Просторово-часовий розподіл небезпечних сухих гроз в Україні

Early warning and response systems use meteorological forecasts to initiate public health interventions to reduce health impacts associated with weather variables, including climate variability. Effective systems are based on reliable meteorological forecasts; robust understanding of the cause- and effect relationships between meteorological factors and health outcome (allowing identification of high risk thresholds); response measures that can be implemented in a timely manner to reduce morbidity and mortality; and institutions and civil society actors with the resources, capacity, knowledge, and political will to undertake the response measures. Because these systems are effective in reducing adverse health impacts, it is widely assumed that such systems also will reduce the impacts of climate change. However, early warning systems are often designed without consideration of climate change. They establish exposure-response relationships and design response measures based on recent conditions and do not consider the implications for the effectiveness of systems of future and different weather and development patterns. Extreme weather and climate events are projected to increase in frequency, intensity, and duration, and may occur at times of the year different from today. Acclimatization, housing, the demographic structure of the population, technology development, and other factors will influence future vulnerability. Therefore, early warning systems should have flexible structures and processes to support modifications as climate and development baselines shift. Engagement with a broad range of stakeholders will facilitate timely adjustments to early warning and response systems to maintain or improve their effectiveness however the climate changes.

The extreme climatic events are increasing worldwide and are becoming more and more recurrent. It is becoming urgent to take action to reduce them. The Mediterranean region is one of the most affected areas by the effects of climate change. The recent events in France, Spain, Greece, Turkey or even Libya reflect the violence of hydro-meteorological phenomena due in part to the rising sea temperatures. To limit the occurrence of such events, it is necessary to have some feedback. PREDICT uses post-event spatial imaging techniques such as rapid mapping to obtains geospatial data in just a few hours after the event. This permits to manage and document the consequences of such events. Furthermore, PREDICT has been developing a program called COSPARIN (COntribution du SPAtial à la gestion du Risque INondation) for few years now, with European Space Agency (ESA) and the National Centre of Spatial studies (CNES) approval. The main goal of this program is to better understand and monitor extreme events before, during and after their occurrence. This relies on an innovative method based on the use of the most recent satellite data available as well as algorithms and artificial intelligence to establish a global estimation of precipitations and an estimation of potential flooding areas. To improve forecasting and deploy early warning systems, Europe has set up the HORIZON program which includes a new project called GOBEYOND (GeO and weather multi-hazard impact Based Early warning and response systems supporting rapid deploYment of first respONders in EU and beyond). The aim of this project is to improve existing tools and methods by designing two platforms of Multi Risk Impact-based Early Warning System (MR-IEWS) for Europe and Mediterranean area (North Africa included). This draws on all available data, including COSPARIN satellite data, to forecast hydro-meteorological risks (floods, storms, heatwaves) and geological risks (earthquakes, volcanic eruptions, tsunamis). Also, this aims to promote rapid and effective operational response by those in charge of safety and to move from simple hazard forecasting to risk forecasting by taking vulnerability into account. Keywords: Climate change, Spatial data, Forecast, Innovation

