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Clinical risk phenotypes in diabetes and their associations with adverse cardiovascular events:A report from the Silesia Diabetes-Heart Project
No full textAims: Diabetes is characterized by clinical heterogeneity. This study aimed to identify different clinical phenotypes of real-world people with diabetes and to assess their associations with major adverse cardiovascular events (MACEs). Methods: From a prospective Polish registry of people with diabetes, hierarchical cluster analysis was performed based on 19 variables, including co-morbidities and cardiovascular (CV) risk factors. The primary outcome was the risk of MACEs (CV death, acute coronary syndrome and myocardial revascularizations, ischaemic stroke, new onset heart failure, and hospitalization for CV reasons). Secondary exploratory outcomes were each MACE component and all-cause death. Results: On 2109 participants (median age 60 years, interquartile range [IQR] 45–69, 51.3% men) included, three different phenotypes were identified: (i) cluster 1 (27.8%) – young with type 1 diabetes; (ii) cluster 2 (42.0%) – elderly with type 2 diabetes and high complexity; (iii) cluster 3 (30.2%) – middle-aged with type 2 diabetes and cardiometabolic risk factors. Compared to cluster 1, the risk of primary outcome was higher for clusters 2 and 3 (adjusted hazard ratio [aHR] 2.93, 95% CI 1.60–5.36, and aHR 1.85 95% CI 1.07–3.20, respectively). Using cluster 3 as the reference, cluster 1 was associated with a lower risk (aHR 0.54, 95% CI 0.31–0.94), and cluster 2 with a higher risk (aHR 1.58, 95% CI 1.08–2.33). Conclusions: People with diabetes aggregate into different clinical phenotypes, each with different risks of MACEs. Integrated approaches tailored to these diverse clinical profiles are needed to improve outcomes in this heterogeneous and multifaceted disease.</p
Enhanced electron transfer pathway of zero-valent iron particles immobilized on coconut shell derived carbon for prolonged Cr(VI) removal
No full textImmobilizing zero-valent iron (ZVI) with modulated structure and good dispersibility is of great potential for the elimination of Cr(VI) in contaminated water or soil, but exploring how the support facilitate and prolong the removal of Cr(VI) by ZVI in terms of the electron transfer is inadequate. Coconut shell derived carbon could be promising for Cr(VI) removal due to the features of large specific surface area and porosity. Accordingly, ZVI particles are dispersed into low-cost and scalable coconut shell derived carbon (CSC) matrix uniformly in this study, with enhanced contact area and interaction of ZVI with CSC for effective elimination of Cr(VI). The optimal ZVI/CSC-0.65 demonstrates an equilibrium adsorption capacity of 307.8mg/g and long-life Cr(VI) cleanup ability over 144h. The interface reaction mechanism between ZVI/CSC and Cr contaminant have been systematically studied by various techniques including XRD, SEM/TEM and XPS, etc. ZVI’s aggregation and passivation in ZVI/CSC-0.65 have been greatly alleviated. Further analysis by Tafel polarization and DFT computation suggests ZVI/CSC-0.65 undergoes fast electron transfer, with a much higher adsorption energy (-2.90eV) than that of pristine ZVI (-0.63eV). The Bader charge analysis demonstrates that there is 4.40 |e| charge transferring the ZVI to the carbon, encouraging the Cr(VI) adsorption. A micro-battery based mechanism is proposed which provides an additional electron transfer pathway in ZVI/CSC for long-term Cr(VI) removal
Techno-economic evaluation of retrofitting power-to-methanol:grid-connected energy arbitrage vs standalone renewable energy
No full textThe power-to-methanol (PtMeOH) will play a crucial role as a form of renewable chemical energy storage. In this paper, PtMeOH techno-economics are assessed using the promising configuration from the previous work (Mbatha et al. [1]). This study evaluated the effect of parameters such as the CO2 emission tax, electricity price, and CAPEX reduction on the product methanol economic parity with respect to a reference case. Superior to previous economic studies, a scenario where an existing methanol synthesis infrastructure is 100 % retrofitted with the promising electrolyser is assessed in terms of its economics and the associated economic parity. The volatile South African electricity market is considered as a case study. The sensitivity of the PtMeOH and green H2 profitability are checked. Grid-connected and standalone renewable energy PtMeOH scenarios are assessed. Foremost, generalisable effect trends of these parameters on the net present value (NPV) and the levelized cost of methanol(LCOMeOH) and H2 (LCOH2) are discussed. The results show that economic parity of H2 (LCOH2 = current selling price = 4.06 €/kg) can be reached with an electricity price of 30 €/MWh and 70 % of the CAPEX. While the LCOMeOH will still be above 2 €/kg at 80 % of the CAPEX and electricity price of 20 €/MWh. This indicates that even if the CAPEX reduces to 20 % of its original in this study, and the electricity price reduces to about 20 €/MWh, the LCOMEOH will still not reach economic parity (LCOMeOH > current selling price = 0.44 €/kg). The results show that to make the retrofitted plant, with a minimum of 20 years of life span, profitable, a feasible reduction in the electricity price to below 10 €/MWh along with favourable incentives such as CO2 credit and reduction in CAPEX, particularly that of the electrolyser, and treatment of the PtMeOH as a multiproduct plant will be required
Compliance to prescribed training among recreational swimmers using augmented-reality swim goggles:A randomised controlled trial
No full textComplying with prescribed training plans is an important challenge for swimmers, as deviations from intended intensity or duration can reduce gains in performance and fitness. This randomised controlled trial investigated whether real-time visual feedback enhances compliance with prescribed training protocols among recreational swimmers. Fifty-seven participants were randomised into feedback (FB) and non-feedback (NFB) groups and completed 35 workouts over 12 weeks across three training volumes (small, medium, large). The FB group used FORM Goggles to receive real-time visual feedback; the NFB group used printed instructions and standard timing tools. Metrics included workout length count, workout effort, incomplete workouts, interval effort, rest time, and stroke type. Compliance was analysed using generalised linear mixed-effects models. The FB group demonstrated significantly better compliance with workout length count than the NFB group in the small and large plans (p < 0.004), with large effect sizes. Interval effort compliance was also higher in the FB group for the large training plan (69% vs. 58%, p = 0.044). Other metrics showed no meaningful group differences. These findings suggest that real-time visual feedback improves adherence to prescribed workout length and, to a lesser extent, interval effort, supporting its potential value in recreational swim training programmes.</p
