Displaying publications 1 - 20 of 52 in total

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  1. 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
  2. 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
  3. 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
  4. 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
  5. 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*
  6. 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
  7. 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
  8. 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
  9. 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
  10. 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*
  11. 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
  12. Fulltext Effects of temperature, humidity, air quality and anthropic activities on the transmission of SARS-CoV-2: a systematic review protocol
    Jeevananthan C, Muhamad NA, Jaafar MH, Hod R, Ab Ghani RM, Md Isa Z, et al.
    BMJ Open, 2020 11 04;10(11):e039623.
    PMID: 33148753 DOI: 10.1136/bmjopen-2020-039623
    INTRODUCTION: The current global pandemic of the virus that emerged from Hubei province in China has caused coronavirus disease in 2019 (COVID-19), which has affected a total number of 900 036 people globally, involving 206 countries and resulted in a cumulative of 45 693 deaths worldwide as of 3 April 2020. The mode of transmission is identified through airdrops from patients' body fluids such as during sneezing, coughing and talking. However, the relative importance of environmental effects in the transmission of the virus has not been vastly studied. In addition, the role of temperature and humidity in air-borne transmission of infection is presently still unclear. This study aims to identify the effect of temperature, humidity and air quality in the transmission of SARS-CoV-2.

    METHODS AND ANALYSIS: We will systematically conduct a comprehensive literature search using various databases including PubMed, EMBASE, Scopus, CENTRAL and Google Scholar to identify potential studies. The search will be performed for any eligible articles from the earliest published articles up to latest available studies in 2020. We will include all the observational studies such as cohort case-control and cross-sectional studies that explains or measures the effects of temperature and/or humidity and/or air quality and/or anthropic activities that is associated with SARS-CoV-2. Study selection and reporting will follow the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines and Meta-Analysis of Observational Studies in Epidemiology guideline. All data will be extracted using a standardised data extraction form and quality of the studies will be assessed using the Newcastle-Ottawa Scale guideline. Descriptive and meta-analysis will be performed using a random effect model in Review Manager File.

    ETHICS AND DISSEMINATION: No primary data will be collected, and thus no formal ethical approval is required. The results will be disseminated through a peer-reviewed publication and conference presentation.

    PROSPERO REGISTRATION NUMBER: CRD42020176756.

