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The rapid progress of flexible electronics has met the growing need for detecting human movement information in exoskeleton auxiliary equipment. This study provides a review of recent advancements in the design and fabrication of flexible electronics used for human motion detection. Firstly, a comprehensive introduction is provided on various self-powered wearable flexible sensors employed in detecting human movement information. Subsequently, the algorithms utilized to provide feedback on human movement are presented, followed by a thorough discussion of their methods and effectiveness. Finally, the review concludes with perspectives on the current challenges and opportunities in implementing self-powered wearable flexible sensors in exoskeleton technology. [ABSTRACT FROM AUTHOR]

Lumbar spine injuries resulting from heavy or repetitive lifting remain a prevalent concern in workplaces. Back-support devices have been developed to mitigate these injuries by aiding workers during lifting tasks. However, existing devices often fall short in providing multidimensional force assistance for asymmetric lifting, an essential feature for practical workplace use. In addition, validation of device safety across the entire human spine has been lacking. This paper introduces the Bilateral Back Extensor Exosuit (BBEX), a robotic back-support device designed to address both functionality and safety concerns. The design of the BBEX draws inspiration from the anatomical characteristics of the human spine and back extensor muscles. Using a multi–degree-of-freedom architecture and serially connected linear actuators, the device's components are strategically arranged to closely mimic the biomechanics of the human spine and back extensor muscles. To establish the efficacy and safety of the BBEX, a series of experiments with human participants was conducted. Eleven healthy male participants engaged in symmetric and asymmetric lifting tasks while wearing the BBEX. The results confirm the ability of the BBEX to provide effective multidimensional force assistance. Moreover, comprehensive safety validation was achieved through analyses of muscle fatigue in the upper and the lower erector spinae muscles, as well as mechanical loading on spinal joints during both lifting scenarios. By seamlessly integrating functionality inspired by human biomechanics with a focus on safety, this study offers a promising solution to address the persistent challenge of preventing lumbar spine injuries in demanding work environments. Editor's summary: Repetitive tasks involving lifting of objects such as in industrial settings can cause injuries to the spine and back muscles. Although wearable exoskeleton suits can be used in these settings to alleviate risks to injury, they may not provide multidimensional movement during asymmetric lifting. Kim et al. have developed an active Bilateral Back Extensor Exosuit capable of multidimensional force assistance during lifting tasks. The wearable device offers multiple degrees of freedom in range of motion and was shown to provide back muscle force assistance and to decrease compression on the spines of human participants during asymmetric and symmetric lifting tasks. —Amos Matsiko [ABSTRACT FROM AUTHOR]

Researchers have been focusing on developing intelligent sensing devices capable of capturing human movements or signals, particularly in sports and healthcare. These signals, encompassing biophysical and biochemical data, are increasingly utilized across various fields for purposes such as sports training, medical diagnostics, preventive healthcare, and rehabilitation. The urgency for real-time health monitoring has been underscored by the coronavirus disease 2019 (COVID-19) pandemic, hastening the demand for portable, flexible sensors enabling remote health signal measurement. Our investigation delves into recent advancements in flexible smart sensor technologies tailored for monitoring crucial signals in healthcare and sports. Within this comprehensive study, we provide an overview of the latest progress in smart sensors and systems engineered to track fitness and health indicators. These encompass a range of signals including electrocardiograms (ECGs), inertial data, motion, heart rate, and biological fluids (such as blood and sweat). Furthermore, we introduce a novel approach involving multimodal and integrated multidimensional sensing systems distinguished by their high sensitivity. Additionally, we elucidate current challenges in the field and offer insights into the future trajectory of smart sensors in capturing vital signals effectively. It is evident that the integration of modern fabrication techniques, stretchable electronic devices, the Internet of Things (IoT), and artificial intelligence (AI) algorithms has significantly augmented the capacity to monitor and utilize these signals for human health monitoring, including disease prediction. Smart sensors and systems that are multidimensional and multimodal might provide a more adaptable and comprehensive solution to meet the escalating healthcare demands. [ABSTRACT FROM AUTHOR]

Flexible pressure sensors are valuable in applications such as electronic skin, smart robots, artificial prosthetics, and wearable electronics. In this study, a fully packaged, flexible, self‐powered, long‐term stable sensor array based on piezoelectrets is developed for pressure monitoring. A pressure sensor with a microcavity structure and a thickness of 500 µm achieved an impressive piezoelectric coefficient of 23.8 pC N−1 and a fast response time of 93 ms. The sensor yielded an output voltage of 0.26 V when subjected to a force using 0.3 g soybeans, and it displayed a remarkable linear relationship (R2 = 0.992) between force and electricity with pressure ranging from 1.4 to 13.6 N and a sensitivity of 9 mV N−1. Real‐time monitoring of sound vibration, radial artery pulse, and finger movement is demonstrated along with the successful recording of dynamic pressure changes within the porcine knee joint. It holds potential for fields such as monitoring pressure changes in the movement of human bodies and robotics and can contribute significantly to pressure assessment during total knee replacement. [ABSTRACT FROM AUTHOR]

Developing stretchable strain sensors with high sensitivity and stability is crucial for various applications such as prosthetic hands, human health monitoring, and human‐machine interactions. However, achieving these qualities simultaneously remains challenging. Here, an inherently stretchable strain sensor is presented that integrates ultrahigh sensitivity and robust stability, enabling stretch, press, or bend sensing capabilities. This sensor employs a softer elastomeric channel filled with liquid metal (LM) as the conductive path. A stiffer elastomer convex integrated into the channel serves as a strain‐manipulated gate, controlling opening gap of electrical current flow path. During deformation, the softer elastomer undergoes cross sectional reduction due to the Poisson effect, while the stiffer convex gate retains its geometry. This heterogeneous deformation behavior leads to significant contraction or closure of the LM channel, resulting in increased resistance and a remarkable enhancement in sensitivity by more than two orders of magnitude. The all‐soft design maintains exceptional stability even under extended or repetitive substantial deformations. With the ability to monitor subtle and large human body movements, detect grip actions of soft grippers reliably, and monitor the gradual and extended growth process of plants, this sensor holds significant potential for advancements in flexible electronics. [ABSTRACT FROM AUTHOR]

Sport diversification provides opportunities for individuals to develop physical literacy, establish a growth mindset, become more agile in varied environments, and develop robust strategies to improve performance. One could say the same for biomechanists, who study the control and dynamics of human movements in the context of sport. Through the lens of sport, we have focused on the ongoing interaction between the nervous system, musculoskeletal system, and the environment by using integrated experimental and modelling approaches to study well-practiced, goal-directed tasks in controlled laboratory and realistic field settings. By integrating multiple sources of information in real time to provide timely, relevant, usable, and easy to understand (TRUE) feedback during skill acquisition, we have found these resources also support learning and opportunities for self-discovery of proficiencies by coaches and athletes. Managing multimodal data acquired with emerging technological advances has also benefited from the use of FAIR data management principles, where data are findable, accessible, interoperable, and reusable. By listening, clarifying goals, and exploring together with coaches and athletes, we can bridge the gaps between what we know and what we do. [ABSTRACT FROM AUTHOR]

