Sensorimotor control of the arm and hand can be disturbed in various health conditions causing deviant and unfavourable movement patterns. This study aimed to evaluate a new clinical test protocol for pointing acuity using the Leap Motion Controller (LMC) and assess the concurrent validity against a standard laboratory motion capture system (Qualisys Motion Capture, QMC). Twenty participants (mean age 44.4 years) performed the pointing acuity tests with eyes open and closed, and with left and right hand. Outcome measure was the mean absolute error (AE) in millimeters (mm) of 10 trials. Pearson’s r or Spearman’s rho were used to analyse the correlations between LMC and QMC, and Bland-Altman were used to analyse the agreement. There were moderate to very strong correlations (r: 0.67 to 0.95) between LMC and QMC in tests with eyes closed. In contrast, tests with eyes open showed weak correlations; rho 0.27 for both left and right hand. Bland-Altman showed median bias of ≤ 1.4 mm (LoA 0.1 to 17.5 mm) between LMC and QMC with eyes open, and ≤ 5.1 mm (LoA −31.6 to 21.5 mm) with eyes closed. Assessing pointing acuity using LMC with a standardized test protocol has an acceptable concurrent validity when compared to a 3D-based multi camera system (QMC), supporting its potential use in clinical settings. Future research should evaluate its psychometric properties regarding discriminative validity, reliability and responsiveness in various populations.

The proposed evolutionary learning model, EvoLearning, is aimed towards maximizing student mastery through effort and processing, scaffolding, and meaningfulness. The model was formed by inductive development of courses in software engineering and pervasive computing over two decades, inspired by agile and evolutionary software engineering principles. The model is built on the teacher being a guide on the side instead of a sage on the stage, with the aim to allow for maximum degrees of freedom for students to pursue interests and goals while providing scaffolding for the learning process.

The design of the model has been informed by theoretical frameworks from educational psychology and pedagogy. For example, course designs utilize taxonomies for defining objectives and to construct consecutive activities, that stepwise increase the cognitive and knowledge dimensions of learning. The steps are designed as incremental and interwoven, with increasingly challenging objectives and activities. The model emphasizes students’ feeling of ownership and purpose, through freedom of will, which leads to increased meaningfulness and motivation. The learning environments invite and require active participation, from the start of a course. The design benefit from student diversity that enhances the collaborative learning experience, while allowing for non-linear increments, that may cater for unexpected yet significant learning experiences for the students.

In practice, courses are composed by interwoven individual and group activities constructively linked to learning objectives, which the proposed model conceptualizes as 'heartbeats'. Weekly tutored meetings are of an utmost importance to guide the students towards taking incremental steps and for the teacher to qualitatively assess progress with the students, as well as to define, prioritize and plan the next activities. This illustrates the emphasis of consistent and scaffolded activation of students, keeping pace throughout a course, while allowing freedom of will and use of prior knowledge, that lead to high levels of processing and perceived meaningfulness in multiple contexts.

Recent developments in artificial intelligence (AI) have been increasingly driven by technoscientific and corporate approaches that emphasise large-scale datasets and autonomous generation systems. In response, Human-Centred AI proposes an alternative framework foregrounding human agency, values, and creative control. This paper contributes to this discourse by examining collaborative performance with AI-augmented instruments through a practice-led experiment involving two musicians. In this way, we investigate how musical agency is distributed across humans and multiple AI systems. Conducted through a laboratory process leading up to a live performance, this project went through five phases: 1) introduction of interfaces, 2) curation of datasets, 3) training of neural audio synthesis models and applying corpus-based synthesis techniques, 4) working with the intelligent instruments in rehearsals and performance, and 5) analysing the outcomes. Through combining qualitative, phenomenologically grounded methods and practice-led artistic exploration, we identify emergent creative relationships between performers and AI-augmented instruments. By analysing the agency at play, we unpack how creative control is distributed between human and machine. Situating our work within the framework of professional collaborative performance, we address the lack of phenomenological research into intelligent instruments whilst contributing methodologies for accountable, artist-centred AI development in musical contexts.

Denna sammanställning omfattar till Arbetsmiljöverket och Försäkringskassan anmälda arbetsolyckor och arbetssjukdomar i privata byggindustrin i företag verksamma inom bygg- och anläggning. Arbetsskador inträffade under år 2025, som är anmälda och registrerade till och med sista maj 2026, ingår i redovisningen. Sammanställningen visar inte vilka av dessa anmälda arbetsskador som blir godkända. 

Accurate estimation of railway track geometry degradation and reliable prediction of remaining useful life (RUL) are essential for cost-effective infrastructure management. This paper presents a self-contained Kalman filtering framework that integrates unsupervised tamping detection and Settling-aware post-tamping state management, jointly addressing state estimation, short-term forecasting, and RUL prediction for longitudinal level and twist parameters using only historical onboard monitoring (OBM) and measurement train (MT) data, without requiring traffic load, environmental, or maintenance metadata. The framework comprises four integrated stages: (i) source-specific outlier detection exploiting the distinct noise characteristics of OBM and MT instruments; (ii) unsupervised tamping detection through adaptive jump analysis on monthly representative values; (iii) a damped Kalman filter with adaptive noise modelling and spatial fusion of neighboring positions, augmented by a Settling-aware state management mechanism that detects the rapid post-tamping consolidation phase and injects physically informed velocity priors to prevent filter lag; and (iv) a RUL prediction module that propagates the final Kalman state forward under damped dynamics until the predicted geometry violates the Alert Limit (AL) or Immediate Action Limit (IAL) defined by EN 13848-5. The complete pipeline is evaluated on 50 consecutive track positions spanning a 50-metre segment of Sudostbahn line 870 (Switzerland), using 6 MT and 33 OBM observations per position collected over five years (2016-2021). Results demonstrate accurate estimation through both degradation and maintenance phases, with six-month forecast confidence bands. The multi-position RUL analysis classifies 98% of positions as safe beyond a 2-year horizon and identifies the remaining positions with finite RUL values, enabling spatially targeted maintenance prioritization.

The jet mass of W bosons decaying to a quark-antiquark pair is measured in W+jets events from proton-proton collisions at a center-of-mass energy of 13 TeV. The data used were collected by the CMS experiment at the CERN LHC and correspond to an integrated luminosity of 138 fb−1. Hadronic decays of W bosons with high momenta produce strongly collimated decay products due to the large Lorentz boost, and are reconstructed as single large-radius jets. These jets have a characteristic substructure that is exploited to distinguish them from the large background of quark- and gluon-initiated jets. The jet mass is computed using the soft-drop algorithm, which suppresses soft wide-angle radiation that leads to a broadening of the jet mass distribution. For the first time, unfolded measurements are presented of the double-differential W+jets cross section as a function of the jet transverse momentum and soft-drop mass. From these distributions, the W boson mass is obtained, with a value of 80.83 ± 0.55 GeV in a scheme with mass-dependent width, achieving the smallest uncertainty available today from an all-jets final state at a hadron collider.

