Anthropogenic Forcing of the Baltic Sea Thallium Cycle.

Anthropogenic activities have fundamentally changed the chemistry of the Baltic Sea. According to results reported in this study, not even the thallium (Tl) isotope cycle is immune to these activities. In the anoxic and sulfidic ("euxinic") East Gotland Basin today, Tl and its two stable isotopes are cycled between waters and sediments as predicted based on studies of other redox-stratified basins (e.g., the Black Sea and Cariaco Trench). The Baltic seawater Tl isotope composition (ε205Tl) is, however, higher than predicted based on the results of conservative mixing calculations. Data from a short sediment core from East Gotland Basin demonstrates that this high seawater ε205Tl value originated sometime between about 1940 and 1947 CE, around the same time other prominent anthropogenic signatures begin to appear in the same core. This juxtaposition is unlikely to be coincidental and suggests that human activities in the surrounding area have altered the seawater Tl isotope mass-balance of the Baltic Sea.

Quantitative analysis of the impact of climate variability and human activities on grassland productivity of the Qilian Mountain National Park, China.

To quantitatively analyze the impact of climate variability and human activities on grassland productivity of China's Qilian Mountain National Park, this study used Carnegic-Ames-Stanford Approach model (CASA) and Integrated Vegetation model improved by the Comprehensive and Sequential Classification System (CSCS) to assess the trends of grassland NPP from 2000 to 2015, the residual trend analysis method was used to quantify the impact of human activities and climate change on the grassland based on the NPP changes. The actual grassland NPP accumulation mainly occurred in June, July and August (autumn); the actual NPP showed a fluctuating upward trend with an average increase of 2.2 g C·m-2 a-1, while the potential NPP increase of 1.6 g C·m-2 a-1 and human-induced NPP decreased of 0.5 g C·m-2 a-1. The annual temperature showed a fluctuating upward trend with an average increase of 0.1°C 10a-1, but annual precipitation showed a fluctuating upward trend with an average annual increase of 1.3 mm a-1 from 2000 to 2015. The area and NPP of grassland degradation caused by climate variability was significantly greater than that caused by human activities and mainly distributed in the northwest and central regions, but area and NPP of grassland restored caused by human activities was significantly greater than that caused by climate variability and mainly distributed in the southeast regions. In conclusion, grassland in Qilian Mountain National Park showed a trend of degradation based on distribution area, but showed a trend of restoration based on actual NPP. Climate variability was the main cause of grassland degradation in the northwestern region of study area, and restoration of grassland in the eastern region was the result of the combined effects of human activities and climate variability. Under global climate change, the establishment of Qilian Mountain National Park was of great significance to the grassland's protection and the grasslands ecological restoration that have been affected by humans.

Human Activity Recognition in a Free-Living Environment Using an Ear-Worn Motion Sensor.

Human activity recognition (HAR) technology enables continuous behavior monitoring, which is particularly valuable in healthcare. This study investigates the viability of using an ear-worn motion sensor for classifying daily activities, including lying, sitting/standing, walking, ascending stairs, descending stairs, and running. Fifty healthy participants (between 20 and 47 years old) engaged in these activities while under monitoring. Various machine learning algorithms, ranging from interpretable shallow models to state-of-the-art deep learning approaches designed for HAR (i.e., DeepConvLSTM and ConvTransformer), were employed for classification. The results demonstrate the ear sensor's efficacy, with deep learning models achieving a 98% accuracy rate of classification. The obtained classification models are agnostic regarding which ear the sensor is worn and robust against moderate variations in sensor orientation (e.g., due to differences in auricle anatomy), meaning no initial calibration of the sensor orientation is required. The study underscores the ear's efficacy as a suitable site for monitoring human daily activity and suggests its potential for combining HAR with in-ear vital sign monitoring. This approach offers a practical method for comprehensive health monitoring by integrating sensors in a single anatomical location. This integration facilitates individualized health assessments, with potential applications in tele-monitoring, personalized health insights, and optimizing athletic training regimes.

A Comparative Study of Physically Accurate Synthetic Shadow Datasets in Agricultural Settings with Human Activity.

