Explore the vast range of titles available.

Bismuth vanadate (BiVO 4 ) has been widely investigated as a photocatalyst or photoanode for solar water splitting, but its activity is hindered by inefficient cocatalysts and limited understanding of the underlying mechanism. Here we demonstrate significantly enhanced water oxidation on the particulate BiVO 4 photocatalyst via in situ facet-selective photodeposition of dual-cocatalysts that exist separately as metallic Ir nanoparticles and nanocomposite of FeOOH and CoOOH (denoted as FeCoO x ), as revealed by advanced techniques. The mechanism of water oxidation promoted by the dual-cocatalysts is experimentally and theoretically unraveled, and mainly ascribed to the synergistic effect of the spatially separated dual-cocatalysts (Ir, FeCoO x ) on both interface charge separation and surface catalysis. Combined with the H 2 -evolving photocatalysts, we finally construct a Z-scheme overall water splitting system using [Fe(CN) 6 ] 3−/4− as the redox mediator, whose apparent quantum efficiency at 420 nm and solar-to-hydrogen conversion efficiency are optimized to be 12.3% and 0.6%, respectively. Artificial photosynthesis offers an integrated means to convert light to fuel, but efficiencies are often low. Here, authors report a Z-scheme system utilizing Ir and FeCoO x co-catalysts to enhance charge separation on BiVO 4 facets that achieves high quantum efficiencies for overall water splitting.

We propose a new mechanism of (geometric) moduli stabilisation in type IIB/F-theory four-dimensional compactifications on Calabi–Yau manifolds, in the presence of 7-branes, that does not rely on non-perturbative effects. Complex structure moduli and the axion-dilaton system are stabilised in the standard way, without breaking supersymmetry, using 3-form internal fluxes. Kähler class moduli stabilisation utilises perturbative string loop corrections, together with internal magnetic fields along the D7-branes world-volume leading to Fayet-Iliopoulos D-terms in the effective supergravity action. The main ingredient that makes the stabilisation possible at a de Sitter vacuum is the logarithmic dependence of the string loop corrections in the large two-dimensional transverse volume limit of the 7-branes.

A bstract We study string loop corrections to the gravity kinetic terms in type IIB com- pactifications on Calabi-Yau threefolds or their orbifold limits, in the presence of D 7-branes and orientifold planes. We show that they exhibit in general a logarithmic behaviour in the large volume limit transverse to the D 7-branes, induced by a localised four-dimensional Einstein-Hilbert action that appears at a lower order in the closed string sector, found in the past. Here, we compute the coefficient of the logarithmic corrections and use them to provide an explicit realisation of a mechanism for Kähler moduli stabilisation that we have proposed recently, which does not rely on non-perturbative effects and lead to de Sit- ter vacua. Our result avoids no-go theorems of perturbative stabilisation due to runaway potentials, in a way similar to the Coleman-Weinberg mechanism, and provides a counter example to one of the swampland conjectures concerning de Sitter vacua in quantum grav- ity, once string loop effects are taken into account; it thus paves the way for embedding the Standard Model of particle physics and cosmology in string theory.

In electric vehicles (EVs), the batteries are arranged in the battery pack (BP), which has a small layout space and difficulty in dissipating heat. Therefore, in EVs, the battery thermal management systems (BTMSs) are critical to managing heat to ensure safety and performance, particularly under higher operating temperatures and longer discharge conditions. To solve this problem, in this article, the thermal analysis models of a 3-battery-cell BP were created, including scenarios (1) natural air cooling without a BTMS; (2) natural air cooling with water cooling hybrid BTMS; and (3) forced air cooling plus water cooling composite BTMS. The thermal performances of the pack-level BPs were simulated and analyzed based on computational fluid dynamics (CFD). A variety of boundary conditions and working parameters, such as ambient temperature, inlet coolant flow rate and initial temperature, discharge rate, air flow rate, and initial temperature, were considered. The results show that without a BTMS (Scenario 1), the maximum temperature in the BP rises rapidly and continuously to reach 63.8 °C, much higher than the upper bound of the recommended operating temperature range (ROTR between +20 °C to +35 °C) under the extreme discharge rate of 3 C and even if the discharge rate is 2 C. With a hybrid BTMS (Scenario 2), the maximum temperature in BP rises to about 38.7 °C, slightly above the upper bound of the ROTR. Lowering the coolant (water) initial temperature can effectively lower the temperature up to 5.7 °C in BP, but the water flow rate cannot since the turbulence model. While with a composite BTMS (Scenario 3), the temperature can be further lowered up to 1.5 °C under the extreme discharge rate of 3C, just reaching the upper bound of the ROTR. In addition, lowering the initial coolant temperature or air temperature can effectively decrease the temperatures up to 5.1 and 1.0 °C, respectively, in BP, but the coolant flow rate (due to the turbulence model) and the air flow rate cannot. Finally, the thermal performances of the different battery cells in the BP with different cooling systems and at the different positions of the BP were compared and analyzed. The present work may contribute to the design of BTMSs in the EV industry.

