Displaying publications 41 - 52 of 52 in total

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  1. Fulltext Diversity and host associations of Myrsidea chewing lice (Phthiraptera: Menoponidae) in the tropical rainforest of Malaysian Borneo
    Madrid RS, Sychra O, Benedick S, Edwards DP, Efeykin BD, Fandrem M, et al.
    Int J Parasitol Parasites Wildl, 2020 Dec;13:231-247.
    PMID: 33294362 DOI: 10.1016/j.ijppaw.2020.10.011
    The tropical rainforests of Sundaland are a global biodiversity hotspot increasingly threatened by human activities. While parasitic insects are an important component of the ecosystem, their diversity and parasite-host relations are poorly understood in the tropics. We investigated parasites of passerine birds, the chewing lice of the speciose genus MyrsideaWaterston, 1915 (Phthiraptera: Menoponidae) in a natural rainforest community of Malaysian Borneo. Based on morphology, we registered 10 species of lice from 14 bird species of six different host families. This indicated a high degree of host specificity and that the complexity of the system could be underestimated with the potential for cryptic lineages/species to be present. We tested the species boundaries by combining morphological, genetic and host speciation diversity. The phylogenetic relationships of lice were investigated by analyzing the partial mitochondrial cytochrome oxidase I (COI) and the nuclear elongation factor alpha (EF-1α) genes sequences of the species. This revealed a monophyletic group of Myrsidea lineages from seven hosts of the avian family Pycnonotidae, one host of Timaliidae and one host of Pellorneidae. However, species delimitation methods supported the species boundaries hypothesized by morphological studies and confirmed that four species of Myrsidea are not single host specific. Cophylogenetic analysis by both distance-based test ParaFit and event-based method Jane confirmed overall congruence between the phylogenies of Myrsidea and their hosts. In total we recorded three cospeciation events for 14 host-parasite associations. However only one host-parasite link (M. carmenae and their hosts Terpsiphone affinis and Hypothymis azurea) was significant after the multiple testing correction in ParaFit. Four new species are described: Myrsidea carmenaesp.n. ex Hypothymis azurea and Terpsiphone affinis, Myrsidea franciscaesp.n. ex Rhipidura javanica, Myrsidea ramonisp.n. ex Copsychus malabaricus stricklandii, and Myrsidea victoriaesp.n. ex. Turdinus sepiarius.
    Matched MeSH terms: Human Activities
  2. Fulltext Fractionation of as, co, cu and zn by sequential extraction in surface sediment of Kuala Terengganu river estuary
    Muhammad Nazirul Mubin Abd Halim Shah, Lee, Siang Hing, Mohd Yusoff Nurulnadia, Meng, Chuan Ong
    Malaysian Journal of Applied Sciences, 2020;5(2):57-68.
    MyJurnal
    Development and urbanization processes around Terengganu River estuary are expected to release a significant amount of heavy metals into the existing surface sediment. However, information on how and why these metals are attached into specific fraction of sediments is still lacking. Therefore, this study aimed to explain the heavy metal concentration distribution in each available fraction in Terengganu River estuary. In this study, nine surface sediments originated from various human activities area in Terengganu River estuary were collected during four different sampling sessions in 2017. Heavy metal content from the collected sediments were extracted using 3-steps BCR sequential extraction method followed by detection using Inductively Coupled Plasma Mass-Spectrometer (ICP-MS) and we discovered that the total concentration of arsenic (As), cobalt (Co), copper (Cu), and zinc (Zn) ranged from 2.18 to 17.48 mg/kg dry wt., 2.53 to 20.53 mg/kg dry wt., 1.01 to 13.13 mg/kg dry wt., and 6.10 to 65.71 mg/kg dry wt., respectively. Dominance of metals in each fraction can be arranged as follows: As: residual > reducible > exchangeable > oxidizable; Co: residual > exchangeable > reducible > oxidizable; Cu: residual > oxidizable > reducible > exchangeable; Zn: residual > exchangeable > reducible > oxidizable. Availability of metals in the sediment at Terengganu River estuary is limited since that majority of metals resides in non-mobilisable fraction of the sediment. In essence, the sequential extraction provides information regarding the metals’ fractionation, availability and mobility, which could be used in assessing the environmental contamination in the area.
