Tropical peat comprises decomposed dead plant material and acts like a sponge to absorb water,making it fully saturated.However,drought periods dry it readily and increases its vulnerability to fire.Peat fires emit gr...Tropical peat comprises decomposed dead plant material and acts like a sponge to absorb water,making it fully saturated.However,drought periods dry it readily and increases its vulnerability to fire.Peat fires emit greenhouse gases and particles contributing to haze,and prevention by constructing fire-break canals to reduce fire spread into forest reserves is crucial.This paper aims to determine peat physical and chemical properties near a fire-break canal at different fire frequency areas.Peat sampling was conducted at two forest reserves in Malaysia which represent low fire frequency and high fire frequency areas.The results show that peat properties were not affected by the construction of a fire-break canal,however lignin and cellulose content increased significantly from the distance of the canal in both areas.The study concluded that fire frequency did not significantly influence peat properties except for porosity.The higher fibre content in the high frequency area did not influence moisture content nor the ability to regain moisture.Thus,fire frequency might contribute differently to changes in physical and chemical properties,hence management efforts to construct fire-break canals and restoration efforts should protect peatlands from further degradation.These findings will benefit future management and planning for forest reserves.展开更多
During emergency evacuation,it is crucial to accurately detect and classify different groups of evacuees based on their behaviours using computer vision.Traditional object detection models trained on standard image da...During emergency evacuation,it is crucial to accurately detect and classify different groups of evacuees based on their behaviours using computer vision.Traditional object detection models trained on standard image databases often fail to recognise individuals in specific groups such as the elderly,disabled individuals and pregnant women,who require additional assistance during emergencies.To address this limitation,this study proposes a novel image dataset called the Human Behaviour Detection Dataset(HBDset),specifically collected and anno-tated for public safety and emergency response purposes.This dataset contains eight types of human behaviour categories,i.e.the normal adult,child,holding a crutch,holding a baby,using a wheelchair,pregnant woman,lugging luggage and using a mobile phone.The dataset comprises more than 1,5o0 images collected from various public scenarios,with more than 2,9oo bounding box annotations.The images were carefully selected,cleaned and subsequently manually annotated using the Labellmg tool.To demonstrate the effectiveness of the dataset,classical object detection algorithms were trained and tested based on the HBDset,and the average detection accuracy exceeds 90%,highlighting the robustness and universality of the dataset.The developed open HBDset has the potential to enhance public safety,provide early disaster warnings and prioritise the needs of vulnerable individuals during emergency evacuation.展开更多
Eruptive fires are one of the main causes of human losses in forest fire fighting. The sudden change in fire behaviour due to a fire eruption is extremely dangerous for fire-fighters because it is unpredictable. Very ...Eruptive fires are one of the main causes of human losses in forest fire fighting. The sudden change in fire behaviour due to a fire eruption is extremely dangerous for fire-fighters because it is unpredictable. Very little literature is available to support either modelling or occurrence prediction for this phenomenon. In this study, an unsteady physical model of fire spread is detailed, which describes the initiation and development of eruptive fires with an induced wind sub-model. The latter phenomenon is proposed as the mainspring of fire eruptions. Induced wind is proportional to the rate of spread and the rate of spread is in a non-linear relationship with induced wind. This feedback can converge or diverge depending on the conditions. The model allows both explaining why an eruption can occur and predicting explicitly its occurrence according to meteorological conditions, topographic parameters, fuel bed properties and fire front width. The model is tested by comparing its results to a set of experiments carried out at laboratory scale and during an outdoor wildfire, the Kornati accident.展开更多
基金This research was funded by the Ministry of Higher Education Malaysia via the Fundamental Research Grant Scheme(FRGS/1/2020/WAB03/UPM/02/1)。
文摘Tropical peat comprises decomposed dead plant material and acts like a sponge to absorb water,making it fully saturated.However,drought periods dry it readily and increases its vulnerability to fire.Peat fires emit greenhouse gases and particles contributing to haze,and prevention by constructing fire-break canals to reduce fire spread into forest reserves is crucial.This paper aims to determine peat physical and chemical properties near a fire-break canal at different fire frequency areas.Peat sampling was conducted at two forest reserves in Malaysia which represent low fire frequency and high fire frequency areas.The results show that peat properties were not affected by the construction of a fire-break canal,however lignin and cellulose content increased significantly from the distance of the canal in both areas.The study concluded that fire frequency did not significantly influence peat properties except for porosity.The higher fibre content in the high frequency area did not influence moisture content nor the ability to regain moisture.Thus,fire frequency might contribute differently to changes in physical and chemical properties,hence management efforts to construct fire-break canals and restoration efforts should protect peatlands from further degradation.These findings will benefit future management and planning for forest reserves.
基金funded by the Hong Kong Research Grants Council Theme-based Research Scheme(T22-505/19-N)the National Natural Science Foundation of China(52204232)MTR Research Fund(PTU-23005).
文摘During emergency evacuation,it is crucial to accurately detect and classify different groups of evacuees based on their behaviours using computer vision.Traditional object detection models trained on standard image databases often fail to recognise individuals in specific groups such as the elderly,disabled individuals and pregnant women,who require additional assistance during emergencies.To address this limitation,this study proposes a novel image dataset called the Human Behaviour Detection Dataset(HBDset),specifically collected and anno-tated for public safety and emergency response purposes.This dataset contains eight types of human behaviour categories,i.e.the normal adult,child,holding a crutch,holding a baby,using a wheelchair,pregnant woman,lugging luggage and using a mobile phone.The dataset comprises more than 1,5o0 images collected from various public scenarios,with more than 2,9oo bounding box annotations.The images were carefully selected,cleaned and subsequently manually annotated using the Labellmg tool.To demonstrate the effectiveness of the dataset,classical object detection algorithms were trained and tested based on the HBDset,and the average detection accuracy exceeds 90%,highlighting the robustness and universality of the dataset.The developed open HBDset has the potential to enhance public safety,provide early disaster warnings and prioritise the needs of vulnerable individuals during emergency evacuation.
文摘Eruptive fires are one of the main causes of human losses in forest fire fighting. The sudden change in fire behaviour due to a fire eruption is extremely dangerous for fire-fighters because it is unpredictable. Very little literature is available to support either modelling or occurrence prediction for this phenomenon. In this study, an unsteady physical model of fire spread is detailed, which describes the initiation and development of eruptive fires with an induced wind sub-model. The latter phenomenon is proposed as the mainspring of fire eruptions. Induced wind is proportional to the rate of spread and the rate of spread is in a non-linear relationship with induced wind. This feedback can converge or diverge depending on the conditions. The model allows both explaining why an eruption can occur and predicting explicitly its occurrence according to meteorological conditions, topographic parameters, fuel bed properties and fire front width. The model is tested by comparing its results to a set of experiments carried out at laboratory scale and during an outdoor wildfire, the Kornati accident.
