Search Results (2,060)

The 1742–1743 plague epidemic profoundly impacted populations along the Camino Real, the main colonial trade route connecting Buenos Aires and Lima, with Córdoba, Argentina, as a critical focal point. This research examines the multidimensional repercussions of the epidemic, emphasizing its demographic, social, and economic consequences. Utilizing parish records as primary sources, this study quantifies the excess mortality, revealing a dramatic increase, with death rates in Córdoba peaking at 12 times the pre-epidemic average in May 1743. Marginalized groups, particularly enslaved and Indigenous populations, were disproportionately affected, which exacerbated existing social inequalities. Through a comprehensive analysis of historical data, this study contributes to understanding epidemic responses in colonial Latin America, illuminating how public health crises intersect with demographic and socio-economic structures. The findings provide insights that are relevant to contemporary public health perspectives on crisis management and resilience, highlighting how historical epidemics offer valuable understanding into the social dynamics of health emergencies. Full article

This paper explores the possibility of counteracting the crisis of culture and institutions by investing in the identity values of the user-actor within digital spaces built for the purpose. The strategy is applied to the analysis of three Catalan cloisters (Spain), with a focus on the representation of the cloister of Sant Cugat (Barcelona). Heuristic picklocks are found in the semantic richness proposed by Marius Schneider exclusively on the verbal level. The authors interpret the contents and transcribe them into graphic signs and digital denotations of sounds and colors. They organize proprietary ontologies, or syntagmatic lines, to be entrusted to the management of computer algorithms. The syncretic culture that characterized the medieval era allowed the ability to mediate science and faith to be entrusted to the mind of the praying monk alone in every canonical hour. The hypothesis that a careful direction has programmed the ways in which to orient souls to “navigate by sight” urges the authors to find the criteria that advanced statistics imitates to make automatic data processing “Intelligent”. In step with the times and in line with the most recent directions for the Safeguarding of Heritage, the musical, astral, and narrative rhythms feared by Schneider are used to inform representative models, to increase not only the visual perception of the user (XR Extended Reality) but also to solicit new analogies and illuminating associations. The results return a vision of the culture of the time suitable for shortening the distances between present and past, attracting the visitor and, with him, the resources necessary to protect and enhance the spaces of the Romanesque era. The methodology goes beyond the contingent aspect by encouraging the ‘remediation’ of contents with the help of machine learning. Full article

In the current global context of the natural resource crisis and the need for environmental protection, sustainable economy strategies are becoming imperative. These strategies aim to optimize wastewater treatment processes, with a particular focus on the removal of biological and chemical quality indicators such as BOD, COD and TSS. By developing and implementing advanced technologies and effective resource management methods, this article explores ways the industry can reduce its negative environmental impact and contribute to a sustainable future. The proposed research investigates the impact of 40% ferric chloride on the purification processes of domestic wastewater using biological contactors. The study evaluates the efficiency of pollutant removal through measurements such as biochemical oxygen demand over 5 days (BOD), chemical oxygen demand (COD) and total suspended solids (TSS). Through the statistical analysis of the obtained results, the research identifies opportunities for innovative strategies in the sustainable economy, thus contributing to the optimization of purification process efficiency and significantly reducing pollution’s impact on the environment. In conclusion, this research highlights the use of 40% ferric chloride as an effective and sustainable method to improve the efficiency of wastewater treatment processes, focusing on BOD, COD and TSS removal. The findings demonstrate significant pollutant reduction and environmental impact mitigation, underlining its potential for Sustainable Development Goals. The study supports innovative strategies for optimizing water quality and recommends further evaluation of long-term impacts on human and environmental health. Full article

Efficiency is one of the tenets in assessing the financial health of an enterprise. Ultimately, the form of asset management has a major impact on growth but also on the decline of profit. It also reveals how the enterprises are positioned within the competitive market environment. For this reason, the aim of this article is to define the level of business activity in the Visegrad Four in the pre-crisis, during-crisis and post-crisis periods of the COVID-19 pandemic. The investigation included 48,650 enterprises from Slovakia, Czechia, Poland, and Hungary over the period 2017–2023. We determined the median values of eleven business efficiency indicators separately for each country and sector. The Friedman test and Kruskal–Wallis test confirmed significant differences between years and countries. Furthermore, multiple pairwise comparisons revealed analogies between the pre-crisis and post-crisis periods, as well as similarities between the two pandemic years for the tested ratios. One can observe that the results serve as the foundation for regional and international benchmarks, particularly for enterprises from former Eastern Bloc countries. Full article

