Julia Valentim Tavares, Emanuel Gloor, Thiago S. F. Silva
et al.
Abstract Amazon rainforests face intensifying water stress due to increases in vapour pressure deficit and changing hydrological regimes. Embolism resistance (Ψ50) is a critical metric of tree survival under drought conditions, it is defined as a plant’s capacity to resist disruption of xylem water flow due to air bubble formation from water stress. However, measurements of Ψ50 are only available for a limited number of Amazon locations and species. Conversely, data on forest taxonomic composition are abundant across Amazonia, and if Ψ50 is conserved phylogenetically, these data could provide a way to scale-up drought resistance patterns. Here we evaluate Ψ50 measurements across non-flooded Amazonian tree taxa and reveal a moderate phylogenetic signal, with phylogenetic conservatism evident at the family-level. Notably, Fabaceae is amongst the most embolism-resistant tree families in Amazonia. Leveraging the phylogenetic signal we use species composition and tree size data from 448 forest plots across Amazonia to produce a macroecological assessment of Amazonian vulnerability to embolism. The resulting estimate spatial pattern reveals that forests in the Brazilian and Guiana Shield regions, where Fabaceae abundance is high, show strong resistance to embolism. In contrast, tree communities in Western Amazonia appear more vulnerable to embolism, suggesting a reduced capacity to withstand future drought conditions.
Faezeh Amirteimoury, Farshid Keynia, Elaheh Amirteimoury
et al.
Abstract Wind is a renewable, sustainable, and clean source of energy. This has led to wind gaining a lot of attention in recent decades as a reliable alternative to fossil fuels. However, wind speed fluctuations complicate its integration with power grids. To tackle this issue, this paper proposes a new wind speed prediction model that combines four techniques: Discrete Wavelet Transform, which smooths the wind speed signal; Mutual Information, which selects the most informative part of the wind speed time series; Coot Optimization Algorithm for optimal feature selection; and Bidirectional Long Short-Term Memory for capturing complex patterns. To evaluate the efficiency of the proposed model, its performance was measured using error metrics such as mean squared error, mean absolute error, mean absolute percentage error, coefficient of determination ( $$R^2$$ ), and median absolute error. The proposed model was examined using two different wind speed datasets and achieved high prediction accuracy. Additionally, 14 different benchmark models were created, and their prediction results were compared with those of the proposed model. A comparison between the results of the proposed model and benchmark models demonstrated the superiority of the proposed model.
Sahra Svensson-Hoglund, Jennifer D. Russell, Jessika Luth Richter
et al.
The Circular Economy (CE) concept continues to garner significant attention from stakeholders. Yet, what a CE entails in its realized state remains insufficiently articulated, particularly for product users. This study introduces a multilevel systems model that conceptualizes an economy-wide, fully implemented CE from the perspective of the product user. The focus is on the foundational tenets of CE theory, namely the flows of materials, products, and components in the specific context of durable consumer products. The model's development follows a sequential method and is empirically tested and refined through a Delphi study involving 14 experts in CE and sufficiency. First, this model clarifies the composition, elements, and structure of the consumption system in a realized CE. Notably, elements often relegated to the background, such as contextual settings, are foregrounded to enable a more comprehensive analysis of factors involved in CE behaviors. The model provides a structured foundation for systematic exploration of potential implications for product users, which can be expanded in future research to include additional dimensions (e.g., social and ecological). Second, the multileveled nature of the model and mapping of the diverse flows and interactions shaping product users' reality in a realized CE allows for systematic integration of consumption (i.e., more concrete from the perspective of the product user) and production (i.e., more abstract) systems. As such, the model introduces an integrated product-system lens for bridging multi- and interdisciplinary areas of sustainable consumption and production. The paper concludes by outlining avenues for future research and potential applications of the model.
Andrzej Gębura, Andrzej Szelmanowski, Ilona Jacyna-Gołda
et al.
