Models can help to obtain the desired properties of steel by predicting when different microstructures form during phase transformations in manufacturing processes. One prominent model for low-alloy steel is the Kirkaldy–Venugopalan model but it has not been evaluated for wear-resistant ultra-high-strength steels (UHSS). A modified Kirkaldy-type model was developed in this work for the phase transformation kinetics in a wear-resistant UHSS. A modified incremental Koistinen–Marburger model was used for the martensite transformation which considers the gradual start of the transformation. The framework was validated by simulating the dilatometry experiments in a finite element model. Good agreement was obtained for the low cooling rates 2.5 to 15 °C/s yielding ferrite, pearlite, and bainite, as well as for the high cooling rates 20 to 50 °C/s yielding bainite and martensite. The model was also applied to the steel Hardox 450 where it predicted the formation of 99.7% martensite at the experimental critical cooling rate for full martensite formation of 12 °C/s found in the literature, which demonstrates the model’s capability to be used more generally on wear-resistant UHSS. The predicted hardness also captured the general trend seen in the hardness measurements.

The transition to a climate-neutral economy has intensified demand for critical raw materials (CRM), prompting the European Union to adopt the Critical Raw Materials Act (CRMA) to secure supply and promote circularity. Among its objectives, the CRMA seeks to accelerate the recovery of CRM from extractive waste. However, the implementation of these ambitions depends on existing legal frameworks that were not designed for large-scale resource recovery. This article examines the institutional and legal challenges arising from the interaction between the CRMA and key areas of EU environmental law, with a particular focus on the Water Framework Directive and EU waste legislation. Through a doctrinal analysis supported by case law and examples from northern Sweden, the paper demonstrates how overlapping regulatory regimes create structural tensions between the CRMA's acceleration logic and established environmental safeguards. Water law imposes strict non-deterioration requirements that may delay or prevent project authorization, while waste law introduces significant uncertainty regarding material classification and marketability. The article identifies structural regulatory tensions: While the CRMA promotes the recovery of CRM and advances circularity and net-zero ambitions, it operates within legal frameworks that impose stringent constraints. In doing so, it effectively collides with environmental protection rules that pursue the same overarching goals, risking the obstruction of circular solutions. This tension reflects deeper inconsistencies in EU law and calls for greater legal coherence, clearer rules, and better alignment between environmental and industrial objectives.

Background: Anxiety-related symptoms are prevalent and can negatively affect concentration, motivation, and overall well-being. Traditional treatments such as cognitive behavioral therapy and medication work well for clinical anxiety disorders. However, individuals with anxiety often struggle with access, adherence, and staying engaged in treatment. Emerging technologies such as virtual reality (VR) and gamification offer new opportunities to enhance user engagement and motivational processes within digital mental health applications.

Objective: This study introduces Cleanify, a gamified VR cleaning simulation designed using the Octalysis framework and the persuasive system design model. The objective was to evaluate whether gamification elements improve user engagement, focus, and satisfaction compared to a nongamified version among individuals experiencing anxiety symptoms. We hypothesized that the gamified version would outperform the nongamified version in enhancing user engagement, immersion, and overall user experience.

Methods: A pilot experimental study was conducted with 50 participants aged 18 to 39 years recruited from the general population in northern Sweden. Participants were randomly assigned to either a gamified or nongamified version of the Cleanify VR application and completed a single 15-minute VR session using the Oculus Quest headset. Baseline anxiety symptoms were assessed using the Generalized Anxiety Disorder–7 scale for descriptive purposes only. Postintervention outcomes included focus and immersion measured using the Flow State Scale and user experience measured using the short version of the User Experience Questionnaire. Group differences were analyzed using 2-tailed independent-sample t tests.

Results: Participants using the gamified VR version demonstrated higher engagement and immersion than those using the nongamified version. The gamified group reached higher in-game levels overall, with a greater proportion of participants reaching level 3 (17/25, 68% vs 8/25, 32%), and reported higher recommendation scores (mean 4.20, SD 0.76 vs 3.36, SD 0.86). Significant group differences were observed for overall flow (t48=3.87; P<.001), fluency (t48=4.36; P<.001), and absorption (t48=2.80; P=.008). User Experience Questionnaire results indicated higher pragmatic quality, hedonic quality, and overall user experience in the gamified condition.

Conclusions: Integrating gamification into a VR environment significantly enhanced user engagement, focus, and immersion in this pilot sample. These findings provide preliminary evidence that gamified VR design elements can positively influence user experience outcomes. Further research incorporating longitudinal designs and clinical outcome measures is needed to determine potential relevance.

The increasing demand for personalized, non-invasive, and real-time medical monitoring has motivated the integration of digital twin technologies into healthcare systems. This paper proposes a Digi-Phy Twin, an augmented framework that couples digital and physical twins into a closed-loop, resulting in an adaptive system for medical applications. The physical twin represents the real-world anatomical and physiological structure of the head, while the digital twin incorporates physics-based modeling, data-driven analytics, and artificial intelligence to replicate underlying mechanisms and estimate internal states. Real-time data exchange between the physical and digital domains enables continuous learning, predictive analysis, and feedback-driven adaptation. The proposed architecture supports multimodal data fusion, real-time prediction, and visualization, facilitating personalized monitoring and clinical decision support. Key challenges related to privacy, interoperability, scalability, and power-efficient computation are discussed. The Digi-Phy Twin framework establishes a foundation for next-generation intelligent healthcare systems and demonstrates strong potential for non-invasive brain monitoring and precision medicine applications. 

A search is performed for dark matter produced in association with bottom quarks and a pair of electrons or muons in data collected with the CMS detector at the LHC, corresponding to 138 fb−1 of integrated luminosity of proton-proton collisions at a center-of-mass energy of 13 TeV. For the first time at the LHC, the associated production of a bottom quark-antiquark pair and a new heavy neutral Higgs boson (H) that subsequently decays into a leptonically decaying Z boson and a pseudoscalar (a) is explored. The latter acts as a dark matter mediator in the context of the two Higgs doublet model plus a pseudoscalar (2HDM+a). Multivariate techniques that target a wide range of mass configurations for the H and a particles are used. The observations are consistent with the expectations from standard model processes. Upper limits at 95% confidence level are set on the product of cross section and branching fraction of the new particles, ranging from 10−2 pb for an H mass of 400 GeV to 10−3 pb for an H mass of 2000 GeV. Constraints on the parameter space of a benchmark 2HDM+a model are derived and compared with expectations in the context of cosmological predictions.

The geometrical process conditions of thin foils in laser cutting result in higher local surface tension forces, increasing the susceptibility to dross attachment. Drop-like dross formation patterns and modes in laser cutting of 100 μm thin stainless steel foils were investigated using high-speed imaging and run length analysis. Laser power and cutting speed significantly influenced dross formation. Three distinct dross patterns were identified: an alternating side pattern, single-sided formation, and irregular formations on both sides. These patterns, observed via bottom-view high-speed imaging, correspond to the three melt flow modes A, S, and I respectively, each associated with different line energy regimes. Mode S tends to occur at low line energy, with about 3 ms duration until drop completion, while Mode A is more likely at high line energy, with about 18 ms completion time. A run length was introduced, where analysis revealed up to 36 consecutive dross deposits within the single-sided formation pattern. The preferred movement of the melt, either to the one or to the other cut edge, is based on details of the complex fluid mechanics, which could partially be explained, aided by high-speed imaging. Occasional drop merging events were observed during single-sided formation when molten dross remained in contact with a newly formed drop before solidification. Dross diameter and kerf width were measured, as key criteria for controlled laser cutting. These findings offer new insights into how process parameters govern dross behavior in thin-sheet laser cutting, aiming to keep one side dross-free.

