Using the parametrically designed battery pack in Grasshopper, a Voronoi grain-based discrete element model was established in the three-dimensional distinct element code, incorporating progressively increasing grain equivalent diameters. A grain-based stress corrosion model, enhanced with an improved stress corrosion theory, was further proposed to simulate both the direct shear characteristics and shear creep behaviour of unsaturated sandstone under varying normal stresses. The mesoscopic parameters, including the contact properties between grains and the stress corrosion parameters, were then calibrated. This ensured that the model could accurately reproduce the mechanical properties of sandstone observed in laboratory tests. The modelling results indicated that tensile cracks were the dominant cracks generated during the shear process under various saturations and normal stresses, along with a few shear cracks. A significant negative correlation was observed between the joint roughness coefficient (JRC) and the ratio of long-term to peak shear strength. Additionally, increased normal stress or decreased saturation were both found to accelerate the time-dependent failure process, leading to a shorter time-to-failure under constant shear loading. We summarize that the proposed model effectively characterizes the direct shear and creep behaviour of unsaturated sandstone at varying roughnesses and normal stresses.
<i>Background</i>: Defining the optimal fleet portfolio is a crucial process in airline planning. The published efforts in literature provide ways to anticipate the disruption effects on the passenger demand; however, the proposed solution in this paper provides visibility on the impact of sustainable disruption and the way an airline can resist it. <i>Methods</i>: This paper proposes a two-stage methodology to find the best portfolio for airline operational requirements under the impact of disruption. The first stage considers optimization for normal airline operations under a specific fleet portfolio using an Integer Linear Programming (ILP) model. The second stage of the analysis is a mapping for the scenario-based methodology to find a way out for an airline subjected to some given disruption in operations. <i>Results</i>: The result of the two-stage analysis shall define the best fleet portfolio to withstand sustained disruptions by mapping the results in a disruption funnel and showing the impact of the supply and demand gap on the airline’s sustainable profitability. <i>Conclusions</i>: This paper provides a novel, practical way of evaluating strategic decisions to choose the best fleet portfolio and make airlines rely on the mapping of the disruption funnel to modify their network while increasing supply chain resilience.
Transportation and communication, Management. Industrial management
The Bullwhip Effect, describing the amplification of demand variability up the supply chain, poses significant challenges in Supply Chain Management. This study examines how the COVID-19 pandemic intensified the Bullwhip Effect across U.S. industries, using extensive industry-level data. By focusing on the manufacturing, retailer, and wholesaler sectors, the research explores how external shocks exacerbate this phenomenon. Employing both traditional and advanced empirical techniques, the analysis reveals that COVID-19 significantly amplified the Bullwhip Effect, with industries displaying varied responses to the same external shock. These differences suggest that supply chain structures play a critical role in either mitigating or intensifying the effect. By analyzing the dynamics during the pandemic, this study provides valuable insights into managing supply chains under global disruptions and highlights the importance of tailoring strategies to industry-specific characteristics.
Luigi Russo, Antonietta Sorriso, Silvia Liberata Ullo
et al.
Land Cover (LC) mapping using satellite imagery is critical for environmental monitoring and management. Deep Learning (DL), particularly Convolutional Neural Networks (CNNs) and Vision Transformers (ViTs), have revolutionized this field by enhancing the accuracy of classification tasks. In this work, a novel approach combining a transformer-based Swin-Unet architecture with seasonal synthesized spatio-temporal images has been employed to classify LC types using spatio-temporal features extracted from Sentinel-1 (S1) Synthetic Aperture Radar (SAR) data, organized into seasonal clusters. The study focuses on three distinct regions - Amazonia, Africa, and Siberia - and evaluates the model performance across diverse ecoregions within these areas. By utilizing seasonal feature sequences instead of dense temporal sequences, notable performance improvements have been achieved, especially in regions with temporal data gaps like Siberia, where S1 data distribution is uneven and non-uniform. The results demonstrate the effectiveness and the generalization capabilities of the proposed methodology in achieving high overall accuracy (O.A.) values, even in regions with limited training data.