The evolution of aircraft power systems has been driven by increasing electrical demands and advancements in aviation technology. Background: This study provides a comprehensive review and experimental validation of on-board electrical network development, analyzing power management strategies in both conventional and modern aircraft, including the Mi-24 helicopter, F-22 multirole aircraft, and Boeing 787 passenger airplane. Methods: The research categorizes aircraft electrical systems into three historical phases: pre-1960s with 28.5 V DC networks, up to 2000 with three-phase AC networks (3 × 115 V/200 V, 400 Hz), and post-2000 with 270 V DC networks derived from AC generators via transformer–rectifier units. Beyond theoretical analysis, this work introduces experimental findings on hybrid-electric aircraft power solutions, particularly evaluating the performance of the Modular Power System for Aircraft (MPSZE). The More Electric Aircraft (MEA) concept is analyzed as a key innovation, with a focus on energy efficiency, frequency stability, and ground power applications. The study investigates the integration of alternative energy sources, including photovoltaic-assisted power supplies and fuel-cell-based auxiliary systems, assessing their feasibility for aircraft system checks, engine startups, field navigation, communications, and radar operations. Results: Experimental results demonstrate that hybrid energy storage systems, incorporating lithium-ion batteries, fuel cells, and photovoltaic modules, can enhance MEA efficiency and operational resilience under real-world conditions. Conclusions: The findings underscore the importance of MEA technology in the future of sustainable aviation power solutions, highlighting both global and Polish research contributions, particularly from the Air Force Institute of Technology (ITWL).
Chaos Engineering (CE) is an engineering technique aimed at improving the resilience of distributed systems. It involves intentionally injecting faults into a system to test its resilience, uncover weaknesses, and address them before they cause failures in production. Recent CE tools automate the execution of predefined CE experiments. However, planning such experiments and improving the system based on the experimental results still remain manual. These processes are labor-intensive and require multi-domain expertise. To address these challenges and enable anyone to build resilient systems at low cost, this paper proposes ChaosEater, a system that automates the entire CE cycle with Large Language Models (LLMs). It predefines an agentic workflow according to a systematic CE cycle and assigns subdivided processes within the workflow to LLMs. ChaosEater targets CE for software systems built on Kubernetes. Therefore, the LLMs in ChaosEater complete CE cycles through software engineering tasks, including requirement definition, code generation, testing, and debugging. We evaluate ChaosEater through case studies on small- and large-scale Kubernetes systems. The results demonstrate that it consistently completes reasonable CE cycles with significantly low time and monetary costs. Its cycles are also qualitatively validated by human engineers and LLMs.
Sharon Guardado, Risha Parveen, Zheying Zhang
et al.
The integration of Large Language Models (LLMs) in Requirements Engineering (RE) education is reshaping pedagogical approaches, seeking to enhance student engagement and motivation while providing practical tools to support their professional future. This study empirically evaluates the impact of integrating LLMs in RE coursework. We examined how the guided use of LLMs influenced students' learning experiences, and what benefits and challenges they perceived in using LLMs in RE practices. The study collected survey data from 179 students across two RE courses in two universities. LLMs were integrated into coursework through different instructional formats, i.e., individual assignments versus a team-based Agile project. Our findings indicate that LLMs improved students' comprehension of RE concepts, particularly in tasks like requirements elicitation and documentation. However, students raised concerns about LLMs in education, including academic integrity, overreliance on AI, and challenges in integrating AI-generated content into assignments. Students who worked on individual assignments perceived that they benefited more than those who worked on team-based assignments, highlighting the importance of contextual AI integration. This study offers recommendations for the effective integration of LLMs in RE education. It proposes future research directions for balancing AI-assisted learning with critical thinking and collaborative practices in RE courses.