As shallow mineral resources become progressively depleted, mining activities are increasingly extending to greater depths. Rockburst incidents induced by the high-stress conditions associated with deep mining have become a major threat to personnel safety and operational continuity. Destress blasting is widely regarded as one of the most effective methods for reducing rockburst risk. It uses controlled explosions to fracture the rock mass ahead of the working face and redistributes concentrated stresses away from the working face. However, accurately evaluating its effectiveness remains a major challenge. Building upon existing numerical simulation results obtained using the LS-DYNA finite element software, this study employed the 3DEC discrete element software to perform dynamic simulations of destress blasting. A new energy-based quantitative evaluation method was developed and compared with conventional stress-based evaluation approaches. The findings reveal that dynamic loading can more accurately simulate the blasting effects, whereas traditional single-stress indicators cannot effectively assess destressing effectiveness. In contrast, the proposed energy-based method accounts for the 3D stress state of deep underground rock masses and provides a more comprehensive assessment of destress blasting performance. The differences in destress blasting effectiveness revealed by comparing various cross-sections and sampling intervals were also analyzed, and a recommended method for evaluating destress effects using energy density was proposed. The findings provide important insights into the accurate assessment of destress blasting effectiveness in deep mining operations.

Tailings affected by neutral mine drainage (NMD) have received comparatively little attention despite their potential to release trace elements, including rare earth elements (REE) and sulphate to downstream hydrological systems. This study presents a comprehensive geochemical characterisation of apatite iron ore (AIO) tailings from Kiirunavaara mine in northern Sweden, providing baseline geochemical and mineralogical data needed to evaluate the long-term stability of REE and other elements of potential concern in NMD conditions. Three vertical cores (KI_01, KI_02, and KI_03; up to 10 m deep) and groundwater samples were examined. The tailings were dominated by silicate minerals (ca. 74 wt%, albite, biotite, quartz, actinolite) with smaller amounts of carbonates (calcite, dolomite), sulphides (pyrite, chalcopyrite) and sulphates (gypsum, anhydrite). Hydraulic sorting during deposition created clear textural and compositional differences, with coarser material near the discharge points and finer material further away. The tailings contained average light REE and heavy REE content of 1100 ppm and 180 ppm, respectively, hosted in primarily unaltered apatite with minor monazite and allanite. This is supported by strong correlations between REE, P, F and As, and by intact grain boundaries in mineral analyses. The groundwater in the tailings was circumneutral to alkaline (pH 6.8–9.7) and mostly anoxic. Dissolved REE concentrations in groundwater were low, with ΣREE average value of 0.29 µg/L, showing that REE mobility is limited under current conditions. Sulphide minerals displayed minimal oxidation, reflected by low dissolved O2 and the absence of secondary iron phases, suggesting restricted oxygen ingress. In contrast, gypsum and anhydrite showed signs of dissolution, contributing to elevated sulphate and calcium in groundwater, especially during spring melt. 

Förekomst av järnsulfider i berg som bryts för infrastrukturprojekt har på senare åruppmärksammats som en potentiell miljörisk, eftersom oxidation av sulfidmineral kan ge upphovtill sura och metallhaltiga lakvatten. Trots att den totala mängden berg som hanteras inominfrastruktursektorn är mindre än avfallsvolymerna från gruvindustrin är kunskapen begränsadom hur sulfidförande bergmaterial kan användas utan att orsaka negativa miljöeffekter. Dettaprojekt (2021–2024) har finansierats av Trafikverket och syftar till att öka förståelsen förgeokemiska processer i sulfidförande berg. Studierna omfattar främst metamorfa sedimentärabergarter med förhöjda svavelhalter jämfört med den genomsnittliga kontinentala jordskorpan.De dominerande sulfiderna är pyrit och magnetkis, medan mindre mängder kopparkis, blyglansoch zinkblände också har identifierats. Mineralogiska och geokemiska analyser har genomförtsmed bland annat polarisationsmikroskopi, mikro-XRF, SEM, EMPA och ICP-teknik för attkarakterisera både bergmaterial och lakvatten.Kinetiska lakningsförsök har utförts på bergmaterial från Förbifart Stockholm och tunnelbyggemed material från Tunnelbygge Högdalen. Resultaten visar att magnetkis vittrar betydligtsnabbare än pyrit, som förblev i stort sett opåverkad efter fyra års lakning av Förbifart Stockholm.Bergmaterialet från Tunnelbygge Högdalen uppvisar fortfarande neutrala förhållanden och lågutlakning av metaller. Sulfider som är inkapslade i mer motståndskraftiga mineral, såsom kvarts,visar lägre benägenhet att oxidera än frilagda sulfider eller sulfider inneslutna i lättvittrade mineral,såsom biotit. Utlakningen domineras av metallerna Cu, Co och Ni, vilka främst är associerademed magnetkis. Inledningsvis har karbonater och cement buffrat pH och därmed fördröjtförsurningen. Kinetisk lakning begränsas av att försöken är både tidskrävande och kostsamma,vilket kan försvåra tillämpningen som beslutsunderlag i projekt med korta planerings- ochgenomförandetiderFältstudier av tidigare bergupplag i Kil, Hemavan och Uddevalla visar att sura och metallhaltigavatten kan förekomma långt efter att upplagen har avlägsnats. Förhöjda halter av bland annat Cu,Co, Ni och Al har uppmätts nedströms upplagsområdena. Det är dock osäkert om själva upplagenutgör den enda källan eller om bidraget från sekundära utfällningar och omkringliggande upplagär betydande. Resultaten visar att långvarig lagring av krossat sulfidförande berg i öppna upplagskapar gynnsamma förhållanden för sulfidoxidation genom hög exponering för syre ochbetydande vattentransport.Projektet visar också att de statiska testmetoderna ABA och NAG ofta ger osäkra resultat för bergmed låga halter av sulfider och karbonater. Bedömningen av miljörisker bör därför baseras på enkombination av geologiska, mineralogiska och geokemiska analyser. Något generellt gränsvärdeför svavelhalt som avgör om ett bergmaterial är lämpligt att använda är svårt att fastställa, eftersombland annat när och hur länge ett surt lakvatten kan förekomma beror på mängden bergmassorsom är begränsad i exempelvis en vägkonstruktion. En möjlig strategi är att använda sulfidförandeberg i vägkonstruktioner, där kompaktering och asfalttäckning kan begränsa syretillförseln ochdärmed minska sulfidoxidationen och utlakningen. Kunskapen om geokemiska processer i sådanakonstruktioner är dock fortfarande begränsad och ytterligare forskning behövs. Reaktivtransportmodellering är ett lovande verktyg för att förutsäga långsiktig sulfidoxidation ochutlakning under olika förhållanden för syre- och vattentillgång. Vetenskapliga publikationer omdetta arbete är under framtagande och kommer att komplettera rapportens resultat.

This study investigates whether a naturally formed dry crust in sulfide-rich soil can serve as abarrier that limits oxygen and water penetration, thereby reducing sulfide oxidation, and isfunded by the Swedish Transport Agency (Trafikverket)t. Sulfide soils contain iron sulfides that,when exposed to oxygen and moisture, generate acidity and mobilize metals. In many miningenvironments, prolonged oxidation leads to the formation of hardpan, a dense, cemented layerdominated by secondary minerals such as Fe-oxyhydroxides and gypsum, which can restrict thetransport of water and oxygen. The present work examines whether the dry crust identified inÖverkalix, Sweden, exhibits similar properties.Soil samples were collected from the crust and analyzed chemically and mineralogically usingICP-SFMS, XRD, and SEM-EDS; the results were compared with those from previouslystudied acid sulfate soils (ASS). The dry crust contained very low sulfur but high iron andphosphorus, indicating nearly complete sulfide oxidation and extensive secondary mineralformation. XRD confirmed a silicate-dominated matrix with 4.5% ferrihydrite. SEM-EDSrevealed Fe-O-rich precipitates and accessory minerals, including REE-bearing phosphates.Compared with ASS profiles, the crust showed elevated As and Cu, consistent with strong metalretention by Fe oxyhydroxides.Despite high Fe content and fine-grained structure, the dry crust did not qualify as a hardpan. Ithad unusually high water content (~49%), lacked cementation, and could not be recovered as anintact monolith, rendering oxygen diffusion testing impossible. Its behavior suggests that whilethe crust can temporarily retain metals through adsorption and co-precipitation, rewetting mayremobilize them. Thus, although the crust shows characteristics favorable for potential barrierformation, it does not currently exhibit the mechanical strength or low permeability associatedwith hardpans. Understanding these processes, however, may help guide strategies tointentionally induce hardpan-like stabilization in sulfide-bearing soils.

