High-risk Zone Research Articles

PHYSICAL RECREATION IN STUDENT’S EDUCATIONAL SPACE

A person's health reflects the most important aspect of social life and is fundamentally related to his right to physical, spiritual, mental, and social well-being. Strengthening and maintaining citizens' health is one of the prior directions of the state policy of Russia and Belarus. Young students belong to the group of the population that is at high-risk zone of disease, due to the need for more psychological, emotional and mental load, study and adaptation to new conditions of the population, etc. To optimize the level of students’ psychophysical condition, it is necessary to increase the amount of their movement activity. The aim of the research is to identify the theoretical and practical significance of sports in the educational space. To determine students' attitudes towards physical recreation. Research methods and organization: The following methods were used in this research: study of the scientific-methodical literature on the research problem, a sociological survey, a questionnaire, a pedagogical observation, mathematical statistics. 957 students took part in the survey. As a result of the research, it was revealed that according to the results of a social survey among students of the Republic of Belarus, girls preferred forms of independent physical-culture rehabilitation activities: hiking, sectional trainings in various sports, morning hygienic gymnastics, etc. Students' attitudes towards sports as a means of physical recreation were revealed. Of the twenty sports offered, 80% of the students mentioned nine. Male students are interested in football, swimming, basketball, tourism, and female students are interested in aerobics, swimming, table tennis, and tourism.

The Role of Wildlife and Pests in the Transmission of Pathogenic Agents to Domestic Pigs: A Systematic Review.

Wild animals and pests are important reservoirs and vectors of pathogenic agents that can affect domestic pigs. Rapid globalization, anthropogenic factors, and increasing trends toward outdoor pig production facilitate the contact between domestic pigs and wildlife. However, knowledge on the transmission pathways between domestic pigs and the aforementioned target groups is limited. The present systematic review aims to collect and analyze information on the roles of different wild animal species and pests in the spread of pathogens to domesticated pigs. Overall, 1250 peer-reviewed manuscripts published in English between 2010 and 2022 were screened through the PRISMA framework using PubMed, Scopus, and Web of Science databases. A total of 84 studies reporting possible transmission routes of different pathogenic agents were included. A majority of the studies (80%) focused on the role of wild boars in the transmission of pathogenic agents to pig farms. Studies involving the role of rodents (7%), and deer (6%) were the next most frequent, whereas the role of insects (5%), wild carnivores (5%), wild birds (4%), cats (2%), and badgers (1%) were less available. Only 3.5% of studies presented evidence-based transmission routes from wildlife to domestic pigs. Approximately 65.5% of the included studies described possible risks/risk factors for pathogens' transmission based on quantitative data, whereas 31% of the articles only presented a hypothesis or qualitative analysis of possible transmission routes or risk factors and/or contact rates. Risk factors identified include outdoor farms or extensive systems and farms with a low level of biosecurity as well as wildlife behavior; environmental conditions; human activities and movements; fomites, feed (swill feeding), water, carcasses, and bedding materials. We recommend the strengthening of farm biosecurity frameworks with special attention to wildlife-associated parameters, especially in extensive rearing systems and high-risk zones as it was repeatedly found to be an important measure to prevent pathogen transmission to domestic pigs. In addition, there is a need to focus on effective risk-based wildlife surveillance mechanisms and to raise awareness among farmers about existing wildlife-associated risk factors for disease transmission.

Clinical and Analytic Accuracy of Simultaneously Acquired Hemoglobin Measurements: A Multi-Institution Cohort Study to Minimize Redundant Laboratory Usage.