Gels with excellent ionic conductivity and resilience to temperature extremes are sought in practical applications due to their potential for building flexible and wearable soft electronics. In this work, highly conductive eutectogels are fabricated by one‐step polymerization of (3‐acrylamidopropyl) trimethyl ammonium chloride in deep eutectic solvents (DESs). Owing to the ion‐conductive polymer networks facilitating ion conduction, these eutectogels exhibit a significant enhancement in ionic conductivity (27.1 mS cm−1), demonstrating an order of magnitude higher than that of pure DES. Furthermore, due to several unique properties of DES, these eutectogels exhibit excellent stretchability (>1000%), good adhesion, as well as nonflammability performance over a wide temperature range (−40 to 100°C). Moreover, the strain sensor based on the eutectogel shows a highly sensitive response (gauge factor = 6.92) to a broad strain range (from 450% to 550%), enabling the accurate and reliable detection of various human movements. Additionally, our findings reveal that the eutectogel‐based sensor displays a comparable pattern of response curves with negligible signal deterioration, even at extreme temperatures of −20 and 60°C. The results of this work will promote novel applications in the construction of flexible and safe devices. [ABSTRACT FROM AUTHOR]

Human fingers possess stable high sensitivity and a wide range of tactile perception, attributed to the gradient microstructure and the interlocking collagen fiber on the skin's surface. However, challenges persist in achieving simultaneous enhancement of multiple functionalities in artificial skin. Inspired by the unique structure of the skin, a two‐step process involving ion diffusion‐induced and strong‐weak topological crosslinking is synergistically employed to fabricate a bilayer gradient hydrogel. Zn2+ initially diffuses to induce the formation of weak bonds, imparting elasticity. Subsequently, Fe3+/Zn2+ diffusion constructs a strong‐weak topologically crosslinked network, enhancing the toughness of the gel while reducing the brittleness associated with robust bonds. Due to its distinctive design, the gel employs an adaptive energy dissipation strategy subjected to large and small stress, ensuring high sensitivity (3.31 kPa−1, 0–2 kPa), wide sensing range (0.4–40.6 kPa), and exceptional stability (500 cycles). This flexible approach enables programmable design in three dimensions, including ion diffusion type, direction, and shape. This gel can detect the gentle brushing of feathers and human body movements. It utilizes significant differences generated by magnitudes of stress to perform binary information encryption. This study introduces a novel strategy for preparing skin‐like gels, offering promising potential for expanding their applications in complex scenarios. [ABSTRACT FROM AUTHOR]

Globalization is turning into a phenomenon that shapes and accelerates migration. The displacement of global capital movements, regional wars, and the intersection of the search for a better life with technology has made the phenomenon of migration one of the important agenda items of the global public sphere. Countries have difficulty stopping human movements that push the boundaries. The economic and social consequences of the increasing migration on a global scale are more frequently encountered by local people. The media is an important tool in shaping public opinion, as it is a platform where the topic of migration is discussed from various perspectives. Similarly, local media is an area where the issue of migration, a global problem, is addressed from different angles. In local media, which mostly reports on local events, global events are inevitably covered as well. The issue of migration has become one of the important topics for local media. In this context, the problem of the study is to understand the opinions of local journalists about the issue of migration. This study aims to determine how local journalists approach the phenomenon of migration in the context of globalization. In addition, the study analyzes local journalists' thoughts about globalization, economy, socio-cultural and media. In the study, phenomenology, one of the qualitative research designs, was conducted. In-depth interviews were conducted as a data collection technique in the study. According to the findings, the themes that come to the fore in journalists' thoughts under the heading of globalization are; It is stated that global forces cause migrations, developments in the field of communication have an impact on global migrations, migrations create cheap labor and global migration management is unfair [ABSTRACT FROM AUTHOR]

Detecting respiration and heartbeat information from human chest movement is crucial for non‐contact vital sign detection. Due to the small frequency difference between respiration and heartbeat signals and the relatively low amplitude of the heartbeat signal, it is challenging to separate and extract them completely. This letter proposes a successive variational mode extraction method based on spectrum trend for the extraction of respiration and heartbeat signals. The proposed method adaptively determines the initial centre frequency and extraction order of the desired signal modes by analyzing the spectrum trend of the signal to be decomposed. Additionally, a recursive framework is introduced to sequentially extract the desired signal modes. Both simulation and practical experiment results show that the method is effective in extracting respiration and heartbeat signals, significantly improving the accuracy of estimating respiration and heartbeat frequencies. [ABSTRACT FROM AUTHOR]

Humans use their fingers to perform a variety of tasks, from simple grasping to manipulating objects, to typing and playing musical instruments, a variety wider than any other species. The more sophisticated the task, the more it involves individuated finger movements, those in which one or more selected fingers perform an intended action while the motion of other digits is constrained. Here we review the neurobiology of such individuated finger movements. We consider their evolutionary origins, the extent to which finger movements are in fact individuated, and the evolved features of neuromuscular control that both enable and limit individuation. We go on to discuss other features of motor control that combine with individuation to create dexterity, the impairment of individuation by disease, and the broad extent of capabilities that individuation confers on humans. We comment on the challenges facing the development of a truly dexterous bionic hand. We conclude by identifying topics for future investigation that will advance our understanding of how neural networks interact across multiple regions of the central nervous system to create individuated movements for the skills humans use to express their cognitive activity. [ABSTRACT FROM AUTHOR]

As both the proportion of older people and the length of life increases globally, a rise in age-related degenerative diseases, disability, and prolonged dependency is projected. However, more sophisticated biomedical materials, as well as an improved understanding of human disease, is forecast to revolutionize the diagnosis and treatment of conditions ranging from osteoarthritis to Alzheimer's disease as well as impact disease prevention. Another, albeit quieter, revolution is also taking place within society: human augmentation. In this context, humans seek to improve themselves, metamorphosing through self-discipline or more recently, through use of emerging medical technologies, with the goal of transcending aging and mortality. In this review, and in the pursuit of improved medical care following aging, disease, disability, or injury, we first highlight cutting-edge and emerging materials-based neuroprosthetic technologies designed to restore limb or organ function. We highlight the potential for these technologies to be utilized to augment human performance beyond the range of natural performance. We discuss and explore the growing social movement of human augmentation and the idea that it is possible and desirable to use emerging technologies to push the boundaries of what it means to be a healthy human into the realm of superhuman performance and intelligence. This potential future capability is contrasted with limitations in the right-to-repair legislation, which may create challenges for patients. Now is the time for continued discussion of the ethical strategies for research, implementation, and long-term device sustainability or repair. [ABSTRACT FROM AUTHOR]

