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Growth of Renewable Energy:A Review of Drivers from the Economic Perspective
Get PDFGlobal modern renewable energy based on geothermal, wind, solar, and marine resources has grown rapidly over the last decades despite low energy density, intermittent supply, and other qualities inferior to those of fossil fuels. What is the explanation for this growth? The main drivers of growth are assessed using economic theories and verified with statistical data. From the neo-classic viewpoint that focuses on price substitutions, the growth can be explained by the shift from energy-intensive agriculture and industry to labour-intensive services. However, the energy resources complemented rather than substituted for each other. In the evolutionary idea, investments supported by policies enabled cost-reducing technological change. Still, policies alone are insufficient to generate the growth of modern renewable energy as they are inconsistent across countries and in time. From the behavioural perspective that is preoccupied with innovative entrepreneurs, the value addition of electrification can explain the introduction of modern renewable energy in market niches, but not its fast growth. Instead of these mono-causalities, the growth of modern renewable energy is explained by technology diffusion during the pioneering, growth, and maturation phases. Possibilities that postpone the maturation are pinpointed.</p
Impact of elevated lipoprotein(a) on epicardial coronary flow conductance and endoluminal atherosclerotic disease distribution
Get PDFBackground: Elevated lipoprotein(a) [Lp(a)] is associated with accelerated progression of coronary plaques, a higher prevalence of thin-cap fibroatheroma, and an increased risk of spontaneous myocardial infarction. However, Lp(a)'s impact on coronary artery disease (CAD) and the resulting coronary flow dynamics have yet to be fully determined. Objective: To evaluate the effects of elevated Lp(a) levels on epicardial coronary flow and endoluminal disease pattern (focal or diffuse). Methods: In a propensity-score matched (PSM) cohort from the ongoing PIONEER IV trial (NCT04923191), participants with de novo CAD and elevated Lp(a) (>50 mg/dL or 120 nmol/L) were matched to controls based on traditional CAD risk factors. Epicardial flow velocity was assessed using the Quantitative Flow Ratio (QFR), with the virtual QFR-Pressure Pullback Gradient Index (QFR-PPGi) characterizing the endoluminal disease phenotype. A QFR ≤ 0.80 indicated significant epicardial flow limitation. Results: Among 672 consecutively enrolled participants with available Lp(a) measurements, elevated levels were observed in 23 % (152/672). Complete risk profiles for traditional CAD risk factors were available for 391 participants with de novo CAD, of whom 75 had elevated Lp(a) levels. After propensity matching, 75 pairs (150 participants) were eligible for analyses. QFR analyses were completed in 392/450 (87 %) vessels. The median difference in baseline QFR between matched vessels was −0.045 (p = 0.005), while the mean difference in QFR-PPGi was −0.028 (p = 0.013). Vessels from participants with elevated Lp(a) demonstrated significantly higher rates of QFR ≤ 0.80 compared to matched controls (31 % vs. 19 %, absolute risk difference 12 %; 95 % CI: 2.7 %–21 %, p = 0.011). Conclusions: Elevated plasma levels of Lp(a) were associated with increased epicardial flow limitation and a more diffuse endoluminal disease pattern. Condensed abstract: In this PIONEER IV sub-study (NCT04923191), we investigated the effect of elevated lipoprotein(a) [Lp(a)] on epicardial coronary flow and endoluminal disease distribution. We analyzed 392 vessels from 75 propensity-matched pairs using Quantitative Flow Ratio (QFR) to assess flow limitation and the virtual QFR-Pressure Pullback Gradient Index (QFR-PPGi) to characterize disease distribution patterns (focal versus diffuse). Vessels exposed to elevated Lp(a) exhibited significantly higher rates of epicardial flow limitation (QFR ≤ 0.80) than controls (31 % vs. 19 %, absolute risk difference 12 %; 95 % CI: 2.7 %–21 %, p = 0.011). The median difference in baseline QFR between matched vessels was −0.045 (p = 0.005), while the mean difference in QFR-PPGi was −0.028 (p = 0.013). These findings demonstrate that elevated Lp(a) levels are associated with both greater epicardial flow limitation and a more diffuse pattern of coronary artery disease.</p
