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BACKGROUND: A significant proportion of young at-risk patients and nonsmokers are excluded by the current guidelines for lung cancer (LC) screening, resulting in low-screening adoption. The vision of the US National Academy of Medicine to transform health systems into learning health systems (LHS) holds promise for bringing necessary structural changes to health care, thereby addressing the exclusivity and adoption issues of LC screening. OBJECTIVE: This study aims to realize the LHS vision by designing an equitable, machine learning (ML)-enabled LHS unit for LC screening. It focuses on developing an inclusive and practical LC risk prediction model, suitable for initializing the ML-enabled LHS (ML-LHS) unit. This model aims to empower primary physicians in a clinical research network, linking central hospitals and rural clinics, to routinely deliver risk-based screening for enhancing LC early detection in broader populations. METHODS: We created a standardized data set of health factors from 1397 patients with LC and 1448 control patients, all aged 30 years and older, including both smokers and nonsmokers, from a hospital's electronic medical record system. Initially, a data-centric ML approach was used to create inclusive ML models for risk prediction from all available health factors. Subsequently, a quantitative distribution of LC health factors was used in feature engineering to refine the models into a more practical model with fewer variables. RESULTS: The initial inclusive 250-variable XGBoost model for LC risk prediction achieved performance metrics of 0.86 recall, 0.90 precision, and 0.89 accuracy. Post feature refinement, a practical 29-variable XGBoost model was developed, displaying performance metrics of 0.80 recall, 0.82 precision, and 0.82 accuracy. This model met the criteria for initializing the ML-LHS unit for risk-based, inclusive LC screening within clinical research networks. CONCLUSIONS: This study designed an innovative ML-LHS unit for a clinical research network, aiming to sustainably provide inclusive LC screening to all at-risk populations. It developed an inclusive and practical XGBoost model from hospital electronic medical record data, capable of initializing such an ML-LHS unit for community and rural clinics. The anticipated deployment of this ML-LHS unit is expected to significantly improve LC-screening rates and early detection among broader populations, including those typically overlooked by existing screening guidelines.

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Injection molding is a common plastic processing technique that allows melted plastic to be injected into a mold through pressure to form differently shaped plastic parts. In injection molding, in-mold electronics (IME) can include various circuit components, such as sensors, amplifiers, and filters. These components can be injected into the mold to form a whole within the melted plastic and can therefore be very easily integrated into the molded part. The brain-computer interface (BCI) is a direct connection pathway between a human or animal brain and an external device. Through BCIs, individuals can use their own brain signals to control these components, enabling more natural and intuitive interactions. In addition, brain-computer interfaces can also be used to assist in medical treatments, such as controlling prosthetic limbs or helping paralyzed patients regain mobility. Brain-computer interfaces can be realized in two ways: invasively and noninvasively, and in this paper, we adopt a noninvasive approach. First, a helmet model is designed according to head shape, and second, a printed circuit film is made to receive EEG signals and an IME injection mold for the helmet plastic parts. In the electronic film, conductive ink is printed to connect each component. However, improper parameterization during the injection molding process can lead to node displacements and residual stress changes in the molded part, which can damage the circuits in the electronic film and affect its performance. Therefore, in this paper, the use of the BCI molding process to ensure that the node displacement reaches the optimal value is studied. Second, the multistrategy differential evolutionary algorithm is used to optimize the injection molding parameters in the process of brain-computer interface formation. The relationship between the injection molding parameters and the actual target value is investigated through Latin hypercubic sampling, and the optimized parameters are compared with the target parameters to obtain the optimal parameter combination. Under the optimal parameters, the node displacement can be optimized from 0.585 to 0.027 mm, and the optimization rate can reach 95.38%. Ultimately, by detecting whether the voltage difference between the output inputs is within the permissible range, the reliability of the brain-computer interface after node displacement optimization can be evaluated.

