Solid-state ammonia storage supports the transition towards safe and efficient low-carbon energy storage and transportation. Alkaline earth metals halides (AEMHs) based materials, such as strontium chloride (SrCl2), can be utilized to efficiently store ammonia with high capacity and mitigate ammonia toxicity but suffer from large volume expansion during ammonia absorption and slow thermal desorption kinetics. Here, SrCl2 was structured into SrCl2-carbon nanofiber composites (SrCs) by electrospinning and a subsequent three-step carbonization process. Polyvinylpyrrolidone (PVP) was used as a carrier polymer in water/ethanol solution in electrospinning and as a carbon source for stabilizing SrCs with high SrCl2 loadings. Chemical and structural changes of the nanofiber structures during carbonization were investigated with different surface characterization techniques, including XRD, SEM, and FTIR. The SrCs could be loaded with up to 90 wt% of SrCl2 salt, resulting in remarkable high, and stable ammonia sorption uptake capacity of 671 mg/g over four cycles, mechanical integrity and more than 4 times faster desorption kinetics compared to SrCl2 powder.

Tungsten (W) is an emerging contaminant of concern whose high mobility in the environment is of scientific debate. It is also a critical raw material whose mining is expected to increase. Mine waste management aims to create an anoxic environment of neutral pH to limit sulfide oxidation and acid mine drainage. This study evaluates the stability of W-minerals under such geochemical conditions to develop recommendations for W mine waste management. Intact cores of legacy mine tailings from Morkulltjärnen, Sweden, were collected and analyzed using whole rock geochemistry and XRD. Monolayers with minutes amounts of W were generated by hydroseparation and studied using SEM-EDS, automated mineralogy, and microprobe analysis. Scheelite concentrates from the decommissioned processing plant were analyzed with XRF and 7 step sequential extractions. Groundwater was collected from eight wells in the Morkulltjärnen tailings in 2023 and 2024 and analyzed for 71 elements, anions and chemophysical parameters. This is one of the first field studies presenting mineralogical signs of scheelite weathering with tabular morphology, rod-shaped and porous structure, and loss of W from the crystal lattice, leading to very high concentrations (up to 23 mg/L) of dissolved W in anoxic and alkaline groundwater. This shows that standard mine waste management practices are unsuitable for scheelite, and action is needed to limit W mobilization into the surrounding environment from scheelite-rich tailings. Adsorption onto Fe-(hydr)oxides may be effective for controlling W mobilization from legacy mine waste, but in new mines, sulfides and tungstates should be separated in the processing plant and stored under anoxic and oxic conditions, respectively.

A digital innovation (DI) process with multiple stakeholder involvement is complex. While a reductionist approach might be occasionally favourable, embracing the complexity is more beneficial. In terms of researchers, it is social scientists’ central task to study complex phenomena in society. For practitioners, complexity causes innovation due to heterogeneous actors and dynamic interactions (i.e., non-linearity). Thus, this paper aimed to unveil the complexity mechanisms in DI processes’ structures and how interactions through these mechanisms foster DI. Two case studies of DI processes were conducted, where data was collected through interviews with project participants and through observing project meetings. The complexity mechanisms in DI processes’ structures include open systems, nested systems, distributed control, and dependence on key stakeholders (i.e., hubs). This research offers a theoretical contribution to understanding DI process complexity by identifying how these mechanisms can foster and hinder innovation. The complex interplay of these mechanisms could also bring change in how the DI process works and its boundary definition. Research about the complexity of DI has focused on DI ecosystems. This paper shifts the focus to the complexity of DI processes that produce DIs and cause their ecosystems to come into existence and evolve, and even cause the ecosystem’s extinction.

Urban Snow and Ice Removal and Storage (USIRS) is an essential and demanding component of urban management in areas experiencing harsh weather during the winter. This present study offers a systematic review of the literature in the USIRS field of study through analyzing 81 peer-reviewed academic papers listed in Scopus and Web of Science (published between 1960 and mid-2024). The pertinent research was thoroughly synthesized using content analysis by manual coding in ATLAS.ti software (to assist textual analysis), network visualization, and co-occurrence analyses (including temporal and geographical distributions). The results shed light on regional and temporal trends, links, and patterns across USIRS goals and strategies, and snow/ice reuse and deposit/dumping forms. These patterns also revealed common and not well-known connections in the literature. The findings indicate that the literature has divided USIRS goals into four categories: traffic, economic, human safety, and environmental. The most often discussed goals are connected to traffic (37%), with cost-efficiency coming in second (29%). Mechanical snow removal (41.96%) and de-icing (30.36%) are the most used USIRS strategies. While most defined snow/ice deposit or dumping forms are categorized as central (22.08%) or local (19.48%) deposits, emerging innovative practices like storing snow for cooling highlight the possibility of energy savings as the most prevalent type of snow/ice reuse outlined in literature. This study provides a beneficial resource for urban planners, academics, and policymakers by highlighting several hotspots and gaps within USIRS research. It aims to strengthen urban resilience to deal with winter difficulties and maximize the benefits of USIRS practices. 

Iraq has faced escalating water scarcity over the past two decades, driven by climate change, upstream water withdrawals, and prolonged economic instability. These factors have caused deterioration in irrigation systems, inefficient water distribution, and growing social unrest. As per capita water availability falls below critical levels, Iraq is entering a period of acute water stress. This escalating water scarcity directly impacts water and food security, public health, and economic stability. This study aims to develop a general framework combining decision support systems (DSSs) with Integrated Comprehensive Water Management Strategies (ICWMSs) to support water planning, allocation, and response to ongoing water scarcity and reductions in Iraq. Implementing such a system is essential for Iraq to alleviate its continuing severe situation and adequately tackle its worsening water scarcity that has intensified over the years. This integrated approach is fundamental for enhancing planning efficiency, improving operational performance and monitoring, optimizing water allocation, and guiding informed policy decisions under scarcity and uncertainty. The current study highlights various international case studies that show that DSSs integrate real-time data, artificial intelligence, and advanced modeling to provide actionable policies for water management. Implementing such a framework is crucial for Iraq to mitigate this critical situation and effectively address the escalating water scarcity. Furthermore, Iraq’s water management system requires modifications considering present and expected future challenges. This study analyzes the inflows of the Tigris and Euphrates rivers from 1933 to 2022, revealing significant reductions in water flow: a 31% decrease in the Tigris and a 49.5% decline in the Euphrates by 2021. This study highlights the future 7–20% water deficit between 2020 and 2035. Furthermore, this study introduces a flexible, tool-based framework supported by a DSS with the DPSIR model (Driving Forces, Pressures, State, Impacts, and Responses) designed to address and reduce the gap between water availability and increasing demand. This approach proposes a multi-hazard risk matrix to identify and prioritize strategic risks facing Iraq’s water sector. This matrix links each hazard with appropriate DSS-based response measures and supports scenario planning under the ICWMS framework. The proposed framework integrates hydro-meteorological data analysis with hydrological simulation models and long-term investment strategies. It also emphasizes the development of institutional frameworks, the promotion of water diplomacy, and the establishment of transboundary water allocation and operational policy agreements. Efforts to enhance national security and regional stability among riparian countries complement these actions to tackle water scarcity effectively. Simultaneously, this framework offers a practical guideline for water managers to adopt the best management policies without bias or discrimination between stakeholders. By addressing the combined impacts of anthropogenic and climate change, the proposed framework aims to ensure rational water allocation, enhance resilience, and secure Iraq’s water strategies, ensuring sustainability for future generations.