Background: The forest products industry plays a significant role in global carbon emissions, highlighting the need for sustainable practices to address the climate crisis. Reverse logistics (RL), focusing on the return, reuse, and recycling of materials, offers a promising approach to decarbonizing supply chains. However, its application within forest products supply chains remains underexplored. Methods: This study conducts a review of the literature on RL, its environmental implications, and its potential to reduce carbon emissions in forest products supply chains. Key areas examined include greenhouse gas reduction, waste management, and the promotion of circular economy principles. Additionally, the study evaluates case studies and models that integrate RL practices into forest-based industries. Results: The findings reveal that RL can significantly reduce greenhouse gas emissions by optimizing transportation routes, minimizing waste, and extending product life cycles. Incorporating these practices into forestry operations reduces the environmental impact and aligns with sustainable forestry goals. The study identifies gaps in current research, particularly regarding empirical data and the scalability of RL solutions. Conclusions: RL represents a critical strategy for decarbonizing forest products supply chains and advancing sustainable development. Future research should focus on developing standardized methodologies, enhancing technological integration, and fostering policy support to maximize its impact. These steps are essential to fully leverage RL as a tool for mitigating climate change and promoting a circular economy. Full article

Accurate remaining capacity prediction (RCP) of lithium-ion batteries (LIBs) is crucial for ensuring their safety, reliability, and performance, particularly amidst the growing energy crisis and environmental concerns. However, the complex aging processes of LIBs significantly hinder accurate RCP, as traditional prediction methods struggle to effectively capture nonlinear degradation patterns and long-term dependencies. To tackle these challenges, we introduce an innovative framework that combines evolutionary learning with deep learning for RCP. This framework integrates Temporal Convolutional Networks (TCNs), Bidirectional Gated Recurrent Units (BiGRUs), and an attention mechanism to extract comprehensive time-series features and improve prediction accuracy. Additionally, we introduce a hybrid optimization algorithm that combines the Sparrow Search Algorithm (SSA) with Bayesian Optimization (BO) to enhance the performance of the model. The experimental results validate the superiority of our framework, demonstrating its capability to achieve significantly improved prediction accuracy compared to existing methods. This study provides researchers in battery management systems, electric vehicles, and renewable energy storage with a reliable tool for optimizing lithium-ion battery performance, enhancing system reliability, and addressing the challenges of the new energy industry. Full article

This study examines the effectiveness of various spatial data sources and pathfinding algorithms for route determination in forested environments, focusing on the Mazovia region of Poland. Accurate and efficient forest route planning is critical for both military operations and crisis management, highlighting the need for reliable data and robust algorithms. The analysis centers on three primary spatial data sources that can support forest routing: the civilian Topographic Objects Database (TOD) and OpenStreetMap (OSM), along with the military-specific Vector Map Level 2 (VML2). Two commonly used pathfinding algorithms, Dijkstra and A* (the latter with six heuristic variations), were tested to assess their suitability and performance in these contexts. This study was conducted across ten of the largest forested areas in Mazovia, with route determinations performed between selected pairs of start and end points within each forest area. The findings indicate that the TOD database yielded the most stable and consistent routes, while the A* algorithm with Euclidean distance heuristics proved to be the fastest among the tested variants. In contrast, OSM data presented challenges due to inconsistencies, resulting in some routes being undeterminable, where connections between start and end points were lacking. These results underscore the importance of data quality and algorithm selection in effective forest route planning. Full article

The INterpolated FLOod Surface (INFLOS) tool was developed to meet the operational needs of the Copernicus Emergency Management Service (CEMS) Rapid Mapping (RM) component, which delivers critical crisis information within hours during and after disasters. With increasing demand for accurate and real-time flood depth estimates, INFLOS provides a rapid, adaptable solution for estimating floodwater depth across diverse flood scenarios, using remotely sensed data and high-resolution Digital Terrain Models (DTMs). INFLOS calculates flood depth by interpolating water surface elevation from sample points along flooded area boundaries, derived from satellite imagery. This tool is capable of delivering flood depth estimates in a rapid mapping context, leveraging a multistep interpolation and filtering process for improved accuracy. Tested across fourteen regions in Europe and South America, INFLOS has been successfully integrated into CEMS RM operations. The tool’s computational optimisations further enhance efficiency, improving computation times by up to 15-fold, compared to similar techniques. Indeed, it is able to process areas of up to 6000 ha in a median time of 5.2 min, and up to 30 min at most. In conclusion, INFLOS is currently operational and consistently generates flood depth products quickly, supporting real-time emergency management and reinforcing the CEMS RM portfolio. Full article