Shadow, a natural phenomenon resulting from the absence of light, plays a pivotal role in agriculture, particularly in processes such as photosynthesis in plants. Despite the availability of generic shadow datasets, many suffer from annotation errors and lack detailed representations of agricultural shadows with possible human activity inside, excluding those derived from satellite or drone views. In this paper, we present an evaluation of a synthetically generated top-down shadow segmentation dataset characterized by photorealistic rendering and accurate shadow masks. We aim to determine its efficacy compared to real-world datasets and assess how factors such as annotation quality and image domain influence neural network model training. To establish a baseline, we trained numerous baseline architectures and subsequently explored transfer learning using various freely available shadow datasets. We further evaluated the out-of-domain performance compared to the training set of other shadow datasets. Our findings suggest that AgroSegNet demonstrates competitive performance and is effective for transfer learning, particularly in domains similar to agriculture.

Human Activity Recognition Algorithm with Physiological and Inertial Signals Fusion: Photoplethysmography, Electrodermal Activity, and Accelerometry.

Inertial signals are the most widely used signals in human activity recognition (HAR) applications, and extensive research has been performed on developing HAR classifiers using accelerometer and gyroscope data. This study aimed to investigate the potential enhancement of HAR models through the fusion of biological signals with inertial signals. The classification of eight common low-, medium-, and high-intensity activities was assessed using machine learning (ML) algorithms, trained on accelerometer (ACC), blood volume pulse (BVP), and electrodermal activity (EDA) data obtained from a wrist-worn sensor. Two types of ML algorithms were employed: a random forest (RF) trained on features; and a pre-trained deep learning (DL) network (ResNet-18) trained on spectrogram images. Evaluation was conducted on both individual activities and more generalized activity groups, based on similar intensity. Results indicated that RF classifiers outperformed corresponding DL classifiers at both individual and grouped levels. However, the fusion of EDA and BVP signals with ACC data improved DL classifier performance compared to a baseline DL model with ACC-only data. The best performance was achieved by a classifier trained on a combination of ACC, EDA, and BVP images, yielding F1-scores of 69 and 87 for individual and grouped activity classifications, respectively. For DL models trained with additional biological signals, almost all individual activity classifications showed improvement (p-value < 0.05). In grouped activity classifications, DL model performance was enhanced for low- and medium-intensity activities. Exploring the classification of two specific activities, ascending/descending stairs and cycling, revealed significantly improved results using a DL model trained on combined ACC, BVP, and EDA spectrogram images (p-value < 0.05).

Critical Analysis of Data Leakage in WiFi CSI-Based Human Action Recognition Using CNNs.

WiFi Channel State Information (CSI)-based human action recognition using convolutional neural networks (CNNs) has emerged as a promising approach for non-intrusive activity monitoring. However, the integrity and reliability of the reported performance metrics are susceptible to data leakage, wherein information from the test set inadvertently influences the training process, leading to inflated accuracy rates. In this paper, we conduct a critical analysis of a notable IEEE Sensors Journal study on WiFi CSI-based human action recognition, uncovering instances of data leakage resulting from the absence of subject-based data partitioning. Empirical investigation corroborates the lack of exclusivity of individuals across dataset partitions, underscoring the importance of rigorous data management practices. Furthermore, we demonstrate that employing data partitioning with respect to humans results in significantly lower precision rates than the reported 99.9% precision, highlighting the exaggerated nature of the original findings. Such inflated results could potentially discourage other researchers and impede progress in the field by fostering a sense of complacency.

Adaptive temporal compression for reduction of computational complexity in human behavior recognition.

The research on video analytics especially in the area of human behavior recognition has become increasingly popular recently. It is widely applied in virtual reality, video surveillance, and video retrieval. With the advancement of deep learning algorithms and computer hardware, the conventional two-dimensional convolution technique for training video models has been replaced by three-dimensional convolution, which enables the extraction of spatio-temporal features. Specifically, the use of 3D convolution in human behavior recognition has been the subject of growing interest. However, the increased dimensionality has led to challenges such as the dramatic increase in the number of parameters, increased time complexity, and a strong dependence on GPUs for effective spatio-temporal feature extraction. The training speed can be considerably slow without the support of powerful GPU hardware. To address these issues, this study proposes an Adaptive Time Compression (ATC) module. Functioning as an independent component, ATC can be seamlessly integrated into existing architectures and achieves data compression by eliminating redundant frames within video data. The ATC module effectively reduces GPU computing load and time complexity with negligible loss of accuracy, thereby facilitating real-time human behavior recognition.

