Search Results (78)

The use of prosthetic devices can significantly improve the quality of life of individuals with limb amputations. However, existing prosthetic hands face multiple engineering and manufacturing challenges, making them economically inaccessible to a large portion of the population. This study focuses on the design and analysis of a cost-effective prosthetic hand capable of performing five fundamental grasp types: tripod, cylindrical, spherical, lateral, and pinch. The development process began with a biomechanical analysis of the human hand, followed by the derivation of a kinematic model. To ensure anthropomorphic fidelity, finger trajectories were synthesized using a computer vision-based algorithm that captured natural human motion. These trajectories were then mapped to the prosthetic control system. Experimental validation was conducted through rigorous goniometric analysis of the prototype’s execution. The results demonstrated the system’s effectiveness in replicating functional grasps, with a Root Mean Square Error (RMSE) within acceptable thresholds for assistive tasks. While the prototype achieved high motion correspondence, higher deviations were observed in distal joints due to mechanical transmission resistance and spring-return torque requirements. This work provides a scalable framework for tendon-driven prostheses, balancing advanced trajectory synthesis with a robust and accessible mechanical architecture. Full article

Objectives: This study aimed to evaluate the influence of tumor geometry on dose distribution and delivery accuracy, and to assess the impact of the Automatic Lower Dose Objective (ALDO) function on dosimetric performance. Methods: Computed tomography images of a Rando anthropomorphic phantom were used to simulate intracranial multiple metastases. Two contour groups were generated on the same CT dataset, consisting of two spherical targets with diameters of 1 cm and 2 cm, respectively. For each group, target pairs were created with edge-to-edge separation distances ranging from 1 to 6 cm. Automated single-isocenter stereotactic radiosurgery plans were generated using the HyperArc workflow, both with and without the ALDO function. Dosimetric performance was evaluated using the RTOG conformity index, Paddick conformity index, gradient index, and homogeneity index. Patient-specific quality assurance was performed using electronic portal imaging device-based verification and radiochromic film dosimetry. Gamma analysis with multiple criteria was applied to assess the impact of target size and geometric separation on delivery accuracy. Results: The use of ALDO improved dose conformity and gradient performance but resulted in increased dose heterogeneity and higher hot spots. In non-ALDO configurations, the agreement between EPID portal dosimetry and film measurements varied according to target size, gamma criteria, and spatial position. For the 2 cm targets, EPID portal dosimetry generally demonstrated higher gamma passing rates than film measurements, whereas the 1 cm targets showed mixed results depending on measurement position and gamma criteria. These differences likely reflect the distinct detector characteristics and spatial sensitivities of the two QA methodologies. Larger discrepancies were observed under stricter gamma criteria and at off-axis positions, indicating potential influences of target geometry and high-dose gradient regions within the simplified phantom configurations evaluated in this study. Conclusions: Within the simplified two-target phantom configurations evaluated in this study, tumor geometric distribution significantly affects both dosimetric characteristics and QA outcomes in HyperArc SRS. Film measurements provide greater sensitivity, whereas EPID-PD alone may be insufficient for evaluating small-target high-gradient regions under strict gamma criteria. Full article

This study examines the psychological mechanisms through which anthropomorphic artificial intelligence (AI) relates to consumer adoption intentions in fragile, low-trust economies. Integrating the Stimulus–Organism–Response framework with the Computers Are Social Actors paradigm, Institutional Trust Theory, and Privacy Calculus Theory, we investigate how human-like AI design shapes cognitive and affective responses within Sierra Leone’s banking sector. Using survey data from 277 banking customers and partial least squares structural equation modeling, we find that AI anthropomorphism exhibits no direct association with adoption intention (β = −0.013, p = 0.760). Instead, its influence is entirely indirect—transmitted in parallel through perceived social presence (β = 0.144, 95% CI [0.062, 0.226]) and trust in the AI system (β = 0.139, 95% CI [0.068, 0.210]). Critically, customer skepticism—shaped by institutional fragility—functions as a boundary condition that substantially attenuates both pathways: among highly skeptical users (+1 SD), anthropomorphism’s conditional effect on social presence becomes non-significant (β = 0.098, p = 0.124) compared to low-skepticism users (β = 0.412, p < 0.001), while its effect on trust is reduced by more than half (β = 0.118 vs. 0.284). These findings identify a critical boundary condition on human-like AI design: in low-trust environments, anthropomorphism operates not as a standalone adoption driver but as a relational amplifier whose efficacy depends on foundational trust and is substantially weakened when skepticism is high. The study challenges universalist assumptions in human–AI interaction research and underscores the need for institutionally sensitive design approaches in fragile economies. Full article

