Holographic Surgical Care in Military Settings Through Localized Parallel Computing

Demand-driven Corporate Social Responsibility: Symbolic <i>versus</i> Substantive Change after Environmental Disasters 

The article highlights the relevance of studying the evolution and strategic importance of marketing innovations within the system of enterprise economic security under the conditions of digitalization. Based on an analysis of scientific sources as well as international and domestic research, it reveals how marketing innovations have transformed from tactical tools (focused on specific elements of the marketing mix) into a strategic component of corporate management, ensuring the resilience, adaptability, and competitiveness of enterprises. The article emphasizes the systematization of the key stages in the development of marketing innovations in the 21st century, starting from their official recognition as a distinct type of innovation (2000–2010), through their integration into business models (2010–2020), and up to their acquisition of functions related to crisis management, ESG compliance, and long-term strategic positioning (post-2020). Particular attention is given to the role of digital tools – such as Big Data, CRM systems, omnichannel strategies, and automated communication platforms – in strengthening the enterprise’s economic security. The article emphasizes that modern marketing innovations should be viewed not only as a means of improving promotional effectiveness but as a multifactorial tool for risk management, resilience support, and alignment with global standards (notably ESG). It highlights that the integration of marketing innovations into the system of enterprise economic security enables proactive risk management, flexible responses to global challenges (pandemics, armed conflicts, environmental disasters), and enhanced long-term competitiveness. The article concludes that further theoretical research is necessary to develop methodological approaches for assessing the impact of marketing innovations on the economic security of enterprises, as well as to produce practical recommendations for their effective integration into corporate development strategies.

The January 2021 flash flood in Wake Village, Batu Benawa Subdistrict, Hulu Sungai Tengah Regency, South Kalimantan, was triggered by severe upstream environmental degradation, where deforestation accelerated erosion and landslides, leading to the collapse of natural dams. This study aims to develop a settlement revitalisation plan that integrates environmental standards and disaster mitigation strategies to strengthen resilience in floodprone communities. Using a mixed-methods approach that combines spatial analysis, stakeholder interviews, and a review of secondary data, the research identified key challenges: High building density that risks slum formation if reconstruction follows the old layout, topographical vulnerability in lowlying flood zones, and community resistance to relocation despite government support. Weaknesses in current mitigation measures were found in settlement patterns, green space provision, building structures, and utility infrastructure. To address these issues, five strategic actions are proposed: Optimising spatial and environmental layouts, constructing physical barriers with slope revegetation, developing adaptive drainage systems, promoting resilient building designs, and restructuring settlement layouts based on disaster risk reduction principles. The novelty of this study lies in combining land readjustment with infrastructure reinforcement as a dual strategy, offering a replicable model for enhancing settlement resilience against flash floods in vulnerable areas.

Nujiang Lisu Autonomous Prefecture Yunnan Province, China (hereinafter the Nujiang Autonomous Prefecture for short) set of frontier, ethnic religion, poverty, ecological and natural disasters in one, is a typical representative of the southwest frontier ethnic regions. Harsh geological and ecological environment and frequent natural disasters make the frontier ethnic masses in poverty and backwardness, backward and poverty and aggravate the ecological environment deterioration, this vicious cycle of long-term role, the formation of “Nujiang problem”, has become a long-term control of the development bottleneck of Nujiang Autonomous Prefecture. In mountainous and towns from the perspective of the development of ecological migration, transforming the mode of production of the steep slope land reclamation, the elimination of bottlenecks in the development, "break a leap forward development path". The southwest frontier ethnic regions achieve 2020 completely solve poverty mission objectives, built southwest borderland “the construction of ecological civilization pacesetter, ethnic unity and progress of the demonstration zone” has practical significance.

This paper contains the results of computer simulations performed for several realistic scenarios of electromagnetic (EM) field evaluation near the cellular base station (CBS) and some analysis of obtained results. The motivation of this study is exposure assessment of EM energy on biological objects. There are scenarios of field distribution indoors, near the hill, obstacle like a wall and finally the field distribution inside the human body is computed. These scenarios are general and frequently encountering in practice inside space covered by wireless networks. Using the reasonable computational resources and keeping the accuracy of the solution acceptable for problems involving the electrically large objects the method of auxiliary sources (MAS) (R. Zaridze et al., 2002) (D. Kakulia et al., 2004) is used to simulate the EM field for the cases mentioned above.