Objectives Fear of falling (FoF) is a common problem among older adults. It can lead to reduced quality of life and less physical activity, which increases fall risk. Earlier work has shown that FoF can be a manifestation of executive dysfunction in adults over 50 years, but studies on people over age 75 years are lacking. Executive functions (EFs) are cognitive functions associated with the frontal lobes and the prefrontal cortex. The aim of this study was to assess associations of EFs and FoF among people aged 80 years or older.

Methods This cross-sectional study was based on data from the Northern Sweden Silver-MONICA study and included 434 participants aged 80 years or older. EFs were assessed with the Frontal Assessment Battery (FAB) and FoF with the Falls Self-Efficacy Scale–International (FES-I). Multivariable linear regression analysis was used to examine associations among EF, FoF, and a comprehensive set of adjustment factors. Pearson correlation analysis was used to evaluate associations of FES-I and the subitems of the FAB.

Results EFs as measured by FAB were inversely associated with FoF (β = -0.23; 95% confidence interval, -0.42 to -0.03; p = 0.021), even after comprehensive adjustments. The FAB subitems measuring lexical fluency, inhibitory control, sustained attention, self-organization, motor programming, and planning also were inversely associated with FoF.

Conclusions Lower EF is associated with higher FoF among people aged 80 years or older. This information is important for treating and preventing FoF in this population.

Understanding the limitations of incorporating conventional machine learning synergy led to the inclusion of physics knowledge. This study presents the potency of physics-informed feature engineering for machine learning to enhance fault detection in gears, shafts, and bearings at three constant-speed running conditions. AI models such as Decision Tree (DT), Random Forest (RF), and Support Vector Machine-Radial Basis Function (SVM-RBF) are constructed to verify traditional statistical performance metric and physics-based signal descriptors. Additionally, time-frequency domain representation as spectrogram images is fed into the CNN-oriented ResNet-152 architecture to demonstrate the skillfulness of the model’s ability. Based on the results obtained, RF is observed to be supreme with 98.42% upon applying physics-centric parameters when compared with statistical variables. To make an inference, further comparison of the best classification model’s accuracy using physics expertise when accounted with ResNet image-based categorization, physics-grounded RF models have premier achievements. Thus, it is concluded that physical laws are expedient in offering exceptional outcomes for identifying various defects in complex industrial rotary machines in different operating modes.

This single-case study focuses on 91 Swedish upper secondary students’ educational encounters with a local historical archive via a place-based educational intervention. Furthermore, it investigates how this event stimulated the students’ historical empathy through their meetings with historical sources at the local archive. Focus group interviews with participating students before and after the visits revealed changes in their expressions of historical empathy. The students’ expressions of historical contextualisation and perspective taking were challenged after the visit. Many students did not view the local past they encountered at the archive as actual history. Most students related the traditional Western, European, and nation-state-centred history they had met through their previous history education to their historical perspective. However, the affective connection to local history primarily increased after the visit, which helped promote a full manifestation of historical empathy among the students. Before the visits, the affective connection had not been visual among the students at all, which shows that local historical archives, as cultural heritage institutions, can help students connect empathically with the past. The results further show that the local past met through the archival visits was a previously unknown historical perspective to the students.

The total cement energy consumption is around 5% of global industrial energy usage. In cement plants, mills consume half of this energy for dry grinding particles. However, grinding in tumbling mills is a random process, and a maximum of 5% of this energy would be directly devoted to particle size reduction. Thus, understanding interactions between operation variables and the mill energy consumption factors would be essential for sustainable cement production and green transition. Surprisingly, few investigations were conducted to study the energy consumption indexes of cement mills. Using a conscious lab “CL” as an advanced AI structure for industrial-scale problems could facilitate such an understanding of interactions within cement mill variables and promote controlling energy consumption for sustainable production. To fill the gap, this study developed a CL by examining different AI models (Random Forest, Support Vector Regression, Convolutional Neural Network, extreme gradient boosting, CatBoost, and SHapley Additive exPlanations) for modeling energy consumption indexes of a close ball mill circuit in a cement plant to address the effectiveness of operating variables. Explainable AI modeling highlighted interactions and measured the effectiveness of operating variables on mill energy consumption indexes. The airlift current and separator variables ranked the most effective operating factors on the mill energy consumption indexes. CatBoost, as an advanced AI model, showed the highest prediction accuracy for modeling (R2: 0.90). Such a CL model for a cement mill can be used for training operators, controlling the process, saving time and energy, reducing laboratory work, and scaling issues, and finally enhancing sustainability.

Advancements in satellite technology have led to the development of large constellations in Low Earth Orbit, which presents new challenges in orbital management. Controlling and managing these large numbers of satellites efficiently becomes un-scalable due to the high computational and telemetry demands. This article addresses the problem of station-keeping, where each satellite in a constellation independently calculates correction maneuvers to compensate deviations from the nominal orbit caused by orbital perturbations. Using Reinforcement Learning, a decentralized satellite station-keeping policy is trained in a high-fidelity simulation environment, to output a low-thrust finite-pulse maneuver plan. The trained neural network policy requires minimal computational resources and can be readily deployed onboard resource-constrained space-grade computers. The proposed framework employs the model-free Soft Actor-Critic algorithm, which observes the relative orbital elements between the satellite’s current and ideal/desired trajectory and outputs a maneuver plan consisting of thrust direction, start time, and duration. Two policies are trained to account for both in-plane and out-of-plane tracking. To this end, a realistic and fuel-efficient mission scenario is designed, keeping orbit-plane errors within specified bounds. Furthermore, the performance of the proposed framework is compared with an optimal-control-based station-keeping approach. The efficacy and robustness of the proposed framework is demonstrated through a series of Monte-Carlo simulations and benchmarked against the traditional optimization-based approach, on a wide array of initial conditions.

The wood preservation industry has contaminated numerous sites with polycyclic aromatic hydrocarbons (PAH). The aim of this study was to investigate the use of electrochemical oxidation (EO), without chemical additives, for in situ degradation of PAHs in soil co-contaminated with arsenic. Two 1 m3 boxes, each containing a 20 cm layer of contaminated sand overlaid with 20 cm of peat, were equipped with iron electrodes and placed outdoors. EO was applied using a pulsating direct current with alternating polarity, and its impact on PAH concentrations in soil and soil solution, as well as on the associated microbial community was assessed. Soil solution was sampled from the boxes over two seasons and analysed for PAH16 concentrations, showing an average decrease of 82 % by the end of the second season. This reduction was mainly observed in the medium and high molecular weight PAH fractions, suggesting that EO can effectively degrade more recalcitrant PAH compounds. The least reduction was seen in the low molecular weight PAH16, likely due to the replenishment from PAHs sorbed to the soil. Soil samples were taken from 12 different locations within the boxes, showing that PAH16 concentrations significantly decreased in 10 out of 12 sampling points, with a greater average reduction in sand (84 % decrease) compared to peat (69 % decrease). Microbial analyses of the soil samples revealed no significant changes in DNA concentrations across all taxa over time, with Gammaproteobacteria remaining the most abundant microorganisms in all samples, suggesting its ability to persist complex contamination.

