Natural rubber (NR) is a high-performance elastomer valued for its elasticity, tensile strength, and tear resistance, making it indispensable in diverse industrial applications, including automotive tires, vibration-damping systems, medical devices, and consumer goods. However, conventional synthetic additives used in NR compounding pose environmental and health concerns due to their persistence, toxicity, and non-renewable origin. This review critically examines recent developments in natural-based additives—sourced from plants (e.g., lignin and vegetable oils), animals (e.g., collagen and chitosan), and minerals (e.g., silica and clay)—and their integration into NR compounding and processing. The discussion highlights how these bio-derived alternatives influence rheological, mechanical, thermal, and chemical properties of NR compounds, with comparative insights against conventional formulations. Challenges related to additive availability, processing behavior, cost, and suitability for high-performance applications are highlighted, alongside their potential to reduce reliance on petroleum-based materials. Finally, emerging directions such as smart/nano-enabled additives and AI/ML-driven formulation optimization are considered, underscoring the potential for natural-based additives to advance environmentally sustainable practices while meeting evolving industrial demands.
Attribution of historical climate changes is particularly challenging for periods before the mid-20th century due to sparse and low-quality observational data, raising questions about which temporal scales best capture the climate system’s response to external forcings and can robustly constrain future projections. To address these issues, this study performs a multi-scale attribution analysis of temperature extremes over the period 1901–2020 and examines the robustness of attribution-based scaling factors across different time scales. Despite uncertainties in the early data, the newly developed homogenized observations show pronounced warming in both cold and hot extremes, along with a lengthening of the growing season during 1901–2020. These trends intensified markedly after the 1950s, with the magnitude of changes approximately doubling for some extreme indices. Coupled Model Intercomparison Project Phase 6 (CMIP6) models successfully reproduce the overall warming trends in observations, although they underestimate the magnitude of changes, particularly in the pre-1950 period. Using optimal fingerprinting, more than 70% of the observed changes are attributed to greenhouse gas forcing, with aerosols offsetting less than 35% of the greenhouse gas-induced warming. Attribution analysis conducted within a large-ensemble model framework across multiple time scales shows that the ranges of best estimates and confidence intervals (CIs) for scaling factors decrease as the time period lengthens. The century-scale attribution yields the narrowest CIs and most robust best estimates, indicating the most robust detection results. Despite the robustness of century-scale results, scaling factors from 1951–2020 are selected to constrain projections due to more reliable observational constraints. Constrained end-of-century (2081–2100) projections show amplified increases of 20.3%–33.1% for most extremes compared to raw projections, highlighting the critical impact of attribution period selection and providing a transferable framework for regional climate risk assessment.
In order to handle the increasing complexity of software systems, Artificial Intelligence (AI) has been applied to various areas of software engineering, including requirements engineering, coding, testing, and debugging. This has led to the emergence of AI for Software Engineering as a distinct research area within the field of software engineering. With the development of quantum computing, the field of Quantum AI (QAI) is arising, enhancing the performance of classical AI and holding significant potential for solving classical software engineering problems. Some initial applications of QAI in software engineering have already emerged, such as test case optimization. However, the path ahead remains open, offering ample opportunities to solve complex software engineering problems cost-effectively with QAI. To this end, this paper presents a roadmap towards the application of QAI in software engineering. Specifically, we consider two of the main categories of QAI, i.e., quantum optimization algorithms and quantum machine learning. For each software engineering phase, we discuss how these QAI approaches can address some of the tasks associated with that phase. Moreover, we provide an overview of some of the possible challenges that need to be addressed to make the application of QAI for software engineering successful.
Jayanaka L. Dantanarayana, Savini Kashmira, Thakee Nathees
et al.
AI-Integrated programming is emerging as a foundational paradigm for building intelligent systems with large language models (LLMs). Recent approaches such as Meaning Typed Programming (MTP) automate prompt generation by leveraging the semantics already present in code. However, many real-world applications depend on contextual cues, developer intent, and domain-specific reasoning that extend beyond what static code semantics alone can express. To address this limitation, we introduce Semantic Engineering, a lightweight method for enriching program semantics so that LLM-based systems can more accurately reflect developer intent without requiring full manual prompt design. We present Semantic Context Annotations (SemTexts), a language-level mechanism that allows developers to embed natural-language context directly into program constructs. Integrated into the Jac programming language, Semantic Engineering extends MTP to incorporate these enriched semantics during prompt generation. We further introduce a benchmark suite designed to reflect realistic AI-Integrated application scenarios. Our evaluation shows that Semantic Engineering substantially improves prompt fidelity, achieving performance comparable to Prompt Engineering while requiring significantly less developer effort.
P. L. Rosendahl, J. Schneider, G. Bobillier
et al.