Kazuki Numazato, Keiichiro Kan, Masaki Kitagawa
et al.
Flapping-wing drones have attracted significant attention due to their biomimetic flight. They are considered more human-friendly due to their characteristics such as low noise and flexible wings, making them suitable for human-drone interactions. However, few studies have explored the practical interaction between humans and flapping-wing drones. On establishing a physical interaction system with flapping-wing drones, we can acquire inspirations from falconers who guide birds of prey to land on their arms. This interaction interprets the human body as a dynamic landing platform, which can be utilized in various scenarios such as crowded or spatially constrained environments. Thus, in this study, we propose a falconry-like interaction system in which a flapping-wing drone performs a palm landing motion on a human hand. To achieve a safe approach toward humans, we design a trajectory planning method that considers both physical and psychological factors of the human safety such as the drone's velocity and distance from the user. We use a commercial flapping platform with our implemented motion planning and conduct experiments to evaluate the palm landing performance and safety. The results demonstrate that our approach enables safe and smooth hand landing interactions. To the best of our knowledge, it is the first time to achieve a contact-based interaction between flapping-wing drones and humans.
Nowadays, the corporate governance quality (CGQ) of a company has become increasingly important as it is the benchmark for the investment decision-making process. However, there are not many studies of CGQ in the Vietnamese public sector. In this regard, this study adopted and adapted the Association of Southeast Asian Nations (ASEAN) corporate governance scorecard and the Vietnamese Listed Company Awards scorecard to evaluate the CGQ of state-owned enterprises (SOEs) in Vietnam. Data from 220 equitized SOEs were collected from a self-administered survey and double-checked with information from in-depth interviews with the company’s managers and their documents. Collected data were analyzed to score the CGQ of surveyed companies. Results showed that the CGQ of survey companies met the basic requirements that comply with the legal framework. The analysis also showed the differences between two groups of state-owned enterprises: listed versus unlisted joint-stock companies. Based on the findings, we suggested companies improve and adjust their governance policies following 77 scorecard criteria and apply international best practices in corporate governance to promote their brand value to become more appealing to investors.
Automatic drone landing is an important step for achieving fully autonomous drones. Although there are many works that leverage GPS, video, wireless signals, and active acoustic sensing to perform precise landing, autonomous drone landing remains an unsolved challenge for palm-sized microdrones that may not be able to support the high computational requirements of vision, wireless, or active audio sensing. We propose AIRA, a low-cost infrared light-based platform that targets precise and efficient landing of low-resource microdrones. AIRA consists of an infrared light bulb at the landing station along with an energy efficient hardware photodiode (PD) sensing platform at the bottom of the drone. AIRA costs under 83 USD, while achieving comparable performance to existing vision-based methods at a fraction of the energy cost. AIRA requires only three PDs without any complex pattern recognition models to accurately land the drone, under $10$cm of error, from up to $11.1$ meters away, compared to camera-based methods that require recognizing complex markers using high resolution images with a range of only up to $1.2$ meters from the same height. Moreover, we demonstrate that AIRA can accurately guide drones in low light and partial non line of sight scenarios, which are difficult for traditional vision-based approaches.
Marieke Wesselkamp, Matthew Chantry, Ewan Pinnington
et al.
Most useful weather prediction for the public is near the surface. The processes that are most relevant for near-surface weather prediction are also those that are most interactive and exhibit positive feedback or have key role in energy partitioning. Land surface models (LSMs) consider these processes together with surface heterogeneity and forecast water, carbon and energy fluxes, and coupled with an atmospheric model provide boundary and initial conditions. This numerical parametrization of atmospheric boundaries being computationally expensive, statistical surrogate models are increasingly used to accelerated progress in experimental research. We evaluated the efficiency of three surrogate models in speeding up experimental research by simulating land surface processes, which are integral to forecasting water, carbon, and energy fluxes in coupled atmospheric models. Specifically, we compared the performance of a Long-Short Term Memory (LSTM) encoder-decoder network, extreme gradient boosting, and a feed-forward neural network within a physics-informed multi-objective framework. This framework emulates key states of the ECMWF's Integrated Forecasting System (IFS) land surface scheme, ECLand, across continental and global scales. Our findings indicate that while all models on average demonstrate high accuracy over the forecast period, the LSTM network excels in continental long-range predictions when carefully tuned, the XGB scores consistently high across tasks and the MLP provides an excellent implementation-time-accuracy trade-off. The runtime reduction achieved by the emulators in comparison to the full numerical models are significant, offering a faster, yet reliable alternative for conducting numerical experiments on land surfaces.
