Aalborg University

The recycling of lithium-ion batteries (LIBs) has been dogged by air pollutants containing fluoride (e.g. HF, PF5, POF3). Pyrolysis is a technique that can eliminate polyvinylidene fluoride (PVDF) from the cathode electrode sheets of spent LIBs, effectively separating the cathode material from the aluminum (Al) foil. Nonetheless, the HF gas generated during pyrolysis not only corrodes equipment but also presents serious environmental risks. To address this, a novel, eco-friendly strategy is introduced for the direct upgrading of cathode active materials (CAM). The strategy's cornerstone involves incorporating a minor amount of calcium into the original cathode material's coating, and it leverages mechanical stirring during the waste battery material separation process to ensure the electrode is fully detached from the current collector at a reduced temperature. The pyrolysis mechanism elucidates that fluorine-containing organic pollutants are converted into metal fluorides and deposited on the surface of cathode particles during aerobic pyrolysis, thereby improving the interfacial stability of lithium nickel cobalt manganese oxide (NCM) materials, reducing transition metal dissolution. This strategy not only eliminates the release of fluorine-containing organic pollutants during pyrolysis but also achieves direct regeneration of CAM. This work underscores the importance of the cathode materials' manufacturing process in facilitating the recycling of spent LIBs and provides an environmentally friendly and economically viable solution for the battery recycling industry.

Lithium-ion batteries have emerged as the primary power source for electric mobilities. To ensure the safe operation of the batteries throughout their lifespan, accurate remaining useful lifetime (RUL) prediction is required. This paper proposes ensemble strategies that integrate two different Gaussian process regression (GPR) methods and model-based methods to enhance the robustness of the prediction. The first GPR is based on the forward extrapolation of the measured capacity sequence. The second GPR is based on the extrapolation of the measured feature and then inputs the predicted feature into a capacity estimation model. The first ensemble strategy is the weighted ensemble method, which uses the least squares method to determine the weighted coefficients. The second strategy is a more conservative method, which chooses the fastest degradation path between two basic methods at each prediction step. The third strategy is particle filter (PF) to combine the predicted data from different methods. The batteries aged by a real forklift aging profile and open access dataset are used to verify the proposed methods. The results of all methods based on different percent of data are analyzed. The results show that individual methods may obtain different prediction results, while ensemble strategies have accurate and robust predictions. The PF for capacity-based and feature-based methods has the best performance with the absolute error of RUL less than 23 full equivalent cycles, error of prediction steps less than 1, and negligible simulation time for forklift dataset.

We extend a classical result by Triebel on boundedness of bandlimited multipliers on $L^p({\mathbb R}^n)$, $0<p\le 1$, to a vector-valued and matrix-weighted setting with boundedness of the bandlimited multipliers obtained on $L^p(W)$, $0<p\le 1$, for matrix-weights $W:{\mathbb R}^n\rightarrow \mathbb {C}^{N\times N}$ that satisfy a matrix Muckenhoupt $A_p$-condition.

Objectives Accurate prevalence estimates of diffuse large B‐cell lymphoma (DLBCL) are important for numerous purposes including orphan drug designation. A key criterion for orphan drug designation is a disease prevalence of less than 5/10,000 persons. The objective is to apply and compare different methods of prevalence assessment. Methods In the present nationwide Danish cohort study, the prevalence of DLBCL was assessed using different methodologies, including register‐based and formula‐based approaches. Results The prevalence calculations were based on 9,492 patients diagnosed with DLBCL since year 2000. Incidence increased and survival improved in the period, resulting in higher prevalence of DLBCL. In year 2023, the 2‐,3‐,5‐,10‐, and 20‐year prevalences were 1.53, 2.19, 3.45, 6.08, and 8.80 per 10,000 adults using the register‐based approach. The formula‐based approach was generally accurate when using restricted mean survival. However, when using median survival, the total prevalence was estimated at 8.1 per 10,000 adults. Furthermore, when extrapolating the median survival from the 5‐year survival under constant hazard assumption as done in some orphan drug designation reports, the prevalence was estimated at 6.6 per 10,000 adults. Conclusions In conclusion, the estimated DLBCL prevalences are sensitive to the applied method. DLBCL would disqualify from orphan drug designation in some of the mentioned scenarios.

