Search Results (94)

In the Anthropocene, the environmental crisis necessitates a radical repositioning of the human-nature relationship. This paper examines the sijo poetry in Musan Cho Oh-hyun’s For Nirvana through an interdisciplinary framework bridging Zen philosophy with material ecocriticism. The study elucidates how Musan deconstructs anthropocentric exceptionalism by restoring agency to the non-human world. Textual analysis reveals three arguments. First, elemental forces like wind and waves are subjectified as primordial teachers through mujō-seppō (non-sentient beings preaching the Dharma), dismantling sovereign human scriptural authority. Second, visceral encounters with animals and insects critique logocentric domination, proposing “epistemological silence” and “radical humility” as alternative eco-politics. Finally, bodily decay and trans-corporeal porosity are reframed as generative pathways toward a radical “ecological Nirvana”—a physical matrix of cyclical renewal. By synthesizing Jane Bennett’s vital materialism with Dōgen’s Zen vision of “walking mountains”, this study deploys a Zen materialism lens that enriches Western theory with the Buddhist soteriology of compassion (karuna). Ultimately, Musan reconfigures Nirvana not as an escapist transcendence, but as a profound somatic descent into the material mesh, where ultimate spiritual realization lies in the ego’s total dissolution into the “walking, talking minerals” of a sacred, suffering ecosystem. Full article

Animals feature regularly on children’s television, exposing children to implicit and explicit messages about the natural world. Although animal representation and anthropomorphism have been examined across children’s media, film, literature, and popular culture, less is known about how animals are portrayed in preschool television, particularly in relation to biological and ecological accuracy. This study examined animal representations in programs aired on ABC Kids, the Australian Broadcasting Corporation’s free-to-air children’s television channel, using quantitative and qualitative content analysis. A total of 947 episodes from 92 programs were analyzed, with a subset of 265 episodes (43 h and 45 min) from 39 programs examined in greater detail. Animals were prominently featured, appearing on screen for 88% (171 h and 40 min) of total programming time, with programs featuring animals as central characters accounting for 41% (81 h). Nearly all central animal characters exhibited anthropomorphic traits, such as walking, talking, dressing, or behaving like humans. Inaccuracies in species depiction were common, including portrayals of animals in human-like social structures, unrealistic environments, and mixed predator–prey groupings, alongside a hierarchy based on roles and relationships. These findings indicate that realistic portrayals of animals and the natural world were uncommon. The study highlights the need for greater consideration of how animal characters are represented in preschool television, and how imaginative animal portrayals might be balanced with biologically and ecologically grounded depictions. Further research should examine how such portrayals influence children’s understanding of animals and their connection with nature. Full article

Walking is one of many basic human motor functions, yet replicating it in robotic systems remains a complex problem. Historically, the design of walking mechanisms has relied on human intuition and iterative refining. Some well-known mechanisms, like Theo Jansen, have been invented by artists rather than engineers. In this paper, we present a novel, automated pipeline that includes dataset generation, filtering, and an optimization procedure for synthesizing 1-DOF geometrically feasible walking mechanisms. Four million mechanisms were simulated and evaluated for 25 mechanism types, for a total of 100 million mechanisms. Quantitative design criteria for walking motion were identified and applied to retain a total of 23,250 valid, stable walking mechanisms. We then apply a custom optimization process to adjust near-walking mechanisms whose joints run into the ground. A custom function is used to minimize the error related to ground intersection and step uniformity. The computational generation and optimization of walking linkages demonstrated in this work aims to systematically generate a large number of design concepts for walking mechanisms. While the focus of this work is on the synthesis of mechanisms for walking robots, the same methodology could be generalized to identify mechanisms for a wide range of applications beyond walking robots. Full article

People living in India are experiencing some of the hottest summers on the planet. Conditions are particularly harsh in Indian cities, like Kolkata, where high temperatures are combined with high humidity. Understanding how conditions in Kolkata have evolved could provide an important addition to the growing study of the problems facing megacities in the hot, humid tropics. Yet in Kolkata, this understanding is obscured by different, often incompatible, methods of assessing the intensity of heat stress. This narrative review considers the problems encountered when attempting to develop a clear understanding of past increases or even to quantify current conditions using conventional meteorological or remote sensing data. Rather than trying to arrive at a precise quantification of how much hotter it is now in Kolkata than in the past, we argue for more fine-grained, individual-level understanding of how heat is experienced. An example of this approach is provided by a study that used telemetric devices to continuously monitor the temperature and humidity to which elderly residents of slum areas in Kolkata were exposed during 24h periods as they went about their daily lives. This study indicates that individuals experience a diversity of heat conditions that are inadequately represented by outdoor temperatures. Living in dwellings where indoor temperatures are often hotter than outdoor temperatures, the daily heat stress experienced by this vulnerable group varies between conditions that are stressful but endurable to those that approach the limits of human heat tolerance. Given the likelihood of even hotter environments in the future, urban planners will need access to more comprehensive heat studies, focusing on continual monitoring of heat stress and physiological responses of individuals from different walks of life. Full article