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BACKGROUND: Sepsis survivors experience high morbidity and mortality. Though recommended best practices have been established to address the transition and early post hospital needs and promote recovery for sepsis survivors, few patients receive recommended post-sepsis care. Our team developed the Sepsis Transition and Recovery (STAR) program, a multicomponent transition intervention that leverages virtually-connected nurses to coordinate the application of evidence-based recommendations for post-sepsis care with additional clinical support from hospitalist and primary care physicians. In this paper, we present findings from a qualitative pre-implementation study, guided by the Consolidated Framework for Implementation Research (CFIR), of factors to inform successful STAR implementation at a large learning health system prior to effectiveness testing as part of a Type I Hybrid trial. METHODS: We conducted semi-structured qualitative interviews (n = 16) with 8 administrative leaders and 8 clinicians. Interviews were transcribed and analyzed in ATLAS.ti using a combination deductive/inductive strategy based on CFIR domains and constructs and the Constant Comparison Method. RESULTS: Six facilitators and five implementation barriers were identified spanning all five CFIR domains (Intervention Characteristics, Outer Setting, Inner Setting, Characteristics of Individuals and Process). Facilitators of STAR included alignment with health system goals, fostering stakeholder engagement, sharing STAR outcomes data, good communication between STAR navigators and patient care teams/PCPs, clinician promotion of STAR with patients, and good rapport and effective communication between STAR navigators and patients, caregivers, and family members. Barriers of STAR included competing demands for staff time and resources, insufficient communication and education of STAR's value and effectiveness, underlying informational and technology gaps among patients, lack of patient access to community resources, and patient distrust of the program and/or health care. CONCLUSIONS: CFIR proved to be a robust framework for examining facilitators and barriers for pre-implementation planning of post-sepsis care programs within diverse hospital and community settings in a large LHS. Conducting a structured pre-implementation evaluation helps researchers design with implementation in mind prior to effectiveness studies and should be considered a key component of Type I hybrid trials when feasible. TRIAL REGISTRATION: Clinicaltrials.gov, NCT04495946 . Registered August 3, 2020.

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Malaria is an infectious and communicable disease, caused by one or more species of Plasmodium parasites. There are five species of parasites responsible for malaria in humans, of which two, Plasmodium Falciparum and Plasmodium Vivax, are the most dangerous. In Djibouti, the two species of Plasmodium are present in different proportions in the infected population: 77% of P. Falciparum and 33% of P. Vivax. In this study we present a new mathematical model describing the temporal dynamics of Plasmodium Falciparum and Plasmodium Vivax co-infection. We focus briefly on the well posedness of this model and on the calculation of the basic reproductive numbers for the infections with each Plasmodium species that help us understand the long-term dynamics of this model (i.e., existence and stability of various eqiuilibria). Then we use computational approaches to: (a) identify model parameters using real data on malaria infections in Djibouti; (b) illustrate the influence of different estimated parameters on the basic reproduction numbers; (c) perform global sensitivity and uncertainty analysis for the impact of various model parameters on the transient dynamics of infectious mosquitoes and infected humans, for infections with each of the Plasmodium species. The originality of this research stems from employing the FAST method and the LHS method to identify the key factors influencing the progression of the disease within the population of Djibouti. In addition, sensitivity analysis identified the most influential parameter for Falciparium and Vivax reproduction rates. Finally, the uncertainty analysis enabled us to understand the variability of certain parameters on the infected compartments.

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This paper pioneers a novel approach in electromagnetic (EM) system analysis by synergistically combining Bayesian Neural Networks (BNNs) informed by Latin Hypercube Sampling (LHS) with advanced thermal-mechanical surrogate modeling within COMSOL simulations for high-frequency low-pass filter modeling. Our methodology transcends traditional EM characterization by integrating physical dimension variability, thermal effects, mechanical deformation, and real-world operational conditions, thereby achieving a significant leap in predictive modeling fidelity. Through rigorous evaluation using Mean Squared Error (MSE), Maximum Learning Error (MLE), and Maximum Test Error (MTE) metrics, as well as comprehensive validation on unseen data, the model's robustness and generalization capability is demonstrated. This research challenges conventional methods, offering a nuanced understanding of multiphysical phenomena to enhance reliability and resilience in electronic component design and optimization. The integration of thermal variables alongside dimensional parameters marks a novel paradigm in filter performance analysis, significantly improving simulation accuracy. Our findings not only contribute to the body of knowledge in EM diagnostics and complex-environment analysis but also pave the way for future investigations into the fusion of machine learning with computational physics, promising transformative impacts across various applications, from telecommunications to medical devices.

