Forest fire is a major ecological disaster, which has economic, social and environmental impacts on humans and also causes the loss of biodiversity. Forest officials issue the warnings to the public on the basis of fire danger index classes. There is no fire danger index for the country India due to the sparsely distributed meteorological stations. Previous studies suggest that Static Fire Danger Index (SFDI) as well as Dynamic Fire Danger Index (DFDI) have been derived from the satellite datasets. In this study, we have made an attempt to integrate both the Static and Dynamic fire danger indices and also used the Near Real Time (NRT) data sets that can be available for download through NASA FTP server after one hour of the satellite overpass. In this study, DFDI has been calculated from the MODIS Terra NRT Land Surface Temperature (MOD11_L2) and MODIS TERRA NRT surface reflectance MOD09. Finally, The Forest Fire Danger Index (FFDI) has been developed from the static and dynamic fire danger indices by the additive model and the overall accuracy was around 81.27%. Thus, the FFDI has been useful to predict the fire danger accurately and can be useful anywhere, where the meteorological stations are un-available.

Introduction: While preparing for dive exams, divers are trained to perform rescue exercises. Currently, divers think they will manage and control their body at any time.Methods: At the bottom of the water, the student takes the instructor who simulates a sickness, up to the surface, by controlling the going up. Then, if the exercise is wrong, both divers go down and exercise again. If the exercise is successful, both divers go down to perform another exercise until they are tired or until they have no more air. Results: I discovered that there was a mistake in the preparation of divers for rescue training due to Descartes’ error, “I think, thus I am”, which is part of our collective memory. Going up and going down after a sickness simulation is a repetitive action of the body and going down is therefore part of the automatisms of rescue, which is very dangerous. Conclusions: The body is not necessarily controlled by the present thinking, and if divers forget sickness while going up in a real rescue situation, they will go down automatically because their bodies were trained like this. In addition, it is very easy to forget sickness. Divers should not go down after rescue, they should be on the dive boat or on the land. Descartes’ error has to be reversed: “I am, thus I think” is right. The divers’ rescue exercises must be simplified and divers should never go down after the simulation of a sickness.

Introduction: One of the key strategies to achieve sustainable development goal by reduce preventable maternal deaths below 70 per 100,000 is increasing awareness about major obstetrics danger sign. Mother's awareness of obstetric danger signs in developing country is relatively low regardless of attempts trough many local and national program. It is crucial to increase women’s knowledge of obstetric danger signs through education and awareness-raising efforts. Despite its importance to reduce maternal deaths, Studies are limited only to the Northern Ethiopia. Objective: This study aimed to assess prevalence of knowledge of obstetric danger signs and associated factors among women who delivered in the last 12 months in Sidama region, Ethiopia. Methods: We conducted a community-based cross-sectional study from October to November, 2022. A multi-stage sampling procedure was utilized to select study participants. A structured and pretested questionnaire was utilized to collect data. A multilevel mixed-effects negative binomial regression model was used to control effects of clusters and confounders.Results: Out of 1,140 study participants, only 1,130 study participants took part in the study making a response rate of 99.12%. The overall prevalence of knowledge of obstetrics danger sign was estimated as 22.3% (95%CI: 18.7, 25.9). Government employed women (AIRR = 1.37; 95% CI: 1.2, 1.6), women who had access to mass media (AIRR = 1.16; 95% CI: 1.08, 1.25), women who had received model family training (AIRR = 1.15; 95% CI: 1.1, 1.25), autonomous women (AIRR = 1.15; 95% CI: 1.04, 1.25), women who had faced health problems during pregnancy (APR = 1.21; 95% CI: 1.11, 1.32) and urban women (APR = 1.22; 95% CI: 1.09, 1.62) were factors associated positively with knowledge of obstetrics danger sign. Conclusion: The prevalence of knowledge of obstetrics danger sign is found to be lower compared with the national average. Residence, mass-media exposure, model family planning, problem faced during pregnancy and women autonomy are the main predictors of knowledge of obstetrics danger sign. Public health policy makers should enhance health literacy at for focused on rural community. Awareness creation should be improved through mass media. Health providers should educate women during maternity health services to empower women for decision making process.

Prescriptions for fuels management are universally applied across forest types in British Columbia, Canada, to reduce fire behaviour potential in the wildland-urban interface. Fuel thinning treatments are assumed to reduce the potential for sustained ignition and crown fire initiation by reducing surface fuel loading. We hypothesized that these prescriptions are not appropriate for the coastal wet forests in the Whistler region of the province. Our study measured the efficacy of fuel thinning treatments in four stands located in the Whistler community forest. We examined several in-stand microclimate variables during snow melt in the spring and at the height of fire danger in late summer, at thinned and unthinned locations paired using GIS- analysis. We found that thinning increased the wildfire risk based on the differences between unthinned and thinned areas in the same forest stand.