<p>Understanding crack phenomena in the snowpack and their role in avalanche formation is imperative for hazard prediction and mitigation. Many studies have explored how structural properties of snow contribute to the initial instability of the snowpack, focusing particularly on failure initiation within weak snow layers and the onset of crack propagation. This work addresses the subsequent stage, the effect of slab touchdown after weak-layer failure in mixed-mode loading (compressive anticrack (mode I) and shear (mode II) loading). Our results demonstrate that slab touchdown reduces the energy release rate, which can lead to crack arrest even under static conditions. This challenges the idea that only the dynamic properties of snow layers and spatial snowpack variations govern arrest, emphasizing instead the crucial role of mechanical interactions between the slab, weak layer, and base layer. By integrating these findings into the broader context of snowpack stability analysis, we contribute to a more nuanced understanding of avalanche initiation mechanisms. The analysis is provided in a comprehensive open-source model (<span class="uri">https://github.com/2phi/weac</span>, last access: 11 June 2025).</p>
The fluid-structure interaction problem of cylindrical flow around the cylinder is a classic problem, especially in many fields such as energy engineering, fluid dynamics, engineering structure, energy utilization and development, and environmental protection. In this article, we have heard about the simplified model and analyzed it with the help of the classical cylindrical flow model. In this paper, the flow field was pre-processed on the Workbench software platform, and the pressure distribution and eddy current phenomenon were observed by post-processing given the velocity of the flow field of 1m/s. There is a large number of thunder (2.799552×106) and the Carmen Vortex appears around the cylinder. The vortex falls off periodically, and the wake structure is relatively irregular. The maximum pressure appears at the bottom of the cylinder and the maximum deformation at the top. Then, the pressure term of the simulation results was transferred to the Ansys solver for finite element structural analysis to observe the stresses and strains of the structure under the action of fluids. Finally, the response signal is based on Variational Mode Decomposition (VMD). Its periodic signal corresponded to the flow field, and its components were analyzed in detail. The application of VMD in this area provides innovative inspiration for related fields.
Meloidogyne javanica and Ralstonia solanacearum are the highly specialized soil-born plant parasites with economic importance causing root-knot and bacterial wilt diseases in tomatoes, respectively. The occurrence and intensity of the bacterial wilt escalated in the presence of root-knot nematodes and R. solanacearum concurrently detected in different vegetable crops. Sampling and preparation of leaf extract were done to investigate the activity of catalase (CAT), superoxide dismutase (SOD), and peroxidase (POX) enzymes at 24, 48, 72, and 120 hours post-inoculation (hpi) of tomato plants with R. solanacearum and M. javanica. The enzyme activity was measured at each time interval. The CAT and SOD enzymes exhibited maximum activity levels at 120 and 48 hpi in the nematode treatment, respectively. Meanwhile, the levels of POX enzyme peaked at 48 and 72 hpi in the nematode and nematode-bacterium treatments, respectively. Pathogen stress eventually led to a decrease in the SOD and POX enzymes 120 hours after inoculation and a significant increase in CAT during nematode-bacterium treatment. The results revealed apparent enzyme activity variations in tomato plants infected with both pathogens at different time intervals after inoculation.
Seyed Amir Hossein Alavi, Kambiz Shahroudi, Mohammad Doostar
et al.
This study aims to design a model for the relationships between the indices of a creative city and creative economy development in the creative city of Rasht, Guilan Province, Iran. In this research, the grounded theory was employed to conduct a qualitative analysis, and semi-structured interviews were carried out for data collection. For this purpose, eight interviews were given to senior and middle-ranking city managers on the research topic. Based on Strauss and Corbin’s systematic approach, a model was proposed for the relationships between the indices of a creative city and creative economy development through open coding, axial coding, and selective coding. The results of coding the interviews indicated that the creative economy was the axial category in the model. Creative economy development included eight components with different weights, among which “creative industries” and “creative citizens” had pivotal roles. According to the experts, different factors such as the characteristics of a creative city affect creative economy development, and urban management is considered to play a key role in this process.
Eriks Klotins, Michael Unterkalmsteiner, Tony Gorschek
Software start-ups are new companies aiming to launch an innovative product to mass markets fast with minimal resources. However, most start-ups fail before realizing their potential. Poor software engineering, among other factors, could be a significant contributor to the challenges that start-ups experience. Little is known about the engineering context in start-up companies. On the surface, start-ups are characterized by uncertainty, high risk, and minimal resources. However, such a characterization isn't granular enough to support identification of specific engineering challenges and to devise start-up-specific engineering practices. The first step toward an understanding of software engineering in start-ups is the definition of a Start-Up Context Map - a taxonomy of engineering practices, environment factors, and goals influencing the engineering process. This map aims to support further research on the field and serve as an engineering decision support tool for start-ups. This article is part of a theme issue on Process Improvement.