Utilizing wind power alongside flexible resources such as power‐to‐gas technology, gas‐fuel generator, demand response (DR) program, grid‐enhancing technologies, and carbon capture and storage can help to low carbon operation of integrated electricity‐gas systems (IEGSs). Accordingly, this paper proposes a low‐carbon economic dispatch model for the IEGS, in which gas‐fuel generators, DR, and gas‐fuel generator are considered to realize the economic and environmentally friendly operation of these systems. Also, the flexible AC transmission system device as one of the grid‐enhancing technologies is innovatively included in IEGSs to guarantee that wind power is deliverable entire the electricity system. Besides, power‐to‐gas equipped with hydrogen storage is used to absorb the excess wind power to produce CH4. On the other hand, to capture the inherent flexibility of the gas network, the gas‐storing characteristic of pipelines is shown by line pack modelling. To manage uncertainties associated with wind power and DR program, the proposed model is formulated as an IGDT‐stochastic problem. For efficient computation purposes, the present work follows the mixed‐integer linear programming framework. Different case studies are performed on an integrated test system. Numerical simulation results show that the proposed model leads to reducing the total cost, carbon emissions, and wind curtailment by 29%, 16.4%, and 100%, respectively. It can be seen that the proposed low‐carbon ED model is environmentally friendly and has economic benefits.

High solar evaporation efficiency combined with enhanced desalination and antifouling performance is key in the application of the solar-driven interfacial water evaporation (SIWE) technology. In this study, we have designed a dual-crosslinked and dual-networked hydrogel (CSH) for interfacial solar vapor generation (ISVG). Through adjusting the proportions of matrix components and balancing the degree of crosslinking between cellulose and epichlorohydrin, it is feasible to obtain the hybrid hydrogel with elastic behaviors. The resulted hydrogel has a porous structure enabling the transport of water molecules, while the doped component of iron-based metal–organic frameworks provides this hydrogel with strong light absorbance, achieving an evaporation rate of 2.52 kg·m−2·h−1 under 1 kW·m−2 solar irradiation and an evaporation efficiency of 89.32%. The porosity also creates salt resistance through capillary forces. Practical applications of such CSH hydrogels in the field of seawater desalination and wastewater purification are conducted under outdoor light conditions, and the concentrations of metal ions are revealed to be reduced by orders of magnitude below the WHO threshold ones, while pigments are found to be absent from the condensate contained in the treated wastewater.

Fibropapillomatosis (FP) is an emerging neoplastic disease associated with chelonid herpesvirus 5 (ChHV5; Scutavirus chelonidalpha 5) that affects all species of marine turtles worldwide, mainly green turtles (Chelonia mydas) at coastal feeding sites. This report describes the case of a juvenile green turtle stranded alive on the coast of Veracruz, Mexico that presented 41 lesions suggestive of FP distributed on the eyes, neck, front flippers, axillary/inguinal regions and plastron. Morphologically, the lesions varied in size, shape and appearance of the surface. A tumour was collected and analysed by histopathology revealing a benign neoplasm with fibropapilloma characteristics (dermal and epidermal proliferation) and cytopathic effects consistent with herpesvirus infection, such as ballooning, reticular, and vacuolar degeneration, cell necrosis, eosinophilic intranuclear inclusion bodies, and inflammatory cell infiltration. The tumour tested positive for ChHV5 through conventional PCR targeting the UL30, UL18, UL22, and UL27 genes. Phylogenetic analysis of the DNA Polymerase (UL30) placed the Veracruz variant in the Western Atlantic/Eastern Caribbean cluster along with sequences from Florida, Colombia, Barbados, and Brazil. Additional identification of the CMA1.1 DNA mitochondrial haplotype for this individual supports the connectivity between green turtles from the northern and southern regions of the Gulf of Mexico (GoM) and the Caribbean. It also suggests a potential risk route for ChHV5 infection. This report details the first case of FP linked to ChHV5 in Veracruz and the southwestern GoM. Further research on FP and ChHV5 in these areas is crucial due to their role as habitats for five sea turtle species across various life stages.