The remediation of per-and polyfluorinated alkyl substances (PFAS) has received increasing attention because of their toxicity at even low concentration and their ubiquitous presence in water and soil resources. Electrochemical treatment with Boron Doped Diamond (BDD) electrodes has emerged as a powerful technology for PFAS removal and destruction from surface water, groundwater, and wastewater, including streams originating from the remediation of contaminated soils. Impressive removal rates and full mineralization can be achieved for several classes of PFAS with minimum demand of chemicals, an extremely low amount of waste generation and reduced energy requirements compared to other destruction technologies. Within this critical review, reaction pathways, solution chemistry and operational parameters are discussed, and recent advances of the electrochemical cell are summarized. Finally, synergic combination with other remediation techniques is presented, with the aim to promote the development of a robust approach to tackle PFAS contamination.

The stabilization of single-atom catalysts on semiconductor substrates is pivotal for advancing photocatalysis. TiO2, a widely employed photocatalyst, typically stabilizes single atoms at oxygen vacancies—sites that are accessible but prone to agglomeration under illumination. Here, we demonstrate that cation vacancies in Ti-deficient TiO2 nanosheets provide highly stable anchoring sites for Pt single atoms, enabling persistent photocatalytic hydrogen evolution. Ultrathin TiO2 nanosheets with intrinsic Ti4+ vacancies are synthesized via lepidocrocite-type titanate delamination and Pt single atoms are selectively trapped within these vacancies through a simple immersion process. The resulting Pt-decorated nanosheets exhibit superior photocatalytic hydrogen evolution performance, outperforming both Pt nanoparticle-loaded nanosheets and benchmarked Pt single-atom catalysts on P25. Crucially, Pt atoms anchored at Ti4+ vacancies display remarkable resistance to light-induced agglomeration, a key limitation of conventional single-atom photocatalysts. Density functional theory calculations reveal that Pt incorporation into Ti4+ vacancies is highly thermodynamically favorable and optimizes hydrogen adsorption energetics for enhanced catalytic activity. This work highlights the critical role of cation defect engineering in stabilizing single-atom co-catalysts and advancing the efficiency and durability of photocatalytic hydrogen evolution.

As mining progresses to greater depths, the challenges of high stress become more pronounced, often resulting in rockbursts that significantly impact deep underground mining operations. To address these challenges, Zinkgruvan mine in Sweden is testing destress drilling as a proactive measure to reduce the propensity for rockbursts and enhance the long-term stability of the mining drift, particularly in the roof and shoulders. Destress drilling holes, in this study, were drilled at 20° inclination on the periphery of the exploration drift and strategically placed ahead of development blasts. Laser scans of the drift were conducted before and after scaling, and the point cloud data was analysed using Cloud Compare software, with the Cloud-to-Cloud (C2C) algorithm employed to detect profile changes. This allowed for a comparison between blast rounds with and without destress drilling to assess the technique’s effectiveness. Results demonstrated that destress drilling reduced stress concentrations in the surrounding rockmass, as evidenced by reduced profile change. Blast rounds with destress drilling had up to 2.5 m3 less volume added per metre. C2C analysis showed 20 % to 30 % lower standard deviation and consistently lower mean deviation, indicating improved profile uniformity. These findings highlight the technical and operational benefits of destress drilling. 

The Rävliden North volcanogenic massive sulfide (VMS) deposit, in northern Sweden underwent polyphase deformation and greenschist to lower amphibolite facies metamorphism during the Svecokarelian orogeny. This caused remobilisation and recrystallisation of ore minerals, whose composition was analysed using laser ablation inductively coupled plasma mass spectrometry (LA-ICP-MS) and electron probe microanalysis (EPMA). Sphalerite, chalcopyrite, and pyrrhotite chemistry mirrors zonation of undeformed VMS deposits. Chalcopyrite-rich mineralisation contains higher Cu, Co, In, and lower Mn concentrations than sphalerite-rich mineralisation. Besides galena, Ag occurs in sulfosalts, tellurides, antimonides, and amalgams, which possibly formed through exsolutions from α-galena in syn- to post-tectonic structures. LA-ICP-MS imaging shows Ag-rich minerals in early syngenetic pyrite, in contrast to syn-metamorphic pyrite, indicating remobilisation during deformation. Despite sampling effects accounted for through linear mixed effects (LME) modelling, the results indicate that syn-metamorphic recrystallisation and remobilisation did not lead to substantial compositional changes in ore minerals. Instead, these processes partitioned Ga between sphalerite and chalcopyrite and enriched Ag, Cd, and Sb in minerals associated with younger parageneses. Zeolite-bearing veins in the hanging wall host sphalerite with the highest Ga, Ge, Cu, and Sb contents and galena with the lowest Bi, Te, and Tl contents. An origin potentially linked to far-field effects of the opening of the Iapetus Ocean or waning Timanian orogeny is discussed based on similarities to other vein- and breccia-hosted Zn Pb deposits in Northern Sweden. This study provides the first multiple-mineral in-situ trace element dataset for a VMS deposit in the Skellefte district, enhances understanding of element redistribution during metamorphism, and identifies remobilised elements potentially vectoring mineralisation at depth. Moreover, this study enables tracing of penalty and by-product elements in downstream beneficiation processes.

The Swedish government has introduced a requirement to conduct a climate declaration for new buildings, and climate impact limit levels are set to be implemented by 2025. This has put pressure on structural engineers, who are an important part of the decisionmaking process regarding materials for and design of the superstructure of buildings. Since climate calculations usually occur towards the end of the project and are a timeconsuming and manual effort, structural engineers seldom evaluate the climate impact of their designs daily. The literature also suggests that while integrations of building information modelling software for structural engineering with climate calculation software exist, there is still a long way to go before this becomes fully automated. Moreover, even if automated, does this really help the housing companies and consultants where they are today? The aim is, therefore, to discuss the role of digital workflows in lowering the climate impact of residential buildings. This paper presents a case study examining a recently built apartment building in Northern Sweden in collaboration with the structural engineers and the housing company. The digital workflow used during the design phase was mapped, and several redesign ideas (e.g., climate-improved concrete, timber instead of concrete, innovative foundation design) were evaluated for their climate impact, cost, and feasibility from the perspectives of the architect, the client, and the contractor. The connection between theredesign ideas and the digital workflow is discussed. The results show that while there is potential for enhancing the building information models and tools used in the project, the digital workflow is not the primary challenge to reducing the climate impact. Instead, other factors, such as limited access to climate-improved concrete and a lack of early discussion of the climate impact of the design ideas between the different actors involved, present more significant challenges.