This paper presents an immersive teleoperation framework for service robots that combines real-time 3D human pose estimation with a Virtual Reality (VR) interface to support intuitive, natural robot control. The operator is tracked using MediaPipe for 2D landmark detection and an Intel RealSense D455 RGB-D (Red-Green-Blue plus Depth) camera for depth acquisition, enabling 3D reconstruction of key joints. Joint angles are computed using efficient vector operations and mapped to the kinematic constraints of an anthropomorphic arm on the CHARMIE service robot. A VR-based telepresence interface provides stereoscopic video and head-motion-based view control to improve situational awareness during manipulation tasks. Experiments in real-world object grasping demonstrate reliable arm teleoperation and effective telepresence; however, vision-only estimation remains limited for axial rotations (e.g., elbow and wrist yaw), particularly under occlusions and unfavorable viewpoints. The proposed system provides a practical pathway toward low-cost, sensor-driven, immersive human–robot interaction for service robotics in dynamic environments. Full article

Background: In computed tomography (CT), automatic exposure control (AEC) determines the tube current and thus the radiation dose based on scout images. We investigated CT dose modulation using two versions of CARE Dose 4D, Siemens AEC software. Methods: A cylindrical phantom and an anthropomorphic phantom with the upper extremities raised or down were imaged. The CT tube current was determined using two versions of CARE Dose 4D and different scout directions: the posteroanterior scout image alone (PA scout), the lateral scout image alone (Lat scout), and the combination of the PA and Lat scout images (PA + Lat scout). The new version is designed to utilize the Lat image solely for off-center correction when both PA and Lat images are available. Experiments were performed at various vertical positions and with various scout imaging parameters. Results: The influence of the scout direction on CT dose was demonstrated, with variations depending on the imaging object and software version. The CT dose determined with the PA scout varied according to vertical positioning, presumably due to changes in image magnification. Such effects were small with the Lat scout or PA + Lat scout. Decreasing the tube voltage or tube current in scout imaging affected CT dose modulation with the Lat scout but not with the PA scout. With the PA + Lat scout, the effects of scout parameters were evident using the previous version but minimal using the new version. Conclusions: Off-center correction in the new version functioned appropriately. Because the behavior of an AEC system is complicated, it is recommended to examine the characteristics of each AEC system under various imaging conditions. Full article

As brain organoids and organoid-based computational models grow in complexity, they increasingly exhibit electrophysiological patterns consistent with plasticity and information processing. This article explores a central question at the intersection of neuroscience, synthetic biology, and philosophy of mind: Can intelligent behavior be meaningfully separated from an intelligent being? In other words, can adaptive, goal-directed behavior exist independently of subjective awareness—a question that challenges conventional definitions of cognition and consciousness. Drawing from neuroscience, artificial intelligence, and philosophy, I propose a tiered framework based on neural complexity and environmental responsiveness. This includes a simple level analysis and a context-sensitive benchmark for evaluating intelligence in organoid systems without presupposing sentience. Ethical and ontological implications are also addressed, particularly the risk of anthropomorphizing synthetic cognition and the importance of developing context-aware definitions of intelligence. By distinguishing functional sophistication from subjective experience, the framework aims to guide responsible scientific inquiry while clarifying the boundaries of synthetic cognition. Full article

