Loss Of Human Life Research Articles

Fighting against forest fire: A lightweight real-time detection approach for forest fire based on synthetic images

Forest fires are known for their high level of randomness and unpredictability, which often lead to significant ecological damage and human life loss. Existing forest fire detection technologies are not capable of detecting small-scale flames or smoke in real time, thus failing to meet the demands of real-time detection of forest fires using Unmanned Aerial Vehicles (UAVs). To overcome these limitations, we propose an efficient and lightweight forest fire detection method that utilizes synthetic images and UAVs to achieve real-time and high-precision detection of forest fires against complex backgrounds. Firstly, we propose the Dilation Repconv Cross Stage Partial Network (DRCSPNet), which enhances the detection capabilities for multiscale flames and smoke using multi-branch parallel joint dilation convolution and batch normalization, while effectively extracting features from different stages of forest fires. Secondly, to mitigate challenges associated with extreme lighting in forest scenes and large contrast variation in fire images, we propose a Global Mixed-Attention (GMA) model across feature pyramids to enhance information lost in high-dimensional feature maps and increase the robustness of the model through a multiscale fusion strategy. Finally, we present the Lite-Path Aggregation Network (Lite-PAN) with varying scales to improve effective feature flow for multilevel forest fires, addressing challenges that arise from various climatic conditions. Furthermore, we employ Unreal Engine 5 to generate forest fire datasets in four scenarios to address the issue of relatively limited aerial forest fire datasets. According to the results of the experiment, our proposed method achieves 58.39% mAP(mean Average Precision) with 5.703 GFLOPs (Giga Floating Point Operations Per Second) while yielding a frame rate of 33.5 Frames Per Second (FPS) on NVIDIA Jetson NX. Extensive experiment results demonstrate our method has the advantage of being in real time, extremely accurate, and easily implementable compared to state-of-the-art techniques.

Aplicación de redes neuronales artificiales a la modelación de lluvia-escorrentía en la cuenca del río Chancay Lambayeque

Between the months of December to April, regions of northern Peru, including Lambayeque, are affected by maximum extreme events, wreaking havoc on homes, flooding crop fields, collapsing hydraulic works, and the most irreparable loss of human lives. In this line, the objective of this research was to apply Artificial Neural Networks to rain-runoff modeling in a basin in northern Peru, namely, the Chancay Lambayeque river basin belonging to the Pacific slope. For this purpose, records of precipitation and flows of 30 years (hydrological normal) were collected from 12 hydrometeorological stations belonging to the basin and neighboring it. Thus, applying a model of Long and Short Term Memory Networks (LSTM) we proceeded to model the rain, seeking to follow the behavior of the flows observed in the Racarrumi hydrometric station, with 80 % of the information the model was trained and with 20 % it was validated. In short, it was obtained that in the modeling validation stage, the Nash coefficient was 0.93, corresponding to the qualifier "very good".

Death and mourning in times of COVID-19. The experience of Zimbabwean migrants in South Africa

The paper deals with the effects of the regulations and restrictions on the handling of corpses and funerals among Zimbabwean migrant families who lost relatives in South Africa during the COVID-19 epidemic in the years 2020 and 2021. Interviews were conducted with members of this migrant community. The interviews revealed a range of affective and material dimensions entangled in these multiple losses, highlighting therefore experiences of truncated grief. Restrictions on funerals and burials forced immobility on the living and their dead; on bereaved communities, themselves and their corpses are forms of biopower that multiplied losses among cross-border migrant communities. The loss of autonomy of communities around death rituals and burial places constitute realms where the materiality of death is revealed. I look at both the loss of human life and the emotional losses associated with the limitations imposed on the dead body under COVID-19.