A combined statistical analysis of searches for heavy vector boson resonances decaying into pairs of W, Z, or Higgs bosons, as well as into quark pairs (qq, bb, tt, tb) or lepton pairs (ℓ⁺ℓ⁻, ℓν), with ℓ = e, μ, τ, is presented. The results are based on proton-proton collision data at a center-of-mass energy of 13 TeV, corresponding to an integrated luminosity of 138 fb−1, collected by the CMS experiment from 2016 to 2018. No significant deviation from the expectations of the standard model is observed. The results are interpreted in the simplified heavy vector triplet (HVT) framework, setting 95% confidence level upper limits on the production cross sections and on the coupling strengths of the HVT bosons to standard model particles. The results exclude HVT resonances with masses below 5.5 TeV in a weakly coupled scenario, below 4.8 TeV in a strongly coupled scenario, and up to 2.0 TeV in the case of production via vector boson fusion. The combination provides the most stringent constraints to date on new phenomena predicted by the HVT model.

A search for Higgs boson (H) production at high transverse momentum (pT) in the WW decay channel is presented. The analysis uses proton-proton collisions at √s = 13 TeV recorded by the CMS experiment in 2016–2018, corresponding to an integrated luminosity of 138 fb−1. The visible decay products of the Higgs boson are reconstructed as a single large-radius jet with one isolated lepton or none (1ℓ and 0ℓ, respectively; ℓ = e, μ). The H-candidate jets are identified using an advanced transformer-based algorithm and are calibrated with the Lund jet plane reweighting technique. The 1ℓ channel is further split into gluon fusion, vector boson fusion, and associated production with hadronically decaying vector boson categories, while the 0ℓ channel considers all production processes inclusively. The measured cross section times the H → WW branching fraction relative to the standard model expectation is µ = −0.19^+0.48_-0.46 , indicating no evidence of a signal above the background. This measurement represents the first dedicated study of highly Lorentz-boosted H → WW decays, complementing earlier searches for high-pT Higgs boson in other decay channels.

As humanity seeks to decarbonise hard-to-abate sectors, electrolytic hydrogen production, based on renewable electricity, is expected to grow significantly. Co-location of hydrogen and electricity infrastructure is sometimes desirable for reasons of cost and practicality and is unavoidable at the electrical grid interface of an electrolyser. In this work, the risks of co-location for GW-scale hydrogen electrolysers and their grid-connection substation are explored in an interdisciplinary manner by considering the ignition sources presented by electrical substation infrastructure along with the accident scenarios that might arise with varying degrees of co-location, given the properties of hydrogen gas. Corona discharge and insulator discharge are identified as the two greatest hazards present. Mitigation measures are discussed in relation to the accident scenarios through a synthesis of electrical design and hydrogen fire safety design, considering jet flame and explosion risks with electrical assets as ignition sources. In many cases, ignition sources and fuel leak risk cannot be eliminated but physical separation and inclusion of barriers can offer adequate mitigation. Detailed recommendations for future research are identified. 

This analysis presents an observation of the photon-fusion production of W boson pairs using the CMS detector at the LHC. The total cross section of the W+W− production in photon fusion is measured using proton-proton collision data with an integrated luminosity of 138 fb−1 collected with the CMS detector in 2016–2018 at a center-of-mass energy of √s = 13 TeV. Events are selected in the final state with one isolated electron and one isolated muon, and no additional tracks associated with the electron-muon production vertex. The total and fiducial production cross sections are  643⁺⁸²_-78 fb and 3.96^+0.53_-0.51 fb, respectively, in agreement with the standard model predictions of 631 ± 126 fb and 3.87 ± 0.77 fb. This agreement enables stringent constraints on anomalous quartic gauge couplings within a dimension-8 effective field theory framework.

The high-luminosity upgrade of the CERN LHC requires the replacement of the CMS tracking detector to cope with the increased radiation fluence while maintaining its excellent performance. An extensive R&D program, aiming at using 3D pixel silicon sensors in the innermost barrel layer of the detector, has been carried out by CMS in collaboration with the FBK (Trento, Italy) and CNM (Barcelona, Spain) foundries. The sensors will feature a pixel cell size of 25 × 100μm², with a centrally located electrode connected to the readout chip. The sensors are read out by the RD53A and CROCv1 chips, developed in 65 nm CMOS technology by the RD53 Collaboration, a joint effort between the ATLAS and CMS groups. This paper reports the results achieved in beam test experiments before and after irradiation, up to a fluence of approximately 2.6 × 10¹⁶ n_eq/cm². Measurements of assemblies irradiated to a fluence of 1 x 10¹⁶ n_eq/cm² show a hit detection efficiency higher than 96% at normal incidence, with fewer than 2% of channels masked, across a bias voltage range greater than 50 V. Even after irradiation to a higher fluence of 1.6 x  10¹⁶ n_eq/cm², similar performance is maintained over a bias voltage range of 30 V, remaining well within CMS requirements.

A search for a new neutral gauge boson, Z′, produced in association with one or two jets, including at least one b jet, and decaying into a pair of muons is presented. The analysis uses proton-proton collision data collected with the CMS detector at √s = 13 TeV, corresponding to an integrated luminosity of 138 fb−1. No significant deviation from background expectations is observed. Upper limits at 95% confidence level on the product of cross section, branching fraction to dimuons, acceptance, and efficiency, from 0.2 to 2 fb, are set for Z′ boson masses between 125 and 350 GeV. Process-dependent products of acceptance and efficiency, and model-independent limits on the signal yield are provided. These are the only results to date in the 125–200 GeV mass range and the most stringent for b quark fusion production modes in the 200–350 GeV range, complementing inclusive Z′ boson searches.

A novel anomaly detection algorithm is presented. The Wasserstein normalized autoencoder (WNAE) is a normalized probabilistic model that minimizes the Wasserstein distance between the learned probability distribution—a Boltzmann distribution where the energy is the reconstruction error of the autoencoder (AE)—and the distribution of the training data. This algorithm has been developed and applied to the identification of semivisible jets—conical sprays of visible standard model (SM) particles and invisible dark matter states—with the CMS experiment at the CERN LHC. Trained on jets of particles from simulated SM processes, the WNAE is shown to learn the probability distribution of the input data in a fully unsupervised fashion, such that it effectively identifies new physics jets as anomalies. The model exhibits stable, convergent training and recovers strong classification performance for a wide range of signals against the selected background process, for which a standard AE fails because of outlier reconstruction. In addition, the model improves upon standard normalized autoencoders while remaining fully agnostic to the signal. The WNAE directly tackles the problem of outlier reconstruction, a common failure mode of autoencoders in anomaly detection tasks.

A search for Higgs boson pair production is presented, targeting final states whereone Higgs boson decays to a bottom quark-antiquark pair and the other Higgs boson decaysto two W bosons, both of which decay leptonically, to an electron or a muon, and a neutrino.For the first time, the search is conducted with proton-proton collision data from the LHCat √s = 13.6TeV, recorded with the CMS detector in 2022 and 2023 and correspondingto an integrated luminosity of 62fb−1. The results are consistent with the standard modelpredictions. An upper limit of 12.0 times the standard model prediction at 95% confidencelevel is set on the Higgs boson pair production cross section, with an expected limit of 18.5.The results are also used to constrain the strength of the trilinear self-coupling of the Higgsboson, as well as of the quartic coupling between two Higgs bosons and two vector bosons.