Предметом дослідження є вивчення фактора абсентеїзму Бредфорда як інструменту для аналізу відсутності співробітників на робочому місці, зокрема його використання для оцінювання регулярності та частоти відсутностей за допомогою інтерактивного дашборду. Мета дослідження – розроблення аналітичної інформаційної системи на основі фактора абсентеїзму Бредфорда для ефективного моніторингу та управління відсутностями співробітників у компанії. У статті необхідно виконати такі завдання: проаналізувати останні дослідження й публікації щодо фактора абсентеїзму Бредфорда; виокремити не розв’язані раніше частини загальної проблеми; створити ER-діаграми інформаційної системи; визначити необхідний функціонал аналітичної інформаційної системи та її архітектури; розробити необхідні візуалізації та розмістити їх на відповідних сторінках інформаційної системи; провести експеримент для визначення трендів і закономірностей фактора Бредфорда на основі тестових показників; виявити результати досліджень і проаналізувати їх; сформулювати висновки та перспективи подальшого розвитку. Методи дослідження. Проаналізовано інформацію про відсутність працівників, що зберігаються в базі даних SQL, для визначення зв’язків між таблицями та підготовки інформації для подальшого аналізу. З метою візуалізації та аналізу використано інструмент Power BI, що дає змогу інтегрувати інформацію з різних джерел і надавати візуалізовані показники абсентеїзму. Основним аналітичним інструментом став розрахунок фактора Бредфорда, що оцінює вплив частих короткострокових відсутностей на загальну роботу команди. Крім того, впроваджено методи моделювання баз даних та побудови ER-діаграм для забезпечення правильних взаємозв’язків між елементами системи. Досягнуті результати: проаналізовано останні дослідження та публікації щодо фактора абсентеїзму Бредфорда; виокремлено не розв’язані раніше частини загальної проблеми; створено ER-діаграму аналітичної інформаційної системи; визначено необхідний функціонал аналітичної інформаційної системи та її архітектури; розроблено необхідні візуалізації та розміщено їх на відповідних сторінках інформаційної системи; проведено експеримент для визначення трендів та закономірностей фактора Бредфорда; виявлено й обговорено результати дослідження та сформульовано висновки й перспективи подальшого розвитку. Висновки. Унаслідок виконаної роботи розроблено аналітичну інформаційну систему для аутсорсингової компанії, що ґрунтується на факторі абсентеїзму Бредфорда. Використання Power BI як основного інструменту візуалізації та аналізу інформації дало змогу створити інтерактивний дашборд, що забезпечує зручний доступ до ключових показників відсутності працівників на різних рівнях – від окремих співробітників до департаментів і проєктів. Це допомагає керівникам швидко й ефективно аналізувати ситуацію з абсентеїзмом, приймати обґрунтовані рішення щодо управління персоналом і вчасно реагувати на можливі проблеми.
The objective of this article is to evaluate the structural changes that have occurred in the export of mechanical engineering from Ukraine. These changes have been influenced by significant transformations in the country's foreign economic policy, which have been primarily caused by a shift in the country's foreign policy as a result of military aggression from Russia, which has been a leading foreign trade partner for a considerable period of time. Methodology. The study is based on the authors' previous works, which are devoted to the study of the features of the export-oriented development of the country and the industry. In particular, the studies examine the strengthening of the innovative component in the export potential and the formation of mechanisms and strategies for ensuring the development of exports on a high-tech basis. The methodological basis of this work was also formed by the scientific studies of leading Ukrainian and foreign researchers devoted to the development of the export of mechanical engineering, problems and prospects of the industry in the implementation of foreign economic activity. In order to calculate structural changes in exports, a generalised methodology has been developed based on the systematisation of scientific papers that outline different approaches to assessing structural changes and disproportions. The main content of the proposed methodology is to form a system of indicators that allow for a comprehensive assessment of structural changes in the exports of a particular industry. To calculate and test the methodology, open statistical data on exports of mechanical engineering products were used. Results. The research findings indicated that, despite the pivotal role of the mechanical engineering sector in the national economy, the sector's performance in Ukraine has exhibited a discernible negative trajectory in terms of overall production and sales volumes, export volumes, and the patterns of expansion observed in export operations. In Ukraine, the contribution of mechanical engineering to the national economy is 8%, whereas in industrialised countries, this figure ranges between 30 and 50%. The long-term orientation of mechanical engineering enterprises towards the conventional Russian market has not provided the impetus for the innovative development of such enterprises. Objective changes in Ukraine's foreign economic policy related to Russia's military invasion have created a field of uncertainty for mechanical engineering companies. The search for partners in new foreign markets was rather slow and not always effective. All this led to structural changes in the export of mechanical engineering products. Calculations have shown the existence of imbalances in the structure of exports of mechanical engineering products. In particular, for a long time there was a predominance of heavy engineering products. Conversely, the calculations demonstrated that products with competitive advantages in foreign markets account for a relatively minor proportion of exports. This provides a rationale for a shift in strategy with regard to the expansion of exports in the mechanical engineering sector, with a focus on the increased export of competitive high-tech products. Practical implications. A complex of indicators was employed to calculate the structural changes in the export of mechanical engineering. This enabled the identification of those priority groups of mechanical engineering products with the greatest export potential. Value / Originality. The developed methodological approach, which integrates a set of indicators for the analysis of structural changes in exports, provides a foundation for the formulation of management decisions and the development of export strategies for the advancement of mechanical engineering enterprises within the context of an export-oriented economic model.
