To fulfil climate change commitments and transition towards net zero carbon economies, there is a need for Nordic nations to explore the development and deployment of alternative fuels. In this context, this inquiry presents the first comparative analysis of drivers for and barriers to implementing green hydrogen and hydrogen-based fuels (hydrogen, methanol, ammonia) within potential hydrogen hubs in Nordic ports. To support the development of hydrogen hubs to meet ambitious decarbonization targets for maritime shipping and relevant industry, this study focuses on five case studies from Sweden and Iceland. 64 semi-structured interviews with diverse stakeholders are used to examine the potential for ports to become hydrogen hubs. This is done by exploring the relative strength of drivers and barriers related to different phases of hydrogen hub development and the influential context-specific and necessary enabling factors. Key findings illustrate market opportunities, consumer demand, and environmental commitments as shared drivers for each case, while high costs, insufficient policy instruments, and limited energy resources and grid capacity emerge as shared barriers. The frequently dualistic nature of these factors underscores the need for policy frameworks to bridge the gap between current limitations and future potential. The main policy implications include a need for provision of funding for more Nordic pilot projects, increasing renewable electricity capacity, and implementing sufficient economic incentives to support hydrogen-based fuel upscaling and ensure cost-competitiveness in comparison with existing fossil fuel-based alternatives. This can help to encourage strategic investments in infrastructure and coordinated policy measures to unlock the potential of hydrogen hubs.

Swedish wildlife management is characterized by multi-level governance, stakeholder conflict, and strong cultural ties to hunting. In this contested arena, frontline wildlife officers play a central role in mediating between state authority and local interests, making professional legitimacy a critical institutional resource. Drawing on semi-structured interviews with wildlife officers from 20 out of 21 County Administrative Boards, this article examines how wildlife officers in Sweden build and sustain professional legitimacy through everyday practices within a gendered governance landscape and analyses legitimacy as a situated accomplishment enacted through bureaucratic neutrality, relational and emotional labour, and alignment with cultural norms. Findings show that while objectivity and rule adherence are formally universal ideals, access to legitimacy-enhancing practices is shaped by gendered expectations, amplifying credibility for some while constraining others, particularly women. The study contributes to research on environmental governance by integrating institutional work and gender theory, revealing how legitimacy is maintained in practice.

Controllable cutting performance is essential for handheld saws in both craft and professional wood processing. Despite the widespread use of side-bevelled handsaw teeth in manual and electrically powered tools, the combined influence of tooth geometry, set pattern, as well as cutting direction on cutting forces is not well understood. A Design of Experiments (DoE) approach was applied to evaluate the effects of tooth type (universal, UT; compound, GT), side-bevel, set, set pattern, and cutting direction on main (Fm) and normal (Fn) cutting forces. Experiments were conducted on Norway spruce blocks sourced from a single log, which was conditioned to achieve uniform moisture content and characterised using X-ray computed tomography. A custom-built cutting rig measured Fm and Fn under controlled cutting speed and depth conditions. A 2⁵⁻¹ fractional factorial design was applied as an exploratory DoE screening approach to capture main and two-factor interaction effects. Fm was dominated by cutting direction and kerf-related factors, with GT teeth exhibiting lower Fm than UT teeth in rip cutting. Fn was primarily governed by tooth type, bevel, and set pattern. Interaction effects between geometry and cutting direction were statistically significant, highlighting the multivariate nature of the cutting process. Overall, cutting forces are governed by interactions between tooth geometry, set configuration, and cutting direction. The results provide a quantitative basis for further development of handsaw tooth design and cutting efficiency.

Teachers must, in one way or another, address issues of Artificial Intelligence (AI) and generative AI (GAI). The urgency became even more acute when the chatbot ChatGPT was launched in November 2022, changing the conditions for teaching and learning in schools, creating tensions within the educational system. The aim of the study is to deepen understanding of teachers' experiences of how generative AI, specifically ChatGPT, shapes teaching and education in secondary and upper secondary schools. The theoretical framework is cultural-historical activity theory. Focus group interviews were performed on two occasions with 15 Swedish teachers. The reflexive thematic analysis resulted in three themes, elucidating tensions and contradictions: Navigating professional autonomy and institutional support in a new teaching landscape; Transforming relationships in the age of AI; and Rethinking the purpose of education in a dynamic educational context. Cultural-historical activity theory regards tensions and contradictions as prerequisites for transformation and development. Therefore, there is potential that generative AI and ChatGPT can become useful educational tools if teachers can negotiate and manage the uncertainties, and learn how to integrate the tools successfully into their teaching.

Management for Industry 4.0/5.0 will require considerable engineering effort. To reduce such efforts by cost and time is a considerable challenges. This paper highlight that the usage of Service Oriented Architecture and microservice architecture technologies can create substantial saving on engineering for production automation. The impact on engineering cost and time has been investigated by the Arrowhead Tools project. For the purpose 28 industrial use cases were defined. For each of the 28 use cases a baseline for the engineering cost and time was defined, using for each industrial partner the available technology and competence. Based on use case requirements, a dedicated edge microservice architecture was defined and implemented using the Eclipse Arrowhead architecture and reference implementation, complemented by use case-specific edge microservices and tool chain interoperability. The engineering cost and time for implementing the microservice architecture for the use cases were calculated. Costs, as time savings were then determined for each use case. The resulting median cost and time savings were just above 80%, with a range from 30% to 95%.

Background

Knowing how consumers understand energy labels is important for predicting behaviour and interpretations of environmental impact.

Aims

The present study evaluates how different visual cues in the design of EU energy labels help consumers understand the energy scale.

Method

In two preregistered behavioural experiments, internet-based samples of participants from the UK general population completed rating tasks. Two key types of energy label designs were evaluated. One type of energy label, the class arrow, highlights a colour-coded letter that represents one of seven levels in the energy scale. The other type of energy label, the full scale display, shows a spatial layout where all seven colour-coded letters are arranged.

Results

A scale that contains multiple ‘A’ and ‘A+’ letter symbols implied a skewed scale structure, leading to biased overestimations. This skewness effect was found with the class arrow, indicating that this label type is understood based on its highlighted letter. However, with the full scale display, these skewed letters were overshadowed by the linear spatial layout which implied equidistance within the energy scale. This mitigated cognitive bias as a skewed cue was overshadowed by a linear cue. Colour coding was an additional visual cue in energy labels that had no measurable effect on ratings in the presence of more dominant visual cues (letters and spatial layout).

Discussion and Conclusion

Results indicate how cues compete for visual attention when energy labels are evaluated. We propose two design conditions that support a linear understanding of graded eco-label scales: Cue linearity and cue dominance.

This study examines time-related constraints on circularity by applying Shannon entropy to measure distributional dispersion across end-of-life fates in plastic waste management. Using OECD data for 1990–2018, end-of-life shares for recycling, incineration, landfill, mismanaged, and littered waste are combined with entropy to assess structural change over time. Entropy makes that structural shift visible and comparable across time. During the early 1990s, plastic waste management was highly predictable due to landfill dominance. From the mid-2010s onward, entropy stabilized at a high level despite continued growth in recycling. That plateau is consistent with persistent dispersion across competing end-of-life routes rather than convergence toward recycling dominance.