The emotional responses of psychotherapists to their patients, known as countertransference, can yield valuable insights into the patient's psychological functioning. Albeit from a different perspective, the Rorschach test also provides information about the patient's psychological processes. In particular, the Rorschach human movement response (M) has been shown to be a useful measure of higher-level psychological functioning. In an attempt to bridge these two largely different perspectives, the aim of this study was to explore the association between M responses in the Rorschach protocols of psychotherapy patients and emotional responses exhibited by their therapists. To this end, a convenience sample of 149 outpatients were administered the Rorschach according to the Comprehensive System, and their therapists completed the Therapist Response Questionnaire. Through a series of regression models, controlling for response style, response complexity, and degree of psychopathology, M demonstrated a significant association with the therapists' emotional responses. A lower number of M responses was associated with the therapists' feelings of disengagement, and a higher number of M responses was associated with the therapists' feelings of being more involved with the patient. Taken together, these results suggest a potential relationship between the number of M responses the respondent gives in the Rorschach and the subsequent development of the therapeutic alliance between the respondent and their therapist. [ABSTRACT FROM AUTHOR]

Background: Application of numerous malaria control interventions has led to reduction in clinical malaria cases and deaths but also the realisation that asymptomatic parasite carriers play a key role in sustaining transmission. This study assessed the effectiveness of using the Ultra-sensitive NxTek eliminate RDT (uRDT) and conventional SD Bioline HRP2 RDT (cRDT) in diagnosing asymptomatic parasitaemia while measuring the impact of mass testing, treatment and tracking (MTTT) on the prevalence of asymptomatic malaria over a 1-year period in Ghana. Methods: A total of 4000 targeted participants from two towns, Obom and Kofi Kwei, with their surrounding villages, were tested for asymptomatic malaria four times over the study period using uRDT (intervention) and the cRDT (control) respectively. Participants carrying malaria parasites were followed by home visit and phone calls for compliance to treatment, and filter paper blood blots collected from participants were used to determine true parasite carriage by PET-PCR. A mathematical model of the study site was developed and used to test the impact of test sensitivity and mass migration on the effect of MTTT. Results: The start and end point sensitivities of the cRDT were 48.8% and 41.7% and those for the uRDT were 52.9% and 59.9% respectively. After a year of MTTTs, asymptomatic parasite prevalence, as determined by PCR, did not differ statistically in the control site (40.6% to 40.1%, P = 0.730) but decreased at the intervention site (55.9% to 46.4%, P < 0.0001). Parasite prevalence by RDT, however, indicated statistical reduction in the control site (25.3% to 22.3%, P = 0.017) and no change in the intervention site (35.1% to 36.0%, P = 0.614). The model predicted a mild effect of both diagnostic sensitivity and human movement in diminishing the impact of MTTT in the study sites. Conclusions: Asymptomatic parasite prevalence at the molecular level reduced significantly in the site where the uRDT was used but not where the cRDT was used. Overall, the uRDT exhibited higher sensitivity relative to the cRDT. Highly sensitive molecular techniques such as PET-PCR should be included in parasite prevalence estimation during MTTT exercises. [ABSTRACT FROM AUTHOR]

Freezing is one of the most extensively studied defensive behaviors in rodents. Both reduced body and gaze movements during anticipation of threat also occur in humans and have been discussed as translational indicators of freezing but their relationship remains unclear. We thus set out to elucidate body and eye movements and concomitant autonomic dynamics in anticipation of avoidable threat. Specifically, 50 participants viewed naturalistic pictures that were preceded by a colored fixation cross, signaling them whether to expect an inevitable (shock), no (safety), or a potential shock (flight) that could be avoided by a quick button press. Body sway, eye movements, the heart rate and skin conductance were recorded. We replicated previously described reductions in body sway, gaze dispersion, and the heart rate, and a skin conductance increase in flight trials. Stronger reductions in gaze but not in body sway predicted faster motor reactions on a trial‐wise basis, highlighting their functional role in action preparation. We failed to find a trait‐like relationship between body and gaze movements across participants, but their temporal profiles were positively related within individuals, suggesting that both metrics partly reflect the same construct. However, future research is desirable to assess these response patterns in naturalistic environments. A more ethological examination of different movement dynamics upon threat would not only warrant better comparability between rodent and human research but also help determine whether and how eye‐tracking could be implemented as a proxy for fear‐related movements in restricted brain imaging environments. Our article unravels the relationship between body and eye movements in human threat anticipation, drawing parallels with freezing behavior in rodents. We reveal a physio‐behavioral defensive state, emphasize eye‐tracking's potential in neuroimaging, and spotlight the need for improved translational research practices across species. [ABSTRACT FROM AUTHOR]

Community roads are crucial for efficient navigation in residential areas. However, current navigation maps often lack comprehensive coverage of these roads. To address this issue, we present a Human-Flow Model (HFM) to identify roads within residential communities by utilizing abundant low-frequency trajectory data from human movements. First, the HFM leverages human movement density in residential zones to estimate the likelihood of road existence. Using the probability distribution of human movement and neighboring building footprints, we construct a Human-Flow Probability Field (HFPF), which serves as a distribution representation for modeling human movement within densely populated built-up areas. Then, the flow paths are extracted from the HFPF using hydrological analysis techniques, which facilitates the identification of main paths and smaller branches within the community road network. Finally, the road network is refined using morphological methods. Our model was tested using six residential communities located in Wuhan, China. It consistently outperformed other methods by detecting more roads with higher accuracy, especially intricate branches. By incorporating flow semantics, our model capitalizes on sparse trajectory data to enhance the fine-scale community road networks. This improvement enhances the last-mile navigation experience in sustainable cities, contributing to overall urban mobility and convenience for residents. [ABSTRACT FROM AUTHOR]

Flexible force sensors show great potential applications in smart wearable devices based on their softness and ease of deformation. Textiles are extremely compatible with the human body, where mechanical forces generated by human movements and physiological activities create rich application scenarios for flexible force sensors. Currently, the bottleneck in the development of flexible sensors depends on the breakthrough of mass production efficiency, which has been a research focus in recent years to accelerate the application of smart textiles in various fields. This work reviews the research progress of flexible force sensors based on fiber assemblies with mass production efficiency, which is discussed in four aspects: sensing mechanisms, structure design, preparation methods, and application. Finally, the challenges faced by mass production of flexible force sensors are analyzed and summarized, and a few inspirations for their future development direction are provided. [ABSTRACT FROM AUTHOR]