An investigation of the difficulties that confronted SMMEs in uMhlathuze Municipality because of the COVID-19 crisis was the purpose of this research. Given the novelty and severity of the COVID-19 pandemic, this research was motivated by the desire to obtain information that can be used by both the government and the SMMEs to prepare for future disasters. The research embraced a qualitative research design and a case study research approach and used semi-structured interviews to extract data from 12 purposefully sampled SMMEs. The thematic analysis technique and Atlas.ti software version 23.2.3.27778 were used to analyse the collected data. Research findings revealed that a loss of customers and income; disruption of business operations; health-related challenges; difficulties in accessing government aid; challenges in fulfilling financial obligations; business activities not compatible with power saving; government corruption and incompetency; increases in operating costs; losses due to stock which decayed; and supply chain disruptions were the challenges that confronted SMMEs during COVID-19. Basing on these findings, the researchers recommend that the government should equip SMMEs with disaster management skills to enable these entities to better circumvent future challenges. Additionally, in these volatile times, SMMEs should strive to set some savings aside to fall back on in times of need. Full article

Leveraging Twitter data for crisis management necessitates the accurate, fine-grained geolocation of tweets, which unfortunately is often lacking, with only 1–3% of tweets being geolocated. This work addresses the understudied problem of fine-grained geolocation prediction for Arabic tweets, focusing on the Kingdom of Saudi Arabia. The goal is to accurately assign tweets to one of thirteen provinces. Existing approaches for Arabic geolocation are limited in accuracy and often rely on basic machine learning techniques. Additionally, advancements in tweet geolocation for other languages often rely on distinct datasets, hindering direct comparisons and assessments of their relative performance on Arabic datasets. To bridge this gap, we investigate eight advanced deep learning techniques, including two Arabic pretrained language models (PLMs) on one constructed dataset. Through a comprehensive analysis, we assess the strengths and weaknesses of each technique for fine-grained Arabic tweet geolocation. Despite the success of PLMs in various tasks, our results demonstrate that a combination of Convolution Neural Network (CNN) and Long Short-Term Memory (LSTM) layers yields the best performance, achieving a test accuracy of 93.85%. Full article

Background/Objectives: The rapid spread of antimicrobial resistance (AMR) presents a critical threat to global health. Primary care plays a significant role in this crisis, with oral antibacterial drugs among the most prescribed medications. Antibacterial prescribing rates are often high and complicated in out-of-hours (OOH) services, including weekdays outside regular hours, weekends, and holidays, potentially exacerbating AMR. This review aims to identify the existing literature on oral antibacterial drug prescribing within primary care OOH services. Methods: This review followed established frameworks, adhered to PRISMA-ScR guidelines, and the protocol was registered on Open Science Framework. Seven databases were searched from 2017 to May 2022. Data were summarised, tabulated, and presented narratively to explore themes and patterns that aligned with the review objectives. Results: The search identified 28 studies from nine high-income countries, mainly the UK (n = 6) and Belgium (n = 5). Most were quantitative studies (n = 23). Key areas identified included common oral antibacterial prescriptions, prescribing trends, presentations and conditions managed in OOH services, factors and predictors associated with prescribing, prescribing appropriateness, the impact of interventions on prescribing, prescribing in the context of COVID-19, patient satisfaction and expectations, and the challenges encountered, such as factors influencing prescribing behaviour and decision making, safety netting, and communication. Conclusions: This review highlights key areas around oral antibacterial prescribing in primary care OOH services. Despite the numerous articles identified covering various areas within OOH services, the variability in OOH services approaches across countries and studies complicates the comparison of practice. Further research is needed to better understand practices in these settings. Full article