Frequent diagnostic blood sampling contributes to anemia among critically ill children. Reducing duplicative hemoglobin testing while maintaining clinical accuracy can improve patient care efficacy. The objective of this study was to determine the analytical and clinical accuracy of simultaneously acquired hemoglobin measurements with different methods. Retrospective cohort study. Two U.S. children's hospitals. Children (< 18 yr old) admitted to the PICU. None. We identified hemoglobin results from complete blood count (CBC) panels paired with blood gas (BG) panels and point-of-care (POC) devices. We estimated analytic accuracy by comparing hemoglobin distributions, correlation coefficients, and Bland-Altman bias. We measured clinical accuracy with error grid analysis and defined mismatch zones as low, medium, or high risk-based on deviance from unity and risk of therapeutic error. We calculated pairwise agreement to a binary decision to transfuse based on a hemoglobin value. Our cohort includes 49,004 ICU admissions from 29,926 patients, resulting in 85,757 CBC-BG hemoglobin pairs. BG hemoglobin was significantly higher (mean bias, 0.43-0.58 g/dL) than CBC hemoglobin with similar Pearson correlation ( R2 ) (0.90-0.91). POC hemoglobin was also significantly higher, but of lower magnitude (mean bias, 0.14 g/dL). Error grid analysis revealed only 78 (< 0.1%) CBC-BG hemoglobin pairs in the high-risk zone. For CBC-BG hemoglobin pairs, at a BG hemoglobin cutoff of greater than 8.0 g/dL, the "number needed to miss" a CBC hemoglobin less than 7 g/dL was 275 and 474 at each institution, respectively. In this pragmatic two-institution cohort of greater than 29,000 patients, we show similar clinical and analytic accuracy of CBC and BG hemoglobin. Although BG hemoglobin values are higher than CBC hemoglobin values, the small magnitude is unlikely to be clinically significant. Application of these findings may reduce duplicative testing and decrease anemia among critically ill children.

Climate-induced vegetation dynamics associated with the prevalence of charcoal oak disease in Zagros forests

Zagros forests are the important ecosystem for water and soil protection. Recently, these forests have been affected by extensive die-back associated with drought and pathogens. In this study, an epidemiological approach was adopted to investigate oak die-back in four sites of unexplored Zagros forests of western Iran, Lorestan. The pathogenic agent was identified and an index of disease severity (IDS) was designated for more than 100 trees in each site. High-risk zone mapping was determined based on disease dispersal and inverse distance weighting (IDW) was utilized for interpolation analysis. To evaluate the precipitation role in the regeneration of oak sites, remote sensing (RS) analysis (using NDVI) was carried out based on the processing of Sentinel-2A satellite images from 2016 to 2019, and the logistic regression (LR) model was used to study the correlation between disease incidence and climatic factors. Mean annual maximum temperature (MAMT), mean annual relative humidity (MARH), mean annual precipitation (MAP), and wet-bulb temperature (WBT) of 10 years were included as independent variables. An ITS-based phylogenetic analysis showed that the isolated fungus infecting the oak trees was Biscogniauxia mediterranea. Also, the Sarab-Doure area with a value of 321.5 ranked as the most infected area followed by Mela-Shabane (232.3) and Sarab-Navkesh (168.6), and Kaka-Reza was the healthy area, Kruskal-Wallis test (chi-square; p-value < 0.0001). Based on the LR model, the highest risk was predicted at MAMT of 28 °C (Waldχ2 = 6.61, p-value < 0.05), MARH of 38% (Waldχ2 = 6.20, p-value < 0.01), and wet-bulb temperature (WBT) of 11 °C (Waldχ2 = 4.84, p-value < 0.02). Compared to the infected areas, the healthy site, Kaka-Reza, had a significantly greater degree of NDVI from 2016 to 2019. In 2019, NDVI value for all studied sites increased which is promising for the rehabilitation of the infected forest areas. IDW analysis showed that the west of the province is more prone to die-back. Overall, this study showed that oak decline syndrome is extremely a drought-induced phenomenon in the Zagros regions.

