Scholars and practitioners who seek to understand collective decision-making and governance utilize frameworks and theories to identify what matters (and does not) in shaping public policy creation, implementation, and revision. Useful frameworks and theories will attempt to identify the primary elements of the policy-making process and characterize the critical interactions among them. The Advocacy Coalition Framework (ACF) was developed for that purpose. In the four decades since its first articulation in the early 1980s, the Framework has been widely applied, criticized, and revised in cases across the globe. This chapter briefly recounts that development and evolution, highlighting key milestones and adaptations that have shaped its current form. We present the three main theories embedded in the Framework and offer a characterization of the “textbook” version of the ACF that one is likely to encounter in overviews, applications, and courses on theories of the policy process. We then provide an overview of this volume's empirical chapters, highlighting a cross-section of the most exciting new scholarly developments within and outside this textbook version.

This open access book provides an updated and integrated analysis of the Advocacy Coalition Framework, more than thirty years after its conception. First devised in the 1980s to help explain change within public policy processes, the framework has been widely applied, criticized and revised over the last three decades, implemented by policy scholars across the globe, and in hundreds of different languages. 

Edited by the two foremost ACF scholars, this book analyses the current state and future directions of the Advocacy Coalition Framework. The first section provides a concise overview of the purpose of the framework, as well as its application in previous research. This is followed by eleven cross-section case studies that showcase some of the most exciting developments in ACF scholarship around the globe. It concludes with recommendations for adapting the ACF for future use, as well as new directions of research.

Milling force is a parameter affecting wood processing quality, tool life, and energy consumption, and its variation is influenced by the multi-factor coupling of cutting parameters and tool geometric factors. This study systematically investigates milling forces during the processing of pine wood (Pinus sylvestris var. mongholica Litv.) using a hybrid modeling approach combining principal component analysis (PCA) and multiple linear regression (MLR). Firstly, PCA was employed to reduce the dimensionality of the tool rake angle (γ), helix angle (λ), cutting depth (h), feed per tooth (U_z), and triaxial milling forces (F_x, F_y, F_z); this eliminated the multicollinearity among variables and extracted the integrated features. Subsequently, an MLR model was constructed using the principal components as independent variables to quantitatively evaluate the contribution of each factor to milling forces. The results support the conclusion that PCA successfully extracted the first four principal components (cumulative variance contribution rate: 92.78%), with PC1 (49.16%) characterizing the comprehensive milling force effect and PC2 (15.03%) primarily reflecting the characteristics of the tool geometric parameters. The established MLR model demonstrated a high significance (R²: F_x = 0.915, F_y = 0.907, F_z = 0.852). The cutting depth exerted a significant positive driving effect on the triaxial milling forces via PC1 (each 1 mm increase in depth increased the PC1 score by 0.64 units, resulting in increases of 27.2%, 26.6%, and 21.8% for F_x, F_y, and F_z, respectively). The helix angle significantly suppressed F_y through PC2 (β = −0.090, p < 0.001), whereas the rake angle exhibited a weak negative effect on F_x via PC3 (β = −0.015). Parameter optimization identified the combination γ = 25°, λ = 30°, h = 0.5 mm, and U_z = 0.1 mm∙z⁻¹ as optimal, which reduced the triaxial milling forces by 62.3% compared to the experimental maximum. This study provides a theoretical foundation and novel parameter optimization strategy for the efficient, low-damage processing of wood materials.

The use of physical vehicles and environments during vehicle research and development is highly resource-intensive, particularly for autonomous vehicles. Recently, digital models are therefore increasingly used instead, which require high levels of fidelity and validity. While the two aforementioned qualities are often lacking, an absence of versatility for multi-purpose use is even more prevalent in current digital models. In response to these challenges, this work presents a novel real-time multi-physics digital twin of an offroad vehicle with high levels of fidelity and validity, both regarding the vehicle dynamics and hydraulics, as well as regarding the visual representation of the environment and the exteroceptive sensor emulation. The versatility of the digital twin enables its usage for vehicle development tasks concerning mechanical components and driveline, as well as for visual machine learning tasks, such as generation of auto-annotated visual training data. Development of control algorithms leveraging both visual input and mechanical systems is also enabled. Furthermore, the real-time capability allows for Hardware-in-the-Loop and Vehicle-in-the-Loop simulation. The modeling, calibration, and real-world validation of the digital twin is presented, with an emphasis on the vehicle dynamics and hydraulics. The shown validity enables advancements in the development of autonomous offroad vehicles.

In response to growing concerns over water scarcity and climate impacts in northern Europe, this study evaluates the performance and sustainability of rainwater harvesting (RWH) systems for non-potable use in Scandinavia. This study examined nine RWH systems for non-potable purposes across Denmark, Sweden, and Norway, ranging from small pilot installations to large urban developments. Semi-structured interviews were conducted with operators of nine systems. Generally, simpler systems with minimal treatment achieved better reliability, lower energy use, and higher user satisfaction than complex installations. Challenges for RWH systems included limited experience with the systems, under-dimensioned storage, regulatory uncertainties and temperatures below zero. Generally, the owners of the systems reported high levels of contentment with the systems and the quality of the water. Motivations for installing the systems varied between regions – from groundwater protection in Denmark to sustainability certification in Sweden. Difficulties encountered mostly pertained to issues that arose from the design, such as subdimensioned storage. Overall, RWH might not be a panacea for water shortages induced by climate change in northern Europe, but can offer a viable complement to municipal water supply when designed appropriately for local climate and governance conditions.

Most studies of policy change within the ACF have been descriptive, single-instance explorations that confirm one or more of its four pathways: external shocks, internal shocks, negotiated agreements, and policy-oriented learning. To advance ACF studies of policy change, this chapter takes a different approach to assessing the drivers of change. Rather than looking to confirm one of the four pathways for a single instance of change, we inductively explore what policy actors cite as “antecedents”—or potential influencing, motivating, or driving factors—for multiple policy changes in a textbook subsystem. We identify antecedents through policy actor references in the public discourse related to 77 new or modified policies in the Colorado oil and gas subsystem between 2007 and 2021, drawing on public and government documents, public testimony, and news articles. We then compare the inductively identified antecedents of each policy change with the four pathways of change outlined within the ACF. We find references to internal antecedents were the most commonly mentioned, followed by references to external antecedents. We conclude by discussing the significance of these results for studying policy change, the importance of including both inductive and deductive methods to expand the existing pathways, and guidelines for conducting a medium-sized analysis of policy change over time.

