Warning Levels Research Articles

Early ecological security warning of cultivated lands using RF-MLP integration model: A case study on China's main grain-producing areas

The evaluation and early warning of the ecological security of cultivated land are crucial to food security and social stability in the post-epidemic era. In this study, we construct a cultivated ecological security assessment index system based on the pressure-state-response (PSR) model in the main grain-producing areas of China. The entropy method and the multi-factor comprehensive weighting method are used to evaluate the ecological security of the cultivated land in the study area from 2007 to 2018. We propose an integrated model that combines random forest (RF) and multilayer perceptron (MLP), i.e., RF-MLP, to estimate ecological security levels of cultivated lands in the study area from 2019 to 2028. We also present specific policy suggestions to improve the ecological status of cultivated lands. We found that: (1) the ecological security index (E index) of cultivated land showed an overall upward trend from 0.6379 in 2006 to 0.7754 in 2018, despite the fact that this increasing pattern was not considerably strong. The warning situation was changed from medium warning to light warning. The warning levels present a notable spatial imbalance, with higher warning levels occurring in the northern provinces. (2) From 2019 to 2028, although the E index of cultivated land in the main grain-producing areas is expected to be further improved, its values are between 0.7 and 0.8, with most provinces lying in the light warning grades. During the forecasting period, the spatial pattern of the warning situations is stable. In some regions, e.g., North and Northeast China, the cultivated land ecology is expected to be developed towards a positive direction. Although the ecological security of cultivated land in the middle and lower reaches of the Yangtze River region is generally satisfactory, the E index presents a declining trend, thus demanding further attention. The results from this study are expected to benefit decision-makers in designing mitigating strategies for better ecological security management and maintenance of cultivated lands in China's main grain-producing areas.

Analysis of Debris Flow Triggering Conditions for Different Rainfall Patterns Based on Satellite Rainfall Products in Hengduan Mountain Region, China

Rainfall is the main factor that induces debris flow. Satellite rainfall products provide a new source of data in terms of debris flow-triggering conditions to overcome the lack of rainfall data coverage from ground-based rainfall gauges in large-scale mountainous regions. In this study, the applicability of four satellite rainfall products (CMORPH, GPM, MSWEP, and PERSIANN) in the Hengduan Mountain region (HMR) was evaluated with reference to ground observation data from 2000 to 2020. The critical rainfall and rainfall thresholds under different rainfall patterns and warning levels that trigger debris flows were analyzed according to the empirical cumulative distribution function (ECDF) and cumulative probability. The results showed that CMORPH (comprehensive indicator score (CI = 0.72) and GPM (CI = 0.70) performed better in the simulation of daily rainfall sequence consistency and extreme rainfall conditions in the study area. CMORPH also had the highest reconstruction rate for correctly capturing rainfall events that triggered debris flows, with a value of 89%. Approximately half of the rainfall patterns that cause debris flows are antecedent-effective-rainfall-dominated. Both intraday-rainfall-dominated and intraday-antecedent-rainfall-balanced patterns were below 30%. There were evident differences in the critical rainfall for different rainfall patterns under the same warning level. By comparing the results of previous studies on rainfall thresholds, it is believed that the results of this study confirm the application of satellite rainfall products; in addition, the calculated rainfall thresholds can provide a reference for the early warning of debris flows in the HMR. In general, this work is of great significance to the prediction and early warning of debris flow hazards.

A Risk Evaluation Method of Coastal Oil Depots for Heavy Rainfall Vulnerability Assessment

Oil depots in the coastal areas of China are prone to disasters caused by heavy rain due to the monsoon climate. Studies focusing on heavy rainfall vulnerability in coastal oil depots are limited. Therefore, we evaluated the safety of oil depots based on four factors in this study: personnel, equipment and facility, environment, and resilience. Complex networks, analytic hierarchy processes, and information entropy theory were used to establish an evaluation index system including four first-level indicators, nine second-level indicators, and 40 third-level indicators. Scores of 40 evaluation indicators were taken as the input, a vulnerability level of oil depots affected by heavy rain was gained as the output, and results were presented visually (different warning levels distinguished by color) to help oil depot enterprises improve their safety performance under extreme weather conditions.

