Mechanically Robust and Spectrally Selective Convection Shield for Daytime Subambient Radiative Cooling

As a passive cooling strategy, radiative cooling is becoming anappealing approach to dissipate heat from terrestrial emitters to the outer space. However, the currently achieved cooling performance is still underperforming due to considerable solar radiation absorbed by the emitter and nonradiative heat transferred from the surroundings. Here, we proposed a mechanically robust and spectrally selective convection shield composed of nanoporous composite fabric (NCF) to achieve daytime subambient radiative cooling. By selectively reflecting ∼95% solar radiation, transmitting ∼84% thermal radiation, and suppressing the nonradiative heat transferred from warmer surroundings, the NCF-based radiative cooler demonstrated an average daytime temperature reduction of ∼4.9 °C below the ambient temperature, resulting in an average net radiative cooling power of ∼48 W/m2 over a 24 h measurement. In addition, we also modeled the potential cooling capacity of the NCF-based radiative cooler and demonstrated that it can cover the cooling demands of energy-efficient residential buildings in most regions of China. Excellent spectral selectivity, mechanical strength, and weatherability of the NCF cover enable a much broader selection for the emitters, which is promising in the real-world deployment of direct daytime subambient radiative cooling

Metformin Uniquely Prevents Thrombosis by Inhibiting Platelet Activation and mtDNA Release

Thrombosis and its complications are the leading cause of death in patients with diabetes. Metformin, a first-line therapy for type 2 diabetes, is the only drug demonstrated to reduce cardiovascular complications in diabetic patients. However, whether metformin can effectively prevent thrombosis and its potential mechanism of action is unknown. Here we show, metformin prevents both venous and arterial thrombosis with no significant prolonged bleeding time by inhibiting platelet activation and extracellular mitochondrial DNA (mtDNA) release. Specifically, metformin inhibits mitochondrial complex I and thereby protects mitochondrial function, reduces activated platelet-induced mitochondrial hyperpolarization, reactive oxygen species overload and associated membrane damage. In mitochondrial function assays designed to detect amounts of extracellular mtDNA, we found that metformin prevents mtDNA release. This study also demonstrated that mtDNA induces platelet activation through a DC-SIGN dependent pathway. Metformin exemplifies a promising new class of antiplatelet agents that are highly effective at inhibiting platelet activation by decreasing the release of free mtDNA, which induces platelet activation in a DC-SIGN-dependent manner. This study has established a novel therapeutic strategy and molecular target for thrombotic diseases, especially for thrombotic complications of diabetes mellitus

Focus on vulnerable populations and promoting equity in health service utilization ––an analysis of visitor characteristics and service utilization of the Chinese community health service

Background Community health service in China is designed to provide a convenient and affordable primary health service for the city residents, and to promote health equity. Based on data from a large national study of 35 cities across China, we examined the characteristics of the patients and the utilization of community health institutions (CHIs), and assessed the role of community health service in promoting equity in health service utilization for community residents. Methods Multistage sampling method was applied to select 35 cities in China. Four CHIs were randomly chosen in every district of the 35 cities. A total of 88,482 visitors to the selected CHIs were investigated by using intercept survey method at the exit of the CHIs in 2008, 2009, 2010, and 2011. Descriptive analyses were used to analyze the main characteristics (gender, age, and income) of the CHI visitors, and the results were compared with that from the National Health Services Survey (NHSS, including CHIs and higher levels of hospitals). We also analyzed the service utilization and the satisfactions of the CHI visitors. Results The proportions of the children (2.4%) and the elderly (about 22.7%) were lower in our survey than those in NHSS (9.8% and 38.8% respectively). The proportion of the low-income group (26.4%) was apparently higher than that in NHSS (12.5%). The children group had the lowest satisfaction with the CHIs than other age groups. The satisfaction of the low-income visitors was slightly higher than that of the higher-income visitors. The utilization rate of public health services was low in CHIs. Conclusions The CHIs in China appears to fulfill the public health target of uptake by vulnerable populations, and may play an important role in promoting equity in health service utilization. However, services for children and the elderly should be strengthened

Report from Working Group 3: Beyond the standard model physics at the HL-LHC and HE-LHC