Over 30 years ago, Paul Sabatier and Hank Jenkins-Smith edited a seminal volume that described and applied the Advocacy Coalition Framework (ACF). Building on over three decades of subsequent research on the ACF, this volume's primary contribution comes from an orientation to ACF scholarship that involves distinctions between textbook and non-textbook applications. This orientation raises a question: What adjustments do scholars make when applying the ACF outside its textbook “comfort zone”? This chapter addresses this question with the following argument: The ACF has always simplified and structured the complexity of policy processes by implicitly situating subsystems and the broader system architecture on key conceptual scales and levels, wherein the placement of the textbook ACF is just one possible (albeit very useful) configuration among many. When researchers apply the ACF outside the textbook context, they consciously or unconsciously adjust these levels or operate on entirely different scales and levels. Moving forward, critical tasks for ACF scholars will be to make these scales and levels explicit, recognize the configurations that apply to any given research project, engage in conduct cross-scale and cross-level research, and focus inquiry into which scales and levels are most crucial in advancing knowledge about policy processes and contributing to a better world.

As sixth-generation (6G) wireless networks evolve into increasingly Artificial Intelligence (AI)-driven, user-centric ecosystems, traditional reactive handover mechanisms demonstrate limitations, especially in mobile edge computing and autonomous agent-based service scenarios. This manuscript introduces the Wireless AI Agent Network (WAAN), a cross-layer framework designed to enable intent-aware and proactive handovers in 6G networks. WAAN embeds lightweight Tiny Machine Learning (TinyML) agents as autonomous, negotiation-capable entities across heterogeneous edge nodes that contribute to intent propagation and network adaptation. To ensure continuity across mobility-induced disruptions, WAAN incorporates semi-stable Rendezvous Points (RPs) that serve as coordination anchors for context transfer and state preservation. The framework’s operational capabilities are demonstrated through a multimodal environmental control case study, highlighting its effectiveness in maintaining user experience under mobility. Finally, the article discusses key challenges and future opportunities associated with the deployment and evolution of WAAN.

This paper introduces the Laminated Partial-Composite Plate Theory (LPCPT), as an extension of the classical laminated plate theory (CLPT), incorporating the effects of partial-interaction imperfection at the constituting layers’ interfaces. The interlayer interaction effects are modelled through out-of-plane shear stresses based on a shear spring model in terms of the relative displacements/slips at the interfaces. The proposed LPCPT extends a recently developed model for multilayer composite beam/column elements with interlayer partial-interaction imperfection. The model’s governing equations, as well as the extended classical boundary conditions, are formulated. Analytical solution schemes are introduced for free vibrations and buckling of partial-composite plates. The analytical solutions can flexibly capture any number of constituent layers. The validity and high accuracy of the established approach are demonstrated via comparative numerical results based on 3-D finite element analysis (FEA). It is shown how the buckling loads and natural vibration frequencies degrade from those predicted based on CLPT with perfect-bonding ideal assumptions, considering different levels of interlayer interaction. For a special case where the interlayer interaction modulus is set to the equivalent layers’ transverse shear modulus, the results of the present model are shown to match those of thick integrated plates based on higher-order shear deformation theory (HSDT).

While sport psychology has long emphasized mental and cognitive aspects of performance, sports medicine has traditionally focused on musculoskeletal and physiological aspects, largely overlooking the brain's central role in athletic performance. This narrative review aims to bridge this gap by introducing Computational Sports Medicine, a novel framework that integrates cognitive neuroscience with established physiological and biomechanical measures. Using alpine skiing as a primary example, this review examines the critical role of working memory updating in dynamic environments, discusses how neural processes enable adaptation, and proposes Computational Sports Medicine as a unifying predictive framework. This approach moves beyond descriptive analysis to provide objective, quantifiable metrics, testable models, and the ability to simulate “what-if” scenarios for proactive intervention. Practical implications for training include developing sport-specific cognitive tasks, individualizing variability in motor and cognitive learning, and leveraging technologies like virtual reality and wearable sensors. The review primarily targets elite and sub-elite athletes, for whom cognitive and environmental demands are most pronounced. This brain-inclusive framework offers a personalized approach to performance optimization, injury prevention, and safe return-to-play decisions, positioning the brain as the central organ to the future of sports medicine.

This study assesses the precision of NASA Power meteorological data in Iraq over a 12-year period, utilizing data from 10 meteorological stations. The research focuses on key meteorological parameters, i.e., average daily temperatures, rainfall, wind speed, solar radiation, and relative humidity. Through transparent data analysis and comparison, the validity of NASA Power in local climate monitoring within Iraq is evaluated. Through statistical analysis, the correlation between NASA Power data and meteorological station data is evaluated using Kendall's Tau correlation coefficient test and Mean Bias Error (MBE). The comparison of NASA Power meteorological data with observed data from ten meteorological stations in Iraq revealed significant findings. NASA Power data displayed a high correlation (0.748-0.912) with observed temperatures, indicating accuracy in temperature assessment. The data also showed weak to strong correlations (0.105-0.526) for rainfall and weak to moderate correlations (0.105-0.427) for wind speed, suggesting potential supplementary use, albeit with the need for calibration. For solar radiation, NASA Power data exhibited a strong to very strong correlation (r = 0.636-0.834), making it suitable for solar assessments. For relative humidity, a very strong correlation (r = 0.636-0.834) was demonstrated, indicating the need for further analysis. Despite its reliability as a meteorological data source in Iraq, NASA Power data should undergo validation across various applications and regions to ensure its accuracy and dependability.

The development of advanced absorbents for effectively capturing carbon dioxide is crucial in mitigating greenhouse gas emissions. This study introduced a series of deep eutectic solvents (DESs) for CO2 capture and identified the most promising DESs with the stepwise screening method based on their absorption capacity, absorption rate, thermal stability, desorption efficiency, and apparent activation energy. Consequently, compared to the monoethanolamine (MEA), in the 30 wt% aqueous solutions, [1,2,3-Triazolium chloride][diethylenetriamine] ([TrizCl][DETA]) and [Piperazinium chloride][diethylenetriamine] ([PzCl][DETA]) improved the CO2 absorption capacities by 31% and 34%, absorption rates by 12% and 30%, and the amounts of CO2 desorbed by 42% and 23%, as well as reduced the apparent activation energies by 9% and 28%, respectively. Meanwhile, their thermal stabilities (degradation onset temperatures, Tonset) were enhanced by 101% and 32%, respectively. The FTIR and NMR analyses were conducted to provide deeper insights into the chemical absorption mechanism of CO2 by the DESs. 