The sustainable extraction of manganese (Mn), a critical element used in many high-tech industries, has attracted considerable attention. However, the complex mineral composition makes the processing of manganese oxide ores challenging. This has led to an interest in green chemistry and alternative solvents to reduce environmental impacts. Deep Eutectic Solvents (DES) are recognized for their unique characteristics and environmental benefits. In this study, various DES comprising choline chloride as a hydrogen-bond acceptor (HBA) and organic acids such as oxalic acid (OX), tartaric acid (TA), and lactic acid (LA) as hydrogen-bond donors (HBDs) were synthesized. The key parameters, including HBA:HBD molar ratios, particle size, agitation speed, time, pulp density, and temperature, were systematically studied to optimize manganese recovery. By optimizing the parameters, 99.5% manganese extraction was achieved under the conditions of 40 g/L solid pulp, a molar ratio of 1:2, stirring speed of 200 rpm, temperature of 50 °C, and a leaching time of 1 h using ChCl:OX. Moreover, Density functional theory (DFT) was applied to evaluate the stability of various geometry-optimized Mn2+ complexes with the existing ligands such as OX, LA, TA, and Cl ion, by calculating the Gibbs free energy (ΔG(complex)) for each complex. The theoretical outcomes showed agreement with experimental results. DFT assessment revealed that Mn(OX)₂ has the most negative ΔG(complex) among the DES containing organic acid, followed by tartaric and lactic acid systems, indicating its potential for forming stable complexes with Mn2+. These findings highlight the potential of DES in efficient Mn recovery and simplify the traditional two-step pyro-hydro process by combining it into one step.

This paper examines whether concentration in electricity generation affects the deployment of wind and solar capacity across the 27 European Union member states over 2000–2022. Market concentration is measured by the generation share of the largest electricity producer, and wind and solar deployment is measured as installed wind and solar capacity relative to total national electricity capacity. Using a country-level panel and dynamic estimation methods that account for persistence, unobserved heterogeneity, and reverse causality—including fixed-effects OLS and Arellano–Bond GMM estimators with lagged concentration as an internal instrument—the analysis finds no systematic relationship between market concentration and subsequent wind or solar deployment. The result is robust across alternative concentration and outcome measures, bias-corrected dynamic panel estimators, balanced samples, region-specific linear time trends and heterogeneity analyses across regions, transition stages, energy-system characteristics, and policy regimes. The findings suggest that within the EU institutional setting, market concentration per se is not a first-order constraint on renewable uptake; instead, deployment is primarily driven by policy settings and the path dependence inherent in installed capacity.

Log parsing is a fundamental prerequisite for automated system monitoring, anomaly detection, and root cause analysis in large-scale distributed environments. However, existing parsing approaches often rely on heuristic rules, manually engineered features, or fixed similarity thresholds, limiting their adaptability to heterogeneous and evolving log structures. To address these challenges, this study presents SemBERT, a fully unsupervised log parsing framework that integrates semantic BERT embeddings, Incremental Principal Component Analysis (IPCA), HDBSCAN clustering, and adaptive centroid-based cluster merging for robust template mining. Unlike conventional methods that employ fixed merging criteria, SemBERT adaptively determines semantic merging thresholds according to the distribution of cluster centroids, reducing parameter sensitivity across datasets. Experiments on the LogHub HDFS benchmark demonstrated that SemBERT achieved perfect Grouping Accuracy (GA = 100.00%), Parsing Accuracy (PA = 95.82 ± 0.44%), and Template Accuracy (TA = 76.59 ± 4.49%), outperforming several classical parsers in semantic template discovery while maintaining competitive parsing performance. Repeated reproducibility experiments using three different random seed initializations (42, 123, and 999) produced identical adaptive thresholds and final template counts across all evaluated data scales, confirming the deterministic behavior and reproducibility of the proposed framework. Additional scalability experiments on randomly sampled subsets ranging from 50K to 1M log entries demonstrated stable threshold estimation and controlled template growth, highlighting the effectiveness of the adaptive semantic merging strategy. To validate the practical utility of the extracted templates, downstream anomaly detection experiments were conducted using representative machine learning, deep learning, and transformer-based models. The semantic templates generated by SemBERT provide informative representations suitable for downstream log analytics. Furthermore, experiments on the BGL dataset indicate that the framework generalizes effectively to heterogeneous logging environments through lightweight dataset-specific preprocessing. These findings suggest that SemBERT provides a scalable and semantically informed solution for unsupervised log parsing in modern distributed systems.

Carbonaceous asteroids such as Ryugu and Bennu have experienced the catastrophic disruption of their parent bodies and subsequent cratering impacts. Their internal structure resembles a rubble pile, and some of their boulders have large cracks on their surfaces. Indeed, regolith grains sampled and returned to Earth were found to have numerous cracks, some of which extended through the entire interior of the samples. In an impact, pre-existing large cracks are likely to affect subsequent disruption and fragmentation. Hence, pre-existing large cracks affect the size distribution and lifetime of these boulders, – and thereby asteroids. However, it is unclear how exactly fracturing is influenced by pre-existing large cracks. The purpose of this study is to investigate how pre-existing large cracks influence the fragmentation of targets. We therefore conducted impact experiments on carbonaceous meteorites with and without internal cracks. A spherical alumina projectile with 1.0 mm and a glass projectile with 0.80 mm in diameter were fired into 1–2-cm-sized Allende (CV3), Murchison (CM2), and Aguas Zarcas (CM2) targets at nominal impact velocities of 2.0 and 4.0 km ⁠s^-1, respectively. X-ray computed tomography imaging on the meteorite before impact and on the largest fragment after impact were performed. The experiments show that, for CV and CM meteorites, several of the newly exposed surfaces on the largest impact fragments were formed along pre-existing large cracks. Measurements of fragment size distributions from the disruption of CM meteorites revealed a deficiency of small fragments, potentially explaining the scarcity of fine particles on small carbonaceous asteroids.

Introduction: 

Flexible fused-deposition-modelled propellers can lose endurance when deformation changes the designed incidence, while thin sections that appear aerodynamically superior may be structurally inadmissible. This study develops a constraint-driven aero-structural workflow for a 9.4 in two-bladed glycol-modified polyethylene terephthalate (PETG) multirotor propeller at a per-rotor hover thrust of 4.5 N.

Methods: 

Six chord-twist and airfoil candidates were evaluated over 2-5 N using blade-element momentum theory (BEMT) and orthotropic finite-element analysis (FEA). Candidates were screened against a 40 MPa stress allowable and a 0.03R tip-deflection limit. The most efficient feasible blade was then refined by constrained stationwise twist redesign using local Reynolds number, inflow angle, and Cl/Cd information while retaining chord, airfoil schedule, diameter, root geometry, and section thickness. The final design was independently checked using steady RANS computational fluid dynamics (CFD) and pressure-mapped FEA.

