Search Results (102)

The debate around corporate sustainability has become increasingly heated, with opinions ranging from denial of climate change to fatalistic acceptance of an impending collapse of civilization. This study examines how multinational enterprises, which contribute to nearly half of global GDP, internalize knowledge of planetary boundaries and take action within the organization or externally with investors, industry bodies, and policymakers. The research is grounded in empirical analysis of longitudinal data on two large samples of multinationals. It is found that, despite warnings from scientists about breaching planetary boundaries, multinationals, at best, follow an incremental approach to sustainability initiatives that collectively fail to drive positive global change. This well-entrenched practice remains unquestioned by external stakeholders, such as regulators, investors, and lenders. The research explains this behavior through (a) our inability to link global scientific findings to non-financial performance imperatives for individual businesses, and (b) our reliance on traditional enterprise risk management models that are less effective in a non-ergodic world. Full article

This study proposes a heterogeneous fusion path planning framework for unmanned aerial vehicles (UAVs) operating in complex emergency rescue and civil environments. Existing single-mechanism metaheuristics—including Particle Swarm Optimization (PSO), Ant Colony Optimization (ACO), and Genetic Algorithms (GAs)—suffer from fundamental limitations in three-dimensional kinematic path planning: PSO converges rapidly but stagnates at local optima due to population variance collapse; ACO offers robust local exploitation but incurs prohibitive cold-start overhead; GAs maintain diversity at the cost of expensive crossover operations. To address these complementary deficiencies simultaneously, the proposed framework introduces a spherical coordinate representation that reduces computational complexity and naturally enforces UAV kinematic constraints, combined with adaptive weight factors and a serial PSO-ACO fusion strategy, and subsequently incorporates adaptive weight factors. A serial fusion strategy is then introduced, wherein the sub-optimal trajectory generated by the Spherical PSO phase is mapped into the ACO pheromone field via a Gaussian Kernel Density Mapping (GKDM) mechanism, enabling the ACO phase to perform fine-grained local exploitation within a kinematically feasible corridor. Various constraints along the flight path are formulated into distinct cost functions, which cover aircraft track length, pitch angle variation, altitude difference variation, obstacle avoidance, and smoothness; the core task of the algorithm is to find the flight path with the minimum total cost. The proposed algorithm is dedicated to UAV path planning in complex emergency rescue environments (disaster-stricken areas, hazardous zones) and is further applicable to civil low-altitude logistics delivery, industrial facility inspection, ecological environment monitoring and urban air mobility (UAM) scenarios with complex obstacle constraints. It can effectively improve the safety and efficiency of UAVs in reaching rescue points, delivering emergency supplies, conducting disaster surveys, and completing various civil low-altitude operation tasks. Full article

Architecture, as a profession, discipline and practice, has played a vital role in designing, constructing and maintaining modern culture. The creative work of imagining and building places, infrastructure and dwellings for the complex activities of contemporary life has contributed to the global world we now inhabit. There are, however, indications that this edifice of modernity is cracking because of external and internal forces that undermine our global society. Climate change, species extinction, and worldwide threats to democracy and governance, along with new technologies, converge and reveal the uncomfortable possibility that modern industrial global culture and civilization may collapse. As a response, an expanding body of ‘stories of collapse’ has emerged to interpret causes, processes, and scenarios. This essay engages with key voices (Rees, Bendell, Lewis, Hagens, de Oliveira, and Macy), to describe in what ways architecture is complicit in this moment, and suggests what ethical and place-based responsibilities may be required of architects and placemakers as collapse unfolds. Full article

This article argues that John Calvin’s account of the church in Institutes of the Christian Religion IV is best read through the formative logic of worship. Calvin famously identifies preaching the Word and administering the sacraments as the marks of the visible church. Rather than regarding these marks merely as identifiers, this study interprets them as worship practices that shape Christian life “in-between” church and society. First, the preached Word is not simply received as information but functions as the medium through which faith is generated and sustained, forming a community tasked to bear truth publicly. Second, the sacraments operate as embodied theology: baptism initiates believers into ecclesial belonging through cleansing, renewal, and confession, while the Lord’s Supper repeatedly schools the church in remembrance, thanksgiving, unity, and mutual love. Finally, by situating the Word and sacrament within the church’s maternal nurture and the ministry of reconciliation, the article shows how worship extends beyond the sanctuary, cultivating conscience and communal practices oriented toward public peace and responsibility. In Calvin’s Reformation vision, worship is thus the hinge that links ecclesiology to social ethics without collapsing the distinction between the church and civil society. Full article