An integrated model chain for future flood risk prediction under land-use changes

Flood is a very destructive natural disaster in the world that is strongly influenced by land-use change. Therefore, a comprehensive flood risk modeling considering the change in land-use is essential for understanding, predicting, and mitigating flood risk. However, most existing single modeling ignored the derivative effect of land-use change, which may reduce the reality of results. To further address the issue, this study presented an integrated model chain by coupling the Markov-FLUS model, the multiple linear regression and the improved TOPSIS model. By applying it in Guangdong Province, the future land-use simulation, spatialization of hazard-bearing bodies, and determination of flood risk were realized. The results show that the coupled model chain allows for good prediction of flood risk under different scenarios, which could be expressed by flood risk composite index (FRSI). In the natural growth scenario, the flood risk will show markedly increasing trend from 2020 to 2030 (FRSI = 2.06), with the high and highest risk zones will expand significantly. Spatially, these increased high flood risk zones mainly distributed on the periphery of existing built-up lands. On the contrary, the flood risk in ecological protection scenario tends to stabilize (FRSI = 1.98), which may be a reference for alternative development paths. These dynamic information identified by this model chain provides a deeper insight into the spatiotemporal characteristics of future high flood risk areas, which can facilitate reasonable flood mitigation measures to be developed at the most critical locations in the region. In further applications, more efficient spatialization models and climate factor are suggested to be introduced.

High-risk spatiotemporal patterns of cutaneous leishmaniasis: a nationwide study in Iran from 2011 to 2020

BackgroundCutaneous leishmaniasis (CL) is a wide-reaching infection of major public health concern. Iran is one of the six most endemic countries in the world. This study aims to provide a spatiotemporal visualization of CL cases in Iran at the county level from 2011 to 2020, detecting high-risk zones, while also noting the movement of high-risk clusters.MethodsOn the basis of clinical observations and parasitological tests, data of 154,378 diagnosed patients were obtained from the Iran Ministry of Health and Medical Education. Utilizing spatial scan statistics, we investigated the disease’s purely temporal, purely spatial, spatial variation in temporal trends and spatiotemporal patterns. At P = 0.05 level, the null hypothesis was rejected in every instance.ResultsIn general, the number of new CL cases decreased over the course of the 9-year research period. From 2011 to 2020, a regular seasonal pattern, with peaks in the fall and troughs in the spring, was found. The period of September–February of 2014–2015 was found to hold the highest risk in terms of CL incidence rate in the whole country [relative risk (RR) = 2.24, P < 0.001)]. In terms of location, six significant high-risk CL clusters covering 40.6% of the total area of the country were observed, with the RR ranging from 1.87 to 9.69. In addition, spatial variation in the temporal trend analysis found 11 clusters as potential high-risk areas that highlighted certain regions with an increasing tendency. Finally, five space-time clusters were found. The geographical displacement and spread of the disease followed a moving pattern over the 9-year study period affecting many regions of the country.ConclusionsOur study has revealed significant regional, temporal, and spatiotemporal patterns of CL distribution in Iran. Over the years, there have been multiple shifts in spatiotemporal clusters, encompassing many different parts of the country from 2011 to 2020. The results reveal the formation of clusters across counties that cover certain parts of provinces, indicating the importance of conducting spatiotemporal analyses at the county level for studies that encompass entire countries. Such analyses, at a finer geographical scale, such as county level, might provide more precise results than analyses at the scale of the province.Graphical

Inerter-based damping isolation system for vibration control of offshore platforms subjected to ground motions

Offshore structures are critical infrastructure in the field of ocean engineering, which may under the threat of earthquake shaking, especially when they are located in the high-risk seismic zones. This paper proposes using an innovative inerter-based damping isolation system (IDIS) for the seismic protection of offshore platforms. The concept of IDIS is firstly proposed by integrating the conventional bearings with inerter-based dampers (IDs). An analytical model of the system is established, and the corresponding equilibrium equations of motion are derived. Without loss of generality, the deck acceleration, displacement on the jacket cap, and isolating layer deformation are chosen as the performance objectives, and parametric analyses are performed to investigate the influences of the key parameters of IDIS on each performance objective. A performance-based multi-objective optimization framework is then developed for determining the optimal parameters of IDIS. The control effectiveness of IDIS is demonstrated in the frequency domain and compared to that of the traditional damping isolation system (DIS). Finally, case studies are performed to further investigate the performance of IDIS subjected to natural seabed ground motions. The results show that properly optimized IDIS is more effective than the conventional DIS in terms of not only the structural responses but also the deformation of the isolating layer. The proposed IDIS can be an attractive and effective alternative to the conventional DIS in the vibration control of offshore platforms.