Results: 

The aerodynamic-only winner failed the 0.03R deflection constraint, whereas the APC/E216 configuration satisfied both stress and stiffness requirements and was selected as the feasible baseline. Across 2-5 N, the local twist redesign reduced the required rotational speed by approximately 11% and increased the hover figure of merit by up to 4.6%. At the 4.5 N design point, outward load migration increased the torque arm while the lower rotational speed reduced combined stress by 24.2%. The associated reduction in centrifugal stiffening increased tip displacement by 45.5%, but the displacement remained within the prescribed limit. RANS-CFD and pressure-mapped FEA recovered the target thrust and confirmed structural feasibility.

Discussion: 

The results show that aerodynamic improvement cannot be separated from printed-blade stiffness. Twist-induced radial load redistribution can simultaneously improve hover efficiency, reduce centrifugal stress, and decrease centrifugal stiffening. The proposed tiered BEMT/FEA-to-CFD/FEA workflow therefore provides a physically traceable route for balancing aerodynamic performance and structural admissibility in printable low-Reynolds-number multirotor propellers.

To effectively mitigate NOX emissions in pulverized coal combustion, Computational Fluid Dynamics (CFD) is frequently employed as a predictive tool. Because fuel-bound nitrogen is the primary source of NOX in these applications, modelling largely relies on coal characterization—data that are often uncertain even with costly experimental measurements.This study introduces a systematic framework for estimating missing fuel‑nitrogen characteristics from standard proximate and ultimate analyses based on correlations from rapid coal devolatilization experiments. Utilizing a conventional CFD approach, we simulated coal combustion in a 150-kW facility, which mimics rotary kiln conditions. A sensitivity analysis evaluates the impact of uncertainties in volatile nitrogen yields, precursor compositions (HCN/NH3), and char surface reactivity on NOX predictions. Results indicate that input uncertainties may account for up to 25% of the total predicted NOX. The ratio of nitrogen released as volatiles versus char-bound nitrogen and the initial char surface area of the coal particles are both critical parameters, while variations in volatile precursor ratios had a negligible effect (<1%). The model was validated against measured NOX emissions from the 150-kW facility, demonstrating that while the framework provides a robust estimation tool, high-fidelity characterization of char properties remains vital for precise emission modelling.

Urban stormwater sediments can accumulate a broad range of contaminants, yet municipalities often lack theresources for extensive laboratory analyses to determine appropriate management of these sediments. Thisstudy evaluates colour analysis, X-ray fluorescence (XRF), and optical microscopy as low-resource methods forcharacterising sediments and identifying anthropogenic particle sources. The assessment used sediment samplesfrom 17 stormwater ponds and 26 gully pots in Sweden, which were earlier examined for metal and organiccontaminants. The colour analysis showed spatial patterns for stormwater pond sediments, with loweranthropogenic pollutant levels and lighter colours in Örebro and darker sediments from Växjö and Stockholmshowing higher pollutant concentrations, indicating the potential for colour as preliminary pollution indication.Microscopy revealed different anthropogenic materials, including clothing fibres, plastics, metal fragments, andglass beads, highlighting multiple contributing sources. In contrast, gully pot sediments displayed weakerrelationships between colour, metals and pollutants, likely due to difference in catchments characteristics.Overall, the combined methods provided valuable insights into sediment composition and contaminationpatterns, however, further analysis is needed to confirm the observed trends of colour and pollutant levels.

Drawing on blue humanities scholarship and narratological theory, this article develops a ‘blue’ narratological approach to examine how hydrological conditions influence narrative organization in Maja Lunde’s speculative climate novel Blå. Through close readings, I trace how shifting water conditions, including storms, tides, drought and water scarcity, influence both narrative duration and narrative progression across the novel’s dual timelines. In the first storyline, which follows the climate activist Signe on her solo voyage across the Atlantic, calm seas open up space for memory and reflection, while storms compress narrative time and force action. In the second storyline, set decades later in a drought-stricken France, limited access to water constrains movement and produces periods of stasis. Prolonged waiting slows narrative progression and generates forms of temporal suspension, yet recurring water-related crises repeatedly disrupt this stasis and propel the narrative forward. Across both storylines, hydrological conditions influence when action becomes possible and when it is deferred or brought to a halt. I also examine how the novel’s ending departs from conventional narrative closure. Rather than resolving narrative desire through the completion of its protagonists’ journeys, Blå redirects attention toward the anticipated return of water and the hydrological cycles that connect its two timelines. By analyzing water as a structuring force, the article demonstrates how environmental change may help shape narrative organization as well as thematic representation.

Likvärdig utbildning är en utmaning för det svenska skolsystemet. Samtidigt påverkar allt fler globala trender och aktörer såväl utbildningens villkor som vad som värderas som god och likvärdig utbildning. Likväl har en re-centralisering inom offentlig sektor lett till andra styr- och kontrollmekanismer. Det systematiska kvalitetsarbetet (SKA) syftar till att upprätthålla och utveckla kvalitet och likvärdighet i utbildningen, vilket ställer allt högre krav på såväl lärare som rektorer och förvaltningsledning gällande vetenskapligt förhållningssätt. Dock har skolledare och skolchefer relativt svag koppling till vetenskapliga kvalifikationer och tolkar innebörden olika. Denna studie belyser och problematiserar styrning och ledning av SKA med fokus på en likvärdig utbildning i förskolan inom ramen för en verksamhetslednings (VL’s) deltagande i en utvecklings- och forskningspraktik – ett aktionsforskningsprojekt i nära samverkan mellan huvudman och akademi. Resultatet visar hur VL balanserar mellan styrning och ledning och friutrymme i SKA genom att arbeta för en kultur som stödjer delaktighet, dialog och agens. Vidare synliggörs den komplexa processen gällande transformering av analys och resultat i styrkedjans växelstationer, där friktioner kan uppstå på grund av olika sätt att se på kvalitet. Analysen framträder som den ram för likvärdighet som kan komplettera standardiserade underlag samtidigt som professionskompetensen värdesätts och diskrepansen mellan olika kvalitetslogiker överbyggs.

The aim of this review is to analyse the data available in the literature concerning the dynamics associated with pain perception in cases of discopathy and intervertebral disc degeneration. Determined by multifactorial factors, within the optimisation of the assessment processes typical of evidence-based medicine (EBM), discopathy – that is, intervertebral disc degeneration – and pain in the lumbar or cervical regions represent different stages of the same degenerative process, clearly linked to dysfunctional mechanical conditions. The complexity of the diagnostic procedure stems from the difficulty of distinguishing clinical findings from imaging findings, or rather, of interpreting the latter in the light of a pain physiology that has been largely overlooked, which encompasses aspects of histology, neurophysiology and neuropathology.