Machine learning (ML) is changing cybersecurity by enabling progressed discovery, anticipation and reaction instruments. This paper gives a comprehensive survey of ML's part in cybersecurity, looking at both hypothetical systems and down to earth usage. It diagrams the rising dangers focusing on ML models, such as ill-disposed assaults, information harming and show reversal assaults and examines state-of-the-art defense procedures, counting ill-disposed preparing, vigorous models and differential protection. Furthermore, the paper investigates different ML applications in cybersecurity from interruption location to malware classification, highlighting their affect on improving security measures. An peculiarity induction calculation is proposed for the early discovery of cyber-intrusions at the substations. Cybersecurity has ended up a imperative investigate range. The paper concludes with a discourse on the key inquire about bearings and best hones for making secure and versatile ML frameworks in a data-driven world. This paper dives into how Machine Learning (ML) revolutionizes cybersecurity, enabling progressed discovery, avoidance, and reaction components. It offers a exhaustive investigation of ML's urgent part in cybersecurity, enveloping hypothetical systems and viable applications. It addresses rising dangers like ill-disposed assaults and information harming, nearby cutting-edge defense techniques such as antagonistic preparing and strong models.

AbstractIntroduction: Increasing knowledge of obstetric danger signs (ODS) and encouraging birth preparedness and complication readiness (BPCR) practices are strategies to increase skilled maternal health service utilization in low-income countries. One of the methods to increase mothers' knowledge about ODS and promote BPCR practice is through health education intervention (HEI). However, the effect of context-specific community-based health education led by women's groups on these outcomes has not been comprehensively studied, and the existing evidence is inconclusive. Thus, we aimed to evaluate the effect of a context-specific community-based HEI led by women's groups on mothers' knowledge regarding ODS and BPCR practices in southern Ethiopia.Methods: An open-label, two-arm parallel group cluster-randomized controlled trial was conducted from January to August 2023 on pregnant women from 24 clusters (kebeles) (12 interventions and 12 controls) in the northern zone of the Sidama region. The Open Data Kit smartphone application was utilized to collect data. The intention-to-treat analysis was used to compare outcomes between groups. We fitted multi-level mixed-effects modified Poisson regression with robust standard error to account for between and within cluster effects. Results: One thousand and seventy pregnant women (540 in the intervention and 530 in the control clusters) responded to this study, making the overall response rate 95.02%. Excessive vaginal bleeding (94.3% in the interventional group vs. 88.7% in the control group) was the commonest ODS mentioned during childbirth. Overall, 68.7% of women in the intervention group and 36.2% of mothers in the control group had good knowledge of ODS (P-value < 0.001). Saving money and materials (97.1% in the interventional group vs. 92.7% in the control group) was the most frequently practiced BPCR plan. Overall, 64.3% of women in the intervention group and 38.9% of mothers in the control group practiced BPCR (P-value < 0.001). HEI significantly increased overall knowledge of ODS (adjusted risk ratio [ARR]: 1.71; 99% CI: 1.14-2.57) and improved overall BPCR practice (ARR: 1.55; 99% CI: 1.02-2.39). Conclusions: A community-based HEI led by women's groups improved mothers' knowledge regarding ODS and BPCR practices in a rural setting in southern Ethiopia. Interventions designed to increase women's knowledge of ODS and improve BPCR practice must implement context-specific, community-based HEI that aligns with World Health Organization recommendations. Trial registration: NCT05865873

Forest fires though part of a natural forest renewal process, when frequent and assuming large-scale proportions have detrimental impacts on biodiversity, agroforestry systems, soil erosion, air and water quality, infrastructures, and economy. Portugal (PT) endures extreme forest fires, with a record of burned area in 2017. These extreme wildfire events (EWE) concentrated in few days but with high burned areas, are among other factors, linked to severe fire weather conditions. In this study a comparison between several fire danger indices is performed for a reference period 2001‒2021 and 2017 (May‒October) for the Fire Weather Index (FWI), Continuous Haines Index (CHI), Keetch-Byram drought index (KBDI), Burning Index (BI) and Fire Danger index (FDI). A daily analysis for the so-called Pedrogão Grande wildfire (June 17th) and the October major fires (October 15th) included the Spread Component (SP) and Ignition Component (IC). Results revealed high above average values for all indices for 2017 in comparison with 2001‒2021 particularly, for October. High statistically significant monthly correlations between FWI, FDI and BI were found, along with lower between FWI and CHI. These correlations are depicted in the spatial patterns between FWI and FDI for the two EWE. The spatial distribution of FDI, SC and IC had the best performance in capturing the locations of the occurrence of the two EWEs’. The outcomes allowed to conclude, that since fire danger depends on several factors a multi-index’s diagnosis is highly relevant, though calibration and scale adjustment are needed for PT. The implementation of a Multi-index’s Prediction System should be able to further enhance the ability of tracking and forecast unique EWE, since the shortcomings of some indices are compensated by the information retrieved by others as shown in this study. Overall, a new forecast system can help ensuring the development of appropriate spatial preparedness plans, proactive responses by the civil protection regarding firefighter’s management, suppression efforts to minimize the detrimental impacts of wildfires in Portugal.