Background: In the United Kingdom, specialist teams managing crises in dementia make efforts to reduce inpatient admissions by supporting people to live for longer in their own homes. However, fluctuations in the health and social circumstances of both the person with dementia and/or their family caregiver can lead to ‘crisis’: a breakdown in home care often leading to inpatient admissions. At this point, a team managing crises in dementia (TMCD) might become involved. These multidisciplinary teams begin with a rapid assessment to establish the needs of the person with dementia and their caregiver(s), followed by intensive but short-term interventions to reduce the risk of inpatient admission. There is little evidence as to how family caregiver experience input from a TMCD. Methods: A thematic analysis of qualitative interviews with caregivers supporting a family member with dementia to live at home and who have received support from a TMCD. Results: The personal troubles of family caregivers are intertwined with their experience of engaging with and trying to gain support from human services, particularly TMCDs. While respondents describe how specific individuals have addressed or added to the troubles they face, the most impactful interventions from their perspectives are medication for managing challenging behaviour and knowing that there is a service they can telephone in a crisis. Conclusions: Efforts to reduce inpatient admissions by supporting people to live in their own homes for as long as is practicable, even when carers may have reached the limits of their caring capacities, can, at best, only delay inpatient to long-stay residential accommodation. This is because when one frail older person has the responsibility of caring for another frail older person, even with support from TMCD and other health and social care services, crises are difficult to manage. Full article

The COVID-19 pandemic has meant a serious risk to the economic viability of companies and the sustainability of employment in the restaurant sector, a high-impact activity for the economy and employment in Mexico and Spain. This paper analyzes the causes of the prolonged and intense damage to companies and employees via multilevel analysis techniques and a qualitative, inductive methodology drawing on multiple sources. Research propositions posit that the sectoral structure, management practices, and institutional actions during and after the pandemic are predictors of recovery or continued losses. The balanced result of these three levels of analysis, in a severe crisis situation, such as the global pandemic, reveals that the combination of low institutional protection at the macro level, a hostile industry structure at the meso level, and a focus solely on economic sustainability as the primary business objective resulted in widespread resignation and put survival at risk, particularly for smaller companies and entrepreneurs. Analysis of the firms’ and stakeholders’ actions also shed light on the inter-relations, such as the negative effect of macro general policies on a fragmented, asymmetric meso level. Inter-relations among customers and firms’ behavior gave insights that could increase resilience before general critical events. Finally, the balanced results recommend a simultaneous effort from firms and policy makers to make possible a profound change while addressing the sector’s shortcomings. Firms’ effort in managing key assets, such as human capital, to acquire the capacity for the flexibility, adaptability, and innovation essential for change and renewal, must be endorsed by institutional support and customer recognition of the contributions of this singular service and cultural industry. Full article

Shipping activities continue to experience growth across a multitude of industrial sectors within the Arctic, hence there are risks in terms of severity and likelihood of accidents. The Arctic region is inherently dangerous to transportation and human existence due to its extreme climate and environmental conditions, and hence the complexities associated with emergency situations within the maritime domain are amplified when operating within the Arctic and North-Atlantic (ANA). The definition and characterisation of potential seaborne disasters and catastrophic incidents in the ANA region are significant enablers in providing a set of critical and sustainable tools for Search and Rescue (SAR), Oil Spill Response (OSR), and emergency management practitioners. Therefore, in this paper we aim to identify and characterise high-priority potential seaborne disasters and catastrophic incidents in the ANA region such as cruise ship accidents, oil leaks, radiological leaks, and fishing boat groundings. These were compiled as an outcome of a set of workshops carried out as part of the ARCSAR, EU Horizon 2020 funded project, and from analysis of the literature. We also provide root cause analysis techniques, tools for strategic decision-making, and means of mitigation. We demonstrate how such tools can be used by applying some of them to a selective case study and drawing lessons learned from the application of root cause analysis, which can help emergency response organisations with preparedness work and hence more efficient response. In doing so, we provide a set of tools that can be used for strategic and operational learning. Such approaches can help standardise the definition and characterisation of potential seaborne disasters and catastrophic incidents in the ANA region in both prospective and retrospective analysis. Full article

In recent years, we have witnessed an increase in emergency and catastrophic events. It is now crucial for professionals involved in emergency management to stay updated on issues and statistics related to decision-making in emergency situations. Emergency Decision-Making (EDM) has emerged as a strategic field of study, guiding the training and education of those responsible for managing and responding to emergencies, making it a critical area for national security. This original work advances the study of EDM by offering key insights into research trends over the years, the geographical distribution of scientific output, the methodologies employed, yearly statistics, and the spread of key terms and the most-explored topics. The main findings of this study on EDM literature from 1977 to 2023 highlight the dominant role of China and the USA in scientific productivity in the field. Moreover, a significant absolute increase in scientific productivity has been observed from 2020 to the present. Full article