Background: Quantifying absorbed doses from radiopharmaceuticals within human organs necessitates advanced computational modeling, as direct in vivo measurement remains impractical. Methods: In this study, three Monte Carlo-based simulation codes, Monte Carlo N-Particle version 6 (MCNP6), GEANT4 Application for Tomographic Emission (GATE), and GEANT4-based Architecture for Medicine-Oriented Simulations (GAMOS), were employed to evaluate internal dosimetry following the Medical Internal Radiation Dose (MIRD) formalism. As an illustrative case, simulations were first performed for ^99mTc-MIBI uptake in the myocardium using the anthropomorphic phantom, with the heart modeled as the source organ to assess energy deposition in key target organs. Dose assessments were conducted at two time points: immediately post-injection and at 60 min post-injection (representing the cardiac rest phase), allowing comparison against established clinical reference data. Results: Across all codes, organ-specific dose distributions exhibited strong consistency. The pancreas absorbed the highest dose (GATE: 21%, GAMOS: 20%, MCNP6: 22%), followed by the gallbladder (GATE: 18%, GAMOS: 17%, MCNP6: 18%) and kidneys (GATE: 16%, GAMOS: 15%, MCNP6: 16%). These findings established a consistent organ dose ranking: pancreas > gallbladder > kidneys > spleen > heart/liver, corroborating previously published empirical data. To demonstrate the versatility of the framework, additional simulations were performed with ¹⁸F in an anthropomorphic phantom and with spherical tumor models using therapeutic radionuclides (¹⁷⁷Lu and ²²⁵Ac). This broader application underscores the adaptability of the tri-code approach for both diagnostic and therapeutic scenarios. Conclusions: This comparative analysis highlights the complementary advantages of each Monte Carlo platform. GATE is well-suited for high-fidelity clinical applications where anatomical and physical accuracy are critical. GAMOS proves advantageous for rapid prototyping and iterative modeling workflows. MCNP6 remains a reliable benchmark tool, particularly effective in scenarios requiring robust radiation transport validation. Together, these Monte Carlo frameworks form a validated and adaptable toolkit for advancing internal dosimetry in personalized nuclear medicine, supporting both clinical decision-making and the development of safer, more effective radiopharmaceutical therapies. Full article

This paper presents the development of a 32-degree-of-freedom (DoF) humanoid robotic system designed for autonomous piano performance. The system integrates a vision-based music sheet reader with a YOLOv8 neural network for real-time detection and classification of musical symbols, achieving a mean average precision (mAP) of 96% at IoU 0.5. A heuristic-based synchronization and motion planning module computes optimal finger trajectories and hand placements, enabling expressive and temporally accurate performances. The robotic hardware comprises two anthropomorphic hands mounted on linear rails, each with independently actuated fingers capable of vertical, horizontal, and rotational movements. Experimental validation demonstrates the system’s ability to execute complex musical passages with precision and synchronization. Limitations related to dynamic expressiveness and symbol generalization are discussed, along with proposed enhancements for future iterations. The results highlight the potential of AI-driven robotic systems in musical applications and contribute to the broader field of intelligent robotic performance. Full article

The accelerating global population aging has brought increasing attention to the loneliness and emotional needs experienced by older adults due to shrinking social networks and the loss of relatives and friends, which significantly impair their quality of life and psychological well-being. In this context, companion robots powered by artificial intelligence are increasingly regarded as a scalable and sustainable form of emotional intervention that can address older people’s affective and social requirements. This study systematically reviews research trends in this field, analyzing the structure of emotional needs among older users and their acceptance mechanisms toward robot functionalities. First, a keyword co-occurrence analysis was conducted using VOSviewer on relevant literature published between 2000 and 2025 from the Web of Science database, revealing focal research topics and emerging trends. Subsequently, questionnaire surveys and in-depth interviews were carried out to identify emotional needs and functional preferences among elderly users. Findings indicate that the field is characterized by increasing interdisciplinary integration, with affective computing and naturalistic interaction becoming central concerns. Empirical results reveal significant differences in need structures across age groups: the oldest-old prioritize safety monitoring and daily assistance, whereas the young-old emphasize social interaction and developmental activities. Regarding emotional interaction, older adults generally prefer natural and non-intrusive expressive styles and exhibit reserved attitudes toward highly anthropomorphic designs. Key factors influencing acceptance include practicality, ease of use, privacy protection, and emotional warmth. The study concludes that effective companion robot design should be grounded in a nuanced understanding of the heterogeneous needs of the aging population, integrating functionality, interaction, and emotional value. Future development should emphasize adaptive and customizable capabilities, adopt natural yet restrained interaction strategies, and strengthen real-world cross-cultural and long-term evaluations. Full article