We report on the observation and measurement of astrometry, photometry, morphology, and activity of the interstellar object 3I/ATLAS, also designated C/2025 N1 (ATLAS) with the NSF-DOE Vera C. Rubin Observatory. Comet 3I/ATLAS, the third known interstellar object, was discovered on UT 2025 July 1. Rubin Observatory had coincidentally collected images of the object’s region of the sky during routine commissioning. Facilitated by Rubin’s high resolution and large aperture, we successfully recovered object detections from Rubin observations spanning UT 2025 June 21 (10 days before discovery, when 3I/ATLAS was 4.5 au from the Sun) through the date of discovery, and we acquired additional images through UT 2025 July 20 as part of commissioning. We measure on-sky locations of 3I/ATLAS in Rubin ugrizy bands, with a typical precision of ∼70 mas, and briefly describe the reason this is coarser than our measured static source astrometric precision of ∼3 mas in Rubin images. We measure grizy magnitudes of 3I/ATLAS photometry at ∼0.01 mag precision, detecting no short-term photometric variability above 0.01 mag. We derive an estimated near-nucleus dust-to-nucleus scattering cross-sectional ratio of η ≳ 13 on UT 2025 July 2 based on Rubin photometry and an upper limit nucleus size computed from Hubble Space Telescope observations. We find Rubin colors of g − r =  (0.657 ± 0.013) mag, r − i =  (0.235 ± 0.018) mag, i − z = (0.147 ±  0.042) mag, and z − y =  (0.047 ± 0.052) mag. These data represent the earliest observations of this object by a large (≳8 m class) telescope and illustrate the type of measurements (and discoveries) Rubin’s Legacy Survey of Space and Time will provide after it begins in early 2026.

A search for exotic Higgs boson decays H → 𝒜𝒜, with 𝒜 → γγ is presented, using events with a semi-merged topology. One of the hypothetical particles, 𝒜, is assumed to decay promptly into a semi-merged diphoton system reconstructed as a single photon-like object, while the other 𝒜 decays into two resolved photons. The search is performed using proton-proton collision data collected by the CMS experiment at  √s = 13 TeV, corresponding to an integrated luminosity of 138 fb−1. The data agree with the standard model background expectation. Upper limits are set on the product of the Higgs boson production cross section and the branching fraction, σ(pp → H) ℬ(H → 𝒜𝒜 → 4γ), which range from 0.264 to 0.005 pb at 95% confidence level, for 𝒜 masses in the range 1 < m𝒜 < 15 GeV. These limits are the most stringent to date in the 1–5 GeV m𝒜 range.

A search is conducted for standard model Higgs bosons with large transverse momentum (pT) decaying to bottom quark pairs and produced in association with a hadronically decaying W or Z boson at the LHC. The result is based on a dataset of proton-proton collisions at a center-of-mass energy of 13 fb-1 TeVcollected with the CMS detector in 2016–2018, corresponding to an integrated luminosity of 138. Boosted Higgs, W, and Z boson decays are reconstructed using large-radius jets with pT > 450 GeV and identified with heavy-flavor classifiers based on a graph convolutional neural network. The observed signal strength relative to the standard model expectation is utional neural network. The observed signal strength relative to the standard model expectation is µ = 0.72 ^+0,75_-0,71  including statistical and systematic uncertainties.

Anomaly detection methods used in a recent search for new phenomena by CMS at the CERN LHC are presented. The methods use machine learning to detect anomalous jets produced in the decay of new massive particles without depending on a specific theory model. The effectiveness of these approaches in enhancing sensitivity to various simulated signal samples is studied and compared using data collected in proton–proton collisions at a center-of-mass energy of 13 TeV. In an example analysis, the capabilities of anomaly detection methods are further demonstrated by identifying large-radius jets consistent with Lorentz-boosted hadronically decaying top quarks in a model-agnostic framework.

A search for heavy long-lived charged particles at the LHC is presented. Particles traversing the CMS muon detector across several bunch crossings are searched for using a data sample of proton-proton collisions at √𝑠 = 13.6 TeV collected with the CMS detector in 2024, corresponding to an integrated luminosity of 3.7 fb-1. This is the first search using the novel level-1 trigger scouting data set collected without any trigger selection, allowing correlations between bunch crossings to be analyzed. The results are interpreted as upper limits on the cross sections of several benchmark processes with pair production of heavy long-lived charged particles. Upper limits on the fiducial cross section of a heavy long-lived charged particle with pT > 500 GeV and |η| < 0.83 are also set in different ranges of ß = v/c. This analysis is a proof of concept for the level-1 trigger data scouting system and complements existing searches for heavy long-lived charged particles by extending the sensitivity to lower β values.

The existence of a new spin-zero particle with a mass below the electroweak scale is predicted by severaltheoretical models. Searches for resonant production of photon pairs at the LHC are able to probe thesemodels. We present a search for a narrow resonance produced through gluon fusion that decays into a pairof photons with an invariant mass between 10 and 70 GeV, using a proton-proton collision dataset from theCMS experiment. This dataset, corresponding to an integrated luminosity of 54.4 fb−1, was recorded in2018 at a center-of-mass energy of 13 TeV using a newly introduced diphoton trigger that enabledexploration of the low-mass diphoton spectrum. No significant excess above the expected background isobserved. Upper limits are set on the product of the gluon fusion production cross section and the branchingfraction of the diphoton decay of a narrow resonance. An interpretation of these limits within an effectivefield theory framework for axionlike particles is also provided.

A search for a Higgs boson produced in association with a W or Z boson and decaying via a soft unclustered energy pattern (SUEP) is presented. The analysis is based on proton-proton collision data corresponding to an integrated luminosity of 138fb-1 collected between 2016 and 2018 at the LHC. Final states with a leptonic W or Z boson decay associated with a high multiplicity of low-momentum charged particles are explored for the first time. The results show no significant excess over the standard model background expectation. Limits are set on the production cross section of a Higgs boson that decays to a SUEP, for a range of parameters of the SUEP model. Material is provided to facilitate further interpretation of the results.

A variety of fluorinated ethers have been used to improve key performance metrics in lithium–sulfur batteries, such as specific energy and cycle life. However, most previous articles employed only one fluorinated ether, and there is currently a lack of criteria or consensus about which fluorinated ether provides the largest performance improvement. This stems from a lack of fundamental understanding of the correlation between the chemical formula of the fluorinated ether and its effect on lithium–sulfur battery reactions. In this work, we systematically investigated nine fluorinated ethers and tested their effects in lithium–sulfur batteries representative of commercially relevant conditions. Electrochemical measurements were complemented by ex situ and operando NMR characterization of the lithium metal anode reactions, ex situ SEM and EDX imaging of cycled lithium and sulfur electrodes, and ab initio calculations and molecular dynamics simulations of the electrolytes, thus revealing the key motifs in the chemical structure of the fluorinated ethers that lead to performance improvements. The best-performing fluorinated ethers were those that interact with lithium ions with a strength similar to that of the other solvent(s) in the electrolyte, and as a result, fast solvent exchange reactions and high lithium-ion mobility are promoted. This benefited both the lithium and the sulfur electrode reactions, with the suppression of lithium dendrites, the formation of a more protective lithium SEI, and the promotion of polysulfide reactions that avoid the passivation of the carbon in the sulfur electrode. The best results were obtained with TFMP (1,1,2,2-tetrafluoro-3-methoxypropane), which had not been used before in lithium–sulfur batteries.