Digitalization is transforming the job market at an unprecedented pace, necessitating a skilledworkforce capable of innovating and adapting in a rapidly evolving professional landscape. Thisshift presents a challenge for education systems to rethink competence-building processes toalign with emerging digital demands. Traditional degree-centric models of education areincreasingly inadequate for addressing the ongoing needs for reskilling and upskilling in theworkforce. This paper explores how higher education can integrate into the fast-paced ecosystemof professional skill development and examines the role of co-design methodology in analyzingthe demand for digital skills in the job market. Through a single-case study, this researchinvestigates how co-design approaches can bridge the gap between the practical needs of the jobmarket and the academic perspectives on future digital competencies. This study addresses acritical research gap by offering insights into effective methodologies for aligning educationalpractices with evolving digital skill requirements.

Steel is a durable material and is 100% recyclable from all stages of a product’s life. When steel scrap is properly processed, it retains most of its properties. With the transformation of the industry towards low-carbon steel production, the adoption of new technological innovations, also regarding the utilisation of scrap, including post-consumer scrap, is of great importance. An accurate chemical analysis of the scrap composition enables tramp- or alloying elements to be used as resources instead of being considered impurities. This work presents a concept to integrate an optical-spectroscopic sensor chain for scrap characterisation into an existing scrap preparation process. X‑Ray Fluorescence (XRF) and laser-induced breakdown spectroscopy (LIBS) are used for chemical analysis and are coupled with optical sensors, RGB‑D camera and Light Detection and Ranging (LiDAR), to simultaneously determine the material volume and mass and asses the presence of some tramp elements. A concept for transferring, synchronising and merging large amounts of data is outlined to develop a machine learning model to support scrap characterisation. First results demonstrate the potential of AI to advance digitalisation in sustainable steelmaking. In order to successfully implement these technologies in companies and to ensure acceptance among employees, their skills must be further developed. The workforce accordingly needs to be prepared and ready to embrace new challenges and learning opportunities. From a socio-technical perspective, such technology can only be introduced optimally through the joint optimisation of people, technology and organisation.

Safe quadruped locomotion over hazardous terrain cannot be reduced to selecting the locally safest foothold, because a geometrically reachable contact may still deteriorate the subsequent support phase, increase actuator demand, approach friction limits, or leave insufficient clearance for the robot body. This work presents STEP-CBF+, a reduced-order predictive foothold-and-posture screening framework that couples terrain-risk memory, DCM/ZMP-based support prediction, discrete-time barrier screening, energy-aware motion regularization, three-joint torque feasibility, friction-cone constraints, and anticipatory body-height adaptation within a single candidate-selection layer. The study considers a 13.5 kg quadruped with 3-DOF legs executing a crawl gait with duty factor 0.75 and evaluating 49 foothold candidates at each decision. Six hazardous-terrain conditions are examined over 25 randomized terrain and payload realizations and compared with nominal, risk-only, and reduced-order convex-MPC strategies. Performance is quantified using independent fall and slip outcomes, distance before failure, dynamic support margins, tracking accuracy, actuator and friction utilization, computation time, and component-wise ablations. Across all scenarios, STEP-CBF+ reduces the mean fall rate to 4.0% while increasing the stable-step fraction to 77.3%, compared with 16.7–18.7% falls and 72.2–73.7% stable steps for the three comparators. A particularly revealing result appears on inclined terrain, where stable time reaches 86.0% and the minimum dynamic margin improves to -0.032 m while peak actuator utilization is reduced. In the confined duck-under scenario, anticipatory posture adaptation enables collision-free traversal and zero falls, whereas all non-height-adaptive strategies fail. The method retains a mean selection time of approximately 13.6 ms, compared with 30.5 ms for the reduced-order convex-MPC implementation. The results indicate that the main benefit of STEP-CBF+ emerges when contact feasibility must be evaluated together with the consequences of the next support phase and body posture, providing an interpretable intermediate safety layer between terrain perception and lower-level locomotion control. The conclusions remain limited to reduced-order randomized simulation and do not constitute a whole-body nonlinear invariance proof or hardware validation.

An integrated hydrometallurgical process was developed for the selective recovery of molybdenum from spent iron-molybdate catalysts generated during formaldehyde production. Ammoniacal buffer leaching was statistically optimized using response surface methodology based on central composite design to evaluate the effects of NH4OH:(NH4)2SO4 molar ratio, total ammonia concentration, pulp density, temperature, and time on molybdenum dissolution. The developed quadratic model demonstrated strong predictive capability and identified the ammonia-ammonium ratio as the most influential parameter. The kinetic analysis revealed that leaching proceeds through a multi-step mechanism involving NH3-NH4+ coupled solution equilibria, surface reactions, and interfacial transport. Under optimized conditions, 98.3% molybdenum leaching was experimentally achieved with only 2.1% iron co-dissolution in ammoniacal solution. The pregnant leach solution was directly treated by solvent extraction using protonated tri-n-octylamine modified with tributyl phosphate in kerosene. Selective extraction of 96% molybdenum was achieved at pH 5.4 without measurable iron co-extraction. Two-stage counter-current extraction enabled an overall extraction efficiency of 99.96%, demonstrating strong potential for continuous and scalable operation. Quantitative stripping with NaOH solution produced a molybdenum-rich solution (∼103 g·L−1 Mo), from which high-purity sodium molybdate (Na2MoO4.2H2O) crystals were recovered by evaporative crystallization. The integrated process combines NH4OH (NH4)2SO4 leaching, liquid-liquid separation, and crystallization techniques into an efficient hydrometallurgical route for recovering molybdenum from spent Fe Mo catalysts and provides a promising strategy for the efficient utilization of secondary molybdenum resources.

Mining is entering a phase in which decarbonisation, safety, and productivity targets are being pursued through tightly coupled technological and governance changes. This paper develops and applies an integrated socio-technical framework that links major technology families (automation/robotics, AI (artificial intelligence) and digital twins, advanced drilling and blasting, electrification/renewables, and frontier domains such as deep-sea and space mining) to sustainability outcomes across underground, open-pit, processing, and tailings/closure contexts. Using an interdisciplinary synthesis of peer-reviewed research, industry evidence and policy principles (including Sustainable Development Goals (SDGs) and International Council of Mining and Metals (ICMM) Mining Principles), the study moves beyond a catalogue of innovations by making boundary conditions and second-order effects explicit. As an example, autonomy can reduce exposure to hazards but increase dependence on cyber-physical assurance, connectivity, and functional safety verification; electrification can eliminate underground diesel emissions, yet its net climate benefit and operational resilience depend on charging logistics, power quality, and grid carbon intensity. A maturity–impact mapping is introduced to distinguish scaled technologies from pilots and to prioritise where evidence is transferable versus context-dependent. The framework is grounded in 25 global case studies spanning regions, commodities, and organisational types, enabling comparative interpretation of what works, where, and why. The central contribution is a decision-relevant synthesis that identifies integrated “solution bundles” and the enabling infrastructure, workforce transitions, and governance mechanisms required for credible scale-up, providing a practical agenda for achieving safer, lower-carbon, and socially accepted mining systems.