More than two-thirds of dementia cases are attributed to Alzheimer’s disease (AD), while the remaining cases include frontotemporal dementia (FTD), vascular dementia, and other related disorders. Electroencephalography (EEG) is among the most cost-effective methods for supporting the diagnosis of these conditions and can serve as a valuable source of information for AI-assisted diagnostic systems. This paper focuses on the classification of EEG data from patients with FTD, Alzheimer’s disease, and healthy controls. Our study focused on two key issues in this setting. First, the reliable differentiation between FTD and AD. Secondly, EEG data are noisy, difficult to Pre-process, and often limited in their ability to capture long-range relationships. Modern Hopfield networks offer a promising direction because they are effective in pattern storage and retrieval and are closely related to attention mechanisms. In this work, four neural network architectures integrated with modern Hopfield networks are investigated on a publicly available dataset. A standardized workflow was adopted so that all models were trained and evaluated under identical conditions. The models were assessed using 5-fold stratified cross-validation together with hold-out evaluation. The best-performing model achieved 96% accuracy in the present experimental setting. Overall, the results show that the more expressive Hopfield-based architectures improve performance within the proposed model family and suggest that modern Hopfield networks are a promising component for EEG-based dementia classification. 

With the advancement in computations and the introduction of renewable energy sources, flexible demands can play the main role in load frequency control. Some industrial and domestic loads can provide synthetic inertia that aids the natural inertia provided by conventional generators. This enhances small signal stability performance, as the total system inertial would be larger. The load frequency control (LFC) system with flexible demand-side control capability, in general, forms a time delay system. In this paper, a new approach to analyse the stability of the LFC system with controllable loads and communication delay is proposed by transforming the transcendental characteristic equation to a nonlinear equation in the complex frequency domain. The marginal frequencies are used to determine the DM. The sweeping test, together with the binary iteration algorithm, is implemented to find these marginal frequencies, yielding accurate DM computation. The method offers advantages in terms of accuracy and easy implementation. The paper also examines the influence of the flexible demand response on frequency stability as the flexible load participation factor is increased beyond 0.2. As the flexible demands participation factor is increased beyond 0.2, the stability of the system becomes delay-dependent. 

Sulphide soil is an organic soil characterised by high water content and poor geotechnical properties. When excavated, it oxidises and becomes an environmental hazard due to leached metals and acid drain. To avoid excavation, methods for utilizing more sulphide soil as a subgrade material are being developed. However, precise characterisation of sulphide soil is challenging, as its inherent properties make it prone to sample disturbance, introducing large scatter into geotechnical test results. To minimise the scatter in laboratory test results, a portion of the characterisation could be based on reconstituted samples. This study explores the applicability of the slurry deposition method to produce homogeneous, repeatable and representative sulphide soil samples. The reconstituted samples were assessed by comparing their initial index properties and triaxial behaviour against those of the intact samples. The index properties of the tested reconstituted samples precisely and accurately matched the average results of the intact samples. The undrained triaxial behaviour and derived critical state line of the reconstituted samples and the intact samples were found to be comparable. Neither type of sample reached critical state in drained triaxial testing. In conclusion, this study suggests that the slurry deposition method is suitable for reconstituting sulphide soil samples.

Calcination, where CaCO3 is thermally decomposed to CaO and CO2, is one of the most important chemical reactions. In this work, a CFD model is developed to investigate the flash calcination behavior of lime mud under various temperatures and gas atmospheres. Simulation results are compared with experimental data obtained from a pilot-scale flash calcination experimental campaign conducted in a drop tube furnace at the RISE site in Piteå. The simulated flash calcination temperature ranges from 700 to 1350 °C, with 50 °C increments, under different atmospheric conditions: 100% N2, CO2, and H2O vapor, as well as a 50/50 mixture of CO2 and H2O vapor. The comparison shows overall good agreement between simulations and experiments, with most flash calcination thresholds accurately captured, particularly in cases involving CO2-containing atmospheres. The particle residence time, temperature, and the presence of CO2 and H2O vapor were identified as the most influential parameters affecting flash calcination conversion and the evolution of the specific surface area.

Sugarcane bagasse (SCB), a recalcitrant lignocellulosic biomass, necessitates pretreatment to enhance the release of soluble organics for effective utilization. This study evaluated three pretreatment methods including alkaline (ALK), acid (AC) and hydrothermal (HTL), to extract sugars from SCB, subsequently evaluating these hydrolysates as substrates for bioelectricity generation in dual-chamber microbial fuel cells (MFCs). The study was conducted in two phases. Phase I utilized tap water to dilute the SCB hydrolysate, while Phase II replaced tap water with sewage. Results from Phase I indicated that ALK hydrolysate yielded the highest current density (414.00 mA/m²), followed by AC (339.00 mA/m²) and HTL (316.13 mA/m²). The corresponding chemical oxygen demand (COD) degradation rates were 57.60% for ALK, 46.67% for AC, and 37.30% for HTL hydrolysates. Phase II introduced sewage as a diluent, enhanced HTL hydrolysate performance (415.05 mA/m²; 53.50% COD removal) due to improved ionic conductivity and nutrient availability, which fostered better biofilm formation and electron transfer. The blending of AC and ALK hydrolysates facilitated in-situ pH neutralization, optimizing substrate complexity and buffering stability, culminating in a peak specific power yield of 1011 W/kgCOD. Cyclic voltammetry (CV) confirmed the development of an electroactive biofilm, indicating effective electron mediation. The study demonstrated that integrating optimized pretreatment with cost-effective methodologies, such as sewage repurposing can amplify energy recovery from SCB. This approach not only improves green energy production but also aligns with circular bioeconomy principles by valorizing agricultural residues and wastewater, presenting a scalable model for sustainable bioenergy systems.

Generative AI is rapidly entering introductory programming, yet evidence about how learners coordinate with AI, especially in dyads, remains limited, and open datasets that support reproducible, trace-based evaluation are scarce. I present ASTRA (Adaptive Socially-intelligent Team Reasoning Agents), a multi-agent tutoring prototype and benchmark framework for studying collaborative programming with socially differentiated agents. ASTRA supports three configurations: alone_tutor (one learner with a Tutor agent), pair_tutor (two learners with a Tutor agent), and pair_multiagent (two learners with Tutor and Facilitator agents, where the Facilitator prompts coordination and balanced participation). As access to research participants is not yet available, I release an open synthetic benchmark dataset that mirrors ASTRA’s logging schema and a prespecified between-subjects design (𝑁 = 540 participants; 360 sessions; 1440 task episodes) across a bank of 20 short Python programming tasks. The dataset includes turn-level dialogue traces and task-level artefacts designed to support log-operational research questions about interaction dynamics, participation balance and reciprocal engagement in dyads, and performance and verification behaviours. Descriptive summaries and illustrative models indicate that the benchmark yields measurable condition-differentiated patterns consistent with the simulation assumptions. I emphasise that these findings are simulated evidence intended for benchmarking, measurement feasibility, and reproducible pipeline development, not causal estimates of learning effects, while providing a transparent analysis blueprint for future ethics-approved validation studies. 

Generative AI and additive manufacturing (AM) are shifting orthotic design from generic devices to data-driven, patient-specific solutions. This paper presents a systematic review of Generative AI in Design for AM (DfAM) for orthotic devices. It examines how AI-driven methods generate customised, lightweight orthoses via 3D printing, improving both design efficiency and anatomical fit. The review identifies biomechanical and workflow challenges that hinder adoption and outlines how Generative AI can advance orthotic DfAM, providing a conceptual workflow and suggestions for future research.