Anthropogenic disturbance affects calling and collective behaviour in corvid roosts.

Acoustic communication plays an important role in coordinating group dynamics and collective movements across a range of taxa. However, anthropogenic disturbance can inhibit the production or reception of acoustic signals. Here, we investigate the effects of noise and light pollution on the calling and collective behaviour of wild jackdaws (Corvus monedula), a highly social corvid species that uses vocalizations to coordinate collective movements at winter roosting sites. Using audio and video monitoring of roosts in areas with differing degrees of urbanization, we evaluate the influence of anthropogenic disturbance on vocalizations and collective movements. We found that when levels of background noise were higher, jackdaws took longer to settle following arrival at the roost in the evening and also called more during the night, suggesting that human disturbance may cause sleep disruption. High levels of overnight calling were, in turn, linked to disruption of vocal consensus decision-making and less cohesive group departures in the morning. These results raise the possibility that, by affecting cognitive and perceptual processes, human activities may interfere with animals' ability to coordinate collective behaviour. Understanding links between anthropogenic disturbance, communication, cognition and collective behaviour must be an important research priority in our increasingly urbanized world. This article is part of the theme issue 'The power of sound: unravelling how acoustic communication shapes group dynamics'.

Eco-environmental changes due to human activities in the Erhai Lake Basin from 1990 to 2020.

Human activities have increased with urbanisation in the Erhai Lake Basin, considerably impacting its eco-environmental quality (EEQ). This study aims to reveal the evolution and driving forces of the EEQ using water benefit-based ecological index (WBEI) in response to human activities and policy variations in the Erhai Lake Basin from 1990 to 2020. Results show that (1) the EEQ exhibited a pattern of initial degradation, subsequent improvement, further degradation and a rebound from 1990 to 2020, and the areas with poor and fair EEQ levels mainly concentrated around the Erhai Lake Basin with a high level of urbanisation and relatively flat terrain; (2) the EEQ levels were not optimistic in 1990, 1995 and 2015, and areas with poor and fair EEQ levels accounted for 43.41%, 47.01% and 40.05% of the total area, respectively; and (3) an overall improvement in the EEQ was observed in 1995-2000, 2000-2005, 2005-2009 and 2015-2020, and the improvement was most significant in 1995-2000, covering an area of 823.95 km2 and accounting for 31.79% of the total area. Results also confirmed that the EEQ changes in the Erhai Lake Basin were primarily influenced by human activities and policy variations. Moreover, these results can provide a scientific basis for the formulation and planning of sustainable development policy in the Erhai Lake Basin.

A Multi-Modal Egocentric Activity Recognition Approach towards Video Domain Generalization.

Egocentric activity recognition is a prominent computer vision task that is based on the use of wearable cameras. Since egocentric videos are captured through the perspective of the person wearing the camera, her/his body motions severely complicate the video content, imposing several challenges. In this work we propose a novel approach for domain-generalized egocentric human activity recognition. Typical approaches use a large amount of training data, aiming to cover all possible variants of each action. Moreover, several recent approaches have attempted to handle discrepancies between domains with a variety of costly and mostly unsupervised domain adaptation methods. In our approach we show that through simple manipulation of available source domain data and with minor involvement from the target domain, we are able to produce robust models, able to adequately predict human activity in egocentric video sequences. To this end, we introduce a novel three-stream deep neural network architecture combining elements of vision transformers and residual neural networks which are trained using multi-modal data. We evaluate the proposed approach using a challenging, egocentric video dataset and demonstrate its superiority over recent, state-of-the-art research works.

Human Action Recognition and Note Recognition: A Deep Learning Approach Using STA-GCN.