The transition to sustainable transportation fuels requires investment in emerging biomass-to-liquid production pathways under uncertain market and policy conditions. This study applies a real options analysis framework to evaluate the economic viability and timing of investments in biomass- and power-to-liquid pathways by identifying conditions where an investor should invest, defer, or abandon investments. The analysis is conducted for Sweden, reflected by its large biomass base and well-developed forest industry and ambitious defossilization policies. Results indicate that large price gaps between feedstock and produced fuels are not by themselves sufficient to trigger investment; in volatile markets, investors may still defer because the option to wait has economic value. Thus, even at identical price levels across scenarios, outcomes range from commitment to inaction depending on volatility. Moreover, when investments do occur, they are consistently deferred until the final year of the investment window. While modest subsidies may suffice under stable price conditions, volatile markets with high drifts require significantly greater support to counteract the incentive to defer investments. Electricity cost structures and carbon pricing must be targeted to support the transition toward electrified fuel production pathways. The insights from this study can inform the design of policy instruments that align investor incentives with global transition goals.

We propose a neural network training method capable of accounting for the effects of systematic variations of the data model in the training process and describe its extension towards neural network multiclass classification. The procedure is evaluated on the realistic case of the measurement of Higgs boson production via gluon fusion and vector boson fusion in the ττ decay channel at the CMS experiment. The neural network output functions are used to infer the signal strengths for inclusive production of Higgs bosons as well as for their production via gluon fusion and vector boson fusion. We observe improvements of 12 and 16% in the uncertainty in the signal strengths for gluon and vector-boson fusion, respectively, compared with a conventional neural network training based on cross-entropy.

Urbanization and climate change are putting pressure on urban drainage systems, particularly in cities with aging combined sewers. This study evaluates the effectiveness of green infrastructure (GI) in reducing combined sewer overflow (CSO) durations and pollutant loads under current and future climate conditions. Using a spatially realistic, stakeholder-informed implementation approach, the study addresses modelling uncertainty, evaluation of practical GI implementation strategies, and identifies subcatchments contributing to pollutants in the overflow. An ensemble-based hydrological model and a simplified pollutant load assessment were applied to an urban case study in Trondheim, Norway, using scenarios developed in collaboration with local stakeholders to implement bioretention cells in the catchment. Results showed that GIs limited to public land can reduce CSO durations by up to 63% under the current climate and 65% in the future climate. Evaluation of pollutant loads from critical subcatchments highlighted areas for additional water quality benefits. The study demonstrates that stakeholder-informed modelling provides practical insights for municipalities and supports the development of integrated stormwater management plans.

Deep learning models, especially for object detection have gained immense popularity in computer vision. These models have demonstrated remarkable accuracy and performance, driving advancements across various applications. However, the high computational complexity and large storage requirements of state-of-the-art object detection models pose significant challenges for deployment on resource-constrained devices like mobile phones and embedded systems. Knowledge Distillation (KD) has emerged as a prominent solution to these challenges, effectively compressing large, complex teacher models into smaller, efficient student models. This technique maintains good accuracy while significantly reducing model size and computational demands, making object detection models more practical for real-world applications. This survey provides a comprehensive review of KD-based object detection models developed in recent years. It offers an in-depth analysis of existing techniques, highlighting their novelty and limitations, and explores future research directions. The survey covers the different distillation algorithms used in object detection. It also examines extended applications of knowledge distillation in object detection, such as improvements for lightweight models, addressing catastrophic forgetting in incremental learning, and enhancing small object detection. Furthermore, the survey also delves into the application of knowledge distillation in other domains such as image classification, semantic segmentation, 3D reconstruction, and document analysis.

The impressive advancements in semi-supervised learning have driven researchers to explore its potential in object detection tasks within the field of computer vision. Semi-Supervised Object Detection (SSOD) leverages a combination of a small labeled dataset and a larger, unlabeled dataset. This approach effectively reduces the dependence on large labeled datasets, which are often expensive and time-consuming to obtain. Initially, SSOD models encountered challenges in effectively leveraging unlabeled data and managing noise in generated pseudo-labels for unlabeled data. However, numerous recent advancements have addressed these issues, resulting in substantial improvements in SSOD performance. This paper presents a comprehensive review of 28 cutting-edge developments in SSOD methodologies, from Convolutional Neural Networks (CNNs) to Transformers. We delve into the core components of semi-supervised learning and its integration into object detection frameworks, covering data augmentation techniques, pseudo-labeling strategies, consistency regularization, and adversarial training methods. Furthermore, we conduct a comparative analysis of various SSOD models, evaluating their performance and architectural differences. We aim to ignite further research interest in overcoming existing challenges and exploring new directions in semi-supervised learning for object detection.

This study presents a multiscale investigation of mycelium-based biocomposites produced via solid-state cultivation of Ganoderma lucidum on agro-food sidestreams. Three lignocellulosic residues, wheat bran (in two particle sizes), rice straw, and spent coffee grounds, were selected based on global availability and chemical diversity. The biocomposites were characterized to investigate how substrate composition and mycelial growth influence microstructure and macroscopic performance.

Monosaccharide analysis and scanning electron microscopy (SEM) revealed that wheat bran supported enhanced mycelial growth. Fine wheat bran-based composites exhibited compressive strengths up to 449 kPa at 30 % strain and tensile moduli of 15–25 MPa, significantly higher than expanded polystyrene (EPS), a conventional insulator. All biocomposites showed intrinsic surface hydrophobicity (water contact angles of 106–120°). Thermal analyses, including thermogravimetric analysis (TGA) and hot-plate conductivity measurement, confirmed their suitability as porous insulation. Cone calorimetry demonstrated improved fire safety in wheat bran-based composites, with reduced peak heat release rates (112–115 kW/m2).

Embodied energy and carbon footprint assessments indicated up to 89 % lower energy demand and 72 % lower CO2 emissions compared with EPS. Through multiscale characterization and direct benchmarking, this study shows how substrate selection and fungal-substrate interactions can be utilized to tailor performance. The findings provide insights into converting low-value biomass into scalable, fire-safer, and environmentally responsible insulation materials.