Leon Scheiber, Nivedita Sairam, Mazen Hoballah Jalloul
et al.
Abstract The economies and livelihoods of many coastal megacities are at serious risk from flooding, despite investments in flood defenses. For instance, in Ho Chi Minh City, the construction of a large‐scale ring‐dike has mitigated negative effects from storm surges, yet damage is still frequently caused by high‐intensity rainfalls leading to nuisance flooding, which is responsible for the highest proportion of flood losses in the city today. Because sustainable flood risk management requires detailed spatial information, we analyze the local risk and its components based on a chain of novel models previously calibrated and validated for Ho Chi Minh City. Furthermore, we assess the effectiveness of two decentralized adaptation options, namely private precautionary measures and rainwater retention, for mitigating pluvial flooding. Our integrated risk assessment reveals that the approaches are complementary, which is a major advantage for their implementation. Implementation of both approaches has the potential to reduce the expected annual damage and the number of annually affected households by 16% and 56%, respectively. This is also reflected in a significant reduction of annual losses per household, which we propose as an additional, people‐centered indicator of flood risk. Moreover, these measures are well‐suited to strengthen citizen participation in risk reduction beyond top‐down protection schemes. Complementing the ring‐dike with decentralized adaptation options can therefore be seen as an effective and generic strategy to alleviate the impacts of nuisance flooding in coastal megacities, such as Ho Chi Minh City, and should be incentivized by decision‐makers. Aside from hydrological and metocean site conditions, both the methodology and findings of this study are transferrable to any coastal megacity facing similar challenges.
Jinqi Luo, Tianjiao Ding, Kwan Ho Ryan Chan
et al.
Large Language Models (LLMs) are being used for a wide variety of tasks. While they are capable of generating human-like responses, they can also produce undesirable output including potentially harmful information, racist or sexist language, and hallucinations. Alignment methods are designed to reduce such undesirable outputs via techniques such as fine-tuning, prompt engineering, and representation engineering. However, existing methods face several challenges: some require costly fine-tuning for every alignment task; some do not adequately remove undesirable concepts, failing alignment; some remove benign concepts, lowering the linguistic capabilities of LLMs. To address these issues, we propose Parsimonious Concept Engineering (PaCE), a novel activation engineering framework for alignment. First, to sufficiently model the concepts, we construct a large-scale concept dictionary in the activation space, in which each atom corresponds to a semantic concept. Given any alignment task, we instruct a concept partitioner to efficiently annotate the concepts as benign or undesirable. Then, at inference time, we decompose the LLM activations along the concept dictionary via sparse coding, to accurately represent the activations as linear combinations of benign and undesirable components. By removing the latter ones from the activations, we reorient the behavior of the LLM towards the alignment goal. We conduct experiments on tasks such as response detoxification, faithfulness enhancement, and sentiment revising, and show that PaCE achieves state-of-the-art alignment performance while maintaining linguistic capabilities.
Building up competencies in working with data and tools of Artificial Intelligence (AI) is becoming more relevant across disciplinary engineering fields. While the adoption of tools for teaching and learning, such as ChatGPT, is garnering significant attention, integration of AI knowledge, competencies, and skills within engineering education is lacking. Building upon existing curriculum change research, this practice paper introduces a systems perspective on integrating AI education within engineering through the lens of a change model. In particular, it identifies core aspects that shape AI adoption on a program level as well as internal and external influences using existing literature and a practical case study. Overall, the paper provides an analysis frame to enhance the understanding of change initiatives and builds the basis for generalizing insights from different initiatives in the adoption of AI in engineering education.
Horia Mărgărit, Amanda Bowman, Krishnageetha Karuppasamy
et al.
In this work, we present a case study in implementing a variational quantum algorithm for solving the Poisson equation, which is a commonly encountered partial differential equation in science and engineering. We highlight the practical challenges encountered in mapping the algorithm to physical hardware, and the software engineering considerations needed to achieve realistic results on today's non-fault-tolerant systems.