Human action recognition (HAR) is growing in machine learning with a wide range of applications. One challenging aspect of HAR is recognizing human actions while playing music, further complicated by the need to recognize the musical notes being played. This paper proposes a deep learning-based method for simultaneous HAR and musical note recognition in music performances. We conducted experiments on Morin khuur performances, a traditional Mongolian instrument. The proposed method consists of two stages. First, we created a new dataset of Morin khuur performances. We used motion capture systems and depth sensors to collect data that includes hand keypoints, instrument segmentation information, and detailed movement information. We then analyzed RGB images, depth images, and motion data to determine which type of data provides the most valuable features for recognizing actions and notes in music performances. The second stage utilizes a Spatial Temporal Attention Graph Convolutional Network (STA-GCN) to recognize musical notes as continuous gestures. The STA-GCN model is designed to learn the relationships between hand keypoints and instrument segmentation information, which are crucial for accurate recognition. Evaluation on our dataset demonstrates that our model outperforms the traditional ST-GCN model, achieving an accuracy of 81.4%.

Human Activity Recognition Based on Deep Learning and Micro-Doppler Radar Data.

Activity recognition is one of the significant technologies accompanying the development of the Internet of Things (IoT). It can help in recording daily life activities or reporting emergencies, thus improving the user's quality of life and safety, and even easing the workload of caregivers. This study proposes a human activity recognition (HAR) system based on activity data obtained via the micro-Doppler effect, combining a two-stream one-dimensional convolutional neural network (1D-CNN) with a bidirectional gated recurrent unit (BiGRU). Initially, radar sensor data are used to generate information related to time and frequency responses using short-time Fourier transform (STFT). Subsequently, the magnitudes and phase values are calculated and fed into the 1D-CNN and Bi-GRU models to extract spatial and temporal features for subsequent model training and activity recognition. Additionally, we propose a simple cross-channel operation (CCO) to facilitate the exchange of magnitude and phase features between parallel convolutional layers. An open dataset collected through radar, named Rad-HAR, is employed for model training and performance evaluation. Experimental results demonstrate that the proposed 1D-CNN+CCO-BiGRU model demonstrated superior performance, achieving an impressive accuracy rate of 98.2%. This outperformance of existing systems with the radar sensor underscores the proposed model's potential applicability in real-world scenarios, marking a significant advancement in the field of HAR within the IoT framework.

Adopting Graph Neural Networks to Analyze Human-Object Interactions for Inferring Activities of Daily Living.

Human Activity Recognition (HAR) refers to a field that aims to identify human activities by adopting multiple techniques. In this field, different applications, such as smart homes and assistive robots, are introduced to support individuals in their Activities of Daily Living (ADL) by analyzing data collected from various sensors. Apart from wearable sensors, the adoption of camera frames to analyze and classify ADL has emerged as a promising trend for achieving the identification and classification of ADL. To accomplish this, the existing approaches typically rely on object classification with pose estimation using the image frames collected from cameras. Given the existence of inherent correlations between human-object interactions and ADL, further efforts are often needed to leverage these correlations for more effective and well justified decisions. To this end, this work proposes a framework where Graph Neural Networks (GNN) are adopted to explicitly analyze human-object interactions for more effectively recognizing daily activities. By automatically encoding the correlations among various interactions detected through some collected relational data, the framework infers the existence of different activities alongside their corresponding environmental objects. As a case study, we use the Toyota Smart Home dataset to evaluate the proposed framework. Compared with conventional feed-forward neural networks, the results demonstrate significantly superior performance in identifying ADL, allowing for the classification of different daily activities with an accuracy of 0.88. Furthermore, the incorporation of encoded information from relational data enhances object-inference performance compared to the GNN without joint prediction, increasing accuracy from 0.71 to 0.77.

Soil quality assessment in low human activity disturbance zones: a study on the Qinghai-Tibet Plateau.