    Matched MeSH terms: Human Activities*
  13. Fulltext Air quality changes during the COVID-19 lockdown over the Yangtze River Delta Region: An insight into the impact of human activity pattern changes on air pollution variation
    Li L, Li Q, Huang L, Wang Q, Zhu A, Xu J, et al.
    Sci Total Environ, 2020 Aug 25;732:139282.
    PMID: 32413621 DOI: 10.1016/j.scitotenv.2020.139282
    The outbreak of COVID-19 has spreaded rapidly across the world. To control the rapid dispersion of the virus, China has imposed national lockdown policies to practise social distancing. This has led to reduced human activities and hence primary air pollutant emissions, which caused improvement of air quality as a side-product. To investigate the air quality changes during the COVID-19 lockdown over the YRD Region, we apply the WRF-CAMx modelling system together with monitoring data to investigate the impact of human activity pattern changes on air quality. Results show that human activities were lowered significantly during the period: industrial operations, VKT, constructions in operation, etc. were significantly reduced, leading to lowered SO2, NOx, PM2.5 and VOCs emissions by approximately 16-26%, 29-47%, 27-46% and 37-57% during the Level I and Level II response periods respectively. These emission reduction has played a significant role in the improvement of air quality. Concentrations of PM2.5, NO2 and SO2 decreased by 31.8%, 45.1% and 20.4% during the Level I period; and 33.2%, 27.2% and 7.6% during the Level II period compared with 2019. However, ozone did not show any reduction and increased greatly. Our results also show that even during the lockdown, with primary emissions reduction of 15%-61%, the daily average PM2.5 concentrations range between 15 and 79 μg m-3, which shows that background and residual pollutions are still high. Source apportionment results indicate that the residual pollution of PM2.5 comes from industry (32.2-61.1%), mobile (3.9-8.1%), dust (2.6-7.7%), residential sources (2.1-28.5%) in YRD and 14.0-28.6% contribution from long-range transport coming from northern China. This indicates that in spite of the extreme reductions in primary emissions, it cannot fully tackle the current air pollution. Re-organisation of the energy and industrial strategy together with trans-regional joint-control for a full long-term air pollution plan need to be further taken into account.
    Matched MeSH terms: Human Activities
  14. Microplastics in the freshwater and terrestrial environments: Prevalence, fates, impacts and sustainable solutions
    Wong JKH, Lee KK, Tang KHD, Yap PS
    Sci Total Environ, 2020 Jun 01;719:137512.
    PMID: 32229011 DOI: 10.1016/j.scitotenv.2020.137512
    The ubiquitous occurrences of microplastics in the environment have raised much concern and resulted in voluminous studies related to microplastics. Studies on microplastics pollution of the marine environment have received significantly higher attention compared to those of the freshwater and terrestrial environments. With the impetus to better understand microplastics in the freshwater and terrestrial environments, this review elucidates the findings of >100 articles related to the prevalence, fates and impacts of microplastics therein and the sustainable solutions, mostly in the past 10 years. This review shows the interconnection between terrestrial and freshwater microplastics with wastewater and sewage treatment plants as the most significant contributors of environmental microplastics via sludge and effluent discharges. Microplastics in both ecosystems comprise the primary and secondary forms with the latter resulted from weathering of the former. Besides retaining in soil and infiltrating with rainwater underground, terrestrial microplastics also enter the freshwater environment. The environmental microplastics interact with the biotic and abiotic components resulting in entrainment, settlement, biofouling, degradation, fragmentation and entry into the food chain, with subsequent transfer across the food chain. The abundance of environmental microplastics is attributed to population density and urbanization though tidal cycle, storms, floods and human activities can affect their distribution. The leaching of additives from microplastics poses major health concern and sustainable solutions target at reduction of plastics use and disposal, substitution with bioplastics and wastewater treatment innovations. Further studies on classification, detection, characterization and toxicity of microplastics are necessary to permit more effective formulation of solutions.
    Matched MeSH terms: Human Activities
  15. Sumatran rhinoceros on the brink of extinction
    Lam SS, Ma NL, Peng W, Sonne C
    Science, 2020 May 29;368(6494):958.
    PMID: 32467384 DOI: 10.1126/science.abc2202
    Matched MeSH terms: Human Activities*
  16. Individuals in urban dwelling primate species face unequal benefits associated with living in an anthropogenic environment
    Marty PR, Balasubramaniam KN, Kaburu SSK, Hubbard J, Beisner B, Bliss-Moreau E, et al.
    Primates, 2020 Mar;61(2):249-255.
    PMID: 31773350 DOI: 10.1007/s10329-019-00775-4