Tarleton Gillespie, Ryland Shaw, Mary L. Gray
et al.
As generative AI technologies find more and more real-world applications, the importance of testing their performance and safety seems paramount. "Red-teaming" has quickly become the primary approach to test AI models--prioritized by AI companies, and enshrined in AI policy and regulation. Members of red teams act as adversaries, probing AI systems to test their safety mechanisms and uncover vulnerabilities. Yet we know far too little about this work or its implications. This essay calls for collaboration between computer scientists and social scientists to study the sociotechnical systems surrounding AI technologies, including the work of red-teaming, to avoid repeating the mistakes of the recent past. We highlight the importance of understanding the values and assumptions behind red-teaming, the labor arrangements involved, and the psychological impacts on red-teamers, drawing insights from the lessons learned around the work of content moderation.
Understanding the complex interactions between land surface and atmosphere is essential to improve weather and climate predictions. Various numerical experiments have suggested that regions of strong coupling strength (hotspots) are located in the transitional climate zones. However, atmospheric processes in these hotspots are found to have different responses to the perturbation of surface properties. Here we establish analytical relationships to identify key role of soil moisture variances in controlling the coupling hotspots. Using the most recent numerical experiments, we find different signs of feedback in two such hotspots, suggesting the coupling can either reinforce or attenuate persistent extreme climates. We further uncover new coupling hotspots in regions where precipitation is highly sensitive to soil moisture perturbation. Our results highlight the importance of both signs and magnitudes of land-atmosphere interactions over extensive regions, where the ecosystems and communities are particularly vulnerable to the extreme climate events.
Sudipan Saha, Tushar Verma, Dario Augusto Borges Oliveira
With the global population on the rise, our cities have been expanding to accommodate the growing number of people. The expansion of cities generally leads to the engulfment of peripheral areas. However, such expansion of urban areas is likely to cause increment in areas with increased land surface temperature (LST). By considering each summer as a data point, we form LST multi-year time-series and cluster it to obtain spatio-temporal pattern. We observe several interesting phenomena from these patterns, e.g., some clusters show reasonable similarity to the built-up area, whereas the locations with high temporal variation are seen more in the peripheral areas. Furthermore, the LST center of mass shifts over the years for cities with development activities tilted towards a direction. We conduct the above-mentioned studies for three different cities in three different continents.
Lucas V. S. Oliveira, Daniel P. Marques, Luan C.A. Rocha
et al.