Artificial intelligence (AI) can revolutionize the development industry, primarily electrical and electronics engineering. By automating recurring duties, AI can grow productivity and efficiency in creating. For instance, AI can research constructing designs, discover capability troubles, and generate answers, reducing the effort and time required for manual analysis. AI also can be used to optimize electricity consumption in buildings, which is a critical difficulty in the construction enterprise. Via machines gaining knowledge of algorithms to investigate electricity usage patterns, AI can discover areas wherein power may be stored and offer guidelines for enhancements. This can result in significant value financial savings and reduced carbon emissions. Moreover, AI may be used to improve the protection of creation websites. By studying statistics from sensors and cameras, AI can locate capacity dangers and alert workers to take suitable action. This could help save you from injuries and accidents on production sites, lowering the chance for workers and enhancing overall safety in the enterprise. The impact of AI on electric and electronics engineering productivity inside the creation industry is enormous. AI can transform how we layout, build, and function buildings by automating ordinary duties, optimising electricity intake, and enhancing safety. However, ensuring that AI is used ethically and responsibly and that the advantages are shared fairly throughout the enterprise is essential.
The perception of the value and propriety of modern engineered systems is changing. In addition to their functional and extra-functional properties, nowadays' systems are also evaluated by their sustainability properties. The next generation of systems will be characterized by an overall elevated sustainability -- including their post-life, driven by efficient value retention mechanisms. Current systems engineering practices fall short of supporting these ambitions and need to be revised appropriately. In this paper, we introduce the concept of circular systems engineering, a novel paradigm for systems sustainability, and define two principles to successfully implement it: end-to-end sustainability and bipartite sustainability. We outline typical organizational evolution patterns that lead to the implementation and adoption of circularity principles, and outline key challenges and research opportunities.
Mobile software engineering has been a hot research topic for decades. Our fellow researchers have proposed various approaches (with over 7,000 publications for Android alone) in this field that essentially contributed to the great success of the current mobile ecosystem. Existing research efforts mainly focus on popular mobile platforms, namely Android and iOS. OpenHarmony, a newly open-sourced mobile platform, has rarely been considered, although it is the one requiring the most attention as OpenHarmony is expected to occupy one-third of the market in China (if not in the world). To fill the gap, we present to the mobile software engineering community a research roadmap for encouraging our fellow researchers to contribute promising approaches to OpenHarmony. Specifically, we start by presenting a literature review of mobile software engineering, attempting to understand what problems have been targeted by the mobile community and how they have been resolved. We then summarize the existing (limited) achievements of OpenHarmony and subsequently highlight the research gap between Android/iOS and OpenHarmony. This research gap eventually helps in forming the roadmap for conducting software engineering research for OpenHarmony.
Knowledge engineering is the process of creating and maintaining knowledge-producing systems. Throughout the history of computer science and AI, knowledge engineering workflows have been widely used because high-quality knowledge is assumed to be crucial for reliable intelligent agents. However, the landscape of knowledge engineering has changed, presenting four challenges: unaddressed stakeholder requirements, mismatched technologies, adoption barriers for new organizations, and misalignment with software engineering practices. In this paper, we propose to address these challenges by developing a reference architecture using a mainstream software methodology. By studying the requirements of different stakeholders and eras, we identify 23 essential quality attributes for evaluating reference architectures. We assess three candidate architectures from recent literature based on these attributes. Finally, we discuss the next steps towards a comprehensive reference architecture, including prioritizing quality attributes, integrating components with complementary strengths, and supporting missing socio-technical requirements. As this endeavor requires a collaborative effort, we invite all knowledge engineering researchers and practitioners to join us.
Short-fiber-reinforced composites (SFRC) are high-performance engineering materials for lightweight structural applications in the automotive and electronics industries. Typically, SFRC structures are manufactured by injection molding, which induces heterogeneous microstructures, and the resulting nonlinear anisotropic behaviors are challenging to predict by conventional micromechanical analyses. In this work, we present a machine learning-based multiscale method by integrating injection molding-induced microstructures, material homogenization, and Deep Material Network (DMN) in the finite element simulation software LS-DYNA for structural analysis of SFRC. DMN is a physics-embedded machine learning model that learns the microscale material morphologies hidden in representative volume elements of composites through offline training. By coupling DMN with finite elements, we have developed a highly accurate and efficient data-driven approach, which predicts nonlinear behaviors of composite materials and structures at a computational speed orders-of-magnitude faster than the high-fidelity direct numerical simulation. To model industrial-scale SFRC products, transfer learning is utilized to generate a unified DMN database, which effectively captures the effects of injection molding-induced fiber orientations and volume fractions on the overall composite properties. Numerical examples are presented to demonstrate the promising performance of this LS-DYNA machine learning-based multiscale method for SFRC modeling.