The history of mankind is replete with movements of human beings across our large planet. People moved or were forced to move because they could no longer live at one place in peace and stability. Wars and famines were common reasons to uproot the tents and move on to greener pastures but there were other reasons as they are still today. One of the major causes of leaving one's homeland is inter-tribal or inter-ethnic rivalry and the other is religious persecution. As we see in Middle-East or West Asia as commonly designated now, these causes are still alive and festering like unhealed wounds making people leave the safety of their homes and family for a more hospitable environment. Into this mixture of geo-politics and ideologies promoted by the super-powers, a complex situation has become more complicated leading countries to take sides and fight proxy wars. The 'migrants' and 'refugees' streamed in via various routes especially by crossing the Mediterranean. Whether these asylum seekers could be classified as migrants or refugees is contentious. One study examines how the very concepts of 'migrant' and 'refugee' are used in different contexts, and introduces questions about mobility, citizenship and the nation state. The media and the press continued to refer to the people leaving conflict zones as migrants looking for a better economic opportunity. Responsible journalists depicted refugees and their desperate situation in a balanced and sympathetic context but a majority of news media channels took the sensationalist and ideologically biased road, playing on people's anti-immigration sentiment, anxiety and fears. [ABSTRACT FROM AUTHOR]

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Automatic segmentation is essential for enhancing human activity recognition, especially given the limitations of publicly available datasets that often lack diversity in daily activities. This study introduces a novel segmentation method that utilizes skeleton data for a more accurate and efficient analysis of human actions. By employing an autoencoder, this method extracts representative features and reconstructs the dataset, using the discrepancies between the original and reconstructed data to establish a segmentation threshold. This innovative approach allows for the automatic segmentation of activity datasets into distinct segments. Rigorous evaluations against ground truth across three publicly available datasets demonstrate the method's effectiveness, achieving impressive average annotation error, precision, recall, and F1-score values of 3.6, 90%, 87%, and 88%, respectively. This illustrates the robustness of the proposed method in accurately identifying change points and segmenting continuous skeleton-based activities as compared to two other state-of-the-art techniques: one based on deep learning and another using the classical time-series segmentation algorithm. Additionally, the dynamic thresholding mechanism enhances the adaptability of the segmentation process to different activity dynamics improving overall segmentation accuracy. This performance highlights the potential of the proposed method to significantly advance the field of human activity recognition by improving the accuracy and efficiency of identifying and categorizing human movements. [ABSTRACT FROM AUTHOR]

In order to carry out mathematical modeling of the drug particles or drop movement in the human respiratory system, an approach to reverse prototyping of the studied areas based on the medical data (computed tomography) results is presented. To adapt the computational grid, a mathematical model of airflow in channels of complex geometry (respiratory system) has been developed. Based on the data obtained, the results of computational experiments for a single-phase system are presented. [ABSTRACT FROM AUTHOR]

The accurate analysis of human dynamic behavior is very important for overcoming the limitations of movement diversity and behavioral adaptability. In this paper, a wearable device-based human dynamic behavior recognition method is proposed. The method collects acceleration and angular velocity data through a six-axis sensor to identify information containing specific behavior characteristics in a time series. A human movement data acquisition platform, the DMP attitude solution algorithm, and the threshold algorithm are used for processing. In this experiment, ten volunteers wore wearable sensors on their bilateral forearms, upper arms, thighs, calves, and waist, and movement data for standing, walking, and jumping were collected in school corridors and laboratory environments to verify the effectiveness of this wearable human movement recognition method. The results show that the recognition accuracy for standing, walking, and jumping reaches 98.33%, 96.67%, and 94.60%, respectively, and the average recognition rate is 96.53%. Compared with similar methods, this method not only improves the recognition accuracy but also simplifies the recognition algorithm and effectively saves computing resources. This research is expected to provide a new perspective for the recognition of human dynamic behavior and promote the wider application of wearable technology in the field of daily living assistance and health management. [ABSTRACT FROM AUTHOR]

Predictive neuromuscular simulations are a powerful tool for studying the biomechanics of human walking, and deriving design criteria for technical devices like prostheses or biorobots. Good agreement between simulation and human data is essential for transferability to the real world. The human foot is often modeled with a single rigid element, but knowledge of how the foot model affects gait prediction is limited. Standardized procedures for selecting appropriate foot models are lacking. We performed 2D predictive neuromuscular simulations with six different foot models of increasing complexity to answer two questions: What is the effect of a mobile arch, a toe joint, and the coupling of toe and arch motion through the plantar fascia on gait prediction? and How much of the foot's anatomy do we need to model to predict sagittal plane walking kinematics and kinetics in good agreement with human data? We found that the foot model had a significant impact on ankle kinematics during terminal stance, push-off, and toe and arch kinematics. When focusing only on hip and knee kinematics, rigid foot models are sufficient. We hope our findings will help guide the community in modeling the human foot according to specific research goals and improve neuromuscular simulation accuracy. [ABSTRACT FROM AUTHOR]

Although advanced robots can adeptly mimic human movement and aesthetics, they are still unable to adapt or evolve in response to external experiences. To address this limitation, we propose an innovative approach that uses parallel-processable retention-engineered synaptic devices in the control system. This approach aims to simulate a human-like learning system without necessitating complex computational systems. The retention properties of the synaptic devices were modulated by adjusting the amount of Ag/AgCl ink sprayed. This changed the voltage drop across the interface between the gate electrode and the electrolyte. Furthermore, the unrestricted movement of ions in the electrolyte enhanced the signal multiplexing capability of the ion gel, enabling device-level parallel processing. By integrating the unique characteristics of the synaptic devices with actuators, we successfully emulated a human-like workout process that includes feedback between acute and chronic responses. The proposed control system offers an innovative approach to reducing system complexity and achieving a human-like learning system in the field of biomimicry. [ABSTRACT FROM AUTHOR]

Innovative applications of human movement analysis, for example, for mitigating/slowing down certain pathological conditions, have recently emerged from the modeling and automated measurement of full‐body expressive midlevel individual and group movement qualities, at a higher complexity level than movement qualities derived directly from physical signals, still not characterizing any gesture in a specific way. More in general, the availability of automated analysis techniques of midlevel expressive movement qualities can contribute to interaction design incorporating body‐based performance practices inspired by artistic theories in dance and music. This work investigates how such practices and techniques can support embodied interaction design by enabling automated measuring of cues of leadership, cohesion, and fluidity in full‐body movement in group settings. In particular, the dance‐inspired scientific approach, the data collection protocol, and the analysis techniques adopted for assessing movement qualities connected to leadership and cohesion within the group and fluidity of the dancers' full‐body movement are described. Finally, future developments of this research are outlined. [ABSTRACT FROM AUTHOR]

Flexible electronic devices could collect dynamic motion information of human body for various sharing operations and became increasing importance in strategic emerging fields of the digitization and intellectualization. However, developing a strain sensor with a wide effective sensing range and high sensitivity remains challenging. In this work, smart Wearable polyurethane nanofiber-based graphene oxide powder (GO)/silver nanowires (Ag NWs) composite membrane was fabricated and demonstrated as strain sensors to monitor various human movements. Ag NWs (0.2 wt%) With varying content of GO (0.3, 0.4 and 0.5 wt%) in N, N-dimethylformamide to fabricated different doped membrane (DF) followed interlayers spraying by graphene nanosheets. Finally, the composite film with three-dimensional multilayer structure was obtained, and named as sandwich membrane (SF). The experimental results show that the sensing mechanism of the composite structure is different under different stresses. When the strain was small, DF plays a major role, and when the strain was large, SF plays a major role. The microstructure of sandwich membrane (SF) composed of fibrous layers based on doped membrane (DF) and interlayers spraying by graphene nanosheets determines various sensitivities to relative resistance variation at different stages during deformation process. The DF appears much sensitive at small strain and the SF mainly occurs at large deformation. Especially, the SF with 11 layers presents extremely synchronous output resistance signals with input strain, and its effective strain range widens to 53.16% with 774.19 sensitivity, moreover, when its durability and stability exceed 1000 cycles, its fast response lag time reduces to 0.002 s. [ABSTRACT FROM AUTHOR]