Whiteleg shrimp (Penaeus vannamei) is currently facing significant challenges related to severe disease outbreaks. As the importance of the host–microbiota relationship is being revealed, modulating this relationship has become a key strategy in disease management. Xylooligosaccharides (XOS)—short-chain sugar molecules—have been gaining attention for their potential health benefits in the prebiotics field. In this study, an XOS-rich extract from Salicornia ramosissima was incorporated into shrimp feeds to evaluate its impact on gut health, with the main focus on gut proteomics and microbiota. XOS were incorporated at 0.1% (XOS_0.1) and 1% (XOS_1) concentrations, and a 14-day feeding trial, followed by a bacterial challenge with Vibrio harveyi, was performed. The effects of XOS were evaluated by assessing zootechnical parameters, gene expression in the hepatopancreas, and gut microbiota and proteome. The results showed no significant differences in zootechnical parameters and gene expression after the 14-day trial between animals fed XOS diets and control. However, shrimp fed XOS_1 showed an increased diversity of the microbial communities in the gut when compared with those fed control. Also, known shrimp gut symbionts, such as Ruegeria, Leisingera, and Demequina, were significantly enriched in groups fed XOS after the feeding trial. XOS also modulated the regulation of proteins in the gut. Nevertheless, stressful conditions appear to alter the effects of XOS and the dynamics of gut bacteria. Further studies are warranted to understand the impacts of long-term inclusion of XOS extracts, especially on health-related parameters and disease resistance.

The long-term effects of integrated nutrient management (INM) on crop performance and soil health—particularly within sub-humid environments—remain insufficiently explored. This research aimed to quantify the relationship between the soil quality index (SQI) and overall system productivity. The SQI represents a numerical indicator of soil functioning and its biological and chemical integrity, while system productivity reflects the economic yield generated by the cropping system. A long-term experiment initiated in 1972 formed the foundation for this study, which was conducted from 2019 to 2021 and included eleven nutrient management treatments. These comprised the following treatments: inorganic fertilizers alone (100% NPK, 150% NPK, 100% NP, 100% N, and 100% NPK without sulfur); combinations of organic and inorganic inputs (50% NPK + FYM and 100% NPK + FYM); lime with inorganic fertilizers (100% NPK + lime); zinc with inorganics (100% NPK + Zn); hand weeding with inorganics (100% NPK + HW); an unfertilized control. The study was implemented in a maize–wheat rotation under the sub-humid climatic conditions of Palampur, Himachal Pradesh, India. System productivity was estimated using wheat grain equivalent yield, and SQI values were generated from selected soil properties. These indicators—along with the sustainable yield index (SYI)—were applied to assess the effectiveness of each treatment. The results showed that the 100% NPK + FYM combination produced the highest SQI, followed by 100% NPK + lime, whereas the 100% N treatment yielded the lowest value. Overall, the findings highlight the crucial role of adopting sustainable nutrient management practices to maintain soil quality and optimize productivity in sub-humid agricultural systems.

We present lightcurves, rotation periods, and colors for the first asteroid discoveries made with the NSF-DOE Vera C. Rubin Observatory. These are the first science results derived from the 2103 asteroid discoveries released as part of the Rubin First Look (RFL) media event on 2025 June 23, in which the first LSST Camera commissioning images were released. The ∼340,000 observations in which the discoveries were made span nine nights between 2025 April 21 and May 5. With a limiting single-epoch 5σ depth of ∼23–25 mag and dense temporal sampling under an irregular, commissioning-driven cadence, the RFL observations provide an ideal test bed for determination of rotation periods, including sensitivity to rapid rotation. We model lightcurves and derive rotation periods and colors for the ∼2000 objects. We find 75 main-belt asteroids (MBAs) and one near-Earth object (NEO) with reliable rotation periods spanning 0.031–21.3 hr and a photometric precision in the range of 0.05–0.15 mag. We find 19 superfast rotators with periods shorter than the 2.2 hr spin barrier. Rubin-discovered MBA 2025 MN45 is the fastest-rotating d > 0.5 km known asteroid with a rotation period of 1.9 minutes; along with NEO 2025 MJ71 (1.9 minutes) and Rubin-discovered MBAs 2025 MK41 (3.8 minutes), 2025 MV71 (13 minutes), and 2025 MG56 (16 minutes), these five super- to ultrafast rotators join a couple of NEOs as the fastest-spinning subkilometer asteroids known. As this study demonstrates, even in early commissioning, Rubin is successfully probing a previously sparsely sampled region of the subkilometer size−spin rate regime for MBAs.

Composite solid electrolytes (CSEs) combine the flexibility of polymers with the stability of inorganic electrolytes, making them promising candidates for next-generation solid-state lithium-metal batteries (LMBs). However, their practical application is limited by low room-temperature ionic conductivity, primarily due to poor polymer-inorganic interfacial compatibility that hinders Li+ transport. In this work, we introduce a polymer-compatible ionic liquid (IL) to mediate the interphase between the polymer and ceramic components, simultaneously preventing ceramic particle aggregation for uniform dispersion and activating ceramic-polymer interfaces to construct continuous Li+ transport pathways across ceramic domains and interfacial boundaries. The interfacial engineered CSEs exhibit a substantial enhancement in room-temperature ionic conductivity to 1.64 × 10−3 S cm−1. At the ambient temperature, the Li||Li symmetric cells demonstrate stable and reversible lithium plating/stripping for 4000 h, and the Li||LiFePO4 cell delivers an initial specific capacity of 172.1 mAh g−1 at 0.5C with 90.4% capacity retention after 300 cycles. Furthermore, the Li||LiNi0.8Co0.1Mn0.1O2 cells demonstrate stable performance even under high-voltage operation (4.5 V). This work provides a practical interfacial design strategy for developing high-performance CSEs in the next-generation solid-state LMBs.

This open access book provides the uniaxial, triaxial, fatigue, creep, creep-fatigue loading tests in order to investigate the salt mechanical response and the related damage mechanisms defining relation between plastic deformation, dilatancy and damage. Based on the experimental results, a new creep-fatigue constitutive model for salt rock that considers creep-fatigue interaction was established by incorporating a state variable that characterizes rock hardening level on the basis of the Norton creep model. Different salt rock mechanical test data sets with varying stress paths were used to validate this creep-fatigue constitutive model. The fit curves and test curves of the stress paths demonstrate good consistency, indicating that the model comprehensively considers the effects of time, load, and state on salt rock's creep-fatigue, effectively describing the creep-fatigue plastic deformation features of salt rock under different stress paths.

