How to improve the energy-saving performance of China's transport sector? An input-output perspective.

The transport sector proves a major energy consumer in China, but improving energy-saving performance in China's provincial transport sector from the lifecycle perspective remains unresolved. Thus, this study employs the environmentally extended multi-region input-output (MRIO) method, structural path analysis, and the newest MRIO table of China from 2017, to investigate how to improve the energy-saving performance from final demand structure, supply chain, and pathway perspectives. The relevant results are threefold. (1) Regarding the final demand structure level, the embodied energy consumption of China's transport sector is predominantly driven by investment from the production side, while that of the consumption side is primarily caused by exports. (2) At the supply chain level, production-side embodied energy consumption primarily occurs along a three-echelon supply chain, while that from the consumption side mostly occurs via a two-echelon supply chain. (3) At the pathway level, the production-side energy-saving performance of China's provincial transport sector is dominated by two pathways along the construction sector, including transport sector → construction sector → final demands, and transport sector → intermediate inputs → construction sector → final demands, while that of the consumption side is chiefly determined by three pathways along internal transportation chains.

Thermal Decomposition Mechanism of P(DAC-AM) with Serial Cationicity and Intrinsic Viscosity.

The thermal decomposition of the thermodynamic, kinetic and mechanisms of copolymer P(DAC-AM) samples with serial cationicity and intrinsic viscosity ([η]), and the control samples of homopolymer PAM and PDAC, were studied and analyzed using TG, DSC, FTIR. The results of the thermal decomposition thermodynamics confirmed that the thermal decomposition processes of the serial P(DAC-AM) samples and the two control samples could be divided into two stages. It was found that the processes of the copolymer P(DAC-AM) samples were not a simple superposition of those of homopolymers, whose monomers had composed the unit structures of the copolymer, but there were interactions between the two suspension groups. The results of thermal decomposition kinetics showed that the apparent activation energy (E) of the thermal decomposition process of all polymer samples had different varying trends in the terms of weight-loss rate (α). The reaction order (n) of the thermal decomposition of P(DAC-AM) in Stage I and II was close to 1, but in the former and the latter it tended to be 2 and 0.5, respectively. Finally, the thermal decomposition mechanism of copolymer P(DAC-AM) samples was discussed. The above research could not only fill in the knowledge vacancy of the thermal decomposition of the thermodynamic, kinetic and mechanisms of P(DAC-AM), but could also lay a foundation for the study of thermal decomposition mechanisms of the other types of polymers, including cationic polymers.

An efficient ANN SoC for detecting Alzheimer's disease based on recurrent computing.

Alzheimer's Disease (AD) is an irreversible, degenerative condition that, while incurable, can have its progression slowed or impeded. While there are numerous methods utilizing neural networks for AD detection, there is a scarcity of High-performance AD detection chips. Moreover, excessively complex neural networks are not conducive to on-chip implementation and clinical applications. This study addresses the challenges of high misdiagnosis rates and significant hardware costs inherent in traditional AD detection techniques. A novel and efficient AD detection framework based on a recurrent computational strategy is proposed. The framework harnesses an Artificial Neural Network (ANN) embedded within a System on Chip (SoC) to perform sophisticated Electroencephalogram (EEG) analysis. The approach began by employing a reduced IEEE754 single-precision encoding method to hardware-encode the preprocessed EEG data, thereby minimizing the memory storage area. Next, data remapping techniques were utilized to ensure the continuity of the input data read addresses and reduce the memory access pressure during neural network computations. Subsequently, hierarchical and Processing Element (PE) reuse technologies were leveraged to perform the multiply-accumulate operations of the ANN. Finally, a step function was chosen to establish binary classification circuits dedicated to AD detection. Experimental results indicate that the optimized SoC achieves a 70 % reduction in area and a 50 % reduction in power consumption compared to traditional designs. For various neural network models, the detection model proposed in this paper incurs less overhead, with a training speed 3 to 4 times faster than other traditional models, and a high accuracy rate of 98.53 %.