Since the early 2000s, the EU has funded many R&D projects on border security. However, devices developed through these programmes have met ‘violent ends’: firstly, by appearing to face abrupt ends after EU funding ceased; secondly, by materialising to serve as violent border devices. This article traces the fate of devices funded by the EU’s 7th Framework Programme for managing human and non-human movements. Theoretically, it adds to the literature on ignorance by approaching debates on non-knowledge through a Bourdieusian analytical lens, and methodologically, it proposes to mobilise the concept of ‘symbolic violence’ to investigate and compare what is acknowledged and what is not, what is obscured or obfuscated in and through R&D projects. Empirically, the paper examines several projects that aim to manage people and things on the move, and which have materialised in more or less overt ways. The article argues that projects focused on border security play a precursory role in the enactment of violent devices, and simultaneously obfuscate the non-knowledge of ‘violent ends’ that shape the future of the EU’s border governance. It shows that even if these projects do not apparently materialise, they nevertheless find ways of achieving their violent ends. [ABSTRACT FROM AUTHOR]

Achieving highly efficient self‐powered fibrous sensing systems is desirable for smart electronic textiles but remains a great challenge. Here, a bifunctional hydrogel is proposed by introducing of polyacrylic acid grafted MXene (MXene‐g‐PAA) into polyacrylamide/chitosan, achieving high‐sensitivity sensor and a stable hydrogel electrolyte of battery. The MXene‐g‐PAA flakes act as ion transport "highway", significantly enhance ionic conductivity, thereby increasing the sensitivity of sensors and facilitating Zn2+ diffusion in Zn‐ion battery (ZIB). The rich hydrogen bonding network in the hydrogel improves its mechanical properties and limits water molecule movement, thus reducing side reactions and prolonging the stability of ZIB. As a result, the fibrous sensor exhibits high strain sensitivity (gauge factor of 2.4) with a wide detection range (0–800%), and the fibrous Zn‐ion battery exhibits high capacity (353 mAh cm−3) with long cycling stability (400 cycles). The hydrogel‐based sensor and ZIB can be easily integrated into a flexible self‐powered sensing system, which effectively detects human movement and 3D ball motion. The bifunctional hydrogel with the integrated fibrous system will shed light on the development of next‐generation smart electronic textiles. [ABSTRACT FROM AUTHOR]

Understanding human movement patterns is crucial for comprehending how a city functions. It is also important for city planners and policymakers to create more efficient plans and policies for urban areas. Traditionally, human movement patterns were analyzed using origin–destination surveys, travel diaries, and other methods. Now, these patterns can be identified from various geospatial big data sources, such as mobile phone data, floating car data, and location-based social media (LBSM) data. These extensive datasets primarily identify individual or collective human movement patterns. However, the impact of spatial scale on the analysis of human movement patterns from these large geospatial data sources has not been sufficiently studied. Changes in spatial scale can significantly affect the results when calculating human movement patterns from these data. In this study, we utilized Weibo datasets for three different cities in China including Beijing, Guangzhou, and Shanghai. We aimed to identify the effect of different spatial scales on individual human movement patterns as calculated from LBSM data. For our analysis, we employed two indicators as follows: an external activity space indicator, the radius of gyration (ROG), and an internal activity space indicator, entropy. These indicators were chosen based on previous studies demonstrating their efficiency in analyzing sparse datasets like LBSM data. Additionally, we used two different ranges of spatial scales—10–100 m and 100–3000 m—to illustrate changes in individual activity space at both fine and coarse spatial scales. Our results indicate that although the ROG values show an overall increasing trend and the entropy values show an overall decreasing trend with the increase in spatial scale size, different local factors influence the ROG and entropy values at both finer and coarser scales. These findings will help to comprehend the dynamics of human movement across different scales. Such insights are invaluable for enhancing overall urban mobility and optimizing transportation systems. [ABSTRACT FROM AUTHOR]

Introduction: Work-related musculoskeletal disorders have a greater negative impact on nurses' well-being, both physically and emotionally. Because of this issue, nurses take excessive leave, are absent from work, quit their profession, and have a lower quality of life. This leads to an increase in human and economic losses to health care institutions. However, there is a paucity of information about work-related musculoskeletal disorders among nurses in the study area. Objective: This study aimed to assess the magnitude of work-related musculoskeletal disorders and its factors associated among nurses in Hospitals of West Shoa Zone, Central Ethiopia, 2021. Materials and methods: A cross-sectional facility-based study was conducted with 406 systematically selected nurses from March 2021 to June 2021. A simple random sampling was used to select study subjects. Data were gathered through face-to-face interviews. The data has been cleaned and checked, entered Epi Data 3.1, and exported into Statistical Package for Social Sciences software version 25 for further analysis. Bivariable and multiple logistic regressions were used to determine the factors associated with outcome variable. The level of statistical significance was reported to be less than 0.05. Results: The prevalence of work-related musculoskeletal disorders was 73.8% and the highest report 247 (62.2%) was seen in the lower back which was followed by the neck 182 (45.8%) and elbow 126 (31.7%), while the lowest affected body part was ankle 46(11.6%). Working in the medical ward (COR = 5.91, 95%CI: 2.53–13.79), surgical ward (COR = 9.63, 95%CI: 3.64–15.51), and operation room theater (COR = 6.02, 95%CI: 2.37–15.27) had higher risk to work-related musculoskeletal discomfort as compared to those who were worked in outpatient department. Nurses those experienced bending or twisting back as a problem during work 2.33 times more likely to have higher risk WMSDs as compared to those who didn't perceive it with (COR 2.33, 95% CI: 1.47–3.71). Conclusion: In this study, the prevalence of work-related musculoskeletal disorders among nurses was high. Working in room/ward, working in malposition, have no on job training to prevent work-related musculoskeletal disorders and bending or twisting back during work were identified as associated factors. Since work-related musculoskeletal disorders are preventable, educating the staff about the use of proper body mechanics and modifying the work environment is essential. [ABSTRACT FROM AUTHOR]