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BACKGROUND: The aggressive nature of Fournier gangrene and the associated health issues can result in a more complex clinical course and potentially a longer hospital stay. This study aimed to assess factors that affect the length of hospital stay (LHS) and its relation to the outcome of Fournier gangrene patients. METHODS: A retrospective study was performed at King Abdulaziz University Hospital (KAUH), Saudi Arabia, on patients diagnosed with Fournier gangrene between 2017 and 2023. Data about length of hospital stay (LHS), age, BMI, clinical and surgical data and outcome was obtained. RESULTS: The mean age of the studied patients was 59.23 ± 11.19 years, the mean body mass index (BMI) was 26.69 ± 7.99 kg/m2, and the mean duration of symptoms was 10.27 ± 9.16 days. The most common presenting symptoms were swelling or induration (64%), 88% had comorbidities with diabetes mellitus (DM) (84%), and 76% had uncontrolled DM. of patients, 24% had a poly-microbial infection, with E. coli being the most common (52%). The mean length of hospital stay (LHS) was 54.56 ± 54.57 days, and 24% of patients had an LHS of more than 50 days. Longer LHS (> 50 days) was associated with patients who did not receive a compatible initial antibiotic, whereas shorter LHS was associated with patients who received Impenem or a combination of vancomycin and meropenem as alternative antibiotics following incompatibility. Reconstruction patients had significantly longer LHS and a higher mean temperature. However, none of the studied variables were found to be predictors of long LHS in the multivariate regression analysis. CONCLUSION: Knowledge of the values that predict LHS allows for patient-centered treatment and may be useful in predicting more radical treatments or the need for additional treatment in high-risk patients. Future multicenter prospective studies with larger sample sizes are needed to assess the needed variables and predictors of long LHS.

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We consider a quasi-one-dimensional Poisson-Nernst-Planck model with two cations having the same valances and one anion. Bikerman's local hard-sphere potential is included to account for ion size effects. Under some further restrictions on the boundary conditions of the two cations, we obtain approximations of the I-V (current-voltage) relations by treating the ion sizes as small parameters. Critical potentials are identified, which play critical roles in characterizing finite ion size effects on ionic flows. Nonlinear interplays between system parameters, such as boundary concentrations and diffusion coefficients, are analyzed. To provide more intuitive illustrations of our analytical results and better understanding of the dynamics of ionic flows through membrane channels, numerical simulations are performed.

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Despite the known benefits of supportive work environments for promoting patient quality and safety and healthcare worker retention, there is no clear mandate for improving work environments within Learning Health Systems (LHS) nor an LHS wellness competency. Striking rises in burnout levels among healthcare workers provide urgency for this topic. Methods: We brought three experts on moral injury, burnout prevention, and ethics to a recurring, interactive LHS training program "Design Shop" session, harnessing scholars' ideas prior to the meeting. Generally following SQUIRE 2.0 guidelines, we evaluated the prework and discussion via informal content analysis to develop a set of pathways for developing moral injury and burnout prevention programs. Along these lines, we developed a new competency for moral injury and burnout prevention within LHS training programs. Results: In preparation for the session, scholars differentiated moral injury from burnout, highlighted the profound impact of COVID-19 on moral injury, and proposed testable interventions to reduce injury. Scholar and expert input was then merged into developing the new competency in moral injury and burnout prevention. In particular, the competency focuses on preparing scholars to (1) demonstrate knowledge of moral injury and burnout, (2) measure burnout, moral injury, and their remediable predictors, (3) use methods for improving burnout, (4) structure training programs with supportive work environments, and (5) embed burnout and moral injury prevention into LHS structures. Conclusions: Burnout and moral injury prevention have been largely omitted in LHS training. A competency related to burnout and moral injury reduction can potentially bring sustainable work lives for scholars and their colleagues, better incorporation of their science into clinical practice, and better outcomes for patients.

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Informed consent is a cornerstone of ethical human research. However, as cluster randomized trials (CRTs) are increasingly popular to evaluate health service interventions, especially as health systems aspire toward the learning health system, questions abound how research teams and research ethics boards (REBs) should navigate intertwining consent and data-use considerations. Methodological and ethical questions include who constitute the participants, whose and what types of consent are necessary, and how data from people who have not consented to participation should be managed to optimize the balance of trust in the research enterprise, respect for persons, the promotion of data integrity, and the pursuit of the public good in the research arena. In this paper, we report the findings and lessons learned from a qualitative study examining how researchers and REB members consider the ethical dimensions of when data can be collected and used in CRTs in the evolving research landscape.