Smart solutions comprise a synergy of products, services, software, connectivity, data, and intelligence. This study examines the evolution of a manufacturer into a smart solution provider, highlighting the role of value-creation capabilities, activities, and practices. Through a longitudinal, in-depth single-case study of a leading technology provider, we scrutinize the value-creation capabilities, activities, and practices that enable a manufacturer to convert its product-focused capabilities and activities into those of a smart solution provider. Particularly, we uncovered three key value-creation-related capabilities that are crucial for the successful transition, namely: 1) visualization capability, 2) integration capability, and 3) scaling capability to revamp the business model for the adoption of smart solutions logic. The findings highlight the role of sensing, seizing, and transforming in a product manufacturer's transition. For managers, our study provides a framework that helps identify, manage, and alter a firm's capabilities and activities when steering the firm toward smart solutions.

To maximize resource efficiency in the direct reduction ironmaking process, effectively recirculating DRI fines is essential. These fines, which account for 1–2% by weight of DRI production, are generated during handling, production, and transportation processes. The DRI fines, rich in iron and comprised of finer size fractions, present a significant opportunity for innovation; however, their effective recycling poses challenges without proper agglomeration. Briquetting emerges as a highly promising solution to this challenge. This research explores the application of both organic and inorganic binders in briquetting DRI fines, aiming to unlock their full potential. The H2-based reduction behavior of the resulting briquettes is analyzed, utilizing a reduction process powered by 100% H2. The impact of various binders on critical parameters such as mechanical strength, moisture content, and compaction pressure of the briquettes is explored. The optimized briquettes undergo rigorous hydrogen-based reduction through thermogravimetric analysis (TGA), followed by comprehensive characterization using XRD, and LECO methods. Impact of carbon addition on the mechanical strength of the briquettes was also investigated. Among the various binders tested, the optimal combination for enhancing the mechanical strength of DRI fines briquette was determined to be 2% polyacrylamide binder with 0.2–0.8% sodium silicate. Furthermore, the incorporation of 5% biocarbon into DRI fines effectively maintained mechanical integrity, emphasizing the critical role of binders in producing durable briquettes suitable for industrial processes. This research contributes to advancing sustainable practices in the industry and paves the way for a greener future in iron and steel manufacturing.

Poniższy tekst zawiera informacje o XXXVI Konferencjiz Historii Matematyki, która odbyła się w Będlewie w dniach 18–22maja 2025 roku.

Witold Więsław był matematykiem i historykiem matematyki oraz jej popularyzatorem. W matematyce zajmował się algebrą i teorią ciał topologicznych. W latach 1966-2012 pracował w Instytucie Matematycznym Uniwersytetu Wrocławskiego w Zakładach Algebry, Algebry i Teorii Liczb oraz Historii i Metodologii Matematyki. W 1971 roku uzyskał doktorat z matematyki na Uniwersytecie Wrocławskim, a w 2006 roku habilitację w Instytucie Historii Nauki PAN. Był członkiem Polskiego Towarzystwa Matematycznego (PTM) w latach 1986--2014. Opublikował ponad 220 prac oraz w 1988 roku monografię {\it Topological Fields}. Był pierwszym redaktorem czasopisma {\it Antiquitates Mathematicae}. Był on także recenzentem 132 prac i książek dla MathSciNet w latach 1972-2013 oraz 228 prac i książek dla ZblMath w latach 1974-2013. W 2003 roku przyznano mu Nagrodę Główną PTM im. Samuela Dicksteina „za osiągnięcia w dziedzinie historii matematyki i popularyzacji matematyki”.

Magnetotelluric (MT) survey results from the Late Archean Manica greenstone belt, an extension of the Odzi-Mutare greenstone belt of the Zimbabwe Craton, are presented. A total of 33 MT stations were acquired on an irregular grid with an average station spacing of approximately 5 km. This data set represents the first MT survey in Mozambique. The results from the 3-D modeling indicate a conductive mid-crustal structure in the central part of the surveyed greenstone belt and the presence of narrow sub-vertical conductive structures connecting the mid-crustal conductor with shallow structures. These sub-vertical conductive structures are tentatively interpreted as marking the location of fluid pathways for mineralizing fluids associated with gold occurrences. The modeled mid-crustal conductor mapped in the Manica greenstone belt does not have a western continuation toward the adjacent Odzi-Mutare greenstone belt in Zimbabwe.

Józef Marcinkiewicz, będąc w Londynie w 1939 r. napisał list do Williama Fellera, pracującego w tym czasie w Sztokholmie. Niestety, listu, którego nadawcą był Marcinkiewicz nie udało się dotychczas odnaleźć, przetrwała natomiast datowana na 11 lipca 1939 r. odpowiedź Fellera. List ten zachował się zapewne tylko dlatego, że nie zastał adresata w Londynie i odesłano go do Polski z bardzo dużym opóźnieniem (zdaje się, że już po wojnie). Poza komentarzami dotyczącymi bieżących spraw politycznych i osobistych, zawiera on wynik autorstwa Józefa Marcinkiewicza, odnoszący się do obliczeń Haralda Craméra (opublikowanych w jego książce {\it Random Variables and Probability Distribution} z 1937 r.) i do tego lapidarny komentarz samego Fellera. List pisany jest po niemiecku. Załączamy jego fotokopię, tłumaczenie na język polski oraz komentarz do rezultatu matematycznego. Oryginał znajduje się w rękach prywatnych -- w archiwum spadkobierców rodziny Marcinkiewiczów.

The round goby, an invasive fish from the Black and Caspian Seas, has spread to Swedish waters, threatening recreational fisheries. We modeled impacts on the future recreational fishery in Sweden using data from a recreational fishing survey, and estimated effects of the round goby on other fish species. Values attached to recreational fishing were estimated using a travel cost approach. Catch and consumer surplus were compared before and after a 10-year increase in round goby abundance. Overall impacts of a 10-year increase in round goby abundance in Swedish waters would reduce the present value of consumer surplus by SEK 379-million (EUR 33-million). Any management action keeping a status quo in round goby impact with a price less than about 3 million EUR yearly would hence be justified. This study highlights how aquatic invasive species can cause substantial social losses and that proactive management may be cost-efficient.

