Human Activities Research Articles

A Projected OMP-Hybridized Discriminative K-SVD-Based Dictionary Learning Algorithm for Human Activity Recognition From Accelerometer Signals

A Projected OMP-Hybridized Discriminative K-SVD-Based Dictionary Learning Algorithm for Human Activity Recognition From Accelerometer Signals

A systematic review of nurse objects as safe sites for seedling establishment and implications for restoration.

Direct human activity and global climatic changes are threatening the existence of many vegetated habitats. Seedling establishment, one of the riskiest plant life stages, must be successful for such habitats to persist. The establishment of seedlings is known to be enhanced by nurse effects, but most studies to date have looked at the nursing effects of plants while sidelining inanimate objects. Nevertheless, nurse objects can support seedling establishment via diverse mechanisms such as moderating abiotic stresses like extreme temperatures and drought, reducing negative biological interactions such as herbivory while enhancing positive processes like seed dispersal, and providing protection from physical disturbances such as trampling and fire. The robust nature of nurse objects highlights their potential in habitat restoration. The addition of nurse objects allows a simple, single-effort rehabilitation strategy that can later draw on natural seed dispersal and establishment. By achieving a better understanding of the processes in which nurse objects are involved we should be able to better predict vegetation dynamics and manipulate them to minimize adverse processes and support regeneration in natural habitats.

Heavy metal concentrations in soil and ecological risk assessment in the vicinity of Tianzhu Industrial Park, Qinghai-Tibet Plateau

Industrial parks in China are centers of intensive chemical manufacturing and other industrial activities, often concentrated in relatively small areas. This concentration increases the risk of soil pollution both within the parks and in surrounding areas. The soils of the Tibetan Plateau, known for their high sensitivity to environmental changes, are particularly vulnerable to human activity. In this study, we examined the concentrations (mg/kg) of 10 metal elements (As, Cd, Cr, Cu, Hg, Mn, Ni, Pb, Se, and Zn) in soils at depths of 0–10 cm, 10–20 cm, and 20–30 cm from the surface at three distances (500 m, 1000 m, and 1500 m from the park boundary) on the east, south, west, and north sides of the Tianzhu Industrial Park on the Qinghai-Tibet Plateau. The concentrations of As, Cr, Mn, and Pb were close to the standard reference values for the Qinghai-Tibet Plateau, while Cu, Ni, Se, and Zn levels were found to be 1.6-2.2 times higher than the reference values. Cd and Hg concentrations were particularly concerning, at 8.0 and 6.5 times higher than reference values, respectively. The potential ecological risk indexes indicated persistent risk levels for Cd and Se across various directions and distances. Variations in soil depth and direction were observed for the concentrations of As, Cd, Hg, Pb, Se, and Zn, underscoring the need for regular or long-term monitoring. Cd, in particular, presents a significant hazard due to its high concentration and its propensity for uptake by plants in the study area.

CDFL: Efficient Federated Human Activity Recognition Using Contrastive Learning and Deep Clustering

CDFL: Efficient Federated Human Activity Recognition Using Contrastive Learning and Deep Clustering

Elucidating the Molecular Mechanisms and Comprehensive Effects of Sludge-Derived Plant Biostimulants on Crop Growth: Insights from Metabolomic Analysis.

The utilization of urban waste for land management plays a crucial role in reshaping material flows between human activities and the environment. Sewage sludge alkaline thermal hydrolysis (ATH) produces sludge-derived plant biostimulants (SPB), which have garnered attention due to the presence of indole-3-acetic acid. However, there remains a gap in understanding SPB's molecular-level effects and its comprehensive impact on crops throughout their growth cycle. In this study, non-targeted and targeted metabolomic approaches are employed to analyze 51 plant hormones and 1,177 metabolites, revealing novel insights. The findings demonstrate that low concentrations of SPB exerted multiple beneficial effects on rice roots, leaves, and the root-soil system, facilitating rapid cell division and enhancing antioxidant defense mechanisms. These results provide a vital foundation for understanding ATH metabolic pathways and advocating for widespread SPB application, offering significant implications for sustainable land management.

High-resolution multimodal profiling of human epileptic brain activity via explanted depth electrodes.

