Search Results (331)

This study aimed to create and evaluate an optimization-based sensor fusion algorithm that combines Optical Motion Capture (OMC) and Inertial Motion Capture (IMC) measurements to provide a more efficient and reliable gap-filling process for OMC measurements to be used for future research. The proposed algorithm takes the first and last frame of OMC data and fills the rest with gyroscope data from the IMC. The algorithm was validated using data from twelve participants who performed a hand cycling task with an inertial measurement unit (IMU) placed on their hand, forearm, and upper arm. The OMC tracked a cluster of reflective markers that were placed on top of each IMU. The proposed algorithm was evaluated with simulated gaps of up to five minutes. Average total root-mean-square errors (RMSE) of <1.8° across a 5 min duration were observed for all sensor placements for the cyclic upper limb motion pattern used in this study. The results demonstrated that the fusion of these two sensing modalities is feasible and shines light on the possibility of more field-based studies for human motion analysis. Full article

Nowadays, society is becoming increasingly committed to traveling by plane for work, tourism, and leisure in general. However, either due to internal, specific factors or to external determinants, like terrorism and climate changes, a growing number of travelers have experienced the so-called fear of flying, a persistent, irrational fear of flight-related situations for which a clear, efficacious therapy does not yet exist. Based on the usual interaction with the surrounding environment, conducted by means of the five human senses, and particularly on the neurophysiological pathway followed by the chemical senses, in this study, we revise the findings in the related literature on the topic, proposing an alternative way to alleviate the anxiety related to the fear of flight. This is based on chemosensory stimulation being applied directly during a flight and is possibly concerned with the consumption of meals, an usual activity performed onboard. After an introductory section aimed at understanding the problem, we present some studies related to chemosensory perception during the flight, highlighting the specificities of the scenarios, followed by a description of findings related to the meals proposed by flight companies in this context, and finally wrapping up the possible alternative approaches that could be conducted by such providers to alleviate the fear of flying condition through chemosensory stimulation vehiculated by meals, and enhance the quality of flight experience related to food consumption onboard. Full article

The eye is one of our five sense organs, where optical and neural structures are integrated. It works in synchrony with the brain, enabling the formation of meaningful images. However, lack of function, complete absence or structural abnormalities of cone cells in the cone cells in the retina causes the emergence of types of Color Vision Deficiency (CVD). This deficiency is characterized by the lack of clear vision in the use of colors in the same region of the spectrum, and greatly affects the quality of life of the patient. Therefore, it is important to develop filters that enable colors to be combined successfully. In this study, an original filter design was improved, built on a five-stage systematic structure that complements and supports itself. But optimization regarding performance value needs to be tested with objective methods independent of human decision. Therefore, in order to provide performance analyses based on objective evaluation criteria, original and enhanced images simulated by patients with seven different Color Vision Deficiency (CVD) types were classified with the MobileNet transfer learning model. The classification results show that the developed final filter greatly improves the differences in color perception levels in both eyes. Thus, color stimulation between the two eyes is more balanced, and perceptual symmetry is created. With perceptual symmetry, environmental colors are perceived more consistently and distinguishably, and the visual difficulties encountered by color blind individuals in daily life are reduced. Full article

A brain–computer interface (BCI) provides a direct communication pathway between the human brain and external devices, enabling users to control them through thought. It records brain signals and classifies them into specific commands for external devices. Among various classifiers used in BCI, those directly classifying covariance matrices using Riemannian geometry find broad applications not only in BCI, but also in diverse fields such as computer vision, natural language processing, domain adaption, and remote sensing. However, the existing Riemannian-based methods exhibit limitations, including time-intensive computations, susceptibility to disturbances, and convergence challenges in scenarios involving high-dimensional matrices. In this paper, we tackle these issues by introducing the Bures–Wasserstein (BW) distance for covariance matrices analysis and demonstrating its advantages in BCI applications. Both theoretical and computational aspects of BW distance are investigated, along with algorithms for Fréchet Mean (or barycenter) estimation using BW distance. Extensive simulations are conducted to evaluate the effectiveness, efficiency, and robustness of the BW distance and barycenter. Additionally, by integrating BW barycenter into the Minimum Distance to Riemannian Mean classifier, we showcase its superior classification performance through evaluations on five real datasets. Full article