    Matched MeSH terms: Human Activities
  3. Review of chitosan composite as a heavy metal adsorbent: Material preparation and properties
    Begum S, Yuhana NY, Md Saleh N, Kamarudin NHN, Sulong AB
    Carbohydr Polym, 2021 May 01;259:117613.
    PMID: 33673980 DOI: 10.1016/j.carbpol.2021.117613
    A large amount of wastewater is typically discharged into water bodies and has extremely harmful effects to aquatic environments. The removal of heavy metals from water bodies is necessary for the safe consumption of water and human activities. The demand for seafood has considerably increased, and millions of tons of crustacean waste are discarded every year. These waste products are rich in a natural biopolymer known as chitin. The deacetylated form of chitin, chitosan, has attracted attention as an adsorbent. It is a biocompatible and biodegradable polymer that can be modified and converted to various derivatives. This review paper focuses on relevant literature on strategies for chemically modifying the biopolymer and its use in the removal of heavy metals from water and wastewater. The different aspects of chitosan-based derivatives and their preparation and application are elucidated. A list of chitosan-based composites, along with their adsorptivity and experimental conditions, are compiled.
    Matched MeSH terms: Human Activities
  4. Fulltext BeSafe B2.0 Smart Multisensory Platform for Safety in Workplaces
    Márquez-Sánchez S, Campero-Jurado I, Robles-Camarillo D, Rodríguez S, Corchado-Rodríguez JM
    Sensors (Basel), 2021 May 12;21(10).
    PMID: 34066186 DOI: 10.3390/s21103372
    Wearable technologies are becoming a profitable means of monitoring a person's health state, such as heart rate and physical activity. The use of the smartwatch is becoming consolidated, not only as a novelty but also as a very useful tool for daily use. In addition, other devices, such as helmets or belts, are beneficial for monitoring workers and the early detection of any anomaly. They can provide valuable information, especially in work environments, where they help reduce the rate of accidents and occupational diseases, which makes them powerful Personal Protective Equipment (PPE). The constant monitoring of the worker's health can be done in real-time, through temperature, falls, noise, impacts, or heart rate meters, activating an audible and vibrating alarm when an anomaly is detected. The gathered information is transmitted to a server in charge of collecting and processing it. In the first place, this paper provides an exhaustive review of the state of the art on works related to electronics for human activity behavior. After that, a smart multisensory bracelet, combined with other devices, developed a control platform that can improve operators' security in the working environment. Artificial Intelligence and the Internet of Things (AIoT) bring together the information to improve safety on construction sites, power stations, power lines, etc. Real-time and historic data is used to monitor operators' health and a hybrid system between Gaussian Mixture Model and Human Activity Classification. That is, our contribution is also founded on the use of two machine learning models, one based on unsupervised learning and the other one supervised. Where the GMM gave us a performance of 80%, 85%, 70%, and 80% for the 4 classes classified in real time, the LSTM obtained a result under the confusion matrix of 0.769, 0.892, and 0.921 for the carrying-displacing, falls, and walking-standing activities, respectively. This information was sent in real time through the platform that has been used to analyze and process the data in an alarm system.
    Matched MeSH terms: Human Activities
  5. ADA-SR: Activity detection and analysis using security robots for reliable workplace safety
    Zhang G, Jing W, Tao H, Rahman MA, Salih SQ, Al-Saffar A, et al.
    Work, 2021;68(3):935-943.
    PMID: 33612535 DOI: 10.3233/WOR-203427
    BACKGROUND: Human-Robot Interaction (HRI) has become a prominent solution to improve the robustness of real-time service provisioning through assisted functions for day-to-day activities. The application of the robotic system in security services helps to improve the precision of event detection and environmental monitoring with ease.

