Achieving decent living standards in emerging economies challenges national mitigation goals for CO2 emissions

Emerging economies, low- and middle-income countries experiencing rapid population and GDP growth, face the challenge of improving their living standards while stabilizing CO2 emissions to meet net-zero goals. In this study, we quantify the CO2 emissions required for achieving decent living standards (DLS) in emerging economies. The results show that, compared to other regions, achieving DLS in emerging Asian and African economies will result in more additional CO2 emissions, particularly in the DLS indicators of Mobility and Electricity. Achievement of DLS in emerging economies will result in 8.6 Gt of additional CO2 emissions, which should not jeopardize global climate targets. However, a concerning trend arises as more than half of the emerging economies (62 out of 121) will face substantial challenges in aligning their expected emission growth for achieving DLS with their national emission mitigation targets

Phenotypic, fermentation characterization, and resistance mechanism analysis of bacteriophage-resistant mutants of Lactobacillus delbrueckii ssp. bulgaricus isolated from traditional Chinese dairy products

peer-reviewedBacteriophage infection is a large factor in dairy industrial production failure on the basis of pure inoculation fermentation, and developing good commercial starter cultures from wild dairy products and improving the environmental vigor of starter cultures by enhancing their phage resistance are still the most effective solutions. Here we used a spontaneous isolation method to obtain bacteriophage-resistant mutants of Lactobacillus delbrueckii ssp. bulgaricus strains that are used in traditional Chinese fermented dairy products. We analyzed their phenotypes, fermentation characteristics, and resistance mechanisms. The results showed that bacteriophage-insensitive mutants (BIM) BIM8 and BIM12 had high bacteriophage resistance while exhibiting fermentation and coagulation attributes that were as satisfying as those of their respective parent strains KLDS1.1016 and KLDS1.1028. According to the attachment receptor detection, mutants BIM8 and BIM12 exhibited reduced absorption to bacteriophage phiLdb compared with their respective bacteriophage-sensitive parent strains because of changes to the polysaccharides or teichoic acids connected to their peptidoglycan layer. Additionally, genes, including HSDR, HSDM, and HSDS, encoding 3 subunits of a type I restriction-modification system were identified in their respective parent strains. We also discovered that HSDR and HSDM were highly conserved but that HSDS was variable because it is responsible for the DNA specificity of the complex. The late lysis that occurred only in strain KLDS1.1016 and not in strain KLDS1.1028 suggests that the former and its mutant BIM8 also may have an activatable restriction-modification mechanism. We conclude that the L. bulgaricus BIM8 and BIM12 mutants have great potential in the dairy industry as starter cultures, and their phage-resistance mechanism was effective mainly due to the adsorption interference and restriction-modification system

Curing Condition and NaOH Concentration on the Mechanical Properties of Fly Ash Based Geopolymer Mortars

This paper describes the effect of different curing conditions on the mechanical properties of fly ash-based geopolymer mortars activated by different NaOH solution molarity. The influence of different curing initial curing temperatures (40℃,50℃,60℃), initial curing time (4h,8h,12h), and NaOH solution concentration (8mol, 10mol, 12mol) was studied by orthogonal design and the single-variable method. Results indicated that the fluidity of the geopolymer mortar sample followed the opposite trend with NaOH molarity increase. The most sensitive factor is NaOH molarity without considering curing age based on the data from orthogonal experiments. Furthermore, the study revealed a tendency to increase the flexural and compressive strength with rising NaOH concentration, initial curing temperature, and time, despite the mechanical strength development speed at different ages being various, and the optimum NaOH molarity may be 10mol while NaOH pellets dosage and CO2 emission were considered

The Default Mode Network Supports Episodic Memory in Cognitively Unimpaired Elderly Individuals: Different Contributions to Immediate Recall and Delayed Recall

While the neural correlates of age-related decline in episodic memory have been the subject of much interest, the spontaneous functional architecture of the brain for various memory processes in elderly adults, such as immediate recall (IR) and delayed recall (DR), remains unclear. The present study thus examined the neural correlates of age-related decline of various memory processes. A total of 66 cognitively normal older adults (aged 60-80 years) participated in this study. Memory processes were measured using the Auditory Verbal Learning Test as well as resting-state brain images, which were analyzed using both regional homogeneity (ReHo) and correlation-based functional connectivity (FC) approaches. We found that both IR and DR were significantly correlated with the ReHo of these critical regions, all within the default mode network (DMN), including the parahippocampal gyrus, posterior cingulate cortex/precuneus, inferior parietal lobule, and medial prefrontal cortex. In addition, DR was also related to the FC between these DMN regions. These results suggest that the DMN plays different roles in memory retrieval across different retention intervals, and connections between the DMN regions contribute to memory consolidation of past events in healthy older people

Learning Optimal Deterministic Auctions with Correlated Valuation Distributions

In mechanism design, it is challenging to design the optimal auction with correlated values in general settings. Although value distribution can be further exploited to improve revenue, the complex correlation structure makes it hard to acquire in practice. Data-driven auction mechanisms, powered by machine learning, enable to design auctions directly from historical auction data, without relying on specific value distributions. In this work, we design a learning-based auction, which can encode the correlation of values into the rank score of each bidder, and further adjust the ranking rule to approach the optimal revenue. We strictly guarantee the property of strategy-proofness by encoding game theoretical conditions into the neural network structure. Furthermore, all operations in the designed auctions are differentiable to enable an end-to-end training paradigm. Experimental results demonstrate that the proposed auction mechanism can represent almost any strategy-proof auction mechanism, and outperforms the auction mechanisms wildly used in the correlated value settings