Artificial intelligence (AI) in mineral processing has developed almost entirely within a single methodological family: predictive models that convert measured inputs into numerical outputs. Since 2022, large language models (LLMs) and agentic AI systems have transformed adjacent fields, including chemistry, materials science, and geoscience, by enabling reasoning over unstructured evidence and autonomous multi-step action. Mineral processing has not yet adopted these capabilities. To the best of the author’s knowledge, this paper is among the first to systematically position the AI trajectory in mineral processing against this broader trend, rather than treating LLMs as an incremental extension of existing predictive modeling workflows. The paper identifies five cross-cutting opportunity areas, each grounded in a documented precedent from an adjacent field: agentic multimodal process copilots, domain-specific foundation models, self-driving laboratories, retrieval-augmented knowledge synthesis, and language-enabled digital twins. The paper also identifies five structural barriers explaining the current adoption lag, including data fragmentation, an unresolved verification methodology, and a workforce gap. These findings are synthesized into a prioritized, phased research agenda. This agenda offers the mineral processing research community a concrete, actionable path into this underexploited capability space, rather than a general appeal to adopt AI.

Electronic waste (e-waste) is among the fastest-growing global waste streams, with 62 million tonnes generated in 2022 and only 22.3% formally recycled, while generation is projected to reach 82 million tonnes by 2030. Fragmented record-keeping, weak chain-of-custody controls, and limited transparency enable illegal dumping, fraudulent recycling claims, and loss of recoverable materials. This paper presents a narrative literature review of blockchain and distributed ledger technologies applied to electronics and e-waste management. Following a structured search and relevance screening of major scientific databases, publisher platforms and grey literature (last search was run on 15 July 2026), 110 sources published between 2017 and 2026 were thematically synthesized across four domains: technological developments, sustainability and circular-economy implications, traceability and compliance mechanisms, and e-waste management applications. Blockchain delivers value primarily through an auditable chain-of-custody built on three pillars: standardized item and batch identities, standardized lifecycle event schemas, and licensed-actor attestations supported by off-chain evidence anchored on-chain. Recurring design patterns include permissioned consortium networks, hybrid storage, and role-based access control. Persistent barriers include data-input integrity, scalability, governance, and cost. Blockchain immutability protects stored records but cannot correct falsified inputs, making trusted data capture and governance the decisive factors for reliability. Adoption should be evaluated against measurable indicators, such as traceability completeness, audit-time reduction, fraud rates, and verified material recovery, rather than assumed benefits.

This study examined whether three tiers of bioanalytical tools, acute toxicity (Microtox®), in-vitro cytotoxicity,and receptor-specific bioassays, can serve as indicators of contaminant burden in sediments from 17 Swedishstormwater ponds which were previously analysed for 259 legacy and contemporary contaminants. Acutetoxicity was rarely observed, and Microtox® responses showed no clear correspondence with chemicalcontamination. In contrast, cytotoxicity derived from methanol-based sediment extracts strongly aligned withchemical contamination indicators, including the number of quantified substances and exceedances ofenvironmental threshold frameworks. Receptor-based assays (estrogenic receptor, aryl hydrocarbon receptorand oxidative stress) revealed widespread biological activities, frequently exceeding benchmark levels derivedfrom reference-compound PNECs, and illustrated mechanistic responses not captured by apical tests. Takentogether, the results show that while Microtox® is poorly suited for assessing hazards in organic-rich stormwatersediments, cytotoxicity and receptor-based bioassays provide sensitive and complementary insights into mixtureeffects and contaminant loading. These findings support integrating effect-based tools with chemical monitoringto improve sediment quality assessment.

Mitigating structural failure and improving the fire safety of concrete infrastructure during severe thermal events depends critically on the high-temperature resilience of Portland cement paste. Given the increasing production of Portland blended cements, understanding their high-temperature behavior is crucial for ensuring the safety of building structures. This study investigates the effects of exposure to high temperatures (up to 1200 °C) on Portland cement pastes containing ground granulated blast furnace slag and quartz powder, focusing on their thermal stability and the chemical reactions occurring under these conditions. In situ X-ray diffraction (XRD) with a heating module was employed to observe real-time phase transformations as the temperature increased, supported by ex situ scanning electron microscopic analysis. The results showed changes in the mineralogical composition, with particular attention to the decomposition of calcium hydroxide and the formation of melilite above 900 °C. These transformations suggest thermal reactions between cement hydrate products (calcium silicates and aluminates) in the presence of slag and quartz powder. Mixtures containing quartz powder exhibited increased porosity and phase transformation shifts at lower temperatures, reflecting the combined effects of quartz addition, reduced reactive binder content, and an increased effective water-to-binder ratio. Notably, lower strength-grade cements containing fly ash (additional alumina source) show higher degrees of formation of calcium–aluminate silicate phases such as melilite upon heating. Compared to conventional studies, the novelty of this study lies in the use of an in situ experimental setup, which uniquely identifies the temperature thresholds of chemical changes and the formation of new phases such as melilite in cement–selected slag mixes, while also capturing their recrystallization upon cooling.

Background: The relationship between sedentary behavior and cognitive health remains insufficiently understood. The aim of this study was to examine associations between occupational and leisure-time sitting and self-reported cognitive dysfunction.

Methods: Data from men and women, aged 50–64 years old, participating in the Umeå site of the cross-sectional Swedish CArdioPulmonary bioImage Study (SCAPIS) was used. Occupational and leisure-time sitting were measured using a thigh-worn accelerometer over seven days. Work hours were reported in a diary. Cognitive dysfunction was measured using the Cognitive Dysfunction Questionnaire. A Hurdle model was used to firstly assess the association of domain-specific sitting time with any cognitive dysfunction (logistic regression) and secondly with the severity of cognitive dysfunction (beta-binomial regression). Analyses were adjusted for sex, age, educational level, moderate- to vigorous physical activity, sleep duration, risky alcohol intake and emotional health limitations.

Results: A total of 1559 participants were included. In the fully adjusted model, each additional hour of occupational sitting was associated with 8% lower odds of reporting cognitive dysfunction (odds ratio (OR) 0.92, 95% confidence interval (CI) 0.87–0.98), while each additional hour of leisure-time sitting was associated with 12% higher odds (OR 1.12, 95% CI 1.03–1.21). Neither occupational nor leisure-time sitting was significantly associated with the severity of cognitive dysfunction among individuals reporting any dysfunction.

Conclusions: Domain-specific sitting time was associated with the occurrence, but not the severity, of self-reported cognitive dysfunction in middle-aged adults. The contrasting associations observed for occupational and leisure-time sitting suggest that the context of sedentary behavior may be relevant for cognitive health. Future longitudinal studies using device-based measurements are needed to clarify causality and to explore the potential role of cognitively active and passive sedentary behaviors.