Automated condition monitoring of railway switches and crossings (S&C) requires classification models whose reported accuracy reflects genuine generalization rather than evaluation artefacts. This paper presents a methodologically rigorous, leak-free machine-learning framework for vibration-based event classification, evaluated on accelerometer data from a full-scale outdoor S&C test facility. The pipeline enforces strict ordering (split, select, augment, standardize, train, evaluate) and partitions the data at the level of physical events, so that all measurements of a given event are assigned together to either the training or the test subset. A symmetric tabular autoencoder generates synthetic minorityclass samples through latent-space interpolation. Twenty-one classifiers spanning eight families are benchmarked on held-out data and by group-aware five-fold cross-validation. The strongest models reach 81.5% held-out accuracy (ROC-AUC ≈0.94) and 80.4%±2.1% under cross-validation; ensemble methods are the most stable. Feature standardization is essential: without it, neural networks collapse below chance level. Computational profiling (inference latency 0.005–0.63 ms per one-second segment; model size 0.002–2.4 MB) maps three deployment scenarios to specific algorithm recommendations. Because the minority crossing class has only six held-out samples, its per-class metrics carry wide confidence intervals and should be interpreted with caution.

Objective

Internet-delivered psychotherapies show promise for adolescent major depression (MDD), but little is known about which baseline characteristics predict improvement or moderate differential effects between internet-delivered cognitive-behavioral therapy (ICBT) and psychodynamic therapy (IPDT). This study examined pretreatment predictors and moderators of symptom change in a randomized trial comparing the treatments.

Method

This secondary analysis used data from a non-inferiority trial (n = 272) in which adolescents with MDD received 10 weeks of guided ICBT or IPDT. Mixed-effects models tested predictors and moderators of change in self-rated depression. Anxiety symptoms, length of depressive episode, emotion regulation, personality disorder severity, attachment, self-compassion, suicidal ideation, and baseline depression were examined as predictors and moderators.

Results

Higher self-compassion predicted steeper improvement across treatments, as the only significant predictor. Comorbid anxiety moderated outcome where higher anxiety was associated with significantly greater improvement in IPDT relative to ICBT, with no difference at average levels. At the lowest levels of anxiety, ICBT showed significantly better outcomes. No other significant moderators emerged.

Conclusions

Findings suggest that baseline variables may influence the rate of improvement in internet-delivered therapy for adolescent MDD, as well as help guide treatment selection. Replication is needed to establish the clinical utility of these variables.

In this work, the hydrogenation behavior of a near-equiatomic Ti-V-Zr-Nb-Mo-Hf-Ta-W refractory high-entropy alloy (R-HEA) exposed to pressurized hydrogen has been thoroughly investigated. Isothermal gas-phase hydrogen absorption experiments have been performed and a maximum uptake of 1.13 wt.% H has been achieved after exposure to a pure H2 atmosphere at 350 °C and 60 bar H2 for 6 h. This hydrogen absorption capacity is rather low compared to previous literature, where capacities as high as 2.7 wt.% have been reported. The presence of two distinct (Hf,Zr)-mixed oxides at the surface of the particles has been deduced from X-ray diffraction analyses and identified as the main reason for the relatively low H uptake and the minimal impact onto the mechanical integrity of the R-HEA after hydrogenation. The results hereby reported suggest that R-HEAs containing strong oxide-forming elements such as Hf, Zr, and Ti undergo surface hydrogenation-regeneration upon intermittent exposure to a hydrogen atmosphere. The cyclic nature of such phenomena should be further investigated, as it could lead to the development of novel, self-regenerating protective materials against hydrogen diffusion and embrittlement to be potentially used as permeation barriers.

Conventional validation of low-carbon stabilization binders often relies on short curing periods and batch leaching tests, which do not adequately capture long-term durability or transport-controlled contaminant release. In this study, a bioash–GGBFS binder was evaluated for stabilization/solidification of metal-contaminated soil from Näsudden, Sweden, using an integrated program of extended curing, wet–dry and freeze–thaw cycling, and standardized percolation and diffusion leaching tests. The treated mixture (50% soil, 35% bioash, and 15% GGBFS) developed unconfined compressive strength in the MPa range and maintained high strength after durability exposure, with 1635 ± 308 kPa after wet–dry cycling and 2047 ± 100 kPa after freeze–thaw cycling. Percolation testing at L/S = 10 showed strong reductions in leaching compared with untreated soil, including 96% for As, 98% for Cd, 90% for Pb, 92% for Zn, 88% for Ni, and 65% for Cu. Diffusion testing confirmed low release for most elements, while Cu showed the highest cumulative release and mobility, indicating an element-specific limitation. Overall, the results demonstrate that the bioash–GGBFS binder can provide both durable mechanical performance and sustained immobilization of most priority contaminants under transport-relevant conditions. The findings support its potential as a low-carbon alternative for stabilization and reuse of contaminated soils, although additional measures may be needed where Cu governs compliance

Little is known about the outdoor activities of older people in the Nordic Arctic region, who face long and snowy winters, long distances, underdeveloped transportation and limited community facilities. Addressing the lack of region-specific evidence on outdoor mobility for promoting healthy and independent ageing in Arctic environments, this study aims to examine self-reported barriers and enablers of outdoor mobility among adults aged 70 years or older living in Iceland and northern parts of Finland and Sweden. A cross-sectional postal questionnaire focusing on outdoor mobility and environment was circulated in 2024 and collected 811 responses, a 41% response rate (50% women). In this paper, three open-ended survey questions on barriers and enablers of outdoor activities were exploratorily analysed with conventional content analysis.

On barriers, the most common response was No barriers. Other outdoor mobility barriers were divided into three themes: personal (health complications, laziness, loneliness when going out, fear of falling, lack of time), community-driven (maintenance issues in the living environment, remoteness or absence of infrastructure or services), and environmental (poor and risky weather during long and snowy Arctic winters). The responses on enablers suggested several main themes: active lifestyle; infrastructure, roads and public spaces; nature and weather; promoting health; mobility with aid, including appropriate clothing. The most identified enablers were recreational outdoor activities, maintaining health, accessible infrastructure, and supportive physical and natural environments, with some variation across countries and sexes. By removing these barriers and promoting enablers, older residents in the Nordic Arctic region can be better supported in healthy ageing in place.

This article presents the collected output of the longitudinalMathematics Inclusion and Equity (MInE) project. Specifically, itreports on the third and final phase of the study, in which resultsfrom phases one and two were further explored. The project wasundertaken in close collaboration with teachers in two Swedishschools. The overall purpose was to promote equity and inclusionin mathematics teaching. In phase three of the study, key conclusionswere developed into a framework that allows for the contextualisa-tion of inclusion and equity in teaching. This contextualisation wasachieved by identifying specific ethical dilemmas that arise in teach-ing and the teachers’ corresponding professional judgements onhow to act. Importantly, the prerequisites for decision-makingneeded to be understood in relation to the level at which responsi-bilities and resources lay, i.e. at the school-, classroom- or individuallevel. A key outcome of the research is a framework that enablespolicy makers, teachers, and researchers to develop inclusion andequity as interrelated, fluid, and contextual pedagogical processes byaddressing school-, classroom- and individual levels.

Nigeria is endowed with vast solid mineral resources that remain largely underexploited, despite their potential to catalyse national infrastructure development and drive inclusive economic growth. This study examines the challenges and prospects of linking Nigeria’s mineral wealth with infrastructure development, within the broader context of sustainable development and regional integration. Drawing from empirical data, national reports, and comparative global case studies, the research identifies institutional fragmentation, policy and regulatory bottlenecks, weak governance, and limited financing mechanisms as key barriers to integrated mineral-infrastructure planning. It also explores successful models from countries such as Mozambique, Guinea, Botswana, Chile, and South Africa, providing strategic insights applicable to Nigeria’s context. The study proposes a framework for developing mineral corridors, embedding infrastructure obligations in mining licenses, leveraging resource-for-infrastructure partnerships, and enhancing inter-agency coordination. By implementing these reforms, Nigeria can unlock the transformative potential of its mining sector, bridge its infrastructure gap and foster long-term economic diversification. The findings contribute to policy discourse on resource-based development and offer actionable recommendations for stakeholders across government, industry, and development finance institutions.

Background: Chronic pain (CP) is a leading cause of disability worldwide and is associated with substantial psychological, functional, and social burden. Acceptance and Commitment Therapy (ACT) has demonstrated efficacy in improving pain acceptance and functioning; however, access to psychological care remains limited. Internet-based interventions (IBIs) may help bridge this gap. To date, no ACT internet-based intervention has been developed and tested for individuals with CP in Italy. The present pilot randomized controlled trial (RCT) evaluated feasibility, acceptability, usability, and exploratory changes in pain acceptance of MobACT, a guided internet-based ACT intervention translated and adapted for the Italian context.