This is the third out of five chapters of the final report [1] of the Workshop on Physics at HL-LHC, and perspectives on HE-LHC [2]. It is devoted to the study of the potential, in the search for Beyond the Standard Model (BSM) physics, of the High Luminosity (HL) phase of the LHC, defined as $3$ ab$^{-1}$ of data taken at a centre-of-mass energy of 14 TeV, and of a possible future upgrade, the High Energy (HE) LHC, defined as $15$ ab$^{-1}$ of data at a centre-of-mass energy of 27 TeV. We consider a large variety of new physics models, both in a simplified model fashion and in a more model-dependent one. A long list of contributions from the theory and experimental (ATLAS, CMS, LHCb) communities have been collected and merged together to give a complete, wide, and consistent view of future prospects for BSM physics at the considered colliders. On top of the usual standard candles, such as supersymmetric simplified models and resonances, considered for the evaluation of future collider potentials, this report contains results on dark matter and dark sectors, long lived particles, leptoquarks, sterile neutrinos, axion-like particles, heavy scalars, vector-like quarks, and more. Particular attention is placed, especially in the study of the HL-LHC prospects, to the detector upgrades, the assessment of the future systematic uncertainties, and new experimental techniques. The general conclusion is that the HL-LHC, on top of allowing to extend the present LHC mass and coupling reach by $20-50\%$ on most new physics scenarios, will also be able to constrain, and potentially discover, new physics that is presently unconstrained. Moreover, compared to the HL-LHC, the reach in most observables will, generally more than double at the HE-LHC, which may represent a good candidate future facility for a final test of TeV-scale new physics

Flexible Zoom Telescopic Optical System Design Based on Genetic Algorithm

The performance of current liquid zoom systems is severely limited by their initial structure’s construction and solution. In this study, an automatic search method based on genetic algorithm (GA) was proposed for obtaining the optimal initial structure of a double liquid lens zoom optical system. This method was used to design a zoom telescopic objective with a fast response characteristic. The zoom equation of the zoom system was derived based on the Gaussian bracket method, and an initial structure evaluation function that integrated the aberration, the system length, and the smoothness of the focal power change in the liquid lenses was designed. This evaluation function was used as the fitness function in GA to automatically retrieve the optimal initial structure of the zoom system. Finally, an optical design software was used to optimize the design of the zoom system to obtain the final structure of the zoom system. Image quality analysis and tolerance analysis of the zoom system revealed that the system exhibited excellent imaging capability and could be manufactured easily. In addition, the analysis of the zoom curve revealed that the optical system exhibited a smooth continuous zooming capability

Wind farm micro-siting by Gaussian particle swarm optimization with local search strategy

The micro-siting of wind farms has recently attracted much attention due to the booming development of wind energy. The paper aims to maximize the electrical power extracted from a wind farm while satisfying the required distance between turbines for operation safety. The micro-siting problem is by nature a constrained optimization problem, in which the coupling of wake effects is strong and the number of position constraints between turbines is large. An improved Gaussian particle swarm optimization algorithm is proposed to optimize the positions of turbines in the continuous space. To prevent the premature of the algorithm, a local search strategy based on differential evolution is incorporated to search around the promising region achieved by the particle swarm optimization. A simple feasibility-based method is employed to compare the performance of different schemes. Comprehensive simulation results demonstrate that the micro-siting schemes obtained by the proposed algorithm increase the power generation of the wind farm. Moreover, the execution time of the algorithm is significantly reduced, which is important especially for large-scale wind farms

A novel technique for lymphadenectomy along the left recurrent laryngeal nerve during minimally invasive esophagectomy: a retrospective cohort study

Abstract Background In the context of esophageal cancers, lymph nodes located along the left recurrent laryngeal nerve (RLN) exhibit significant involvement, posing significant challenges for lymphadenectomy. The objective of this study is to assess the safety and efficacy of a novel technique for lymphadenectomy called "elastic suspension of left RLN" method, comparing it with the conventional approach. Methods Between January 2016 and June 2020, a total of 393 patients who underwent minimally invasive esophagectomy with gastroplasty and cervical esophagogastric anastomosis were enrolled in the study. Among them, 291 patients underwent the "elastic suspension of left RLN" method, while 102 patients underwent the conventional method. We compared the number of harvested lymph nodes along the left RLN and assessed postoperative complications between these two groups. Additionally, the overall survival (OS) rate was calculated and analyzed for the entire cohort. Results In comparison to the conventional group, the elastic suspension group exhibited a higher yield of harvested lymph nodes along the left RLN (5.36 vs 3.07, P < 0.001). Moreover, the incidence of postoperative hoarseness was lower in the elastic suspension group (10.65% vs 18.63%, P = 0.038). The average duration of lymphadenectomy along the left RLN was 11.85 min in the elastic suspension group and 11.51 min in the conventional group, although this difference was not statistically significant (P = 0.091). Notably, the overall 5-year OS was markedly higher in the elastic suspension group compared to the conventional group (64.1% vs. 50.1%, P = 0.020). Conclusions The findings suggest that the novel "elastic suspension of left RLN" method for lymphadenectomy along the left RLN in minimally invasive esophagectomy is both safe and effective. This technique holds promise for widespread adoption in esophagectomy procedures