Understanding labour exploitation as a social determinant of health in migrant populations – insights from the Danish population-based registers

Abstract Despite recent global events such as the COVID-19 pandemic and the Qatar world cup drawing attention to the significant loss of human lives of labour migrants in precarious working conditions, evidence on the comparative mortality of migrant workers remains sparse and inconsistent. Studying the comparative mortality of migrant workers with local workers is challenging for several reasons. Data on migrants’ employment conditions and health outcomes are often incomplete, and when available captured in separate databases. In addition, there is a need to disentangle the healthy migrant effect from the healthy worker effect, which presents challenges in the selection of reference populations and temporality when studying mortality of migrants. In view of such challenges, Denmark provides an ideal context to study labour migration and mortality. The Danish population-based registers offer linked longitudinal data on migrants’ employment history and mortality outcomes. Denmark’s migrant integration policy also has a history of weaving immigration and labour policies, and migrants in Denmark continue to be prone to labour exploitation in high-risk industries such as construction and agriculture. This presentation shall use Denmark as a case study to compare the mortality outcomes of migrant workers with Danish workers to better understand labour exploitation as a social determinant of health.

Machine Learning Ensemble Methodologies for the Prediction of the Failure Mode of Reinforced Concrete Beam–Column Joints

One of the most critical aspects in the seismic behavior or reinforced concrete (RC) structures pertains to beam–column joints. Modern seismic design codes dictate that, if failure is to occur, then this should be the ductile yielding of the beam and not brittle shear failure of the joint, which can lead to sudden collapse and loss of human lives. To this end, it is imperative to be able to predict the failure mode of RC joints for a large number of structures in a building stock. In this research effort, various ensemble machine learning algorithms were employed to develop novel, robust classification models. A dataset comprising 486 measurements from real experiments was utilized. The performance of the employed classifiers was assessed using Precision, Recall, F1-Score, and overall Accuracy indices. N-fold cross-validation was employed to enhance generalization. Moreover, the obtained models were compared to the available engineering ones currently adopted by many international organizations and researchers. The novel ensemble models introduced in this research were proven to perform much better by improving the obtained accuracy by 12–18%. The obtained metrics also presented small variability among the examined failure modes, indicating unbiased models. Overall, the results indicate that the proposed methodologies can be confidently employed for the prediction of the failure mode of RC joints.

Effect of Vertical Aspect Ratio on Mass, Stiffness and Vertical Geometric Irregular Structures

Occurrence of earthquake will highly damage the structures which are not modelled and designed to resist such loads it may cause sometimes the collapse of structures leads to loss of human lives. The size of the building and slenderness ratio are the important factors for the efficient structural design. The aspect ratio of buildings is considerably effects under seismic loads. It is important to know the influence of aspect ratio on the irregular structures in high seismic zone areas. The research has been performed on the 15 storey building frames of five cases with mass, stiffness and vertical geometric irregularities. Each case comprises of four models having various vertical aspect ratios. The purpose of this study is to investigate the influence of vertical aspect ratio on mass, stiffness and vertical geometric irregular structures and how the structure will vary under seismic loads. The irregularities are incorporated in to the buildings by increasing the loads and column height in particular storeys and by eliminating the columns and beam sections compared to adjacent storeys. Linear dynamic analysis is performed. It is observed that with the decrease of vertical aspect ratio the structural response will increases the response of storey displacement, base shear, storey drift and over turning moment in the cases of mass and stiffness irregular structures. In the case of vertical geometric irregular structures with the decrease of vertical aspect ratio the storey displacement and storey drift values decrease for all the models, the base shear, overturning moment and stiffness increases with the decrease of vertical aspect ratio. Key words: Mass irregularity, Stiffness irregularity, Vertical geometric irregularity, Vertical aspect ratio, Linear dynamic analysis.

Study of the influence of rheological parameters on the behavior of a submarine debris flow

Submarine debris flows are events that occur with great frequency on a geological scale and can cause major damage to offshore structures or even loss of human life. Understanding the mechanisms involved in the development of a debris flow requires not only knowledge of soil mechanics, but also knowledge of fluid mechanics, since the incorporation of water during the flow causes changes in shear strength. Unlike soils, the shear strength of fluids is represented by mathematical models called rheological models. The objective of this paper is to propose a new rheological model capable of representing the changes caused by water entrainment into a submarine debris flow, by correlating it with the Liquidity Index of the soil. A rheological analysis of marine clay samples from the pre-salt layer has made it possible to represent the variation in strength due to water content. The influence of water content and velocity on flow behavior has also been studied through the analysis of centrifuge tests performed in a previous study.