Methods: A two-arm pilot RCT with parallel groups (1:1 allocation) was conducted. Forty adults with CP were randomized to either the MobACT intervention (n = 20) or a waitlist control group (n = 20). The seven-week guided intervention was delivered via the Iterapi platform. Feasibility outcomes included recruitment, retention, usability, satisfaction, intervention experience, and participant feedback. The primary exploratory clinical outcome was pain acceptance, measured using the Chronic Pain Acceptance Questionnaire (CPAQ-20) at baseline (T0) and post-intervention (T1). Feasibility, usability, and participant experiences were explored through post-intervention questionnaires and semi-structured interviews analyzed using thematic analysis.

Results: Pain acceptance significantly improved over time across participants (p < .001), but the time × group interaction was not significant (p = .978), indicating no evidence of a differential change between MobACT and waitlist during the pilot period. Reliability of the CPAQ-20 was good at both time points (α = 0.802–0.842). Qualitative findings indicated high usability, satisfaction, and perceived applicability of ACT strategies. Participants reported increased awareness, reduced rumination, improved emotional regulation, and greater engagement in valued activities, despite limited changes in pain intensity.

Conclusions: Findings support the feasibility, acceptability, and usability of MobACT as a culturally adapted internet-based ACT intervention for CP in Italy. Exploratory outcome findings should not be interpreted as evidence of efficacy; rather, they support further evaluation in a larger, fully powered RCT with more comprehensive engagement monitoring and longer follow-up.

Background:Evidence-based psychological interventions are usually not accessed by marginalized groups such as refugees. Culturally adapted psychological interventions have reported larger effect sizes than nonadapted psychological interventions. However, the cultural adaptation of interventions is a lengthy process, entailing a challenge. One potential solution to overcome this challenge is the use of artificial intelligence (AI).

Objective:The aim of this study was to investigate and compare the perceived cultural relevance and acceptability of 2 common cognitive behavioral therapy (CBT) techniques when translated and culturally adapted by AI versus a human psychologist.

Methods:In a 2×2 factorial design, the text generator type (AI vs human psychologist) and the CBT technique (cognitive restructuring vs behavior modification) were compared. CBT technique texts translated and culturally adapted either by AI or by a human psychologist were blindly rated using the Cultural Relevance Questionnaire and the Theoretical Framework of Acceptability. Raters were Arabic-speaking refugees and immigrants, aged between 18 and 69 years, residing in Sweden, Denmark, and Germany. Raters were randomly allocated to 1 of 4 conditions. Each condition consisted of 2 stimuli. Two-factor between-subject design analyses were used to analyze the data.

Results:A significant main effect of the text generator domain type (P=.02; η²=0.045) was found in the first rating, with texts adapted by the AI domain perceived as more culturally relevant than those adapted by the human domain. No significant main effect of the CBT technique was found in the first rating (P=.10; η²=0.022). There were no differences in the second rating. Regarding acceptability, no significant main effects of text generator domain type (P=.09; η²=0.024) or the CBT technique (P=.88; η²=0.001) were found in either of the ratings.

Conclusions:CBT technique materials adapted by AI may be perceived as similarly culturally relevant as those adapted by a human psychologist. This finding implies the potential to accelerate the cultural adaptation of psychological interventions. However, AI still needs to be used with caution and in accordance with rigorous safety standards and robust frameworks.

The reverse water-gas shift (RWGS) reaction offers a sustainable pathway for converting CO2 into CO, thereby facilitating syngas production. A stable and efficient catalyst is essential for ensuring practical applications without the risk of deactivation. In this study, perovskite oxide supports FeMnO3 (FM), ZrCaO3 (ZC), LaFeO3 (LF), and LaCoO3 (LC) were synthesized via the scalable and facile Pechini sol-gel method and impregnated with 5 wt% Ni and 5 wt% Cu to regulate the redox activity, reducibility, and thermal stability. The comprehensive characterization, including ICP-SFMS, XRD, H2-TPR, TGA, N2 Physisorption, XPS, and SEM, were conducted, confirming successful supported metals addition, high perovskite crystallinity, surface NiO/CuO formation, lower reduction temperatures and enhanced thermal stability. Catalytic testing from 200 to 700°C with different CO2:H2 ratios and feed compositions revealed high RWGS performance at 700°C with 15 vol% CO2, and CO2:H2 = 1:4. Under these improved conditions, Ni- and Cu-impregnated LaCoO3 achieved approximately 66% CO2 conversion with 98–100% CO selectivity. The catalysts demonstrated almost stable performance over 70 h with CO2 conversion stabilizing at 59.2% and maintaining a high CO selectivity (97.5%), with Cu contributing to improved stability by mitigating Ni deactivation. The catalyst retained the structural stability which was revealed by post-reaction XRD and SEM showing high crystallinity and minimal morphological changes. The higher performance of the LC catalyst is attributed to preserved Co3 +/Co2+ redox chemistry, and Ni and Cu supported metals effects, resulting in enhanced CO2 activation and electron transfer. This work demonstrates a dual Ni-Cu impregnation approach on LaCoO3 that enhances stability and RWGS performance, establishing it as a durable catalyst for RWGS applications.

Recent progress in nanostructuring and targeted doping has accelerated thermoelectric research by enabling more effective control over coupled electrical and thermal transport. In this context, transition-metal chalcogenides such as MnSe have emerged as promising candidates because their electronic structure and carrier concentration can be systematically tuned through compositional engineering to optimize transport performance. Thermoelectric power generators therefore offer a compelling, environmentally benign route for converting waste heat into electricity, operating silently and without direct pollutant emissions. This study presents a simple technique for synthesized the MnSe nanoparticles, along with an analysis of their structural, morphological, and thermoelectric characteristics. By using the hydrothermal technique to successfully synthesise the p-type pristine and MnSe nanoparticles. Various methods of characterisation including X-ray diffraction (XRD), scanning electron microscope (SEM), and Energy-dispersive X-ray spectroscopy (EDX) were used to characterize the synthesized nanoparticles. Results confirmed that nanoparticles crystalline in a cubic crystal structure with an average crystallite size 56 nm. The electrical conductivity of the MnSe material decreases as the Al content increases from 478 to 92 S/cm, primarily because of the decrease in the number of charge carriers. However, the Seebeck coefficient values are increased from 29 to 92 µV/K. The findings demonstrated a significant enhancement in the thermoelectric characteristics of MnSe nanoparticles with the introduction of Al doping, employing the hydrothermal technique. Consequently, the power factor increased from 42 to 79 µWm–1K–2, respectively. This study presents a cost-effective and efficient approach for producing a substantial amount of Al-codoped MnSe at low temperatures. The resulting material exhibits excellent thermoelectric properties, making it suitable for various practical applications.

Accurate prediction of foot pattern throughout various terrains is essential for achieving stable and adaptive control in powered lower-limb prostheses. In this study, a range of predictive models, including regression-based approaches, ensemble machine learning methods, and recurrent neural networks (RNNs), were systematically compared to determine their capability in generating oscillatory gait signals from tibial angular position while walking on varied terrain. The training and testing data were collected from ten healthy individuals while level-ground walking, ascending and descending stairs. Statistical models had poor accuracy and generalization, while ensemble methods (Gradient Boosting, Histogram-Based Gradient Boosting, and eXtreme Gradient Boosting) had moderate performance but remained sensitive to outliers and inter-subject variation. In contrast, recurrent architectures, i.e., long short-term memory networks (LSTM), had the highest predictive accuracy, with a mean correlation of 0.88, and a low root mean square error of 0.09 rad. Although the LSTM-based method provided slightly lower accuracy than some of the control methods inspired by the central pattern generators in the literature, it required only prosthesis-embedded sensors, avoiding the need for sensors on the intact or residual limb. Moreover, the compact network size and low computational load make it well-suited for embedded deployment, reducing cost, power consumption, and user burden. These findings place RNN-based approaches in line with ongoing research trends toward dynamic pattern generator–inspired controllers, offering robust, volitional-like control while maintaining practicality for real-world implementation.

