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

Modeling the turbulent flow within different draft tube configurations in a cost-effective way is essential for efficient turbine optimization and exploring the underlying flow mechanics. In this study, a convolution neural network (CNN) based surrogate model was proposed to predict local flow parameters within different inclined Francis turbine draft tubes. Three symbolic representations denoting the complex geometry and boundary conditions were set as the input, and pressure and velocity were the output. The adopted CNN framework consists of the U-Net architecture with a contracting path and four expansive paths. Six representative hyperparameters were considered to analyze their influence on the performance and generalization ability of the CNN model. The results show that the predicting accuracy of the CNN model with a U-Net network is 7.53% higher than the traditional CNN model, as skip connections improve image segmentation accuracy. The CNN model with a larger convolution kernel can more comprehensively capture the main features of the flow field. The model with three input variables improves prediction accuracy by 2.4% as more geometrical features correlate with the key flow patterns. For the four different image resolutions, the model with a resolution of 200 × 400 performs exceptionally well. In addition, appropriately increasing the number of convolutional layers or blocks can significantly improve the prediction accuracy of the CNN model. The proposed innovative surrogate model is useful for facilitating the optimization of hydraulic turbine components. 

This study is an investigation of the impact of grinding-generated surface roughness on the wear and fatigue life of rails. The grinding process, essential for rail maintenance, creates a rough surface that significantly influences wheel/rail contact conditions. Using a combined experimental and numerical approach, this work replicates and integrates grinding-generated roughness into an elastoplastic rolling contact model. To evaluate the effects of surface roughness on rail degradation, Archard's wear equation is applied for wear assessment, while fatigue is analysed using the Jiang-Sehitoglu fatigue parameter. The results show that rough surfaces induce localised high stresses and plastic strains, accelerating material degradation, particularly in early rolling cycles. In contrast, smoother surfaces exhibit more stable plastic strain evolution over fewer cycles. Additionally, the grinding-generated roughness significantly increases the damage accumulation, highlighting its role in reducing rail life. These findings emphasize the need to incorporate surface roughness into predictive maintenance models and optimise grinding practices to ensure rail longevity and operational reliability.

In Finland and Scandinavia, even-aged forest management predominates, often including mechanical site preparation and manual planting. Growing labor shortages and increased demand for sustainability have driven interest in mechanized and autonomous planting systems. This study evaluates two automated Coverage Path Planners (CPP), Pathfinder and TerraTrail, developed to optimize planting routes for mechanized forest regeneration. Their performance is compared to the routes of the manually operated mechanized planting machine, PlantMax. Three operational sites in Sweden, representing varied terrain and hydrological conditions are evaluated. The evaluation focuses on coverage, Euclidean and Dubins path lengths. Both CPPs incorporate Digital Elevation Models (DEM), Depth-to-Water (DTW) maps and vehicle-specific kinematics to generate planting routes. Two scenarios are evaluated: one where the CPPs neglect the DTW map, and another where the CPPs are constrained to avoid DTW values below 0.3 m. Results show that automated CPPs achieve 15–19% higher coverage than manual planning on average. Pathfinder showed similar normalized path lengths in an unconstrained scenario as the manual operator, but 14% shorter in the constrained environment. TerraTrail shows 7% longer normalized path lengths in an unconstrained scenario, while the constrained scenario shows similar path lengths as the manual operator. These findings emphasize the potential of deploying automated CPP systems to enhance precision, sustainability, and labor efficiency of silvicultural operations. The CPPs support both autonomous deployment and decision support tool for operators. Further refinement, including combining both CPPs to leverage the best functions of each, along with reversible path planning, could enhance their value in forestry practices.

This study investigated the relationship between biological effects and contamination profiles in sediments from 17 stormwater sedimentation facilities across four Swedish municipalities. Sediment extracts were examined using a battery of in vitro bioassays targeting five modes of action: aryl hydrocarbon (AhR), estrogenic (ER), androgenic (AR), antiandrogenic (AntiAR) receptors, and oxidative stress (Nrf2) activities, along with cytotoxicity. Significant differences in biological activities across cities aligned with patterns in previously characterized profiles of 259 urban-sourced organic substances. Concentrations of bioactive substances correlated positively with biological activities; however, they explained only a limited fraction (typically <10%) of the observed effects, suggesting the influence of unmonitored substances. Among the examined substances, polycyclic aromatic hydrocarbons and alkylphenols were the most prominent drivers of chemically explained biological activities across end points. A masking effect between AR and AntiAR was revealed, with negative correlations between AR responses and agonist concentrations indicating dominance of antiandrogenic activity. Cytotoxicity rankings across samples did not align with acute toxicity previously measured on the same sediments using Microtox. However, overall cytotoxicity correlated significantly with chemical contamination indicators. These findings highlight the relevance of effect-based tools in capturing mixture effects in stormwater sediments and support their integration alongside chemical analysis in sediment quality assessments.

Cross-laminated timber (CLT) is increasingly used in sustainable construction but is sensitive to moisture, which can compromise durability and promote microbial growth. Damage to temporary weather protection during construction can lead to unintended moisture exposure. In such cases, it is important to understand how the material may have been affected. While X-ray computed tomography (CT) is not suitable for field application, it offers a detailed means of studying internal moisture behaviour and contributes to a better understanding of moisture-related risks in CLT following barrier failure.This study uses CT to investigate how moisture distributes and changes within CLT after protective covering damage. Eight CLT specimens from Scots pine were subjected to controlled wetting and drying cycles. Time-resolved CT scanning, combined with image processing techniques, was used to capture internal moisture variation with high spatial resolution. The method enabled detailed observation of both absorption and drying, revealing transport patterns between layers and interfaces.The study demonstrates that CT effectively reveals both moisture spread and slow drying in CLT following barrier failure.

This research-in-progress applies the Double Diamond design model and Living Lab (LL) methodology to co-create rural digital policy in the cross-border regions of Muodoslompolo (Sweden) and Muonio (Finland). Drawing on participatory speculativeand critical design methods, such as cultural probes, design fiction, and scenario-basedworkshops, the project engages diverse stakeholders through the PentaHelix model. Early activities, including interdisciplinary workshops, interviews, and communityengagement, have informed a draft policy prototype and a prioritization matrix aligned with key Sustainable Development Goals (SDGs). The study contributes both practically, by developing a context-sensitive digital policy and action plan, and academically, by advancing LL practices in rural, cross-border settings. The proposed approach offers areplicable model for inclusive and participatory policymaking in underrepresentedregions.

Efficient biomass production through management like thinning is crucial for increasing the supply of renewable and carbon neutral feedstock. However, change in growth rates may alter feedstock properties and affect subsequent bioenergy conversion, material and chemical production. This study evaluated the effects of thinning treatments and stem diameter on the fuel, elemental, and structural composition of stemwood and bark from second-rotation poplar plantation (original stand: 1100 stumps ha−1). Two different thinning methods were applied: row thinning (removing all stems for every other row of plantation and reducing stump density to 550 stumps ha−1) and stem thinning (retaining only the single largest stem per stump). The results showed that thinning method and stem diameter affect fuel and lignocellulosic composition. Single-stem trees at high stump density had the best fuel traits, with low ash and high volatile matter to fixed carbon (VM/FC) ratios, reflecting reduced growth competition. Smaller stems contained more ash and VM/FC in bark. Carbon, hydrogen, and nitrogen contents were not affected by treatments. Single-stem trees had higher hemicelluloses and lower lignin, indicating more complete cell wall development, while crowded, multi-stem conditions increased lignin. Highest extractives were found in bark from low-density single-stem trees. Both total biomass and structural components yields were highest for single-stem trees without row thinning. It highlights the benefits of stem thinning. This study suggests that both quality and quantity of biomass from second-rotation poplar plantation can be influenced by thinning treatments and stem diameter, with potential implications for bioenergy and bio-based chemicals or fuels.