The use of wearable sensors, such as inertial measurement units (IMUs), and machine learning for human intent recognition in health-related areas has grown considerably. However, there is limited research exploring how IMU quantity and placement affect human movement intent prediction (HMIP) at the joint level. The objective of this study was to analyze various combinations of IMU input signals to maximize the machine learning prediction accuracy for multiple simple movements. We trained a Random Forest algorithm to predict future joint angles across these movements using various sensor features. We hypothesized that joint angle prediction accuracy would increase with the addition of IMUs attached to adjacent body segments and that non-adjacent IMUs would not increase the prediction accuracy. The results indicated that the addition of adjacent IMUs to current joint angle inputs did not significantly increase the prediction accuracy (RMSE of 1.92° vs. 3.32° at the ankle, 8.78° vs. 12.54° at the knee, and 5.48° vs. 9.67° at the hip). Additionally, including non-adjacent IMUs did not increase the prediction accuracy (RMSE of 5.35° vs. 5.55° at the ankle, 20.29° vs. 20.71° at the knee, and 14.86° vs. 13.55° at the hip). These results demonstrated how future joint angle prediction during simple movements did not improve with the addition of IMUs alongside current joint angle inputs. [ABSTRACT FROM AUTHOR]

Composite indoor human activity recognition is very important in elderly health monitoring and is more difficult than identifying individual human movements. This article proposes a sensor-based human indoor activity recognition method that integrates indoor positioning. Convolutional neural networks are used to extract spatial information contained in geomagnetic sensors and ambient light sensors, while transform encoders are used to extract temporal motion features collected by gyroscopes and accelerometers. We established an indoor activity recognition model with a multimodal feature fusion structure. In order to explore the possibility of using only smartphones to complete the above tasks, we collected and established a multisensor indoor activity dataset. Extensive experiments verified the effectiveness of the proposed method. Compared with algorithms that do not consider the location information, our method has a 13.65% improvement in recognition accuracy. [ABSTRACT FROM AUTHOR]

Developing mmWave radar sensors for indoor crowd motion sensing and tracking faces a critical challenge: the scarcity of large-scale, high-quality training data. Traditional human experiments encounter logistical complexities, ethical considerations, and safety issues. Replicating precise human movements across trials introduces noise and inconsistency into the data. To address this, this study proposes a novel solution: a movable platform equipped with a life-size mannequin to generate realistic and diverse data points for mmWave radar training and testing. Unlike human subjects, the platform allows precise control over movements, optimising sensor placement relative to the target object. Preliminary optimisation results reveal that sensor height impacts tracking performance, with an optimal sensor placement above the test subject yields the best results. The results also reveal that the 3D data format outperforms 2D data in accuracy despite having fewer frames. Additionally, analysing height distribution using 3D data highlights the importance of the sensor angle—15° downwards from the horizontal plane. [ABSTRACT FROM AUTHOR]

This paper describes a novel bionic eye binocular vision system designed to mimic the natural movements of the human eye. The system provides a broader field of view and enhances visual perception in complex environments. Compared with similar bionic binocular cameras, the JEWXON BC200 bionic binocular camera developed in this study is more miniature. It consumes only 2.8 W of power, which makes it ideal for mobile robots. Combining axis and camera rotation enables more seamless panoramic image synthesis and is therefore suitable for self-rotating bionic binocular cameras. In addition, combined with the YOLO-V8 model, the camera can accurately recognize objects such as clocks and keyboards. This research provides new ideas for the development of robotic vision systems. [ABSTRACT FROM AUTHOR]

Mayaro virus (MAYV; Alphavirus: Togaviridae) is an emerging pathogen in Latin America, causing fever and polyarthritis. Sporadic outbreaks of MAYV have occurred in the region, with reported human cases being imported to Europe and North America. Although primarily a risk for those residing in the Amazon basin's tropical forests, recent reports highlight that urbanization would increase the risk of MAYV transmission in Latin America. Urban emergence depends on human susceptibility and the ability of mosquitos like Aedes aegypti (Linnaeus, 1762) (Diptera: Culicidae) to transmit MAYV. Despite the absence of active MAYV transmission in Argentine, the risk of introduction is substantial due to human movement and the presence of Ae. aegypti in the region. This study aimed to evaluate the susceptibility of different Argentine Ae. aegypti populations to MAYV genotype L (MAYV‐L) using dose–response assays and determine barriers to virus infection, dissemination and transmission. Immature mosquito stages were collected in Buenos Aires, Córdoba and Rosario cities. Female Ae. aegypti (F2) were orally infected by feeding on five concentrations of MAYV‐L, ranging from 1.0 to 6.0 log10 PFU/mL. Abdomens, legs and saliva were analysed using viral plaque assays. Results revealed that MAYV‐L between infection and dissemination were associated with viral doses rather than the population origin. Infection rates varied between 3% and 65%, with a 50% infectious dose >5.5 log10 PFU/mL. Dissemination occurred at 39%, with a 50% dissemination dose of ~6.0 log10 PFU/mL. Dissemination among infected mosquitoes ranged from 60% to 86%, and transmission from disseminated mosquitoes ranged from 11% to 20%. Argentine Ae. aegypti populations exhibited a need for higher viral doses of MAYV‐L than those typically found in humans to become infected. In addition, only a small proportion of infected mosquitoes were capable of transmitting the virus. Understanding MAYV transmission in urban areas is crucial for public health interventions. [ABSTRACT FROM AUTHOR]

The kinematics of the human hand exhibit complex and diverse characteristics unique to each individual. Various techniques such as vision-based, ultrasonic-based, and data-glove-based approaches have been employed to analyze human hand movements. However, a critical challenge remains in efficiently analyzing and classifying hand grasp types based on time-series kinematic data. In this paper, we propose a novel sparse coding feature extraction technique based on dictionary learning to address this challenge. Our method enhances model accuracy, reduces training time, and minimizes overfitting risk. We benchmarked our approach against principal component analysis (PCA) and sparse coding based on a Gaussian random dictionary. Our results demonstrate a significant improvement in classification accuracy: achieving 81.78% with our method compared to 31.43% for PCA and 77.27% for the Gaussian random dictionary. Furthermore, our technique outperforms in terms of macro-average F1-score and average area under the curve (AUC) while also significantly reducing the number of features required. [ABSTRACT FROM AUTHOR]

Gait asymmetries are a common problem in clinical populations, such as those with a history of stroke or Parkinson's disease. The use of a split-belt treadmill is one way to enhance gait symmetry but relies on specialty (and typically expensive) equipment. Alternatively, visual cues have been shown as a method to alter gait mechanics, but their utility in altering gait symmetry has been relatively understudied. Before deploying this method to clinical populations, a proof-of-concept study is needed to explore using visual cues to alter gait symmetry in healthy adults. Therefore, the purpose of this study was to examine the extent to which healthy adults could synchronize to an asymmetric visual cue with a small or large gait asymmetry using wearable sensors to measure gait asymmetries. Seventy-two healthy adults (ages: 23.89 ± 6.08 years) walked on the treadmill for two conditions: with and without the visual cue. Each walking condition lasted 10 min at the participant's preferred walking speed. Inertial sensors were used to measure gait asymmetries. Some participants did not respond to the visual cue, and groups were separated into responders and non-responders. Participants in the small and large asymmetry-responder groups exhibited statistically significant increased asymmetries in single limb support % (p < 0.01) and step duration (s) (p < 0.05, p < 0.01, respectively). Only the large asymmetry-responder group showed statistically significant (p < 0.01) increased asymmetries in stride length. Overall, asymmetrical walking visual cues can alter gait asymmetries, and inertial sensors were sensitive enough to detect small changes in gait asymmetries. [ABSTRACT FROM AUTHOR]