Influence of a fishing harbour on coastal geomorphology of the southwest coast of India and predictions of its future trends

In the present study, we estimated the shoreline change along Ponnani Fishing Harbour (PFH), the coastal part of Malappuram, India, using the Digital Shoreline Analysis System (DSAS) version 5.0. We also assessed the land use and land cover (LULC) changes in the gateway of Ponnani Fishing Harbour (PFH) and its surrounds using Geographical Information System (GIS) and Remote Sensing (RS) techniques. LULC estimated that vegetation cover was more in 2004 than in 2020, supported by the values shown by VSB (Vegetation with Scattered Built-up) and BSV (Built-up with Scattered Vegetation) show a significant areal change in the study area. On the comparison of erosion and accretion in the two zones of the study area, erosional activities are dominant in Ponnani, which is in the southern part. Based on the Linear Regression Rate (LRR) values, the maximum erosion rate was -7.41 m/year at Ponnani. The southern sector is also undergoing a maximum accretion rate of 8.11 m/year in the Ponnani Harbour region. The End Point Rate (EPR) estimated similar trends of the highest erosion -43.24 m/year and highest accretion, 37.34 m/year rates in the southern sector. The Net Shoreline Movement (NSM) values demonstrate that the shoreline retreated by -285.9 m, and the shoreline accreted by 132.32 m at the Ponnani Harbour area over the last three decades. The Kalman filter model predicted that the shoreline at Ponnani and Puthuponnani beaches might experience further erosion of approximately a 100-metre shift by 2030. An increase in the erosion rate could erode another 140 m from the shoreline by 2040. The Ponnani and Puthuponnani beach extents must be considered high erosion risk zones in the perspective of the estimated results. This assessment indicates that the southern stretch of the area under the study of the PFH coastline is eroding. The significant changes in land use and land cover that have been seen over the past few years following the construction of PFH are expected to be the main cause of the severe loss of the coast in the southern part. The main reason for erosion in PFH would be construction operations that put pressure on the coastline side. The limited supply of sand used to replenish the coast naturally causes shoreline change due to the large drop in sediment load from the river, which is evident by the shrinking of the braided island size. The methods employed in this study and the results might help manage the PFH coast sustainably. The outcomes of this assessment suggest that similar studies should be conducted in other regions of India's western coastline to understand the shoreline evolution and develop a better coastal management strategy.

Efficient Adaptation to Flood Risk

This paper studies whether private adaptation to flood risk is economically efficient. We estimate the return to elevating houses, one of the most significant private defensive investments against flooding, using two decades of microdata on the universe of houses and flood damages in high-risk flood zones in the Atlantic and Gulf Coast United States. We find that undertaking adaptation is socially optimal in the highest-risk areas over a house's lifetime but that individual homeowners may underinvest in flood protection because the benefits do not accrue over their average tenure. We identify conditions under which adaptation yields the highest returns.

An Adaptive Fusion Risk-Zone Detection Network and its Application

COVID-19 has caused a pandemic and adverse effects in many fields on a global scale. The city scale quarantine has demonstrated its effectiveness in controlling the epidemic. Conversely, it is costly and risky in inducing economic and social challenges. A compromised solution is to place quarantine measures at high-risk zones on a local scale. Therefore, it is important to investigate risk zones for conducting cost insensitive precautionary measures. The urban data depict the characteristics of different city zones, which offers an opportunity for detecting the high-risk zones. Yet, the high noise-to-signal ratio requires an efficient procedure to rule out irrelevant information in the informative raw urban data and adapt to the risk detection task. In this paper, we propose an Adaptive Fusion Risk-zone Detection Network (AFRDN), which fuses the static and dynamic multi-sourced urban data in an adaptive manner. Specifically, AFRDN first extracts diverse information-rich features from raw urban data with various encoders in the embedding learning module. Then, the AFRDN takes a hierarchical late fusion strategy by fusing the static embedding and the attentive hidden state of dynamic features in the deep latent space. To capture the most relevant information for risk-zone detection, the AFRDN adapts each dimension in the fused embedding with multi-head self-attention blocks. We have collected a real-world dataset including six Chinese cities and conducted extensive experiments to evaluate our framework. Simulation experiments and comparative analysis results show that the AFRDN is effective and feasible for early detection of infectious diseases high-risk zones.