Cross-laminated timber (CLT) is increasingly used for construction of multi-storey buildings. However, ensuring satisfactory vibro-acoustic performance, particularly in the low-frequency range (typically below 200 Hz), remains a significant challenge, often necessitating add-on solutions such as floating floors. In this study, the aim was to investigate how vibration levels for CLT panels can be reduced by using various lamination materials as well as integrated elastomer layers. Finite element (FE) models were developed, calibrated and validated based on experimental modal analysis of CLT panels with and without elastomer layers. Specifically, elastic moduli of spruce, birch and compressed spruce were calibrated to experimentally obtained eigenfrequencies and mode shapes. Moreover, calibrated FE models of selected panels were used to determine and calibrate a viscoelastic material model for the elastomer layer using frequency-dependent stiffnesses and damping. Using the material model for the numerical simulations, the deviations in accelerance root mean square values were less than 1 dB compared to the experimental data. Finally, it was shown that by using birch or compressed spruce instead of spruce the vibration response could be reduced by 30% and 50%, respectively, for a realistic floor panel size. By integrating a 12 mm elastomer layer into the panels, the vibration response could be reduced by an additional 40%, compared to a panel without an elastomer layer.

Large Language Models (LLMs) and foundational models play a central role in multimodal text-to-sound systems, such as text-to-speech and text-to-music, as well as in recent musical agent systems designed to automate music production tasks. We explore an alternative approach that employs LLM-based sonic agents as spatial composition techniques. This method, emerging from the Wilding AI research-creation project, integrates LLMs into Max/MSP, Ableton Live, and spatial sound environments. LLMs are used to generate step-by-step sequences controlling spatial audio parameters, including sound motion in 3D space and other assignable sound properties. This paper details the artistic framework of Wilding AI, a LLM behavior generator, a live performance at the CTM Festival 2025, and discussions on composition, improvisation, time, and agency. Our work repositions LLMs as tools for shaping sonic experiences rather than merely generating finished audio material.

This article is an invitation into an artistic research project on team-based filmmaking. Its format takes a montage approach congenial with the research ambitions: writings on a film process are interspersed by texts on research methods, script scenes, historical images and facts, while loosely traced by comic strips in the intended film’s chronology.

The Sisters B. project responds to a lack of research on and through collaborative film practice. We situate the project in our time’s flood of media-based storytelling and ecological crises. This project’s multifaceted and exploratory approaches are informed by the complexity and entanglement of consequences – to environment, people and otherwise, directly and ideologically – of both cinematic production and its narratives. Our methodological framework draws from several academic fields and artistic disciplines, manifested through the article’s exposition of a filming week on the Bergman Estate.

The Sisters B. explores loss and conditions for creativity through an embodied conversation with the composers Lili (1893-1918) and Nadia (1887-1979) Boulanger. The article proposes a range of embodied montage strategies; intertwining narrative levels, connecting times, activating audience imagination, embracing friction, responding to circumstances, producing by re-using; porously overlapping fact and fiction, theory and practice by relating biography, ecology and rhythmic gestures to script, performance and film editing.

The role of frothers in flotation separation is pivotal because they generate stable bubbles and facilitate the transport of particles to the froth phase. Owing to increasing environmental concerns about hazardous plastic handling and stricter regulations in the mining industry, there is a growing need for less toxic, more environmentally friendly reagents. Frothers, which are typically synthetic, non-green, and unsustainable chemicals, have historically received less attention than other flotation reagents, including collectors and depressants. This is contrary to their significant role as the most critical reagent in plastic flotation separation. They also have a governing influence on ore separation, significantly affecting froth zone height and durability, consequently impacting selectivity and recovery. Alcohol-based frothers are a well-known group of frothers utilized in froth flotation. They are generally divided into aliphatic, aromatic, and cyclic alcohol categories. Several investigations have reported promising results from using natural and non-hazardous alcohol-based frothers, demonstrating enhanced flotation performance and reduced health risks. This comprehensive review focuses on natural alcohol-based frothers (eucalyptus oil, pine oil, and terpineol alcohol), providing an in-depth analysis of their structure, application, effectiveness in flotation separation, and comparison with conventional hazardous frothers. Despite promising results in flotation using alcohol-based frothers, substantial gaps remain, notably interactions and adsorption mechanisms with the particle surface and other flotation reagents, performance across various flotation systems, industrial scalability, etc. This robust review highlights the existing gaps, identifies challenges, and proposes future research directions to advance the adoption of natural alcohol-based frothers.

This study evaluates the tribological performance of fully formulated water-based lubricants (WBLs) under rolling/sliding conditions. The WBLs are composed of water-soluble glycols and glycerol with a functional proportion of water. They exhibited significantly lower friction across all lubrication regimes, with near superlubricity achieved under certain conditions. Unlike conventional oils, WBLs showed minimal shear heating effects and exhibited a low shear response with increasing slide-to-roll ratios. Long-duration tests confirmed consistently lower friction for the WBLs. Post-test analysis showed comparable wear between selected WBLs and the reference oil at 40 °C but greater surface modification for WBLs at higher temperatures. Observed wear mechanisms included abrasive and adhesive wear, with occasional instances of spalling after prolonged tests. X-ray photoelectron spectroscopy (XPS) analysis suggested the presence of chemical species on wear scars, attributed to additive-surface interactions. These findings highlight the potential of fully formulated WBLs in rolling/sliding contacts and their current state in comparison to a conventional gear oil.

Upgrading clean energy fuels, such as biogas, natural gas, and shale gas, requires the capture of CO2 to enhance their heating value. Ionic liquids (ILs) are promising absorbents for this purpose, but the vast number of available ILs necessitates an efficient screening method. The Comparative Absorption Factor (CAF) developed in our previous study can estimate the total annual cost (TAC) of CO2 capture from biogas, which is a key advantage over alternative screening methods. However, CAF does not consider the effects of operating conditions such as CO2 concentration, pressure, and temperature. To address this limitation, a modified CAF (CAFmodified) that incorporates these factors was proposed. Given the linear relationship between the original CAF and TAC, three representative ILs ([C10mpy][DCA], [C1mim][tfo], and [C1py][tfo]) were selected from 490 ILs based on their melting point, viscosity, and original CAF values. Subsequently, process simulations for these ILs were conducted using Aspen Plus, considering a wider range of operating conditions: CO2 concentrations of 30–50 vol%, temperatures of 303.15–318.15 K, and pressures of 7–15 bar. These simulations were used to determine the Aspen Plus-derived TAC, which served as the basis for proposing CAFmodified. Finally, data for Aspen Plus-derived TAC from the previous study and for 6 additional ILs over a broad range of operating conditions were used to compare with the TAC values estimated by CAFmodified. The results showed an average relative deviation of 16%, indicating that CAFmodified is effective for screening ILs for CO2 capture under varying operating conditions.