This paper addresses the complex hydrothermal evolution of Metasomatic Iron and Alkali-Calcic (MIAC) mineral systems based on a review of the lithogeochemical footprints of IOA and IOCG deposits in the northern Norrbotten province (Sweden) and the Cloncurry district (Australia). The use of Na-Ca-Fe-K-Mg molar barcodes on a lithogeochemical diagram tailored for these mineral systems allows to depict the evolution of MIAC systems along diagnostic metasomatic paths from high (HT) to low temperature (LT) alteration facies as follows: 1) HT or LT Na alteration (300–1000 °C); 2) HT Ca Fe alteration (400–1000 °C); 3) HT K Fe alteration (350–450 °C); 4) HT K and HT K-Ca-Mg alteration; 5) LT K Fe, Na-Ca-Mg-Fe, and/or Na-Ca-Mg alteration (≤ ~350 °C); and 6) epithermal alteration (≤ 150 °C) and later stage hydrothermal veining. A distinct range of whole rock compositions and metal associations characterizes each alteration facies and can be captured by diagnostic molar barcodes and alteration indices. In northern Norrbotten, the IOA deposits are hosted in HT Ca Fe alteration facies but regionally intensely albitized regions are overprinted by K Fe alteration. The IOCG deposits are hosted in MIAC systems with zones of early Na ( Ca) alteration related to the regionally extensive albitite or scapolite alteration (Facies 1) and localized skarns. These are overprinted by HT Ca Fe alteration (Facies 2) and HT to LT K Fe alteration (Facies 3 and 5). The Cu Au mineralization is not systematically associated with the iron oxide-rich breccias and the intense K-feldspar- or sericite-rich K Fe alteration typical of many IOCG deposits worldwide. Instead, the lesser intensity of alteration and the abundance of mafic and ultramafic rocks in the environment lead to pattern enriched in Mg with relic of amphibole-rich alteration remaining in the assemblage as demonstrated for the Nautanen North IOCG deposit (Sweden). Consequently, the geochemical footprints of the Norrbotten Cu Au deposits are distinct from magnetite-group (e.g., Great Bear magmatic zone, Canada) and hematite-group (e.g., Olympic Dam, Australia) IOCG deposits even if they have all the known alteration facies of MIAC systems. Conversely, IOCG deposits in northern Norrbotten show similarities to certain deposits in the Cloncurry district of Australia. In both regions, the IOCG deposits are associated with HT Ca Fe and K Fe alteration facies that commonly overprint early Na and/or Na Ca alteration. In northern Norrbotten, IOA deposits are characterized by early Na alteration evolving towards Na Ca alteration, then Fe-rich Ca Fe alteration. These hydrothermal alteration types are subsequently superimposed by later K Fe alteration. We conclude that the use of Na-Ca-Fe-K-Mg molar barcodes provides new insights to understand the evolution of MIAC systems and is a powerful approach for unraveling superimposed alteration trends, which can serve as an exploration targeting tool from the district- to the deposit-scale in complex metasomatized areas.

The green transition necessitates manufacturing companies to address climate change and incorporate sustainable and resilient solutions into their production. The best opportunity to achieve such solutions in production is during development, especially in greenfield projects with fewer constraints from existing production solutions. So far, the knowledge of how to achieve this is limited. Therefore, this paper aims to elaborate on how manufacturing companies can attain sustainable and resilient production. The focus is on development practices in greenfield projects and their relation to active ownership, collaboration and learning, potentially supporting the lasting impact of change initiatives.

The paper builds on results from a multiple case study, including three greenfield production development projects. A research design, involving 22 semi-structured interviews and four workshops, was applied. An analytical framework was developed to support the analysis, including active ownership, collaboration and learning. In total, 21 different development practices were identified and categorised into active ownership, collaboration, and learning. The paper contributes a new perspective on production development. As an alternative to the traditional planning-and-control perspective, a learning perspective on production development was applied, which is increasingly required for production development processes addressing new domains, such as the green transition.

Background: National and international guidelines advocate for a multimodal approach to treating adult attention-deficit/hyperactivity disorder (ADHD), combining pharmacotherapy with psychological interventions. While recent reviews support cognitive behavioral therapy (CBT) as a viable treatment for ADHD in adults, evidence remains limited. Another challenge is the availability of psychological interventions, with stimulants remaining the primary treatment choice for adults with ADHD. One promising approach to increasing access to psychological interventions is the dissemination of internet-delivered CBT.

Objective: This study evaluated the feasibility, acceptability, and effects of a guided web-based stress management program specifically designed for working adults with ADHD. The intervention aimed to enhance quality of life by addressing stress, exhaustion, anxiety, and depression, commonly experienced by this population.

Methods: Thirty-six participants took part in a single-arm open trial, with assessments before, during, and after the intervention. The intervention consisted of 12 modules based on CBT principles, focusing on executive functioning, stress management, and emotion regulation, with clinician support on demand. Primary and secondary outcomes included quality of life (Adult ADHD Quality of Life Scale [AAQoL]), perceived stress (Perceived Stress Scale [PSS-10]), exhaustion (Karolinska Exhaustion Disorder Scale [KEDS]), anxiety (Generalized Anxiety Disorder 7-item Scale [GAD-7]), depression (Patient Health Questionnaire [PHQ-9]), and ADHD symptoms (the World Health Organization Adult ADHD Self-Report Scale [ASRS]).

Results: Results indicated a statistically and clinically significant improvement in quality of life (Cohen d=0.84), and a reduction in ADHD symptoms (d=0.98), as well as statistically significant reductions in perceived stress (d=0.83), exhaustion (d=1.12), anxiety (d=1.70), and depression (d=1.25). Improvements were sustained at a 12-week follow-up. A clinically significant improvement in quality of life was observed in 36% (13/36) of participants. Participants reported high satisfaction with the program and the guidance. Adherence was high, with an overall assessment response rate of 84%, a mean of 78% of modules opened, and no explicit dropouts. Twelve of the 36 participants reported negative effects. Qualitative content analysis of participants’ written feedback revealed positive experiences and suggestions for improvement.

Conclusions: This study suggests promise for web-delivered interventions tailored to the needs of adults with ADHD, pending further research and development in controlled studies.

Ever since the rise of the Internet, digitalization has been increasingly shaping firms’ business processes and models. The rapid rise of digital technologies has perhaps even changed some of the fundamentals of strategy, including strategic decision-making, cognition, discourse and practices. However, limited effort has been made to structure the strategy-oriented academic discourse in the digitalization era. This essay begins by defining and positioning the digital transformation from a strategic perspective. We provide grounds for this essay based on the existing research on a literature search and review to add theoretical structure to the field through the bibliometric mapping of 626 digital transformation studies from high-quality academic journals (AJG3 and AJG4 journal quality levels). Based on the analysis, we identify four clusters of research perspectives: (1) digital transformation, (2) digital strategy, (3) digital business model innovation and (4) digital marketing. Our essay contributes to the emerging digital transformation literature by positioning these different perspectives in digital phenomena that are relevant and adjacent to strategy and strategizing. Finally, we position the special issue papers in the research perspectives identified, and we suggest potential avenues for future research.