    In primates, living in an anthropogenic environment can significantly improve an individual's fitness, which is likely attributed to access to anthropogenic food resources. However, in non-professionally provisioned groups, few studies have examined whether individual attributes, such as dominance rank and sex, affect primates' ability to access anthropogenic food. Here, we investigated whether rank and sex explain individual differences in the proportion of anthropogenic food consumed by macaques. We observed 319 individuals living in nine urban groups across three macaque species. We used proportion of anthropogenic food in the diet as a proxy of access to those food resources. Males and high-ranking individuals in both sexes had significantly higher proportions of anthropogenic food in their diets than other individuals. We speculate that unequal access to anthropogenic food resources further increases within-group competition, and may limit fitness benefits in an anthropogenic environment to certain individuals.
    Matched MeSH terms: Human Activities
  17. Human Activities Attract Harmful Mosquitoes in a Tropical Urban Landscape
    Lee JM, Wasserman RJ, Gan JY, Wilson RF, Rahman S, Yek SH
    Ecohealth, 2020 03;17(1):52-63.
    PMID: 31786667 DOI: 10.1007/s10393-019-01457-9
    Knowledge of the interrelationship of mosquito communities and land use changes is of paramount importance to understand the potential risk of mosquito disease transmission. This study examined the effects of land use types in urban, peri-urban and natural landscapes on mosquito community structure to test whether the urban landscape is implicated in increased prevalence of potentially harmful mosquitoes. Three land use types (park, farm, and forest nested in urban, peri-urban and natural landscapes, respectively) in Klang Valley, Malaysia, were surveyed for mosquito larval habitat, mosquito abundance and diversity. We found that the nature of human activities in land use types can increase artificial larval habitats, supporting container-breeding vector specialists such as Aedes albopictus, a dengue vector. In addition, we observed a pattern of lower mosquito richness but higher mosquito abundance, characterised by the high prevalence of Ae. albopictus in the urban landscape. This was also reflected in the mosquito community structure whereby urban and peri-urban landscapes were composed of mainly vector species compared to a more diverse mosquito composition in natural landscape. This study suggested that good environmental management practices in the tropical urban landscape are of key importance for effective mosquito-borne disease management.
    Matched MeSH terms: Human Activities*
  18. 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
  19. Pervasive human-driven decline of life on Earth points to the need for transformative change
    Díaz S, Settele J, Brondízio ES, Ngo HT, Agard J, Arneth A, et al.
    Science, 2019 12 13;366(6471).
    PMID: 31831642 DOI: 10.1126/science.aax3100
    The human impact on life on Earth has increased sharply since the 1970s, driven by the demands of a growing population with rising average per capita income. Nature is currently supplying more materials than ever before, but this has come at the high cost of unprecedented global declines in the extent and integrity of ecosystems, distinctness of local ecological communities, abundance and number of wild species, and the number of local domesticated varieties. Such changes reduce vital benefits that people receive from nature and threaten the quality of life of future generations. Both the benefits of an expanding economy and the costs of reducing nature's benefits are unequally distributed. The fabric of life on which we all depend-nature and its contributions to people-is unravelling rapidly. Despite the severity of the threats and lack of enough progress in tackling them to date, opportunities exist to change future trajectories through transformative action. Such action must begin immediately, however, and address the root economic, social, and technological causes of nature's deterioration.
    Matched MeSH terms: Human Activities/trends*
  20. Landslide spatial modelling using novel bivariate statistical based Naïve Bayes, RBF Classifier, and RBF Network machine learning algorithms
    He Q, Shahabi H, Shirzadi A, Li S, Chen W, Wang N, et al.
    Sci Total Environ, 2019 May 01;663:1-15.
    PMID: 30708212 DOI: 10.1016/j.scitotenv.2019.01.329
    Landslides are major hazards for human activities often causing great damage to human lives and infrastructure. Therefore, the main aim of the present study is to evaluate and compare three machine learning algorithms (MLAs) including Naïve Bayes (NB), radial basis function (RBF) Classifier, and RBF Network for landslide susceptibility mapping (LSM) at Longhai area in China. A total of 14 landslide conditioning factors were obtained from various data sources, then the frequency ratio (FR) and support vector machine (SVM) methods were used for the correlation and selection the most important factors for modelling process, respectively. Subsequently, the resulting three models were validated and compared using some statistical metrics including area under the receiver operating characteristics (AUROC) curve, and Friedman and Wilcoxon signed-rank tests The results indicated that the RBF Classifier model had the highest goodness-of-fit and performance based on the training and validation datasets. The results concluded that the RBF Classifier model outperformed and outclassed (AUROC = 0.881), the NB (AUROC = 0.872) and the RBF Network (AUROC = 0.854) models. The obtained results pointed out that the RBF Classifier model is a promising method for spatial prediction of landslide over the world.
    Matched MeSH terms: Human Activities
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