Aiming to support Retrieval Augmented Generation (RAG) in the design process, we present a method to identify explicit, engineering design facts - {head entity :: relationship :: tail entity} from patented artefact descriptions. Given a sentence with a pair of entities (based on noun phrases) marked in a unique manner, our method extracts the relationship that is explicitly communicated in the sentence. For this task, we create a dataset of 375,084 examples and fine-tune language models for relation identification (token classification) and elicitation (sequence-to-sequence). The token classification approach achieves up to 99.7 % accuracy. Upon applying the method to a domain of 4,870 fan system patents, we populate a knowledge base of over 2.93 million facts. Using this knowledge base, we demonstrate how Large Language Models (LLMs) are guided by explicit facts to synthesise knowledge and generate technical and cohesive responses when sought out for knowledge retrieval tasks in the design process.
Thomas J. Galarneau Jr., Xubin Zeng, Ross D. Dixon
et al.
Abstract Mesoscale convective systems (MCSs) in the tropics play an integral role in the water cycle, are associated with local hazardous weather conditions, and have significant remote impacts on the midlatitude jet stream. Although it is known that MCSs occur in relatively moist environments, it is unclear how far in advance favorable ingredients (lift, instability, and moisture) in the mesoscale environment precede MCS formation. In this study, an automated MCS tracking algorithm and global reanalyses are used to examine the pre‐MCS environment for 3295 MCSs that occurred in the tropics in a 3‐month period. Results showed that increased water vapor and mesoscale ascent implied by low‐level convergence and upper‐level divergence preceded MCS formation by up to 24 h. Regional variations in pre‐MCS environment conditions were apparent and are discussed. Future work will study to what extent these moisture and wind anomalies can be used to predict MCS formation.
Dissolved black carbon (DBC), the particular component of black carbon that can be dissolved in the water, which accounts for ~10% of the organic carbon cycle in the earth’s water body, is an essential member of the dissolved organic matter (DOM) pool. In contrast to DOM, DBC has a higher proportion of conjugated benzene rings, which can more efficiently encourage the degradation of organic micropollutants in the aquatic environment or more rapidly generate reactive oxygen species to photodegrade the organic micropollutants. Therefore, it is of great significance to study the changes and mechanisms of DBC photochemical activity affected by different factors in the water environment. Our work reviewed the main influencing factors and mechanisms of the photochemical activity of DBC. It focuses on the methodologies for the quantitative and qualitative investigation of the photochemical activity of DBC, the impact of the biomass source, the pyrolysis temperature of biochar, and the primary water environmental parameters on the photochemical activity of DBC and the indirect photodegradation of pollutants. Based on this, a potential future study of DBC photochemical activity has been prospected.
Quantitative differentiation of climate and human activities on runoff is important for water resources management and future water resources trend prediction. In recent years, runoff in the middle reaches of the Yellow River (MRYR) has decreased dramatically. Many studies have analyzed the causes of runoff reduction, but there is still a lack of understanding of the spatial differences in runoff contributions and their causes. Therefore, this study quantitatively distinguishes the contributions of climate and human activities to runoff changes in nine sub-basins of the MRYR based on the Budyko framework and analyses the differences in the contributions of different basins and their causes. The results show that the runoff in the nine sub-basins decreases significantly and the precipitation increases from northwest to southeast. The contribution of human activities to runoff is greater than that of climate change, especially in the Huangfuchuan (HF) River and Kuye (KY) River basins, where the contribution of human activities to runoff exceeds 90%. The greater impact of human activities in HF River and KY River is due to the significantly higher water use growth rate and normalized vegetation index trends than in other areas.
HIGHLIGHTS
Spatial differences in the causes of runoff variation in nine small watersheds in the middle reaches of the Yellow River were analyzed.;
The influence of NDVI and human water extraction cannot be ignored.;
River, lake, and water-supply engineering (General), Physical geography
The work presents elecode, open-source software for various electrical engineering applications that require considering electromagnetic processes. The primary focus of the software is power engineering applications. However, the software does not impose any specific limitations preventing other uses. In contrast to other open-source software based on the Finite Difference Time Domain (FDTD) method, elecode implements various thin wire modeling techniques which allow simulating complex objects consisting of wires. In addition, implemented graphical user interface (GUI) helps modify models conveniently. The software provides auxiliary numerical methods for simulations and measurements of the electrical soil properties, allows conducting lightning-related simulations (including those involving isolation breakdown models), and calculations of grounding characteristics. The part of the code responsible for FDTD simulations is well tested in previous works. Recently, the code was rewritten in order to add a convenient interface for using it as a library, command-line program, or GUI program. Finally, the code was released under an open-source license. The main capabilities of the software are described in the work. Several simulation examples covering main software features are presented. elecode is available at https://gitlab.com/dmika/elecode.