Links between disasters of natural origin, economic development and gender inequality: evidence for latin America and the Caribbean

AbstractThis paper explores the relationship between gender and disasters of natural origin (ND) by considering how the level of economic growth influences the losses caused by these events and how disasters also contribute to gender inequality. To this end, the effects of ND occurring during the 2000–2021 period in 20 countries in Latin America and the Caribbean are analysed using a structural equation approach with fixed effects panel data (SEM-FE) as an analytical tool. The results obtained are in line with previous literature that identifies population growth and economic growth as variables that drive the increase in loss of human life from NDs. The results show evidence of the mitigating effects of gender equality on the impact of NDs measured in terms of human life and of the negative effects of the impact of NDs on gender equality. The findings reinforce gender equality as a key element in preventing disasters and rebuilding a sustainable environment in their aftermath.

Multiple relocation cases in disaster-related deaths after the Fukushima nuclear accident

IntroductionRelocation after a disaster often has negative health effects on vulnerable people, particularly older adults. However, little is known about the number of repeated evacuations and relocations after disasters among those experienced disaster-related deaths. This case series aimed to summarize the secondary problems and mortality risks in patients involved in relocation after disasters.MethodsWe analyzed and summarized the data on 520 disaster-related deaths in Minamisoma City, a municipality near the Fukushima Daiichi Nuclear Power Plant, focusing on the number of relocations. Herein, we describe the cases of three individuals over 80 years old, who died after eight or more relocations, which were certified as disaster-related deaths.ResultsWe discuss following three cases, which are (1) a female patient in her 90s who had a stroke and was completely disabled, (2) a female patient in her 80s whose cancer was detected later due to her failing to see her family doctor, and (3) a male patient in his 80s whose regular postoperative treatment was disrupted following cancer recurrence.DiscussionOur findings indicate that evacuation led to the worsening of chronic diseases and eventually to post-disaster death. Prolonged wide-area evacuation and repeated relocation likely interrupted the continuum of care and led to the failure of adequate care provision. To reduce the loss of human life, it is necessary to review the number of wide-area evacuations and relocations over the medium to long term after large-scale disasters, including nuclear disasters, which may occur in the future. Simultaneously, it is important to establish a medical system at the evacuation site so that disaster victims can continue to receive medical care even after evacuation or relocation.

Landslides: A Review from the Southern Western Ghats of India

ABSTRACT Landslides are the most unpredictable catastrophic events in mountainous and hilly regions. South Western Ghats one among of the regions in India that have experienced recurring landslides due to increase in pre-and post-monsoon rains. Landslides often cause damage to buildings, roads, natural vegetation, even result in the loss of human lives in South Western Ghats with indirect impact on the socioeconomic status of the country. It is crucial to depict the current trend technologies and their performance in landslide studies, offering valuable insights into mitigation strategies. This article highlights on prevalent techniques for mapping landslide zones, along with major conditional factors and assess the performance of these techniques in landslide susceptibility mapping of the South Western Ghats. Landslide studies of the South Western Ghats reveal 20% of the research work with the Landsat 8 OLI for mapping landslides, 13.3% of the studies using the higher spatial resolutions 2.5m and 10m of Cartosat DEM, the rest of the studies were based on the spatial resolution of 12.5m and 30m from ALOS PALSAR, ASTER and SRTM DEM. About 26% landslide studies have employed quantitative approaches primarily the frequency ratio of bivariate. Subsequently machine learning techniques such as random forest and support vector machine have gained prominence, accounting for 33% landslide studies. The literature depicts precipitation as a significant triggering factor and slope as the major conditional factor in the occurrence of landslides within the South Western Ghats. Prioritized densely populated areas in landslide studies have increased attention to smaller geographical areas and forest regions in the South Western Ghats to protect both inhabitants and ecosystems.