Efficient SOM’s Application to Seismic Fumarolic Tremor for the Detection of Anomalous Hydrothermal Activity in Campi Flegrei Volcano (Italy)

In recent decades, the Campi Flegrei caldera (Italy) showed unrest characterized by increases in seismicity, ground uplift, and hydrothermal activity. Currently, the seismic and hydrothermal phenomena are mostly concentrated in the Solfatara–Pisciarelli area, which presents a wide fumarolic field and mud emissions. The main fumarole in Pisciarelli is associated with a boiling mud pool. Recently, episodes of a sudden increase in hydrothermal activity and expansion of mud and gas emissions occurred in this area. During these episodes, which occurred in December 2018 and September 2020, Short Duration Events (SDEs), related to the intensity of mud pool boiling, were recorded in the fumarolic seismic tremor. We applied a Self-Organizing Map (SOM) neural network to recognize the occurrence of SDEs in the fumarolic tremor of Campi Flegrei, which provides important information on the state of activity of the hydrothermal system and about the possible phreatic activity. Our method, based on an ad hoc feature extraction procedure, effectively clustered the seismic signals containing SDEs and separated them from those representing the normal fumarolic tremor. This result is useful for improving the monitoring of the Solfatara–Pisciarelli hydrothermal area which is a high-risk zone in Campi Flegrei.

Application of source-sink theory and MCR model to assess hydrochemical change risk in Lhasa River basin, Tibet, China

The Lhasa River, one of the tributaries of the Yarlung Zangbo River on the Tibetan Plateau, provides water for the vast majority of residents in Tibet. The main purpose of this study is to identify regional environmental risk factors that may be affecting the Lhasa River basin. The minimum cumulative resistance (MCR) model was used to determine the “source-sink” risk pattern of the Lhasa River by identifying the sources of metal ions and trace elements in the basin. The results show that loss of soil ions, climate conditions and environmental factors contribute to the hydrochemical characteristics of the Lhasa River basin. The distribution of soil type greatly overlaps with the likelihood of soil ions loss. The low-risk and high-risk zones mainly consist of calcic soils and litho soils, respectively. In terms of the climate and environmental risk factors, rainfall erosivity poses the greatest risk during the low-flow season, vegetation coverage during the high-flow season, and freeze-thaw erosivity during the normal-flow season. High-risk sources and low-risk sources were identified by the MCR model. The high-risk sources were the vegetated and unused lands, which are vulnerable to weathering of Ca and Mg rich sedimentary and mixed rocks, while low-risk sources were the urban, construction, industrial and mining areas and forest land. This study can contribute to the development of policies for the sustainable management of water resources and the protection of the water environment in the Lhasa River Basin.

An Integrative Explainable Artificial Intelligence Approach to Analyze Fine-Scale Land-Cover and Land-Use Factors Associated with Spatial Distributions of Place of Residence of Reported Dengue Cases