Twin transition, the concurrent development of digitalization practices and circular practices, are believed to be the key to firm performance. However, empirical evidence is currently scarce regarding the effect of twin transition on firm performance and the complementary practices that enable and realize it. This study draws on a survey of 188 Swedish manufacturing firms and employs PLS structural equation modelling to test the hypothesis. The results indicate that firm performance can be improved through digitalization practices but with circular practices as a mediator. Moreover, our results show a significant and positive moderation effect of supply chain orchestration and supply chain business model innovation as complementary mechanisms for enabling and realizing the process. This implies time sequencing between the two twin transition dimensions. These results carry theoretical implications for twin transition research and circular economy literatures. The results also provide novel managerial insights by showing that digital investments must be coupled with early orchestration of supply chain partners and aligned business model innovation to effectively scale circular practices and realize performance benefits.

Groundwater in semi-arid regions is increasingly stressed by intensive abstraction and contamination, while aquifer productivity and intrinsic vulnerability are commonly evaluated separately. This study investigates whether structured integration of groundwater potential and intrinsic vulnerability can provide a more reliable basis for sustainable groundwater management in the Halabja–Khwrmal area, northeast Iraq. Groundwater potential was delineated using the Analytical Hierarchy Process (AHP) applied to seven hydrogeological and environmental factors, whereas intrinsic vulnerability was assessed using the standard DRASTIC model. Both indices were independently validated prior to integration. Receiver Operating Characteristic (ROC) analysis based on 430 well discharge records yielded an Area Under the Curve (AUC) of 0.751, indicating acceptable discrimination of productive zones. Regression between DRASTIC index values and measured nitrate concentrations showed a strong positive relationship (R² = 0.797), supporting vulnerability reliability. Cross-classification of the validated ordinal groundwater potential and vulnerability indices generated nine PotentialVulnerability zones, where highly productive areas largely coincide with low intrinsic vulnerability. High groundwater potential occupies 49.24% of the basin, while high intrinsic vulnerability covers 20.65%, with limited spatial overlap between highly productive and highly vulnerable conditions. The class-preserving integration prevents compensatory masking between productivity and susceptibility and provides a transparent spatial framework for regulated abstraction and priority protection. The proposed Potential–Vulnerability framework offers a transferable spatial basis for groundwater management in hydrogeologically variable semi-arid aquifer systems.

Transitioning to a resource-efficient and sustainable circular economy is vital for tackling climate- and environmental-related challenges. This study demonstrates a closed-loop strategy for upcycling agricultural biowaste eggshells. Water- insoluble proteins were extracted from both shell membranes and shell fragments by boiling in water using protein denaturants. The ground eggshells also were used to prepare calcium acetate (Ca(CH₃COO)₂) and to treat Scots pine (Pinus sylvestris L.) sapwood. Mineralisation of the wood was achieved by performing a two-step impregnation process using aqueous solutions of ammonium dihydrogen phosphate (NH₄H₂PO₄) and Ca(CH₃COO)₂ salts. Morphological studies revealed the relatively low saturation of wood matrix with mineral, with cell lumina mostly unfilled, while elemental mapping confirmed homogeneous distribution of Ca and P within the wood matrix. Powder X-ray diffraction (XRD) analysis revealed that wood treatment resulted in the in-situ co-precipitation of low-crystallinity hydroxyapatite (Ca₁₀(PO₄)₆(OH)₂), and spectroscopic analysis indicated carbonate substitution within the Ca₁₀(PO₄)₆(OH)₂ crystal lattice, suggesting the formation of carbonated hydroxyapatite (Ca_10-x(PO₄)_6-x(CO₃)_x(OH)_2-x-2y(CO₃)_y). Microscale combustion calorimeter (MCC) and cone calorimeter (CC) measurements of mineralised wood revealed a reduction in the total heat release (THR) compared with untreated wood, indicating potential for further optimisation of wood modification process. Results suggest that the proposed aqueous solution-based processing approach for converting an abundant resource, chicken eggshells, into value-added products has potential for new technology and bioeconomy development and represents a promising pathway towards improved sustainability.

This study examines the transformative potential of generative artificial intelligence (GAI) on the knowledge management (KM) systems and capabilities in organizations. GAI enables efficient processing and summarization of an organization’s proprietary data, generating actionable outputs and further insights (i.e. new data) for its knowledge workers. This improved visibility and leverage to organizational data has the potential to establish a better understanding of what an organization knows and to discover and integrate latent knowledge, leading to, e.g. enhanced KM and better-informed, more consistent, and quicker decisions. However, to reap the potential benefits of GAI, organizations must go beyond the mere adoption of this new digital technology. Organizations should first prepare and transform themselves to enhance their readiness for integrating GAI into their knowledge-related processes and work practices. We argue that KM, both its capability and system views, takes a key role in this transformation. Accordingly, we describe how GAI augments organizational KM capabilities and systems, and how these capabilities and systems act as catalysts for successful GAI use and value creation. In essence, this study outlines the steps for successful KM and GAI integration in organizations and suggests alternative starting points and configurations to achieve it.

Here, we introduce a fluorine-free and flame-retardant dual-salt electrolyte formulation based on a new electron-deficient aromatic and Hückel-type lithium 4,6-diethoxy-2-oxo-2H-1,3,5-triazin-5-ide (LiDET) salt in combination with lithium bis(oxalato)borate (LiBOB) salt, both dissolved in a triethyl phosphate (TEP) solvent with vinylene carbonate (VC) as an additive. The interactions between BOB and DET anions promote a loosely coordinated Li+ solvation sheath, which results in enhanced interfacial stability of electrodes. The Li||Li symmetric cells maintain stable overpotentials during prolonged cycling, while the full cells demonstrate a capacity retention of 91% in Li||NMC811 cell after 500 cycles and 94% in Li||LTO cell after 200 cycles. The dual-salt electrolyte facilitates the formation of stable cathode electrolyte interphase (CEI) and solid electrolyte interphase (SEI) layers as confirmed with X-ray photoelectron spectroscopy (XPS), where the presence of species such as Li3N, LiNxOy, B–N, and Li–B–O is evident. Overall, this study paves the way for development of fluorine-free and flame-retardant electrolytes for lithium metal batteries (LMBs), especially for high-voltage cathodes like NMC811.

Uranium (U) is a chemically toxic contaminant, and elevated concentrations in groundwater pose environmental and public health concerns in mining-impacted regions. U mobilization from mine-affected bedrock can contribute to groundwater contamination, yet the geochemical controls governing U release and transport under environmentally relevant conditions remain incompletely understood. This study investigates the roles of pH, complexing ligands, and mineralogy on U mobilization from pegmatite and trachyandesite rocks collected from Leveäniemi open pit, an active iron ore mine in Northern Sweden. Elevated U concentrations have been detected in groundwater entering the open pit through rock fractures. A systematic experimental approach combining batch leaching and dynamic flow through experiments was applied across a range of geochemical conditions, including varying acid concentrations, pH (acidic to alkaline), and ligand concentrations representative of groundwater (SO₄²⁻, NO₃⁻, Cl⁻, and HCO₃⁻) at environmentally relevant concentrations. The results demonstrate that U mineral dissolution and U mobilization are governed by distinct geochemical controls. Significant dissolution occurs under acidic and strongly alkaline conditions, with uraninite identified as the primary reactive U-bearing mineral, whereas other U-bearing minerals are comparatively resistant. Under neutral pH conditions representative of groundwater, U release is limited and controlled by surface-mediated processes rather than bulk mineral dissolution. However, the carbonate ligand promotes the formation of stable uranyl-carbonate and Ca-uranyl-carbonate complexes, enhancing U solubility and transport. These findings show that even limited mineral reactivity can sustain dissolved U concentrations over time and that groundwater composition plays a critical role in controlling U mobility. This is environmentally significant because such conditions are typical of groundwater systems, indicating that U can remain mobile and contribute to long-term contamination of water resources and downstream aquatic ecosystems. This study distinguishes the geochemical conditions that dissolve U-bearing minerals from those that sustain dissolved U transport under environmentally relevant groundwater conditions, providing new insight into mechanisms governing U transport in mine-impacted groundwater and supporting improved prediction, monitoring, and mitigation of U contamination of downstream recipients.