Sharp iteration asymptotics for transfer operators induced by greedy β-expansions
No full textWe consider base-β expansions of Parry's type, where a0≥a1≥1 are integers and a0<β<a0+1 is the positive solution to β2=a0β+a1 (the golden ratio corresponds to a0=a1=1). The map x↦βx−⌊βx⌋ induces a discrete dynamical system on the interval [0,1) and we study its associated transfer (Perron–Frobenius) operator P. Our main result can be roughly summarized as follows: we explicitly construct two piecewise affine functions u and v with Pu=u and Pv=β−1v such that for every sufficiently smooth F which is supported in [0,1] and satisfies ∫01Fdx=1, we have PkF=u+β−k(F(1)−F(0))v+o(β−k) in L∞. This is also compared with the case of integer bases, where more refined asymptotic formulas are possible.</p
The future of clean transportation:Hydrogen, batteries, ammonia, and green methane in perspective
No full textAmid growing efforts to decarbonize the transport sector, this review examines portable energy solutions for clean mobility, focusing on hydrogen, batteries, ammonia, green methane, methanol, biodiesel, and sustainable aviation fuel (SAF). We discuss the fundamentals, production routes, storage requirements, and application feasibility of each carrier, alongside recent advancements and persisting challenges. Ammonia, while valued for its favorable storage and carbon-free combustion, faces constraints such as toxicity, indirect carbon emissions, and ammonia slip, though emerging approaches like direct air capture offer promising mitigation pathways. Green methane, ethanol, biodiesel, and SAF are identified as complementary fuels suited for sector-specific deployment, whereas batteries and hydrogen show long-term promise. Cradle-to-Grave emission analyses indicate that battery technologies deliver the lowest overall greenhouse gas footprint among the cleaner energy options, while hydrogen and methanol–gasoline blends also demonstrate notably competitive performance. Cost-per-kilometer analysis indicates the lowest value for methane (0.029 /km). Lithium-ion batteries, despite high efficiency, yield moderate cost benefits (0.058 $/km) under our 100 kg energy storage system model, though high-capacity batteries could improve real-world economics. Drawing on literature data and our own assessments, we underscore that the future of clean mobility will not hinge on a single technology, but on a synergistic, multi-fuel strategy integrating the strengths of diverse energy carriers to meet the evolving demands of sustainable transport.</p
Lewatit 1065 VP OC for direct air capture; an analysis of adsorption characteristics using dynamic column breakthrough
No full textDirect air capture based on solid sorbents (S-DAC) is increasingly being recognized as an essential part of climate change mitigation strategies. This work investigates the adsorption characteristics for Lewatit 1065 OC VP which is considered a benchmark sorbent for S-DAC processes. A novel dynamic column breakthrough (DCB) instrument suited for DAC compositions is developed and described. Using both DCB experiments and volumetric measurements, we have identified a significant intra-batch variability in the CO2 loading capacity. The observed difference in CO2 capacity is found to depend entirely on the particle size from the manufacturer; thus, highlighting the importance of standardized sample selections when evaluating the properties of amine-functionalized materials. This ensures a solid database for process modeling and avoids misleading performance results of S-DAC processes. The mass transfer kinetics has been evaluated from both adsorption and desorption breakthrough experiments combined with modeling. The modeling effort includes a comparison between the commonly used linear driving force (LDF) model and a dual-site (DS) model to describe the CO2 uptake. A significant dependence on macropore resistance is confirmed, though an additional transport resistance is required to fully describe the mass transfer kinetics. We hypothesize that the CO2-amine reaction is the key contributor to the added mass transfer resistance prompting the research on new amine-functionalized materials to focus on enhancing both the macropore resistance and reaction kinetics.</p
PosGNN:A Graph Neural Network Based Multimodal Data Fusion for Indoor Positioning in Industrial Non-Line-of-Sight Scenarios
Full text linkIn industrial environments, the wireless infrastructure is functional for offering services such as communication and positioning of industrial assets. However, the frequently occurring Non-Line-of-Sight (NLoS) conditions in industrial scenarios cause the wireless receiver to have positional information from a limited and varying number of wireless transmitters between consecutive time steps, leading to ambiguities in wireless infrastructure-based positioning. In this paper, we propose PosGNN, a novel data fusion solution based on the Graph Neural Network (GNN) approach that allows us to estimate the position of the User Equipment (UE) by fusing the positional information from the available wireless transmitters at each time step with the UE sensor technology. The performance of the proposed method is assessed using an experimental setup of Ultra-Wideband (UWB) technology as wireless infrastructure at 3.7 - 4.2GHz frequency band, the Inertial Measurement Unit (IMU) as UE-side sensor, and the Automated Guided Vehicle (AGV) as the target UE to be positioned. The experimental results demonstrate the exceptional performance of our approach over the conventional model-based approach, Extended Kalman Filter (EKF), and the data-driven approach, Deep Neural Network (DNN), achieving an average positioning error of less than 15 cm in harsh industrial environments.</p