The continued expansion of infill development in unconventional basins has increased the occurrence and operational impact of fracture-driven interactions (FDIs) between child and parent wells. These interactions, ranging from poro-elastic stress effects to direct hydraulic connections, can reduce production, result in loss of recoverable reserves, degrade parent well integrity, and introduce significant uncertainty in stimulation design. Conventional detection methods rely on post-job analysis or advanced downhole instrumentation, which are often impractical to scale. This work introduces a real-time, automated workflow that detects and classifies FDIs using only surface pressure data from nearby shut-in parent wells. The workflow utilizes time-aligned surface pressure data from multiple parent-wells and applies a set of engineered signal features to characterize pressure deviations associated with stimulation. These features, including pressure derivatives, temporal lags, waveform asymmetry, and dynamic response patterns, are used to train and infer from a supervised machine learning model that classifies each detected event into one of three FDI categories. This multi-class classification framework allows differentiation between interaction mechanisms and provides stage-level context for each pressure anomaly. Inferred pressure lag patterns are further analyzed to estimate fracture propagation velocity and directional bias. Its architecture supports centralized as well as localized processing. This flexibility ensures rapid model inference even in bandwidth-constrained or remote field environments. Model performance was evaluated on historical data from several multi-well pads across major North American unconventional plays. Fracture velocity estimates derived from pressure signal timing revealed spatial propagation patterns that helped estimate depletion gradients and geologic heterogeneities. The multi-class classification model demonstrated high predictive accuracy, effectively distinguishing between overlapping and sequential FDIs across multiple parent wells. Classification outputs correlated well with known interference patterns and post-treatment diagnostics. The ability to resolve FDI type at the stage level supports actionable decisions, including diverter deployment, treatment schedule modifications, and fluid design adjustments. In addition to operational insights, these outputs enhance reservoir simulation calibration by providing high-resolution pressure interaction labels for history matching. This proposed workflow advances the operational monitoring of FDIs. By eliminating the need for downhole sensors or spatially dependent modeling, the workflow generalizes across diverse pad configurations. Its ability to resolve three distinct FDI types, tied to specific stages and wells, enhances the diagnostic resolution available to completions engineers. A key advantage is the system's support for edge deployability, allowing near-wellsite execution of detection logic and significantly reducing latency between data availability and engineering response. This combination of scalable architecture, interpretability, and multi-class intelligence represents a substantial advancement in frac surveillance and real-time completions optimization.

In recent years, Unmanned Aerial Vehicles (UAVs) have become essential for important tasks like search and rescue (SAR), environmental monitoring, disaster assessment, and surveillance. The effectiveness of these AI-driven vision systems depends greatly on stable communication links between UAVs and ground stations. Packet loss and transmission issues often reduce the quality of visual data. This, in turn, lowers the accuracy of automated detection and analysis. To address this problem, this paper presents a Hybrid Dual-Domain Attention and Transformer Network (Hybrid DDAC) designed to restore UAV images that are damaged by communication-related distortions. The network combines multi-scale convolutional feature extraction, spatial and channel attention, transformerbased global reasoning, and an Adaptive Communication-Aware Correction Layer (ACCL) to effectively recover local details while keeping global consistency. A dataset containing 2,405 original UAV images and 2,405 corresponding packet-lossdistorted images from an open-source repository was used for training and evaluation. The proposed Hybrid DDAC outperforms traditional models by achieving better PSNR, SSIM, and MAE values. Experimental results show that the Hybrid DDAC significantly improves image clarity and structural integrity, making UAV-based vision systems more robust under unreliable communication conditions.

Under the impact of urbanization as well as the influence of cultural exchanges between ethnic groups and the need to develop housing in the modernity direction of the northern mountainous people, the construction of buildings with traditional housing architecture characteristics is gradually decreased. It is highly possible that the traditional heritage of ethnic communities will gradually be forgotten if the mutual influence relationship between traditional culture and architectural housing is not being paid sufficient attention. In order to provide insight into traditional housing architecture and guide the preservation and development of residential architecture, the research aims to identify the influences of traditional culture on residential architectural space. The article employs a variety of techniques, including field surveys, analysis and evaluation of real-world scenarios, overviews of related studies, and the inheritance of secondary data from earlier research endeavors to accomplish the suggested research goals. The study focuses on analysing the following five categories of traditional housing architecture, which are: 1) The influence of living culture; 2) The influence of costume culture; 3) The influence of labor and production culture; 4) The influence of culture, lifestyle, local customs and practices; and 5) The influence of religious culture.