With the proliferation of intra-state conflicts in Kenya, traditional methods of detecting potential conflicts and recommending timely intervention measures have proved to be largely ineffective. This paper explores how integrating Artificial Intelligence (AI) in conflict early warning systems can enhance peace and security in Kenya. It focuses on how AI can augment human capabilities in facilitating effective proactive measures in de-escalating conflicts. The paper is guided by three research objectives: first, assessing the efficacy of AI in systematically identifying indicators and patterns of conflict-triggers in Kenya; second, examining how AI can enhance real-time sharing of information to the pertinent actors for timely interventions; and third, evaluating the impact of integrating AI in Kenya’s conflict prevention initiatives. The paper is guided by the securitization theory as propounded by Ole Wæver of the Copenhagen School. The theory dispels the notion that national security issues arise naturally. Instead, the securitizing agents within the state designate select issues as threatening, consequently moving them from low to high priority security concerns, thereby necessitating concerted action. The paper adopts an explorative research design and secondary data on conflict early warning and artificial intelligence from published and unpublished sources. It finds that AI can effectively analyse vast amounts of historical, current and emerging data in order to systematically identify conflict triggers and patterns for effective conflict prevention. Moreover, it submits that AI has unmatched capacity to sustain real-time sharing of information among the relevant actors in conflict prevention infrastructure thereby guaranteeing timely responses to check the escalation of conflicts. The paper also observes that AI has the potential of enhancing Kenya’s preparedness and response capabilities thereby minimising economic, political and human losses occasioned by violent conflicts. It recommends the urgent need to revamp the existing EarlyWarning Response Systems (EWRS) by integrating AI capabilities to amplify human capabilities. In addition, the paper recommends fast-tracking of the roll-out of Information Communication Technology (ICT) infrastructure to enhance real-time sharing of information and creating a lean Infrastructure for Peace (I4P) for effective delivery. Lastly, the paper underscores the inevitability of visionary leadership to provide policy guidelines on integrating AI in conflict prevention initiatives and the general operationalization of Early Warning and Response Systems (EWRS).

A statistical analysis of the distribution of convective windstorms and their radar characteristics in Hubei Province, China. Part 2

Using big data and artificial intelligence to establish a multi-point monitoring, early warning, and disposal system to achieve early warning and intervention of infectious disease outbreaks is an important means of controlling the spread of the epidemic. Taking Xiaoshan district as an example, this study analyzes the monitoring contents, warning methods, and application effectiveness of the infectious disease monitoring, early warning and disposal system. Based on Xiaoshan's health big data resources, the system starts with syndrome, disease diagnosis and etiology. Through advanced technologies such as artificial intelligence and block chain, it realizes early identification of infectious disease outbreaks, data fusion, multi-cross collaboration, and closed-loop management. It has improved the sensitivity of clustered outbreaks monitoring and the effectiveness of epidemic disposal and provided a reference for grassroots disease prevention and control departments to establish an infectious disease monitoring and early warning system.

The EU Response to COVID-19: From Reactive Policies to Strategic Decision-Making.

Today, most European destinations consider the event tourism organization as a tool for socio-economic development. The tourism industry development contributes to the income sources diversification in the territories budget, which in combination with other sources can be a factor in shaping the socio-economic development of a particular territory. The event tourism development should be developed as a soft tourism form that is well adapted to local diversity. The purpose of the article is to study the event tourism development in Ukraine and its impact on the territory promotion. The article uses general scientific methods and content analysis. Despite the significant scientific achievements on this issue, in the Ukrainian tourism discourse, event tourism has not become the object of due attention. This fully applies to the significant reputational event tourism potential, its ability to act as a factor in shaping the tourist attractiveness of the territory. The structure of event tourism development in Ukraine by events types is investigated in the article. It is determined that the largest share is made up of music festivals and competitions. There are also a significant number of gastronomic festivals, national holidays and festivals, film and theater festivals and theatrical shows, which also attract a large number of visitors. According to the territorial distribution results of events by regions of Ukraine, the largest number of events takes place in the largest cities and regions of Ukraine – Kyiv and Kyiv region, Lviv region, Odessa region and Kharkiv region. The paper proposes the concept use of event marketing in tourism, which is due to the fact that in modern conditions it is becoming increasingly difficult to capture the attention of tourists; and as old marketing tools lose their effectiveness over time, destinations are forced to invent new concepts that fully meet the requirements of today's target audience. One of the tools that allows you to retain and attract visitors and investors is event marketing. Event marketing in tourism is the event use to promote a certain area and attract consumers who do not find a tourist destination attractive enough to visit it for no reason.