Francisco G. Blanco, Francisco G. Blanco, Roberto Vázquez
et al.
Polymeric nanoparticles (NPs) present some ideal properties as biomedical nanocarriers for targeted drug delivery such as enhanced translocation through body barriers. Biopolymers, such as polyhydroxyalkanoates (PHAs) are gaining attention as nanocarrier biomaterials due to their inherent biocompatibility, biodegradability, and ability to be vehiculized through hydrophobic media, such as the lung surfactant (LS). Upon colonization of the lung alveoli, below the LS layer, Streptococcus pneumoniae, causes community-acquired pneumonia, a severe respiratory condition. In this work, we convert PHA NPs into an antimicrobial material by the immobilization of an enzybiotic, an antimicrobial enzyme, via a minimal PHA affinity tag. We first produced the fusion protein M711, comprising the minimized PHA affinity tag, MinP, and the enzybiotic Cpl-711, which specifically targets S. pneumoniae. Then, a PHA nanoparticulate suspension with adequate physicochemical properties for pulmonary delivery was formulated, and NPs were decorated with M711. Finally, we assessed the antipneumococcal activity of the nanosystem against planktonic and biofilm forms of S. pneumoniae. The resulting system displayed sustained antimicrobial activity against both, free and sessile cells, confirming that tag-mediated immobilization of enzybiotics on PHAs is a promising platform for bioactive antimicrobial functionalization.
V. I. Menshchikova, N. K. Rodionova, A. A. Burmistrova
The article reveals the reasons for the introduction of economic sanctions and restrictions against Russia. The commodity structure of exports and imports of the Russian Federation was analyzed; the grouping of Russian regions by the volume of exports and imports with countries of the far and near abroad was carried out, which allowed identifying at least 20 regions of the Russian Federation in each group that are actively engaged in foreign economic activity. It is concluded that the industrial specialization of the regions of the Russian Federation determines the nature of their foreign economic activity: the leading regions in export are the regions in which the raw materials industries of the economy are concentrated, and in import — the regions in which the manufacturing industries, primarily mechanical engineering, are concentrated. It is proved that only nine subjects of the Russian Federation (Moscow and St. Petersburg, the Republic of Tatarstan, Krasnodar and Krasnoyarsk Territories, Sverdlovsk, Rostov, Nizhny Novgorod and Moscow regions) are key regions in the implementation of comprehensive foreign trade operations. At the same time, there are regions that work mainly for import — Kaliningrad and Kaluga regions, Primorsky Krai, and others — mainly for export — Sakhalin and Irkutsk regions. The conclusion is made about the need to develop high–tech industries, IT–sphere (hardware and software), mechanical engineering, production of own feed, seeds, plant protection products in the Russian regions.
L. Scheiber, M. Hoballah Jalloul, C. Jordan
et al.
<p>Hydro-numerical models are increasingly important to
determine the adequacy and evaluate the effectiveness of potential flood
protection measures. However, a significant obstacle in setting up
hydro-numerical and associated flood damage models is the tedious and
oftentimes prohibitively costly process of acquiring reliable input data,
which particularly applies to coastal megacities in developing countries and
emerging economies. To help alleviate this problem, this paper explores the
usability and reliability of flood models built on open-access data in
regions where highly resolved (geo)data are either unavailable or difficult
to access yet where knowledge about elements at risk is crucial for
mitigation planning. The example of Ho Chi Minh City, Vietnam, is taken to
describe a comprehensive but generic methodology for obtaining, processing
and applying the required open-access data. The overarching goal of this
study is to produce preliminary flood hazard maps that provide first insights
into potential flooding hotspots demanding closer attention in subsequent,
more detailed risk analyses. As a key novelty, a normalized flood severity
index (<span class="inline-formula"><i>I</i><sub>NFS</sub></span>), which combines flood depth and duration, is proposed to
deliver key information in a preliminary flood hazard assessment. This index
serves as an indicator that further narrows down the focus to areas where
flood hazard is significant. Our approach is validated by a comparison with
more than 300 flood samples locally observed during three heavy-rain events
in 2010 and 2012 which correspond to <span class="inline-formula"><i>I</i><sub>NFS</sub></span>-based inundation hotspots in
over 73 % of all cases. These findings corroborate the high potential of
open-access data in hydro-numerical modeling and the robustness of the newly
introduced flood severity index, which may significantly enhance the
interpretation and trustworthiness of risk assessments in the future. The
proposed approach and developed indicators are generic and may be replicated
and adopted in other coastal megacities around the globe.</p>
Lindomar Matias Gonçalves, Clara Mendoza-Martinez, Elém Patrícia Alves Rocha
et al.