Human topoisomerase III beta (hTOP3B) is a unique and important enzyme in human cells that plays a role in maintaining genome stability, affecting cellular aging, and potentially impacting viral replication. Its dual activity on both DNA and RNA makes it a valuable target for therapeutic interventions. hTOP3B has been shown to be required for the efficient replication of certain positive-sense ssRNA viruses including Dengue. We performed in silico screening of a library comprising drugs that are FDA-approved or undergoing clinical trials as potential drugs to identify potential inhibitors of hTOP3B. The topoisomerase activity assay of the identified virtual hits showed that bemcentinib, a compound known to target the AXL receptor tyrosine kinase, can inhibit hTOP3B relaxation activity. This is the first small molecule shown to inhibit the complete catalytic cycle of hTOP3B for the potential interference of the function of hTOP3B in antiviral application. Additional small molecules that share the N⁵,N³-1H-1,2,4-triazole-3,5-diamine moiety of bemcentinib were synthesized and tested for the inhibition of hTOP3B relaxation activity. Five compounds with comparable IC₅₀ to that of bemcentinib for the inhibition of hTOP3B were identified. These results suggest that the exploration of tyrosine kinase inhibitors and their analogs may allow the identification of novel potential topoisomerase inhibitors. Full article

The urban heat island (UHI) effect and its associated extreme weather events have adverse impacts on human environment-coupled systems. However, the spatiotemporal variations in the UHI effect, as well as potential influencing factors, across climate zones remain poorly understood. This study explored how climate zones influenced the spatiotemporal variation in, trends in, and drivers of summer daytime surface UHI intensity (SUHII) in 220 Chinese cities located in five climate zones from 2000 to 2020. SUHII was quantified using MODIS land surface temperature (LST) data and remote sensing-derived urban built-up area masks were used to quantify SUHII. The Mann–Kendall test was applied to detect long-term SUHII trends, while Pearson correlation and stepwise multiple regression analyses were performed to identify key climatic and geographic drivers across different climate zones. The results indicated summer daytime SUHII values of 1.75 °C ± 1.19 °C, 1.74 °C ± 0.81 °C, 2.37 °C ± 0.75 °C, 2.14 °C ± 1.00 °C, and 2.36 °C ± 0.91 °C for the middle temperate zone (MTZ), south temperate zone (STZ), north subtropical zone (NSZ), middle subtropical zone (MSZ), and south subtropical zone (SSZ), respectively. In most cities, the SUHII increased significantly over time (p < 0.05). Pearson’s correlation analysis indicated that the enhanced vegetation index (EVI) and net radiation (NR) were moderately correlated with the SUHII in the MTZ, with correlation coefficients (r) of 0.465 and 0.42 (p < 0.05). Using a multivariate stepwise regression model, the relative contributions of various influencing factors to the UHI effect were quantified, explaining 27.1% to 57.2% of the variation across different climate zones. In particular, the economic vulnerability index and population density were the main factors affecting the SUHII in the MTZ and SSZ. Our findings support the development of policies aimed at mitigating the UHI effect by addressing the specific requirements of different climate zones to reduce. Full article

The underlying design of large language models (LLMs), trained on vast amounts of human texts, implies that chatbots based on them will almost inevitably retain some human personality traits. That is, we expect that LLM outputs will tend to reflect human-like features. In this study, we used the ‘Big Five’ personality traits tool to examine whether several chatbot models (ChatGPT versions 3.5 and 4o, Gemini, and Gemini Advanced, all tested in both English and Polish), displayed distinctive personality profiles. Each chatbot was presented with an instruction to complete the International Personality Item Pool (IPIP) questionnaire “according to who or what you are,” which left it open as to whether the answer would derive from a purported human or from an AI source. We found that chatbots sometimes chose to respond in a typically human-like way, while in other cases the answers appeared to reflect the perspective of an AI language model. The distinction was examined more closely through a set of follow-up questions. The more advanced models (ChatGPT-4o and Gemini Advanced) showed larger differences between these two modes compared to the more basic models. In IPIP-5 terms, the chatbots tended to display higher ‘Emotional Stability’ and ‘Intellect/Imagination’ but lower ‘Agreeableness’ compared to published human norms. The spread of characteristics indicates that the personality profiles of chatbots are not static but are shaped by the model architecture and its programming as well as, perhaps, the chatbot’s own inner sense, that is, the way it models its own identity. Appreciating these philosophical subtleties is important for enhancing human–computer interactions and perhaps building more relatable, trustworthy AI systems. Full article