The Spanish conquest of the Inca empire in the early 16th century stands as one of the most striking examples of asymmetric historical collapse. In this paper, a simplified mathematical formulation is developed being inspired by Lotka–Volterra dynamics to describe, in a stylized quantitative manner, the interactions between the Inca state and the invading Spanish forces. The model is not intended to explain the historical events in a causal or predictive sense, but rather to capture and represent key mechanisms commonly identified in historical analyses. These include the demographic and political weakening caused by smallpox epidemics prior to direct contact, the internal fragmentation produced by the civil war and the introduction of external shocks such as the capture of Atahualpa and the fall of Cusco. Although intentionally minimalistic, the framework provides a dynamical illustration of how combined internal and external pressures can destabilize a complex society. This descriptive perspective situates the Inca collapse within the broader conceptual language of complex systems, emphasizing how nonlinear interactions, feedback and structural asymmetry shape trajectories of resilience and failure. Full article

This article examines how the Southern lady is represented in three major Southern women’s novels set during the American Civil War: Macaria (1864) by Augusta Jane Evans Wilson, The Battle-Ground (1902) by Ellen Glasgow, and Gone with the Wind (1936) by Margaret Mitchell. Although separated by over seven decades and distinct historical perspectives—Wilson as a contemporary witness, Glasgow as a postwar observer, and Mitchell as a nostalgic inheritor—their works collectively shaped enduring images of the South in American popular culture. Through textual analysis, the study explores how each author depicts female endurance, illness, and mortality to symbolize both individual and social transformation. The heroines (Wilson’s Electra and Irene, Glasgow’s Betty Ambler, and Mitchell’s Scarlett O’Hara) embody resilience amid collapse, assuming active roles in the reconstruction of Southern identity. Their struggles reflect broader tensions between traditional femininity and emerging female agency. Ultimately, the article argues that portrayals of women’s frailty and death function as metaphors for the decline of the antebellum order and the inevitable demise of the Southern lady ideal, revealing illness and death as physical and cultural markers of the South’s transformation in war and its aftermath. Full article

Reinforced concrete walls (RC walls) are widely used in transportation, building structures, and civil air defence engineering. RC walls are vulnerable to low-velocity impact, such as the fall of components caused by earthquakes or explosions, for example, and the impact from road objects, such as vehicles, during their service life. When subjected to instantaneous high-energy impact, RC walls at key positions are prone to severe damage, which can further lead to structural collapse. Therefore, it is necessary to consider improving the impact resistance of key RC walls in a structure. Using a porous honeycomb structure with excellent energy absorption performance to provide impact protection for key RC walls is an effective way to reduce the damage of RC walls and thereby enhance the impact resistance of a structure. Therefore, based on the author’s previous series of experimental and numerical studies on the impact resistance of RC walls, as well as the high-mass pendulum impact experimental study on the honeycomb sandwich panel composite RC wall (HSP-RC wall), this paper adopts a multi-scale modelling method in micro-mechanics and macro-mechanics to establish a pendulum impact finite element model (FEM) for the HSP-RC wall. The representative volume element (RVE) and periodic boundary condition (PBC) are used to calculate the elastic property parameters of the honeycomb, which guide the establishment of the FEMs for the HSP-RC wall. The FEMs can avoid the computational difficulty caused by refined simulation, analyse the impact damage of the HSP-RC walls more accurately, quantify the impact protection effect of the honeycomb sandwich panel, and thus facilitate the parametric analysis of the impact resistance of HSP-RC walls with different honeycomb panel structural parameters in subsequent studies. Full article

Assessing seismic vulnerability is a critical step in evaluating the resilience of existing buildings, and fragility curves are widely used to quantify the probability of damage under varying levels of seismic intensity. However, traditional methods for generating these curves often rely on generalized assumptions that may not accurately capture the seismic behavior of diverse building types within a region. This limitation is particularly evident for non-engineered masonry buildings, which typically lack standardized designs. Their irregular and informal construction makes them difficult to assess using conventional approaches. Transformer-based models, a type of machine learning (ML) technique, offer a promising alternative. These models can identify patterns and relationships in available data, making them well suited for developing seismic fragility curves with improved efficiency and accuracy. While transformers are relatively new to civil engineering, their application to seismic fragility assessment has been largely unexplored. This study presents a pioneering effort to apply transformer models for deriving fragility curves for non-engineered masonry buildings. A comprehensive dataset of 646 masonry buildings observed in Malawi is used to train the models. The transformers are trained to predict the probability of four damage states: Light Damage, Severe Damage, Near Collapse, and Collapse based on Peak Ground Acceleration (PGA). The performance of the transformer-based approach is compared with other ML methods, demonstrating its strong potential for more efficient and accurate seismic fragility assessment. Future work could adopt the proposed methodology and extend the approach by incorporating larger datasets, additional regional contexts, and alternative ML techniques to further enhance predictive performance. Full article