Infection caused by antibiotic-resistant microorganisms (ARMs) has been declared a global threat to public health. Polymeric nanoparticles (PNPs) formed by antimicrobial peptides (AMPs) and synthetic PNPs against ARM infections are emerging. PNPs are also considered to be a promising natural biological preservative that prevents microbial spoilage through food processing and preservation. We engineered CNMs, a novel nanocomposite antibacterial agent based on chitosan nanoparticles and AMP microcin J25. In this study, we aimed to evaluate the comprehensive antimicrobial activity, potential antimicrobial mechanism, and anti-inflammatory activity of CNMs. We demonstrated that CNMs harbor excellent bactericidal activity against clinical foodborne pathogens and ARMs. CNMs caused fast mortality against different growth phases of tetracycline (Tet)-resistant enterotoxigenic E. coli (ETEC) and significantly killed Tet-resistant ETEC in food biological environments. Mechanistically, CNMs have the ability to bind lipopolysaccharides (LPS), neutralize endotoxin, and promote diaphragm permeability by damaging the cell membrane. CNMs did not cause mouse RAW264.7 cell cytotoxicity. Notably, CNMs significantly reduced the cytotoxicity of RAW264.7 macrophages induced by LPS. The LPS-induced inflammatory response was significantly ameliorated by CNMs by reducing the levels of nitric oxide and proinflammatory cytokines, including tumor necrosis factor α, interleukin (IL)-6, IL-8, IL-1β, Toll-like receptor 4, and nuclear factor κB (NF-κB), in LPS-challenged RAW264.7 macrophages. CNMs downregulated the NF-κB and mitogen-activated protein kinase signaling pathways, thereby inhibiting inflammatory responses upon LPS stimulation. Taken together, CNMs could be applied as effective antimicrobial/anti-inflammatory agents with lower cytotoxicity in food, medicine, and agriculture to prevent bacterial contamination and infection, respectively.

To investigate the evolutionary mechanism of coal mine gas explosion, and to improve the level of system safety. In this paper, a multi-factor coupled risk deduction model for coal mine gas explosion is constructed by using the system dynamics method. The two subsystems of ‘human factors’ and ‘material factors’ are simulated by using Vensim software. To obtain the evolutionary trend of system gas explosion risk and the risk change trend of index variables. The risk level of coal mine gas explosion is predicted by adjusting the parameters of the index variables, and the influence of various coupled factors on the development trend of the risk level is analyzed. Through the application of the model, the results reveal that the model has certain applicability and feasibility. The risk values of system variables have a certain degree of hysteresis and cumulativeness in their impact on other variables and the system risk level, and the ‘emergence’ and ‘mutation’ phenomena of accident evolution.