High-energy partons lose energy while propagating through the hot, strongly interacting medium produced in ultrarelativistic nucleus-nucleus collisions, leading to a suppression of particle production at high transverse momentum (pT). The dependence of this energy loss on the size of the colliding nuclear system has yet to be firmly established experimentally. This Letter presents a systematic study of charged-particle suppression across four different nucleus-nucleus collision systems using nuclear modification factors (RAA) measured by the CMS Collaboration at the CERN LHC. Previous CMS measurements of RAA in oxygen-oxygen, xenon-xenon, and lead-lead collisions are recast with identical pT intervals and are complemented by the first measurement of the charged-particle RAA in neon-neon collisions at  √𝑠NN= 5.36TeV. The neon-neon data correspond to an integrated luminosity of 0.76 nb-1. The RAA in all collision systems examined show similar qualitative trends as a function of pT, but have a magnitude which is ordered with the nucleon number A. The RAA feature a downward slope at low pT, a local minimum at around 5–7 GeV, and an upward slope with increasing pT. The RAA are also compared in terms of A1/3, which is proportional to the nuclear radius. Models including only initial-state nuclear effects fail to reproduce the observed trends, whereas energy loss models reproduce the trends in the region pT > 9.6 GeV.

Production fraction ratios of 𝐵+, 𝐵0, and 𝐵0𝑠 mesons are measured in proton-proton collisions at √𝑠=13  TeV using a special dataset recorded in 2018 with high-rate triggers designed to collect an unbiased sample of 1⁢010 𝑏 hadrons with the CMS experiment at the LHC. These data allow the study of the open-charm decays of 𝐵 mesons (𝐵(𝑠) →𝜋⁢𝐷(𝑠)) where the 𝐷 meson decays into fully hadronic final states. By utilizing known branching fractions and precise theoretical calculations, production fraction ratios as functions of 𝐵 meson transverse momentum (𝑝T) and rapidity (𝑦) are measured using open-charm decays in the kinematic range of 8<𝑝T<60  GeV and |𝑦| <2.25. In addition, the same dataset is used to measure the relative production fraction ratios with the charmonium decay channels [𝐵(𝑠) →𝑋⁢𝐽/𝜓 with 𝑋 indicating a 𝐾+, 𝐾*(892)0, or 𝜙⁡(1020) meson] where the 𝐽/𝜓 meson decays into a pair of muons. The open-charm results are used to normalize the relative production fraction ratios obtained from the charmonium samples. Measurements of the ratios of branching fractions of 𝐵 meson decays to charmonium and open-charm final states are also reported, which will improve the world-average values of these ratios. Finally, we test isospin invariance in 𝐵 meson production in proton-proton collisions and observe that it holds within the experimental precision.

At hadron colliders, the net transverse momentum of particles that do not interact with the detector (missing transverse momentum, →𝑝missT) is a crucial observable in many analyses. In the standard model, →𝑝missT originates from neutrinos. Many beyond-the-standard-model particles, such as dark matter candidates, are also expected to leave the experimental apparatus undetected. This paper presents a novel deep neural network based →𝑝missT estimator, deepmet, developed by the CMS Collaboration at the LHC. The deepmet algorithm produces a weight for each reconstructed particle based on its properties. The estimator is based on the negative vector sum of the weighted transverse momenta of all reconstructed particles in an event. Compared with other estimators currently employed by CMS, deepmet improves the →𝑝missT resolution by 10%–30%, shows improvement for a wide range of final states, is easier to train, and is more resilient against the effects of additional proton-proton interactions accompanying the collision of interest.

Lignin holds great promise for fabricating multifunctional carbon materials, yet it is hindered by the difficulty of simultaneous pore structure regulation and heteroatom doping. To address this challenge, we propose a novel molten KCl-thiourea synergistic strategy for the scalable synthesis of N/S co-doped lignin-derived porous carbons without additional chemical activators. Molten KCl provides a growing environment to inhibit carbon agglomeration, guiding the formation of a hierarchical micropore-mesopore structure. Thiourea serves as a multi-role precursor: it crosslinks lignin-derived fragments to prevent structural collapse, acts as an in-situ N/S source, and reacts with residual Na+in lignin to form water-soluble salts for in-situ desalination. The optimal sample exhibits a high surface area of 1802 m2⋅g 1, a balanced pore size distribution, and a heteroatom content of N/S for CO2 adsorption (5.7 mmol⋅g 1) and supercapacitor applications (345F⋅g 1 at 0.1 A⋅g 1 and 98% capacitance retention after 10,000 cycles). This work not only provides a sustainable and cost-effective route for lignin valorization but also reveals the synergistic mechanism of molten salts and heteroatom precursors in regulating carbon structures. The resulting carbons exhibit great potential for practical applications in carbon capture and electrochemical energy storage.

The Higgs boson (H) decay width is determined from the ratio of off- and on-shell production of 𝐻 →𝑊⁢𝑊 →𝑒⁢𝜈⁢𝜇⁢𝜈 using proton–proton collision data corresponding to an integrated luminosity of 138  fb−1 collected at √𝑠 =13  TeV by the CMS experiment at the Large Hadron Collider. The off-shell signal strength is measured as 𝜇off−shell =1.2+0.8−0.7. The Higgs boson total decay width is Γ𝐻 =3.9+2.7−2.2  MeV, in agreement with the standard model prediction. The uncertainty in this result represents a factor of 3 improvement over the previous CMS result in this decay channel.

The ratio between the 𝐵+𝑐→𝐽/𝜓⁢𝜏+⁢𝜈𝜏 and 𝐵+𝑐→𝐽/𝜓⁢𝜇+⁢𝜈𝜇 branching fractions is measured using a data sample of proton-proton collisions collected by CMS at a center-of-mass energy of 13  TeV in the years 2016–2018 and corresponding to an integrated luminosity of 138  fb−1. The 𝐽/𝜓 meson is identified through its 𝐽/𝜓 →𝜇+⁢𝜇− decay and the tau lepton is reconstructed in the hadronic three-prong final state. The measured ratio of branching fractions in this tau decay mode, ℛhad𝐽/𝜓 =1.0⁢4+0.50−0.44, is combined with the previous analysis based on the 𝜏+→𝜇+⁢𝜈𝜇⁢¯𝜈𝜏 leptonic decay channel, leading to ℛ𝐽/𝜓 =0.49±0.26. As this result is consistent with the standard model prediction of 0.258 ±0.004, no evidence of lepton flavor universality violation is found.

Solid biomass oxy-fuel combustion with external recirculation was demonstrated in a 100-kW atmospheric, down-fired combustor operated close to stoichiometry. The external recirculation setup comprised a particulate filter, a condenser, a fan and O2 addition before the burner inlet. A theoretical description of the recirculation process is presented and validated. Two fuels, softwood (SW) and forest residues (FR), with similar residence times, were compared. Gaseous species, including potassium (K) compounds (atomic K, KOH, and KCl), and gas temperature were quantified in real-time by tunable diode laser absorption spectroscopy (TDLAS) and photofragmentation TDLAS at two locations in the reactor core. Major species (CO2, H2O, O2, and N2) were also measured at the exhaust. Flue gas particles collected with a low-pressure impactor at the exhaust were analyzed by X-ray powder diffraction and scanning electron microscopy. The average CO2 purity (dry) was 90 % for SW and 86 % for FR. The NO concentration was higher for FR due to the larger nitrogen content in the fuel. The gaseous K species concentrations were higher for FR than for SW (factor 2–3), but not as high as expected from the difference in fuel K content (factor 7), likely due to the high content of Si and Al in FR. Gas-phase K was significantly lower than predicted by thermodynamic equilibrium calculations (TEC), probably due to K adsorption by soot particles. The fine and coarse particle concentrations were significantly higher for FR than for SW due to the higher ash content of FR. The FR fine mode particles consisted mainly of K2SO4 and KCl, in good quantitative agreement with TEC of gas phase condensation. Apatite, Ca5(PO4)3OH, likely formed from vaporized Ca and P, was found in the fine mode in all recirculation cases.