Background In patients with atrial fibrillation (AF), the impact of peripheral artery disease (PAD) on oral anticoagulant (OAC) therapy use and the risk of outcomes remains unclear. Objective To analyse the epidemiology of PAD in a large cohort of European and Asian AF patients, and the impact on treatment patterns and risks of adverse outcomes. Methods We analysed AF patients from two large prospective observational registries. OAC prescription and risk of outcomes were analysed according to the presence of PAD, using adjusted Logistic and Cox regression analyses. The primary outcome was the composite of all-cause death and major adverse cardiovascular events (MACE). Interaction analyses were also performed. Results Fifteen-thousand-four-hundred-ninety-seven patients with AF (mean age 68.9, SD 11.6 years; 38.6% female, 30% from Asia) were included in the analysis. PAD was found in 941 patients (6.1%), with a higher prevalence among European individuals compared to Asian (8.1% vs 1.2%, p < 0.001). On logistic regression analysis, European patients had sixfold higher odds of presenting with PAD compared with Asians (OR 6.23, 95% CI 4.75–8.35). After adjustments, PAD was associated with lower use of OAC (OR: 0.59, 95% CI: 0.50–0.69). On Cox regression analysis, PAD was associated with a higher risk of the primary composite outcome (HR 1.28, 95% CI: 1.08–1.52) and all-cause death (HR 1.40, 95% CI: 1.16–1.69). A significant interaction was observed between PAD and age, with higher effects of PAD found in younger patients (< 65 years) for the risk of the primary outcome (pint = 0.014). Conclusions In patients with AF, PAD is associated with lower use of OAC and a higher risk of adverse outcomes, with a greater risk seen in younger patients.

Background In patients with treatment resistant depression (TRD), the ESCAPE-TRD study showed esketamine nasal spray was superior to quetiapine extended release. Aims To determine the robustness of the ESCAPE-TRD results and confirm the superiority of esketamine nasal spray over quetiapine extended release. Method ESCAPE-TRD was a randomised, open-label, rater-blinded, active-controlled phase IIIb trial. Patients had TRD (i.e. non-response to two or more antidepressive treatments within a major depressive episode). Patients were randomised 1:1 to flexibly dosed esketamine nasal spray or quetiapine extended release, while continuing an ongoing selective serotonin reuptake inhibitor/serotonin norepinephrine reuptake inhibitor. The primary end-point was achieving a Montgomery–Åsberg Depression Rating Scale score of ≤10 at Week 8, while the key secondary end-point was remaining relapse free through Week 32 after achieving remission at Week 8. Sensitivity analyses were performed on these end-points by varying the definition of remission based on timepoint, threshold and scale. Results Of 676 patients, 336 were randomised to esketamine nasal spray and 340 to quetiapine extended release. All sensitivity analyses on the primary and key secondary end-point favoured esketamine nasal spray over quetiapine extended release, with relative risks ranging from 1.462 to 1.737 and from 1.417 to 1.838, respectively (all p < 0.05). Treatment with esketamine nasal spray shortened time to first and confirmed remission (hazard ratio: 1.711 [95% confidence interval 1.402, 2.087], p < 0.001; 1.658 [1.337, 2.055], p < 0.001). Conclusion Esketamine nasal spray consistently demonstrated significant superiority over quetiapine extended release using all pre-specified definitions for remission and relapse. Sensitivity analyses supported the conclusions of the primary ESCAPE-TRD analysis and demonstrated robustness of the results.

The differential influence of sex on premature mortality in schizophrenia is unclear. This study assessed the differences in all-cause and specific cause mortality risks in people with schizophrenia compared to several control groups stratified by sex. We conducted a PRISMA 2020-compliant systematic review and random-effects meta-analysis of cohort studies assessing mortality relative risk (RR) for people with schizophrenia, comparing by sex. We measured publication bias and conducted a quality assessment through the Newcastle-Ottawa scale. We meta-analyzed 43 studies reporting on 2,700,825 people with schizophrenia. Both males and females with schizophrenia had increased all-cause mortality vs. comparison groups (males, RR=2.62, 95%CI 2.35–2.92; females, RR=2.56, 95%CI 2.27–2.87), suicide (males, RR=9.02, 95%CI 5.96–13.67; females, RR=12.09, 95%CI 9.00–16.25), and natural cause mortality (males, RR=2.11, 95%CI 1.88–2.38; females, RR=2.14, 95%CI 1.93–2.38). No statistically significant differences in sex-dependent mortality risk emerged. There was an age-group-dependent increased mortality risk in females < 40 years vs. >/=40 years old (RR=4.23/2.17), and significantly higher risk of death due to neurological disorders (dementia) in males vs. females (RR=5.19/2.40). Increased mortality risks were often associated with specific modifiable risk factors. The increased mortality risk did not improve over time, calling for more studies to identify modifiable factors, and for better physical healthcare for males and females with schizophrenia.