Cross-country skiers employ various techniques, where the ski is exposed to different forces during the motion. This study utilized a novel sled tribometer to investigate the combined effects of load and positioning of the skier on the coefficient of friction (COF) between the skis and snow. Three different loads (40 kg, 80 kg and 120 kg) were applied to the sled, and the center of mass was systematically varied between three positions behind the binding position: 70mm (leaning forward), 140mm (centered) and 210mm (backward). A variety of skis were used, including different models of skate skis and one classic-style ski with grip wax. The results consistently demonstrated that increasing the load on the sled reduced the COF by up to 15% (from the lowest to highest load), regardless of the position of the center of mass. The position of the center of mass had a minimal effect on COF in most tests. An exception was observed when using grip wax, where a forward-leaning position combined with a heavy load significantly increased the COF (~ 8%) compared to what is expected without grip wax. This load-dependent reduction in the COF was observed across different skis and test sessions. The ski camber profile was measured for all skis in all configurations. In general, increasing the load increases the glide zone length but at the same time increasing the average pressure. The position of the center of mass has little to no effect on the rear glide zone but slightly alters the length and position of the front glide zone. While the mechanisms of friction are discussed, a complete understanding of these mechanisms has not yet been reached. 

The construction industry, being material-intensive, is a major target for sustainability initiatives due to its significant consumption of energy and resources. In response, circular economy principles are gaining interest from the construction industry, since they benefit the environment and promote sustainable societal development. Nevertheless, the implementation of these circular economy principles has not been widely adopted by construction companies within the construction industry, indicating substantial room for improvement in areas such as coordinating policies, market conditions, and business model development for different actors within the project-based construction industry. The objective of this research is to explore the content of circular business models for construction companies, as they play a critical role in promoting the implementation of circular economy principles to foster responsible consumption of raw materials and mitigate the environmental impact of the industry. Current research in this field lacks systematic views, which may enhance understanding and provide a theoretical basis for researchers and construction companies transitioning toward circular economy principles. This article employs a literature review method of including 53 journal articles, where the content of analysis reveals 34 aspects related to circular business models for construction companies. Additionally, the study outlines future research directions, focusing on the intersections between different elements within the business model. The findings of this study offer valuable insights for policymakers on how to strengthen external support and the development of circular value networks to promote the adoption of circular business models from the perspective of construction companies.

Existing vibration and buckling analysis models for the partial-composite beam/column elements are restricted to a limited number of constituting layers. This is due to the escalated complexity of the governing equations with an increase in the number of layers. The present study formulates the stability and vibration problems of columns and beams composed of any number of identical constituting layers, incorporating the effects of interlayer partial-interaction imperfection. A Timoshenko/Engesser-hypothesis-based partial-composite (TEPC) model is developed and a novel analytical solution scheme is implemented into the extracted governing differential equations. As a result, efficient conversion coefficients are introduced, converting the well-known classical Euler column buckling and beam vibration formulae to those of multilayer elements having interlayer partial-interaction imperfection based on the TEPC model. The validity of the proposed approach is verified through comparison with available experimental data and the conducted 3-D FEA. It is shown that the most significant reduction in the predicted buckling capacity of partial-composite multilayer columns, when transitioning from the EBPC model to TEPC, occurs for the columns with the highest interlayer interaction. Furthermore, it is shown that the influence of interlayer interaction level on the Euler-to-Timoshenko/Engesser conversion coefficients becomes less pronounced as the number of constituting layers increases.

Forming hybrid structures into complex shapes is key to address lightweighting of automotive parts. Recently, an innovative joining technique between aluminium and Carbon Fibre-Reinforced Polymer (CFRP) based on mechanical interlocking through sheet punching has been developed. However, scaling up the solution requires the assessment of challenges, such as multi-material forming and joint integrity, after forming operations. Therefore, this work proves the feasibility of forming aluminium–CFRP prepreg panels into complex omega-shaped profiles following a conventional cold-stamping process. Forming without defects was possible even in specimens featuring mechanical joints generated through punching. The effect of the CFRP position (in the inner or the outer side of the formed profile), the number of mechanical joints, the addition of a Glass Fibre-Reinforced Polymer (GFRP) intermediate layer to prevent galvanic corrosion and adequate lubrication on necking, cracking, springback behaviour and the final geometry after curing were studied. Compression tests were performed to assess the mechanical response of the hybrid profile, and the results showed that the addition of CFRP in the aluminium omega profile changed the buckling behaviour from global bending to axial folding, increasing the maximum compression load. Additionally, the presence of mechanical interlocking joints further improved the mechanical performance and led to a more controlled failure due to buckling localization in the geometric discontinuity.

The current standard model of cosmology successfully describes a variety of measurements, but the nature of its main ingredients, dark matter and dark energy, remains unknown. Euclid is a medium-class mission in the Cosmic Vision 2015–2025 programme of the European Space Agency (ESA) that will provide high-resolution optical imaging, as well as near-infrared imaging and spectroscopy, over about 14 000 deg2 of extragalactic sky. In addition to accurate weak lensing and clustering measurements that probe structure formation over half of the age of the Universe, its primary probes for cosmology, these exquisite data will enable a wide range of science. This paper provides a high-level overview of the mission, summarising the survey characteristics, the various data-processing steps, and data products. We also highlight the main science objectives and expected performance.

Superlattice hydrogen storage alloys have attracted much attention due to their high capacity, excellent cyclic stability, and moderate operating conditions. This review, focusing on journal articles published between 2020 and 2025, comprehensively covers the impacts of doping with different rare-earth elements and the substitution of different elements on superlattice hydrogen storage alloys and details the influence mechanisms of different preparation methods, such as arc melting and powder metallurgy, on the phase structure of alloys. A thorough analysis is conducted on how rare-earth element doping alters the crystal structure, lattice parameters, and phase stability of alloys, thereby affecting their hydrogen storage performance. Meanwhile, the differences in the effects of different substituting elements at various substitution sites on the phase structure and hydrogen storage performance of alloys are explored, and the regular patterns and influencing factors are summarized. This review provides a new perspective for the design and development of high-performance superlattice hydrogen storage alloys and is expected to contribute to the long-term and sustainable development of clean hydrogen energy.

