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Bearings are widely used in various electrical and mechanical equipment. As their core components, failures often have serious consequences. At present, most parameter adjustment methods are still manual adjustments of parameters. This adjustment method is easily affected by prior knowledge, easily falls into the local optimal solution, cannot obtain the global optimal solution, and requires a lot of resources. Therefore, this paper proposes a new method for bearing fault diagnosis based on wavelet packet transform and convolutional neural network optimized by a simulated annealing algorithm. Firstly, the original bearing vibration signal is extracted by wavelet packet transform to obtain the spectrogram, and then the obtained spectrogram is sent to the convolutional neural network for parameter adjustment, and finally the simulated annealing algorithm is used to adjust the parameters. To verify the effectiveness of the method, the bearing database of Case Western Reserve University is used for testing, and the traditional intelligent bearing fault diagnosis methods are compared. The results show that the new method for bearing fault diagnosis proposed in this paper has a better and more reliable diagnosis effect than the existing machine learning and deep learning methods.

Nuclear factor erythroid 2-related factor 2 (NRF2) is a transcription factor that regulates the cellular defense against toxic and oxidative insults through the expression of genes involved in oxidative stress response and drug detoxification. NRF2 activation renders cells resistant to chemical carcinogens and inflammatory challenges. In addition to antioxidant responses, NRF2 is involved in many other cellular processes, including metabolism and inflammation, and its functions are beyond the originally envisioned. NRF2 activity is tightly regulated through a complex transcriptional and post-translational network that enables it to orchestrate the cell’s response and adaptation to various pathological stressors for the homeostasis maintenance. Elevated or decreased NRF2 activity by pharmacological and genetic manipulations of NRF2 activation is associated with many metabolism- or inflammation-related diseases. Emerging evidence shows that NRF2 lies at the center of a complex regulatory network and establishes NRF2 as a truly pleiotropic transcription factor. Here we summarize the complex regulatory network of NRF2 activity and its roles in metabolic reprogramming, unfolded protein response, proteostasis, autophagy, mitochondrial biogenesis, inflammation, and immunity.

Suboptimal diets are a leading risk factor for death and disability. Nutrition labelling is a potential method to encourage consumers to improve dietary behaviour. This systematic review and network meta-analysis (NMA) summarises evidence on the impact of colour-coded interpretive labels and warning labels on changing consumers' purchasing behaviour. We conducted a literature review of peer-reviewed articles published between 1 January 1990 and 24 May 2021 in PubMed, Embase via Ovid, Cochrane Central Register of Controlled Trials, and SCOPUS. Randomised controlled trials (RCTs) and quasi-experimental studies were included for the primary outcomes (measures of changes in consumers' purchasing and consuming behaviour). A frequentist NMA method was applied to pool the results. A total of 156 studies (including 101 RCTs and 55 non-RCTs) nested in 138 articles were incorporated into the systematic review, of which 134 studies in 120 articles were eligible for meta-analysis. We found that the traffic light labelling system (TLS), nutrient warning (NW), and health warning (HW) were associated with an increased probability of selecting more healthful products (odds ratios [ORs] and 95% confidence intervals [CIs]: TLS, 1.5 [1.2, 1.87]; NW, 3.61 [2.82, 4.63]; HW, 1.65 [1.32, 2.06]). Nutri-Score (NS) and warning labels appeared effective in reducing consumers' probability of selecting less healthful products (NS, 0.66 [0.53, 0.82]; NW,0.65 [0.54, 0.77]; HW,0.64 [0.53, 0.76]). NS and NW were associated with an increased overall healthfulness (healthfulness ratings of products purchased using models such as FSAm-NPS/HCSP) by 7.9% and 26%, respectively. TLS, NS, and NW were associated with a reduced energy (total energy: TLS, -6.5%; NS, -6%; NW, -12.9%; energy per 100 g/ml: TLS, -3%; NS, -3.5%; NW, -3.8%), sodium (total sodium/salt: TLS, -6.4%; sodium/salt per 100 g/ml: NS: -7.8%), fat (total fat: NS, -15.7%; fat per 100 g/ml: TLS: -2.6%; NS: -3.2%), and total saturated fat (TLS, -12.9%; NS: -17.1%; NW: -16.3%) content of purchases. The impact of TLS, NS, and NW on purchasing behaviour could be explained by improved understanding of the nutrition information, which further elicits negative perception towards unhealthful products or positive attitudes towards healthful foods. Comparisons across label types suggested that colour-coded labels performed better in nudging consumers towards the purchase of more healthful products (NS versus NW: 1.51 [1.08, 2.11]), while warning labels have the advantage in discouraging unhealthful purchasing behaviour (NW versus TLS: 0.81 [0.67, 0.98]; HW versus TLS: 0.8 [0.63, 1]). Study limitations included high heterogeneity and inconsistency in the comparisons across different label types, limited number of real-world studies (95% were laboratory studies), and lack of long-term impact assessments. Our systematic review provided comprehensive evidence for the impact of colour-coded labels and warnings in nudging consumers' purchasing behaviour towards more healthful products and the underlying psychological mechanism of behavioural change. Each type of label had different attributes, which should be taken into consideration when making front-of-package nutrition labelling (FOPL) policies according to local contexts. Our study supported mandatory front-of-pack labelling policies in directing consumers' choice and encouraging the food industry to reformulate their products. PROSPERO (CRD42020161877).