Reliability modeling and analysis of uncertain competing failure systems

The competing failure system is quite common in real life, such as automotive system and aerospace system. Two distinct failure modes usually coexist in this type of system: gradual internal degradation and sudden external shock, where the occurrence of any failure mode can lead to the system failure. The system failure such as aircraft accident may cause a terrible loss of human life and property. Hence, it is worth reducing the rate of system failure by improving the system reliability. While, reliability model is the foundation of any research of reliability and epistemic uncertainty exists in the process of modeling and estimating. Therefore, we introduce uncertainty theory to investigate the competing failure system with epistemic uncertainty. The internal degradation and external shock of the system are characterized by an uncertain differential equation and an uncertain renewal process, respectively. The system reliability is defined as the uncertain measure that neither internal degradation nor external shock surpass corresponding thresholds. In addition, some reliability formulas are derived for the systems subjected to extreme shock, cumulative shock, running shock and δ-shock, respectively. Finally, a case study with respect to micro-electro-mechanical system is given to show how the proposed model is implemented.

Specific Technologies of Aviation Rescue and Firefighting Operations over the Years

The twentieth century was a period of rapid development in aviation. It was a time of great progress in the construction of aircraft, the development of ground infrastructure and the improvement of qualifications of aviation personnel. Over the years, aviation has gone from cruise flights in simple aircraft to transatlantic flights, complex aircraft with hundreds of passengers on board. Military aviation can operate at speeds exceeding the speed of sound and in all weather conditions. The infrastructure of large airports has reached the size of cities. Over the past century, the development of aviation has been followed by changes in the organization and equipment of rescue and fire-fighting services. The public does not accept the loss of human life or large-scale material damage and environmental damage. In the search for effective extinguishing agents and methods of administering them, people began to look for ways other than field fire services. Specialized firefighting units have appeared at airports, which are designed for specific rescue and firefighting operations on aircraft and infrastructure. For many years, attempts have been made to select special rescue and firefighting technologies capable of improving the effectiveness and safety of operations. Quite quickly, the car was chosen as the means of transport for airport rescue services. But over time, the need was recognized for it to have different characteristics and equipment than a vehicle for village and town safety units. As a result of various experiments, unusual technical solutions designed to solve basic problems specific to airports were developed. Equally interesting was the path to determining the best fire extinguishing agents and their means of administration. From hand-held fire extinguishers to remote-controlled cannons administering thousands of liters or kilograms of extinguishing agents over a long distance. The publication provides information on the history and development of basic firefighting technologies specific to aviation and some pointers to the future.

A stringent failure: Regulators do not use available tools to protect aquatic ecosystems from clearcut forestry impacts in New Zealand

