Paving the path towards carbon neutrality: revealing the contribution of the "Belt and Road" Initiative in mitigating carbon intensity.

This study explores the crucial contribution of the "Belt and Road" Initiative (BRI) in diminishing carbon intensity and facilitating progress towards carbon neutrality, addressing the pressing global issue of climate change. Given its status as the world's foremost carbon emitter, China encounters significant pressure to alleviate its emissions. Launched in 2013, the BRI emphasizes economic development along its route while highlighting environmental protection in the regions involved. Despite extensive analyses of the BRI's economic impact, its environmental consequences have received insufficient attention, hindering a comprehensive evaluation of the initiative and obstructing the constructing of an environmentally optimal road. Empirical findings reveal a substantial reduction in carbon emission intensity in provinces along the BRI route, with robustness tests (change the time window period and dynamic effect) validating result consistency. The BRI achieves this reduction by alleviating congestion, enhancing transportation infrastructure, fostering commuting agglomeration, optimizing energy utilization, and lowering carbon intensity. Further analysis uncovers a mediating chain effect, establishing a conduction mechanism of "BRI brings on transportation infrastructure effect and then leads to travel agglomeration effect and then to congestion improvement effect and then to energy utilization effect and then eventuates carbon intensity reduction." This study offers crucial insights for policymakers aiming to make informed decisions towards the green road construction of the BRI, contributing to global efforts to combat climate change.

De Novo Sequencing of Synthetic Bis-cysteine Peptide Macrocycles Enabled by "Chemical Linearization" of Compound Mixtures.

A "chemical linearization" approach was applied to synthetic peptide macrocycles to enable their de novo sequencing from mixtures using nanoliquid chromatography-tandem mass spectrometry (nLC-MS/MS). This approach─previously applied to individual macrocycles but not to mixtures─involves cleavage of the peptide backbone at a defined position to give a product capable of generating sequence-determining fragment ions. Here, we first established the compatibility of "chemical linearization" by Edman degradation with a prominent macrocycle scaffold based on bis-Cys peptides cross-linked with the m-xylene linker, which are of major significance in therapeutics discovery. Then, using macrocycle libraries of known sequence composition, the ability to recover accurate de novo assignments to linearized products was critically tested using performance metrics unique to mixtures. Significantly, we show that linearized macrocycles can be sequenced with lower recall compared to linear peptides but with similar accuracy, which establishes the potential of using "chemical linearization" with synthetic libraries and selection procedures that yield compound mixtures. Sodiated precursor ions were identified as a significant source of high-scoring but inaccurate assignments, with potential implications for improving automated de novo sequencing more generally.

Impact of inverse unequal height strut structure on the functional performance of an additively manufactured cardiovascular stent.

Recently, additive manufacturing (AM) has been investigated as an innovative method to manufacture stents due to its capability in producing complex and customized structures. In this paper, the cardiovascular stents of M-type and N-type with inverse unequal height strut structure and N-type with equal height strut structure were designed and manufactured by Selective Laser Melting (SLM). Following surface polishing, balloon expansion, plane compression and three-point bending experiments were carried out to evaluate the mechanical performance of the stent. The stents designed with inverse unequal height strut structure showed higher radial support performance and lower radial recoil when compared to the stents with uniform design. This study proved the feasibility of SLM in rapid manufacturing of cardiovascular stents that can be used for performance evaluation in design stage.

[Three-dimensional printed 316L stainless steel cardiovascular stent's electrolytic polishing and its mechanical properties].

The interventional therapy of vascular stent implantation is a popular treatment method for cardiovascular stenosis and blockage. However, traditional stent manufacturing methods such as laser cutting are complex and cannot easily manufacture complex structures such as bifurcated stents, while three-dimensional (3D) printing technology provides a new method for manufacturing stents with complex structure and personalized designs. In this paper, a cardiovascular stent was designed, and printed using selective laser melting technology and 316L stainless steel powder of 0-10 µm size. Electrolytic polishing was performed to improve the surface quality of the printed vascular stent, and the expansion behavior of the polished stent was assessed by balloon inflation. The results showed that the newly designed cardiovascular stent could be manufactured by 3D printing technology. Electrolytic polishing removed the attached powder and reduced the surface roughness Ra from 1.36 µm to 0.82 µm. The axial shortening rate of the polished bracket was 4.23% when the outside diameter was expanded from 2.42 mm to 3.63 mm under the pressure of the balloon, and the radial rebound rate was 2.48% after unloading. The radial force of polished stent was 8.32 N. The 3D printed vascular stent can remove the surface powder through electrolytic polishing to improve the surface quality, and show good dilatation performance and radial support performance, which provides a reference for the practical application of 3D printed vascular stent.

Diagnosis of COVID-19 via acoustic analysis and artificial intelligence by monitoring breath sounds on smartphones.

Scientific evidence shows that acoustic analysis could be an indicator for diagnosing COVID-19. From analyzing recorded breath sounds on smartphones, it is discovered that patients with COVID-19 have different patterns in both the time domain and frequency domain. These patterns are used in this paper to diagnose the infection of COVID-19. Statistics of the sound signals, analysis in the frequency domain, and Mel-Frequency Cepstral Coefficients (MFCCs) are then calculated and applied in two classifiers, k-Nearest Neighbors (kNN) and Convolutional Neural Network (CNN), to diagnose whether a user is contracted with COVID-19 or not. Test results show that, amazingly, an accuracy of over 97% could be achieved with a CNN classifier and more than 85% on kNN with optimized features. Optimization methods for selecting the best features and using various metrics to evaluate the performance are also demonstrated in this paper. Owing to the high accuracy of the CNN model, the CNN model was implemented in an Android app to diagnose COVID-19 with a probability to indicate the confidence level. The initial medical test shows a similar test result between the method proposed in this paper and the lateral flow method, which indicates that the proposed method is feasible and effective. Because of the use of breath sound and tested on the smartphone, this method could be used by everybody regardless of the availability of other medical resources, which could be a powerful tool for society to diagnose COVID-19.