Excellent ductility is crucial not only for shaping but also for strengthening metals and alloys. The ever most widely used eutectic alloys are suffering from the limited ductility and losing competitiveness among advanced structural materials. Here we report a distinctive concept of phase-selective recrystallization to overcome this challenge for eutectic alloys by triggering the strain hardening capacity of the duplex phases completely. We manipulate the strain partitioning behavior of the two phases in a eutectic high-entropy alloy (EHEA) to obtain the phase-selectively recrystallized microstructure with a fully recrystallized soft phase embedded in the skeleton of a hard phase. The resulting microstructure fully releases the strain hardening capacity in EHEA by eliminating the weak boundaries. Our phase-selectively recrystallized EHEA achieves a high ductility of ∼35% uniform elongation with true stress of ∼2 GPa. This concept is universal for various duplex alloys with soft and hard phases and opens new frontiers for traditional eutectic alloys as high-strength metallic materials. The ever most widely used eutectic alloys often suffer from limited ductility. Here the authors propose a distinctive concept of phase-selective recrystallization to significantly improve their ductility and strength and pave the way for new applications of the widespread eutectic alloys.

Strong evidence indicates that reduction of salt intake lowers blood pressure and reduces the risk of cardiovascular disease (CVD). The WHO has set a global target of reducing the population salt intake from the current level of approximately 10 g daily to <5 g daily. This recommendation has been challenged by several studies, including cohort studies, which have suggested a J-shaped relationship between salt intake and CVD risk. However, these studies had severe methodological problems, such as reverse causality and measurement error due to assessment of salt intake by spot urine. Consequently, findings from such studies should not be used to derail vital public health policy. Gradual, stepwise salt reduction as recommended by the WHO remains an achievable, affordable, effective, and important strategy to prevent CVD worldwide. The question now is how to reduce population salt intake. In most developed countries, salt reduction can be achieved by a gradual and sustained reduction in the amount of salt added to food by the food industry. The UK has pioneered a successful salt-reduction programme by setting incremental targets for >85 categories of food; many other developed countries are following the UK’s lead. In developing countries where most of the salt is added by consumers, public health campaigns have a major role. Every country should adopt a coherent, workable strategy. Even a modest reduction in salt intake across the whole population can lead to a major improvement in public health and cost savings.

The realization of the efficient hydrogen conversion with large current densities at low overpotentials represents the development trend of this field. Here we report the atomic active sites tailoring through a facile synthetic method to yield well-defined Rhodium nanocrystals in aqueous solution using formic acid as the reducing agent and graphdiyne as the stabilizing support. High-resolution high-angle annular dark-field scanning-transmission electron microscopy images show the high-density atomic steps on the faces of hexahedral Rh nanocrystals. Experimental results reveal the formation of stable sp –C~Rh bonds can stabilize Rh nanocrystals and further improve charge transfer ability in the system. Experimental and density functional theory calculation results solidly demonstrate the exposed high active stepped surfaces and various metal atomic sites affect the electronic structure of the catalyst to reduce the overpotential resulting in the large-current hydrogen production from saline water. This exciting result demonstrates unmatched electrocatalytic performance and highly stable saline water electrolysis. While water electrolysis represents a promising means for renewable hydrogen production, the catalysts needed must be efficient at high current densities. Here, authors show rhodium nanocrystals on graphdiyne to efficiently evolve hydrogen from saline water.

Abstract Context Accurate methods for early gestational diabetes mellitus (GDM) (during the first trimester of pregnancy) prediction in Chinese and other populations are lacking. Objectives This work aimed to establish effective models to predict early GDM. Methods Pregnancy data for 73 variables during the first trimester were extracted from the electronic medical record system. Based on a machine learning (ML)-driven feature selection method, 17 variables were selected for early GDM prediction. To facilitate clinical application, 7 variables were selected from the 17-variable panel. Advanced ML approaches were then employed using the 7-variable data set and the 73-variable data set to build models predicting early GDM for different situations, respectively. Results A total of 16 819 and 14 992 cases were included in the training and testing sets, respectively. Using 73 variables, the deep neural network model achieved high discriminative power, with area under the curve (AUC) values of 0.80. The 7-variable logistic regression (LR) model also achieved effective discriminate power (AUC = 0.77). Low body mass index (BMI) (≤ 17) was related to an increased risk of GDM, compared to a BMI in the range of 17 to 18 (minimum risk interval) (11.8% vs 8.7%, P = .09). Total 3,3,5′-triiodothyronine (T3) and total thyroxin (T4) were superior to free T3 and free T4 in predicting GDM. Lipoprotein(a) was demonstrated a promising predictive value (AUC = 0.66). Conclusions We employed ML models that achieved high accuracy in predicting GDM in early pregnancy. A clinically cost-effective 7-variable LR model was simultaneously developed. The relationship of GDM with thyroxine and BMI was investigated in the Chinese population.