Climate-related failures pose increasing risks to railway operation and maintenance, requiring robust assessments to support effective resilience planning. Accordingly, this study presents a comprehensive risk assessment to identify, quantify, and prioritize climate-related failure modes (CRFMs). CRFMs were identified through text mining of 15-year corrective maintenance records across all five Swedish railway regions. A probabilistic model was then developed to analyze the risk of identified CRFMs, considering both operational and maintenance costs. Risk distributions were derived using Monte Carlo simulation and summarized by Expected Value of Risk (EVoR) and Conditional Value at Risk (CVaR0.90). As a result, 14 CRFMs were identified, accounting for 47% of all failure records. Their annual trend and seasonal distribution align well with periods of increased extreme weather events and dominant seasonal climate hazards. Furthermore, clustering the regional profile of CRFMs reveals regional similarity. The CRFMs were then prioritized, showing that 4 modes (i.e., Track deformation, Snow and ice, Buckling, and Rail breakage/crack) account for 73% of total CVaR0.90. The amplification of total risk in the tail indicates a 74% higher cost burden under extreme conditions. Finally, regional climate vulnerability was assessed using risk metrics normalized by track length and traffic density.

Development of sustainability systems for assessment of environmental impacts remains a paramount challenge for green and circular manufacturing of polymers. In this study, a comprehensive life cycle assessment (LCA) framework is developed for European polymeric waste by integrating OpenLCA, Ecoinvent v3.11, and Python-based machine learning (ML) algorithms. Cradle-to-gate, service-life, and cradle-to-grave assessments are performed for representative thermoplastic composite systems, including PP–PET–cotton, HDPE–glass fiber, and PEEK–carbon fiber composites, covering domestic, engineering, and high-performance polymer categories. The results demonstrate that raw material extraction and manufacturing stages dominate environmental impacts, contributing the highest shares to climate change, ecotoxicity, and non-renewable energy consumption. PP-based composite systems exhibit the lowest overall environmental burdens due to lower processing energy and simpler molecular structures, while HDPE-based systems show moderate impacts. PEEK-based composites present the highest impacts per unit mass, driven by energy-intensive synthesis and high processing temperature. Environmental impacts are evaluated using EF v3.1 and ReCiPe methodologies, supported by Monte Carlo simulations and ML-assisted uncertainty quantification. Monte Carlo simulations and ML-assisted LCA provide probabilistic ranges, uncertainty quantification, and predictive insights into impact indicators, enabling the development of a quantitative sustainability system based on probability–impact relationships. A Europe-wide assessment of 57 Mt of polymeric waste highlights that environmental burdens are concentrated in countries with high polymer production and consumption, emphasizing the importance of energy mix, recycling efficiency, and waste management strategies. Overall, this work demonstrates that digitalized LCA coupled with ML offers a powerful decision-support framework for sustainable polymer design, recycling optimization, and circular economy policy development, supporting the transition toward low-carbon and resource-efficient polymer systems in Europe.

Steel, a cornerstone of modern civilization and economic growth, has experienced exponential production growth, resulting in substantial carbon emissions owing to reliance on fossil-based reducing agents and fuels. Although conventional steelmaking methods have improved, achieving the climate targets outlined in the Paris Agreement requires the adoption of transformative technologies. This review examines potential green energy sources and their integration into the value chain of green steel production to address industry challenges. Electricity, hydrogen, and biocarbon have emerged as primary reducing agents and heating sources, enabling disruptive steelmaking processes. Pathways toward green steel remain constrained by the need to secure sustainable energy supplies, ensure high-grade iron ore availability, and advance energy-efficient technologies for integration into existing or new steelmaking processes. Emphasis is placed on a holistic approach to renewable energy usage and CO2 emission reduction. Future research directions and development priorities for achieving carbon-neutral steelmaking are outlined, with attention to systematically addressing hard-to-abate subprocesses and progressively decarbonizing the entire value chain of green steel production.

Current research indicates that Virtual Reality (VR) can serve as an effective tool for evaluating and training postural control responses and its processing, to distorted sensory input. The aim was to evaluate posturographic spectral responses and adaptation to repeated visual VR-stimulation in young and older adults with wavelet analysis. Twenty-eight young (mean 25.3 years) and 25 older (mean 74.8 years) adults were included. Participants were standing on a force plate performing two control tests (eyes open and closed) and thereafter repeatedly watched a 120-second VR-simulation of a roller-coast ride five times. The first VR session produced a marked two-fold stability response: (1) significant spectral energy increased within 0.4–8.5 Hz in anteroposterior and lateral directions, and (2) significant spectral energy decreased within 0.03–0.13 Hz in anteroposterior direction. Older adults used significantly more high frequency energy and less low frequency energy. Repeated VR sessions significantly decreased high frequency energy in both groups. Wavelet analysis indicates that both younger and older adults employed similar spectral response patterns in response to immersive visual stimulation. However, older adults showed larger shifts in spectral characteristics, suggesting age-related differences in resilience. Postural control appeared capable of rapidly adapting to adjust biomechanical strategies and sensory weighting.

Background

Traumatic neck pain is prevalent and often linked to sensorimotor impairments, including reduced cervical movement velocity, prolonged task completion time and delayed reaction times. The cervical reaction acuity (CRA) test provides a virtual reality (VR) based objective assessment of a combination of these deficits.

Objectives

To evaluate the CRA test in terms of discriminant and construct validity, associations among CRA-variables, and test-retest reliability.

Design

Cross-sectional and test-retest reliability study.

Method

Adults aged 18–65 years with chronic traumatic neck pain (CTNP) (n = 42) and asymptomatic controls (CON) (n = 38) completed the VR-based CRA test (measuring peak velocity, reaction time, and task completion time) twice, one week apart. Discriminant validity was examined through group comparisons and sensitivity and specificity analyses. Construct validity was examined through correlations with patient-reported outcomes. Associations among CRA variables were examined using correlational analyses. Test-retest reliability (CTNP n = 41, CON n = 29) was examined via intraclass correlation coefficients (ICC), standard error of measurement and minimal detectable change.

Results

Participants with CTNP showed significantly lower peak velocity and longer task completion times than CON. Reaction time did not differ between groups. Peak velocity had the best discriminative ability (sensitivity 83%, specificity 71%), while task completion time showed lower sensitivity (45%) but higher specificity (90%). Correlations with self-reported measures were low to moderate. ICC ranged from 0.54 to 0.94 (95% CI 0.23–0.97).

Conclusion

The CRA test assesses cervical movement performance, with reliability ranging from poor to excellent. Individuals with CTNP demonstrate slower cervical movement velocity and prolonged task completion times than CON.

We present Rubin Data Preview 1 (DP1), the first data from the National Science Foundation–Department of Energy Vera C. Rubin Observatory, comprising raw and calibrated single-epoch images, coadds, difference images, detection catalogs, and ancillary data products. DP1 is based on 1792 optical–near-infrared exposures acquired over 48 distinct nights by the Rubin Commissioning Camera (LSSTComCam) on the Simonyi Survey Telescope at the Summit Facility on Cerro Pachón, Chile in late 2024. DP1 covers ∼15 deg² distributed across seven roughly equal-sized noncontiguous fields, each independently observed in six broad photometric bands, ugrizy. The median FWHM of the point-spread function across all bands is approximately "14, with the sharpest images reaching about "58. The 5σ point-source depths for coadded images in the deepest field, the Extended Chandra Deep Field South, are u = 24.55, g = 26.18, r = 25.96, i = 25.71, z = 25.07, and y = 23.1. Other fields are no more than 2.2 mag shallower in any band, where they have nonzero coverage. DP1 contains approximately 2.3 million distinct astrophysical objects, of which 1.6 million are extended in at least one band in coadds, and 431 solar system objects, of which 93 are new discoveries. DP1 is approximately 3.5 TB in size and is available to Vera C. Rubin Observatory data rights holders via the Rubin Science Platform, a cloud-based environment for the analysis of petascale astronomical data. While small compared to future LSST releases, its high quality and diversity of data support a broad range of early science investigations ahead of full operations in 2026.