Humans form deep attachments to some nonhuman animals, yet these attachments are unequally distributed across the tree of life. Drawing on evolutionary biology, comparative cognition, neuroscience, and cultural anthropology, this narrative review explains why empathy and affective preference are typically stronger for phylogenetically closer species—especially mammals—than for distant taxa such as reptiles, fish, or arthropods. We synthesize evidence that signal recognizability (faces, gaze, vocal formants, biological motion) and predictive social cognition facilitate mind attribution to mammals; conserved neuroendocrine systems (e.g., oxytocin) further amplify affiliative exchange, particularly in domesticated dyads (e.g., dog–human). Ontogenetic learning and media narratives magnify these effects, while fear modules and disgust shape responses to some distant taxa. Notwithstanding this average gradient, boundary cases—cephalopods, cetaceans, parrots—show that perceived agency, sociality, and communicative transparency can overcome phylogenetic distance. We discuss measurement (behavioral, psychophysiological, neuroimaging), computational accounts in predictive-processing terms, and implications for animal welfare and conservation. Pragmatically, calibrated anthropomorphism, hands-on education, and messaging that highlights agency, parental care, or ecological function reliably broaden concern for under-represented taxa. Recognizing both evolved priors and cultural plasticity enables more equitable and effective science communication and policy. Expanding empathy beyond its ancestral anchors is not only an ethical imperative but a One Health necessity: safeguarding all species means safeguarding the integrity of our shared planetary life. Full article

As a pivotal carrier of emerging human–computer interaction technologies, artificial intelligence (AI) conversational agents (CAs) hold critical significance for research on the mechanisms of users’ continuance usage behaviour, which is essential for technological optimization and commercial transformation. However, the differential impact pathways of multidimensional perceived interactivity on continuance usage intention, particularly the synergistic mechanisms between technical and affective dual-path dimensions, remain unclear. This study investigates the personalized AI-based CAs project “Dialogue with Great Souls,” launched on a Chinese social platform, using survey data from 305 users. A hybrid approach combining partial least squares structural equation modelling (PLS-SEM) and artificial neural networks (ANN) was employed for empirical analysis. The results indicate that technical dimensions, such as control and responsiveness, are key factors influencing trust, while affective interactive dimensions, including communication, personalization, and playfulness, significantly affect social presence, thereby shaping users’ continuance usage intention. ANN results corroborated most PLS-SEM findings but revealed inconsistencies in the predictive importance of personalization and communication on social presence, highlighting the complementary nature of linear and nonlinear interaction mechanisms. By expanding the interactivity model and adopting a hybrid methodology, this study constructs a novel framework for AI CAs. The empirical findings suggest that developers should strengthen socio-emotional bonds in anthropomorphic interactions while ensuring technical credibility to enhance users’ continuance usage intention. This research not only advances theoretical perspectives on the integration of technical and affective dimensions in agent systems but also provides practical recommendations for optimizing the design and development of AI CAs. Full article

Kinesthetic motor imagery (KMI), the mental rehearsal of a motor task without its actual performance, constitutes one of the most common techniques used for brain–computer interface (BCI) control for movement-related tasks. The effect of neural injury on motor cortical activity during execution and imagery remains under investigation in terms of activations, processing of motor onset, and BCI control. The current work aims to conduct a post hoc investigation of the event-related potential (ERP)-based processing of KMI during BCI control of anthropomorphic robotic arms by spinal cord injury (SCI) patients and healthy control participants in a completed clinical trial. For this purpose, we analyzed 14-channel electroencephalography (EEG) data from 10 patients with cervical SCI and 8 healthy individuals, recorded through Emotiv EPOC BCI, as the participants attempted to move anthropomorphic robotic arms using KMI. EEG data were pre-processed by band-pass filtering (8–30 Hz) and independent component analysis (ICA). ERPs were calculated at the sensor space, and analysis of variance (ANOVA) was used to determine potential differences between groups. Our results showed no statistically significant differences between SCI patients and healthy control groups regarding mean amplitude and latency (p < 0.05) across the recorded channels at various time points during stimulus presentation. Notably, no significant differences were observed in ERP components, except for the P200 component at the T8 channel. These findings suggest that brain circuits associated with motor planning and sensorimotor processes are not disrupted due to anatomical damage following SCI. The temporal dynamics of motor-related areas—particularly in channels like F3, FC5, and F7—indicate that essential motor imagery (MI) circuits remain functional. Limitations include the relatively small sample size that may hamper the generalization of our findings, the sensor-space analysis that restricts anatomical specificity and neurophysiological interpretations, and the use of a low-density EEG headset, lacking coverage over key motor regions. Non-invasive EEG-based BCI systems for motor rehabilitation in SCI patients could effectively leverage intact neural circuits to promote neuroplasticity and facilitate motor recovery. Future work should include validation against larger, longitudinal, high-density, source-space EEG datasets. Full article