Implementing carbon capture and utilization (CCU) technologies is crucial to control the detrimental impact of increasing levels of greenhouse gases on the ecosystem. Among existing technologies for CO2 utilization, methanation stands out as an efficient method, capable of effectively mitigating CO2 concentration while also contributing to meeting energy requirements. The performance of commonly used Ni-based catalysts supported over meso/microporous materials depends upon active sites, metal dispersion, and resistance against deactivation due to coking and sintering. The mentioned characteristics can be tailored by modifying the structural properties of the support material and adding promotors to the catalyst. In this work, Ni-based catalysts were supported over hierarchical zeolite 13X (h13X) and incorporated with different promotors (Mg, Ca, Ce, and La) using the wet-impregnation method. The synthesized catalysts were characterized using XRD, TGA/DSC, SEM, and N2 sorption and desorption techniques to analyze the physical and chemical characteristics. The performance of the catalysts was evaluated for CO2 conversion to methane at different temperatures (250-500 oC), while stability analysis was carried out at 400 oC and 1 bar for 50 hours at GHSV of 60,000 ml.gcat.h-1. Among the promoted catalysts, the 1%La-Ni/h13X catalyst exhibited the highest catalytic activity across all temperatures, with a maximum conversion of 57.6% at 400 oC under atmospheric conditions. Unexpectedly, 1%Mg, Ce, and Ca-promoted catalysts exhibited no significant improvement in the activity of catalysts compared to containing La. The Mg-promoted catalyst showed better stability during 50 hours of methanation reaction compared to La, demonstrating better resilience against deactivation. Likewise, a catalyst containing 3%La displayed superior performance compared to catalysts promoted with 3%Mg, Ce, and Ca. The outstanding performance of 1%La-Ni/h13X catalysts could be ascribed to their high surface area, dispersion, and reducibility. This study highlights the potential of microporous zeolites for CO2 methanation and other heterogeneous reactions.

The construction industry is a major contributor to global CO2 emissions due to high energy consumption in buildings and the production of carbon-intensive materials. Although Big Data is recognized as a transformative tool for improving sustainability by optimizing energy use, resource efficiency, and decision-making, its adoption in construction remains limited. This study aims to identify and analyze the technical, economic, and organizational factors influencing Big Data adoption for sustainability and climate neutrality in Swedish construction companies. A quantitative survey was conducted among 150 industry professionals, and the data were analyzed using descriptive statistics, Spearman correlations, ANOVA, chi-squared (χ2) tests, and principal component analysis (PCA), guided by the diffusion of innovations (DOI) theory. The results indicate that the respondents broadly acknowledge benefits such as energy savings, cost reductions, and improved decision support. The PCA revealed two key dimensions—one capturing technical/environmental benefits, the other economic/regulatory benefits—while barriers included standardization issues, limited digital skills, and investment uncertainties persist. The findings suggest that overcoming these barriers is essential for accelerating a strategic and climate-aligned digital transition in construction, offering actionable insights for policymakers and industry leaders.

We present an innovative statistical test designed to assess the first-order separability of a spatio-temporal point process. Our proposed test employs block permutations and a novel test statistic that incorporates a machine learning technique known as the Hilbert–Schmidt independence criterion. To enhance the practicality of the criterion, we apply the kernel trick. The block permutations are designed to maintain the second-order structure of the point pattern, disrupting it only at the block borders. This design enables the application of our test to a general spatio-temporal point process, which may exhibit small-scale clustering or regularity. We investigated the empirical level of the block permutation-based tests with the new and two previously proposed test statistics for clustered and regular point processes, represented in our study by log Gaussian Cox processes and determinantal point processes. By comparing our results with those obtained from a previously proposed permutation-based test, we confirmed the effectiveness of our method in terms of significance level, power, and notably computational cost. We applied the test to real-world datasets, namely the UK’s 2001 foot-and-mouth disease epidemic and varicella data from Valencia.

Water splitting is a promising and sustainable technology that can address energy and environmental challenges by producing clean H₂ without emissions of harmful pollutants. However, alkaline water oxidation is the most relevant process at the industrial level, and it faces obstacles due to unfavorable thermodynamics and high overpotential. The search for new environmentally friendly materials with high activity and low cost is a significant challenge. Herein, Mn₂P₄O₁₂ microspheres are synthesized from MnO₂ nanosheets via hydrothermal and chemical vapor deposition processes by regulating phosphorization as a new material in water-splitting catalysis. The spherical Mn_₂P_₄O_₁₂ microstructures act as pre-catalysts and undergo surface reconstruction during electrochemical activation, leading to the formation of β-MnO_₂ as the true active phase. Once stabilized, they exhibit outstanding catalytic performance (250 and 510 mV at 10 and 100 mA cm⁻² with Tafel slope as low as 40.80 mV dec⁻¹) and stability for more than 32 h at different potentials. The in situ surface reconstruction highlighted by a multi-technique analysis ensures the catalyst's stability and results in efficient catalytic active sites for adsorbed oxygen, which enhances overall performance. This study provides insights into cyclotetraphosphate catalysis and offers a pathway for developing efficient and cost-effective materials for water electrolysis.

Predicting the energy consumption of buildings plays a critical role in supporting utility providers, users, and facility managers in minimizing energy waste and optimizing operational efficiency. However, this prediction becomes difficult because of the limited availability of supervised labeled data to train Artificial Intelligence (AI) models. This data availability becomes either expensive or difficult due to privacy protection. To overcome the scarcity of balanced labeled data, semi-supervised learning utilizes extensive unlabeled data. Motivated by this, we propose semi-supervised learning to train AI model. For the AI model, we employ the Belief Rule-Based Expert System (BRBES) because of its domain knowledge-based prediction and uncertainty handling mechanism. For improved accuracy of the BRBES, we utilize initial labeled data to optimize BRBES’ parameters and structure through evolutionary learning until its accuracy reaches the confidence threshold. As semi-supervised learning, we employ a self-training model to assign pseudo-labels, predicted by the BRBES, to unlabeled data generated through weak and strong augmentation. We reoptimize the BRBES with labeled and pseudo-labeled data, resulting in a semi-supervised BRBES. Finally, this semi-supervised BRBES explains its prediction to the end-user in nontechnical human language, resulting in a trust relationship. To validate our proposed semi-supervised explainable BRBES framework, a case study based on Skellefteå, Sweden, is used to predict and explain energy consumption of buildings. Experimental results show 20 ± 0.71% higher accuracy of the semi-supervised BRBES than state-of-the-art semi-supervised machine learning models. Moreover, the semi-supervised BRBES framework turns out to be 29 ± 0.67% more explainable than these semi-supervised machine learning models.

This article questions the neoclassical claim that compared to command-and-control instruments such as performance standards, economic policy instruments (e.g., taxes or emission allowance schemes) represent a superior policy approach to mitigate industrial pollution. Based on recent research, we argue that the ongoing transition towards zero-carbon production processes involves specific challenges, which tend to strengthen the case for using standards-based regulations, not least quantitative performance standards. Empirical research suggests that this regulatory approach is not necessarily adopted as crudely as some economic models (and textbooks) suggest. Efforts can be undertaken to reduce the cost of compliance and incentivize technological change. These, in turn, must address the entire set-up of the regulatory systems, including knowledge generation and transfer, social trust, and the forms of relationships between regulators and industry. All in all, it is essential to abstain from simplified normative arguments about instrument choices in environmental policy regardless of the institutional context.

A full set of optimized observables is measured in an angular analysis of the decay B⁰ → K^∗(892)⁰μ⁺μ⁻ using asample of proton-proton collisions at √𝑠 = 13TeV, collected with the CMS detector at the LHC, correspondingto an integrated luminosity of 140 fb⁻¹. The analysis is performed in six bins of the squared invariant mass ofthe dimuon system, 𝑞², over the range 1.1 < 𝑞² < 16GeV². The results are among the most precise experimentalmeasurements of the angular observables for this decay and are compared to a variety of predictions based onthe standard model. Some of these predictions exhibit tension with the measurements.