Steel is a crucial industrial product with applications in various sectors, such as construction, engineering, and industry. However, the steel industry generates significant waste, contributing to greenhouse gas emissions and environmental challenges. To address this issue, incorporating solid waste, especially sludge with high moisture content, into the steel industry’s operations is essential. This study aimed to construct and test an active indirect solar dryer for reducing the moisture content of sludge from a steel drawing industry. By employing principles of the circular economy and the environmental, social, and governance concept, the drying process showed promising results, achieving approximately 42% moisture reduction. This study involved collection and characterization of industrial sludge, design and assembly of a hybrid active indirect solar dryer, fluid dynamic analysis of the behavior of the air inside the device through CFD Ansys software 2012, tests with a thermographic camera to validate the simulation, and optimization of the sludge drying by calculating the thermal efficiency and drying efficiency of the equipment. The adoption of such drying processes can lead to substantial cost reductions in the transportation, handling, and landfilling of steel-drawing sludge, promoting innovation and aiding global steel industries in achieving their solid waste disposal targets.
A physical simulation engine (PSE) is a software system that simulates physical environments and objects. Modern PSEs feature both forward and backward simulations, where the forward phase predicts the behavior of a simulated system, and the backward phase provides gradients (guidance) for learning-based control tasks, such as a robot arm learning to fetch items. This way, modern PSEs show promising support for learning-based control methods. To date, PSEs have been largely used in various high-profitable, commercial applications, such as games, movies, virtual reality (VR), and robotics. Despite the prosperous development and usage of PSEs by academia and industrial manufacturers such as Google and NVIDIA, PSEs may produce incorrect simulations, which may lead to negative results, from poor user experience in entertainment to accidents in robotics-involved manufacturing and surgical operations. This paper introduces PHYFU, a fuzzing framework designed specifically for PSEs to uncover errors in both forward and backward simulation phases. PHYFU mutates initial states and asserts if the PSE under test behaves consistently with respect to basic Physics Laws (PLs). We further use feedback-driven test input scheduling to guide and accelerate the search for errors. Our study of four PSEs covers mainstream industrial vendors (Google and NVIDIA) as well as academic products. We successfully uncover over 5K error-triggering inputs that generate incorrect simulation results spanning across the whole software stack of PSEs.
The article presents the experience of Omsk State Technical University in training of engineering specialists for high-tech economy sectors. The system of innovation engineering education applied in OmSTU includes creation of innovation research-and-production educational centres (resource centres), the program of elite engineering education with aprofound fundamental component, integrated educational programs, the basic departments at research institutes and enterprises.
In presence of multiple objectives to be optimized in Search-Based Software Engineering (SBSE), Pareto search has been commonly adopted. It searches for a good approximation of the problem's Pareto optimal solutions, from which the stakeholders choose the most preferred solution according to their preferences. However, when clear preferences of the stakeholders (e.g., a set of weights which reflect relative importance between objectives) are available prior to the search, weighted search is believed to be the first choice since it simplifies the search via converting the original multi-objective problem into a single-objective one and enable the search to focus on what only the stakeholders are interested in. This paper questions such a "weighted search first" belief. We show that the weights can, in fact, be harmful to the search process even in the presence of clear preferences. Specifically, we conduct a large scale empirical study which consists of 38 systems/projects from three representative SBSE problems, together with two types of search budget and nine sets of weights, leading to 604 cases of comparisons. Our key finding is that weighted search reaches a certain level of solution quality by consuming relatively less resources at the early stage of the search; however, Pareto search is at the majority of the time (up to 77% of the cases) significantly better than its weighted counterpart, as long as we allow a sufficient, but not unrealistic search budget. This, together with other findings and actionable suggestions in the paper, allows us to codify pragmatic and comprehensive guidance on choosing weighted and Pareto search for SBSE under the circumstance that clear preferences are available. All code and data can be accessed at: https://github.com/ideas-labo/pareto-vs-weight-for-sbse.