Copolyester toughened poly(lactic acid) biodegradable material prepared by in situ formation of polyethylene glycol and citric acid.

Polylactic acid (PLA) is a high-modulus, high-strength bio-based thermoplastic polyester with good biodegradability, which is currently a promising environmentally friendly material. However, its inherent brittleness has hindered its widespread use. In this study, we reported a simple and non-toxic strategy for toughening PLA, using biodegradable materials such as polyethylene glycol (PEG) and citric acid (CA) as precursors. Through reactive melt blending with PLA, PEG and CA form PEGCA copolyesters in situ during blending. At the same time, CA can react with PLA and PEG, forming a copolyester structure at the interface of the two phases, improving the interfacial compatibility between PEG and PEGCA with PLA. Fourier transform infrared spectroscopy confirms this. Experimental results show that when the content of PEG/CA reaches 15% (PLA/PEG/CA-15%) in the blends, the impact strength of the blend was 4.47 kJ m-2, and the maximum elongation at break was as high as 360.1%, which were about 2 and 44 times higher than those of pure PLA, respectively. Moreover, the tensile strength was still maintained at the level of 70%. This work can expand the application of PLA in food packaging and medical supplies.

Cinnamaldehyde targets SarA to enhance ß-lactam antibiotic activity against methicillin-resistant Staphylococcus aureus.

Methicillin-resistant Staphylococcus aureus (MRSA) is a current global public health problem due to its increasing resistance to the most recent antibiotic therapies. One critical approach is to develop ways to revitalize existing antibiotics. Here, we show that the phytogenic compound cinnamaldehyde (CIN) and ß-lactam antibiotic combinations can functionally synergize and resensitize clinical MRSA isolates to ß-lactam therapy and inhibit MRSA biofilm formation. Mechanistic studies indicated that the CIN potentiation effect on ß-lactams was primarily the result of inhibition of the mecA expression by targeting the staphylococcal accessory regulator sarA. CIN alone or in combination with ß-lactams decreased sarA gene expression and increased SarA protein phosphorylation that impaired SarA binding to the mecA promoter element and downregulated virulence genes such as those encoding biofilm, α-hemolysin, and adhesin. Perturbation of SarA-mecA binding thus interfered with PBP2a biosynthesis and this decreased MRSA resistance to ß-lactams. Furthermore, CIN fully restored the anti-MRSA activities of ß-lactam antibiotics in vivo in murine models of bacteremia and biofilm infections. Together, our results indicated that CIN acts as a ß-lactam adjuvant and can be applied as an alternative therapy to combat multidrug-resistant MRSA infections.

Tobramycin-resistant small colony variant mutant of Salmonella enterica serovar Typhimurium shows collateral sensitivity to nitrofurantoin.

The increasing antibiotic resistance poses a significant global health challenge, threatening our ability to combat infectious diseases. The phenomenon of collateral sensitivity, whereby resistance to one antibiotic is accompanied by increased sensitivity to another, offers potential avenues for novel therapeutic interventions against infections unresponsive to classical treatments. In this study, we elucidate the emergence of tobramycin (TOB)-resistant small colony variants (SCVs) due to mutations in the hemL gene, which render S. Typhimurium more susceptible to nitrofurantoin (NIT). Mechanistic studies demonstrate that the collateral sensitivity in TOB-resistant S. Typhimurium SCVs primarily stems from disruptions in haem biosynthesis. This leads to dysfunction in the electron transport chain (ETC) and redox imbalance, ultimately inducing lethal accumulation of reactive oxygen species (ROS). Additionally, the upregulation of nfsA/B expressions facilitates the conversion of NIT prodrug into its active form, promoting ROS-mediated bacterial killing and contributing to this collateral sensitivity pattern. Importantly, alternative NIT therapy demonstrates a significant reduction of bacterial load by more than 2.24-log10 cfu/g in the murine thigh infection and colitis models. Our findings corroborate the collateral sensitivity of S. Typhimurium to nitrofurans as a consequence of evolving resistance to aminoglycosides. This provides a promising approach for treating infections due to aminoglycoside-resistant strains.