In this study, new pyrrolo[3',4':3,4]pyrrolo[1,2-a][1,10]phenanthroline derivatives are developed and their stabilities and transformation pathways are investigated. The synthetic approach toward these novel derivatives include a pivotal [3 + 2] cycloaddition of in situ generated ylides, followed by cycloadducts oxidation and other unexpected transformations. The structures of the intermediate and final compounds are proposed based on information obtained from several spectral techniques. Stability study reveal that electron-donating groups in the para position of the phenyl ring promote easier oxidation, whereas electron-withdrawing substituents enhance the stability of the compounds. The acid–base titration of α-monosubstituted 1,10-phenanthroline 6a results in a reversible color change, which is preliminarily explored through spectral methods.

This systematic review comprehensively examines the application and impacts of Educational Data Mining (EDM) over the past decade. It explores the use of various data mining tools and techniques, statistics, and machine learning algorithms in education. The review discusses how EDM helps understand and improve the learning experience, educational strategies, and institutional efficiency. It highlights the iterative process of EDM, its applications, and the benefits it offers to different stakeholders, including students, teachers, and educational institutions. The paper also discusses the challenges related to data ethics, privacy, and security in EDM. Key sections include a methodology for conducting the systematic review, exploring different data mining techniques and learning styles, and using Artificial Intelligence in EDM. The review concludes with a discussion of findings, future research directions, and a summary of the study’s contributions and limitations.

The rapid expansion of Bangladesh’s digital economy has unlocked new opportunities for online businesses, but it has also heightened the challenges of delivering high-quality e-services in an increasingly competitive environment. While previous studies have largely focused on customer satisfaction and consumer experiences, the perspective of business owners—those managing daily operations and strategic decisions—has been notably underexplored. Addressing this gap, the present study aims to identify and prioritize the critical success factors (CSFs) influencing the performance of online businesses in Bangladesh from the owners’ viewpoint.A structured survey has been conducted among 57 online business owners, collecting data across ten essential dimensions, including website infrastructure, information sharing, delivery practices, responsiveness, customization, customer service, reliability, payment security, return policies, and service quality challenges. To ensure a data-driven analysis, the Random Forest algorithm is utilized for feature importance extraction, followed by pairwise comparisons through fuzzy triangular matrices. Subsequently, FAHP and TOPSIS techniques are applied to systematically rank the factors and alternatives. Model validation using the XGBoost classifier results in 93% accuracy and a 93% F1-score, demonstrating the robustness of the proposed approach. The results reveal that service quality concerns, payment security, and effective customer support are the most influential determinants of business success. Beyond offering a methodological advancement that integrates machine learning with MCDM, this study delivers actionable insights for business owners, digital strategists, and policymakers aiming to enhance the competitiveness and resilience of Bangladesh’s e-commerce landscape.

The implementation of water-related environmental measures (WREMs) in agricultural businesses presents an opportunity to align food production with sustainable value creation through ecosystem services. However, the complexity of adopting these measures, and making them a part of business models, necessitates the involvement of intermediaries to facilitate business opportunities that align with governmental sustainability goals. This study investigates the role of government-affiliated intermediaries in facilitating agricultural business plans, motives, and support needs for implementing WREMs. Using baseline and follow-up questionnaire data from 255 agricultural business managers, the study finds that intermediaries play a crucial role in fostering long-term sustainability commitments. Their facilitation, through expert advice, knowledge transfer, and resource mobilization, significantly increased the likelihood of businesses planning semi-long-term WREMs. However, the correlation between WREMs and market-driven value creation remained weak, highlighting the need for enhanced support in developing financially viable sustainability solutions. These findings underscore the importance of aligning intermediation strategies with business managers’ financial incentives to drive broader adoption of sustainable agricultural practices.

The aim of the present paper is to explore, describe and discuss the pedagogical content knowledge of visualizations as representations of grammatical structure. We first illuminate how visualizations are commonly employed in grammatical theory. Secondly, we present a case study where visualizations were adapted to scaffold upper secondary students’ (age 15–17) understanding of grammatical structures in the language classroom. The analyses are founded on two theoretical pillars: the theory of transformation processes as part of pedagogical content knowledge, and theorization of visualizations as found in the field of mathematics and science education. The results show that logical and visual thinking in grammar theory are closely connected and interdependent, as visualizations are used to represent logic of generalizations and patterns. The case study further indicates that simplified representation in the form of visualizations, such as colors and simplified field schemes of sentence structure, can facilitate students’ ability to observe and generalize grammatical structure. By transforming content into learning by using visualizations, students can be scaffolded in their comprehension of abstract grammatical concepts. Finally, the paper discusses instructional strategies that teachers must consider when integrating visualizations into the language classroom.

This study investigates the influence of iron on the optical properties of peralkaline soda aluminosilicate glasses (NASF), focusing on the interplay between glass structure and optical behavior. Fe doping significantly affects the glass network, altering both its structural connectivity and optical properties. Raman spectroscopy reveals that Fe modifies the glass structure by increasing non-bridging oxygens (NBOs), reducing network connectivity. For the highest FeOtot concentration of 8.2 at.%, a more polymerized structural reorganization occurs due to iron self-compensation. Optical analysis shows that Fe incorporation increases the optical absorption and shifts the absorption edge to higher wavelengths. Tauc plot calculations reveal a decrease in the optical bandgap from 4.0 to 3.2 eV, while the Urbach energy indicates an increase of structural disorder. The photoluminescence (PL) excitation and emission related to Fe3+ energy levels is observed, with a broad red emission peak at 700 nm, decreasing with Fe concentration in relation to higher defectivity and clustering. A detailed lifetime analysis shows long lasting tails of the order of milliseconds, also decreasing with Fe content due to non-radiative recombinations and quenching. 

Introduction: Pneumonia is the primary cause of mortality in preterm infants in developing nations; yet, early detection and treatment can significantly reduce mortality rates. Pharmaceutical researchers are diligently striving to identify avariety of drugs that might effectively cure pneumonia.

Method: We are motivated to examine the quantitative structureproperty relationships (QSPR) of anti-pneumonia pharmaceuticals. We employed K-Banhatti topological descriptors and analyzed the findings to achieve this. For estimation of physicochemical properties of pneumonia treatment drugs we utilized linear, quadratic, cubic, and biquadratic regression analyses.

Results and Conclusion: The drugs comprise linezolid, ceftabiprole, and clarithromycin, among others. Topological descriptors enable the exploration of the complexity, connectivity, and other essential attributes of molecules. The quantitative structure-property relationship (QSPR) analysis of pharmaceuticals for illness treatment employing K-Banhatti topological descriptors is an economical approach utilised by pharmaceutical researchers. We performed a QSPR analysis on 20 anti-pneumonia drugs to ascertain the most precise predictions for five properties: enthalpy, flash point, molecular weight, molar volume, and molar refractivity, employing five K-Banhatti indices. To do this, we used linear, quadratic, cubic, and biquadratic regression analyses to find links between molecules and the physical and chemical properties of drugs used to treat pneumonia. Employing molecular descriptors and regression models to investigate chemical patterns is a cost-effective and theoretical methodology.