The origins of the diverse Mesozoic magmatism and present-day intraplate seismicity in central Brazil, part of the South American plate currently undergoing compression/shortening, have been the subjects of long-standing debate. Here, we investigate the structure of the lithosphere using a combined 3-D electrical resistivity and seismic P-wave velocity (Vp) tomography to understand the deformation patterns and the relationship to the distribution of magmatism and seismicity in the region. Magnetotelluric and geomagnetic depth sounding data and P-wave traveltimes from seismological arrays were inverted for the 3-D resistivity and Vp variations in the ∼1500 × 1000 km2 area of study. The results reveal a basic structure consisting of an electrically resistive and seismically fast upper crust, a low-resistivity and low wave speed lower crust, a resistive and fast uppermost mantle and a basal low-resistivity and low wave speed asthenosphere. This layering is thinned in places and dissected by steep cross-cutting NE-SW and NW-SE shear zones of low resistivity and slow wave speeds yielding a fragmented lithosphere. Thick blocks of high resistivity and fast wave speed bounded by the steep shear zones correlate spatially with zones of gravity lows across the Neoproterozoic fold belts. The steep shear zones correlate with the Middle Proterozoic and Mesozoic mafic dyke swarms (likely emplaced during the opening of Neoproterozoic and younger proto-Atlantic oceans) and Cretaceous-Eocene alkaline and alkaline-carbonatite magmatism, indicating that inherited structures controlled the lithospheric deformation and magmatism. They also correlate spatially with zones of high seismicity, suggesting that fluid migration plays a role in generating the ongoing seismicity.

Cenospheres are plausible intermediates in entrained flow gasification (EFG) of biogenic suspension fuels. However, their structure and reactivity remain poorly understood, limiting the accuracy of numerical simulations of entrained flow gasifiers.

The present study characterizes cenospheres from two experiments in a technical entrained flow gasifier using beech wood pyrolysis oil (PO) and char suspensions (slurry; POC) under intentionally low air ratios (0.35). Structural features and CO2 gasification kinetics are investigated, and mass transport effects under technically relevant conditions (1000 – 1500 °C, 10 bar CO2) are evaluated.

A broad particle size distribution of the cenospheres (100 – 800 µm) is identified. Micro computer tomography reveals internal macro-structures ranging from uniformly distributed porosity to a porous shell, with embedded char particles in POC cenospheres.

Micro- and mesoporosity are analyzed via argon physisorption. Only POC cenospheres show microporosity, which is assigned to the char particles. Structural ordering determined by X-ray diffraction (XRD) is stronger for smaller cenospheres. The evolution with increasing particle diameter differs between POC and PO cenospheres, with radial expansion of the graphene layers prevailing in POC cenospheres, while both radial expansion and graphene stacking vary in the structured domains in PO cenospheres.

Reaction kinetics in CO2 (5 – 20 bar, 810 – 850 °C) are determined in the differential fixed bed reactor for 200 – 400 and 600 – 800 µm fractions. The reaction rate increases with increasing temperature and CO2 partial pressure as described by the Arrhenius and the power law model (activation energy: 210 – 242 kJ/mol; reaction order: 0.2 – 0.3). Especially the mantle area-to-volume ratio of the stacked graphene layers derived from XRD and the mesopore surface area are found to impact the reaction rate.

A green transition in aviation requires a drastic upscaling of Sustainable Aviation Fuel (SAF). The power-to-liquid process for the production of CO2-neutral jet fuel via electricity, called electro-SAF (e-SAF), directly replaces fossil jet fuel without having to change infrastructure, aeroplanes, or jet-engines. The process combines green hydrogen with industrial exhaust gas, or captured carbon dioxide, in a circular economy concept. A key element of the e-SAF production plant is the reactor where syngas is produced. Traditional reactors use catalytic technology, which faces severe challenges due to the reduced performance over time because of catalyst degradation, clogging, and breakup due to embrittlement. This results in large operational costs and low process efficiency. A high-potential alternative is the catalyst-free reverse water-gas-shift (RWGS) reactor concept, which has key advantages in energy efficiency, flexibility of operation, potential for upscaling, and applications in related fields.

The primary aim of this paper is to investigate the fundamental aspects of the catalyst-free (non-catalytic) RWGS process, such as reaction kinetics and the interactions between turbulence and chemistry. The secondary aim is to identify how a typical combustion subgrid scale models for Large Eddy Simulations (LES) perform when the chemical reactions are endothermic, in contrast to the strong endothermicity associated with classical combustion. This is done by comparing results from LES with corresponding Direct Numerical Simulations (DNS), which does not rely on any turbulence modeling.

It is found that even small traces of O2 in the CO2 stream can significantly increase the production rate of CO. This is attributed to the increased pool of OH, which benefits the CO production rate. The effect is strongest at atmospheric pressure and less pronounced at higher pressure.

By using the temporal jet framework to study turbulence-chemistry interactions, an algebraic equation for the prediction of the CO conversion time in a turbulent flow as a function of Damköhler number and chemical timescale is employed. This allows for an a priori estimation of the required residence time to reach full conversion of the RWGS reaction for various turbulent shear flows.

Finally, except for some smaller systematic deviations, it is concluded that the partially stirred reactor (PaSR) LES subgrid model designed for combustion reactions perform well also for the endothermic reverse water-gas-shift reaction.

We present a search for the pair production of Higgsinos in final states with large missing transverse momentum and either two reconstructed muons or a reconstructed lepton (muon or electron) and an isolated track. The analyzed data correspond to proton-proton collisions with an integrated luminosity of 137  fb−1, collected by the CMS experiment at √𝑠 =13  TeV in 2016, 2017, and 2018. The signal scenario assumes four nearly mass degenerate Higgsino mass eigenstates: two neutralino states ˜𝜒02 and ˜𝜒01 with a small mass difference in the range 1–10 GeV and two chargino states ˜𝜒±1 with an intermediate mass. The analysis focuses on the decay of the heavier neutralino into the lighter one and a virtual 𝑍 boson, which decays into two same-flavor leptons. The leptons have small transverse momentum and/or a small opening angle between the identified muons. An isolated track is used to recover events in which only one of the two leptons is identified. Multivariate discriminants are used to enhance the sensitivity by efficiently rejecting backgrounds from SM processes or misreconstructed tracks and/or leptons. The search explores a unique phase space and probes a previously unexplored region of the signal model parameter space. Mass differences between the two neutralinos are probed down to 1.5 GeV, assuming a Higgsino mass of 100 GeV. The maximum excluded Higgsino mass is 115 GeV.