Improving soil freeze–thaw retrieval from spaceborne L-Band measurements based on diurnal amplitude variation
Get PDFSoil freeze–thaw (FT) cycles impact soil functions and atmosphere–land interaction, but accurate measurements are very limited. Since surface dielectric properties and microwave emissions are sensitive to the FT state, brightness temperature (TB) measurements at L-band allow retrieval of the FT state. We have demonstrated the potential of a soil FT retrieval algorithm from Soil Moisture Active Passive (SMAP) TB measurements. This retrieval algorithm is formulated regarding Diurnal Amplitude Variation (DAV), which is defined as the difference in TB observations of ascending and descending orbits. The DAV-FT algorithm uses globally fixed parameters. However, parameters should vary regionally considering factors like land cover type, terrain, and climate regions. We introduce Overall Classification Accuracy (OA) to characterize the extraction of DAV annual variation under different parameters. Then, the parameter optimization process, akin to maximum likelihood estimation, selects a combination of parameters to extract the annual variation of the DAV optimally. The DAV-FT algorithm uses optimized parameters, and the results show that compared to using fixed parameters, (a) the area with OA > 0.7 increases from 54.43% to 89.36%; (b) consistency with ERA5-Land and SMAP data has improved in southwestern North America, the Qinghai–Tibet Plateau, and southwestern Eurasia, with regions showing over 0.7 consistency reaching 81.28% for ERA5-Land and 79.54% for SMAP-FT; and (c) in situ stations with higher accuracy outnumber those with lower accuracy (48.11% versus 22.97% for fixed parameters, 35.14% versus 33.51% for SMAP FT). Furthermore, the algorithm achieves the highest median (0.92) and median accuracy (0.88), compared to fixed parameters and SMAP.</p
Quantitative analysis of cooperative upconversion in Al<sub>2</sub>O<sub>3</sub>:Yb<sup>3+</sup>
No full textAmorphous Al2O3 channel waveguides with different Yb3+ concentrations are fabricated by reactive co-sputtering onto thermally oxidized silicon substrates and subsequent reactive ion etching. Pump transmission and bleaching of pump absorption, luminescence spectra, luminescence-decay curves, and the dependence of luminescence intensity on pump power are measured for the transition at 1 μm from the 2F5/2 first excited state of Yb3+ and for the green upconversion luminescence from the second excited state of the single quantum systems formed by Yb3+-Yb3+ pairs. The typical spectroscopic features of cooperative upconversion are observed. Analysis by rate equations reveals that only a microscopic two-ion-class model comprising single ions and ion pairs can quantitatively explain the measured spectroscopic data, i.e., the local chemical environment must be taken into account in the analysis. The fraction of quenched ions determined from the pump-absorption measurements and the fractions of ions forming Yb3+-Yb3+ pairs and producing cooperative upconversion (CU) are found to be equivalent and increase linearly with Yb3+ concentration. It suggests that the observed phenomena of non-saturable absorption and CU are linked to each other and evoked by the same class of ions. Internal net gain in these channel waveguides under the influence of cooperative upconversion is experimentally investigated and successfully modeled by use of the two-ion-class model. This analysis delivers important spectroscopic insight for optimizing the performance of distributed-feedback lasers in these waveguides. Particularly, it will provide a route towards optimizing the Yb3+ concentration for simultaneously ensuring sufficient pump absorption and minimizing non-saturable absorption by CU-quenched Yb3+-Yb3+ pairs that would otherwise significantly increase the laser linewidth, which is the most important performance parameter of such lasers.</p
Cues for odor naming affect performance and brain connectivity