Dengue fever is a prevalent mosquito-borne disease that burdens communities in subtropical and tropical regions. Dengue transmission is ecologically complex; several environmental conditions are critical for the spatial and temporal distribution of dengue. Interannual variability and spatial distribution of dengue transmission are well-studied; however, the effects of land cover and use are yet to be investigated. Therefore, we applied an explainable artificial intelligence (AI) approach to integrate the EXtreme Gradient Boosting and Shapley Additive Explanation (SHAP) methods to evaluate spatial patterns of the residences of reported dengue cases based on various fine-scale land-cover land-use types, Shannon's diversity index, and household density in Kaohsiung City, Taiwan, between 2014 and 2015. We found that the proportions of general roads and residential areas play essential roles in dengue case residences with nonlinear patterns. Agriculture-related features were negatively associated with dengue incidence. Additionally, Shannon's diversity index showed a U-shaped relationship with dengue infection, and SHAP dependence plots showed different relationships between various land-use types and dengue incidence. Finally, landscape-based prediction maps were generated from the best-fit model and highlighted high-risk zones within the metropolitan region. The explainable AI approach delineated precise associations between spatial patterns of the residences of dengue cases and diverse land-use characteristics. This information is beneficial for resource allocation and control strategy modification.

A need to better monitor the effects of coastal defence measures on coastal socio-ecological systems to improve future adaptation solutions

Decision-making in a coastal socio-ecological system involves managing a site-specific complexity arising not only from the interaction between hydrodynamic and morphological conditions, but also from the interactions between human structures and ecological systems, as well as the conflicting needs and interests of local actors. In addition, the projections of climate change impacts on coastal systems have a degree of uncertainty, which increases the general unpredictability of coastal dynamic behaviour, and adds a layer of complexity to the decision-making process. Scientific knowledge can help reduce some of the inherent uncertainties, and is essential when it comes to making sound decisions on the choice of appropriate coastal defence measures (CDMs) that are adapted to specific coastal environments and improve the resilience of coastal communities. This paper is based on a meta-analysis of 355 CDMs case studies drawn from 301 publications. From these published case studies, the objectives were to analyze the geographical and physical contexts in which CDMs monitoring was carried out and, based on the findings, to recommend areas of improvement necessary to help make sound decisions in any type of coastal environment. The meta-analysis showed that study sites are not evenly distributed around the world. Most originate from Europe (n = 106), the USA (n = 151) and Australia (n = 30), while few studies have been carried out in Africa and Asia where dense population resides in high-risk zones. Also noticeable is the absence of sites in high latitude climates where ice plays a major role in the erosion process. Five basic variables (coastal type, sediment type, wave characteristics, tidal range, and currents or sediment transport characteristics) are used in publications to characterize study sites according to their physical components. However, only 13 of the 355 sites included a complete characterization using the 5 variables, of which coastal and sediment types are the most frequently identified (77.2% and 72.7% respectively). In general, CDMs are studied in the context of unconsolidated low shore (59.4%) and in sandy environments (74.6%). Information on tidal range, wave climate, and currents, or sediment transport characteristics, is much scarcer. Since 1990, 3 of the 10 CDMs identified in the studies have received more attention than the others; these are beach nourishments, seawalls and breakwaters, with respective cumulative study sites of 164, 67 and 50. The geomorphological effects of CDMs are the most studied (55.1%), followed by ecological (31.2%), hydrodynamic (9.1%), and social (4.6%). Overall, this meta-analysis helped identify knowledge gaps regarding geographical and physical contexts in which CDMs monitoring was held. It also gave an indication of the kind of improvement necessary for global-scale adaptation planning, and for a better decision-making process to reduce coastal risks in the most vulnerable coastal communities. Finally, the analysis shows that 4.4% of the studies on defence measures include monitoring of their effects on the coastal zone. A conceptual scheme is proposed for the evaluation of adaptation solutions based on the global monitoring of coastal zones to measure coastal change trajectories in the context of climate change.