Research indicates that managers’ leadership behaviors are associated with both subordinates’ and managers’ health and job satisfaction. The aim of this study is to explore the (dis-)agreements between managers’ self-ratings of their own leadership behaviors and their superior managers’ and subordinates’ ratings of the managers’ leadership behaviors, as well as how (dis-)agreements therein affect the managers’ and subordinates’ health and job satisfaction, in the mining industry. Data was collected using surveys distributed to managers, their superior managers, and their subordinates within a mining company. The ratings were analyzed using descriptive-, correlation-, and logistic regression analyses. Findings are that subordinates rated their managers’ leadership behaviors significantly lower than the two manager groups did. Correlation analysis showed that a larger disagreement between the managers’ and subordinates’ ratings for four leadership behavior indices was significantly negatively associated with subordinates’ health and job satisfaction, but not the managers’. Results were partially supported by logistic regression analyses when controlling for background variables. The findings point at the importance of self-other agreement regarding leadership behaviors, as well as how disagreement of ratings between managers and their subordinates are associated with these groups’ health and job satisfaction. The use of self-other feedback tools can be a driver for change, primarily by offering managers valuable insights into how their leadership behaviors are perceived. These insights can prompt reflective processes involving work environment experts, superior managers, and subordinate groups, thereby forming a foundation for the development of actionable improvement plans.

Projects create value for multiple stakeholders, yet project management research offers limited insight into how this value is converted into economic value appropriated by project-based firms and their stakeholders. Drawing on the Value Creation and Appropriation framework, we develop an accounting-based approach to measure stakeholder value appropriation over time and apply it longitudinally to project-based firms in Finland and Sweden. The findings show that economic value is neither automatically nor evenly appropriated by the focal firm but varies across stakeholder groups and institutional and procurement contexts. Customer appropriation can decline while firms protect appropriation by employees and strategic suppliers. The study links project-level value creation with firm-level economic value appropriation, showing that creation and appropriation can diverge across stakeholders and that stable appropriation by critical stakeholders during turbulent periods may indicate organizational resilience and stakeholder buffering. The study extends the Value Creation and Appropriation framework to project-based firms and provides a replicable longitudinal approach to analyzing value appropriation.

Ionic liquids (ILs), together with the confinement strategy, have emerged as promising electrolyte components for advanced lithium-ion batteries due to their nonvolatility, wide electrochemical windows, and excellent thermal stability. However, the influence of IL confinement on solid surfaces on ion-transport behavior remains unclear. In this work, we investigate how IL-loading confined to the surface of SiO2, modulating the confined IL-layer thickness, affects the electrolyte properties and Li⁺ transport, where the IL confined on SiO2 served as a filler in preparing a PEO-based composite electrolyte. It was found that electrolytes with optimized IL thickness exhibit improved ionic conductivity, Li⁺ transference number, and cycling performance. Specifically, insufficient IL thickness reduces the Li⁺ transport area (insufficient confinement), leading to slow ion transport, whereas excessive IL thickness results in bulk-like behavior, primarily influenced by physical ion crowding, thereby reducing the ion transport rates. This slow ion transport ultimately leads to a decline in electrolyte properties and cycling performance. When achieving the optimized confinement thickness, the surface-induced lithium salt dissociation and available transport space reach a synergistic balance, at which point the Li⁺ transport rate reaches its optimum, thus improving performance. For example, the optimized electrolyte demonstrates significantly enhanced ionic conductivity and interfacial stability. Consequently, the assembled LiFePO4//Li coin cells achieve a 77.7% capacity retention after 700 cycles at 0.5 C. These findings suggest that modulating the IL confinement thickness offers a viable approach to optimizing the ion-conduction channels, providing useful insights into IL–solid interfacial interactions and references for designing high-performance solid-state electrolytes. 

Conventional N-curve SRAM stability metrics—static voltage noise margin (SVNM), static current noise margin (SINM), write-trip voltage (WTV) and write-trip current—are extracted from zero crossings or current extrema of the storage-node 𝑉–𝐼 trace. They can under-report low-voltage stability loss when the N-curve current amplitude decreases more rapidly than its zero-crossing separation, approaching exponential dependence as the critical devices enter weak inversion. This letter introduces two area-integrated indicators, static integrated noise margin (SENM) and write-trip integral, together with bistability collapse voltage and noise charge margin as complementary robustness indicators. To first order, SENM ∝SVNM ·SINM, so SENM captures the joint 𝑉–𝐼 degradation not visible from SVNM alone. Post-layout simulation of a 45 nm 6T cell shows that scaling 𝑉DD from 1.0 to 0.4 V decreases SVNM by 79.0% but SENM by 98.4%; across process corners at 0.6 V, SENM spans 12.3 × versus 1.6 × for SVNM. A 1000-point Monte Carlo yields 𝜇/𝜎 =8.22 for SENM, comparable to SVNM and above the current-domain and writeability metrics. The framework complements, not replaces, conventional N-curve analysis.

Grammatical complexity is a feature of more advanced learner writing and, thus, a common goal of language syllabi and a typical scoring component of several high-stakes exams. In this paper, the aim is to explore the influence of 29 grammatical complexity features and the variation of them on grading during a high-stakes EFL exam in Sweden by analysing a learner corpus of 142 graded example texts and 175 teacher graded texts. The graded example texts are graded by expert raters and included in the assessment instructions as benchmarks that should guide teachers in the assessment of their own students’ texts. The results show that Of phrases had the clearest positive relationship with grade in texts assessed by expert raters and teachers. In contrast, verb + that complement clauses had a negative relationship with grade in the expert assessed texts, but not in the teacher assessed texts. Variation of grammatical complexity features had a positive relation with grade in the expert graded texts. The findings have implications for the design of EFL testing and teaching in Sweden, but also contribute to on-going discussions regarding design of high-stakes exams, language syllabi, and writing teaching pedagogy.

Resin Transfer Moulding (RTM) is widely used to manufacture high-quality CFRP components for aerospace applications. Recently, a novel gapped unidirectional (UD) carbon fibre material has been developed, offering new opportunities for composite design and manufacturing. However, the damage mechanisms and defect sensitivity of RTM-manufactured laminates based on this material system remain largely unexplored. The novelty of this work lies in the investigation of damage initiation, propagation, and compressive performance in RTM-manufactured laminates produced from this newly developed gapped UD material, with particular emphasis on the role of manufacturing-induced defects. Thin Teflon films were embedded at the laminate mid-plane to introduce controlled artificial delaminations, enabling a systematic assessment of their influence on damage initiation, damage propagation, and interlaminar stress redistribution. Progressive tensile and compressive testing, combined with stepwise microscopic inspection, was employed to track damage evolution. The results showed that crack initiation under tensile loading is governed primarily by ply orientation, with matrix cracks consistently initiating in the central 90° plies at strains of 0.4–0.5%. Cracking in the ± 45° plies occurred only at the maximum applied strain (1.2%), indicating a higher resistance to failure. Resin-rich areas and binder-related inclusions were not observed to act as preferential crack initiation sites. In contrast, porosity had a pronounced effect on compressive performance. An overall decrease in compressive strength was observed with increasing void content, with the most porous specimens exhibiting reductions of up to approximately 35–40% compared with specimens containing negligible porosity. Artificially induced delaminations reduced the strain threshold for damage initiation and promoted more extensive damage propagation. These results provide new insight into the role of manufacturing defects in damage initiation, propagation, and compressive failure of RTM-manufactured gapped UD CFRP laminates. The findings contribute to the understanding of defect-sensitive damage mechanisms in this emerging material architecture and support the development of more reliable aerospace-grade composite structures. 