Flexibility Contracts (FlexCons), in the context of power and energy systems, aim to address challenges arising from uncertainties in electricity consumption and variable renewable generation using a bilateral short-term instrument. Through such contracts, electricity retailers (RETs) and variable renewable energy producers (VREPs) agree to provide power flexibility to one another to reduce the impact of imbalances on their respective decision-making processes. In this paper, two four-stage decision-making problems are developed for RETs and VREPs to analyze their participation in FlexCons alongside the day-ahead market (DAM), intraday market (IDM), and balancing market (BLM). The proposed models incorporate uncertainties in market prices, electricity consumption, and renewable generation through scenario sets and a stochastic decision-making approach. Additionally, the framework includes single-price and 15-minute imbalance settlement, as well as location-specific considerations within the system. Subsequently, the outcomes of these decision-making problems are integrated into the market-clearing processes of DAM, IDM, and BLM to assess the positive and negative impacts of such bilateral transactions. A two-bus illustrative example and the IEEE 24-bus RTS system are used for simulations. The results indicate higher profit for FlexCons’ participants by 1.6%, lower flexibility prices in the BLM by 6.8%, and an overall reduction in system costs by 4% when FlexCons are used.

Graph Neural Networks (GNNs) are the most common approach for learning complex relational data represented using graph data structures. Although GNNs are effective at learning representations of both nodes and graphs for a given task, the learning process is computationally expensive and as such, time and energy-inefficient. This paper investigates this challenge within the context of recent work on untrained graph representations that only train the solver model. We present Graph Vector Function Architecture (GVFA), a novel alternative to learning graph representations in GNNs that is based on hyperdimensional computing (HDC) principles. GVFA is a general zero-shot approach for graph and node representations without learning. As such, our representations are not task-specific and the computational costs of constructing them is substantially lower compared to learning-based GNN. Empirically, we demonstrate the expressiveness and generalization properties of different GVFA configurations. Our experimental results demonstrate that GVFA outperforms several classic GNNs on their benchmark datasets in terms of classification accuracy for both graph and node classification tasks, while also yielding a substantial reduction in training time.

Space exploration is significant for scientific innovation, resource utilization, and planetary security. Space exploration involves several systems including satellites, space suits, communication systems, and robotics, which have to function under harsh space conditions such as extreme temperatures (− 270 to 1650 °C), microgravity (10⁻⁶ g), unhealthy humidity (< 20% RH or > 60% RH), high atmospheric pressure (~ 1450 psi), and radiation (4000–5000 mSv). Conventional energy-harvesting technologies (solar cells, fuel cells, and nuclear energy), that are normally used to power these space systems have certain limitations (e.g., sunlight dependence, weight, degradation, big size, high cost, low capacity, radioactivity, complexity, and low efficiency). The constraints in conventional energy resources have made it imperative to look for non-conventional yet efficient alternatives. A great potential for enhancing efficiency, sustainability, and mission duration in space exploration can be offered by integrating triboelectric nanogenerators (TENGs) with existing energy sources. Recently, the potential of TENG including energy harvesting (from vibrations/movements in satellites and spacecraft), self-powered sensing, and microgravity, for multiple applications in different space missions has been discussed. This review comprehensively covers the use of TENGs for various space applications, such as planetary exploration missions (Mars environment monitoring), manned space equipment, In-orbit robotic operations /collision monitoring, spacecraft's design and structural health monitoring, Aeronautical systems, and conventional energy harvesting (solar and nuclear). This review also discusses the use of self-powered TENG sensors for deep space object perception. At the same time, this review compares TENGs with conventional energy harvesting technologies for space systems. Lastly, this review talks about energy harvesting in satellites, TENG-based satellite communication systems, and future practical implementation challenges (with possible solutions).

Wearable luminescent solar concentrators (LSCs) hold significant promise for integrating energy harvesting with flexible textiles. However, most currently reported LSCs are either rigid or liquid-filled, making it challenging to achieve flexible devices with high efficiency and mechanical robustness. Here, we introduced Ca²⁺-capped carbon dots (C-dots) as dual-functional agents, simultaneously crosslinking sodium alginate hydrogels and serving as luminophores, eliminating the need for additional dopants. The resulting hydrogels exhibited tunable mechanical strength (0.25 MPa at 50 % strain), high transparency (64 % visible transmittance), and good stability. As a proof-of-concept, we fabricated wearable LSCs by embedding the hydrogel into flattened polyvinyl chloride tubes and weaving them into textiles. Under natural sunlight illumination (50 mW/cm²), the as-fabricated flexible LSC achieved a power conversion efficiency (ηPCE) of 0.26 % and an optical efficiency (ηopt) of 2.60 % with 64 % average visible transmittance. Remarkably, the device retains 72 % of its initial optical efficiency after 24 h continuous ultraviolet illumination (468 mW/cm2). This work demonstrates the first hydrogel-based LSCs for practical wearable energy harvesting.

The continuous rise in anthropogenic CO₂ emissions from fossil fuel combustion underscores the urgency of developing efficient carbon capture technologies. Among various methods, post-combustion CO₂ capture using amine-based solvents remains the most mature and industrially viable. However, conventional aqueous-amine systems suffer from high regeneration energy demands, solvent degradation, and operational challenges. This study systematically reviews recent advances in amine-based solvents and co-solvent formulations designed to enhance absorption efficiency and reduce energy consumption. The discussion covers (i) thermodynamic and kinetic fundamentals of amine–CO₂ interactions, (ii) the effects of co-solvent addition on viscosity, mass transfer, and thermal stability, and (iii) the influence of operating parameters on cyclic capacity and regeneration energy. Emerging classes such as water-lean, biphasic, and nanoparticle-enhanced systems are critically compared based on their absorption kinetics, desorption enthalpy, and stability under cyclic operation. Bibliometric analysis is used to map the evolution of research trends in solvent engineering. The review highlights that co-solvents such as glycols, sulfoxides, and glycol ethers can lower reboiler duty by up to 60% relative to aqueous monoethanolamine while maintaining comparable absorption performance. Remaining challenges include viscosity control, long-term solvent degradation, and scalability. Future research should focus on optimizing solvent composition, integrating process intensification techniques, and developing predictive models linking molecular structure to process performance.

Nitrogen fixation is crucial for agriculture and environmental sustainability, but conventional methods, such as the Haber–Bosch process, suffer from high energy demands, a complex synthesis process, and significant carbon emissions. Here, we report an innovative direct conversion of N2 from water and air via contact-electro-catalysis (CEC) for nitrogen fixation, where dielectric fluorinated ethylene propylene (FEP) micropowder, water, and ultrasonication synergistically enable efficient, simultaneous synthesis of ammonia (NH₃) and nitrate (NO₃⁻), with hydrogen peroxide (H₂O₂) as byproduct under ambient conditions. Another key finding is the role of surfactant, which not only regulates reaction pathways in the nitrogen cycle but also improves the performance of FEP in water. With the assistance of fluorocarbon surfactant (Capstone FS-30), this method achieves a nitrogen fixation rate at 7.9 × 10 ³ μmol L⁻¹ h⁻¹ gcat−1, significantly enhancing the efficiency of current interface-driven nitrogen fixation techniques, and it offers a simplified synthetic process with lower carbon emissions than the traditional method. Our study provides a sustainable and highly efficient catalytic platform, broadening the scope and practicality of nitrogen fixation technologies.