GIS-based flood risk assessment using multi-criteria decision analysis of Shebelle River Basin in southern Somalia

Floods are the most common and expensive natural calamity, affecting every country. Flooding in the Shebelle River Basin (SRB) in southern Somalia has posed a significant challenge to sustainable development. The main goal of this study was to analyze flood hazard, vulnerability and risk in the part of SRB using GIS-based Multi-Criteria Decision Analysis (MCDA). The flood hazard map was constructed using seven important causative factors: elevation, slope, drainage density, distance to river, rainfall, soil and geology. The results demonstrate that very low, low, moderate, high, and very high flood hazard zones correspond to 10.92%, 24.97%, 29.13%, 21.93% and 13.04% of the area of SRB, respectively. The flood vulnerability map was created using five spatial layers: land use/land cover, population density, distance to road, Global man-made impervious surface (GMIS), and Human built-up area settlement extent (HBASE). In addition, the results of the flood susceptibility and vulnerability maps were used to create a flood risk map. The results demonstrate that for the Shebelle River Basin, 27.6%, 30.9%, 23.6%, 12.1%, and 5.7% area correspond to very low, low, moderate, high, and very high flood risk zones, respectively. The Receiver Operating Characteristics-Area Under the Curve (ROC-AUC) of the flood hazard model exhibited a good prediction accuracy of 0.781. The majority of the basin is at risk of flooding in the very low, low, and moderate ranges; however, some tiny areas are at risk of flooding in the high and very high ranges. Flood hazard, vulnerability and risk maps should be provided and distributed the authorities responsible for flood protection so that people are aware flood risk locations.

Ecological restoration of coal mine waste dumps: A case study in Ximing Mine, China

ABSTRACT This study focuses on addressing the issues of spontaneous combustion and slope instability at the coal mine waste (CMW) dump located on the north side of the Yumen River in Ximing Mine, China. We conducted slope restoration and environmental treatment of the CMW dump using shotcrete, deep hole grouting, external soil spray seeding technology with multiple soil fixation measures. The stability of the repaired CMW dump was analysed using LiZheng Slope Stability Analysis Software, and the displacement and stress of high-risk zones were assessed using MIDAS GTS NX Software. Our findings provide valuable insights for slope restoration and evaluation.

Disaster Damage Assessment of Distribution Systems With Incomplete and Incorrect Information

Motivated by the increasing frequency and intensity of extreme weather events, this article aims to effectively assess the scope of zones damaged by natural disasters in distribution systems, which can help for rapid restoration of power supply. Multiple-branch faults and the uncertainty of monitoring information pose huge challenges to Disaster Damage Assessment (DDA). This paper proposes a multiple hypothesis based DDA method to solve this problem. In the proposed method, the novel external environment information is used by updating the objective function with Pre-disaster Prediction Matching Index (PPMI). The reasonable weighting factor of PPMI is theoretically derived. Multiple disaster scenarios are simulated and the DDA results show that the proposed method is more effective. The introduction of PPMI can help to rectify the assessment of zones in high disaster risk zones, thereby improving the overall performance of DDA. Therefore, the proposed DDA method breaks through the assessment bottleneck under limited internal monitoring information. In addition, new technology is proposed to speed up the DDA by setting an effective initial value of optimization, making DDA more efficient.

Forest Fire Risk Zone Mapping in Mizoram Using RS and GIS

In recent years, India’s Northeastern territory has been plagued by wildfires. Mizoram, as per FSI, has been among the most affected regions, with approximately 20,744-recorded wildfires. Forest fire directly or indirectly affects human health, climate change, and the environment. The forest fire risk zonation is generated utilizing remote sensing data, and Geographic Information System based on specified physical and socioeconomic factors. As part of the current study, a Geographical Information System is employed to determine forest fire risk based on predetermined physical and socioeconomic parameters. This study used three distinct models of fire risk zonation index namely FRI (Fire Risk Index), HFI (Hybrid Fire Index), and SFI (Structural Fire Index) to identify the zones in the study area under the risk of forest fires. The Indian state of Mizoram is categorized into five distinct hazard zones based on the probability of wildfire incidents. Fire alerts generated using risk models, and real-time hotspot datasets (forest fire spots) received from MODIS and USGS have been validated. According to the study’s findings, 18.84 km2 of the study area is at low risk of forest fire, 11.072 km2 is at moderate risk, and 5.38 km2 is at high risk. The probability from each metric varies since the input and weightage of the different parameters vary from one another. SFI, therefore, therefore predicts a lower frequency of high-risk wildfire-prone zones than HFI and FRI. In this research, the coefficient of discrimination (R2) is employed to assess the reliability of the projected fire indices being compared with real-time hot spots. Here, FRI possesses the highest accuracy (R2=0.892), HFI has moderate accuracy (R2=0.676), and FRI has the lowest accuracy (R2=0.629).