High-density urban villages pose significant environmental challenges to the aging population. Beyond traditional exposures such as noise and air pollution, older adults may experience heightened physiological stress due to visual exposure within street environments, yet the precise micro-environmental triggers of physiological stress remain poorly understood. This study investigates how street-level visual elements relate to elderly walkers’ physiological stress. We conducted on-site walking experiments and monitored the Skin Conductance Level (SCL) of 81 elderly participants walking through two typical urban villages in Lingnan, China. We used a semantic segmentation algorithm to quantify visual environmental elements from first-person-view images and employed a CatBoost (Categorical Boosting) model to predict stress levels. The explainable model (SHAP, SHapley Additive exPlanations) was then used to interpret the complex relationships. The model demonstrated strong predictive power (e.g., R² = 0.72). SHAP analysis revealed roads and sidewalks as the most dominant predictors of SCL changes, exhibiting significant non-linear effects. Their influence surpassed that of environmental aesthetics like vegetation, which showed a more complex, at times even negative, association with stress reduction. The presence of buildings also exhibited a stress-reducing effect, though less so than roads and sidewalks. Key findings revealed the following: (1) Foundational walking infrastructure is the primary determinant of physiological well-being for elderly pedestrians in high-density environments. (2) The stress-reducing effects of vegetation are context-dependent, while buildings function as a form of “social infrastructure” in mitigating stress. Our findings provide crucial, evidence-based guidance for prioritizing interventions in age-friendly urban renewal projects. Our framework offers a transferable tool for human-centered environmental assessment. Full article

Accurate recognition of lower limb activities is essential for wearable rehabilitation systems and assistive robotics like exoskeletons and prosthetics. This study introduces SDALLE, a custom hardware data acquisition system that integrates surface electromyography sensors (EMGs) and inertial measurement sensors (IMUs) into a wireless, portable platform for locomotor monitoring. Using this system, data were collected from nine healthy subjects performing four fundamental locomotor activities: walking, jogging, stair ascent, and stair descent. The recorded signals underwent an offline structured preprocessing pipeline consisting of time-series augmentation (jittering and scaling) to increase data diversity, followed by wavelet-based denoising to suppress high-frequency noise and enhance signal quality. A temporal one-dimensional convolutional neural network (1D-TCNN) with three convolutional blocks and fully connected layers was trained on the prepared dataset to classify the four activities. Classification using IMU sensors achieved the highest performance, with accuracies ranging from 0.81 to 0.95. The gyroscope X-axis of the left Rectus Femoris achieved the best performance (0.95), while accelerometer signals also performed strongly, reaching 0.93 for the Vastus Medialis in the Y direction. In contrast, electromyography channels showed lower discriminative capability. These results demonstrate that the combination of SDALLE hardware, appropriate data preprocessing, and a temporal CNN provides an effective offline sensing and activity classification pipeline for lower limb activity recognition and offers an open-source dataset that supports further research in human activity recognition, rehabilitation, and assistive robotics. Full article

Creative insight is a core phenomenon of human cognition, often characterized by the sudden emergence of novel and contextually appropriate ideas. Classical models based on symbolic search or associative networks struggle to capture the non-linear, context-sensitive, and interference-driven aspects of insight. In this work, we propose a computational model of insight generation grounded in continuous-time quantum walks over weighted semantic graphs, where nodes represent conceptual units and edges encode associative relationships. By exploiting the principles of quantum superposition and interference, the model enables the probabilistic amplification of semantically distant but contextually relevant concepts, providing a plausible account of non-local transitions in thought. The model is implemented using standard Python 3.10 libraries and is available both as an interactive fully reproducible Google Colab notebook and a public repository with code and derived datasets. Comparative experiments on ConceptNet-derived subgraphs, including the Candle Problem, 20 Remote Associates Test triads, and Alternative Uses, show that, relative to classical diffusion, quantum walks concentrate more probability on correct targets (higher AUC and peaks reached earlier) and, in open-ended settings, explore more broadly and deeply (higher entropy and coverage, larger expected radius, and faster access to distant regions). These findings are robust under normalized generators and a common time normalization, align with our formal conditions for transient interference-driven amplification, and support quantum-like dynamics as a principled process model for key features of insight. Full article