Net-zero by 2050 demands scalable renewables. Ocean wave/tidal energy, though denser than solar and wind, remains at low TRL. A pivotal barrier is the material durability and performance of ocean (hydraulic) rod linear bearings that remain key components of WECs including point absorbers. This study presents a comparative mechanical, thermal, and tribological evaluation of eight commercial and newly developed self-lubricating bearing materials, including thermoplastics, thermosets, elastomers, and a bronze-based alloy, under unlubricated conditions. The materials incorporate diverse reinforcement architectures, including short fibers, woven and continuous fiber fabrics, and solid lubricant particulate fillers. Tribological testing was conducted under an 18 hrs long-duration dry reciprocating pin-on-flat configuration at 0.02–0.1 m/s under 250 N load. Friction behavior, wear rates, and surface degradation were recorded. Post-test analyses using SEM, FTIR, and 3D surface profilometry revealed distinct wear mechanisms and transfer film behavior. Wear maps integrating friction, wear rate, and a normalized multi-parameter performance index reveal sliding-speed-dependent tribological transitions and enable ranking of marine composites. The findings establish a foundational understanding for bearing failure mechanisms, predictive design and material selection in ocean tribological systems.

Hydrogen is emerging as a sustainable energy source that can reduce fossil fuel reliance and associated environmental impact. However, it poses embrittlement challenges for storage and transport materials that affect the widespread deployment of the hydrogen economy. Surface modification of materials by employing coatings, thermochemical, mechanical treatments, and others modifies surface chemistry, microstructure, stress states, and enhances surface integrity. These surface modification methods form physical or chemical barriers that impede hydrogen permeation and lower hydrogen-induced degradation. Though an unfavorable combination of thermodynamic properties, hydrogen solubility, and hydrogen diffusivity of the modified surfaces promotes hydrogen embrittlement mechanisms. This review focuses on a comprehensive overview of various surface modification techniques applied to base materials to counter their hydrogen embrittlement susceptibility. This work emphasizes the relationship between the surface modification methods and their effects on microstructural and mechanical properties, and their contribution to hydrogen storage and transport solutions. Additionally, limitations, challenges, and research gaps related to these surface modification techniques for materials in hydrogen infrastructure are discussed.

To satisfy the high performance requirements of new generation materials in application of aerospace and automotive industries, a series of TiZrHf-based entropic (i.e., entropy-stabilized) alloys with α+β dual-phase microstructure were designed using the CALPHAD (CALculation of PHAse Diagram) methodology. The present work aimed to achieve an balanced performance between mechanical properties, corrosion resistance, and high-temperature oxidation stability by tailoring Zr/Hf ratios. The alloys were comprehensively characterized using X-ray diffraction (XRD), electron channeling contrast imaging (ECCI), and high angle annular dark field scanning transmission electron microscope (HAADF-STEM). Due to the effect of transformation-induced plasticity (TRIP), the homogenized and cryogenic alloys exhibit balanced mechanical properties (i.e., high strength and ductility). Electrochemical tests in 3.5 wt% NaCl solution demonstrated good corrosion resistance, and the stability of the passive film was slightly compromised by both cryogenic treatment and Zr/Hf additions. Moderate high-temperature oxidation tests at 500 and 600 °C showed that the alloys have good oxidation resistance result from the formation of protective scales dominated by TiO2 and Al2O3. However, the formation of less-protective Zr/Hf-oxides at higher temperatures (700 °C) was found to be detrimental. This work provide a CALPHAD-guided design strategy for developing Ti-based entropic alloys with a well-balanced properties for applying in different severe environments.

The production of biogas through anaerobic digestion (AD) of organic-renewable feedstocks is recognized as a viable solution within the renewable energy sector. Biogas typically contains a methane concentration ranging from 60 to 70%, presenting a significant opportunity for energy generation. However, the co-generated carbon dioxide (CO2), which constitutes approximately 30–40% of biogas, poses challenges to overall energy efficiency, thus necessitating the implementation of purification methods to enhance methane concentrations. It is noteworthy that the production of one ton of biomethane results in the generation of approximately two tons of biogenic CO2. This reality opens avenues for carbon capture, storage, and valorization strategies. The biogas industry is beginning to recognize CO2 not merely as a byproduct to be discarded, but as a valuable resource for the synthesis of biomethane, chemicals, fuels, and even building materials. There is a growing interest in utilizing biogenic CO2 as a climate-friendly feedstock, with “bio-Carbon Capture and Utilization” (bio-CCU) practices facilitating the development of sustainable fuels, chemicals, and materials. The article extends to various methods of valorization for biogenic CO2, providing an analysis of techniques for separating and upgrading CO2 derived from biogas. This assessment encompasses both physical and biological methodologies within the carbon capture, utilization, and storage (CCUS) framework. The article further demonstrates both in-situ and ex-situ processes, including biological methodologies that employ microorganisms for CO2 conversion, as well as thermo-physicochemical processes that transform CO2 into biobased products. Additionally, the article demonstrates the economic and environmental advantages associated with the strategic utilization of biogenic CO2. Repurposing this resource is vital for achieving sustainability goals, particularly in renewable energy sectors, where it can significantly enhance energy efficiency and reduce waste. Finally, the article emphasizes the importance of these practices in climate change mitigation, advocating for a circular economy that prioritizes carbon reuse over atmospheric emissions, thus contributing to the advancement of a sustainable future.

3D printing offers the ability to fabricate lightweight structural profiles with controlled infill and geometry. This study examines the mechanical behaviour of 3D-printed polylactic acid (PLA) structures with a 10% infill density under four load conditions (10, 15, 20, and 25 N). Four designs (M1, M2, M3, and M4), representing commonly used structural profiles found in beam and column applications, were analysed using ANSYS finite element simulations. Each design was evaluated under roller and nodal boundary conditions to study deformation, stress, and strain responses. Three-point flexural tests were also carried out on all four designs, and the measured peak flexural stress and apparent flexural modulus were compared with the simulated stiffness values. Both the simulations and experimental results showed that Design M3 exhibited the highest stiffness and more consistent behaviour compared to the other designs, while Design M4 showed higher deformation and lower bending resistance. Roller supports generally reduced deformation through better load distribution, whereas nodal supports increased local stiffness in selected designs. Although the magnitude of stiffness differed between simulation and experiment, the ranking of the designs remained consistent. Overall, the study confirms that the geometry plays an important role in their load-bearing performance, and the numerical model provides a reliable tool for comparing and selecting suitable designs before fabrication.