(1) Background: Detecting long-lie incidents—where individuals remain immobile after a fall—is essential for timely intervention and preventing severe health consequences. However, most existing systems focus only on fall detection, neglect post-fall monitoring, and raise privacy concerns, especially in real-time, non-invasive applications; (2) Methods: This study proposes a lightweight, privacy-preserving, long-lie detection system utilizing thermal imaging and a soft-voting ensemble classifier. A low-resolution thermal camera captured simulated falls and activities of daily living (ADL) performed by ten healthy participants. Human pose keypoints were extracted using MediaPipe, followed by the computation of five handcrafted postural features. The top three classifiers—automatically selected based on cross-validation performance—formed the soft-voting ensemble. Long-lie conditions were identified through post-fall immobility monitoring over a defined period, using rule-based logic on posture stability and duration; (3) Results: The ensemble model achieved high classification performance with accuracy, precision, recall, and an F1 score of 0.98. Real-time deployment on a Raspberry Pi 5 demonstrated the system is capable of accurately detecting long-lie incidents based on continuous monitoring over 15 min, with minimal posture variation; (4) Conclusion: The proposed system introduces a novel approach to long-lie detection by integrating privacy-aware sensing, interpretable posture-based features, and efficient edge computing. It demonstrates strong potential for deployment in homecare settings. Future work includes validation with older adults and integration of vital sign monitoring for comprehensive assessment. Full article

In the face of increasingly complex urban challenges, a critical question arises: can urban ecosystems maintain resilience, vitality, and sustainability when confronted with external threats and pressures? Taking Kunming—a plateau-mountainous city in China—as a case study, this research constructs an urban ecosystem resilience (UER) assessment model based on the DPSIR (Driving forces, Pressures, States, Impacts, and Responses) framework. A total of 25 indicators were selected via questionnaire surveys, covering five dimensions: driving forces such as natural population growth, annual GDP growth, urbanization level, urban population density, and resident consumption price growth; pressures including per capita farmland, per capita urban construction land, land reclamation and cultivation rate, proportion of natural disaster-stricken areas, and unit GDP energy consumption; states measured by Evenness Index (EI), Shannon Diversity Index (SHDI), Aggregation Index (AI), Interspersion and Juxtaposition Index (IJI), Landscape Shape Index (LSI), and Normalized Vegetation Index (NDVI); impacts involving per capita GDP, economic density, per capita disposable income growth, per capita green space area, and per capita water resources; and responses including proportion of natural reserve areas, proportion of environmental protection investment to GDP, overall utilization of industrial solid waste, and afforestation area. Based on remote sensing and other data, indicator values were calculated for 2006, 2011, and 2016. The entire-array polygon indicator method was used to visualize indicator interactions and derive composite resilience index values, all of which remained below 0.25—indicating a persistent low-resilience state, marked by sustained economic growth, frequent natural disasters, and declining ecological self-recovery capacity. Forecasting results suggest that, under current development trajectories, Kunming’s UER will remain low over the next decade. This study is the first to integrate the DPSIR framework, entire-array polygon indicator method, and Grey System Forecasting Model into the evaluation and prediction of urban ecosystem resilience in plateau-mountainous cities. The findings highlight the ecosystem’s inherent capacities for self-organization, adaptation, learning, and innovation and reveal its nested, multi-scalar resilience structure. The DPSIR-based framework not only reflects the complex human–nature interactions in urban systems but also identifies key drivers and enables the prediction of future resilience patterns—providing valuable insights for sustainable urban development. Full article