Repetitive transcranial magnetic stimulation (rTMS) has shown promise as an intervention for pain. An unexplored research question is whether the delivery of rTMS prior to pain onset might protect against a future episode of prolonged pain. The present study aimed to determine whether (1) 5 consecutive days of rTMS delivered prior to experimentally induced prolonged jaw pain has a prophylactic effect on future pain intensity and (2) whether these effects were accompanied by increases in corticomotor excitability (CME) and/or sensorimotor peak alpha frequency (PAF). On each day from day 0 to 4, 40 healthy individuals received a single session of active (n 5 21) or sham (n 5 19) rTMS over the left primary motor cortex. Peak alpha frequency and CME were assessed on day 0 (before rTMS) and day 4 (after rTMS). Prolonged pain was induced via intramuscular injection of nerve growth factor in the right masseter muscle after the final rTMS session. From days 5 to 25, participants completed twice-daily electronic diaries including pain on chewing and yawning (primary outcomes), as well as pain during other activities (eg, talking), functional limitation in jaw function and muscle soreness (secondary outcomes). Compared to sham, individuals who received active rTMS subsequently experienced lower pain on chewing and yawning. Furthermore, active rTMS led to an increase in PAF. This is the first study to show that rTMS delivered prior to prolonged pain onset can protect against future pain. Our findings suggest that rTMS may hold promise as a prophylactic intervention for pain.

Successful collaboration in computer-mediated teams requires awareness among group members of each other’s knowledge, skills, and goals. Large Language Models (LLMs) can play a mediating role in establishing and maintaining this awareness among group members. In an in-situ study, we explored the impact of an LLM-based chatbot on social and cognitive group awareness through a distributed text-based group task. We instructed participants (N = 48) to complete a travel-planning task in sixteen groups of three, with each member given conflicting goals. Each chat was complemented by a chatbot that could be asked for assistance. Through a survey and semi-structured interview, we gained insight into participants’ deliberations on the task and the chatbot’s role. We found that the chatbot’s presence helped increase group awareness as users are forced to clearly and transparently formulate their intentions when prompting the chatbot. The chatbot’s ability to provide suggestions that compromise between user goals based on the chat history helped participants reach a consensus. We present implications for the design of chatbots for collaborative settings.

Online reviews help people make better decisions. Review platforms usually depend on typed input, where leaving a good review requires significant effort because users must carefully organize and articulate their thoughts. This may discourage users from leaving comprehensive and high-quality reviews, especially when they are on the go. To address this challenge, we developed Vocalizer, a mobile application that enables users to provide reviews through voice input, with enhancements from a large language model (LLM). In a longitudinal study, we analysed user interactions with the app, focusing on AI-driven features that help refine and improve reviews. Our findings show that users frequently utilized the AI agent to add more detailed information to their reviews. We also show how interactive AI features can improve users’ self-efficacy and willingness to share reviews online. Finally, we discuss the opportunities and challenges of integrating AI assistance into review-writing systems.

This paper explores the impact of dc-link voltage control (DVC) on the torsional vibration of grid-forming permanent magnet synchronous generator wind turbines (GFM-WTs), where the DVC is adopted by the machine-side converter. This work investigates three widely used DVC strategies, characterized by different physical implications of output of the DVC. First, a reduced-order small-signal model of the GFM-WT is developed. Employing the complex torque coefficients method, the natural frequency and damping ratio of torsional modes for GFM-WTs are derived, which directly show the impacts of different DVCs on torsional modes. This method also allows for an in-depth analysis of torsional dynamics under different control strategies, considering the varying short-circuit ratio (SCR) of ac grid, dclink capacitances, and droop and inertia coefficients of GFM control. Finally, nonlinear time-domain simulations are carried out to confirm the theoretical findings.