Combining machine learning with multi-physics modelling for multi-objective optimisation and techno-economic analysis of electrochemical CO2 reduction process

As a carbon capture and utilization (CCU) technology, gas diffusion electrode (GDE) based electrochemical CO2 reduction reaction (eCO2RR) can convert CO2 to valuable products, such as formate and CO. However, the electrode parameters and operational conditions need to be studied and optimised to enhance the performance and reduce the net cost of the eCO2RR process before its industrial application. In this work, a machine learning algorithm, i.e., extended adaptive hybrid functions (E-AHF) is combined with a multi-physics model for the data-driven three-objective optimisation and techno-economic analysis of the GDE-based eCO2RR process. The effects of eight design variables on the product yield (PY), CO2 conversion (CR) and specific electrical energy consumption (SEEC) of the process are analysed. The results show that the R2 of the E-AHF model for the prediction of PY, CR and SEEC are all higher than 0.96, indicating the high accuracy of the developed machine learning algorithm for the prediction of the eCO2RR process. The process performance experiences a notable improvement after optimisation and is affected by a combination of eight variables, amongst which the electrolyte concentration having the most significant impact on PY and CR. The optimal trade-off single-pass PY, CR and SEEC are 3.25×10−9 kg s−1, 0.663% and 9.95 kWh kg−1 based on flow channels with 1 cm in length, respectively. The SEEC is reduced by nearly half and PY and CR are improved more than two times after optimisation. The production cost of the GDE-based eCO2RR process was approximately $378 t−1product (CO and formate), much lower than that of traditional CO2 utilisation factories ($835 t−1product). The electricity cost accounted for more than 80% of the total cost, amounting to $318 t−1, indicating that cheaper and cleaner electricity sources would further reduce the production cost of the process, which is the key to the economics of this technology

Combining machine learning with multi-physics modeling for multi-objective optimisation and techno-economic analysis of electrochemical CO₂ reduction process

As a carbon capture and utilization (CCU) technology, gas diffusion electrode (GDE) based electrochemical CO2 reduction reaction (eCO2RR) can convert CO2 to valuable products, such as formate and CO. However, the electrode parameters and operational conditions need to be studied and optimised to enhance the performance and reduce the net cost of the eCO2RR process before its industrial application. In this work, a machine learning algorithm, i.e., extended adaptive hybrid functions (E-AHF) is combined with a multi-physics model for the data-driven three-objective optimisation and techno-economic analysis of the GDE-based eCO2RR process. The effects of eight design variables on the product yield (PY), CO2 conversion (CR) and specific electrical energy consumption (SEEC) of the process are analysed. The results show that the R2 of the E-AHF model for the prediction of PY, CR and SEEC are all higher than 0.96, indicating the high accuracy of the developed machine learning algorithm for the prediction of the eCO2RR process. The process performance experiences a notable improvement after optimisation and is affected by a combination of eight variables, among which the electrolyte concentration having the most significant impact on PY and CR. The optimal trade-off single-pass PY, CR and SEEC are 3.25×10-9 kg s-1, 0.663% and 9.95 kWh kg-1 based on flow channels with 1 cm in length, respectively. The SEEC is reduced by nearly half and PY and CR are improved more than two times after optimisation. The production cost of the GDEbased eCO2RR process was approximately $378 t-1product (CO and formate), much lower than that of traditional CO2 utilisation factories ($835 t-1product). The electricity cost accounted for more than 80% of the total cost, amounting to $318 t-1, indicating that cheaper and cleaner electricity sources would further reduce the production cost of the process, which is the key to the economics of this technology.</p