Fine particulate matter (PM_2.5) pollution poses a major threat to human physical and mental health. Smart cities (SCs) provide innovative paths for PM_2.5 pollution prevention and control through Internet of Things (IoT) monitoring, intelligent transportation optimization, and other technological means. Based on the panel data of 2,141 counties in China between 2006 and 2021, this paper constructs a difference-in-differences with multiple time periods (MDID) to systematically assess the impact of SC on PM_2.5 concentration and analyze its mechanism of action by combining the satellite remote sensing PM_2.5 concentration (PM_2.5C) and the list of smart city pilots. This study finds the following: (1) SC significantly reduced the PM_2.5 concentration in the test area by about 3.58%. This conclusion was verified through rigorous robustness testing; (2) SC can effectively reduce PM_2.5C through the innovation effect; (3) High-quality economic development can strengthen the emission reduction effect of SC on PM_2.5C; (4) The environmental benefits of SC show significant spatial heterogeneity, with the largest PM_2.5 reductions occurring in the western regions (4.3% reduction), followed by regions with mature digital infrastructure and cities in high administrative level cities. The results of this study provide a reference for the regional differentiated implementation of the “14th Five-Year Plan for the Development of Innovative Smarter Cities”, and make targeted recommendations for the synergistic management of air quality under the “dual-carbon” goal. Full article

This study explores the intersection of landscape design and ecosystem services, emphasising context-sensitive design and the integration of indigenous and local knowledge (ILK) in forest health bases. Current challenges include disconnects between design practices and local cultural identity, as well as insufficient ecological integration, necessitating systematic approaches that harmonise ecological functions with sociocultural values. While existing research prioritises health benefit assessments, the role of ILK in long-term sustainability remains underexplored. To address this gap, we developed a multidimensional evaluation system integrating ecological, cultural, community, and human health indicators. Using a hybrid Delphi–Analytic Hierarchy Process (AHP), we identified 33 core indicators through literature word-frequency analysis. These indicators were refined via two rounds of expert surveys involving 48 interdisciplinary scholars and empirically validated at the Yuping Mountain Forest Health Base in Sichuan, China. The case study achieved an overall score of 4.371 (Grade I), with “Site location” (weight 0.064) and “Maintenance of the human landscape” (weight 0.056) as pivotal factors. ILK integration enhanced ecological resilience and community cultural engagement. Quantitative data revealed strong performance in five senses of experience (weight 0.056), though cultural resource utilisation requires refinement. The innovation of this study is that it is the first to construct an ILK-driven assessment framework to achieve the deep integration of scientific quantification and local wisdom. The study provides a decision-making tool that is both humanistic and scientific, in order to promote the synergistic development of human health, ecological protection, and cultural heritage and to help sustainable landscape design practice. Full article

Soft anthropomorphic robotic grippers are attractive because of their inherent compliance, allowing them to adapt to the shape of grasped objects and the overload protection needed for safe human–robot interaction or gripping delicate objects with sophisticated control. The anthropomorphic design allows the gripper to benefit from the biological evolution of the human hand to create a multi-functional robotic end effector. Entirely soft grippers could be more efficient because they yield under high loads. A trending solution is a hybrid gripper combining soft and rigid elements. This work describes a prototype of an anthropomorphic, underactuated five-finger gripper with a direct pneumatic drive from soft bending actuators and an integrated resistive tactile sensor array. It is a hybrid construction with soft robotic structures and rigid skeletal elements, which reinforce the body, focus the direction of the actuator’s movement, and make the finger joints follow the forward kinematics. The hand is equipped with a resistive tactile dielectric elastomer sensor array that directly triggers the hand’s actuation in the sense of reflexes. The hand can execute precision grips with two and three fingers, as well as lateral grip and strong grip types. The softness of the actuation allows the finger to adapt to the shape of the objects. Full article