Effective regulation of land-use activities in steepland areas is crucial to protect downstream ecologies and human life as intense rainfall disturbances become more frequent globally. In Aotearoa New Zealand extensive synchronous clear-cutting of Pinus radiata monocultures on steep convergent landforms, and associated earthworks, causes ongoing accelerated erosion, excessive sedimentation, and debris-laden landslides after adverse weather events. This study examines the implementation of national forestry regulations established in 2018, which also enable regional councils to create more stringent rules to protect aquatic ecosystems. All sixteen councils were surveyed in 2021 and 2022 for stringency in planning provisions; and four regions seriously affected by Cyclone Gabrielle in 2023 were resurveyed in 2024. The cyclone caused loss of human life and calamitous damage to housing, infrastructure, and productive land uses from floods exacerbated by clearcut logging debris. All councils had administratively adopted the national regulations into their existing freshwater and coastal resource management plans. Twelve councils retained existing rules that conflicted with the regulations, but these rules do not protect all regionally significant aquatic ecosystems nor minimise landslide risks. No council, except Gisborne, had instigated the expensive and lengthy statutory resource management plan change process, nor taken a ‘strategic and principled’ approach to develop more stringent regulations, such as tougher restrictions on clear-cutting, earthworks, and replanting on steep erosion-prone convergent landforms to protect aquatic ecosystems and vulnerable communities. The government did tighten the management of logging debris after the cyclone, but the national regulations remain ineffective in addressing clear-cut practices maladaptive to intense rainfall and continue to permit replanting on convergent landforms. The regulations need urgent amendment to require councils to develop and implement a strategic and principled approach to stringency to better protect aquatic ecosystems, human life, economic livelihoods, and public infrastructure. Water quality monitoring is also currently inadequate, as no council systematically monitors the effects of forestry activities on sedimentation rates, which inhibits the ability to compare across and between regions. Foresters are not required to monitor water quality, which also stymies assessments of compliance and policy effectiveness. Councils regularly monitor rivers, lakes, and estuaries, but rarely the effects of individual land uses. This means that sediment or other contaminants cannot be parsed to different land use activities, undermining attempts to set catchment limits. It can also take decades at current monitoring levels to defensibly show any water quality improvements after changes to land use regulations. The current environmental limits approach of setting individual water quality attribute targets is highly unlikely to drive changes to maladaptive and ecologically degrading land uses. A new environmental management approach is needed that prohibits or effectively constrains hazardous and adverse activities.

An efficient optimization approach with mobility management for enhanced QoS and secure communication in flying adhoc networks

In biomedical Applications, Network plays a vital role to transmit the health-related information from patient to doctors which helps to save the human lives. During the data transmission, there is the possibility of data loss, delayed delivery, malicious attack and that leads to huge loss in human lives. To overcome this issue, a new technique Smart & secure Flying Adhoc Networks (FANET) architecture was proposed for the Quality-of-Service (QoS) improvement in biomedical applications. This research work helps society to save human lives during any emergency. With the integration of optimization and clustering techniques, the design configuration of the proposed architecture includes mobility management and secure transmission. The first phase includes sensing the patient's information by using Arduino IDE hardware. The sensed data will be transmitted through different types of networks including mobile ad-hoc networks, vehicular ad-hoc networks and flying ad-hoc networks. The routing protocol concentrates on clustering formation, mobility management, secure transmission and optimization. When compared to other type of networks, the proposed Smart & secure Flying Adhoc Networks (FANET) improves the network performance. The quality-of-service was achieved using the parameters such as network throughput, latency, delivery rate, routing overhead and control overhead.

Fatal landslides in Kencho, Shacha & Gozdi villages, Gofa zone, Ethiopia: A detailed investigation (Geological, Geotechnical, geophysical & geospatial) of the July 22, 2024 catastrophe and its socioeconomic repercussions

A landslide is one of the geological hazards that cause the most disaster in densely populated areas. The landslide that occurred in Gezie Gofa woreda, Gofa Zone, Kencho Shacha Gozdi village, killed more than 250 people. Two landslides occurred on July 22, 2024, at 8:30 and 10:40 AM The first landslide killed six people and demolished three houses. The second landslide killed more than 245 people, including those who came to the site to excavate the buried bodies during the first landslide. The primary objective of this research is to evaluate the landslide causative factors, model the landslide susceptibility, and characterize the landslide disaster on socioeconomic effects that occurred on July 22, 2024. The landslide inventory data, field surveys, laboratory analyses, and various geophysical surveys characterized the current and past landslides of the area. The landslide susceptibility model was modeled using a statistical approach in the GIS. environment. The socioeconomic effects were assessed using field surveys and systematic interviews with the victims. The conditioning factors selected for landslide susceptibility modeling are lithology, geological structures, groundwater, slope, land use or land cover, aspect, curvature, and elevation. The major triggering factor of the landslide in the area was heavy rainfall, which occurred on July 21–22, 2024, between 3:00 a.m.-8:30 a.m. The results reveal that the significant conditioning factors of the landslide in the study area are geological structures (both visible and inferred), groundwater, slopes, and human activities. The characterized socioeconomic effects include the destruction of agricultural land, the demolishing of houses, and the loss of human lives, as well as several people evacuated and sheltered under tents and churches. However, the most momentous disaster in the area is the loss of human life. Based on the research results, it would be better to relocate those people living in the high landslide susceptible zones, and all high landslide-prone areas and mountainous terrain in southern Ethiopia should be mapped, and the people should be aware of the landslide risk areas.