Objectives: This study aimed to implement a high-resolution Saudi voxel-based anthropomorphic phantom in the OpenMC Monte Carlo (MC) simulation framework. The objective was to evaluate its applicability in radiological simulations, including radiographic imaging and effective dose calculations, tailored to the Saudi population. Methods: A voxel phantom comprising 30 segmented organs/tissues and over 32 million voxels were constructed from full-body computed tomography data and integrated into OpenMC. The implementation involved detailed voxel mapping, material definition using ICRP/ICRU-116 recommendations, and lattice geometry construction. The simulations included X-ray radiography projections using mesh tallies and anterior–posterior effective dose calculations across 20 photon energies (10 keV–1 MeV). The absorbed dose was calculated using OpenMC’s heating tally and converted to an effective dose using tissue weighting factors. Results: The phantom was successfully modeled and visualized in OpenMC, demonstrating accurate anatomical representation. Radiographic projections showed optimal contrast at 70 keV. The effective dose values for 29 organs were calculated and compared with MCNPX, the ICRP-116 reference phantom, and XGBoost-based machine learning (ML) predictions. OpenMC results showed good agreement, with maximum deviations of −35.5% against ICRP-116 at 10 keV. Root mean square error (RMSE) comparisons confirmed reasonable alignment, with OpenMC displaying higher RMSEs relative to other methods due to expanded organ modeling and material definitions. Conclusions: The integration of the Saudi voxel phantom into OpenMC demonstrates its utility for high-resolution dosimetry and radiographic simulations. OpenMC’s Python (version 3.10.14) interface and open-source nature make it a promising tool for radiological research. Future work will focus on combining MC and ML approaches for enhanced predictive dosimetry. Full article

Artificial intelligence has brought tremendous convenience to human life in various aspects. However, during its application, there are still instances where AI fails to comprehend certain problems or cannot achieve flawless execution, necessitating more cautious and thoughtful usage. With the advancements in EEG signal processing technology, its integration with AI has become increasingly close. This idea of interpreting electroencephalogram (EEG) signals illustrates researchers’ desire to explore the deeper relationship between AI and human thought, making human-like thinking a new direction for AI development. Currently, AI faces several core challenges: it struggles to adapt effectively when interacting with an uncertain and unpredictable world. Additionally, the trend of increasing model parameters to enhance accuracy has reached its limits and cannot continue indefinitely. Therefore, this paper proposes revisiting the history of AI development from the perspective of “anthropomorphic computing”, primarily analyzing existing AI technologies that incorporate structures or concepts resembling human brain thinking. Furthermore, regarding the future of AI, we will examine its emerging trends and introduce the concept of “Cyber Brain Intelligence”—a human-like AI system that simulates human thought processes and generates virtual EEG signals. Full article

High-speed trains are some of the most important transportation vehicles requiring human–computer collaboration. This study investigated the effects of different types of icons on recognition performance and cognitive load during frequent observation and sudden takeover tasks in high-speed trains. The results of this study can be used to improve the efficiency of human–computer collaboration tasks and driving safety. In this study, 48 participants were selected for a simulated driving experiment on a high-speed train. The recognition reaction time, operation completion time, number of recognition errors, number of operation errors, SUS scale, and NASA-TLX questionnaire for the icons were all analyzed using analysis of variance (ANOVA) and the nonparametric Mann–Whitney U test. The results show that anthropomorphic icons can reduce the drivers’ visual fatigue and mental load in frequent observation tasks due to the anthropomorphic facial features attracting driver attention through simple lines and improving visual search efficiency. However, for the sudden takeover human–computer collaboration task, the facial features of the anthropomorphic icons were not recognized in a short period of time. Additionally, due to the positive emotions produced by the facial features, the drivers did not perceive the suddenness and danger of the sudden takeover human–computer collaboration task, resulting in the traditional icons being more capable of arousing the drivers’ alertness and helping them take over the task quickly. At the same time, neither type of icon triggered misrecognition or operation for sufficiently skilled drivers. These research results can provide guidance for the design of icons in human–computer collaborative interfaces for different types of driving tasks in high-speed trains, which can help improve the recognition speed, reaction speed, and safety of drivers. Full article