This study is part of a larger action research (AR) project aimed at improving students’ engagement with fiction and reading skills in Swedish and Swedish as a second language. In this paper, a theoretical framework of value creation in social learning spaces is used to critically explore teachers’ professional learning and experienced value in AR in literature teaching. In total, 15 teachers from primary, secondary and upper secondary schools participated. Data included written teacher reflections and recorded focus group conversations. Reflections were collected at the start and end of the project. Focus group conversations were carried out once a month for three terms. The findings reveal that value was created through collaborative engagement of teachers and researchers, expanding teachers’ professional knowledge on challenges, practice-informed adaptation of teaching and collective learning and shared knowledge. The study emphasises value creation in educational AR through sustained teacher-researcher collaboration, mutual engagement, agency and knowledge co-production. It highlights the role of long-term partnerships and calls for future research to challenge consensus, to include both quantitative and qualitative measures of learning and knowledge, and to engage with scientific texts as well as systematically explore professional learning throughout.

Time-varying (TV) materials have recently gained considerable attention for their ability to manipulate electromagnetic (EM) waves and improve the performance beyond the limits of conventional time-invariant materials. In addition, distributed TV capacitors are becoming more attractive to achieve particular effects. This work presents a systematic approach to modeling TV dielectrics by incorporating TV capacitors in the framework of the partial element equivalent circuit (PEEC) method. Thus, the standard formulation of the PEEC method is modified to include TV dielectrics and lumped elements for general 3D geometries directly in the time domain (TD). It is shown that this is possible through TV capacitances and voltage-controlled current sources. Four numerical examples validate the proposed approach.

Hydrogen production coupled with renewable energy sources (RES) is a promising solution to utilize otherwise curtailed electricity. Hydrogen is also seen as a potential solution in several hard-to-abate industries, such as the maritime industry. This opens the opportunity for hydrogen supply chains utilizing RES to supply hard-to-abate industries with hydrogen. These hydrogen supply chains utilize either electricity cables or hydrogen pipelines to transport energy. Previous studies show that the most cost-efficient means of transportation depends on both the distance and the amount of energy to be transported. In the context of offshore energy production, the problem is further expanded by the conditions of offshore infrastructure. To capture the effect of offshore conditions in the supply chain design, this paper presents a MILP model that minimizes the cost of a power-to-gas hydrogen supply chain, including the decision of where in the supply chain the electrolyzer should be located. The model is applied to a case in southern Sweden with renewable offshore wind supplying electricity to produce hydrogen as maritime fuel for a ferry. The results show that the relation between offshore and onshore distances, together with increased costs for offshore infrastructure, greatly influence the choice of transmission mode. Hence, this study found that a hybrid solution with offshore electricity transmission to the closest point onshore followed by onshore hydrogen transmission through pipelines to be the least cost option for energy transmission in the hydrogen supply chain.

Carpet waste degrades slowly, posing an environmental burden. Incorporating this waste as fibre reinforcement in concrete helps to reduce accumulation and improve the properties of concrete. However, varying fibre characteristics and their interaction in blends are not well established. Hence, understanding hybrid fibre effects is crucial for construction adoption. This study evaluates the mechanical performance, shrinkage, microstructure, pore structure, and interfacial characteristics of hybrid carpet fibre-reinforced concrete. Four fibres, nylon, polypropylene (PP), polytrimethylene terephthalate (PTT), and polyester were analysed using grey relation analysis in six hybrid combinations with three mix ratios at a 0.3% volume fraction. At 28 days, flexural and splitting tensile strengths increased by 4–11% and 8–26%, respectively. All mixtures exhibited reduced shrinkage compared to the control. The nylon/PP 2:1 mix showed the best overall performance. Microstructural analysis revealed pore refinement, reduced porosity, and improved fibre–matrix bonding influenced by fibre hydrophilicity or hydrophobicity. Nanoindentation indicated hydrophilic fibres minimized the interfacial transition zone thickness. The findings indicate fibre combinations exhibit additive effects. This suggests performance can be predicted for blended fibre mixes, potentially allowing the design of mixes using fibre combinations to compensate for poorer performing fibres, enabling their reuse and avoiding landfill disposal.

The first observation of coherent 𝜙⁡(1020) meson photoproduction off heavy nuclei is presented using ultraperipheral lead-lead collisions at a center-of-mass energy per nucleon pair of 5.36 TeV. The data were collected by the CMS experiment and correspond to an integrated luminosity of 1.62  μ⁢𝑏⁻¹. The 𝜙⁡(1020) meson signals are reconstructed via the 𝐾⁺⁢𝐾⁻ decay channel. The production cross section is presented as a function of the 𝜙⁡(1020) meson rapidity in the range 0.3<|𝑦|<1.0, probing gluons that carry a fraction of the nucleon momentum (𝑥) around 10⁻⁴. The observed cross section exhibits little dependence on rapidity and is significantly suppressed, by a factor of  ∼5, compared to a baseline model that treats a nucleus as a collection of free nucleons. Theoretical models that incorporate the nuclear shadowing effect generally provide a better description of the 𝜙⁡(1020) data than those incorporating gluon saturation. This study establishes a powerful new tool for exploring nuclear effects and nuclear gluonic structure in the small-𝑥 regime at a unique energy scale bridging the perturbative and nonperturbative quantum chromodynamics domains.