CFD modelling of infection control in indoor environments: A focus on room-level air recirculation systems

The emergence of viral variants has driven a continuous pandemic with a higher possibility of airborne transmission and a larger scale of infective cases, posing greater demands on indoor risk control. However, the role of room-level air recirculation systems (RRSs) in infection control remains unclear due to insufficient detailed research. There are also fewer analyses of the filtering rating of recirculation filters from the perspective of multi-scale particle size. Thus, a simulation procedure to assess the performance of RRSs on infection control that accounts the transient recirculation of real virus-laden particles in multi-scale sizes was proposed, and focusing on recirculation filter strategies to balance the risk limitation and energy cost. A poorly ventilated winter classroom was selected as a typical environment equipped with RRSs to operate this procedure. Different RRS strategies (i.e., wall-mounted air conditioners (WMAC), floor-standing air conditioners (FSAC) and 4-way cassette air conditioners (WCAC)) were compared. The results show the important contribution of recirculated particles to accumulating the overall infection risk of susceptible occupants towards a high basic reproduction number (Ro > 1). Then, there is a strong correlation of the spatial distributions between high-risk zones and large vortexes at the breathing height of susceptible occupants. Considering rating suitability and filtration effectiveness, the optimization of recirculation filters on energy and cost can be suggested with comparable benefits of infection control.

Adaptation Strategies to Climate Change Impacts in The Area of Sidenreng Lake, Sidenreng Rappang Regency

The research site is one of the high-risk flood district zones along with Panca Lautang, Tellu Limpoe, and Wattang Sidenreng District since it is adjacent to Sidenreng Lake. The issue allied to climate change at the research site is a real phenomenon in which high rainfall occurs, leading the lake water to overflow and the weather, which previously could be calculated by the community in various ways, is currently unpredictable. The clime vulnerability analysis was utilized at the outset of this study to figure out the level of climate vulnerability. Secondly, Spatial Analysis using ArcGIS Software with Overlay Method towards each flood parameter to address the level of high-risk flood at the site of the research. The third analysis used in this research was AHP-SWOT to formulate some alternative strategies to overcome flood disasters brought about by climate change in the area of Lake Sidenreng. The results of the study showed that the level of flood vulnerability was Very Vulnerable, with 130.94 km2, Vulnerable at 134.98 km2, Quite Vulnerable at 88.93 km2, Less Vulnerable at 22.97 km2, and Not Vulnerable at 3.03 km2 with the total amount of 380.85 km2. Subsequently, the analysis results of the climate vulnerability from 27 villages, 14 of which are at a very low level (Class I), 2 villages are at a low level (Class II), 4 villages are at a Medium level (Class III), and 1 village which is Wette Village is at a very high level (Class IV). According to alternative strategy on AHP-SWOT Analysis, it was found that the space matrix is at Quadrant III of the W-O Strategy namely 1) the development of human resources and public role to nourish environmental hygiene and increase the capacity of vulnerable groups by empowering society with several trainings to improve human resources and group capacity and promoting awareness among people who lived in those vulnerable areas to maintain environmental conditions, the formation of climate villages and the formation and development of disaster care communities to minimize the impact of flood disasters that occurred as a result of climate change; 2) Management of climate change risk by local government and stakeholders either structurally or physically (engineering the built environment, technological choices and environment-based services or institutional-based services (choices in law and regulation, economics, policies and programs of government) by involving potential experts; 3) development of flood susceptibility maps and climate change vulnerability risks that are integrated with disaster mitigation maps so that people figure out the areas which have a large impact on climate change disasters