The availability and integration of electrophysiological and molecular data from the living brain is critical to understand and diagnose complex human disease. Intracranial stereo electroencephalography (SEEG) electrodes used for identifying the seizure focus on epilepsy patients could enable the integration of such multimodal data. Here, we report MoPEDE (Multimodal Profiling of Epileptic Brain Activity via Explanted Depth Electrodes), a method that recovers extensive protein-coding transcripts, including cell-type markers, DNA methylation and short variant profiles from explanted SEEG electrodes matched with electrophysiological and radiological data allowing for high-resolution reconstructions of brain structure and function. We find gene expression gradients that correspond with the neurophysiology-assigned epileptogenicity index but also outlier molecular fingerprints in some electrodes, potentially indicating seizure generation or propagation zones not detected during electroclinical assessments. Additionally, we identify DNA methylation profiles indicative of transcriptionally permissive or restrictive chromatin states and SEEG-adherent differentially expressed and methylated genes not previously associated with epilepsy. Together, these findings validate that RNA profiles and genome-wide epigenetic data from explanted SEEG electrodes offer high-resolution surrogate molecular landscapes of brain activity. The MoPEDE approach has the potential to enhance diagnostic decisions and deepen our understanding of epileptogenic network processes in the human brain.

Self-Healing, Electrically Conductive, Antibacterial, and Adhesive Eutectogel Containing Polymerizable Deep Eutectic Solvent for Human Motion Sensing and Wound Healing.

Flexible electronic devices such as wearable sensors are essential to advance human-machine interactions. Conductive eutectogels are promising for wearable sensors, despite their challenges in self-healing and adhesion properties. This study introduces a multifunctional eutectogel based on a novel polymerizable deep eutectic solvent (PDES) prepared by the incorporation of diallyldimethylammonium chloride (DADMAC) and glycerol in the presence of polycyclodextrin (PCD)/dopamine-grafted gelatin (Gel-DOP)/oxidized sodium alginate (OSA). The synthesized eutectogel has reversible Schiff-base bonds, hydrogen bonds, and host-guest interactions, which enable rapid self-healing upon network disruption. GPDO-15 eutectogel has significant tissue adhesion, high stretchability (419%), good ionic conductivity (0.79 mS·cm-1), and favorable antibacterial and self-healing properties. These eutectogels achieve 90% antibacterial effect, show excellent biocompatibility, and can be used as sensors to monitor human activities with strong stability and durability. The in vivo studies indicate that the eutectogels can improve the wound healing process which makes them an effective option for biological dressings.

Effect of climate change on the habitat suitability of the relict species Zelkova carpinifolia Spach using ensembled species distribution modelling.

Zelkova carpinifolia is a Tertiary relict tree distributed in Hyrcanian and Colchic forests. Most of its habitat has been destroyed in the last century. This study aimed to model potentially suitable habitat areas for Zelkova carpinifolia from the past to the future. The Last Glacial Maximum (LGM) and Future (2061-2080) models include 19 bioclimatic variables from the CCSM4 global circulation model Pearson correlation coefficient was used to assess collinearity between variables and ten variables were selected for distribution modelling. Habitat suitability was estimated using the Biodiversity Modelling (BIOMOD) ensemble modelling method by combining the results of ten algorithm models using the R package "biomod2". The area under the curve (AUC) of the receiver operating characteristic (ROC) curve and true skills statistics (TSS) were calculated to evaluate the performance of the models. The contributions of the environmental variables were calculated separately for each algorithm model. According to the results obtained, the most effective bioclimatic variable in the distribution of the species is temperature seasonality (Bio4). The modelling results revealed that Zelkova carpinifolia survived in suitable refuge areas in western Asia during the LGM. These distribution areas have remained largely unchanged and even expanded. The future model results predict that the suitable habitats of the species will narrow in the Hyrcanian forests south of Caspian Sea and that more suitable conditions will be found around the Caucasus. Given the increasing destruction of these valuable plant species due to human activities and the expected negative impacts of climate change in the future, it is important to develop policies and strategies for the protection of Zelkova carpinifolia's habitat, the creation of nature reserves, and sustainability.