No full textHuman olfactory perception and naming represent a complex example of multisensory integration, with growing interest in how cues from different modalities affect olfactory recognition and naming. While studies show that visual cues may support odor naming performance, little is known about how cueing and multisensory integration in odor naming tasks influence neural mechanisms. This study examined the cognitive mechanisms underlying odor identification and the effect of two visual cue types—lexical and color—using behavioral and EEG methods. It also investigated the impact of hedonic ratings, Tip of the Nose phenomenon, familiarity, and subjective recall experiences on odor naming. Forty participants took part in an odor identification task using Sniffin’ Sticks. For each trial, an odorant was first presented, followed by either a visual cue (a color patch associated with the odor source) or a lexical cue (a word fragment). Participants were then asked to name the odor. To examine the neural mechanisms involved in cue-assisted odor identification, the time window during odor naming after the visual cue presentation was analyzed. Connectivity analysis and behavioral performance were assessed to evaluate the effectiveness of the different cue types in supporting identification. Behavioral findings showed that lexical cues improved identification accuracy. Furthermore, hedonic ratings, familiarity, and experiences related to the TON were found to significantly affect naming performance. Odor familiarity and liking levels affected both response accuracy and response time, with more familiar and liked odors being identified both more accurately and more quickly. Granger causality analysis revealed that the color cue condition exhibited more numerous and stronger network connections compared to the lexical cue condition. The lexical cue condition demonstrated more restricted network activation with fewer connections, utilizing focused frontal-temporal and frontal-parietal circuits. In both conditions, prefrontal regions served as strong control hubs, and language networks were preserved. However, additional frontal-occipital connections were observed in the color cue condition, in the form of interhemispheric coordination and visual system integration. The findings demonstrated that cross-modal odor naming utilizes different neural connections depending on cue type, with lexical cues showing more direct access to linguistic areas while color cues exhibit more complex connectivity patterns
Task based evaluation of sparse view CT reconstruction techniques for intracranial hemorrhage diagnosis using an AI observer model
No full textSparse-view computed tomography (CT) holds promise for reducing radiation exposure and enabling novel system designs. Traditional reconstruction algorithms, including Filtered Backprojection (FBP) and Model-Based Iterative Reconstruction (MBIR), often produce artifacts in sparse-view data. Deep Learning Reconstruction (DLR) offers potential improvements, but task-based evaluations of DLR in sparse-view CT remain limited. This study employs an Artificial Intelligence (AI) observer to evaluate the diagnostic accuracy of FBP, MBIR, and DLR for intracranial hemorrhage detection and classification, offering a cost-effective alternative to human radiologist studies. A public brain CT dataset with labeled intracranial hemorrhages was used to train an AI observer model. Sparse-view CT data were simulated, with reconstructions performed using FBP, MBIR, and DLR. Reconstruction quality was assessed using metrics such as Peak Signal-to-Noise Ratio (PSNR), Structural Similarity Index Measure (SSIM), and Learned Perceptual Image Patch Similarity (LPIPS). Diagnostic utility was evaluated using Receiver Operating Characteristic (ROC) analysis and Area Under the Curve (AUC) values for One-vs-Rest and One-vs-One classification tasks. DLR outperformed FBP and MBIR in all quality metrics, demonstrating reduced noise, improved structural similarity, and fewer artifacts. The AI observer achieved the highest classification accuracy with DLR, while FBP surpassed MBIR in task-based accuracy despite inferior image quality metrics, emphasizing the value of task-based evaluations. DLR provides an effective balance of artifact reduction and anatomical detail in sparse-view CT brain imaging. This proof-of-concept study highlights AI observer models as a viable, cost-effective alternative for evaluating CT reconstruction techniques.</p
The dance between symptoms and wellbeing:A case report