The influence of intentional oxygen incorporation in recently developed ternary C-alloyed molybdenum diselenide (MoSeC) coatings has never been investigated. This study addresses this gap by systematically incorporating varying oxygen contents into MoSeC coatings, with the aim of identifying the impact on the structural, mechanical and sliding properties. MoSeC–O coatings with oxygen content in the range of 0 - 21.5 at % were deposited using reactive direct current magnetron sputtering (DCMS). The developed coatings were compact with cauliflower-like surface morphologies. The X-ray diffraction (XRD) and high-resolution transmission electron microscopy (HRTEM) showed that the crystallinity decreased with the introduction of oxygen in the MoSeC coating. The X-ray photoelectron spectroscopy (XPS) chemical bonding investigation confirmed the presence of Mo–O bonds, indicating partial oxidation of Mo. Up to 6.9 at %, the hardness, reduced Young´s modulus, friction and wear showed only minor changes. However, beyond this level, these properties deteriorated substantially. The Raman spectroscopy of the wear tracks revealed formation of low friction MoSe2 tribolayer as the dominant friction reduction mechanism. Whereas, higher oxygen content suppressed the formation of effective tribolayer due to excessive oxidation and loss of MoSe2 crystallinity, in agreement with the structural and chemical bonding results. Overall, the results indicate that a moderate oxygen content can be tolerated in MoSeC coatings, which may allow for more relaxed vacuum and purity conditions during deposition, offering a more cost effective and industrial friendly synthesis route.

A search for dark matter particles produced in association with a Higgs boson decaying to a bottom quark-antiquark pair in proton-proton collisions at √𝑠=13  TeV is presented. The data, collected with the CMS detector at the LHC, correspond to an integrated luminosity of 101  fb−1. The analysis is performed in exclusive categories targeting both Lorentz-boosted (merged) and resolved 𝑏 jet pair topologies, covering a wide range of Higgs boson transverse momentum. A statistical combination is made with a previous search using data collected in 2016 and corresponding to an integrated luminosity of 35.9  fb−1. The observed data agree with the standard model background predictions. Constraints are placed on models predicting new particles or interactions, such as those in the simplified frameworks of baryonic-𝑍′ and 2⁢HDM+𝑎, where the latter is a type-II two-Higgs-doublet model featuring a heavy pseudoscalar with an additional light pseudoscalar. Upper limits at 95% confidence level are set on the production cross section for these models. For the baryonic-𝑍′ model, 𝑍′ boson masses below 2.25 TeV are excluded for a dark matter particle candidate mass of 1 GeV. In the 2⁢HDM+𝑎 model, heavy pseudoscalar masses between 850 and 1300 GeV are excluded for a light pseudoscalar mass of 350 GeV.

Wire-arc additive manufacturing (WAAM) has garnered attention in academia and industry and has huge potential because of its ability to fabricate complex geometry and economic viability compared to other metal additive manufacturing (MAM) processes. Its unparalleled design freedom, ability to manufacture near-net shaped components, less material wastage, ability to produce large components with a variety of materials, and flexibility in influencing material properties by varying the process parameters makes WAAM promising for industry. Ti–6Al–4V is the most prevalent titanium alloy because of its exceptional material properties and extensive application in aerospace industry, the marine sector, and bio-medical implants, among others. However, its use is limited due to its poor wear behavior due to oxidation, delamination, and spalling. Thus, it is crucial to improve the wear resistance of Ti–6Al–4V by surface engineering, manufacturing processes, and post-process treatments. In this review, we aim to provide a brief introduction to popular MAM processes, process parameters of WAAM, and path planning that influence the material properties and explore the wear behavior of the Ti–6Al–4V alloy in different environmental conditions, process parameters, and post-processing treatments. Due to the dearth of literature available on the wear behavior of the Ti–6Al–4V alloy manufactured using WAAM, the wear behavior of Ti–6Al–4V manufactured by L-PBF, SLM, and other conventional methods is reviewed. Additionally, this review paves the path of future exploration of the wear characteristics of the Ti–6Al–4V alloy manufactured by WAAM.

Smishing (SMS phishing) is a form of social engineering attack in which adversaries exploit mobile messaging platforms to deceive users into disclosing sensitive information, installing malicious content, or initiating fraudulent transactions. Unlike traditional cyberattacks that target software vulnerabilities, smishing primarily exploits human trust, making it difficult to detect using conventional rule-based or signature-driven security mechanisms. With the rapid growth of mobile communication and digital financial services, smishing has emerged as a significant cybersecurity threat affecting individuals, enterprises, and communication infrastructures. To address this challenge, this paper proposes a cybersecurity-oriented hybrid fusion framework for smishing detection that integrates advanced feature engineering with deep learning and transformer-based language models. The proposed approach combines semantic embeddings from Sentence-BERT, statistical TF–IDF representations, and SMS-specific security features capturing URL behavior, phone number patterns, sentiment manipulation, linguistic anomalies, and named entity indicators commonly associated with phishing attacks. These heterogeneous features are fused through a multilayer feed-forward neural network, enabling the detection of both known and previously unseen smishing strategies. The framework is evaluated on six publicly available SMS datasets containing English and Bangla messages, ranging from 2,772 to 16,572 samples after preprocessing and class balancing. Experiments follow a stratified train–validation–test split (70%–15%–15%) with 5-fold cross-validation and Optuna-based hyperparameter optimization. The proposed model achieves a peak validation accuracy of 99.67%, an average cross-validation accuracy of 99.38%, and a test accuracy of up to 96.54%, with ROC–AUC values exceeding 98%. The results demonstrate that the proposed approach outperforms traditional machine learning, deep learning, and standalone transformer-based models. To enhance model transparency, Local Interpretable Model-agnostic Explanations (LIME) are incorporated to provide interpretable insights into feature-level decision behavior, demonstrating the effectiveness of the proposed framework for real-world smishing detection in multilingual mobile communication environments.

The increasing generation of wood ash (WA) from biomass combustion presents both an environmental challenge and an opportunity for sustainable resource utilization. This review provides a comprehensive assessment of recent advances in the valorization of WA for the development of sustainable engineering materials within a circular economy framework. Unlike previous studies that primarily focus on isolated applications of WA, this work integrates multiple technical dimensions, including material characterization, advanced manufacturing technologies, mechanical performance evaluation, computational modelling, and industrial commercialization pathways. Wood ash typically exhibits alkaline characteristics (pH 9–13.5) and particle sizes ranging from 1 to 1000 µm, enabling its application in a wide range of material systems. In cementitious materials, partial replacement of cement with WA (0.10–20%) generally improves mechanical performance, whereas excessive incorporation may reduce structural integrity. The high silica content (>62%) in certain WA types also enables its utilization in lightweight glass systems and radiation-shielding materials. Furthermore, WA has emerged as a promising functional filler in polymeric and ceramic composites, where additions above 0.5% can enhance dynamic mechanical properties and thermal stability. The review also examines standardized inspection and testing procedures, including quality control (QC) and quality assurance (QA) frameworks based on American Society for Testing and Materials (ASTM), Canadian Standards Association (CSA), and European standards, to ensure the reliability of WA-derived materials. Recent developments in artificial intelligence, machine learning, and computational modelling are highlighted for predicting mechanical behavior, optimizing processing parameters, and enabling digitalized manufacturing systems. In addition, circular manufacturing strategies and economic evaluation models, including break-even analysis, are discussed to assess the industrial feasibility of WA-based products. By integrating circular economy principles with materials engineering, digital technologies, and economic assessment, this review establishes a holistic framework for transforming wood ash from an industrial residue into value-added sustainable materials for construction, energy, and advanced composite applications.