Short-Latency Trigeminocervical Reflex Obtained Without Muscle Activation: Topographic Distribution and Methodological Approach

Purpose: Electrical stimulation of trigeminal nerve branches elicits early and late reflex responses in the cervical muscles, known as the trigeminocervical reflex (TCR). This study aimed to evaluate the neurophysiological aspects, stimulation patterns, and topographic distribution of short-latency TCR components in humans in the absence of voluntary muscle activation. Methods: This prospective observational study included 30 participants. Trigeminocervical reflex responses were simultaneously recorded from the bilateral sternocleidomastoid, trapezius, and splenius capitis muscles (without voluntary muscle activation), after electrical stimulation of the supraorbital and infraorbital nerves. Two different stimulation protocols were evaluated: a 3 Hz stimulation protocol (using averaging) and a single-pulse stimulation protocol. Results: Using a 3 Hz stimulation protocol, short-latency TCR responses were recorded in the sternocleidomastoid, trapezius, and splenius capitis muscles, ipsilateral and/or contralateral, but with variable recordability depending on the recording site. The most reliable response was obtained in ipsilateral sternocleidomastoid muscle. To the best of our knowledge, this is the first study to demonstrate the elicitation of short-latency TCR components in the sternocleidomastoid muscle in the absence of voluntary muscle activation in humans without craniocervical junction disorders or lower brainstem abnormalities. Conclusions: The choice of an appropriate stimulation protocol is particularly relevant for recording short-latency TCR components, considering that the visualization of early or late responses seems to be facilitated by different stimulation methodologies.

Characterization of the Elemental Composition of Aerosols Emitted in the Dry Season of the Pantanal Wetland, Brazil

The Brazilian Pantanal region experiences intense biomass burning during the dry season, releasing large quantities of gasses and particles into the atmosphere, which have serious implications on both the climate system and public health. Understanding the dynamics of these emissions is crucial for mitigating the impact on the ecosystem, its functioning, and potential anthropogenic disturbances. This study focused on analyzing emissions in the northern Pantanal during the 2022 drought. Concentrations of fine particulate matter (PM2.5), black carbon (BC), and 25 chemical elements were measured using gravimetry, reflectance analysis, and X-Ray fluorescence, respectively, from samples collected between August and October 2022. The average concentrations of PM2.5 and BC increased approximately 4-fold and 2.5-fold, respectively, compared to averages from a decade ago. Significant increases were also observed in elements such as sulfur (S), potassium (K), iron (Fe), and silicon (Si). The maximum concentrations were comparable to values typical of the southern Amazon, a region known for high deforestation rates and land use changes. Elemental analysis revealed substantial shifts in concentrations, primarily associated with biomass burning (BB) and soil suspension. Additionally, enrichment factor (Ef) analysis showed that lead (Pb) levels, correlated with human activities, were 200 times higher than those found under clean atmospheric conditions.

Tackling soil erosion and contamination within the SDGs framework: a case study of the Erdenet copper-molybdenum mine

AbstractMining operations in Mongolia have contributed significantly to national economic growth. However, the lack of adequate mine land rehabilitation practices has resulted in various forms of land degradation, including land cover change, soil erosion and contamination with heavy metals and metalloids. These issues have negatively affected ecosystems and posed potential risks to human health due to the interconnected relationships between land, water and human activities. This study focuses on the copper-molybdenum mining site of Erdenet, the oldest and one of the largest copper mines in Mongolia, to investigate the main drivers of soil degradation and evaluate effective rehabilitation strategies. By combining findings from our previous studies on soil erosion and contamination in the Erdenet area with a review of existing research, we assess key rehabilitation priorities. Taking a Nexus approach, this study explores the interconnections between environmental, economic and social sectors, emphasizing the importance of balancing mining activities with sustainable land management. The previous studies suggest that for the next 10–15 years, priorities should focus on tailings storage facility rehabilitation and soil contamination remediation, while longer-term goals include developing sustainable environmental management strategies that foster cooperation between mining corporations and local communities, enforce regulations and improve monitoring in the Erdenet mining area. The study aligns these priorities with the Sustainable Development Goals, offering science-based recommendations for managing soil erosion and reducing contamination.