Understanding precipitation dynamics in arid regions such as Iraq is of paramount importance in hydrological and climatological studies, as it is a key approach to water resources management and climate change adaptation. This study aims to develop a mathematical predictive model for rainfall isotopic values using machine learning techniques. Stable isotope data for oxygen (δ¹⁸O) and deuterium (δ²H) in precipitation were collected from 32 meteorological stations distributed across Iraq over a 14-year period (2010–2024). The dataset also included meteorological parameters for these stations, including precipitation amount, air temperature, relative humidity, and calculated station elevation. Several machine learning algorithms (i.e., SVM, GBR, ANN, CatBoost, XGBoost, and RF) were employed to compare predicted isotopic values with actual readings, accounting for rainfall characteristics and patterns. The results demonstrated that the RF model achieved superior predictive performance, with a calibration coefficient (R²) of 0.89 in the testing set, indicating strong predictive capability. This model also recorded the lowest mean absolute error (MAE) of 1.39 and the lowest root mean square error (RMSE) of 3.5 compared to the other algorithms, reflecting improved predictive accuracy. These findings confirm the effectiveness of integrating machine learning, particularly the RF approach, in enhancing the modeling of isotopic signature predictions in environmental studies. Furthermore, they highlight the potential of AI-based models as powerful tools for reconstructing historical isotopic datasets, supporting climate variability assessment and sustainable water resources management in arid and semi-arid regions.

Achieving an environmentally sustainable society and meeting international obligations such as the U.N. Paris Agreement, the 2030 Agenda for Sustainable Development, and the EU Green Deal requires all policy sectors to integrate environmental sustainability. Education, as a sector shaping our citizens of tomorrow, has a critical role in this transition.

This study explored how sustainability objectives are prioritized within educational policies, using Sweden as a case study to assess the preconditions for effective environmental education in upper-secondary classrooms.

Applying Environmental Policy Integration as a theoretical framework, the research employs content analysis and crisp set analysis to examine policies governing classroom practices and identify the value hierarchies embedded regarding sustainability. The analysis focuses on the national curriculum (2011) and the syllabi for the eight common subjects in two versions: the 2011 and 2025 revisions.

Findings revealed that Swedish upper secondary policy predominantly favors the social dimension of sustainability. Environmental priorities present in the national curriculum do not consistently trickle down to subject syllabi, and only the subject of science displayed a principled priority of the environment; although this was only evident in earlier versions of the syllabi.

This study concludes that for education to meaningfully contribute to a sustainability transition, policies need a stronger environmental emphasis, particularly policies that inform teachers’ everyday work and the exercise of public authority, such as subject grading criteria.

In iron mining the processing phase broadly consists of sorting, concentrating, and pelletizing of the iron ore, this is to increase the iron content in the final product. In pelletizing, the filtering stage which controls the moisture content in the iron cake plays a crucial role. A Rotatory Vacuum Drum Filter (RVDF) is one of the mining equipment for removing excessive moisture by separating solid iron cake from slurry. A supporting wire which holds the cloth mounted on the frame of the RVDF is one of the critical components. During operation, recursive compression and stretching due to variation in pressure may lead to wire failure. This failure significantly impacts the integrity and efficiency of filter cloth that affects the filtration performance. If the wire failure is not detected promptly, it can lead to prolonged maintenance time, substantial maintenance cost and unplanned downtime, consequently affecting system availability. This work demonstrates health monitoring of filtering system in mining, designed to alert the operators about the emerging failure, to take appropriate maintenance action and minimize further damage, and unplanned downtime.

This paper introduces a computer-vision based monitoring approach that leverages image data of the drum filter during operation. The proposed approach identifies wire-induced degradation pattern on the filter cloth. Extracted video frames from the drum filter are processed to isolate the region of interest. Using Hough transform horizontal sections of the drum are detected followed by a sliding window analysis to evaluate the variations in pixel intensity. For normal surface, the average intensity variations remain low, typically ranging from 5 to 10. However, it spikes up to around 40 when irregular patterns are detected. The focus of this work is on detection and diagnostics, a transition towards prognostics is envisioned by incorporating pressure sensor data. Integrating multi-modal data may enhance the capability of predicting failure and improve the system availability.

High-voltage electric pulse (HVEP) rock fragmentation has demonstrated substantial potential for sustainable fracturing of hard rocks owing to its energy efficiency. The transient nature and highly disruptive characteristics of its physical fracturing process render experimental investigation of the underlying rock-breaking mechanisms challenging. However, existing numerical studies lack comprehensive models that precisely link electrical breakdown phenomena with mechanical disintegration processes. This study combines COMSOL electrical breakdown simulations with four-dimension lattice spring model (4D-LSM) mechanical analysis to establish a coupled HVEP rock fragmentation model. The core concept of the model construction is to import the temperature field of the plasma channel obtained from the electrical breakdown into the mechanical solver to realize the precise connection between the two stages. The validated numerical model elucidates the full process of HVEP-induced fragmentation under varying electrical parameters. Furthermore, the effects of confining pressure and mineral grain size on fragmentation behavior have been investigated. Finally, parametric simulations across 25 electrical parameter combinations demonstrate the critical role of electrode spacing optimization in achieving energy-efficient rock fragmentation. These findings provide a predictive tool for designing efficient HVEP systems in deep resource extraction and mineral processing engineering.

Fast Fourier transform (FFT)-accelerated, integral-equation-based electromagnetic simulators have gained significant attention due to their ability to compute parasitics of arbitrarily shaped and large-scale voxelized structures on desktop computers. However, FFT-based solvers have limitations as they require voxels of the same size across all Cartesian dimensions. This is a significant limitation, especially for printed circuit boards (PCB)-like geometries of very thin copper layers and much thicker dielectric layers, which leads to an excessive number of voxels, and consequently, a large number of unknowns. In addition, in PCB-like structures, vias represent small details that require finer meshing resolution for proper discretization. This work aims to overcome these limitations by developing a systematic anisotropic strategy for computing matrix-vector products using the FFT-based approach and automatically replacing vias with equivalent R–L lumped elements. Furthermore, a zero-thickness conductor mesh is proposed within the partial element equivalent circuit (PEEC) method framework, which is in turn also suitable for use with other integral-equation-based methods. The accuracy, efficiency, and applicability of the proposed FFT-PEEC solver are demonstrated on three examples.

Energy labels help consumers understand the environmental impact of products. This drives consumer behavior. Knowing how label features are perceived can thus have important implications for design, policy, and management. Energy labels contain different design features that convey information about the range of available energy classes. Such contextual information can help people make informed comparisons. Across three behavioral experiments, we evaluate the perception of (EU's eco-design) energy labels based on their letter scale, color-coding, and information about the scale's range of energy classes. These three features had different influences on judgments of environmental friendliness. A letter scale that begins with the alphabet (A to G) helped participants tell apart energy classes linearly. But a scale that extends beyond the alphabet (A+++ to D) led to biased and skewed responses. Color-coding and information about the scale's range of energy classes provided important contextual information that made environmental judgments more discriminating and more stable. We demonstrate novel behavioral evidence on how such contextual information leads to better understanding of energy labels, with a discussion informed by range-frequency theory, the cue-utilization framework, and the heuristics and biases framework. Implications for policy and product marketing strategies with energy labels are discussed.

Attentional Control Theory suggests that acute stress reduces the efficiency of working memory and top-down control, increasing susceptibility to distraction. In contrast, Cognitive Reallocation accounts suggest that acute stress narrows attentional focus and potentially reduces distraction. We tested these competing predictions using a cross-modal oddball task, comparing participants exposed to an acute stressor, via a realistic firefighter training exercise, with an unstressed control group. Participants categorised visual targets preceded by either a standard sound or a rare deviant (a noise burst or a semantically congruent or incongruent word). Both groups were distracted by the deviant sounds, but the effect was larger in those exposed to the stressor, particularly early in the session. Over time, this difference diminished—consistent with recovery from stress exposure and stronger habituation in controls. These results indicate that acute stress is associated with heightened vulnerability to auditory distraction in a pattern resembling reduced working memory availability.