Background: An aging population and an ongoing transition toward person-centered and integrated care highlight the need for health-promoting interventions for older adults, requiring new approaches within municipal health and social care.

Aim: The aim of this study was to explore and describe how professionals in municipal health- and social care discuss opportunities and challenges within their organization related to health-promoting interventions directed at older adults.

Material and Methods: A qualitative focus group design was used. Twenty-two participants, including occupational therapists, development managers, area managers, operations managers, and case managers from seven municipalities, participated in five focus group discussions. Data were analyzed inductively using a focus group analysis approach.

Results: One overarching theme was identified: Creating organizational readiness for health-promoting interventions, comprising three subthemes: Supportive structures for health-promoting work within the organization are needed, A shared health-promoting focus and new collaborations are required, and New working methods are necessary to facilitate health promotion. These subthemes are interrelated together dynamically shape organizational readiness for health-promoting interventions.

Conclusions and Significance: Taken together, The results indicate that organizational readiness within municipal health and social care is currently insufficient for health-promoting interventions to permeate practice yet has inherent potential to be actively created. Creating such readiness therefore requires coordinated, long-term efforts across the organization. For occupational therapy, these results underscore the importance of engaging in ongoing dialogue, both within the profession and across organizational levels, on how their competencies can be utilized and translated from reactive into more proactive and health-promoting approaches into everyday practices.

Thermal radiation emitted by a hydrogen flame is attenuated as it propagates through humid air due to absorption by water vapour contained in the air. This study analyses spectral transmission of energy emitted by the hydrogen flame as it passes through humid air, determining the normalised spectral irradiance, the apparent absorbance, and the percentage of total energy transmitted. The emission spectra of the hydrogen flame and the absorption spectra of humid air are obtained from the HITEMP2010 database, applying the Voigt function under specific conditions for each medium: the hydrogen flame is modelled at 2300K with water vapour mole fraction of 22%, while the humid air is modelled at 300K with relative humidities of 10%, 50% and 100%. The results indicate that humid air can absorb between 10% and 20% of the energy radiated by the hydrogen flame in the first metre of propagation, with total absorption between 20% and 40% over a 10-metre path, depending on relative humidity. Spectral analysis shows that attenuation of total radiation does not follow the exponential behaviour predicted by the Beer–Lambert law, attenuation being determined by spectral mismatch between emission bands of the hydrogen flame and absorption bands of the humid air.

Authorship analysis plays an important role in diverse domains, including forensic linguistics, academia, cybersecurity, and digital content authentication. This paper presents a systematic literature review on two key sub-tasks of authorship analysis; Authorship Attribution and Authorship Verification. The review explores SOTA methodologies, ranging from traditional ML approaches to DL models and LLMs, highlighting their evolution, strengths, and limitations, based on studies conducted from 2015 to 2024. Key contributions include a detailed analysis of methods, techniques, their corresponding feature extraction techniques, datasets used, and emerging challenges in authorship analysis. The study highlights critical research gaps, particularly in low-resource language processing, multilingual adaptation, cross-domain generalization, and AI-generated text detection. This review aims to help researchers by giving an overview of the latest trends and challenges in authorship analysis. It also points out possible areas for future study. The goal is to support the development of better, more reliable, and accurate authorship analysis system in diverse textual domain.

Soil erosion threatens the sustainability of semi-arid watersheds. Sub-watersheds are routinely ranked for intervention, yet the reliability of those ranks is rarely quantified. This study assesses erosion risk across the 3,288 km² Al Khazer River Watershed in northern Iraq and measures the stability of the resulting priorities. Fifteen morphometric parameters were derived for 32 sub-watersheds from a 12.5 m ALOS PALSAR DEM, Sentinel-2 imagery and climate-station records. Criterion weights were obtained by the Analytical Hierarchy Process (consistency ratio = 0.0537). These weights were applied within four ranking models: VIKOR, TOPSIS, SAW and WASPAS. Two erosion models were then applied independently: the Erosion Potential Method (EPM) and the Revised Universal Soil Loss Equation (RUSLE). RUSLE agreed more closely with field observations (Observed-to-Estimated Agreement Ratio (AR) 78.1% and 86.96%) than EPM (49.16% and 53.0%). It was therefore adopted as the internal benchmark for validating the ranking models. All four models identified sub-watersheds 6, 4, 2 and 1 as the highest-priority zones. These watersheds combine steep slopes, high relief and elevated orographic precipitation in the northern mountains. Ordinary least squares regression showed the distance-based methods to correspond most closely with RUSLE (VIKOR R² = 0.763, TOPSIS R² = 0.754), with the two statistically indistinguishable (ΔAIC = 1.21). Input uncertainty was then propagated through the prioritization using 100 Monte Carlo iterations at perturbation levels of ± 10%, ± 20% and ± 30%. Classification stability fell from 96.9% to 84.4% for RUSLE and from 87.5% to 75.0% for TOPSIS. The four highest-priority sub-watersheds retained their classification at every perturbation level. The framework identifies erosion hotspots using only openly available terrain, satellite and climate data. It therefore offers a practical basis for targeting soil conservation investment, for routine environmental monitoring of erosion risk, and for guiding land management and land-use planning in ungauged semi-arid watersheds where field measurement is scarce.

The aim of this project was to develop flame retardants that are safe for both the environment and human health. In the project, harmless molecules that can be found in nature and in various waste streams were selected and investigated to develop flame-retardant candidates for cellulose-based textiles. Among the compounds investigated, creatinine showed particularly promising flame-retardant properties. Combining creatinine with phosphorylation further improved the fire performance of cotton, viscose, and linen, with the strongest effect observed for viscose. The treatments considerably reduced heat release and promoted char formation, although their durability after water exposure still needs to be improved.

Rock mass classification is fundamental to underground engineering design, and accurate conversion between the Q-system and rock mass rating (RMR) is important where the two systems are used together. This study assembles a database of 448 paired Q-system–RMR records from field measurements in Zimbabwe and published tunnel case histories. Statistical reference models are first used to reassess classical conversion formulas. The recalibrated linear relation RMR = 6.70ln(Q) + 46.31 outperforms the two most widely used traditional formulas, while the spline model reveals marked nonlinearity at low and high Q levels, indicating that a global linear relation is insufficient for reliable conversion. To improve predictive performance, multiple regression models are systematically compared, and the extreme gradient boosting model optimized by the opposition-based gravitational-search algorithm (OGSA–XGBoost) is selected, which achieves coefficients of determination (R2) of 0.9506 and 0.9382 on the validation and test sets, together with leading hit rates under several tolerance thresholds and residuals dominated by random scatter rather than systematic bias. Interpretation analysis indicates that rock quality designation (RQD) and joint roughness (Jr) contribute positively to predicted RMR, whereas joint alteration (Ja) and the stress reduction factor (SRF) exert negative effects. Under high stress or weak structure conditions, the adverse effects of Ja and SRF can partially offset the benefits of high RQD and favorable joint roughness. Overall, the results clarify the applicability of classical formulas under the present database and provide an interpretable framework for Q-system to RMR conversion under engineering conditions represented by this database.