Dual tree-boosting framework for estimating warning levels of rainfall-induced landslides

Dual tree-boosting framework for estimating warning levels of rainfall-induced landslides

Research on Food Security Risk Assessment and Early Warning in China Based on BP Neural Network Model

China has always attached great importance to food security issues; especially in today’s changeable world, it is particularly important to build a feasible and accurate food security early warning system. According to the influencing factors in food security, this paper uses the PCA method and the AHP method to construct a food security early warning index system that includes 4 secondary indicators and 13 tertiary indicators of total security, trade security, ecological security, and food security. There are four security levels of no warning, light warning, moderate warning, and heavy warning, and finally the comprehensive evaluation of food security from 2000 to 2019 and the specific early warning levels of various indicators are obtained. This paper constructs a food security evaluation system from the perspective of data, breaks through the limitations of existing research, and improves the completeness of food security early warning indicators. Because the BP neural network is a multilayer feedforward neural network with strong adaptability, it is one of the most widely used and successful neural network models at present. Finally, BP neural network is used to simulate China’s food security early warning system and design standardized risk prevention and control processes and classified response strategies—routine monitoring, risk control, and emergency response—to provide signal guidance and reference for China’s food security to respond to risks early.

Dynamics of surgical smoke in the operating room during spinal surgery: Comparison of particulate matter 2.5-air concentration between the electric scalpel with and without a smoke evacuation pencil: A cross-sectional study

BackgroundSurgical smoke is a vaporous by-product generated during tissue incision and cauterization with an electric scalpel. This smoke contains tissue- and blood/vascular-derived substances, bacteria, viruses, and chemical substances. Among them, it contains many fine particles called particulate matter (PM) 2.5, which are harmful and hazardous to the human body. We aimed to investigate the occurrence of PM2.5 in surgical smoke produced during spinal surgery and to evaluate the efficacy of an electric scalpel with a smoke evacuation pencil. MethodsIn this retrospective observational study, 89 patients who underwent spinal surgery between June 2019 and May 2021 were included. A dust monitor was installed in the operating room to measure the PM2.5 air concentration during the surgery. During each surgery, the total amount of PM2.5, the maximum PM2.5 air concentration, the exposure time to PM2.5, and the average value of PM2.5 air concentration from the start to the end of the surgery were calculated. ResultsWe found that in 29 of the 89 cases (32.6%), the air concentration of PM2.5 increased to a level that could cause health damage during the surgery. Twelve cases (13.4%) reached the level that could cause serious health damage, and 8 cases (9%) reached an emergency warning level. The total amount and the maximum and average levels of PM2.5 were significantly suppressed in the surgery with a smoke evacuation pencil group than in the surgery without a smoke evacuation pencil group. ConclusionWe detected hazardous levels of PM2.5 in the air during spinal surgery, highlighting the importance of considering smoke control or reduction during spinal surgery. We recommend using an electric scalpel with a smoke evacuation pencil for regulating PM2.5 levels in the operating room.

Construction of Economic Management Big Data Platform Based on Artificial Intelligence Algorithm Monitoring and Early Warning

The development of artificial intelligence and the emergence of big data have brought convenience to the development of various fields and also brought great influence to the economic field. There are many data sources for economic management, and the scale is huge, so how to manage these large-scale economic data has become an urgent problem to be solved. In addition, the issue of how to conduct security management on these large-scale data, protect the security of users’ accounts and property, and effectively monitor and prewarn economic market risks is also an urgent issue. This article aims to build an economic market risk monitoring and early warning platform through advanced science and technology such as artificial intelligence and big data to realize an intelligent risk control platform in the economic and financial fields, as well as a data-driven risk management model to create intelligent risk early warning and prevention and the response system to enhance the intelligent level of risk assessment, early warning, prevention, and disposal. Experiments show that the artificial intelligence algorithm monitoring and early warning economic management big data platform constructed in this article shows that its accuracy of economic risk prediction can reach more than 90%.