Non-destructive quantification of the internal progression of thermally induced degradation in wood using X-ray imaging is challenging because the material is inherently heterogeneous and the resulting intensity changes are small, spatially variable, and influenced by acquisition noise and partial-volume effects. A measurement framework was developed for quantifying combustion-front penetration in wood using time-resolved X-ray computed tomography (CT) during controlled one-sided heating. The focus was on defining and measuring the combustion front in CT data rather than detailed physical interpretation of underlying material transformations. A CT-compatible heating arrangement enabled repeated volumetric scanning without interrupting thermal exposure. An automated voxel-wise analysis pipeline was implemented, including baseline normalisation using robust median-based statistics, formation of depth-dependent profiles along the heating direction, and threshold-based front detection with sub-voxel interpolation. Measurements were evaluated within a fixed three-dimensional region of interest located approximately 10 mm inside the specimen boundaries to reduce edge effects and ensure reproducible spatial sampling. Validation against post-exposure visual assessment showed reliable front detection only when a statistically coherent volumetric signature was present. Application to a dynamic experiment enabled extraction of a representative propagation rate of 0.92 mm min⁻¹.

Polymer-based composites have gained prominence in tribological applications due to their lightweight nature, tunable properties, and multifunctional potential. However, existing reviews largely report performance improvements without systematically addressing contradictory trends, testing variability, and emerging manufacturing routes. This review analyses friction and wear mechanisms in fibre-reinforced and particle-reinforced polymers, surface coatings, and additively manufactured polymer composites. Key mechanisms, including load transfer, transfer film formation, thermal dissipation, and interfacial effects, are critically synthesised across thermoset and thermoplastic systems. Representative performance trends are discussed to highlight the influence of reinforcement type, processing route, and operating conditions, along with limitations in current tribological testing practices and the need for standardisation. By integrating mechanistic understanding with comparative performance and future research priorities, this review provides guidance for the design and evaluation of polymer composites in automotive, aerospace, marine, and biomedical tribological applications.

Introduction: 

Cross-laminated timber (CLT) has been recognized as a low-carbon alternative in construction. However, realizing its full potential requires systemic innovation that extends beyond individual projects. Such innovation can be organized through inter-firm collaborations, such as innovation ecosystems which enable co-innovation across multiple building projects.

Methods: 

A longitudinal case study was conducted to examine the development of the organizing of an IE in CLT-based construction over 13 years. Qualitative data from semi-structured interviews, observations, and documents were compiled into a visual map of events and synthesized through temporal bracketing to delineate IE lifecycle phases.

Results: 

Four IE lifecycle phases were identified: birth, expansion, leadership, and transformation. In the early stages, actors shared resources and investment risks through collaborative arrangements and innovative project contracts. As the IE matured, the focus shifted toward tighter organizational control and consolidation through vertical integration of suppliers.

Discussion: 

The study contributes to IE lifecycle theory by showing how IEs evolve through distinct organizing configurations over time and may gradually transform into partially vertically integrated supply-chain structures as technologies and value propositions mature. Methodologically, the study demonstrates how temporal bracketing can be applied to longitudinal IE research to identify shifts in co-innovation, value creation, and organizing across different lifecycle phases. Practically and from a policy perspective, the findings highlight how structured inter-firm collaboration, innovative project contracts, and supportive policy conditions can facilitate risk-mitigated co-innovation and the systemic development of CLT-based construction solutions.

Generative artificial intelligence (GenAI) is used in programming education; however, its adoption can introduce pedagogical misalignment, shallow cognitive engagement, and ethical risks that threaten the sustenance of programming skills of students. This study evaluated the GenAI programming education framework’s ability to sustain higher-order thinking skills (HOTS) and programming logic while mitigating pedagogical, cognitive, and ethical barriers in Java programming. A between-group mixed-methods experiment was conducted amongst 124 undergraduate students (62 in the control group and 62 in the experimental group) over 7 weeks. Learning outcomes were assessed using pretests and posttests, analyzed with baseline-adjusted ANCOVA and MANCOVA, and supplemented with trace-based learning analytics from GenAI logs collected at time points (Weeks 3 and 7). The experimental group showed a baseline-adjusted advantage on HOTS (adjusted mean difference = 0.29; p < 0.001; adjusted Hedges’ g = 0.80) and a smaller but significant improvement in programming logic (adjusted mean difference = 0.21; p = 0.047; adjusted Hedges’ g = 0.36), alongside a multivariate group effect across domains. Log-derived indices also showed larger gains in pedagogical alignment and cognitive engagement, reflected in more frequent task decomposition and debugging behaviors. Ethical engagement has also increased, indicating consistent hallucination and data sensitivity awareness. Path modelling indicated that the intervention increased changes in pedagogical, cognitive, and ethical engagement. Pedagogical alignment and cognitive engagement were positively associated with post-test HOTS and programming logic, whereas ethical engagement was negatively associated with HOTS but not significantly associated with programming logic. Overall, the findings suggest that GenAI becomes more educationally beneficial in programming when guided by a structured approach.

Remediation of former wood treatment sites is challenging due to the presence of contaminants with distinct physicochemical properties, such as arsenic (As) and polycyclic aromatic hydrocarbons (PAHs). This study evaluated a low-voltage electricity-induced soil remediation method designed to immobilize As while simultaneously degrading PAH in situ. A field pilot experiment was conducted at a highly contaminated site using iron (Fe) electrodes supplying pulsed direct current to promote PAH oxidation and Fe release from electrodes for As immobilization. Groundwater in five wells was monitored for concentrations of contaminants, their degradation byproducts, and microbial and fungal community structures. Over two years, dissolved PAH16 concentrations decreased by 62–94% across wells, with no accumulation of oxygenated or nitrogen-containing PAH. Dissolved As concentrations declined by up to 88% at low PAH levels, but reductions were weaker (55–57%) and more variable at very high PAH concentrations (hundreds to thousands μg L–1). Microbial communities, both prokaryotic and fungal, were characterized by taxa often found in contaminated aquifers and soils, with enrichment of PAH-degrading and As-tolerant Pseudomonas, Rugosibacter, and Duganella, but showed no adverse effect of the treatment. Overall, the method promoted concurrent PAH degradation and As immobilization with minimal secondary impacts, demonstrating potential for remediation of mixed-pollutant soils. 

This study investigates how multi-actor collaboration unfolds over time in servitization, where manufacturers increasingly rely on networks of customers, suppliers, and technology partners. Although significant research has examined collaboration in the context of servitization, we still know little about how multi-actor collaboration evolves in this context. Through a qualitative meta-synthesis study of 57 qualitative case studies, our analysis identifies preconditions and three phases – initiation, routinization, and learning and adaptation – each shaped by shifting structural, relational, and cognitive mechanisms. The study contributes by developing a process model of multi-actor collaboration in servitization, showing how mechanisms generate consequences that reshape collaboration over time. It further advances a temporal view of collaboration governance by explaining how structural, relational, and cognitive mechanisms are repeatedly recombined as actor roles, dependencies, and value logics change. For managers, the findings suggest the need to clarify roles, value-sharing agreements, data access, and governance arrangements as servitization networks expand and mature. 

Generative artificial intelligence (AI) excels at producing complex data structures (text, images, videos) by learning patterns from training examples. Across scientific disciplines, researchers are now applying generative models to ‘inverse problems’ to directly predict hidden parameters from observed data along with measures of uncertainty. While these predictive or posterior-based methods can handle intractable likelihoods and large-scale studies, they can also produce biased or overconfident conclusions even without model misspecifications. We present a solution with Frequentist–Bayes (FreB), a mathematically rigorous protocol that reshapes AI-generated posterior probability distributions into (locally valid) confidence regions that consistently include true parameters with the expected probability, while achieving minimum size when training and target data align. We demonstrate FreB’s effectiveness by tackling diverse case studies in the physical sciences: identifying unknown sources under dataset shift, reconciling competing theoretical models, and mitigating selection bias and systematics in observational studies. By providing validity guarantees with interpretable diagnostics, FreB enables trustworthy scientific inference across fields where direct likelihood evaluation remains impossible or prohibitively expensive.