Ultra-lightweight convolution-transformer network for early fire smoke detection

BackgroundForests are invaluable resources, and fire is a natural process that is considered an integral part of the forest ecosystem. Although fire offers several ecological benefits, its frequent occurrence in different parts of the world has raised concerns in the recent past. Covering millions of hectares of forest land, these fire incidents have resulted in the loss of human lives, wild habitats, civil infrastructure, and severe damage to the environment. Around 90% of wildland fires have been caused by humans intentionally or unintentionally. Early detection of fire close to human settlements and wildlife centuries can help mitigate fire hazards. Numerous artificial intelligence-based solutions have been proposed in the past decade that prioritize the detection of fire smoke, as it can be caught through remote sensing and provide an early sign of wildland fire. However, most of these methods are either computationally intensive or suffer from a high false alarm rate. In this paper, a lightweight deep neural network model is proposed for fire smoke detection in images captured by satellites or other remote sensing sources.ResultsWith only 0.6 million parameters and 0.4 billion floating point operations per second, the hybrid network of convolutional and vision transformer blocks efficiently detects smoke in normal and foggy environmental conditions. It outperforms seven state-of-the-art methods on four datasets, including a self-collected dataset from the “Moderate Resolution Imaging Spectroradiometer” satellite imagery. The model achieves an accuracy of more than 99% on three datasets and 93.90% on the fourth dataset. The t-distributed stochastic neighbor embedding of extracted features by the proposed model demonstrates its superior feature learning capabilities. It is remarkable that even a tiny occurrence of smoke covering just 2% of the satellite image area is efficiently detected by the model.ConclusionsWith low memory and computational demands, the proposed model works exceedingly well, making it suitable for deployment in resource constrained devices for forest surveillance and early fire smoke detection.

Time Horizon for Forecasting the Number of Traffic Accidents

On Polish roads, there are more and more automobiles each year. This results in more people using the roadways. Because of this, the threat posed by traffic accidents, which involve the loss of human life and financial resources, is quickly expanding. The rise of motorization and the quickening expansion of the human population are to blame for this. The tiny amount of the dataset that may be utilized for research in this respect is the major problem in anticipating and evaluating data on road accidents. Despite the fact that road accidents result in millions of lives and injuries worldwide each year, they are not very common. Traffic accidents are still quite common despite the pandemic's reduction. The aim of the essay is to evaluate the temporal horizon of the predicted number of traffic accidents. For this reason, an estimate of the expected number of traffic accidents in the future years was made, and its effect on the average percentage error of the projection was computed. Based on the analysis of the input data on the number of road accidents (annual and monthly), the results show that monthly and annual data can be forecast up to six years in advance.

Landslide susceptibility zonation mapping using geospatial technologies and multi criteria evaluation techniques in the upper Didessa sub-basin, Southwest Ethiopia

