Additively manufactured (AM) biodegradable zinc alloys hold huge potential as promising candidates for bone defect and fracture repair, thanks to their suitable biodegradation rates and acceptable biocompatibility. However, the mechanical properties of AM zinc alloys developed so far, ductility in particular, fall short of the requirements for bone substitution. Here, we present Zn-1Mn and Zn-1Mn-0.4Mg alloy implants with unique microstructures, fabricated using laser powder bed fusion (LPBF). Notably, the LPBF Zn-Mn-Mg alloy exhibited an extraordinary balance of strength and ductility, with an ultimate tensile strength of 289 MPa, yield strength of 213.5 MPa, and elongation over 20 %, outperforming all previously reported AM zinc alloys. The simultaneously enhanced strength and ductility of the ternary alloy were attributed to the strong grain-refining effect of the Mg₂Zn₁₁ second phase and the synthetic strengthening caused by the dispersion of the MnZn₁₃ and Mg₂Zn₁₁ second phases inside the grains and at the grain boundaries. In addition, both alloys had similar rates of in vitro biodegradation (∼0.15 mm/year), properly aligned with the bone remodeling process, while also demonstrating favorable biocompatibility and upregulating multiple osteogenic markers. The Zn-Mn-Mg alloy showed even better osteogenic potential than the Zn-Mn alloy, owing to the addition of Mg. The combined attributes of the LPBF Zn-Mn-Mg ternary alloy indicated huge potential for its use as a bone repair material, especially for load-bearing bone fixation. Statement of significance: The mechanical properties of previously developed additively manufactured biodegradable zinc alloys, especially ductility, have not met the requirements for bone repair. Using laser powder bed fusion (LPBF), we fabricated Zn-1Mn and Zn-1Mn-0.4Mg alloy implants with unique microstructures. The LPBF Zn-Mn-Mg alloy demonstrated an exceptional balance of strength and ductility, achieving a tensile strength of 289 MPa, yield strength of 213.5 MPa, and elongation over 20 %, surpassing all reported AM zinc alloys. This study is the first to produce a directly printed biodegradable alloy meeting the mechanical requirements for bone fixation devices without post-processing. Additionally, the alloy exhibited moderate a biodegradation rate and excellent biocompatibility, underscoring its potential for load-bearing bone repair applications.

In this research a machine learning model incorporating uncertainty to enhance the creep-life prediction and high-throughput design of creep-resistant steel is proposed. The framework integrates key physical metallurgical parameter linked to precipitate coarsening and applies transfer learning to correlate short-time tensile properties with the creep performance, all within a Bayesian convolutional neural network. Unlike conventional machine learning models, which often lack an assessment of prediction credibility, this uncertainty-based approach offers more accurate and stable predictions while also providing a measure of prediction credibility. By combining the model with a genetic algorithm, the framework achieves a balance between creep life optimization and uncertainty, thereby supporting robust alloy design. The validation on newly developed martensitic heat-resistant steels with tolerable prediction uncertainty showed excellent alignment between predicted and experimentally determined creep life, underscoring the effectiveness of the framework. These findings highlight the critical role of uncertainty modeling in advancing machine learning applications for alloy design.

Bursts in water distribution systems (WDSs) lead to water wastage and negative environmental impacts. Optimizing pressure sensor placement (PSP) for effective burst detection is crucial for prompt response and adverse event mitigation. While many optimization methods are available for this purpose, their resulting PSP strategies often exhibit large variability caused by background noise or metering accuracy, representing low robustness. This consequently hinders the wide application of existing PSP methods for burst detection in WDSs. This study proposes a novel multi-objective PSP method aimed at maximizing burst detection while minimizing the number of sensors. Within this method, a new threshold metric is introduced to quantify the minimum detectable burst outflow across all pipes. The metric has the key advantage of taking into account the fact that the relative magnitude of the detectable threshold for different pipes is independent of noise, thereby addressing the low robustness issue. The proposed method is applied to three WDSs, and results show that it can consistently achieve robust optimal PSP strategies across diverse noise conditions. Comprehensive comparisons between the proposed method and the other benchmark approaches are conducted using five metrics, including detection performance and layout characteristics. These comparisons reveal the properties of each optimization method and show how detection performance is affected by various placement features. It is anticipated that the proposed method can be useful in engineering practice due to its great robustness in determining the optimal PSP strategies for burst detection in WDSs compared to other alternatives.

To achieve an effective design of additively manufacturable Ni superalloys with decent service performance, a hybrid computational design model has been developed, where the strategy to tailor local elemental segregations was integrated within a scheme of minimizing the cracking susceptibility. More specifically, the phase boundary of primary NbC / γ matrix was introduced into the design routine to tune the spatial distribution of critical solutes at an atomic scale, thereby inhibiting the formation of borides and segregation-induced cracking. Based on the output of the design, new grades of Ni superalloy have been developed with excellent additive manufacturability, as confirmed by the robustness of printing parameters in fabricating low-defect-density samples. The capability of the phase boundaries to evenly distribute boron atoms was validated experimentally, and the cracking induced by uncontrolled boron segregation at grain boundaries was effectively prevented. The newly designed alloys showed good tensile properties and decent oxidation resistance at different service temperatures, which are comparable to those of conventionally produced superalloys. The finding that phase boundaries can be employed to prevent undesirable clustering of boron atoms can be extended to manipulate the distributions of other critical elements, which provides a new path for designing novel Ni superalloys with balanced printability and mechanical properties.

Objective To review the evidence on the effectiveness of public health measures in reducing the incidence of covid-19, SARS-CoV-2 transmission, and covid-19 mortality. Design Systematic review and meta-analysis. Data sources Medline, Embase, CINAHL, Biosis, Joanna Briggs, Global Health, and World Health Organization COVID-19 database (preprints). Eligibility criteria for study selection Observational and interventional studies that assessed the effectiveness of public health measures in reducing the incidence of covid-19, SARS-CoV-2 transmission, and covid-19 mortality. Main outcome measures The main outcome measure was incidence of covid-19. Secondary outcomes included SARS-CoV-2 transmission and covid-19 mortality. Data synthesis DerSimonian Laird random effects meta-analysis was performed to investigate the effect of mask wearing, handwashing, and physical distancing measures on incidence of covid-19. Pooled effect estimates with corresponding 95% confidence intervals were computed, and heterogeneity among studies was assessed using Cochran's Q test and the I 2 metrics, with two tailed P values. Results 72 studies met the inclusion criteria, of which 35 evaluated individual public health measures and 37 assessed multiple public health measures as a "package of interventions."Eight of 35 studies were included in the meta-analysis, which indicated a reduction in incidence of covid-19 associated with handwashing (relative risk 0.47, 95% confidence interval 0.19 to 1.12, I 2 =12%), mask wearing (0.47, 0.29 to 0.75, I 2 =84%), and physical distancing (0.75, 0.59 to 0.95, I 2 =87%). Owing to heterogeneity of the studies, meta-analysis was not possible for the outcomes of quarantine and isolation, universal lockdowns, and closures of borders, schools, and workplaces. The effects of these interventions were synthesised descriptively. Conclusions This systematic review and meta-analysis suggests that several personal protective and social measures, including handwashing, mask wearing, and physical distancing are associated with reductions in the incidence covid-19. Public health efforts to implement public health measures should consider community health and sociocultural needs, and future research is needed to better understand the effectiveness of public health measures in the context of covid-19 vaccination. Systematic review registration PROSPERO CRD42020178692.