Highly pathogenic avian influenza (HPAI) A(H5N1) continues to be endemic in Indonesia, with live bird markets (LBMs) serving as key points for zoonotic transmission. While national assessments exist, there is a lack of local joint risk assessments (JRAs) specifically focused on LBMs. This study conducted the first district-level, multisectoral JRA of HPAI H5N1 at the human-animal-environment interface in LBMs of Bogor District and Municipality, Indonesia, utilizing the FAO-WHO-WOAH JRA Operational Tool adapted for subnational use. A qualitative, participatory JRA was carried out through a structured five-stage process, including governance formation, risk pathway development, stakeholder validation, technical risk analysis, and final consultation. In total, fifty stakeholders from sectors such as human health, animal health, environmental, trade, market, academic, and local government took part. Evidence was triangulated from poultry movement logs, animal and human surveillance data (Integrated Animal Health Information System [iSIKHNAS] and Early Warning and Response System [Sistem Kewaspadaan Dini dan Respons; SKDR), environmental sampling at 15 LBMs, trader interviews, and prior market studies. The risk analysis concentrated on the likelihood, impact, and uncertainty of human H5N1 infection over a 12-month period. More than 90% of approximately 25 million poultry supplied annually to LBMs originated locally, with marked seasonal surges during religious festivals. Risk pathways highlighted poultry trade networks, market handling practices, slaughtering activities, and inadequate sanitation as key transmission nodes. Two environmental samples tested positive for influenza A, but no H5 subtype or human cases were detected locally since 2017. Consensus-based risk estimation classified the likelihood of at least one human H5N1 infection as low, with minor potential population-level impact. Uncertainty was rated moderate due to limited wild bird surveillance, incomplete environmental sampling, and variable data quality across sectors. This district-level JRA identified LBMs as persistent but manageable risk nodes for HPAI H5N1 transmission in Bogor. While the current risk to human health was assessed as low, structural and behavioral vulnerabilities justify proactive mitigation. Priority actions include strengthening LBM biosecurity, improving waste management, enhancing environmental surveillance, and reinforcing integrated One Health coordination. The study demonstrates the feasibility and policy relevance of locally implemented JRAs and provides an operational model for decentralized zoonotic risk assessment in endemic settings.

This study aimed to evaluate the performance of the early warning and response system (EWARS) in the Kebumen District Health Office in 2023, with a focus on the early detection of leptospirosis cases. This descriptive evaluation study used secondary data from the EWARS platform. The data was retrieved from the website web.skdr.surveilans.org. The timeliness of EWARS reporting at community health centers and hospitals in 2023 was 95.42% and 96.23%, respectively. The completeness of EWARS reports at community health centers and hospitals in 2023 was 100%, respectively. Community health centers reported 314 alerts, while 413 were reported from hospitals during 2023. The most frequently reported alert from hospitals was leptospirosis. In the EWARS, 66 cases of leptospirosis were reported at week 11. Alerts from hospital reports from week 1 to week 11 were increasing. An alert from the health center report appeared in the 9th week. The response that has been made is improving health awareness of leptospirosis around the affected community health centers and hospitals in the Kebumen area. The verification of cases from hospitals was 93.46% and verified within 24 hours. The timeliness and completeness of EWARS reporting in Kebumen District have surpassed the national targets of 80% and 90%, respectively. In the case of leptospirosis, community health centers need to increase early detection of symptoms and risk factors for leptospirosis.

Економічні аспекти виробництва і споживання гречки в Україні

BackgroundThe decline of habitat for elephants due to expanding human activity is a serious conservation problem. This has continuously escalated the human–elephant conflict in Africa and Asia. Elephants make extensive use of powerful infrasonic calls (rumbles) that travel distances of up to several kilometers. This makes elephants well-suited for acoustic monitoring because it enables detecting elephants even if they are out of sight. In sight, their distinct visual appearance makes them a good candidate for visual monitoring. We provide an integrated overview of our interdisciplinary project that established the scientific fundamentals for a future early warning and monitoring system for humans who regularly experience serious conflict with elephants. We first draw the big picture of an early warning and monitoring system, then review the developed solutions for automatic acoustic and visual detection, discuss specific challenges and present open future work necessary to build a robust and reliable early warning and monitoring system that is able to operate in situ.FindingsWe present a method for the automated detection of elephant rumbles that is robust to the diverse noise sources present in situ. We evaluated the method on an extensive set of audio data recorded under natural field conditions. Results show that the proposed method outperforms existing approaches and accurately detects elephant rumbles. Our visual detection method shows that tracking elephants in wildlife videos (of different sizes and postures) is feasible and particularly robust at near distances.DiscussionFrom our project results we draw a number of conclusions that are discussed and summarized. We clearly identified the most critical challenges and necessary improvements of the proposed detection methods and conclude that our findings have the potential to form the basis for a future automated early warning system for elephants. We discuss challenges that need to be solved and summarize open topics in the context of a future early warning and monitoring system. We conclude that a long-term evaluation of the presented methods in situ using real-time prototypes is the most important next step to transfer the developed methods into practical implementation.