(1) Background: the community is the most basic place to maintain residents’ lives, and therefore it is urgent to strengthen community resilience construction, which is directly related to the economic development, security and stability of urban communities. (2) Methods: this article combines the SWOT (Strengths, Weaknesses, Opportunities, Threats) qualitative analysis method and the AHP (Analytic hierarchy process) qualitative and quantitative analysis method to explore the current status of community emergency management in Xi’an City. A community emergency management evaluation model is constructed, which is based on six dimensions: infrastructure resilience, community organization resilience, risk hazard governance, emergency material security, emergency force construction, and emergency literacy. The evaluation index for community emergency management is established. Focusing on analyzing the strengths and weaknesses of communities in responding to sudden public health emergencies, opportunities and threats are identified. (3) Results: it was concluded that infrastructure resilience and emergency material support are community strengths, community organizational resilience is a community weakness, the emergency literacy dimension is a community opportunity, and risk and hazard management and emergency force construction are community threats. The results of the data research were further analyzed. (4) Conclusions: The final research results provide new theoretical support for community emergency management, while also providing theoretical and methodological references for emergency capacity evaluation in other fields.

Potato late blight, caused by Phytophthora infestans, is one of the main diseases in potato production, causing major losses in yield. Applying environmentally harmful fungicides is the prevailing and classical method for controlling late blight, thus contaminating food and water. There is therefore a need for innovative research approaches to produce food sustainably. Here, we used a systems approach to identify sustainable management strategies for disease control in potato production in the Netherlands. We focussed not only on ecological processes, the classical approach, but also on decision-making concerning disease management. For that, we performed a literature study, stakeholder interviews and modelling using fuzzy cognitive mapping. Interviews were carried out with farmers, representatives of breeding companies and experts. The fuzzy cognitive map allows to identify major concepts and their influence on late blight management. Three management scenarios were analysed using the fuzzy cognitive map. Results show that published research on the control of potato late blight focusses on agronomic practices, plant breeding for resistance to late blight and chemical-based disease suppression. Farmers are strongly influenced by corporate (such as traders, breeders and retail) and public institutes and policies, each pushing their own objectives and interests. The fuzzy cognitive map showed that social and ecological processes are tightly related. The scenario analysis showed that increasing stakeholder cooperation and a change in market demands towards resistant cultivars could improve sustainability of late blight management. In contrast, policies restricting the use of fungicides would result in increased disease severity if no alternative strategies were implemented. Adoption of such strategies would require social-institutional support and facilitation. We conclude that our systems approach improves the understanding of the system dynamics which is necessary for developing and deploying effective strategies for controlling P. infestans.

BackgroundUsing digital health technologies to aid individuals in managing chronic diseases offers a promising solution to overcome health service barriers such as access and affordability. However, their effectiveness depends on adoption and sustained use, influenced by user preferences.ObjectivesThis study quantifies the preferences of individuals with chronic heart disease (CHD) for features of a mobile health app to self-navigate their disease condition.MethodsWe conducted an unlabeled web-based choice survey among adults older than 18 years with CHD living in Australia, recruited via a web-based survey platform. Four app attributes—ease of navigation, monitoring of blood pressure and heart rhythm, health education, and symptom diary maintenance—were systematically chosen through a multistage process. This process involved a literature review, stakeholder interviews, and expert panel discussions. Participants chose a preferred mobile app out of 3 alternatives: app A, app B, or neither. A D-optimal design was developed using Ngene software, informed by Bayesian priors derived from pilot survey data. Latent class model analysis was conducted using Nlogit software (Econometric Software, Inc). We also estimated attribute importance and anticipated adoption rates for 3 app versions.ResultsOur sample included 302 participants with a mean age of 50.5 (SD 18.2) years. Latent class model identified 2 classes. Older respondents with education beyond high school, prior experience with mobile health apps, and a positive perception of app usefulness were more likely to be in class 1 (257/303, 85%) than in class 2 (45/303, 15%). Class 1 membership preferred adopting a mobile app (app A: β coefficient 0.74, 95% uncertainty interval (UI) 0.41-1.06; app B: β coefficient 0.53, 95% UI 0.22-0.85). Participants favored apps providing postmonitoring recommendations (β coefficient 1.45, 95% UI 1.26-1.64), tailored health education (β coefficient 0.50, 95% UI 0.36-0.64), and unrestricted symptom diary entry (β coefficient 0.58, 95% UI 0.41-0.76). Class 2 showed no preference for app adoption (app A: β coefficient −1.18, 95% UI −2.36 to 0.006; app B: β coefficient −0.78, 95% UI −1.99 to 0.42) or any specific attribute levels. Vital sign monitoring was the most influential attribute among the 4. Scenario analysis revealed an 84% probability of app adoption with basic features, rising to 92% when app features aligned with respondents’ preferences.ConclusionsThe study’s findings suggest that designing preference-informed mobile health apps could significantly enhance adoption rates and engagement among individuals with CHD, potentially leading to improved clinical outcomes. Adoption rates were notably higher when app attributes included easy navigation, vital sign monitoring, feedback provision, personalized health education, and flexible data entry for symptom diary maintenance. Future research to explore factors influencing app adoption among different groups of patients is warranted.