Quantitative laser-based diagnostics like Raman spectroscopy are essential for studying high-temperature processes, but their application in intensely luminous and transient environments such as plasma torches is severely limited by overwhelming background emission. This study focuses on the quantitative thermometry of a 7 kW atmospheric air plasma jet, an environment where such measurements are notoriously difficult. To enable these measurements, a Polarization Lock-In Filtering (PLF) Raman technique is used to suppress the intense and fluctuating plasma background. The method successfully yields high-quality N2 ro-vibrational spectra along the jet’s central axis. Model-based fitting of these spectra produces a detailed axial temperature profile, showing a decay from over 3700 K near the nozzle. Furthermore, the high signal quality enabled the detection of singly ionized nitrogen (N2+) in the plasma core, providing direct evidence of its ionized state. These results represent the first application of PLF for thermometry in a plasma torch and provide critical experimental data for validating magnetohydrodynamic simulations. 

To assess the reliability of green stormwater infrastructure (GSI), including grass swales, it is required to characterize the frequency of system failures. This involves identifying the conditions when the system operates in failure mode, potentially causing downstream flooding. The study utilized three 23-year meteorological time series from three Swedish locations, characterized by frontal, convective, or orographic rainfall events. These time series served as inputs for the simulation of runoff flows using the SWMM model, representing the physical characteristics of a grass swale (GS) located in Luleå, Sweden. Results showed a trend of reduced overflow occurrences and flood risks at higher ksat values of 25 and 31 mm/h. Exceedance curves indicated that ksat values of 2 or 4 mm/h resulted in the swale operating in failure mode 100% of the time. A minimum ksat of 31 mm/h was required to achieve acceptable operation for more than 50% of the time in Gothenburg and Luleå. Östersund was the only location where the studied GS did not operate in failure mode under a high ksat of 31 mm/h. Local climate – especially rainfall distribution and frequency – sets performance thresholds and underscores the need to integrate technical performance with flood risk management in GSI design.

A global decline in students’ motivation and academic performance poses a serious threat to future competence supply, particularly in knowledge-driven economies such as Sweden. Despite higher education’s growing importance for economic and social mobility, the number of students pursuing such education continues to fall. This study employs a mixed-methods design using an explanatory sequential approach to explore how teachers’ leadership identity influences their aspirational engagement in shaping students’ beliefs and intentions to pursue higher education and future career opportunities. The results show that teachers who identify strongly with their leadership role exhibit a type of leadership that influences aspirational engagement with students. This, in turn, may promote students’ beliefs in their potential and intentions to pursue higher education through (1) aspirational engagement in individual dialogues with students, (2) aspirational engagement when introducing new subject areas in whole-class communication, and (3) aspirational engagement related to practical work experience (PRAO). This study demonstrates an understanding of the important potential of teachers’ contributions to elevate society’s future competence supply.

Cyber resilience has emerged as a complementary concept to cybersecurity, expanding the traditional predict and-protect paradigm to include business continuity and adaptive capacities. However, much of the literature remains normative, emphasizing what organizations should do, rather than analyzing what cyber resilience looks like in practice. This paper presents a theoretical framework for analyzing cyber resilience in organizations. Drawing on resilience theory and socio-technical systems theory, the framework identifies four interdependent capabilities—anticipate, withstand, recover, and adapt—and uses the principle of joint optimization to examine how technical and social elements interact within and across these capabilities. The framework was developed using a concept analysis method and is designed to be applied to empirical data, such as interviews or case studies. Its key contribution is to enable structured analysis of how resilience manifests, and how different capabilities compensate for one another depending on the system’s state. We argue that the organization is constantly evolving and changing, meaning that observations are of a temporary system state that has already begun to change. However, analyzing snapshots of the system’s capabilities can help identify areas for improvement. Future research can apply the framework to understand the mechanisms underlying cyber resilience.

Accurate reservoir storage estimation is fundamental to sustainable water resources management; however, small dam projects are often hindered by the prohibitive costs and time required for high-precision topographic surveys. In this study, a rigorous validation of freely available one-arc-second SRTM DEMs was conducted as an alternative approach for estimating volume-elevation relationships at ten small dams in Iraq. High-precision surveys served as benchmarks, enabling statistical validation of DEM-derived estimates using absolute relative error (ARE), root mean square error (RMSE), mean absolute error, and the coefficient of determination (R²). Reservoir basin morphology was further characterised through planimetric indices, including area-to-volume ratio (AVR), shape factor, and solidity. In parallel, terrain complexity within a 5 km buffer zone was quantified using slope variability, curvature, vector ruggedness measure (VRM), and terrain ruggedness index (TRI). A strong structural agreement was demonstrated (R² > 0.98), although substantial variation in volumetric precision was observed. A global sensitivity analysis using the Morris Method identified the standard deviation of the Terrain Ruggedness Index (TRI) as the dominant predictor of accuracy, with µ* values of 83–86, while all other metrics showed minimal influence (µ* ≈ 0–24). These results establish a clear accuracy threshold for one-arc-second SRTM DEMs: they are sufficiently reliable for preliminary planning (< 20% error) in low-ruggedness terrain (TRI SD < 0.1) but become highly unreliable in rugged landscapes, where errors exceed 150% (TRI SD > 0.1). These findings provide a predictive framework for assessing DEM suitability, supporting the integration of satellite topography into small-scale reservoir planning.

The increasing use of lithium-ion batteries (LiBs) in electric vehicles and electronics has made efficient recycling essential for maintaining a reliable and affordable supply of critical metals. Thermal treatment of black mass (BM), the heterogeneous residue from spent LiBs, is a crucial step to improve downstream material separation and recovery. This study investigates the thermal behavior of LiBs BM by analyzing the thermal behavior of its components when heated to 600 °C in an inert (N2) atmosphere or in a mixture of 90 vol % N2 and 10 vol % H2. Thermogravimetric analysis (TGA) was conducted at a heating rate of 10 °C/min with an isothermal hold of 1 h, and coupled with quadrupole mass spectrometry (QMS). The analysis was performed on graphite, activated carbon, lithium hexafluorophosphate (LiPF6), polyvinylidene fluoride (PVDF), synthetic black mass, and lithium nickel manganese cobalt oxide (NMC) industrial BM. Equilibrium calculations conducted in FactSage 8.3 were used to describe and understand the experimental findings. The TGA results indicate that in 100 vol % N2, graphite exhibited the lowest weight loss of 0.1 wt %, followed by activated carbon at 2.9 wt %, PVDF at 56 wt %, and LiPF6 at 81 wt %. Synthetic black mass had a weight loss of 3.4 wt %, while industrial black mass had 1.0 wt %. In 90 vol % N2/10 vol % H2, LiPF6 and PVDF experienced weight losses of 79 and 64 wt %, respectively. Synthetic BM had a weight loss of 15.1 wt %, and industrial BM 15.6 wt % due to enhanced reduction of metal oxides in the presence of hydrogen. 

Flame-resistant and fluorine-free electrolytes based on (combining) the salts lithium saccharinate (LiSac) and lithium bis(oxalato)borate (LiBOB) in a single solvent triethyl phosphate (TEP) solvent and vinylene carbonate (VC) additive are presented and evaluated for lithium metal battery application. The dual salt electrolyte, 1.5 M LiSac + 0.2 M LiBOB in TEP w. 2 % VC, clearly outperforms the single salt ones in terms of electrochemical performance, especially vs. LiNi0.8Mn0.1Co0.1O2 (NMC811) cathodes, properties that originate in a Li+ cation first solvation shell mainly composed of Sac and BOB anions, promoting formation of a mechanically stable, inorganic-rich cathode electrolyte interphase layer, which by X-ray photoelectron spectroscopy was revealed to comprise Li3N, BxOy and SO32− species. Overall, this also results in stable cycling, and a capacity retention of 86 % in both Li||LiFePO4 and Li||NMC811 cells after 500 cycles at 1C rate – hence offering an intrinsically safer electrolyte that also enables the use of both lithium metal anodes and medium-to-high-voltage cathodes. 