Coastal areas are increasingly vulnerable to abrasion, flash floods, and seawater intrusion due to climate change and human activities. Addressing these hazards requires an integrated early warning system that enables timely and adaptive disaster mitigation. This study integrates landscape analysis, Mobile GIS technology, and the Ant algorithm into a unified early warning framework. Landscape analysis identifies geomorphological patterns and coastline changes. Mobile GIS supports real-time spatial data acquisition, while the Ant algorithm optimizes evacuation routes, sensor placement, and hazard prediction through agent-based modeling. The system incorporates spatial, topographic, oceanographic, and socio-economic data processed through spatial and computational methods. Validation using historical records and field data shows enhanced hazard detection, more efficient evacuation planning, and quicker response times. The Ant algorithm adapts routes in real time based on environmental changes. Sensor deployment is optimized for high-risk zones. Mobile GIS ensures continuous updates and spatial visualization. Real-time processing supports rapid decision-making and threat modeling. These integrated components demonstrate a strong potential to build a resilient, data-driven early warning system for coastal communities. In conclusion, the proposed system offers a precise, adaptive, and scalable approach to improve disaster preparedness in vulnerable coastal regions. Keywords: early warning system, Mobile GIS, ant algorithm, landscape analysis, coastal disaster mitigation

Monitoring and early warning system of porcine pasteurellosis based on WebGIS

Loess landslide has become a major geological hazard due to its high distribution, high occurrence frequency, and high loss. More than 1800 landslides occur every year in China, causing an average of more than 400 deaths every year, and the direct economic loss is as high as 4.6 billion yuan. This is a serious threat to the lives and property safety of the people in the disaster area, and caused huge losses to the country. China’s “one belt, one road” like a cooperative project has been like a raging fire in recent years, and the new mode of economic development and regional cooperation has been opened. However, one belt, one road, is seriously threatened by landslides. From 1900 to 2015 one belt, one road, along the more than 330 landslides, caused more than 20 thousand deaths. Therefore, this document collects the basic x-di loess data, uses ArcGIS software and distributed environment network, analyzes the spatial and temporal distribution characteristics of landslides, obtains the factors affecting landslides, and develops the loess landslide monitoring and early warning system. Then this paper studies the sports training, as an important part of college physical education curriculum, should carry out the corresponding reform to play a positive role and effectiveness of sports training. Some colleges and universities lack the scientific management of sports teams in the process of sports training. The overall quality and teaching ability of teachers are not high. We must cultivate students’ interest and further strengthen the reform of sports teaching. It is necessary to strengthen the psychological training of teachers and students, improve the training methods and standard training, in order to improve the effect of sports training. In this paper, through the study of loess landslide geological disasters, the monitoring, and early warning system is developed, which is applied to sports training to make sports training more effective.

This chapter presents the framework to develop advanced early warning systems (EWS) for human and animal health. Based on a malaria EWS case study in Africa, the chapter discusses the needs for developing and implementing effective EWS for several vector-borne diseases affected by climate and environmental factors. Combination of vulnerability assessment, climate forecast, climate monitoring based on remote sensing products and surveillance data compose the basis for creating an EWS. The remaining challenges to get the knowledge into practice and sustain it where it is needed are discussed to improve early warning and early action systems.