Accurate prediction of concrete compressive strength is essential for ensuring structural safety in civil engineering, particularly in road and bridge construction, where inadequate strength can lead to deformation, cracking, or collapse. Traditional non-destructive testing (NDT) methods, such as the Rebound Hammer Test, estimate strength using regression-based formulas fitted with measurement data; however, these formulas, typically optimized via the least squares method, are highly sensitive to initial parameter settings and exhibit low robustness, especially for nonlinear relationships. Meanwhile, AI-based models, such as neural networks, require extensive datasets for training, which poses a significant challenge in real-world engineering scenarios with limited or unevenly distributed data. To address these issues, this study proposes a gradient-boosting artificial bee colony (GB-ABC) algorithm for robust regression curve fitting. The method integrates two novel mechanisms: gradient descent to accelerate convergence and prevent entrapment in local optima, and a point density-weighted strategy using Gaussian Kernel Density Estimation (GKDE) to assign higher weights to sparse data regions, enhancing adaptability to field data irregularities without necessitating large datasets. Following data preprocessing with Local Outlier Factor (LOF) to remove outliers, validation on 600 real-world samples demonstrates that GB-ABC outperforms conventional methods by minimizing mean relative error rate (RER) and achieving precise rebound-strength correlations. These advancements establish GB-ABC as a practical, data-efficient solution for on-site concrete strength estimation. Full article

Dam collapse is a catastrophic event involving an artificial reservoir usually filled with water for hydropower or irrigation purposes. Several cases of dam collapses have overwhelmed entire valleys, reconfiguring their geomorphology, redesigning their landscape, and causing several thousand casualties. These episodes led to more careful regulations and the activation of more effective monitoring and mitigation strategies. A fundamental tool in defining appropriate procedures for alert and risk scenarios is the Dam Emergency Plan (PED), an operational document that establishes the actions and procedures required to manage potential hazards (e.g., geo-hydrological and seismic risk). The aim of this study is to describe a reference methodology for identifying geo-hydrological criticalities based on historical and geomorphological data, applied to civil protection activities. A further objective is to provide a structured inventory of Italian reservoirs, assigning each a potential risk index based on an analytical approach considering several factors (age and construction methodology of the dam, morphological and environmental settings, anthropized environment, and exposed population). The approach identifies that the most significant change in risk over time is not only the dam itself but also the transformation of the territory. This methodology does not incorporate probabilistic forecasting of flood or climate change; instead, it objectively characterizes the exposed territory, offering insights into existing vulnerabilities on which to base effective mitigation strategies. Full article

In this paper, climate evolution is revisited in terms of the theory of dynamical systems, which has been successfully used in predictions of catastrophic events such as avalanches, landslides, or economy and civilization collapses. Such tipping events are announced by warning signs, named “pre-critical fluctuations” or “critical softening”, allowing a tipping date estimate through well-known equations. In the case of climate, the warning signs are extreme events of increasing amplitudes. We show that in such a context, numerical simulations can hardly predict incoming tipping points, due to a divergence in computational time at the singularity. Based on the dynamical systems theory, a recent publication from Copenhagen University shows that the Atlantic Meridional Oceanic Circulation is likely to collapse well before the end of the century, triggering switchover cascades, eventually culminating in global climate tipping. Paleoclimatic studies also show that tipping events occurred in the past, particularly during the PETM period 56 Myrs ago. If this was to happen now, average global temperatures might reach an unbearable level, with a deadline much closer than expected. This extreme emergency has major consequences on the implementation times of sustainability policies and in energy production, mobility, agriculture, housing, etc., that absolutely must be operational on time. Full article

Scouring is an erosional process driven by the water motion over a sediment bed. Scour can lead to structural safety risks of built structures and to riverbanks’ instabilities and collapse. In particular, scouring in river bends is a known phenomenon caused by secondary flow currents. This scouring can result in negative impacts on the economic and social activities that occur on the riverbanks. On the other hand, the erosion and scouring processes of riverbeds are often addressed by means of heavy civil engineering construction works. Aiming at looking for different solutions for the scour in river bends, this research investigates the use of an air bubble screen system to minimize the scouring in river bends by providing detailed measurements of sedimentation patterns and velocity fields in a mild 90-degree bend where an air screen bubble was installed. The air bubble screen is generated by injecting compressed air through a perforated pipe placed on the bed along the outer bend. Different parameters were tested, including the water flow rate in the channel, the air flow rate, the angle of attack between the air bubble screen and the secondary flow, and flow direction. The air bubble screen opposes the direction of the bend’s induced secondary flows, altering the velocity pattern such that the maximum velocity at cross-sections of 45°, 65°, 80°, and 90° were displaced from the outer wall as much as 53%, 68%, 89%, and 84% of the width, respectively. The air bubble screen system also reduced the secondary flow power in the maximum scour zone by 35%. Hence, the maximum scour depth was reduced by 59% to 79.8% for the maximum flow rate by increasing the air bubbles’ angle of attack relative to the primary flow from 0° to 90°. Finally, the limitations of this study and its applicability to real cases is discussed. Full article