Introdução: Ferramentas para determinação de causalidade de Reações Adversas a Medicamentos (RAM) são essenciais para o exercício da farmácia clínica; sobretudo considerando a complexidade terapêutica e vulnerabilidade do neonato sob terapia intensiva. O algoritmo de Naranjo é considerado padrão-ouro, contudo, ao contrário do algoritmo de Du, não foi desenvolvido para UTI neonatal (UTIN). Objetivo: Avaliar a correlação entre dois instrumentos de causalidade na avaliação de RAM suspeitas em NICU e sua reprodutibilidade intravaliadores. Métodos: Este estudo observacional e prospectivo foi desenvolvido em neonatos internados na Unidade de Terapia Intensiva de uma maternidade referência para gestação de alto risco em Natal/Brasil entre janeiro de 2019 e dezembro de 2020. Os casos de RAM suspeitas foram disponibilizados por três farmacêuticas independentes e experientes que aplicaram os algoritmos de causalidade Naranjo et al. e Du et al. O desempenho dos instrumentos foi mensurado pelo Kappa de Cohen (k) aplicado entre os avaliadores e entre os instrumentos. O estudo foi aprovado pelo Comitê de Ética e Pesquisa do Hospital Universitário Onofre Lopes sob nº 2.591.495/2018. Resultados: As farmacêuticas aplicaram os instrumentos em 79 casos de RAM que foram observadas em 57 neonatos do sexo feminino em sua maioria (30; 56,6%), com média de idade gestacional de 30±4 semanas e peso ao nascer de 1.446,0±1.179,3g. As reações mais comuns foram Taquicardia envolvendo Cafeína (14; 17,7%) e Dobutamina (5; 6,3%) e Hipertermia relacionada ao Alprostadil (5; 6,3%). Os métodos não apresentaram correlação significativa quanto a classificação da causalidade de RAM (k global = -0,031; IC95% -0,049 – 0,065). Contudo, o algoritmo de Naranjo apresentou melhor reprodutibilidade interavaliadores (k global = 0,402; IC95% 0,379 – 0,429. Correlação moderada) comparado a Du (k global = 0,108; IC95% 0,064 – 0,149. Correlação fraca). Conclusão: Não houve concordância entre os métodos testados, mas a determinação de causalidade de RAM em neonatos via Algoritmo de Naranjo apresentou melhor reprodutibilidade entre diferentes avaliadores.
Pharmacy and materia medica, Pharmaceutical industry
Ali Kaan Kurbanzade, Ansaar M. Baig, Sanjay Mehrotra
Unmanned aerial vehicles, commonly known as drones, have emerged as a disruptive technology with the potential to revolutionize operations across various industries. Drones are the fast-growing internet-of-things technology and are estimated to have a $100 billion market value in the next decade. Exploring drone operations through research has the potential to yield innovative academic insights and create significant practical effects in diverse industries, offering a competitive edge. Drawing insights from both academic and industry literature, this article describes how technological advancements in UAVs may disrupt traditional operational practices in different industries (e.g., commercial last-mile delivery, commercial pickup and delivery, telecommunication, insurance, healthcare, humanitarian, environmental, urban planning, homeland security), identifies the value of this evolving disruptive technology from sustainability and operational innovation perspectives, argues the significance of this area for operations management by conceptualizing a research agenda. The current state of the art focuses on the computing aspect of analytical models to tackle a variety of synthetic drone-related problems, with mixed integer optimization being the primary tool. There is a very significant research gap that should focus on drone operations management with industry know-how by partnering with actual stakeholders and using a variety of tools (i.e., econometrics, field experiments, game theory, optimal control, utility functions). This article aims to promote research on UAVs from operations management and industry-specific point of view.
Machine learning is expected to enable the next Industrial Revolution. However, lacking standardized and automated assembly networks, ML faces significant challenges to meet ever-growing enterprise demands and empower broad industries. In the Perspective, we argue that ML needs to first complete its own Industrial Revolution, elaborate on how to best achieve its goals, and discuss new opportunities to enable rapid translation from ML's innovation frontier to mass production and utilization.
Haechan Mark Bong, Rongge Zhang, Antoine Robillard
et al.
Safe landing is essential in robotics applications, from industrial settings to space exploration. As artificial intelligence advances, we have developed PEACE (Prompt Engineering Automation for CLIPSeg Enhancement), a system that automatically generates and refines prompts for identifying landing zones in changing environments. Traditional approaches using fixed prompts for open-vocabulary models struggle with environmental changes and can lead to dangerous outcomes when conditions are not represented in the predefined prompts. PEACE addresses this limitation by dynamically adapting to shifting data distributions. Our key innovation is the dual segmentation of safe and unsafe landing zones, allowing the system to refine the results by removing unsafe areas from potential landing sites. Using only monocular cameras and image segmentation, PEACE can safely guide descent operations from 100 meters to altitudes as low as 20 meters. The testing shows that PEACE significantly outperforms the standard CLIP and CLIPSeg prompting methods, improving the successful identification of safe landing zones from 57% to 92%. We have also demonstrated enhanced performance when replacing CLIPSeg with FastSAM. The complete source code is available as an open-source software.