Inundation Analysis of Coastal Urban Area under Climate Change Scenarios

The inundation of urban areas has frequently occurred as a result of the localized heavy precipitation and flash floods in both South Korea and globally. Metropolitan areas with higher property value and population density than rural areas need practical strategies to reduce flood damage. Therefore, this study aims to perform an inundation analysis of coastal urban areas under a climate change scenario. Changwon city is one of the typical coastal metropolitan regions in South Korea. Severe flooding has occurred in this area caused by a combination of precipitation and sea-level rise enhanced by the typhoon, Sanba, in September 2012. At that time, daily precipitation was 65.5 mm, which is lower than the capable amount of rainfall of the drainage system. However, the river stage combined with the tidal wave caused by a typhoon and heavy precipitation exceeded the flood warning level. This study performed the flood inundation analysis for a part of Changwon city using the SWMM model, a two-dimensional urban flood analysis model. Furthermore, we considered the climate change scenarios to predict the potential flood damage that may occur in the future. As a result, as the future target period increases, both the flooding area and the inundation depth increase compared to the results of the inundation simulation according to the current precipitation and sea-level conditions. The inundation area increased by 2.6–16.2% compared to the current state, and the flooded depths would be higher than 1 m or more. We suggest a structural method to reduce inundation damages to consider extreme precipitation and tidal wave effects.

Student Drowsiness During Simulated Solo Flight.

INTRODUCTION: Pilot fatigue is a significant concern in aviation, where efforts are directed at improving rosters, developing models, and improving countermeasures. Little attention has been given to in-flight detection of fatigue/drowsiness. The aims of this research were to determine whether drowsiness is an issue and explore whether infrared reflectance oculography could prove useful for continuous inflight monitoring.METHODS: Nine university-based pilot trainees wore activity monitors and completed diaries, prior to a simulated navigational exercise of approximately 4 h, during the secondary window of circadian low. During the flight they wore a head-mounted device. Oculographic data were collected and converted into a single number, using the Johns Drowsiness Scale (JDS), with increasing values indicating greater drowsiness (range 0.0 to 10.0).RESULTS: Peak JDS values reached 6.5. Values declined from shortly before top of descent, continuing until landing. Two of the nine participants (22.2%), reached drowsiness levels at or above a cautionary warning level, below which is considered safe for driving a motor vehicle.DISCUSSION: The results of this study revealed the timeline and levels of fatigue that might be experienced by student pilots; showing that drowsiness is a potential issue for student pilots operating in flying conditions similar to those in the simulation. Analysis indicated that pilots are likely to experience levels of drowsiness above a cautionary warning level when modeling predicted effectiveness below 90%, indicating a potential drowsiness issue for pilots. It was concluded that oculography is worthy of further investigation for use as an objective fatigue detection tool in aviation.Corbett MA, Newman DG. Student drowsiness during simulated solo flight. Aerosp Med Hum Perform. 2022; 93(4):354-361.

Adaptation capability of rainfall hotspots in water resilient cities using QGIS: a case study of Taichung City in Taiwan

In the context of extreme climate due to global climate transition, rainwater adaptation in resilient cities is a key issue for countries. The purpose of this study is to identify the rainfall hotspots in urban areas and investigate whether these hotspots have environmental conditions for rainfall adaptation. The study site is located in the Taichung area. This study collects rainfall data from rainfall stations at elevations below 500m, employs QGIS (quantum GIS) to create an inverse distance weighted graphical distribution of rainfall to determine the hotspots where the maximum and minimum rainfalls occur, identifies the topography, green spaces, water areas, and buildings in the catchment, integrates the coverage area in the project, and estimates the amount of rainwater that could be directly absorbed by the land within the catchment. The results of this study show that, among the rainfall stations at an elevation below 100m where most urban areas are located, the Taichung rainfall station is the area with the highest number of rainfall events from May to August. Without reliance on gully or river drainage, the natural infiltration of the land in the catchment could only adjust to 80mm of heavy precipitation within 24h of the rainfall warning level of the Central Weather Bureau.