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Bayesian optimization (BO)-assisted screening was applied to identify improved reaction conditions toward a hundred-gram scale-up synthesis of 2,3,7,8-tetrathiaspiro[4.4]nonane (1), a key synthetic intermediate of 2,2-bis(mercaptomethyl)propane-1,3-dithiol [tetramercaptan pentaerythritol]. Starting from the initial training set (ITS) consisting of six trials sampled by random screening for BO, suitable parameters were predicted (78% conversion yield of spiro-dithiolane 1) within seven experiments. Moreover, BO-assisted screening with the ITS selected by Latin hypercube sampling (LHS) further improved the yield of 1 to 89% within the eight trials. The established conditions were confirmed to be satisfactory for a hundred grams scale-up synthesis of 1.

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Importance: The Latino Medical Student Association (LMSA) is a student-run national organization founded in 1972 dedicated to recruiting and retaining members enrolled in health professions programs through academic and social support activities. This study investigates the career impact of member participation in LMSA. Objective: To determine if engagement in LMSA at the individual and school levels contributes to retention, success, and commitment to underserved communities. Design: A voluntary online retrospective 18-question survey sent to LMSA-member medical students in the United States and Puerto Rico from the graduating classes of 2016-2021. Setting: Students in medical schools in the United States and Puerto Rico. Main Outcomes and Measures: There were 18 survey questions. A total of 112 anonymous responses were collected from March 2021 to September 2021. The survey queried levels of engagement with the LMSA and agreement on questions related to support, belonging, and career development. Results: There is a positive relationship between level of engagement in the LMSA and social belonging, peer support, career networking, community engagement, and career commitment to serve Latinx communities. These positive outcomes were enhanced for respondents reporting strong support for their respective school-based LMSA chapters. We did not find a significant relationship between participation in the LMSA and research experiences during medical school. Conclusions and Relevance: Participation in the LMSA is associated with positive individual support and career outcomes for members. Supporting the LMSA as a national organization and within school-based chapters can increase support for Latinx trainees and enhance career outcomes.

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The in-situ resource utilisation (ISRU), in terms of native rocky materials and astronaut wastes, is crucial in contests of soil-based space-farming. Nevertheless, extra-terrestrial soils are very different from Earth soils, lacking any form of organic carbon and associated macro and micronutrients. In this research, we aimed to study and modify two commercially available Lunar and Martian regolith simulants (LHS-1 from Exolith Lab and MMS-1 from Martian Garden) to make them an adequate medium for plant growth. Lettuce was chosen as reference crop to guide the discussion on the results obtained. To reach this main objective, we added to simulants a commercially available monogastric-based organic manure chosen as a substitute of a possible organic amendment produced onboard. The simulant/manure mixture rates were 100:0, 90:10, 70:30, 50:50; w:w. As expected, an approximately linear increase of total and bioavailable contents of macro (N, S, P, Ca, K, Mg) and micro (Fe, Mn, Cu, Zn) nutrients with increasing manure addition to simulants was observed. On the other hand, the very high pH of manure (pH, 9.02) along with its salinity (EC, 6.7 dS m-1) and sodicity (Na, 5.3 g kg-1), did not correct the already high pH of simulants (very high for LHS-1), but rather raised their soluble salt content and sodium amount on the exchange complex. In addition, an increase of toxic soluble aluminium and heavy elements (Pb, Ni, Cr, V) was observed, mainly in the strongly alkaline lunar simulant/manure mixtures. The addition of an organic source also produced a generalised improvement of water retention and hydraulic conductivity of both regolith simulants, in proportion to the percentage of manure addiction. For both situations, the best mixture ratio was 70:30. In terms of water retained, the LHS-1 mixtures benefited more than the MMS-1 ones by manure addition since water was held more in the "dry" (between -100 and -600 cm of matric potential head) than in the "humid" (between -25 and -100 cm of matric potential head) region of water retention. This would make LHS-1 mixtures more useful for cultivation of lettuce, at least in terms of physico-hydraulic properties. Nevertheless, the overall characterisation of the mixtures unveiled that MMS-1-based substrates can ensure better agronomic performances than LHS-1 ones, mainly due to lower pHs and higher nutrient availability; this divergent fertility was particularly evident at 90:10 simulant/manure rate and tend to be mitigated by increasing the levels of manure.