This research delves into the intricate melt flow dynamics observed in asymmetric stainless steel 316L T-joints with dual-beam laser welding, with a particular focus on configurations involving sheets inclined at angles up to 45°. Given the complexity of this welding scenario, the use of filler wire is necessitated, which is only accessible from the top side in this case. To capture the transient melt pool phenomena high-speed imaging techniques (HSI) were employed at both the top and root of the weld. Through HSI observations along with streak analysis, three primary mechanisms were identified: Atypical-flow connection-root and top side, atypical-flow connection-top side and typical-flow disconnection-root side. These observations highlight several critical factors, at the top and bottom of the weld, including intermittent keyhole openings, transient melt flow effects, potential spatter ejection, reabsorption, pronounced bulge and disintegration. These phenomena collectively contribute to weld imperfections, revealing impacts on meandering root and craters. The study provides a comprehensive understanding of 3D asymmetric melt flow dynamics. By categorizing the observed mechanisms, this research is theoretically described and formulated in a systematic flowchart, for laser welding of an asymmetric T-joint. 

Understanding ore texture is essential for optimising comminution and mineral liberation, yet no standardised framework exists for defining and quantifying textural attributes. This review explores the complex relationship between ore texture, breakage mechanisms, and mineral liberation, emphasising the importance of textural parameters such as grain size, mineral intergrowths, and mechanical properties in determining comminution behaviour. The review is structured into key themes: (i) characterisation techniques for ore texture, spanning optical, electron beam, X-ray, and laser-based methods; (ii) conventional and proxy comminution tests for assessing ore hardness and breakage response; (iii) modelling approaches to relate ore texture to mineral liberation, including texture-based and kinetic-based liberation models; and (iv) innovative pre-treatment methods such as microwave heating, high-voltage pulse disintegration, and ultrasonication, which aim to promote non-random breakage and improve mineral liberation. The literature is analysed through a comparative assessment of these methods, evaluating their applicability, limitations, and integration potential in modern mineral processing.

Key takeaways include the need for a standardised classification of ore texture, at least for mineral processing purposes, improved methods for quantifying breakage modes, and the development of more accurate liberation models that incorporate textural heterogeneity. While advanced pre-treatment techniques show promise in promoting non-random comminution, their widespread adoption remains constrained by energy consumption and economic feasibility. By synthesising recent advancements and identifying research gaps, this review contributes to the field by advocating for a geometallurgical approach that integrates ore texture characterization into comminution models. 

Wound infections result in delayed healing, morbidity, and increased risks of sepsis. Early detection of wound infections can facilitate treatment and reduce the need for the excessive use of antibiotics. Proteases are normally active during the healing process but are overexpressed during infection as part of the inflammatory response. Proteases are also produced by the bacteria infecting the wounds, making proteases a highly relevant biomarker for infection monitoring. Here, we show a fluorescence turn-on sensor for real-time monitoring of protease activity in advanced nanocellulose wound dressings for rapid detection of wound pathogens. Colloidal gold nanoparticles (AuNPs) were adsorbed on bacterial cellulose (BC) nanofibrils by using a carefully optimized self-assembly process. The AuNPs could either be homogeneously incorporated in BC dressings or 3D printed in wood-derived cellulose nanofiber (CNF) dressings using a BC-AuNP ink. The BC-adsorbed AuNPs were subsequently functionalized with fluorophore-labeled protease substrates. Cleavage of the substrates by proteases produced by the wound pathogens Staphylococcus aureus and Pseudomonas aeruginosa resulted in a significant increase in fluorescence that correlated with the growth phase of the bacteria. Wound dressing with integrated sensors for the detection of proteolytic activity can enable the sensitive and rapid detection of infections, allowing for optimization of treatment and reducing the risks of complications.

Environmental sustainability dilemmas may be perceived by students as unattainable and difficult to deal with. To circumvent this, research suggests employing encouraging principles such as empowerment, local ownership, collaboration, and resilience. In this study, we sought to explore middle-school students’ learning about science through engaging them in a co-creation process. This meant that fifth graders worked with local urban planning, water usage reduction and water pollution together with science professionals. The classroom observations were analysed through the framework of scientific habits of mind. The results pointed to that co-creation encouraged and enhanced discussions and created opportunities for the students to employ different aspects of science. The discussions were driven by the participants as a curiosity statement could lead to new utterances of rationality, objectivity or suspension of belief. This way of argumentation may socialise the students into a scientific way of reasoning and the interactions become opportunities for learning about science.

The energy transition, which aims to reduce carbon emissions and to slow down climate change, demands an ever-increasing supply of the so-called “critical metals”. Rare-earth Elements and Yttrium (REY) are among the most critical metals, as they are indispensable in most technologies associated with the generation and storage of renewable energy. In recent years there has been a growing interest in the potential of karst bauxites as nonconventional sources of REY and other critical metals such as Sc and Ga. The Sierra de Bahoruco (SW Dominican Republic) contains the most REY-enriched karst bauxites globally. In view of the high potential for hosting important REY contents, the Dominican Republic government has declared the Reserva Fiscal Ávila (RFA), a state-owned area within the Sierra de Bahoruco for assessment and exploration of its REY resources. In this study, we present the first data on the mineralogy and composition of bauxitic rocks from the RFA. The bauxitic deposits comprise clayey bauxites and Fe-rich bauxites that are composed predominantly of Al-oxyhydroxides (gibbsite, boehmite and nordstrandite), kaolinite and Fe-oxyhydroxides. The bauxites are enriched in REY, with a median value of 1,310ppm and up to 2,542ppm, with a consistent enrichment in Light REE (LREE) and Y compared to Middle REE (MREE) and Heavy REE (HREE). The positive correlation between the contents of REY and Th, and negative correlation with K, makes gamma-ray spectrometry an appropriate tool for the exploration. In addition, bauxitic rocks from the RFA contain significant Sc (up to 105ppm) and Ga (up to 54ppm) contents, and their extraction could potentially represent a substantial economic surplus to the revenue generated solely from the aluminum production.

A standard model effective field theory (SMEFT) analysis with dimension-six operators probing nonresonant new physics effects is performed in the Higgs-strahlung process, where the Higgs boson is produced in association with a W or Z boson, in proton-proton collisions at a center-of-mass energy of 13 TeV. The final states in which the W or Z boson decays leptonically and the Higgs boson decays to a pair of bottom quarks are considered. The analyzed data were collected by the CMS experiment between 2016 and 2018 and correspond to an integrated luminosity of 138 fb⁻¹. An approach designed to simultaneously optimize the sensitivity to Wilson coefficients of multiple SMEFT operators is employed. Likelihood scans as functions of the Wilson coefficients that carry SMEFT sensitivity in this final state are performed for different expansions in SMEFT. The results are consistent with the predictions of the standard model.