Prediction of spontaneous combustion tendency of coal based on acetaldehyde release

ABSTRACT Acetaldehyde is one of the crucial gases with odor released during the early stage of spontaneous combustion of coal, and it is essential for predicting the spontaneous combustion tendency of coal. In this study, we systematically investigated the effects of coal metamorphic degree, coal temperature, coal particle size, and oxygen concentration on the release of acetaldehyde using three coal samples with different metamorphic degrees and a combination of oxidation and warming processes as well as gas and liquid chromatography. The results showed that with the increase in temperature, the amount of acetaldehyde released first increased and then stabilized or slightly decreased. The amount of acetaldehyde released at low temperatures was linearly related to oxygen consumption. The higher the degree of coal deterioration, the lower the amount of acetaldehyde released. As the particle size decreased, the amount of acetaldehyde released increased and then decreased. Finally, four warning levels were established based on the concentrations and trends of acetaldehyde together with other index gases.

Seasonal geochemical trends and pollution assessment of bottom sediments in the São Francisco hydrographic basin, Brazil: the Três Marias Reservoir.

The Três Marias Reservoir is the ninth largest reservoir in Brazil, becoming crucial for national strategic development. However, many anthropic activities may affect the sediment quality, promoting the need for a proper environmental assessment. This research appraised the seasonal influences on the Três Marias Reservoir's sediment geochemistry, elucidating possible anthropogenic impacts. The concentrations of Mg, Al, Ca, Cr, Fe, Co, Cu, Cd, Ti, Mn, Ni, Zn, Ba, and Pb were measured in 78 samples of bottom sediments regarding the two seasons of the area, a dry winter and rainy summer. The median ± 2 median absolute deviation (MAD) settled the geochemical background and environmental thresholds for the two seasons. The sediment quality guidelines CONAMA 344/12 highlight the possible adverse ecological effects of pollutants. The hierarchical clustering analysis, the geoaccumulation index, and the pollution load index delineated the polluted zones. The pollution load index ranges from 0.25 to 2.28 in the dry season and 0.56 to 2.11 in the rainy season, defining three affected zones in the reservoir. Forestry and agriculture are the probable pollution sources, reaching warning levels that should be considered in further environmental strategies.

重庆市生态风险预警等级划分及演化趋势模拟

PDF HTML阅读 XML下载 导出引用 引用提醒 重庆市生态风险预警等级划分及演化趋势模拟 DOI: 10.5846/stxb202108032116 作者: 作者单位: 作者简介: 通讯作者: 中图分类号: 基金项目: 重庆市自然科学基金杰出青年基金项目（cstc2020jcyj-jqX0004）；国家社科基金后期资助项目（20FJYB035）；教育部人文社科一般项目（20YJA790016） Classification of ecological risk warning levels and simulation of evolutionary trends in Chongqing Municipality Author: Affiliation: Fund Project: 摘要 | 图/表 | 访问统计 | 参考文献 | 相似文献 | 引证文献 | 资源附件 | 文章评论 摘要:生态风险预警等级评估和演化趋势模拟，可为生态风险管理提供可靠的辅助决策。以重庆市为研究对象，基于驱动力-压力-状态-影响-响应模型，构建重庆市生态风险预警指标体系，采用正态云模型和集对分析法，定量分析重庆市生态风险时空分异特征和演化趋势。研究结果表明：（1）2013-2019年，重庆市生态风险值呈"上升-下降"的波动趋势，综合生态风险隶属于重警等级，生态风险综合值从0.295下降到0.278，生态环境逐年好转；（2）重庆市生态风险有下降、不变、先上升后降低、先降低后上升以及一直升高5种演化趋势，分别占比39%、16%、5%、21%、24%；（3）重庆市生态风险转移分为两个方向，2013-2016年生态风险空间分异性增大，中警、轻警和无警风险等级不断向东北、东南和西部四周分散转移；2016-2019年生态风险分布格局变化较小，重警风险区在东部聚集；（4）演化趋势模拟结果表明，未来重庆市生态风险降低的区县有13个，占比34%，生态环境有向好发展的趋势；生态风险上升的区县有25个，占比66%，生态环境会有所恶化，但是恶化程度较低。将生态风险等级划分与预警演化趋势相结合，能为城市生态风险管理提供科学依据。 Abstract:The level assessment on the ecological risk early warning and the simulation on evolutionary trend can provide reliable auxiliary decision making for the ecological risk management. Taking into account the Driving-Pressure-State-Impact-Response model, this paper establishes an index system for ecological risk early warning to quantitatively analyze the temporal and spatial differentiation characteristics and evolution trend of ecological risk in Chongqing Municipality, using the normal cloud model and the set-pair analysis method. The results demonstrate that:(1) from 2013 to 2019, the ecological risk value of Chongqing presented a "rise-decline" fluctuation trend, and the comprehensively ecological risk was ranked as heavy warning level, while the comprehensive ecological risk value decreased from 0.295 to 0.278, denoting that the ecological environment had been improved. (2) There were five evolutionary trends of ecological risk in Chongqing:decreasing, unchanged, increasing-decreasing, decreasing-increasing and always increasing, accounting for 39%, 16%, 5%, 21% and 24% of all regions, respectively. (3) The ecological risk transferred to ward two orientations in Chongqing. From 2013 to 2016, the medium-alert, light-alert and no-alert risk areas continued to spread to the northeast, southeast and west of Chongqing, which intensified the spatial differentiation of ecological risks; whereas from 2016 to 2019, the high-alert risk areas were concentrated in the east, suggesting the little change in the ecological risk distribution pattern. (4) The simulation results of evolution trend revealed that 13 counties, accounting for 34% of all areas, would experience the declining ecological risk and subsequently improving ecological environment in Chongqing in the future; additionally 25 counties, accounting for 66%, would maintain the deterioration of ecological environment to small extent. The combination of ecological risk classification with early warning evolutionary trend can contribute a scientific basis to urban ecological risk management. 参考文献 相似文献 引证文献