Froth flotation is the most widely applied technique for the beneficiation of low-grade and complex mineral resources, where separation efficiency is fundamentally governed by interfacial phenomena controlled by various factors, particularly flotation reagents, most importantly collectors. Despite substantial progress in collector development and adsorption studies, the characterization of collector behavior at the mineral-solution interface remains fragmented, with limited integration between physicochemical measurements, surface-sensitive analyses, and flotation performance. This lack of a unified, systematic approach hinders the transition from empirical reagent selection to predictive, design-oriented flotation chemistry. This review presents a comprehensive and structured analysis of interfacial physicochemical characterization methods used to investigate collector adsorption and their relevance to flotation systems. Key techniques (such as zeta potential analysis, contact angle measurement, adsorption analysis, and surface tension measurement) and their significant role in collector-related investigations have been comprehensively evaluated. Furthermore, outputs from emerging computational and simulation methods are incorporated to validate and complement experimental findings, enabling a direct link between macroscopic observations and molecular-scale mechanisms. The analysis demonstrates that, rather than relying on a single technique, which is insufficient to fully understand collector behavior, integrating complementary experimental and computational approaches yields the most reliable interpretation of collector-mineral interactions. The integration of these approaches provides a multiscale understanding of collector behavior, bridging bulk solution properties, interfacial interactions, and flotation performance. Based on these insights, the importance of a practical, flexible framework for collector characterization is established, emphasizing the selective, system-specific integration of techniques to optimize experimental design, reduce redundancy, and improve data interpretation. Overall, this work offers a unified, application-oriented perspective on collector characterization, advancing flotation research toward more predictive, efficient, and sustainable methodologies.

Hospitals are among the most energy-intensive building types due to continuous operation, complex technical systems, and stringent indoor environmental requirements. Enhancing energy efficiency in these facilities is therefore critical for reducing operational costs, lowering emissions, and improving system resilience. This paper presents a systematic literature review of 109 peer-reviewed studies published between 2012 and 2022, conducted following PRISMA guidelines. The review synthesises evidence on energy performance outcomes, implementation strategies, and barriers to energy efficiency in hospital buildings. The analysis identifies a growing emphasis on digitalisation, including Internet of Things (IoT), machine learning, smart monitoring, and renewable energy integration. Despite these advances, implementation remains constrained by financial limitations, aging infrastructure, regulatory complexity, and organisational capacity gaps. Drawing on these findings, the paper organises key drivers and barriers into five interdependent dimensions: Financing, Organisational Structures, Building Infrastructure, Energy Systems, and Digital Technologies. The paper contributes a socio-technical framework that conceptualises hospital energy performance as being shaped by interactions between technical building systems and institutional conditions. The framework provides a structured analytical lens for researchers, designers, and facility managers and supports the development of energy-efficient and resilient hospital buildings across diverse contexts.

This report presents an in-depth investigation of four stacking configurations of the van der Waals heterostructure (vdW-HS) of Ti2CO2 and HfSi2N4, conducted to explore their potential for green energy applications. The vdW-HS Ti2CO2/HfSi2N4 has a negligible lattice mismatch of 0.23% between the constituent monolayers, guaranteeing high structural compatibility. The dynamic stability has been confirmed by the phonon band structure, which has no imaginary frequencies throughout the full Brillouin zone. From the electronic band structure analyses, it has been confirmed that all stacking configurations yield identical band characteristics along with an indirect band gap of 0.88 eV calculated by using the Heyd−Scuseria−Ernzerhof (HSE06) functional with spin-orbit coupling (SOC). Remarkably, this vdW-HS displayed type-I band alignment, the electrons tunnel directly from the VBM to CBM of Ti2CO2 monolayer, allowing efficient carrier confinement and recombination, which is advantageous for advanced optoelectronic applications. Moreover, the electronic band edges of the vdW-HS Ti2CO2/HfSi2N4 demonstrate its high suitability for photocatalytic oxygen evolution reaction (OER), but not for hydrogen evolution reaction (HER). The latter unsuitability of the considered vdW-HS is also confirmed by ΔGH > 0.2 or ΔGH < −0.2 eV for all possible sites at the surface of the heterostructure. The considered vdW-HS has a significant static dielectric constant of 4.72, along with noticeable optical absorption in the visible spectrum and intense absorption of 1.50 × 106 cm-1 in the ultraviolet region. The spectroscopic limited maximum efficiency (SLME) of ∼32% is higher than other highly appreciated thin-film photo-responsive absorber materials such as CuInSe2 (∼28%) and CdTe (∼31.5%). The n-type carriers have a higher value of Seebeck coefficient as compared to p-type carriers, which confirms that n-type doping will be more beneficial than p-type. The lattice thermal conductivity κph of the vdW-HS Ti2CO2/HfSi2N4 is 8.41 W/mK at room temperature, which is at least 2.5 and 4.6 times lower than the lattice thermal conductivity of Ti2CO2 and HfSi2N4, respectively. These results highlight the potential of the vdW-HS Ti2CO2/HfSi2N4 as a highly suitable candidate for next-generation optical absorbers and thermoelectric materials for green energy technologies.

Background: 

Previous qualitative research suggests that the biopsychosocial explanatory models of mental illness are not always supported in non-Western cultures. This underscores the importance of exploring explanatory models of mental distress among hard-to-reach groups, such as refugees. The main objective of the current study was to explore what Arabic-speaking refugee youth and young adults conceptualize as their mental distress and if they experience lower functioning or if their mental distress relates to unmet needs in the daily life.

Method: 

A qualitative study design was applied to analyze the responses to the Psychological Outcome Profiles Questionnaire (PSYCHLOPS). An inductive approach using content analysis was utilized to explore what Arabic-speaking refugee youth and young adults conceptualize as their mental health problem. Alderfer’s Existence-Relatedness-Growth theory was employed deductively to understand which needs are unmet in relation to their mental distress.

Results: 

The identified problems fell into one of three main domains: challenges within the self, difficulties in exile and challenges in the encounter with ‘the other’. The mental health problems hindered existence needs and needs of relatedness, as described by the Alderfer’s existence-relatedness-growth (ERG) theory.