Heart failure is a prevalent cardiovascular condition with significant health implications, necessitating effective diagnostic strategies for timely intervention. This study explores the potential of continuous monitoring of non-invasive signals, specifically integrating photoplethysmogram (PPG) and electrocardiogram (ECG), for enhancing early detection and diagnosis of heart failure. Leveraging a dataset from the MIMIC-III database, encompassing 682 heart failure patients and 954 controls, our approach focuses on continuous, non-invasive monitoring. Key features, including the QRS interval, RR interval, augmentation index, heart rate, systolic pressure, diastolic pressure, and peak-to-peak amplitude, were carefully selected for their clinical relevance and ability to capture cardiovascular dynamics. This feature selection not only highlighted important physiological indicators but also helped reduce computational complexity and the risk of overfitting in machine learning models. The use of these features in training machine learning algorithms led to a model with impressive accuracy (98%), sensitivity (97.60%), specificity (96.90%), and precision (97.20%). Our integrated approach, combining PPG and ECG signals, demonstrates superior performance compared to single-signal strategies, emphasizing its potential in early and precise heart failure diagnosis. The study also highlights the importance of continuous monitoring with wearable technology, suggesting a significant stride forward in non-invasive cardiovascular health assessment. The proposed approach holds promise for implementation in hardware systems to enable continuous monitoring, aiding in early detection and prevention of critical health conditions.

Continuous monitoring and research on Poyang Lake is essential to understand its ecological dynamics and promote sustainable development. Spatial and temporal dynamic monitoring and analyses of vegetation changes in the water body of Poyang Lake are still limited. This study fills this gap by using remote sensing and GIS techniques for dynamic monitoring and analysing the changes of water bodies and vegetation in Poyang Lake from 2013 to 2021. We used a combination of Maximum Likelihood Classification (MLC) and Support Vector Machine (SVM) to preprocess and classify 42 Landsat 8 OLI images. The results showed that the stability of the water body and vegetation varied greatly, with the water body showing the obvious change pattern of water rises, vegetation recedes and water recedes, vegetation grows, and the high-frequency inundation area was concentrated in the northeastern part of the lake (accounting for 60% of the total inundation area). Vegetation frequency distribution showed a pattern of sparse in the north and dense in the south, with the middle frequency area being the most, accounting for 19.88%, and the low frequency area being the least, accounting for 16.09%. The results show that the spatial and temporal distribution characteristics of water body and vegetation in Poyang Lake show low stability, which is a highly dynamic ecosystem. This study relatively makes up for the missing analysis of the stability change of water body and vegetation in the cycle of Poyang Lake, and provides a solid scientific basis for the protection and sustainable management work.

Radar is utilized as an active sensing device across many fields. Its waveform optimization is responsible for target signature extraction, profoundly influencing the overall performance. First, the principle of pulse radar waveform design is explored. Waveform design strategies vary based on target models, whether point-like or extended ones, and are often formulated as high-dimensional, non-convex optimization problems with multiple constraints, such as energy, constant modulus, and sidelobe ratios. Second, to address them, techniques like alternating direction method of multipliers (ADMM), semidefinite relaxation (SDR), and minimization-maximization (MM) algorithms are widely employed. Finally, challenges in multimodal sensing collaborative detection, joint multi-tasking, sparse signal recovery, and intelligent perception highlight the need for innovative solutions to meet future demands.

Papillary thyroid carcinoma (PTC) is generally a slow-growing disease with a favorable 10-year survival rate. However, about 10% of PTC cases show significant aggressiveness, with tendencies for local invasion or distant metastasis, the mechanisms of which remain unclear. This study aims to identify predictive indicators and explore new potential targets for clinical treatment, highlighting the need for novel biomarkers and therapeutic targets. We analyzed FBP1 expression in PTC tissues. Cell proliferation, apoptosis, and invasion were evaluated with and without FBP1 overexpression in PTC cells to assess FBP1’s effects. We then investigated whether FBP1 reduces PTC cell tumorigenesis and metastasis by regulating HIF-1α expression. FBP1 expression was reduced in PTC samples and showed a negative correlation with T stage. In vitro experiments indicated that FBP1 acts as a hypoxia response inhibitor, regulating tumor cells. Additionally, FBP1 inhibited the proliferation, apoptosis, and invasion of thyroid cancer cells by modulating HIF-1α expression. Our results provide new insights into the role of FBP1 in PTC progression and indicate that targeting the FBP1-HIF-1α axis could be a promising therapeutic approach for this disease.