Financial Risks Classification Early Warning Analysis of Data Mining Technology

To further improve the informatization level of financial risk early warning, a financial risk classification early warning method based on neural network quantile regression algorithm is proposed. Among them, macro and micro indicators are selected as the index input of early warning, and then the neural network quantile regression algorithm is used to classify and warn the financial risks, finally the specific risk level is output. Simulation results show that the MAE and RMSE of neural network quantile regression algorithm are 7.12e-09 and 1.301e-08, which are lower than those of BP neural network and generalized neural network. Thus the superiority of neural network quantile regression model is verified.

The hazard level set

The hazard rate function plays a fundamental role in survival analysis. Its statistical inference methods have been systemically and exclusively studied. When does the hazard rate reach a particular warning level? This is a basic question of interest to the investigator but largely left to be explored in practice. We define a level set of hazard rate to address this issue and propose a kernel smoothing estimator for such a level set. In terms of the Hausdorff distance, we establish the consistency, convergence rate and asymptotic distribution of the level set estimator. The validity of the proposed confidence set, based on the bootstrap method, for the level set of hazard rate function is theoretically justified. We conduct comprehensive simulation studies to assess the finite-sample performance of the proposed method, which is further illustrated with a breast cancer study.

Levels of Warning in the Text messages Sent by the Saudi Ministry of Health during Covid-19 Pandemic

With the beginning of the Corona pandemic at the beginning of 2019 and its rapid spread, the Kingdom of Saudi Arabia was among the countries that moved very quickly to address this matter. All state institutions played the role related to them. Saudi Ministry of Health launched an intensive package of warning, awareness, and guidance in the form of text messages through multiple electronic platforms to reach the largest segment of society. The study took three sources to collect data, three telecommunications companies, the official account of the Ministry of Health on Twitter and the official website of the Ministry. The current study was based on analyzing these messages in terms of warning levels in various speech acts according to the theories of Austin and Searle, in addition to analyzing the content in terms of its relationship to the actual text and the objective context. The study tried to seek the warning levels in the messages which were classified into three sections: high, moderate, and low and identified the types of actions that represent the levels. The significance of the study lies in revealing how language is used to raise the level of awareness in society. Based on the research methods, this study is analytical and descriptive, based on the theory of speech acts in its foundations, and the development of a reference model for analyzing warning levels that depend on the type of action and its implications, taking into account the indirect speech acts. The results of the study concluded that there is a clear discrepancy in the use of speech verbs to express the three levels of warning.