Geographic flow clustering analysis can effectively reveal human behavioral patterns in movement. Traditional methods for studying human movement patterns are mostly based on first-order quantity analyses of point data, such as hotspots, density or clustering. Currently, relatively few second-order spatial analysis methods based on geographic flows exist. Thus, we developed a new geographic flow method based on spectral clustering and applied it to trajectory data analysis. This article uses the bike-sharing trajectories data in Shanghai in August 2016, spectral clustering analysis was conducted on the group flow patterns before, during and after rainfall, on weekdays and weekends and in the morning and evening peak. Spectral clustering was verified to exhibit better clustering effect by comparing the clustering indices of different clustering methods. This study enriches the analysis method of geographical flows, and the human mobility patterns revealed by its analysis can provide references for formulating urban green travel policies. [ABSTRACT FROM AUTHOR]

Background: Most human movements are supported by its functional capabilities of the anaerobic type, which is dominated by submaximal and maximal sports activities performed in a short time. Functional capabilities of the anaerobic type of man participate in most of his movement activities. Conditioned on adequate and extremely good interaction and functioning of the cardiovascular and respiratory systems, morphological status, metabolic processes, muscle structure, nutritional status, physical activity, etc. Purpose: The research was conducted in the conditions of the so-called field testing with the primary goal of assessing the acute state of anaerobic abilities of physical and sports students, by calculating the fatigue index (FI). Methods: The sample included 80 students of Physical and Sports Education, different geographical regions, average weight 77.88±8.49kg, age 20-23 years. The Running Anaerobic Sprint Test (RAST) was used to assess FI, and the basic statistical parameters were calculated. Results: The average value of the student’s leg power is 573.89 watts (378.42 watts vs. 817.99watts), and the recorded average (FI) of 8.35 watts/sec (3.97watts/sec. vs. 16.93watts/sec.). Conclusions: The obtained results confirmed the average values of anaerobic capacity which are suitable for the examined population of subjects, while the maximal the value of FI suggests a weaker state of the anaerobic capacity of the individuals (less tolerance to lactates). [ABSTRACT FROM AUTHOR]

Ti 3 C 2 T X become a promising electrode and pressure sensing material because of high conductivity. However, poor anisotropy limit its practical applications in high-performance pressure sensor. This study reports one solution for protonated arginine and serine-functionalized graphene quantum dot (RS-GQD+)-regulated synthesis of Ti 3 C 2 T X -Ru monolithic film. Dropped RS-GQD + aqueous solution to Ti 3 C 2 T X aqueous solution to initiate the self-assembly of Ti 3 C 2 T X sheets. Followed by reaction with RuCl 3 to produce Ru nanocrystals and subsequent vacuum filtration. Adjusting amounts of RS-GQD + results in formation of monolithic Ti 3 C 2 T X -Ru film with ultrahigh density of 3.52 g cm−3 and rich anisotropic microstructures. The structure achieves to excellent supercapacitor and pressure sensing behavior. The soft symmetrical supercapacitor with Ti 3 C 2 T X -Ru film electrodes without current collector, conductive agent and binder exhibits ultrahigh specific capacitance of 779.3 F g−1 at 1 A g−1 (1828.7 F cm−3) and energy density of 43.8 W h kg−1 at 225 W kg−1. The Ti 3 C 2 T X -Ru film pressure sensor offers high-resolution distinguish subtle and complex pressure changes with superhigh sensitivity and precision. Its piezoresistive current linearly increases with increasing pressure between 0.02 and 30 kPa with detection limit of 0.0067 kPa. The self-powered pressure sensor shows broad application prospect in monitoring human movement, writing and facial expression. [ABSTRACT FROM AUTHOR]

We recently developed a biomimetic robotic eye with six independent tendons, each controlled by their own rotatory motor, and with insertions on the eye ball that faithfully mimic the biomechanics of the human eye. We constructed an accurate physical computational model of this system, and learned to control its nonlinear dynamics by optimising a cost that penalised saccade inaccuracy, movement duration, and total energy expenditure of the motors. To speed up the calculations, the physical simulator was approximated by a recurrent neural network (NARX). We showed that the system can produce realistic eye movements that closely resemble human saccades in all directions: their nonlinear main-sequence dynamics (amplitude-peak eye velocity and duration relationships), cross-coupling of the horizontal and vertical movement components leading to approximately straight saccade trajectories, and the 3D kinematics that restrict 3D eye orientations to a plane (Listing's law). Interestingly, the control algorithm had organised the motors into appropriate agonist-antagonist muscle pairs, and the motor signals for the eye resembled the well-known pulse-step characteristics that have been reported for monkey motoneuronal activity. We here fully analyse the eye-movement properties produced by the computational model across the entire oculomotor range and the underlying control signals. We argue that our system may shed new light on the neural control signals and their couplings within the final neural pathways of the primate oculomotor system, and that an optimal control principle may account for a wide variety of oculomotor behaviours. The generated data are publicly available at https://data.ru.nl/collections/di/dcn/DSC%5F626870%5F0003%5F 600. [ABSTRACT FROM AUTHOR]

Tendons serve as important weight-bearing structures that smoothly transfer forces from muscles to skeletal parts, allowing contracted muscle movements to be translated into corresponding joint movements. For body mechanics, tendon tissue plays an important role. If the tendons are damaged to varying degrees, it can lead to disability or pain in patients. That is to say, tendon injuries have a significant impact on quality of life and deserve our high attention. Compared to other musculoskeletal tissues, tendons are hypovascular and hypo-cellular, and therefore have a greater ability to heal, this will lead to a longer recovery period after injury or even disability, which will significantly affect the quality of life. There are many causes of tendon injury, including trauma, genetic factors, inflammation, aging, and long-term overuse, and the study of related mechanisms is of great significance. Currently, tendon there are different treatment modalities, like injection therapy and surgical interventions. However, they have a high failure rate due to different reasons, among which the formation of adhesions severely weakens the tissue strength, affecting the functional recovery and the patient’s quality of life. A large amount of data has shown that non coding RNAs can play a huge role in this field, thus attracting widespread attention from researchers from various countries. This review summarizes the relevant research progress on non-coding RNAs in tendon injuries, providing new ideas for a deeper understanding of tendon injuries and exploring new diagnostic and therapeutic approaches. [ABSTRACT FROM AUTHOR]