Existing research has preliminarily achieved stable walking in humanoid robots; however, natural human-like leg motion and adaptive capabilities in dynamic environments remain unattained. This paper proposes a bionic central pattern generator (CPG) gait generation method based on Kimura neurons. The method maps the CPG output to the spatial motion patterns of the robot’s center of mass (CoM) and foot trajectory, modulated by 22 undetermined parameters. To address the vague physical interpretation of CPG parameters, the strong neuronal coupling, and the difficulty of decoupling, this research systematically optimized the CPG parameters by defining an objective function that integrates dynamic balance performance with step constraints, thereby enhancing the naturalness and coordination of gait generation. To further enhance the walking stability of the robot under varying road curvatures, a vestibular reflex mechanism was designed based on the Tegotae theory, enabling real-time posture adjustment during slope walking. To validate the proposed approach, a virtual simulation platform and a physical humanoid robot system were constructed to comparatively evaluate motion performance on flat terrain and slopes with different gradients. The results show that the energy consumption characteristics of robot-coordinated gait are highly consistent with the energy-saving mechanism of human natural motion. In addition, the established reflection mechanism significantly improves the motion stability of the robot in slope transition, and its excellent stability margin and environmental adaptability are verified by simulation and experiment. Full article

Achieving energy-efficient, human-like gait remains a major challenge in bipedal humanoid robotics, as traditional serial actuation architectures often lead to high instantaneous power peaks and uneven load distribution. This study addresses the lack of research on how mechanical symmetry, achieved through parallel actuation, can improve power management in lower-limb joints. We developed a 14-degree-of-freedom (DOF) hip-sized bipedal robot model and conducted simulations comparing a conventional serial configuration—using single-DOF rotary actuators—with a novel parallel configuration that employs paired linear actuators at the hip pitch, hip roll, ankle pitch, and ankle roll joints. Simulation results over a standardized walking cycle show that the parallel configuration reduces peak hip-pitch power by 80.4% and peak ankle-pitch power by 53.5%. These findings demonstrate that incorporating actuator symmetry through parallel joint design significantly reduces actuator stress, improves load sharing, and enhances overall energy efficiency in bipedal locomotion. Full article

This article presents a series of innovative systems developed through student laboratory projects, comprising two autonomous vehicles, a quadrupedal walking robot, an active ankle-foot orthosis, a ball-on-beam balancing mechanism, a ball-on-plate system, and a manipulator arm, all actuated by pneumatic artificial muscles (PAMs). Due to their flexibility, low weight, and compliance, fluidic muscles demonstrate substantial potential for integration into various mechatronic systems, robotic platforms, and manipulators. Their capacity to generate smooth and adaptive motion is particularly advantageous in applications requiring natural and human-like movements, such as rehabilitation technologies and assistive devices. Despite the inherent challenges associated with nonlinear behavior in PAM-actuated control systems, their biologically inspired design remains promising for a wide range of future applications. Potential domains include industrial automation, the automotive and aerospace sectors, as well as sports equipment, medical assistive devices, entertainment systems, and animatronics. The integration of self-constructed laboratory systems powered by PAMs into control systems education provides a comprehensive pedagogical framework that merges theoretical instruction with practical implementation. This methodology enhances the skillset of future engineers by deepening their understanding of core technical principles and equipping them to address emerging challenges in engineering practice. Full article

This study proposes a novel framework integrating Building Information Modeling (BIM) and Geographic Information Systems (GIS) with real-time crowd analytics from Closed-Circuit Television (CCTV) for quantitative walkability assessment. The framework extends open data standards (IFC and CityGML) to model infrastructural and pedestrian flow attributes comprehensively. A walkability scoring mechanism quantifies route quality based on accessibility, efficiency, and physical comfort, differentiating among pedestrian groups, such as individuals sensitive to weather conditions or carrying belongings. Implemented at the Hong Kong University of Science and Technology (HKUST), results indicate that the framework effectively captures variations in walkability scores due to directional differences (uphill vs. downhill), crowd conditions, and operational constraints like facility closures. Statistical tests confirm significant differences in walking costs across these scenarios with variations of up to 30%, demonstrating the framework’s robustness and practical utility for real-time, human-centric urban infrastructure planning. Full article