Within the EU Horizon project OPTAIN (OPtimal strategies to reTAIN and re-use water and nutrients in small agricultural catchments across different soil-climatic regions in Europe, optain.eu) project, the effects of Natural/Small Water Retention Measures (NSWRMs) on water regime, soil erosion and nutrient transport are evaluated at both, catchment- and field-scales for present and future climate conditions. Our goal is to perform an integrated, model-based assessment of the effectiveness of NSWRMs at field scale and cross-validated these results from those obtained from the catchment-scale modelling. The field-scale assessment is based on the adaptation of the SWAP mathematical model to seven pilot sites across three European biogeographical regions and on combined NSWRM – projected climate scenario analyses. The scenarios are designed to evaluate the efficiency and potential of different natural/small water retention measures in improving soil water retention and reducing flash floods and the loss of soil and nutrients under changing climate conditions. We present the harmonized SWAP modelling workflow and the combined scenario analyses, including the implementation of various in-field measures in the SWAP model. Examples of model calibration, validation and scenario results for selected pilot sites will be given.

In China, Energy transition was proposed in the “12th Five-Year Plan” and gained resilient support by “Energy Revolution” announced by President Xi Jinping in 2014. In Paris Agreement, there are targets set up for 2100 to be well below 2 °C, with ambitious target on 1.5 °C. China signed the agreement and will support the global target. In the meantime, large-scale actions were initiated in 2013 by the national action plan on air pollution control for the period from 2013 to 2017. None of these strategies has clear long-term target. In our studies, energy transition will be decided by the long-term target of CO2 emission reduction, air pollutant reduction, and energy security. This paper will present the analysis from IPAC model, by setting up reduction target for CO2 emission under the global 2 °C and 1.5 °C target. Energy transition, CO2 emission, and air pollutant reduction will be discussed based on these targets. For air pollutants, SO2, NOx, PM2.5, black carbon, and mercury will be included. From the results, there will be a significant energy transition by large-scale use of renewable energy, nuclear and the share of coal will be reduced to less than 20% in 2050 from 66% in 2015. Energy transition will also contribute to a drastic reduction in air pollutants.

This paper presents a complete integrated asset model for gas planning and, in particular, the handling of stranded gas during exploration. Uniquely, it models a gas network from the reservoir, right through to product allocation based on contractual obligations. It back-allocates through the network from the market, to ensure accurate supply and product tracking. We show how this type of model provides unprecedented confidence in field evaluation and gas planning. The market-based approach to integrated asset modelling that we used includes an optimal amount of information to deliver a fast, accurate solution. Oil and gas exploration activities do not usually involve planning for stranded or associated gas. Many discovered gas reservoirs are left undeveloped due to a lack of scenario analysis during the exploration phase. A fully integrated gas planning approach is required for effective field development planning in a timely manner. This has been a subject of intense research and development in recent years. Stranded gas reserves can be effectively developed using proactive market planning and strategic gas exploration guided by an integrated gas planning approach. As such, our model includes reservoirs, platforms, facilities, pipelines and markets to effectively understand the network deliverability. In this paper we defined multiple scenarios for bringing up gas from reservoirs, through the facilities and delivery point, right up to allocation of resources to meet the market demand. The case study deals with a market-based approach for modeling 40 gas reservoirs and associated gas products from nearby platforms. The practical constraints such as condensate handling capacities, compressor systems, and contract gas quality are integrated into the model analysis. The unique, fully integrated reservoir to market approach to IAM we demonstrate in this study provides a real breakthrough in E&P companies’' ability to model and run scenarios against their complete gas network in a relatively very short time. In particular the back-allocation from the market provides confidence around their ability to meet their contractual sales obligations and enables product tracking through the whole network.

The ecological decline of ancient Near Eastern civilizations and the violent and explosive characteristics of post‐Columbian colonial ecologies might well remain comfortably remote from us in our twentieth century world were it not for the disturbing parallels that such case histories seem to evoke as we consider our contemporary global circumstance. Just as in ancient times and in the age of colonial expansion, it is in the “remote environments,” usually quite distant from the centers of power, that the crucial indicators of environmental catastrophe first become apparent within the system as a whole. These regions are frequently characterized by weak economies and highly vulnerable ecosystems in our time, just as they were in the past. Accordingly, the environmental circumstances in these regions constitute for the modern world a kind of monitoring device that can provide early warnings of ecological instabilities in the global ecosystem.