specifically by incorporating digital artifacts into their educational routines. The study provides important insights into how digital artifacts can be successfully implemented in practice using Change Laboratories, emphasizing the importance of supporting educators in their zone of proximal development. Using a theoretical perspective grounded in cultural-historical activity theory, the study contributes to understanding how change occurs in the educational field. Full article

Laboratory studies have limitations in screening for anterior cruciate ligament (ACL) injury risk due to their lack of ecological validity. Machine learning (ML) methods coupled with wearable sensors are state-of-art approaches for joint load estimation outside the laboratory in athletic tasks. The aim of this study was to investigate ML approaches in predicting knee joint loading during sport-specific agility tasks. We explored the possibility of predicting high and low knee abduction moments (KAMs) from kinematic data collected in a laboratory setting through wearable sensors and of predicting the actual KAM from kinematics. Xsens MVN Analyze and Vicon motion analysis, together with Bertec force plates, were used. Talented female football (soccer) players (n = 32, age 14.8 ± 1.0 y, height 167.9 ± 5.1 cm, mass 57.5 ± 8.0 kg) performed unanticipated sidestep cutting movements (number of trials analyzed = 1105). According to the findings of this technical note, classification models that aim to identify the players exhibiting high or low KAM are preferable to the ones that aim to predict the actual peak KAM magnitude. The possibility of classifying high versus low KAMs during agility with good approximation (AUC 0.81–0.85) represents a step towards testing in an ecologically valid environment. Full article

Background: Despite the importance of the deltoid to shoulder biomechanics, very few studies have quantified the three-dimensional shape, size, or quality of the deltoid muscle, and no studies have correlated these measurements to clinical outcomes after anatomic (aTSA) and/or reverse (rTSA) total shoulder arthroplasty in any statistically/scientifically relevant manner. Methods: Preoperative computer tomography (CT) images from 1057 patients (585 female, 469 male; 799 primary rTSA and 258 primary aTSA) of a single platform shoulder arthroplasty prosthesis (Equinoxe; Exactech, Inc., Gainesville, FL) were analyzed in this study. A machine learning (ML) framework was used to segment the deltoid muscle for 1057 patients and quantify 15 different muscle characteristics, including volumetric (size, shape, etc.) and intensity-based Hounsfield (HU) measurements. These deltoid measurements were correlated to postoperative clinical outcomes and utilized as inputs to train/test ML algorithms used to predict postoperative outcomes at multiple postoperative timepoints (1 year, 2–3 years, and 3–5 years) for aTSA and rTSA. Results: Numerous deltoid muscle measurements were demonstrated to significantly vary with age, gender, prosthesis type, and CT image kernel; notably, normalized deltoid volume and deltoid fatty infiltration were demonstrated to be relevant to preoperative and postoperative clinical outcomes after aTSA and rTSA. Incorporating deltoid image data into the ML models improved clinical outcome prediction accuracy relative to ML algorithms without image data, particularly for the prediction of abduction and forward elevation after aTSA and rTSA. Analyzing ML feature importance facilitated rank-ordering of the deltoid image measurements relevant to aTSA and rTSA clinical outcomes. Specifically, we identified that deltoid shape flatness, normalized deltoid volume, deltoid voxel skewness, and deltoid shape sphericity were the most predictive image-based features used to predict clinical outcomes after aTSA and rTSA. Many of these deltoid measurements were found to be more predictive of aTSA and rTSA postoperative outcomes than patient demographic data, comorbidity data, and diagnosis data. Conclusions: While future work is required to further refine the ML models, which include additional shoulder muscles, like the rotator cuff, our results show promise that the developed ML framework can be used to evolve traditional CT-based preoperative planning software into an evidence-based ML clinical decision support tool. Full article