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Context: The COVID-19 pandemic has placed a tremendous stress on healthcare systems and caused reorganization. As the pandemic intensifies, identifying the profile of patients with COVID-19 was primordial in order to predict negative outcomes and organize healthcare resources. Age is associated with COVID-19's mortality, but for obvious ethical reasons, chronological age cannot be the sole criterion for predicting negative outcomes. Objective: The objective of this study was to determine the relationship between frailty index (FI) and length of hospital stay, and death in a non-COVID population of patients aged 75 years old and above. Methods and design: A retrospective, analytical, single-centered observational study was performed in the geriatric short-stay accommodation unit at Guadeloupe University Hospital. For this study, 158 patients who were at least 75 years old were recruited from November 2020 to May 2021. FI was calculated as the number of deficits in a participant divided by the total number of deficits considered (the cut-off of FI is.25 in outpatient). Multivariate logistics regression analyses were conducted to assess the association between frailty and death, and length of stay. Results: The average age of the participants was 85.7 ± 6.74 with a range of 75-104. Twenty-four of the patients died during hospitalization. FI was only significantly associated with mortality even after adjustment for age and gender (HR 26.3, 95% CI 1.7-413.4, P = 0.021). The association was stronger in the highest tertile of the FI (age- and gender-adjusted HR 4.6, 95% CI 1.39-15.11, P = 0.01). There was no significant interaction between FI and length of stay. Conclusion: Our study shows an association between FI (in terms of age-related deficit accumulation) and mortality in a non-COVID geriatric short-stay unit in Guadeloupe. The FI seems to have a lower capacity to catch events such as length of stay in this very complex population. Further research studies have to be conducted for better understanding and investigation of our findings.

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The aim of this study was to realize the intercalation of the pyrethroid pesticides beta-cypermethrin (BCT) and lambda-cyhalothrin (LCT) into ZnAl-layered double hydroxides (LDH) and NiZn-layered hydroxide salt (LHS). BCT (LCT)/SBECD-LDH and BCT (LCT)/SBECD-LHS hybrids were obtained with the aid of sulfobutyl ether ß-cyclodextrin (SBECD) through one step method. The hybrids were characterized by powder X-ray diffraction (XRD), Fourier transform infrared spectroscopy (FT-IR), and thermogravimetry and differential thermal analysis (TGA/DTA). The hybrids based on LHS had larger basal spacing than those on LDH. The LDH-hybrids prepared in N-methylpyrrolidone (NMP) had larger basal spacing than those in ethanol. These results were discussed in terms of the matrix structure and solvent properties. The supramolecular structure of the hybrid was reasonably proposed. Furthermore, the release properties of BCT (LCT) from the hybrids were investigated and discussed in two media. The release rate in pH = 5.0 was slower than that in pH = 6.8. The accumulated release amount of pesticide in pH = 5.0 was lower than that in pH = 6.8. LHS-hybrids synthesized in ethanol exhibit a sustainable release property. These depend on the inclusion complexes' arrangement and release medium. The release kinetic processes could be described by pseudo-second order and parabolic diffusion models. The release behavior can be controlled by adjusting the synthesis conditions and the releasing media. This provides the guidance for the application of SBECD and LDH (LHS) in pesticide formulation.

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With the construction of the smart grid, the distribution network with high penetration of the photovoltaic (PV) generator relies more and more on cyber systems to achieve active control; thus, the uncertainty of PV power and the line-switch state will inevitably affect the distribution network. To avoid the situation, a min-max multi-objective two-level planning model is proposed. Firstly, the uncertainty of PV power is considered, and a multi-time PV power model is established. Followed by the analysis of the line-switch state uncertainty in the distribution network, and according to Claude Shannon's information theory, the line-switch state uncertainty model is established under multiple scenarios. After the distribution network reconfiguration, the Latin hypercube sampling (LHS) method is used to determine the line-switch state when the uncertainty budget is different. Finally, considering the worstcase by the uncertainty of PV power and line-switch status, the control model is proposed to improve the stability of the distribution network with the minimal maintenance cost. The model feasibility is verified by the test system and the characteristics of PV power uncertainty, the line-switch state uncertainty is analyzed, and the influence of the scheduling strategy is discussed, thus providing practical technical support for the distribution network.