Drepadom - Establishment of Home Care Services and Hospitalizations for Sickle Cell Disease Patients As Standard Care Since the Covid-19 Pandemic

INTRODUCTIONVaso-occlusive crisis (VOC), hallmark of sickle-cell disease (SCD), is the first cause of patients' emergency room (ER) admissions and hospitalizations. Acute chest syndrome (ACS) is a life-threatening complication that can occur during VOC and prolong hospitalization and is one of the main causes of death in SCD patients. The PRESEV score, established by team members and colleagues, assesses the risk of developing ACS (Bartolucci et al., 2016). In addition, the score has been validated by an international multicenter study, involving 13 centers, distributed in five different countries (PRESEV 2 - ASH 2020). Throughout the first wave of the Covid-19 pandemic, VOC management for SCD patients was a major concern. Our sickle cell referral center set up a hotline to monitor patients suffering from VOC daily, and organized the deployment of home-care services when required. The success of this system during the first wave of the pandemic led to the establishment of DREPADOM, a home-care and hospitalization protocol for VOC management in patients who are at a low risk of developing ACS, as standard care.DESCRIPTION OF SETTINGPatients eligible for DREPADOM are patients that arrive at the ER for a VOC with a low PRESEV score, meaning low risk of developing ACS; or patients that are discharged early following hospitalization for VOC. After physical examination and calculation of the PRESEV score, DREPADOM home hospitalization is systematically offered to patients arriving to the ER with a PRESEV score ≤ 5.If the patient agrees, the DREPADOM nurse coordinator then acts as a link between the pharmacist, the oxygen supplier, the homecare provider, and the DREPADOM medical platform to activate the home hospitalization protocol. This entails the delivery of oxygen supply at the patient's house, dispatch of a medical prescription of opioids and parenteral treatments, twice/thrice-daily visits from homecare nurses, and an on-call SCD expert. DREPADOM relies on a system of daily telephone calls with three levels of expertise and warning and a decision-making algorithm. This is supervised by SCD experts, who arbitrate according to the evolution of the situation (stopping the follow-up, continuing the follow-up as an outpatient, hospitalization) (Fig.1). Furthermore, nurses enter patient vitals in real-time during their daily visits on a dedicated online platform (Link4Life) that contains an integrated automatic alert system. Additionally, a daily phone update between the DREPADOM coordination and the homecare provider's coordination concerning status and evolution of the patient's global condition takes place.RESULTSOver a 6-month period, 39 patients were included in the DREPADOM home hospitalization protocol, 3 of which were included multiple times for a total of 42 inclusions. Mean age was 40 years old [±9], sex ratio was 14/25 (male/female), ER vs early discharge ratio was 21/22, and mean homecare follow-up was 3 days (±1) for both, patients arriving from the ER and early discharge patients. Throughout the third wave of the pandemic, when hospital saturation was a major concern, patients with PRESEV scores 5 ≤ 11 were also offered DREPADOM. Three patients were hospitalized (7%): one for an ACS, who was included during the 3 rd wave of the pandemic with a PRESEV score of 8; one for pyelonephritis unrelated to the VOC; and one for difficulties with venous access. No death was reported.PERSPECTIVESPreliminary satisfaction surveys show a great enthusiasm for DREPADOM, partly due to the high standard of care received, but also due to the shorter length of hospitalization. In fact, median hospital stay for VOC is 4 [3-7] days (Bartolucci, 2016) whereas median homecare follow-up was 3 [1-6] days. [Display omitted] DisclosuresBartolucci: Hemanext: Consultancy; Jazz Pharma: Other: Lecture fees; AGIOS: Consultancy; F. Hoffmann-La Roche Ltd: Consultancy; Emmaus: Consultancy; GBT: Consultancy; INNOVHEM: Other: Co-founder; Bluebird: Consultancy, Research Funding; Novartis: Consultancy, Membership on an entity's Board of Directors or advisory committees, Other: Lecture fees, Steering committee, Research Funding; Addmedica: Consultancy, Other: Lecture fees, Research Funding; Fabre Foundation: Research Funding.