The Q-system and rock mass rating (RMR) are widely used for rock mass classification in tunnels and underground excavations. Reliable prediction of RMR from Q-system parameters can support cross-checking between different classification systems. However, empirical Q–RMR correlations may not fully describe nonlinear effects among individual Q-system parameters. Meanwhile, flexible machine learning models may produce optimistic accuracy and engineering-inconsistent responses when repeated-input states are shared between training and testing data. To address these issues, this study develops a physics-consistent and duplicate-aware machine learning framework for continuous RMR prediction from six Q-system parameters, namely RQD, Jn, Jr, Ja, Jw, and SRF. The framework integrates monotonic gradient boosting, duplicate-aware grouped validation, boundary-sensitive diagnosis around the Poor–Fair transition threshold, and interpretable analysis using SHAP and accumulated local effects. A published field-derived dataset with 356 observations was used for model development. The response distribution was strongly imbalanced: 345 observations (96.9%) were within the Fair range, whereas only 11 observations (3.1%) had RMR ≤ 40. The observed RMR range was 36.00–59.43, and no Very Poor, Good, or Very Good rock mass conditions were represented. The data audit showed a strong repeated-input structure, with only 184 unique six-parameter combinations. Under conventional random splitting, boosting models showed high apparent accuracy, but their performance decreased under duplicate-aware grouped validation. This indicates that repeated Q-system states can lead to optimistic performance estimates if they are not considered during validation. Under duplicate-aware validation, CatBoost achieved the best overall numerical accuracy, while Monotonic XGBoost provided a better balance between prediction performance and engineering consistency. The selected monotonic model eliminated all observed monotonic violations and reduced the boundary-crossing error rate from 53.85% to 38.46% compared with the best unconstrained model. External validation on an independent high-geostress tunnel dataset further showed acceptable transferability, with an R2 of 0.8650. SHAP and ALE analyses identified Ja and SRF as the dominant contributors and confirmed that the learned response directions were consistent with rock engineering knowledge. The results demonstrate that validation reliability and engineering consistency are as important as predictive accuracy in machine learning-based rock mass classification. The proposed framework provides a transparent and physically meaningful tool for supporting RMR estimation from Q-system data, especially within the investigated Fair-dominated range and near the Poor–Fair transition boundary. Accordingly, the proposed model should not be interpreted as an all-class RMR predictor. Its validated scope is limited mainly to continuous RMR estimation within the investigated Fair-dominated range and exploratory diagnosis near the Poor–Fair transition.

Ambulance services in sparsely populated circumpolar regions operate over large distances with limited access to alternative care, potentially expanding their role beyond transport to assessment, decision-making, and healthcare navigation. In such settings, conveyance decisions involving children and young people (CYP) may be complex for ambulance clinicians. This retrospective study examined factors associated with non-conveyance and subsequent healthcare contacts among CYP assessed by ambulance clinicians in northern Sweden and compared characteristics of conveyed and non-conveyed patients. Assessments of patients aged 0–17 during 2020–2023 were included. Logistic regression identified factors associated with non-conveyance, and unscheduled healthcare contacts within 72 hours were examined. Non-conveyance occurred in 39% of assignments. Non-conveyed patients were younger and more often assessed for respiratory conditions and infections, whereas conveyed patients more commonly presented with trauma and neurological conditions. Younger age, assignments outside office hours, and rural location were associated with non-conveyance. One quarter of non-conveyed patients had an unscheduled healthcare contact within 72 hours. The findings suggest that non-conveyance is part of a broader healthcare pathway rather than solely the end of an ambulance assignment. Child-specific guidance for assessment, referral, and follow-up, alongside strengthened decision support for ambulance clinicians, may contribute to safe non-conveyance decision-making in remote regions.

Rationally switching reaction pathways to selectively produce diverse value-added chemicals is crucial for electrochemical CO2 reduction reactions (CO2RRs). However, such a goal is usually achieved by catalyst design, and it remains a challenge for electrolyte engineering. Herein, two structurally similar ionic liquids (ILs) differing only in the C2 substituent (H or –CH3) of the imidazolium cation, namely, 1-butyl-3-methylimidazolium hexafluorophosphate ([BMIM][PF6]) and 1-butyl-2,3-dimethylimidazolium hexafluorophosphate ([BMMIM][PF6]), were employed as electrolytes to modulate the product selectivity of CO2RR over a bimetallic Fe5Sn1NC electrocatalyst. It was found that CO2 reduction product was significantly switched between CO (Faradaic efficiency (FE) = 98.9%) and HCOOH (FE = 99.7%) via changing the IL cation from [BMMIM]+ to [BMIM]+ in the electrolyte, and the current density in the H-cell reached up to 93.7 mA·cm–2 at −2.2 V and 181.4 mA·cm–2 at −2.6 V (vs Ag/Ag+), respectively. Linear sweep voltammetry curves, H/D exchange, in situ/ex situ infrared spectroscopy, and density functional theory calculations suggest that the significant product switching between CO and HCOOH originates from the CO2 reorientation on the catalyst surface driven by different active sites in the imidazolium cations of the ILs, which leads to different reaction pathways in CO2RR.

Heterogeneity in the physical properties and processing behavior of lignins recovered from biorefining process streams can hinder their effective utilization and industrial valorization. This work investigates lignin recovery by acidification from alkaline pretreatment liquors of varying sources (corn stover and hybrid poplar), compositions, solid contents, and incubation temperatures prior to filtration. Recovered lignins were characterized for filtration performance, mass yield, chemical composition, particle size and morphology, and color. A transition temperature was identified corresponding to marked shifts in lignin physical properties and was a function of the solid content and liquor source. Filtration below this temperature yielded slow-filtering, dark, brittle lignins with smoother particle surfaces, whereas precipitation above this threshold yielded fast-filtering, lighter, powdery lignins with rougher particle surfaces. The impact of temperature and concentration on universal cluster aggregation mechanisms was proposed to explain the differences between lignin particle properties and filtration behavior.

Background: The final semester of nursing education is a stressful period as students prepare to transition from academic training to professional practice. This transition requires readiness, including clinical competence, psychological preparedness, and the ability to apply theoretical knowledge in patient care.

Objective: To explore how nursing students nearing graduation describe their transition into their future roles as registered nurses.

Design: Qualitative exploratory design.

Setting: A university in Sweden.

Participants: Twelve final-semester nursing students.

Methods: Data were collected through focus group interviews (n = 3) and individual interviews (n = 5) between November 2022 and January 2023. Inductive qualitative content analysis was applied.

Results: One overarching theme emerged: Leaving security—transitioning into an unpredictable storm with a feeling of ambivalent readiness. Five categories were identified: Reflecting on the nurse’s role, Feeling ready but insecure, Continued learning process after graduation, Challenges and concerns regarding future profession, and Dealing with uncertainty in the new role.

Conclusions: Students felt prepared and eager to begin professional practice while recognizing its complexity and challenges. They remained confident, humble, and optimistic in facing their new role.