Raman spectroscopy is widely used in chemistry, material science and in biomedical applications such as cancer detection. A Raman spectrum of tissue shows DNA, amino acids, lipids, and proteins simultaneously, which makes the evaluation of the spectral content both complete but also challenging. The impact of the technique can increase substantially by access to big databases, but variations in the setup, e.g. quantum efficiency of Raman detectors, transmission profiles and disturbances of optical filters and components, may hinder direct data comparison. We here introduce a step prior to preprocessing that calculates spectral quotients to address system-dependent multiplicative differences and system-inherent background noise, thereby enabling analysis of Raman spectral data from different setups. Pre-processing was performed using an artificial neural network (ANN) trained on synthetic data to deal with fluorescent background and noise. Validation by multivariate analysis of the spectral quotients combined with ANN was performed on randomized synthetic data and, as a proof of principle, on experimental data from brain tumor biopsies. The results demonstrated clustering and feature extraction that was not possible without the introduction of the quotients. Data exploration revealed that the method enabled spectral feature identification even for weak Raman signals that are not in resonance with the excitation wavelength. Note, some distortions persist due to data dependency and additive errors but a system independent clustering was achieved. ANN-based preprocessing combined with spectral quotients for combined evaluation of Raman spectroscopic data from multiple setups opens the possibility for more robust multivariate studies in biomedical and other applications.

Deep eutectic solvents (DESs) have attracted considerable attention as promising alternatives to conventional solvents for mitigating CO2 emissions due to their tunable structures, low volatility, and promising physicochemical properties. In this work, a series of [Triz]Cl/amine DESs were designed and synthesized and then formulated as 30 wt% aqueous solutions (30 wt% DES + 70 wt% H2O) to systematically investigate how the type of hydrogen bond donor (HBD) affects their physicochemical properties, thermal stability, and CO2 capture performance, and to identify the most effective solvent; their CO2 absorption capacity, absorption rate, thermal stability, and desorption efficiency were determined experimentally, and a novel stepwise evaluation strategy was employed for identification. [Triz]Cl/DETA was identified, exhibiting significantly enhanced performance, with CO2 absorption capacity, absorption rate, thermal stability, and cyclic loading increased by 34%, 12%, 114%, and 39%, respectively, when compared with the conventional monoethanolamine (MEA). Its viscosity (both before and after CO2 absorption), oxidative stability, and corrosion resistance were further studied, confirming the superior performance, and the reaction mechanism was also elucidated. This work provides valuable insights into the structure–property relationships of DESs and establishes [Triz]Cl/DETA-based solvents as promising candidates for efficient and sustainable CO2 capture applications.

Twin transition, the simultaneous advancement of technology and circular practices, poses an under-operationalized challenge for industrial firms, particularly in balancing the pace of both dimensions. As a result, twin transition often remains a strategic aspiration rather than an actionable practice. To address this gap, we propose a route optimization model that operationalizes twin transition. Framed within a Socio-Techno-Ecological Systems perspective and developed using design science research, it mandates reduced CO₂ emissions as the default optimization criterion while allowing configurable parameters (i.e., distance, time, and cost). By integrating genetic algorithm with machine-learning techniques, the model enhances adaptive performance in real-time. Achieving 97.7% prediction accuracy with random-forest classifier, the model is validated on established datasets to ensure generalizability. By continuously processing real-time inputs on traffic, road conditions, environmental data, and regulatory constraints, it can improve route safety, efficiency, and compliance. This study aligns technological progress with environmental objectives and societal expectations and offers both theoretical and managerial contributions by helping industrial firms reduce CO₂ emissions, strengthen supply-chain resilience, and accelerate circularity implementation.

Metal oxide heterostructure assemblies made of ZnO-Co₃O₄ core–shell nanowires enable high-performance self-powered optoelectronic devices with potential applications in wireless, autonomous, low maintenance medical implants or environmental sensors. Surprisingly, the experimental study of the single core–shell heterostructures forming the assembly was never reported until now. We unveil the transport phenomena occurring in individual ZnO-Co₃O₄ core–shell nanowires by engineering ionic liquid-gated nanotransistors. The nanostructures are isolated on fabrication substrates and equipped with a set of metallic electrodes probing selectively different sections of the nanowire, in three different configurations labelled core–core, shell–shell and core–shell. The observed electrical responses reflect the properties of the ZnO core, the Co shell and the core–shell heterojunction. The ultrahigh capacitive coupling of the ionic liquid to the nanowire and its conformal feature reveal multiple transport regimes in the same nanodevice: the core, the shell and the core–shell heterojunction act as a linear, nonlinear, and rectifying nanoelectronic components, respectively. This work shines light on the transport properties of individual metal oxide nanowire heterostructures employed in self-powered optoelectronics, suggesting potential applications as multifunctional nanoelectronic components. The methodologies developed in this research set the benchmark for the investigation of nanoscale building blocks of functional semiconductor nanomaterial assemblies for electronic and optoelectronic applications.

Managing the flow of molten steel and the solidification during continuous casting is vital for controlling non-metallic inclusions and ensuring the production of high-quality steel. In this study, a three-dimensional coupled model was employed to predict the molten steel flow, heat transfer, solidification, and inclusion transport. A novel uniformity index, based on the definition of Boltzmann entropy, was introduced to characterize the uniformity of inclusion distribution in the bloom section. The spatial distribution of inclusions was studied under the coupling effect of mold electromagnetic stirring technology (MEMS) and electromagnetic swirling flow in the nozzle (EMSFN) with different current intensities. The results indicate that as EMSFN current intensity increases, the distribution uniformity of inclusions increases at first and then decreases slowly, peaking at MEMS & 500 A EMSFN where maximum rotation velocity rises by 78.8 % versus MEMS & 0 A EMSFN. The experimental results show that the uniformity of the optimal coupling effect (MEMS & 500 A EMSFN) is improved by more than 60 % compared with that of the MEMS & 0 A EMSFN. This study presents a novel quantitative evaluation method for uniformity of inclusion distribution and an effective electromagnetic metallurgy strategy for enhancing the control of inclusion spatial distribution in high-quality clean steel blooms.

This work presents a hybrid analytical-numerical procedure that allows the efficient computation of the flux linkage of two misaligned circular coils placed above a stratified ground, which may include non-magnetic as well as magnetic materials. After deriving the Hankel transform describing the flux linkage, the semi-infinite integration interval is continued to the negative real axis. Next, the part of the integrand that exhibits branch point singularities is expressed in pole-residue form. This task is accomplished through the use of a well-established rational function fitting algorithm. Finally, the integration contour is deformed in the complex plane, so as to surround the pole singularities produced by the fitting algorithm. This makes it possible to apply the residue theorem, which leads to converting the integral representation for the flux into the sum of residues at the poles of the integrand. The derived solution has value in practical applications like wireless power transfer systems for battery electric vehicles charging.