Global energy demand, currently exceeding 600 EJ annually, continues to rise alongside rapid electrification and industrial expansion, while renewable energy contributes only ~15% of total primary energy despite dominating recent capacity additions. This disparity indicates a critical gap, the need for scalable, carbon-neutral energy systems that not only generate power but also valorize waste streams. In this context, anaerobic bioprocessing offers a transformative solution by establishing a dual-pathway bioenergy framework through acidogenic/acetogenic and methanogenic routes. This review critically evaluates the distinct and complementary contributions of acidogenic/acetogenic and methanogenic pathways to green energy recovery. Acidogenic and acetogenic fermentations play a crucial role in sustainable waste management by efficiently converting complex organic materials into valuable biofuels such as green hydrogen and volatile fatty acids (SCCA/MCCA). This high-rate carbon unlocking not only enhances resource recovery (345–589 kg volatile fatty acids (VFA)/ton waste) but also contributes to energy generation (3–10.3 kg H2/ton waste; 334–1123 MJ), aligning with future energy demands and environmental sustainability goals in professional practices. These processes can enable product diversification in renewable fuels and chemicals, establishing a foundation for green chemistry and decentralized biorefineries. Advocating for further exploration and implementation of these pathways is essential for advancing sustainable practices in energy generation and resource management. In contrast, methanogenesis functions as the terminal energy-consolidation stage of anaerobic digestion (AD), transforming organic waste into biomethane (67–193 KgCH4/ton of waste), achieving energy recovery of 3368–9728 MJ, offering stability, scalability, and direct integration into existing energy infrastructures. Together, these pathways create a cohesive carbon and energy cascade, with acidogenic and acetogenic processes unlocking molecular value and flexibility, while methanogenesis ensures efficient energy recovery. This synergistic relationship promotes AD from simple waste treatment to a multi-output biorefinery platform, advancing zero-waste strategies, reducing the reliance on fossil fuels, and fostering resilient circular bioeconomies.

Aside from common challenges in aerial object detection, i.e., small, sparse targets and computational power limitations, detecting vehicles from UAV images in the Nordic regions faces strong visibility challenges and domain gaps caused by diverse levels of snow coverage. Although data annotation is expensive, unannotated data is cheaper to collect by simply flying the drones. Hence, we propose a Sideload-Contrastive-Learning-Adaptation (SCLA) framework to improve lightweight detection model performance utilizing cheap unannotated data. First, we propose the Feature-Map-Patch Contrastive Learning (FM-PaCL) tailored for preserving details while retaining context window to address the unique challenges from large image size and small objects. Then, we propose to fuse features from the FM-PaCL backbone and a frozen YOLO11n backbone in the fine-tuning stage for the detection task to also utilize upstream representation learned from the COCO dataset. Our proposed SCLA framework improves the detection performance by 8.9% in terms of mAP50 on the NVD dataset against the Yolo11n baseline. Code is available at https://github.com/Boyinglby/Sideload-Contrastive-Learning-Adaptation

Thin-walled structures fabricated via Fused Deposition Modeling (FDM) offer high strength-to-weight ratios and geometrically customisable configurations. This study examined how five hexagonal wall architectures of progressively increasing nodal connectivity (D1–D5), three layer heights (0.1, 0.2, and 0.3 mm), and two print angle orientations (0° and 90°) influence the compressive strength, energy absorption, and dimensional accuracy of nylon PA12 tubes under quasi-static uniaxial compression. A Taguchi L30 mixed orthogonal array was used to structure the 30 experimental runs, whereas signal-to-noise (S/N) analysis ranked parameter influence, and Grey Relational Analysis (GRA) resolved the competing compressive-strength and dimensional-accuracy objectives. The most complex architecture (D5) at 90°/0.1 mm achieved the highest compressive strength (144.75 MPa) and energy absorption (4.23 J), whereas the intermediate architecture (D3) at 0°/0.1 mm gave the lowest length error (0.03 mm). Layer height dominated compressive strength (S/N range 0.826 dB), and architected design governed dimensional accuracy (S/N range 4.162 dB). In the multi-response analysis, run T13 (D3/0°/0.1 mm) attained the highest grey relational grade (GRG = 0.8623), whilst the Taguchi main-effect analysis of GRG identified D5/90°/0.1 mm as the optimal factor-level combination, with layer height exerting the greatest combined influence. 

Urbanization increases impervious surfaces, generating higher stormwater volumes and pollutant loads that challenge conventional drainage systems. While nature-based solutions (NBS) are widely used for stormwater management, compact stormwater treatment systems (CSTS) provide a decentralized alternative suited to space-limited urban areas. However, their evaluation is hindered by inconsistent international testing protocols. This study compares three major CSTS performance standards, the British Water Code of Practice (BW CoP), the New Jersey Corporation for Advanced Technology (NJCAT) program, and the Deutsches Institut für Bautechnik (DIBt) protocol, to identify methodological differences and testing implications. Based on this comparison, a modified DIBt protocol is developed, expanded to include nutrient (phosphorus and nitrogen) tests, was applied to a filter chamber under controlled laboratory conditions. Results showed particle removal ranging from 25% at high flow to 97% at low flow conditions, hydrocarbon removal remaining consistently high (98–99%), and metal removal efficiencies varying with flow, copper increasing from 19 to 95% and zinc from slightly negative (release) to 88%. Nutrient tests indicated phosphorus removal from 32 to 84% and negative nitrogen removal rates. These findings highlight the need for harmonized testing frameworks reflecting regional conditions to improve CSTS evaluation and implementation.

Cybersecurity education often struggles to provide scalable, practice-oriented learning experiences while maintaining pedagogical oversight. This paper presents AI-CES, a lecturer-in-the-loop, AI-assisted interactive learning platform designed to support scenario-based cybersecurity education. The system combines AI-generated simulation drafting, lecturer review and approval, student-facing multi-round simulations, and performance tracking within a single web-based environment. We report the design and preliminary evaluation of the prototype rather than a validated effectiveness study. An exploratory evaluation was conducted with 30 participants from one computer science department, including 10 lecturers and 20 students, using role-specific post-use surveys with Likert-scale and open-ended items. Descriptive statistics and thematic coding were used to analyse perceived usability, engagement, content quality, and educational value. Participants generally viewed the platform positively, particularly its interactivity, immediacy of feedback, and lecturer oversight of AI-generated content. Lecturers also valued the visibility provided by the analytics dashboard. However, the study does not establish causal effects on learning outcomes, teaching effectiveness, or long-term skill development. Findings should, therefore, be interpreted as evidence of feasibility and user acceptability in a pilot setting. Limitations include the small single-site sample, reliance on self-reported perceptions, and dependence on third-party AI services. Future work should incorporate controlled comparisons, pre/post knowledge measures, multi-institution evaluation, and longitudinal analysis of learner interaction data.

We present a complete laboratory-scale demonstration of industrial off-gas valorisation that is achieved by integrating enzyme-promoted CO₂ capture and purification, electrochemical conversion and downstream processing. Mixed off-gas was sampled on-site before and after an industrial scrubber, subsequently purified and enriched in methyldiethanolamine (MDEA) using a carbonic anhydrase-enhanced sorption achieving an average CO₂ absorption efficiency of 62.6 ± 2.2% and a desorption efficiency of 81 ± 5%. The 300 L gained gas containing approximately 90% CO₂ was subsequently employed as feedstock for electrochemical CO₂ reduction reaction using tin gas diffusion electrodes (Sn-GDE). After 4 h of operation, 6.9 ± 0.3 L⁻¹ of formate with a coulombic efficiency of 57.8 ± 5.0% was produced in ca. 1.9 L being the combined catholyte solutions of 4 independent replicates. Downstream processing thereof enabled formate extraction and concentration to 189 L⁻¹ formic acid in 10 mL. The overall workflow, from raw off-gas to a purified product, highlights the feasibility of coupling biocatalyst-assisted CO₂ capture with electrochemical conversion for sustainable carbon utilisation. This study provides an important step toward scalable, integrated CO₂ capture and conversion technologies directly applicable to industrial emission sources, by demonstrating the operational challenges that arise only upon process integration.