This study assesses the financial soundness of Sudanese commercial banks during escalating civil conflict by integrating Altman’s Z-score models with scenario-based stress testing. Using audited financial data from 2016 to 2022 (pre-war) and projections through to 2028, the analysis evaluates resilience under low- and high-intensity conflict scenarios. Altman’s Model 3 (for non-industrial firms) and Model 4 (for emerging markets) are applied to capture liquidity, retained earnings, profitability, and leverage dynamics. The findings reveal relative stability between 2017–2020 and in 2022, contrasted by significant vulnerability in 2016 and 2021 due to macroeconomic deterioration, sanctions, and political instability. Liquidity emerged as the most critical driver of Z-score performance, followed by earnings retention and profitability, while leverage showed a context-specific positive effect under Sudan’s Islamic finance framework. Stress testing indicates that even under low-intensity conflict, rising liquidity risk, capital erosion, and credit risk threaten sectoral stability by 2025. High-intensity conflict projections suggest systemic collapse by 2028, characterized by unsustainable liquidity depletion, near-zero capital adequacy, and widespread defaults. The results demonstrate a direct relationship between conflict duration and systemic fragility, affirming the predictive value of Altman’s models when combined with stress testing. Policy implications include the urgent need for enhanced risk-based supervision, Basel II/III implementation, crisis reserves, contingency planning, and coordinated regulatory interventions to safeguard the stability of the banking sector in fragile states. Full article

Given the increasing challenges posed by frequent extreme climatic events, understanding the climate–human connection between the climate system and the transitions of ancient civilizations is crucial for addressing future climatic challenges, especially when examining the relationship between the abrupt events of the Holocene and the Neolithic culture development. Compared with the globally recognized “4.2 ka collapse” of ancient cultures, the initial start time and the cultural significance of the 5.5 ka climatic fluctuation are more complex and ambiguous. The Hongshan culture (6.5–5.0 ka) is characterized by a complicated society evident in its grand public architecture and elaborate high-status tombs. However, the driving mechanisms behind cultural changes remain complex and subject to ongoing debate. This paper delves into the role of climatic change in Hongshan cultural shifts, presenting an integrated dataset that combines climatic proxy records with archaeological data from the Hongshan culture period. Based on synthesized aeolian, fluvial-lacustrine, loess, and stalagmite deposits, the study indicates a relatively cold and dry climatic fluctuation occurred during ~6.0–5.5 ka, which is widespread in the Horqin dune field and adjacent areas. Combining spatial analysis with ArcGis 10.8 on archaeological sites, we propose that the climatic fluctuation between ~6.0–5.5 ka likely triggered the migration of the Hongshan settlements and adjustment of survival strategies. Full article

As high-specification structures, civil aircraft hangars face significant fire risks, including rapid fire propagation and challenging rescue operations. The structural integrity of these hangars is compromised under high temperatures, potentially leading to collapse and making aircraft parking and maintenance unfeasible. The severe consequences of fire in such environments make effective detection essential for mitigating risks and enhancing flight safety. However, conventional fire detectors often suffer from false alarms and missed detections, failing to meet the fire safety demands of large buildings. Additionally, many existing fire detection models are computationally intensive and large in size, posing deployment challenges in resource-limited environments. To address these issues, this paper proposes an improved YOLOv8-based lightweight model for fire detection in aircraft hangars (AH-YOLO). A custom infrared fire dataset was collected through controlled burn experiments in a real aircraft hangar, using infrared thermal imaging cameras for their long-range detection, high accuracy, and robustness to lighting conditions. First, the MobileOne module is integrated to reduce the network complexity and improve the computational efficiency. Additionally, the CBAM attention mechanism enhances fine target detection, while the improved Dynamic Head boosts the target perception. The experimental results demonstrate that AH-YOLO achieves 93.8% mAP@0.5 on this custom dataset, a 3.6% improvement over YOLOv8n while reducing parameters by 15.6% and increasing frames per second (FPS) by 19.0%. Full article