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Autonomous robotic massage holds the potential to alleviate the workload of nurses and improve the quality of healthcare. However, the complexity of the task and the dynamic of the environment present significant challenges for robotic massage. This paper presents a vision-based robotic massage (VBRM) framework that facilitates autonomous robot massaging of the human body while ensuring safe operation in a dynamic environment. The VBRM framework allows the operator to define the massage trajectory by drawing a 2D curve on an RGB image. An interactive trajectory planning method is developed to calculate a 3D massage trajectory from the 2D trajectory. This method accounts for potential movements of the human body and updates the planned trajectory using rigid point cloud registration. Additionally, a hybrid motion/force controller is employed to regulate the motion of the robot's end-effector, considering the possibility of excessive contact force. The proposed framework enables the operator to adjust the massage trajectory and speed according to their requirements. Real-world experiments are conducted to evaluate the efficacy of the proposed approach. The results demonstrate that the framework enables successful planning and execution of the massage task in a dynamic environment. Furthermore, the operator has the flexibility to set the massage trajectory, speed, and contact force arbitrarily, thereby enhancing human–machine interaction. [ABSTRACT FROM AUTHOR]

Understanding the movements of crowds offers more and more opportunities in the field of architecture, in both active and passive methods of implementation. The study briefly describes crowd dynamics, the new field of research as well as the levels of investigation of human mass movements. Due to its expected large-scale spread, it particularly describes the need for and the possibilities of individual human body motion detection in retail facilities while emphasizing the architectural aspects of the rise of innovative commercial facilities. It details and compares the operational processes of modern-day queue-free "convenience" stores, Amazon Go with JWO technology, and Żabka Nano units operating according to AiFi, highlighting their architectural features. [ABSTRACT FROM AUTHOR]

Bio‐compatible strain sensors are indispensable for human monitoring devices, requiring a delicate balance of robustness, flexibility, and sensitivity. However, achieving these attributes concurrently remains a formidable challenge. This article presents a pioneering approach to fabricate three‐dimensional flexible strain sensors using laser‐induced graphene (LIG) on polyimide (PI) substrates. Through a one‐step laser direct writing (LDW) technique, durable LIG/CuSO4 composites with closed‐pore porous structures are synthesized. The integration of copper sulfate within the closed‐cell architecture of LIG establishes a resilient conducting pathway, enhancing sensitivity to deformation under tensile stress. The resulting sensor exhibits exceptional performance in monitoring a wide range of human movements, from vigorous activities to subtle oscillations and physiological signals. Notably, the sensor boasts a remarkable sensing range of up to 25% strain, coupled with a high sensitivity characterized by a gauge factor of approximately 597. Rapid response times of 175 ms and quick recovery times of 200 ms further underscore its efficiency. Moreover, the sensor demonstrates outstanding stability and durability, maintaining consistent performance over 5600 cyclic experiments. This innovative approach represents a significant advancement in bio‐compatible strain sensor technology, offering a versatile solution for diverse monitoring applications. [ABSTRACT FROM AUTHOR]

Flexible wearables have attracted extensive interests for personal human motion sensing, intelligent disease diagnosis, and multifunctional electronic skins. However, the reported flexible sensors, mostly exhibited narrow detection range, low sensitivity, limited degradability to aggravate environmental pollution from vast electronic wastes, and poor antibacterial performance to hardly improve skin discomfort and skin inflammation from bacterial growth under long‐term wearing. Herein, bioinspired from human skin featuring highly sensitive tactile sensation with spinous microstructures for amplifying sensing sensitivity between epidermis and dermis, a wearable antibacterial degradable electronics is prepared from degradable elastomeric substrate with MXene‐coated spinous microstructures templated from lotus leaf assembled with the interdigitated electrode. The degradable elastomer is facilely obtained with tunable modulus to match the modulus of human skin with improved hydrophilicity for rapid degradation. The as‐obtained sensor displays ultra‐low detection limit (0.2 Pa), higher sensitivity (up to 540.2 kPa−1), outstanding cycling stability (>23,000 cycles), a wide detection range, robust degradability, and excellent antibacterial capability. Facilitated by machine learning, the collected sensing signals from the integrated sensors on volunteer's fingers to the related American Sign Language are effectively recognized with an accuracy up to 99%, showing excellent potential in wireless human movement sensing and smart machine learning‐enabled human–machine interaction. [ABSTRACT FROM AUTHOR]

In order to investigate the mechanical properties of basalt fiber-doped tailing sand cemented filler and the evolution of energy damage, a uniaxial compression test was carried out on the basalt fiber-doped tailing sand cemented filler specimens to analyze the energy dissipation characteristics, and the damage constitutive equations with different basalt fiber contents were established based on damage mechanics. The results show that with the increase of fiber doping and fiber length, the uniaxial compressive strength and ductility of the filling body show a trend of increasing and then decreasing; the optimal value of fiber doping is 0.6%, and the optimal value of fiber length is 9 mm; the total strain energy, elastic strain energy and dissipation energy of basalt fiber-modified tailing sand cemented filling body at peak stress show a trend of increasing and then decreasing, and the energy dissipation energy of the filling body shows a trend of increasing and then decreasing. The energy dissipation energy shows a trend of increasing and then decreasing, and the energy dissipation energy shows a trend of increasing and then decreasing. The total strain energy, elastic strain energy, and dissipation energy at the peak stress show a trend of decreasing after increasing with the fiber doping and fiber length, and the energy damage evolution process can be divided into four stages: no damage stage, stable damage development stage, accelerated damage growth stage, and damage destruction; in addition, the existing damage constitutive model of the fiber-filled body was optimized, and the damage correction factor was introduced to obtain the damage constitutive model of the filled body with different fiber contents, and finally, after the verification of experimental and theoretical models, it was found that the two stress–strain curves coincided well. Finally, after the test and theoretical model verification, it is found that the stress–strain curves of the two are in good agreement, which indicates that the established theoretical model has a certain reference value for engineering practice, and at the same time, it has certain limitations. [ABSTRACT FROM AUTHOR]

Despite significant achievements in the artificial narrow intelligence sphere, the mechanisms of human-like (general) intelligence are still undeveloped. There is a theory stating that the human brain extracts the meaning of information rather than recognizes the features of a phenomenon. Extracting the meaning is finding a set of transformation rules (context) and applying them to the incoming information, producing an interpretation. Then, the interpretation is compared to something already seen and is stored in memory. Information can have different meanings in different contexts. A mathematical model of a context processor and a differential contextual space which can perform the interpretation is discussed and developed in this paper. This study examines whether the basic principles of differential contextual spaces work in practice. The model is developed with Rust programming language and trained on black and white images which are rotated and shifted both horizontally and vertically according to the saccades and torsion movements of a human eye. Then, a picture that has never been seen in the particular transformation, but has been seen in another one, is exposed to the model. The model considers the image in all known contexts and extracts the meaning. The results show that the program can successfully process black and white images which are transformed by shifts and rotations. This research prepares the grounding for further investigations of the contextual model principles with which general intelligence might operate. [ABSTRACT FROM AUTHOR]