Quantitative assessment of the ability of the ecosystem service (ES) and its driving forces is of great significance for achieving regional SDGs. In view of the scarcity of existing research that evaluates the sustainability of multiple ES types over a long time series at the township scale in a typical Watertown Region, this study aims to address two key scientific questions: (1) what are the spatiotemporal changes in the ecosystem service supply–demand index (ESSDI) and ecosystem service sustainability index (ESSI) of a typical Watertown Region? and (2) what are the key factors driving the changes in ESSI? To answer the above two questions, this study takes the Yangtze River Delta Integrated Demonstration Zone (YRDIDZ) as the study area, utilizing multi-source remote sensing and other spatiotemporal geographical datasets to calculate the supply–demand levels and sustainable development ability of different ES in the YRDIDZ from 2000 to 2020. The main findings were as follows: (1) From 2000 to 2020, the mean ESSDI values for habitat quality, carbon storage, crop production, water yield, and soil retention all showed a declining trend. (2) During the same period, the mean ESSI exhibited a fluctuating downward trend, decreasing from 0.31 in 2000 to 0.17 in 2020, with low-value areas expanding as built-up areas grew, while high-value areas were mainly distributed around Dianshan Lake, Yuandang, and parts of ecological land. (3) The primary driving factors within the YRDIDZ were human activity factors, including POP and GDP, with their five-period average explanatory powers being 0.44 and 0.26, whereas the explanatory power of natural factors was lower. However, the interaction of POP and soil showed higher explanatory power. The results of this study could provide actionable ways for regional sustainable governance: (1) prioritizing wetland protection and soil retention in high-population-density areas based on targeted land use quotas; (2) integrating ESSI coldspots (built-up expansion zones) into ecological redline adjustments, maintaining high green infrastructure coverage in new urban areas; and (3) establishing a population–soil co-management framework in agricultural–urban transition zones. Full article

The Pandameru River Basin, South India, is affected by high levels of contamination from human activities and the over-exploitation of groundwater for agriculture, both of which pose significant threats to water quality and its availability for drinking and irrigation. To explore sustainable groundwater management, this study presents a machine learning-driven approach to basin-scale groundwater potential zone (GWPZ) mapping by integrating remote sensing (RS), a geographic information system (GIS), and the random forest (RF) algorithm. The research leverages ten thematic layers—including lithology, geomorphology, soil type, lineament density, slope, drainage density, land use/land cover (LULC), NDVI, SAVI, and rainfall—to assess groundwater availability. The RF model, trained with well-distributed groundwater data, provides an optimized classification of GWPZs into five categories: very good (5.84%), good (15.21%), moderate (27.25%), poor (27.22%), and very poor (24.47%). The results indicate that excellent groundwater zones are predominantly located along highly permeable alluvial deposits, whereas low-potential zones coincide with impermeable geological formations and steep terrains. Field validation using piezometric readings and well data confirmed significant variations in water table depths, ranging from 5 m to over 150 m. The groundwater potential map achieved an accuracy of 86%, underscoring the effectiveness of the RF model in predicting groundwater availability. This high-precision mapping technique enhances decision-making for sustainable groundwater management, supporting long-term water conservation, equitable resource allocation, and climate-resilient water strategies. By providing reliable insights into groundwater distribution, this study contributes to the sustainable utilization of groundwater resources in semiarid regions, aiding policymakers and planners in mitigating water scarcity challenges and ensuring water security for future generations. Full article

In the face of rapid infrastructure expansion and escalating anthropogenic activities, it becomes imperative to prioritize the examination of long-term transformations in land cover and ecological quality within the Greater Mekong Subregion (GMS). We developed an ecological evaluation system integrating the land cover data assimilation framework (LCDAF) with the InVEST model to accomplish this goal. The LCDAF compensates for the disadvantages of weather interference, difficulty in recognizing complex scenes, and poor generalization in remote sensing image classification, and also adds temporal continuity that other fusion methods do not have. The synthesized land cover dataset demonstrates superior overall accuracy compared to five existing global products. This enhanced dataset provides a robust foundation for comprehensive analysis and decision making within the ecological evaluation system. We implemented a rigorous and quantitative assessment of changes in land cover and habitat quality spanning 1980 to 2020. The land cover analysis unveiled a noteworthy trend that surfaced in the dynamic interplay between forested areas and croplands, highlighting simultaneous processes of forest restoration and agricultural expansion, albeit at varying rates. Further analysis of habitat quality showed that the GMS generally sustained a moderate level with a slight downward trend observed over the period. Significantly, Laos attained the highest ranking in habitat quality, succeeded by Myanmar, China, Cambodia, Vietnam, and Thailand. In human factors, land use intensity and landscape fragmentation emerge as contributors with detrimental effects on habitat quality. Substantial progress was achieved in implementing forestland conservation measures, exemplified in regions such as Cambodia and Guangxi Province of China, where these endeavors proved effective in mitigating habitat degradation. Despite these positive endeavors, the GMS’s overall habitat quality did not significantly improve. It emphasizes the enduring challenges confronted by the region in terms of ecological management and habitat conservation. Full article