Exterior building materials contribute to stormwater runoff pollution but knowledge of how ecological impacts may vary between different types of building materials remains limited. This study combined chemical analyses of runoff from seven different building surface materials with toxicological response analyses as a contribution to addressing this knowledge gap. Results indicate a range of inorganic (e.g. copper, zinc) and organic substances (e.g. diisononyl phthalate, DINP, and nonylphenol) are mobilised by runoff, with concentrations varying between differing materials and rain events by up to three orders of magnitude. Toxicological analysis involving algae, daphnids and fish embryos, indicated that acute and chronic effects also varied between building materials and events, as well as species. For example, copper sheet runoff (maximum concentration 2900 µg/L) exhibited the strongest acute toxic effect on all three test organisms (≥80 % effect irrespective of event and species). Chronic reproductive effects were reported for Daphnia magna on exposure to PVC and bitumen felt roof runoff. Results show that runoff from several building surface materials commonly found in urban areas can cause acute and chronic effects on aquatic organisms. Findings could support users to identify environmentally sustainable building materials as a contribution to reducing pollution emissions from cities to receiving waters.

This study examined the synergistic and independent effects of soil properties, vegetation cover, conservation practices, and slope on the spatial distribution characteristics of soil erosion in the Abu-Ghraibat watershed in 2024. Soil samples have been collected and analyzed in the laboratory, along with high-resolution satellite imagery, meteorological data, and digital elevation model (DEM) data. The findings indicate that soil erosion in the Abu-Ghraibat watershed in 2024 was minimal, with a progressively increasing severity from north to south. In the studied area, grassland accounts for over 50% of soil erosion, with regions with vegetation coverage > 30% as the primary contributors, all of which are influenced by slope. Moreover, the enhancement of vegetation in the lower strata of the basin and in grasslands, especially on slopes ranging from 10° to 45°, along with the conversion of sloping woodlands and grasslands into terraces, has proven an effective strategy for mitigating soil erosion in the Abu-Ghraibat watershed. The present study has demonstrated that the RUSLEGIS integrated model may serve as an effective instrument for quantitatively and spatially mapping soil erosion at the watershed level in the Abu-Ghraibat, while accounting for the provision of landscape services.

The development of sustainable biomass resources is key to achieving “dual carbon” goals. Waste wood powder, a by-product of traditional wood processing, is often discarded due to its complex structure, leading to resource waste and pollution. In this study, a lithium bromide (∼ 60 wt%) system was used to dissolve and regenerate waste wood powder into three cellulose hydrogels without prior removal of hemicellulose and lignin: OWP-RC, RC, and P-RC. To uncover the mechanisms of dissolution and regeneration, various characterization methods were used to compare regenerated cellulose with the original fibers, at scales ranging from macroscopic to molecular. During dissolution, cellulose reached nanoscale dispersion. The dissolution–regeneration process transformed the cellulose from type I to type II, which exhibited decreased crystallinity and thermal stability. However, the presence of organic powder in OWP-RC improved properties, including crystallinity (27.43 %) and thermal stability. Additionally, the potential for cellulose solution as a medium for integrating other types of waste powders was investigated. Waste wood powder, elemental powders, and compound powders were successfully incorporated into cellulose solutions to facilitate fibration of materials without phase separation. The fibrillated powder composite hydrogels exhibited an increase in compressive strength by 1.8 times and reached a biodegradation rate of 99.15 % under enzymatic conditions. The dissolution–regeneration process yielded a lignin-rich by-product (∼ 54 % of the residue), which can be reused. This work offers an efficient green method for upcycling waste powders and developing functional regenerated cellulose composites.

Denna artikel baseras på resultat från två studier som har undersökt hur VA-organisationer kan ta sig ur den så kallade “data-loopen” – en situation där bristfälliga data gör det svårt att använda modeller och beslutsstöd, vilket i sin tur gör att man tvekar att investera i bättre datainsamling. Artikeln presenterar ett praktiskt ramverk som visar hur man kan komma igång med datadrivet arbete i tillgångsförvaltning av VA-infrastruktur och sedan stegvis bygga vidare och skala upp. Studierna visar också att VA-organisationer faktiskt kan skapa nytta redan från början, även med ofullständiga eller osäkra data samtidigt som de kan lägga grunden för en mer mogen, datadriven tillgångsförvaltning på längre sikt.

This study investigates the occurrence, attenuation, and ecological risks of phthalates and pharmaceuticals in a long-operating wastewater soil infiltration system in northern Sweden. Concentrations of 15 phthalates, 67 pharmaceuticals, caffeine, and acesulfame K were measured in influent wastewater, groundwater, soil, and a downgradient pond across multiple seasons. Results showed that most micropollutant removal occurred in the unsaturated soil zone prior to groundwater recharge, possibly due to processes such as biodegradation and sorption. Substantial reductions were observed for caffeine (>99 %), carbamazepine (>96 %), losartan (>99 %), and phthalates (51 ± 72 % and 92 ± 5 %), with the higher attenuation for phthalates comparable to conventional activated sludge treatment. In contrast, compounds such as metoprolol exhibited moderate reductions (>71 %), while others showed low or even negative attenuation, including diclofenac (46 % and −180 %) and ibuprofen (33 % and −11 %). After groundwater recharge, only ibuprofen showed attenuation beyond dilution, although the mechanisms for this remains unknown. Several pharmaceuticals, including metoprolol, irbesartan, and metformin, were detected in soil samples, though it is unclear whether they were sorbed to the soil matrix or present in porewater. In downgradient surface water, diclofenac and ibuprofen exceeded risk quotient thresholds, while oxazepam surpassed the lowest predicted no-effect concentration (PNEC) in one sample, indicating ecological risks. Overall, the findings highlight both the strengths and limitations of soil infiltration systems in mitigating micropollutant contamination, emphasizing the importance of vadose zone processes while underscoring uncertainties in sorption and degradation mechanisms

The frequency and intensity of extreme weather events pose various types of hazards and vulnerabilities to the railway system. This article aims to challenge the assumption that a single, nationally and internationally aggregated model is sufficient to capture region-specific failure patterns. While useful, these national models are often unable to account for linkages and feedback between heterogeneous regions and associated railway failure mechanisms and processes. This study develops and evaluates regional-national specific machine learning (ML) framework to enhance the classification of climate-related railway failures utilizing a synchronized five-fold cross-validation strategy.

We illustrate the approach through a case study of the Switches and Crossings (S&C) assets across five railway operational regions in Sweden, over a 24-year period, to demonstrate meaningful climate variability. Our methodology provides a consistent procedure that consistently compares national models with region-specific models. Results show that the localized model outperforms the national baseline, with improvements of up to 2.0% in Matthews Correlation Coefficient and 1.2% in F1-score at the 24-hour window preceding failure. Shapley Additive Explanations (SHAP) analysis revealed regionally distinct predictors, such as snow accumulation in the North and weather data coverage issues in the West. The results provide a basis for further process refinement, offering interpretable insights for operation and maintenance planning, risk assessment and supporting climate adaptation actions to achieve a climate-proof and resilient railway system. 