Spatio-temporal evolution and prediction of carbon storage at the source of the Fen River and Sanggan River based on a PLUS-InVEST model

Investigating the evolution of land use and its impact on carbon storage is of significant importance for mitigating regional climate change and promoting green low-carbon development. Ningwu County is located in the source region of the Fenhe and Sanggan River, and its ecological status significantly influences the carbon storage (CS) of the watershed ecosystem. In this study, the PLUS-InVEST model was employed to analyze the land use evolution from 1990 to 2020 in Ningwu County, Shanxi Province, as well as their impacts on CS. Additionally, the study simulated and predicted land use changes in Ningwu County by 2040 under four scenarios: natural development (NDS), ecological protection (EPS), cultivated land protection (CLPS), and urban development (UDS), while estimating the corresponding changes in ecosystem CS. Furthermore, the study utilized optimal parameters-based geographical detector to explore the mechanisms underlying the spatial differentiation of CS. The results indicated that the areas of forest land and construction land in the study area consistently increased from 1990 to 2020, whereas the area of cultivated land continuously declined, with grassland, water bodies, and unused land exhibiting a fluctuating increasing trend. The spatial distribution of CS was highest in the northwest, second highest in the southeast, and lowest in the middle region. Over these 3 decades, CS had shown a continuous increase. It is projected that by 2040, the areas of forest and grassland will experience the most significant increase under the EPS; cultivated land only increase under the CLPS; while construction land display the greatest increase under the UDS. Compared to 2020, these four scenarios for 2040 indicate an increase in regional CS, with the EPS showing the largest increment. The primary factors influencing the spatial differentiation of CS in Ningwu County are human activities, followed by topography and climate change; the interactions among these factors exhibit a reinforcing relationship, with the interaction between the distance from construction land and slope having the most substantial impact on the spatial differentiation of CS.

Neural dynamics of shifting attention between perception and working-memory contents

In everyday tasks, our focus of attention shifts seamlessly between contents in the sensory environment and internal memory representations. Yet, research has mainly considered external and internal attention in isolation. We used magnetoencephalography to compare the neural dynamics of shifting attention to visual contents within vs. between the external and internal domains. Participants performed a combined perception and working-memory task in which two sequential cues guided attention to upcoming (external) or memorized (internal) sensory information. Critically, the second cue could redirect attention to visual content within the same or alternative domain as the first cue. Multivariate decoding unveiled distinct patterns of human brain activity when shifting attention within vs. between domains. Brain activity distinguishing within- from between-domain shifts was broadly distributed and highly dynamic. Intriguingly, crossing domains did not invoke an additional stage prior to shifting attention. Alpha lateralization, a canonical marker of shifting spatial attention, showed no delay when cues redirected attention to the same vs. alternative domain. Instead, evidence suggested that neural states associated with a given domain linger and influence subsequent shifts of attention within vs. between domains. Our findings provide critical insights into the neural dynamics that govern attentional shifts between perception and working memory.

Africanized honeybee venom (Apis mellifera) promotes human complement activation split products storm

IntroductionComplement activation split products are signatures of many immunopathological disorders. Among the laboratory findings observed in these diseases, a reduction in the level of circulating intact complement components can be mentioned, and this change has also been detected in envenomation by multiple Africanized honeybee (Apis mellifera) stings. Although envenomation by these animals elicits diverse life-threatening reactions, the capacity of bee venom (AmV) to activate the human complement system remains elusive.Methods and findingsBy coupling immunochemical and functional approaches, it was observed that AmV strongly consumes components of the alternative pathway (AP) of the complement system in normal human serum (NHS). Additionally, AmV interfered with classical (CP) and lectin pathways (LP) activities. In parallel, a high increase in Ba fragment levels was detected, suggesting that the changes in AP activity were due to its activation. Furthermore, an increase in the level of the C1s-C1INH complex and a decrease in the physiological level of MASP1-C1INH suggested that CP and LP were also activated in the presence of AmV. Strikingly, NHS exposed to increasing AmV concentrations varying from 5 to 1000 µg/mL presented a high generation of C3a, C4a and C5a anaphylatoxins, and sC5b-9 complexes assembly, thus reinforcing that AmV triggers complement activation.ConclusionThese results show that AmV is a strong complement activator. This activation presents a mixed profile, with a predominance of AP activation. This suggests that complement split products can play important roles in the envenomation by Africanized honeybee, as they could induce diverse immunopathological events observed in patients and may also dictate patient clinical prognosis.