    OBJECTIVES: This paper discusses activity detection and analysis (ADA) using security robots in workplaces. The application scenario of this method relies on processing image and sensor data for event and activity detection. The events that are detected are classified for its abnormality based on the analysis performed using the sensor and image data operated using a convolution neural network. This method aims to improve the accuracy of detection by mitigating the deviations that are classified in different levels of the convolution process.

    RESULTS: The differences are identified based on independent data correlation and information processing. The performance of the proposed method is verified for the three human activities, such as standing, walking, and running, as detected using the images and sensor dataset.

    CONCLUSION: The results are compared with the existing method for metrics accuracy, classification time, and recall.

    Matched MeSH terms: Human Activities
  6. MSTCN: A multiscale temporal convolutional network for user independent human activity recognition
    Raja Sekaran S, Pang YH, Ling GF, Yin OS
    F1000Res, 2021;10:1261.
    PMID: 36896393 DOI: 10.12688/f1000research.73175.1
    Background: In recent years, human activity recognition (HAR) has been an active research topic due to its widespread application in various fields such as healthcare, sports, patient monitoring, etc. HAR approaches can be categorised as handcrafted feature methods (HCF) and deep learning methods (DL). HCF involves complex data pre-processing and manual feature extraction in which the models may be exposed to high bias and crucial implicit pattern loss. Hence, DL approaches are introduced due to their exceptional recognition performance. Convolutional Neural Network (CNN) extracts spatial features while preserving localisation. However, it hardly captures temporal features. Recurrent Neural Network (RNN) learns temporal features, but it is susceptible to gradient vanishing and suffers from short-term memory problems. Unlike RNN, Long-Short Term Memory network has a relatively longer-term dependency. However, it consumes higher computation and memory because it computes and stores partial results at each level. Methods: This work proposes a novel multiscale temporal convolutional network (MSTCN) based on the Inception model with a temporal convolutional architecture. Unlike HCF methods, MSTCN requires minimal pre-processing and no manual feature engineering. Further, multiple separable convolutions with different-sized kernels are used in MSTCN for multiscale feature extraction. Dilations are applied to each separable convolution to enlarge the receptive fields without increasing the model parameters. Moreover, residual connections are utilised to prevent information loss and gradient vanishing. These features enable MSTCN to possess a longer effective history while maintaining a relatively low in-network computation. Results: The performance of MSTCN is evaluated on UCI and WISDM datasets using a subject independent protocol with no overlapping subjects between the training and testing sets. MSTCN achieves accuracies of 97.42 on UCI and 96.09 on WISDM. Conclusion: The proposed MSTCN dominates the other state-of-the-art methods by acquiring high recognition accuracies without requiring any manual feature engineering.
    Matched MeSH terms: Human Activities
  7. Fulltext Robust video content analysis schemes for human action recognition
    Aly CA, Abas FS, Ann GH
    Sci Prog, 2021;104(2):368504211005480.
    PMID: 33913378 DOI: 10.1177/00368504211005480
    INTRODUCTION: Action recognition is a challenging time series classification task that has received much attention in the recent past due to its importance in critical applications, such as surveillance, visual behavior study, topic discovery, security, and content retrieval.

    OBJECTIVES: The main objective of the research is to develop a robust and high-performance human action recognition techniques. A combination of local and holistic feature extraction methods used through analyzing the most effective features to extract to reach the objective, followed by using simple and high-performance machine learning algorithms.

    METHODS: This paper presents three robust action recognition techniques based on a series of image analysis methods to detect activities in different scenes. The general scheme architecture consists of shot boundary detection, shot frame rate re-sampling, and compact feature vector extraction. This process is achieved by emphasizing variations and extracting strong patterns in feature vectors before classification.