The Qinghai-Tibet Plateau, located at the Third Pole and known as the "Asian water tower," serves as a crucial ecological barrier for China. Grasping the soil quality on the Qinghai-Tibet Plateau holds paramount importance for the rational and scientific exploitation of soil resources within the region and is essential for vegetation restoration and ecological reconstruction. This study, conducted in Maqin County, Qinghai Province, collected 1647 soil samples (0-20 cm) within a study area of 6300 km2. Sixteen soil indicators were selected that were split into beneficial (N, P, S, and B), harmful (Cr, Hg, As, Pb, Ni, and Cd), and essential (Cu, Zn, Se, Ga, K, and Ca) elements. The Soil Quality Index (SQI) was computed to assess soil quality across diverse geological contexts, land cover classifications, and soil profiles. The results indicate that the overall SQI in the study area was comparatively high, with most regions having an SQI between 0.4 and 0.6, categorized as moderately to highly satisfactory. Among the different geological backgrounds, the highest SQI was found in the Quaternary alluvium (0.555) and the lowest in the Precambrian Jinshuikou Formation (0.481). Regarding different land-use types, the highest SQI was observed in glacier- and snow-covered areas (0.582) and the lowest in other types of grassland (0.461). The highest SQI was recorded in typical alpine meadow soil (0.521) and the lowest in leached brown soil (0.460). The evaluation results have significant reference value for the sustainable utilization and management of soil in Maqin County, Qinghai Province, China.

Global assessment of effective population sizes: Consistent taxonomic differences in meeting the 50/500 rule.

Effective population size (Ne) is a particularly useful metric for conservation as it affects genetic drift, inbreeding and adaptive potential within populations. Current guidelines recommend a minimum Ne of 50 and 500 to avoid short-term inbreeding and to preserve long-term adaptive potential respectively. However, the extent to which wild populations reach these thresholds globally has not been investigated, nor has the relationship between Ne and human activities. Through a quantitative review, we generated a dataset with 4610 georeferenced Ne estimates from 3829 populations, extracted from 723 articles. These data show that certain taxonomic groups are less likely to meet 50/500 thresholds and are disproportionately impacted by human activities; plant, mammal and amphibian populations had a <54% probability of reaching N ̂ e = 50 and a <9% probability of reaching N ̂ e = 500. Populations listed as being of conservation concern according to the IUCN Red List had a smaller median N ̂ e than unlisted populations, and this was consistent across all taxonomic groups. N ̂ e was reduced in areas with a greater Global Human Footprint, especially for amphibians, birds and mammals, however relationships varied between taxa. We also highlight several considerations for future works, including the role that gene flow and subpopulation structure plays in the estimation of N ̂ e in wild populations, and the need for finer-scale taxonomic analyses. Our findings provide guidance for more specific thresholds based on Ne and help prioritise assessment of populations from taxa most at risk of failing to meet conservation thresholds.

Comparative evaluation of soundscapes in human activities spatial contexts of pedestrian spaces adjacent to arterial roads.

Pedestrian spaces adjacent to arterial roads are characterized by the dominance of traffic noise alongside various human activities. Research on the impact of traffic noise on the soundscape evaluation of pedestrian spaces has not considered human activities spatial contexts. To address this research gap, the present study constructed auditory environments for pedestrian spaces in the contexts of commuting, residential, and commercial activities. A total of seven auditory environments were subjected to laboratory auditory evaluations, including perceived dominance of sound source, acoustic comfort, and perceived affective quality of the soundscape. The results indicated that in pedestrian spaces with constant traffic noise, the presence of significant human activity sounds led to a decreased perceived dominance of traffic noise and an increased acoustic comfort, despite the higher acoustic energy. Thus, pedestrian spaces with a variety of human activity received better soundscape evaluations. The elements that reflected the human activities spatial contexts, including the types and intensity of human activities, played a crucial role in soundscape evaluations. Better acoustic comfort was reported in pedestrian spaces characterized by low-intensity residential activities and high-intensity commercial activities. Additionally, pedestrian spaces with more intense activities offered an actively engaging soundscape. The findings can provide reference for a more accurate evaluation of the soundscape in pedestrian spaces and guide the soundscape design of pedestrian environments.

Significant influence of urban human activities and marine input on rainwater chemistry in a coastal large city, China.