No full textMental health care increasingly focuses on the importance of wellbeing. In doing so, therapy is aimed at core values, personal growth, and resilience. For this purpose, the book Kwetsbaar en krachtig can be used. The case of David offered an opportunity to illustrate this. Previous CBT resulted in a lower frequency of overwhelming panic attacks. Nevertheless, David feared that he was seriously ill, because of the palpitations he experienced. He also feared looking foolish, if he needed medical assistance for this. Such symptoms of social phobia proved to be a broader issue. David wanted tools to help him shift his attention from palpitations to more positive aspects of his life. To achieve this goal, well-being therapy was the first step. This was followed by a second phase comprising exposure to social phobic fear. This combination resulted in a decrease in symptoms and increased well-being.</p
Towards vitality:A longitudinal pilot study with a cognitive bias modification e-health intervention (VitalMe) to reduce fatigue in patients with chronic kidney disease
No full textBackground: This longitudinal pilot trial investigated the effects of novel Cognitive Bias Modification (CBM) training targeting fatigue on cognitive biases, fatigue, vitality, and fatigue-related behaviour in people with chronic kidney disease (CKD).Methods: Thirty patients were alternately allocated to a week of CBM training with either attentional bias modification (ABM) or self-identity bias modification (SIBM), followed by a second week with the trainings combined. Twenty-two participants (12 pre-dialysis, 10 dialysis) completed the study, where cognitive biases and self-reported outcomes were measured at baseline, post-training, and follow-up. Possible interaction effects between CBM focus and disease stage were explored.Results: A significant effect of time was found on both cognitive biases; participants' attentional bias (Cohen's d = 0.88–0.99) and self-identity bias (Cohen's d = 1.16–1.28) were significantly more vitality oriented at post and follow-up compared to baseline. On the self-report outcomes, a small beneficial effect was found on vitality, but only for the ABM training.Conclusions: This is the first study to introduce CBM, which targets fatigue, to people with CKD. Despite the limitations in sample size and design, this study revealed strong effects on cognitive biases. It is recommended to replicate these findings in an adequately powered randomised controlled trial.</p
Uitblijven vroege respons bij psychotherapie voor depressie voorspelt behandelnon-respons
No full textAchtergrond: Veel patiënten met een depressieve stoornis verbeteren onvoldoende door psychotherapie. Het is onzeker hoe lang doorbehandelen op dezelfde wijze zinvol is bij uitblijvende resultaten.Doel: Onderzoeken hoe voorspellend vroege non-respons is voor uiteindelijke behandelnon-respons bij matige tot ernstige depressie en wanneer deze voorspellende waarde het hoogst is.Methode: Per-protocolanalyses met 252 patiënten met een matige tot ernstige depressie. Deelnemers kregen willekeurig 16 sessies kortdurende psychodynamische steungevende psychotherapie (KPSP) of cognitieve gedragstherapie (CGT) in 8 weken. De voorspellende waarde van vroege non-respons (< 20% klachtenreductie op de zelfgerapporteerde Inventory of Depressive Symptomatology) in week 1, 2 en 4 voor uiteindelijke behandelnon-respons in week 8 (< 50% klachtenreductie) werd geanalyseerd met logistische regressies.Resultaten: Vroege non-respons was een matige tot sterke voorspeller voor uiteindelijke behandelnon-respons in week 8 waarbij de voorspellende waarde het grootste was in week 4 (oddsratio: 12,1; p < 0,001; AUC: 0,78). De meeste patiënten met vroege non-respons bleven non-responder na de behandeling (84% van de vroege non-responders in week 1 tot 93% van de non-responders in week 4).Conclusie: Bij vroege non-respons is er weinig kans op behandelrespons. Aanbevolen wordt om het behandelbeleid op een eerder moment te bespreken en te wijzigen dan nu gebruikelijk
Electrocatalytic CO<sub>2</sub> reduction with an immobilized iron complex on gas diffusion electrodes
No full textThe immobilization of molecular electrocatalysts on gas diffusion electrodes (GDEs) overcomes mass transport limitations inherent to solution-phase CO 2 reduction. We report the immobilization of the molecular FeTDHPP catalyst on a GDE via supramolecular π-π interactions, achieving a 50-fold catalytic activity increase compared to solution-phase performance.</p