Human and predator presence shape diel activity of urban red squirrels

In urban areas, wildlife has to adapt to human presence and novel predators such as pet species, including the altered conditions of the environment. In such novel settings, the timing of activity is crucial to minimize the risk of mortality. To do so, species may reduce total activity time by increasing activity peaks at specific moments or shifting activity times. We analyzed camera trap data from a citizen science project over four project phases, including spring and autumn before and during the SARS-CoV-2 lockdown, to understand the effects of human, pet (cat, dog), and predator (marten) presence on the activity patterns of urban red squirrels (Sciurus vulgaris; hereafter ‘squirrel’). We examined squirrel activity at seasonal and hourly resolutions in relation to human, garden, urban, and predator factors. We considered human presence as both a direct effect of lockdown and an indirect disturbance measured through urban variables. Results show that direct human presence during lockdown increased squirrel activity intensity in both seasonal and hourly patterns without reducing total activity time. Predator presence affected timing of activity, decreasing total daily activity. Pets, like cats, decreased activity at both resolutions, while martens had a limited effect detected only at the hourly resolution. During lockdown, squirrels may have increased their activity in gardens due to more anthropogenic resources (food or nesting material), but constant threats from pets force them to avoid certain areas despite the benefits. This highlights the delicate balance squirrels must maintain in adapting to human-altered environments while managing predation risks.

Task-agnostic exoskeleton control via biological joint moment estimation.

Lower-limb exoskeletons have the potential to transform the way we move1-14, but current state-of-the-art controllers cannot accommodate the rich set of possible human behaviours that range from cyclic and predictable to transitory and unstructured. We introduce a task-agnostic controller that assists the user on the basis of instantaneous estimates of lower-limb biological joint moments from a deep neural network. By estimating both hip and knee moments in-the-loop, our approach provided multi-joint, coordinated assistance through our autonomous, clothing-integrated exoskeleton. When deployed during 28 activities, spanning cyclic locomotion to unstructured tasks (for example, passive meandering and high-speed lateral cutting), the network accurately estimated hip and knee moments with an average R2 of 0.83 relative to ground truth. Further, our approach significantly outperformed a best-case task classifier-basedmethod constructed from splines and impedance parameters. When tested on ten activities (including level walking, running, lifting a 25 lb (roughly 11 kg) weight and lunging), our controller significantly reduced user energetics (metabolic cost or lower-limb biological joint work depending on the task) relative to the zero torque condition, ranging from 5.3 to 19.7%, without any manual controller modifications among activities. Thus, this task-agnostic controller can enable exoskeletons to aid users across a broad spectrum of human activities, a necessity for real-world viability.

Analysing macroscopic traffic rhythms and city size in affluent cities: insights from a global panel data of 25 cities.

The world is undergoing a rapid process of urbanization. Currently, it is estimated that over 55% of the global population resides in urban areas, a figure projected to reach nearly 70% by 2050. This trend is accompanied by a spatial reorganization of human activities on a global scale, bringing about significant changes in mobility patterns and urban traffic management capabilities. Consequently, it is imperative to evaluate, on a broad scale, how city size influences traffic capacity. This study aims to analyse on-road traffic patterns using a diverse dataset comprising cities of varying population sizes, geographical extents and global locations. Specifically, we conduct an analysis encompassing 25 cities primarily situated in several European countries (France, Italy, Germany, Spain, Switzerland, and the United Kingdom), as well as in North America (Canada) and East Asia (Japan and Taiwan). Our findings shed light on how physical aspects related to urban form influence mobility patterns, offering insights for the implementation of more effective and sustainable traffic management policies.This article is part of the theme issue 'Co-creating the future: participatory cities and digital governance'.