The coastal urban region is generally considered an atmospheric receptor for terrestrial and marine input materials, and rainfall chemistry can trace the wet scavenging process of these materials. Fast urbanization in China's east coastal areas has greatly altered the rainwater chemistry. However, the chemical variations, determinants, and sources of rainfall are unclear. Therefore, the typical coastal city of Fuzhou was selected for 1-year rainwater sampling and inorganic ions were detected to explore above problems. The findings depicted that rainwater ions in Fuzhou were slightly different from those in other coastal cities. Although NO3-, SO42-, Ca2+ and NH4+ dominated the rainwater ions, the marine input Cl- (22 %) and Na+ (11 %) also contributed a considerable percentage to the rainwater ions. Large differences in ion concentrations (2∼28 times) were found in monthly scale due to the rainfall amount. Both natural and anthropogenic determinants influenced the rainwater ions in coastal cities, such as SO2 emission, air SO2 and PM10 content on rainwater SO42-, NO3-, and Ca2+, and soot & dust emission on rainwater SO42-, NO3-, indicating the vital contribution of human activities. Stoichiometry and positive matrix factorization-based sources identification indicated that atmospheric dust/particles were the primary contributor of Ca2+ (83.3 %) and F- (83.7 %), and considerable contributor of SO42- (39.5 %), NO3- (38.3 %) and K+ (41.5 %). Anthropogenic origins, such as urban waste volatilization and fuel combustion emission, contributed 95 % of NH4+, 54.5 % of NO3- and 41.9 % of SO42-, and the traffic sources contribution was relatively higher than fixed emission sources. The marine input represented the vital source of Cl- (77.7 %), Na+ (84.9 %), and Mg2+ (55.3 %). This work highlights the significant influence of urban human activities and marine input on rainwater chemicals and provides new insight into the material cycle between the atmosphere and earth-surface in coastal city.

Adversarial AI applied to cross-user inter-domain and intra-domain adaptation in human activity recognition using wireless signals.

In recent years, researchers have successfully recognised human activities using commercially available WiFi (Wireless Fidelity) devices. The channel state information (CSI) can be gathered at the access point with the help of a network interface controller (NIC card). These CSI streams are sensitive to human body motions and produce abrupt changes (fluctuations) in their magnitude and phase values when a moving object interacts with a transmitter and receiver pair. This sensing methodology is gaining popularity compared to traditional approaches involving wearable technology, as it is a contactless sensing strategy with no cumbersome sensing equipments fitted on the target with preserved privacy since no personal information of the subject is collected. In previous investigations, internal validation statistics have been promising. However, external validation results have been poor, due to model application to varying subjects with remarkably different environments. To address this problem, we propose an adversarial Artificial Intelligence AI model that learns and utilises domain-invariant features. We analyse model results in terms of suitability for inter-domain and intra-domain alignment techniques, to identify which is better at robustly matching the source to target domain, and hence improve recognition accuracy in cross-user conditions for HAR using wireless signals. We evaluate our model performance on different target training data percentages to assess model reliability on data scarcity. After extensive evaluation, our architecture shows improved predictive performance across target training data proportions when compared to a non-adversarial model for nine cross-user conditions with comparatively less simulation time. We conclude that inter-domain alignment is preferable for HAR applications using wireless signals, and confirm that the dataset used is suitable for investigations of this type. Our architecture can form the basis of future studies using other datasets and/or investigating combined cross-environmental and cross-user features.

Quantitative Analysis of Mother Wavelet Function Selection for Wearable Sensors-Based Human Activity Recognition.

Recent advancements in the Internet of Things (IoT) wearable devices such as wearable inertial sensors have increased the demand for precise human activity recognition (HAR) with minimal computational resources. The wavelet transform, which offers excellent time-frequency localization characteristics, is well suited for HAR recognition systems. Selecting a mother wavelet function in wavelet analysis is critical, as optimal selection improves the recognition performance. The activity time signals data have different periodic patterns that can discriminate activities from each other. Therefore, selecting a mother wavelet function that closely resembles the shape of the recognized activity's sensor (inertial) signals significantly impacts recognition performance. This study uses an optimal mother wavelet selection method that combines wavelet packet transform with the energy-to-Shannon-entropy ratio and two classification algorithms: decision tree (DT) and support vector machines (SVM). We examined six different mother wavelet families with different numbers of vanishing points. Our experiments were performed on eight publicly available ADL datasets: MHEALTH, WISDM Activity Prediction, HARTH, HARsense, DaLiAc, PAMAP2, REALDISP, and HAR70+. The analysis demonstrated in this paper can be used as a guideline for optimal mother wavelet selection for human activity recognition.