Clinical studies indicate the etiology of liver disease to be unknown in 5% to 30% of patients. A long-standing hypothesis is the existence of unknown viruses beyond hepatitis A through E virus. We conducted serum virome sequencing in nine patients with cryptogenic liver disease and identified eight contigs that could not be annotated. One was determined to be a contaminant, while two of seven contigs from an individual (Patient 3) were validated by reverse transcription and polymerase chain reaction (RT-PCR) and Sanger sequencing. The possibility of contamination was completely excluded through PCR, with templates extracted using different methods from samples taken at different time points. One of the contigs, Seq260, was characterized as negative-sense single-stranded DNA via enzymatic digestion and genome walking. Digital-droplet PCR revealed the copy number of Seq260 to be low: 343 copies/mL. Seq260-based nested PCR screening was negative in 200 blood donors and 225 patients with liver disease with/without known etiologies. None of the seven contigs from Patient 3 was mapped onto 118,713 viral metagenomic data. Conclusively, we discovered seven unknown contigs from a patient with cryptogenic liver cirrhosis. These sequences are likely from a novel human virus with a negative-sense, linear single-stranded DNA genome. Full article

Neurological disorders like stroke are one of the main causes of motor dysfunction and gait function disabilities in humans. These disorders impact the mobility of patients often leading to weakened and impaired ankle joints which further compromise their balance and walking abilities. Over the span of the last twenty years, there has been a rising interest in designing, developing, and using rehabilitative robots for patients suffering from various ankle joint disabilities. These robotic devices are developed by employing diverse mechanical designs, materials, and control strategies. The aim of this study is to provide a detailed overview of the recent developments in mechanical design, actuation, and control strategies of ankle rehabilitation robots. Experimental evaluation of the discussed ankle robots has also been carried out discussing their results and limitations. This article concludes by highlighting future challenges and opportunities for the advancement of ankle rehabilitation robots, stressing the need for robust and effective devices to better serve patients. Full article

We are working on an innovative approach to outdoor human motion capture, using a wearable device that integrates a low-cost GNSS (Global Navigation Satellite System) receiver and an INS (Inertial Navigation System) via a zero-velocity update (ZUPT) methodology. In this study, we focused on using these devices to reconstruct the foot trajectory. Our work addresses the challenge of capturing precise foot movements in uncontrolled outdoor environments, a task traditionally constrained by the limitations of laboratory settings. We equipped devices that combine inertial measurement units (IMUs) with GNSS receivers in the following configuration: one on each foot and one on the head. We experimented with different GNSS data processing techniques, such as Post-Processed Kinematic (PPK) positioning with and without Moving Base (MB), and after the integration with the IMU, we obtained centimeter-level precision in horizontal and vertical positioning for various walking speeds. This integration leverages a loosely coupled GNSS/INS approach, where the GNSS solution is independently processed and subsequently used to refine the INS outputs. Enhanced by ZUPT and Madgwick filtering, this method significantly improves the trajectory reconstruction accuracy. Indeed, our research includes a study of the impact of moving speed on the performance of these low-cost GNSS receivers. These insights pave the way for future exploration into tightly coupled GNSS/INS integration using low-cost GNSS receivers, promising advancements in fields like sports science, rehabilitation, and well-being. This work seeks not only to contribute to the field of wearable technology, but also to open possibilities for further innovation in affordable, high-accuracy personal navigation and activity monitoring devices. Full article

This paper presents a novel Human Activity Recognition (HAR) framework using wearable sensors, specifically targeting applications in gait rehabilitation and assistive robots. The new methodology includes the usage of an open-source dataset. This dataset includes surface electromyography (sEMG) and inertial measurement units (IMUs) signals for the lower limb of 22 healthy subjects. Several activities of daily living (ADLs) were included, such as walking, stairs up/down and ramp walking. A new framework for signal conditioning, denoising, filtering, feature extraction and activity classification is proposed. After testing several signal conditioning approaches, such as Wavelet transform (WT), Principal Component Analysis (PCA) and Empirical Mode Decomposition (EMD), an autocepstrum analysis (ACA)-based approach is chosen. Such a complex and effective approach enables the usage of supervised classifiers like K-nearest neighbor (KNN), neural networks (NN) and random forest (RF). The random forest classifier has shown the best results with an accuracy of 97.63% for EMG signals extracted from the soleus muscle. Additionally, RF has shown the best results for IMU signals with 98.52%. These results emphasize the potential of the new framework of wearable HAR systems in gait rehabilitation, paving the way for real-time implementation in lower limb assistive devices. Full article