    RESULTS: The proposed schemes are tested on datasets with cluttered backgrounds, low- or high-resolution videos, different viewpoints, and different camera motion conditions, namely, the Hollywood-2, KTH, UCF11 (YouTube actions), and Weizmann datasets. The proposed schemes resulted in highly accurate video analysis results compared to those of other works based on four widely used datasets. The First, Second, and Third Schemes provides recognition accuracies of 57.8%, 73.6%, and 52.0% on Hollywood2, 94.5%, 97.0%, and 59.3% on KTH, 94.5%, 95.6%, and 94.2% on UCF11, and 98.9%, 97.8% and 100% on Weizmann.

    CONCLUSION: Each of the proposed schemes provides high recognition accuracy compared to other state-of-art methods. Especially, the Second Scheme as it gives excellent comparable results to other benchmarked approaches.

    Matched MeSH terms: Human Activities*
  8. Fulltext Incremental Learning of Human Activities in Smart Homes
    Chua SL, Foo LK, Guesgen HW, Marsland S
    Sensors (Basel), 2022 Nov 03;22(21).
    PMID: 36366154 DOI: 10.3390/s22218458
    Sensor-based human activity recognition has been extensively studied. Systems learn from a set of training samples to classify actions into a pre-defined set of ground truth activities. However, human behaviours vary over time, and so a recognition system should ideally be able to continuously learn and adapt, while retaining the knowledge of previously learned activities, and without failing to highlight novel, and therefore potentially risky, behaviours. In this paper, we propose a method based on compression that can incrementally learn new behaviours, while retaining prior knowledge. Evaluation was conducted on three publicly available smart home datasets.
    Matched MeSH terms: Human Activities*
  9. Fulltext Multi-View Human Action Recognition Using Skeleton Based-FineKNN with Extraneous Frame Scrapping Technique
    Malik NUR, Sheikh UU, Abu-Bakar SAR, Channa A
    Sensors (Basel), 2023 Mar 02;23(5).
    PMID: 36904953 DOI: 10.3390/s23052745
    Human action recognition (HAR) is one of the most active research topics in the field of computer vision. Even though this area is well-researched, HAR algorithms such as 3D Convolution Neural Networks (CNN), Two-stream Networks, and CNN-LSTM (Long Short-Term Memory) suffer from highly complex models. These algorithms involve a huge number of weights adjustments during the training phase, and as a consequence, require high-end configuration machines for real-time HAR applications. Therefore, this paper presents an extraneous frame scrapping technique that employs 2D skeleton features with a Fine-KNN classifier-based HAR system to overcome the dimensionality problems.To illustrate the efficacy of our proposed method, two contemporary datasets i.e., Multi-Camera Action Dataset (MCAD) and INRIA Xmas Motion Acquisition Sequences (IXMAS) dataset was used in experiment. We used the OpenPose technique to extract the 2D information, The proposed method was compared with CNN-LSTM, and other State of the art methods. Results obtained confirm the potential of our technique. The proposed OpenPose-FineKNN with Extraneous Frame Scrapping Technique achieved an accuracy of 89.75% on MCAD dataset and 90.97% on IXMAS dataset better than existing technique.
    Matched MeSH terms: Human Activities
  10. Fulltext Native diversity buffers against severity of non-native tree invasions
    Delavaux CS, Crowther TW, Zohner CM, Robmann NM, Lauber T, van den Hoogen J, et al.
    Nature, 2023 Sep;621(7980):773-781.
    PMID: 37612513 DOI: 10.1038/s41586-023-06440-7
    Determining the drivers of non-native plant invasions is critical for managing native ecosystems and limiting the spread of invasive species1,2. Tree invasions in particular have been relatively overlooked, even though they have the potential to transform ecosystems and economies3,4. Here, leveraging global tree databases5-7, we explore how the phylogenetic and functional diversity of native tree communities, human pressure and the environment influence the establishment of non-native tree species and the subsequent invasion severity. We find that anthropogenic factors are key to predicting whether a location is invaded, but that invasion severity is underpinned by native diversity, with higher diversity predicting lower invasion severity. Temperature and precipitation emerge as strong predictors of invasion strategy, with non-native species invading successfully when they are similar to the native community in cold or dry extremes. Yet, despite the influence of these ecological forces in determining invasion strategy, we find evidence that these patterns can be obscured by human activity, with lower ecological signal in areas with higher proximity to shipping ports. Our global perspective of non-native tree invasion highlights that human drivers influence non-native tree presence, and that native phylogenetic and functional diversity have a critical role in the establishment and spread of subsequent invasions.