The traditional deadbeat control-model predictive control (DBC-MPC) algorithm is often adopted to reduce the computational requirements. However, for the selection of the expected voltage vector for a grid-connected power converter with an inductor-capacitor-inductor (LCL) filter, only the current tracking error of the traditional DBC-MPC algorithm has been addressed, while the influence of the filter capacitor voltage tracking error was ignored. Therefore, the calculated output voltage vector of the power converter may deviate from the expected value, degrading the system control performance. In this article, an enhanced DBC-MPC control strategy is proposed. The tracking error of the filter capacitor voltage is also considered, which effectively enhances the performance of the control algorithm. Compared with the traditional DBC-MPC algorithm, the output current THD is reduced by more than 56%. An experimental platform of a three-phase two-level grid-connected power converter with LCL filter is established to evaluate the feasibility and effectiveness of the proposed enhanced DBC-MPC control strategy.

This article presents an analytical derivation of the volt-second error caused by converter nonlinearities such as dead time and the finite switching speeds of the emerging medium voltage silicon-carbide MOSFETs. The analytical model considers the transient behavior of the MOSFETs, the gate-driving circuitry, and the intrinsic parasitic capacitances of the MOSFETs and the power module. The importance of compensating converter nonlinearities has been revived with the utilization of wide bandgap semiconductor devices as the increased switching frequency penalizes the relative impact of dead time squared in motor drive applications. The first-order analytical model is validated with experimental and simulated results of switching events of a 10 kV silicon-carbide MOSFET power module. In addition, the analytical model is utilized to map out the individual contributions from each time period of the switching event.

Anomaly detection is one of the most significant tasks in multivariate time series analysis, while it remains challenging to model complex patterns for improving detection accuracy and to interpret the root causes of anomalies. However, existing studies either consider only the temporal dependencies, or simply reconstruct the original input for detection, both neglecting the hidden relationships among multivariate. We propose an adversarial graph neural network based anomaly detection model, called SGAT-AE, which consists of a S elf-learning G raph AT tention network (SGAT), an A uto- E ncoder (AE), and an adversarial training component. Specifically, SGAT is a prediction model that discovers the graph dependency relationships among multivariate and acts as a sample generator to confuse AE, while AE reconstructs the samples and acts as a discriminator that distinguishes a real sample from a generated one. A novel adversarial training between SGAT and AE is applied to amplify the errors of anomalies such that the prediction performance of SGAT is improved and the overfitting of AE is avoided. In addition, we aggregate the prediction error, the reconstruction error, and the adversarial error for anomaly detection, and develop a graph based anomaly interpretation method that locates the root causes from both local and global perspectives. Extensive experiments with five real-world data offer evidence that the proposed solution SGAT-AE is capable of achieving better performance when compared with the state-of-the-art proposals.

Correlated time series (CTS) forecasting is essential in many practical applications, such as traffic management and server load control. Various deep learning based solutions have been proposed to improve forecasting accuracy. However, while models have become increasingly computationally intensive, they struggle to improve accuracy. This study aims instead to enable more lightweight, accurate models suitable for resource-constrained devices. To achieve this goal, we characterize popular CTS forecasting models, yielding two observations for developing lightweight CTS forecasting. On this basis, we propose the LightCTS framework that adopts plain stacking of temporal and spatial operators instead of alternate stacking which is much more computationally expensive. Moreover, LightCTS features light temporal and spatial operators, L-TCN and GL-Former, offering improved computational efficiency without compromising their feature extraction capabilities. LightCTS also encompasses a last-shot compression scheme to reduce redundant temporal features and speed up subsequent computations. Next, we equip LightCTS with two knowledge distillation modules, Tafd and Caad , that result in LightCTS $^\star$ retaining the original benefits of LightCTS , while also being able to adapt to varying levels of ultra-constrained resources. Experimental studies offer detailed insight into these proposals and provide evidence that both LightCTS and LightCTS $^\star$ are capable of nearly state-of-the-art accuracy at substantially reduced computational costs.