ABSTRACT Landslides have a profound impact on landscape geology, resulting in extensive devastation and loss of human lives. Mapping landslide susceptibility is crucial for effective land use planning in mountainous country like Ethiopia. This study was conducted in the upper Didessa sub-basin, southwestern parts of Ethiopia using Geographic Information System (GIS) and multi criteria evaluation (MCE) technique. This study employed a blend of primary data, encompassing field surveys and interviews with experts, as well as secondary data derived from diverse source, such as remote sensing data, digital soil maps, and geological maps. A total of eleven critical factors were employed to assess the triggers of landslides. These factors include slope, aspect, drainage density, topographic wetness index (TWI), stream power index (SPI), topographic ruggedness index (TRI), hypsometric integral, lithology, land use land cover (LULC), soil texture, and distance from roads. The analytical hierarchy process (AHP) method was used to determine the significance of each indicator through pairwise comparison matrix. The study area was categorized into different zones based on the susceptibility to landslides, namely very high, high, moderate, low, and very low. Results revealed that cultivated land had the highest likelihood of experiencing landslides, with a total of nine incidents out of 25, followed by built-up areas with seven landslides. Conversely, dense forests, sparse forests, and grazing land experienced a lower likelihood of landslides. Out of the 11 factors contributing to landslides, 24% of the surveyed region was deemed to have a moderate susceptibility, with 12% and 6% falling into the categories of high and very high susceptibility to landslides, respectively. The findings of this research provide important information for policymakers to develop efficient measures for preventing and reducing the risks of landslides.

Historical Account of Managing Overabundant Wild Asian Elephants in Myanmar by the Kheddah System of Capture: Philosophy, Principles and Practices.

When standard methods of human-elephant conflict mitigation are not successful, free-ranging wild elephants may continue to come into close contact with people. This results in more frequent and severe conflict, with consequences ranging from crop raiding to loss of human and elephant lives. Understandably, in such situations, local communities may want to be rid of entire herds of elephants. Historically, one of the strategies the Myanmar Government practiced to resolve human-elephant conflict was the capture of whole herds using the Kheddah system. This involved trapping the herd in a stockade, immediately followed by on-site post-capture taming. After taming, the captured elephants were utilized as logging elephants. Elephants worked in timber extraction, retired at age 55 years and were then cared for by the Government until they died. The capture of wild elephants by the Kheddah system was formally banned in Myanmar in 1985 but occasional, small-scale, captures were allowed until 2013 under the strict control of the Myanmar Government. These operations were aimed primarily at capturing elephants involved in human-elephant conflict, rather than to supplement the working elephant population. One of the last Kheddah operations, organized and managed by the author, was conducted in 1996 at the Taikkyi township of the Yangon Region, as a last resort to end human-elephant conflict in an emergency. While chemical immobilization was being used at this time, it was not logistically possible with the high numbers of elephants engaged in the conflict. This review aims to record the history of an activity that was an important element of Myanmar's timber industry more than three decades ago. In this paper, the author presents a description of Kheddah not to endorse it, but to document (1) the Myanmar elephant population management strategy in the past, before it is forgotten, and (2) the practicality of the Kheddha operation when the singly selected commonly- used immobilization or noosing method of elephant capture is unfeasible. The author attempts to shed light on the modern veterinary procedures that may significantly reduce the notorious historical outcome of Kheddha, especially the resulting mortality of captured elephants, should the Kheddha system of capture ever be used as an emergency solution for ongoing problems of human-elephant conflict in the range states of Asia.

Analysis of Suicide Risk in European Countries Using Artificial Intelligence Methods

Suicides are a phenomenon that negatively impacts society. The issues include the loss of human life, negative psychological effects on close ones, increased healthcare costs, lost productivity, and more. Suicides also create social and emotional tensions that can deteriorate the quality of life in affected communities. This is why it is necessary to study and research trends to enable prevention. In the present study, an analysis of suicide risk in some European countries is conducted using an artificial intelligence system implemented with Neural Networks, Random Forests, Support Vector Machines, and more. The study seeks answers to the following questions: Are suicide rates increasing or decreasing? Are there differences in the number of suicides based on age and gender? Do economic factors affect the number and rate of suicides? Can we predict the number of suicides based on the given data? The machine learning methods used for this purpose are created and modeled in the Orange Data Mining environment, which also offers numerous tools for analyzing and visualizing the obtained results. It is important to note that the study of suicide risk is not equivalent to the diagnosis of suicidal disorders; however, the answers to the questions posed in the study are crucial for building and analyzing the overall picture of suicidal intentions.