Anticipation Mechanism for Cold Wave: Forecast Based Financing a Case Study in the Peruvian Andes

In June 2018, the Peruvian provinces of Arequipa and Puno in the southern Andean region were affected by heavy snowfall, which caused severe damage to people and livelihoods in several communities. Using the Forecast-based Financing approach, the Peruvian Red Cross implemented its pre-defined early action protocol before this event, after receiving an extreme snowfall warning (Level 4) from the Peruvian meteorological service. Here, we provide a case study of the approach and event itself, documenting the decision-making thresholds as well as the actions taken. This warning activated the thresholds established in the protocol, and Peruvian Red Cross prioritized 10 communities for pre-disaster support based on the forecasted severity of the event in combination with vulnerability and exposure information. The activation took place 2 days before the extreme snowfall in the communities, and the Red Cross distributed veterinary kits for 50 heads of cattle per family, tarpaulins, tool kits to install a temporary awning for alpacas to protect them from the cold wind and snow, protection kits for alpaca herders and warm clothing for children under five, pregnant women, the elderly, and people with chronic and infectious diseases in 430 highly vulnerable households. This article presents the results of a household survey following the impact of the extreme snowfall. We document the early actions taken by these communities to protect their livelihoods, health, and assets. The evaluation also presents descriptive statistics of household-level outcomes for households receiving pre-snowfall support and those that did not receive any intervention or only received post-disaster assistance. While most households took action to protect their assets, there were fewer extreme losses of alpaca herds reported in the communities who received the early support, and these communities also reported fewer adults suffering from respiratory illnesses. More in-depth research on this type of early action is necessary on a wider scale, especially to evaluate the utility of different support measures and the necessary quantity of support needed. This case study can inform government, civil society, and humanitarian actors of how early action is happening before disasters occur and provide a direction for further investment in research and practice to make use of hydro-meteorological forecasts for the benefit of the most vulnerable.

Effective removal of mercury (Hg) from sediment by acid extraction

Mercury (Hg) is a hazardous and toxic substance that is resistant to decomposition and can accumulate in sediment and enter the living organisms via food chain. Due to the status of level mercury in Indonesia has reached the warning level, the action should be taken to avoid any negative impact on the environment and human health. The purpose of this study to developed a simple, fast, and effective sediment washing method for Hg removal using acid extraction. The results showed that pollution level of sediment in Ciujung watershed and its tributaries was high, especially the pollution of Hg and Cd, according to geoaccumulation index (I geo) and potential ecological risk index (PERI). By the proposed method, Hg-contaminated sediment can be extracted within 21 minutes with 99.5% of extraction rate. CRM ERM CC580 (estuarine sediment) was used for validating the results. The optimal conditions such as vortex time, acid concentration, sonication time were evaluated according to analytical parameters. The method is valuable for designing a remediation protocol for sediment washing in developing countries.

Research on Early Warning for Gas Risks at a Working Face Based on Association Rule Mining

In the process of gas prediction and early warning, outliers in the data series are often discarded. There is also a likelihood of missing key information in the analysis process. To this end, this paper proposes an early warning model of coal face gas multifactor coupling relationship analysis. The model contains the k-means algorithm based on initial cluster center optimization and an Apriori algorithm based on weight optimization. Optimizing the initial cluster center of all data is achieved using the cluster center of the preorder data subset, so as to optimize the k-means algorithm. The optimized algorithm is used to filter out the outliers in the collected data set to obtain the data set of outliers. Then, the Apriori algorithm is optimized so that it can identify more important information that appears less frequently in the events. It is also used to mine and analyze the association rules of abnormal values and obtain interesting association rule events among the gas outliers in different dimensions. Finally, four warning levels of gas risk are set according to different confidence intervals, the truth and reliable warning results are obtained. By mining association rules between abnormal data in different dimensions, the validity and effectiveness of the gas early warning model proposed in this paper are verified. Realizing the classification of early warning of gas risks has important practical significance for improving the safety of coal mines.