The whitepaper provides an overview of how it is possible to extend the lifecycles of connected offerings comprising for instance hardware, software, a support-service system and long-term management of operation. These offerings may span from products, products with add-on or integrated services, solutions, product-service systems, industrial product-service systems, and functions, etc. The selection of business model for an offering has a large impact on how the provider and customer/user can maintain, update and support the offering and keep it operational with a high level of availability and cybersecurity. In addition, new ways of designing the sales agreements or contracts may also lead to an increased level of re-circularity, extension of lifecycles and less ecological impact. This requires that an offering’s lifecycle is foreseen and planned for already at the initial conceptual, requirement engineering, and design phases, rather than trying to add this at a later stage at a much higher cost and effort. The total cost of ownership perspective is applied and compared to the initial cost perspective, which currently often is used.

The hosting capacity of low-voltage (LV) networks is influenced by existing consumption and production in adjacent LV networks under the same medium-voltage (MV) network. Since LV transformers typically lack automatic on-load tap changers, both voltage and current limits require a joint assessment covering the MV network and all underlying LV networks. This paper introduces a methodology to include different MV operating conditions, like reserve operating paths, in the calculation of the hosting capacity for new production at LV networks. The MV voltage profile before the connection of new production, called the background voltage, is modelled to enable such analysis. The methodology is applied to an existing MV/LV network. The hosting capacity was lower, for the studied reserve operating paths, and the limitation was due to overvoltage issues. The proposed methodology is a valuable tool for distribution network planning. It was also shown that it is essential to use an accurate model of the background voltage for hosting capacity calculation of distribution networks.

The integration of renewable energy sources (RES) into modern power grids has enabled decentralized energy generation at the community level, fostering peer-to-peer (P2P) energy trading among prosumers and microgrids. Accurate forecasting of household energy consumption and photovoltaic (PV) generation is critical for optimizing energy flows, enhancing grid reliability, and enabling cost-effective trading decisions. This paper presents an intelligent energy trading platform that integrates machine learning-based forecasting, battery-aware decision-making, and blockchain-enabled transactions to facilitate secure and efficient local energy exchange. Using historical smart meter and weather data from London households, multiple forecasting models including GRU, LSTM, Random Forest, and XGBoost were trained and evaluated. The GRU model achieved superior performance in predicting energy consumption, while Random Forest produced the most accurate PV generation forecasts. These predictions were combined with household battery levels to dynamically determine next-day operational roles: Buyer, Seller, Store, or Use Battery. Unlike conventional fixed-threshold approaches, the framework supports user-defined variable battery thresholds, allowing personalized energy management strategies. The proposed decision-making model achieved an accuracy of 90.72 % for one random block, and extended simulations across 29 different random household blocks confirmed its robustness with an average accuracy of 88.69 % (95 % CI: 87.9–89.6 %). In the trading phase, households participate in a decentralized energy trading platform powered by blockchain and smart contracts. Based on the next-day forecasts, a linear programming-based optimization algorithm matches buyer requests and seller offers to minimize the total system cost while ensuring fairness and efficient energy allocation. To assess its performance, the proposed optimization approach was compared against a greedy matching algorithm where sequential matching is done without a cost optimization and a grid baseline scenario where no storage/sharing of energy takes place. The optimized matching consistently achieved substantially lower trading costs across all households demonstrating superior efficiency, fairness, and scalability compared to the benchmark methods. All transactions are executed securely and transparently on the blockchain through Ethereum-based smart contracts, which automate energy trading, pricing, and settlement. A user-friendly web interface was developed to allow participants to monitor and interact seamlessly with the platform. Overall, this battery-aware, community-driven trading framework showcases how intelligent energy forecasting, cost-optimized decision-making, and blockchain-enabled trading can collectively enhance energy autonomy, cost savings, and renewable energy utilization at both the household and community levels.

The reversible pump turbine (RPT) is likely to enter the S-characteristic zone, thereby inducing pressure fluctuations and oscillations to the grid connection. An innovative optimization framework of RPT runner is presented to mitigate the detrimental flow conditions associated with the S-curve under turbine mode. Comprehensive runner geometric parameters were considered with the optimal Latin hypercube (OLH) sampling technique to generate different designs. Computational fluid dynamics (CFD) was adopted to characterize the RPT hydraulic efficiency and unstable S-characteristics curve, in which the position of the second inflection point was innovatively selected as the objective function. The CFD-driven surrogate-based design methodology was achieved by artificial neural network (ANN). The multi-objective optimization evolutionary algorithm guided the search for the optimal runner configuration with high efficiency and improved S-characteristics. The vortices in the runner channels and high-speed water ring in the vanless area both blocking the flow passage are alleviated in the two selected optimized RPT runners. The total pressure head associated with the intensity of vortices is deceased in the optimized runner, resulting in the improved S-shape characteristic. Runner with higher arches and negative blade lean angle of leading edge is conducive to the smooth streamline and avoidance of the flow separation.

Modeling heat transfer in packed bed processes such as iron ore pelletization is essential for optimizing process operation and furnace design. In these systems, materials like magnetite iron ore undergo thermal and chemical transformations, where heat and mass transfer are often coupled with heat effects from multiple processes — including both exothermic chemical reactions (e.g., oxidation) and endothermic physical changes (e.g., drying and sintering). As the industry moves towards fossil-free ironmaking, it becomes increasingly important to isolate pure heat transfer behavior, independent of chemical reactions, to support the development of sustainable process schemes.

This study investigates convective heat transfer in a packed bed by evaluating several established correlations against the conventionally used modified Ranz–Marshall correlation. Pilot pot-scale experiments were performed by isothermally heating 120 kg of already indurated iron ore pellets at 300 °C to avoid chemical reactions, with additional experiments at 500 °C and 700 °C to assess performance at elevated temperatures typical of industrial pelletization.

Results show that the modified Ranz–Marshall correlation underpredicts heat transfer rates under conditions isolating convective heat transfer, reinforcing the need for this investigation. The Wakao–Funazkri and Rowe–Claxton correlations provided the best agreement with experimental data, particularly at 300 °C. Minor deviations at higher temperatures suggest the influence of unaccounted variables, warranting further study. Sensitivity analysis identified gas velocity as the most significant parameter affecting heat transfer. The results suggest that adopting the Wakao–Funazkri and Rowe–Claxton correlations can provide a stronger basis for predictive pellet heat transfer modeling in future process design and simulation work.

This research investigates the geodiversity of the AL-Zubaidat region in Southeastern Iraq, with a focus on its geomorphological and hydrological characteristics. This information helps researchers identify suitable locations for natural reserves, thereby enhancing the protection of Iraqi biodiversity. The region attracts eco-tourism visitors, benefiting the economy and providing various scientific, cultural, educational, and aesthetic benefits. This research employed the geoinformatics methodology for geospatial analysis, constructing a comprehensive geodatabase, categorizing spatial features through topographic, geological, and hydrological maps, and correlating data with satellite imagery and elevation models. Geodiversity was classified according to physical parameters and international criteria, with the final classification attributes formulated utilizing maps, field photographs, and geodatabases. The research employed specific parameters to analyze the geomorphometric and slope diversity of the Al-Zubaidat area watersheds. The study area in Al-Zubaidat comprises dome-shaped hills, tertiary geological formations, valleys, and badlands. The area encompasses 782.308 km2 and shall consist of three principal watersheds (Al-Sharhani, Abu-Ghraibat, and Al-Shakak), as well as 12 sub-watersheds. The watershed perimeter is correlated with the circulating ratio, form factor, and elongation ratio, with larger perimeters generally indicating larger basin areas. The region encompasses low-slope terrain, with elevated slope values in the northern sections, especially in the headwaters. Geoheritage, geodiversity, geoconservation, and geoparks can facilitate sustainable development, promote healthy lifestyles, and foster cultural diversity. These initiatives are crucial for policymakers and regional stakeholders in semi-arid and developing regions, particularly in Southeast Asia, to enhance income and protect vulnerable natural resources. 