Robust Human Activity Recognition for Intelligent Transportation Systems Using Smartphone Sensors: A Position-Independent Approach

This study explores Human Activity Recognition (HAR) using smartphone sensors to address the challenges posed by position-dependent datasets. We propose a position-independent system that leverages data from accelerometers, gyroscopes, linear accelerometers, and gravity sensors collected from smartphones placed either on the chest or in the left/right leg pocket. The performance of traditional machine learning algorithms (Decision Trees (DT), K-Nearest Neighbors (KNN), Random Forest (RF), Support Vector Classifier (SVC), and XGBoost) is compared against deep learning models (Gated Recurrent Unit (GRU), Long Short-Term Memory (LSTM), Temporal Convolutional Networks (TCN), and Transformer models) under two sensor configurations. Our findings highlight that the Temporal Convolutional Network (TCN) model consistently outperforms other models, particularly in the four-sensor non-overlapping configuration, achieving the highest accuracy of 97.70%. Deep learning models such as LSTM, GRU, and Transformer also demonstrate strong performance, showcasing their effectiveness in capturing temporal dependencies in HAR tasks. Traditional machine learning models, including RF and XGBoost, provide reasonable performance but do not match the accuracy of deep learning models. Additionally, incorporating data from linear accelerometers and gravity sensors led to slight improvements over using accelerometer and gyroscope data alone. This research enhances the recognition of passenger behaviors for intelligent transportation systems, contributing to more efficient congestion management and emergency response strategies.

Transfer learning model for anomalous event recognition in big video data.

Video surveillance faces challenges due to the need for improved anomalous event recognition techniques for human activity recognition. Growing security concerns make standard CCTV systems insufficient because of high monitoring costs and operator exhaustion. Therefore, automated security systems with real-time event recognition are essential. This research introduces a semantic key frame extraction algorithm based on action recognition to minimize frame volume big video data. This approach has not been previously applied with ResNet50, VGG19, EfficientNetB7, and ViT_b16 models for recognizing anomalous events in surveillance videos. The findings demonstrate the effectiveness of this method in achieving high accuracy rates. The proposed method addresses the challenges posed by large volumes of frames generated by surveillance videos, requiring effective processing techniques. A large number of videos from the UCF-Crime dataset were used for proposed model evaluation, including both abnormal and normal videos during the training and testing phase. EfficientNetB7 achieved 86.34% accuracy, VGG19 reached 87.90%, ResNet50 attained 90.46%, and ViT_b16 excelled with 95.87% accuracy. Compared to state-of-the-art models from other studies, the transformer model (ViT_b16) outperformed these algorithms, demonstrating significant improvements in recognizing anomalous events.

Effects of reduced flow gradient on benthic biofilm communities' ecological network and community assembly.

The intensification of human activities has led to flow reduction and cut-off in most global rivers, seriously affecting riverine organisms and the biogeochemical processes. As key indicators of river ecosystems' structure and function, benthic biofilms play a critical role in driving primary production and material cycling in rivers. This research aimed to investigate the characteristics of microbial communities' complexity and stability during river flow reduction. Benthic biofilms were grown in artificial channels and subjected to eight gradients of flow reduction (represented by flow velocity from 0.4 to 110 cm/s). Biofilms' biodiversity, ecological networks and community assembly of bacteria, fungi and algae were investigated by high-throughput sequencing. Results showed significant differences in community composition and structure under different flow conditions. The eight flow gradients' microbial communities were divided into three groups: low, medium and high flows. The flow reduction led to significant decreases in bacterial and fungal communities' Chao1 index. Low flow conditions enriched the bacterial phyla Oxyphotobacteria, Alphaproteobacteria and Mollicutes, but significantly decreased the fungal phylum Chytridiomycota. Lowering flow reduced the fungal network's number of nodes and increased the algal network's number of edges. Cross-domain interactions network analysis showed a gradual increase in node and edge numbers with decreasing flow, while decreasing average path length. The neutral model predicted stochastic processes primarily drove biofilm community assembly, and that model's explanations decreased as the flow gradient decreased. The null model analysis revealed diffusion limitation as the most common stochastic ecological process for bacterial and algal communities, with reduced flow reducing heterogeneous selection and increasing diffusion-limited processes. This study provides an in-depth analysis of flow reduction's effects on biofilm communities' ecological networks and community assembly.