    Matched MeSH terms: Human Activities
  11. Enhancing coastal ecosystem resilience: Investigating the interplay between safety criteria and ferry employee's perceptions to address climate change impacts
    Azmi MA, Mokhtar K, Osnin NA, Razali Chan S, Albasher G, Ali A, et al.
    Environ Res, 2023 Dec 01;238(Pt 1):117074.
    PMID: 37678506 DOI: 10.1016/j.envres.2023.117074
    Coastal ecosystems play an important part in mitigating the effects of climate change. Coastal ecosystems are becoming more susceptible to climate change impacts due to human activities and maritime accidents. The global shipping industry, especially in Southeast Asia, has witnessed numerous accidents, particularly involving passenger ferries, resulting in injuries and fatalities in recent years. In order to mitigate the impact of climate change on coastal ecosystems, this study aimed to evaluate the relationship between employees' perceptions of safety criteria and their own safety behaviour on Langkawi Island, Malaysia. A straightforward random sampling technique was employed to collect data from 112 ferry employees aboard Malaysian-registered passenger boats by administering questionnaires. The findings shed light on the strong connection between providing safety instructions for passengers and safety behaviour among ferry workers. Safety instructions should contain climate-related information to successfully address the effects of climate change. The instructions might include guidance on responding to extreme weather events and understanding the potential consequences of sea-level rise on coastal communities. The ferry company staff should also expand their safe behaviour concept to include training and preparation for climate-related incidents. The need to recognise the interconnectedness between climate change, ferry safety and the protection of coastal ecosystems is emphasised in this study. The findings can be utilised by policymakers, regulatory agencies and ferry operators to design holistic policies that improve safety behaviour, minimise maritime mishaps and preserve the long-term sustainability of coastal ecosystems in the face of difficulties posed by climate change.
    Matched MeSH terms: Human Activities
  12. Contributions of climate change and human activities to grassland degradation and improvement from 2001 to 2020 in Zhaosu County, China
    Zhang M, Zhang F, Guo L, Dong P, Cheng C, Kumar P, et al.
    J Environ Manage, 2023 Dec 15;348:119465.
    PMID: 37924697 DOI: 10.1016/j.jenvman.2023.119465
    Grassland degradation poses a serious threat to biodiversity, ecosystem services, and human well-being. In this study, we investigated grassland degradation in Zhaosu County, China, between 2001 and 2020, and analyzed the impacts of climate change and human activities using the Miami model. The actual net primary productivity (ANPP) obtained with CASA (Carnegie-Ames-Stanford Approach) modeling, showed a decreasing trend, reflecting the significant degradation that the grasslands in Zhaosu County have experienced in the past 20 years. Grassland degradation was found to be highest in 2018, while the degraded area continuously decreased in the last 3 years (2018-2020). Climatic factors for found to be the dominant factor affecting grassland degradation, particularly the decrease in precipitation. On the other hand, human activities were found to be the main factor affecting improvement of grasslands, especially in recent years. This finding profoundly elucidates the underlying causes of grassland degradation and improvement and helps implement ecological conservation and restoration measures. From a practical perspective, the research results provide an important reference for the formulation of policies and management strategies for sustainable land use.
    Matched MeSH terms: Human Activities
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