Writers with decoding and/or spelling difficulties often produce short, lower-quality texts and experience less fluent writing, frequently interrupted by long pauses at the word level. Research suggests that from adolescence onward, such writers become increasingly aware of their difficulties, which influences behaviours such as avoiding difficult-to-spell words and pausing for lexical decisions. The objective of this study was to deepen the understanding of how adolescent students with decoding and spelling difficulties engage in the task of text composition. In this multiple case study, we qualitatively investigated argumentative texts and writing processes produced by three Swedish upper-secondary students with such difficulties. Data were collected through keystroke logging and analyses of texts and keystroke logs provided detailed insights into their individual writing approaches. The results generally align with previous findings but reveal notable differences depending on the severity of the difficulties. Two students with moderate challenges paused extensively to consider spelling, formulation, and word choice, while one student with more pronounced difficulties wrote rapidly and briefly to complete the task quickly. This nuanced analysis highlights the diversity of writing profiles among students with decoding and spelling difficulties and underscores the need for tailored support to help them produce higher-quality texts.

This single-case study was conducted on a 34-year-old woman diagnosed with therapy-resistant depression and co-occurring atypical autism. The subject had been kept on the same medications for eight years despite her condition not improving and at the same time experiencing side effects. Previous studies and patient experiences suggest that many physicians are reluctant to end prescribed medication even if the patient is experiencing inadequate benefits and questionable effects. Co-occurring diseases often share overlapping symptoms, which can make accurate diagnosis and treatment more challenging, such as for patients with depression and autism. The problem becomes even more complicated when looking into the long-term treatment of depression occurring alongside autism spectrum disorder (ASD). The focus of conducting this case study was to determine the effect of DAT on a patient with confirmed therapy-resistant depression and ASD and if DAT would provide long-term benefit for the subject. The study’s results indicate that the patient experienced both quick improvement and long-term positive outcomes of DAT and is now in her 10th-year symptom-free.

Fortsatt urbanisering leder till ökning av ogenomsläppliga ytor samt intensifiering av antropogena aktiviteter,vilket leder till ett behov av noggrann planering för att minska den negativa påverkan på det naturligavattenkretsloppet och recipienterna. I ideala fall börjar detta med att minska föroreningars spridning tilldagvatten, exempelvis genom att använda material som släpper ifrån sig mindre föroreningar (Müller etal., 2023), eller genom gatusopning (Sansalone and Ying, 2008). Det omarbetade EU direktivet om reningav avloppsvatten (Directive (EU) 2024/3019) understryker dessutom vikten av att behandla förorenat dagvattenmer lokalt, exempelvis genom blå-grön infrastruktur (BGI), också kallat naturbaserade lösningar.Samtidigt måste det begränsade utrymmet i stadsområden som är tillgängligt för grönområden användaseffektivt, med hänsyn till multifunktionalitet (Nilsson et al., 2022). BGI används inte bara för dagvatteninfiltrationutan även för förbättring av livskvalitet, rekreationsvärden, estetik, biodiversitet (Taguchi et al.,2020).Vid planeringen av subarktiska städer tillkommer ytterligare en utmaning, i form av långa vintrar och betydandemängder snö. Detta ställer krav på effektiv snöröjning och hantering av snösmältningen. Vanligenär det inte möjligt att lagra all snö i staden, särskilt i täta och centrala områden. Därför transporteras enstor del av snön till centraliserade snötippar, medan snön i bostadsområden lagras på mer lokala snöuppläggningsplatser.Det är till exempel vanligt att snö lagras i diken i anslutning till vägar och parkeringsplatser,en lösning som möjliggör snabb snöröjning och bibehållande av säkra körförhållanden under helavintern. Diken kan därför ses som multifunktionella ytor, där anläggningar för dagvattenhantering kombinerasmed snöhantering (Gavrić et al., 2021; Öhrn Sagrelius et al., 2022). Det behövs dock mer kunskapkring hur snölagring påverkar dikens dagvattenhantering.

Dagvattenutsläpp kan ge upphov till biologiska effekter i urbana vattenmiljöer. I denna studie togs bottensedimentprover från fyra vattendrag, både uppströms och nedströms urbana och industriella områden. Proverna analyserades med fem in vitro-biotester som riktar in sig på aktivering av östrogenreceptorer (ER), androgenreceptorer (AR), arylkolvätereceptorer (AhR), hämning av AR (Anti-AR) samt induktion av oxidativ stress (Nrf2). Resultaten kompletterades med kemisk analys av 82 föroreningar som förekommer i urban avrinning. Så kallad "iceberg-modellering" användes för att identifiera vilka dagvattenrelaterade ämnen som driver de observerade biologiska effekterna.

Provtagningspunkter nedströms dagvattenutsläpp uppvisade förhöjd biologisk aktivitet, särskilt kopplat till AhR-aktivering och oxidativ stress. Intressant nog noterades ökad ER-aktivitet främst vid uppströms punkter med låg urban påverkan och större inslag av jordbruk. Iceberg-modelleringen indikerade att flera högmolekylära PAH:er, såsom benzo[k]fluoranten, potentiellt kan bidra till effekter inom flera av de analyserade verkningsmekanismerna. Resultaten antydde även att 4-tert-oktylfenol teoretiskt skulle kunna påverka främst ER- och Anti-AR-respons. För vissa PFAS visade modelleringen platsberoende potentiella bidrag; vid NrkC föreslogs exempelvis att FOSA kan stå för en del av den beräknade Anti-AR-aktiviteten. I ett enskilt fall (NrkA) var PFOA den enda substans som bidrog till den kemiskt beräknade Nrf2-effekten, men detta utgjorde endast en mycket liten andel av den observerade bioaktiviteten.

Resultaten understryker att urbant dagvatten är en transportväg för biologiskt aktiva föroreningar till recipienter, och att detta vidgar perspektivet bortom vad riktade kemiska analyser ensamma kan avslöja.

Denna studie undersökte ekologiska effekter av urbanisering och urban dagvattenavrinning på vattendrag som passerar genom urbana områden. Diversitet av makroevertebrater (ryggradslösa djur) analyserades med environmental DNA (eDNA) metabarkodning av biofilmprover. Dessa har samlats in vid flera platser längs tre vattendrag i södra Sverige. Proverna samlades in längs en gradient från landsbygd uppströms till urbanpåverkade områden nedströms. Generellt dominerades makroevertebratsamhällena av föroreningståliga taxa, särskilt Chironomidae (fjädermyggor), vilket indikerar antropogent tryck längs provsträckan från uppströms till nedströms. Även om diversiteten ökade nedströms speglade detta en ökad heterogenitet av habitat som gynnar föroreningståliga arter snarare än ekologiska förbättringar, vilket speglar en möjlig påverkan från urbana områden och därmed från dagvatten.