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COVID-19 had been declared a public health emergency by the World Health Organization in the early 2020. Since then, this deadly virus has claimed millions of lives worldwide. Amidst its chaotic spread, several other diseases have faced negligence in terms of treatment and care, of which one such chronic disease is Tuberculosis. Due to huge rise in COVID-19 cases, there had been a drastic decrease in notification of TB cases which resulted in reversal of global TB target progress. Apart from these due to the earlier co-infections of TB with SARS and MERS-CoV viruses, the TB-COVID-19 co-infection posed a severe threat in the spread of the disease. All these factors backed to be major motivation factor in development of this model. Leading with this concern, a TB - COVID-19 co-infection model is developed in this study, considering possibility of waning immunity of both diseases. Considering different epidemiological traits, an epidemiological model with 11 compartments is developed and the co-dynamics is analysed. A detailed stability and bifurcation analysis is performed for the TB only sub-model, COVID-19 only sub-model and the complete TB - COVID-19 model. Impact of key parameters namely, infection rate, waning immunity, and face mask efficacy on disease prevalence is discussed in detail. Sensitivity analysis by means of normalized forward sensitivity index of the basic reproduction number and LHS-PRCC approach is carried to provide a thorough understanding of significance of various parameters in accelerating as well as controlling the disease spread. Optimal control analysis is presented extensively, incorporating controls related to timely and improved TB treatment, and enhanced COVID-19 tests and isolation facilities to curb the spread of these infectious diseases. The simulation results obtained from each of these analyses stress on the importance of different control measures in mitigation of the diseases and are illustrated accordingly. The study suggests that in the times of a pandemic, other disease treatment and care must not be neglected, and adequate care must be taken so that mortality due to co-infection and unavailability of timely treatment can be avoided.

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This invited commentary provides a conceptual history of modern early intervention services, briefly reviews the accomplishments of an international clinical and research community, and offers proposals for how such services might participate in the next generation of progress. In keeping with the theme of this column, we make the argument that such services should orient around bi-directional knowledge translation across basic, clinical and policy domains.

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This study was designed to investigate the transmission dynamics of the novel severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) to inform policy advisory vital for managing the spread of the virus in Nigeria. We applied the Susceptible-Exposed-Infectious-Recovered (SEIR)-type predictive model to discern the transmission dynamics of SARS-CoV-2 at different stages of the pandemic; incidence, during and after the lockdown from 27th March 2020 to 22nd September 2020 in Nigeria. Our model was calibrated with the COVID-19 data (obtained from the Nigeria Centre for Disease Control) using the "lsqcurvefit" package in MATLAB to fit the "cumulative active cases" and "cumulative death" data. We adopted the Latin hypercube sampling with a partial rank correlation coefficient index to determine the measure of uncertainty in our parameter estimation at a 99% confidence interval (CI). At the incidence of SARS-CoV-2 in Nigeria, the basic reproduction number (R0 ) was 6.860; 99%CI [6.003, 7.882]. R0 decreased by half (3.566; 99%CI [3.503, 3.613]) during the lockdown, and R0 was 1.238; 99%CI [1.215, 1.262] after easing the lockdown. If all parameters are maintained (as in after easing the lockdown), our model forecasted a gradual and perpetual surge through the next 12 months or more. In the light of our results and available data, evidence of human-to-human transmission at higher rates is still very likely. A timely, proactive, and well-articulated effort should help mitigate the transmission of SARS-CoV-2 in Nigeria.

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This paper builds an infinity shaped ("∞"-shaped) laser scanning welding test platform based on a self-developed motion controller and galvanometer scanner control gateway, takes the autogenous bead-on-plate welding of 304SS with 3 mm thick specimens as the experimental objects, designs the experimental parameters by the Latin hypercube sampling method for obtaining different penetration depth welded joints, and presents a methodology based on the neuroevolution of augmenting topologies for predicting the penetration depth of "∞"-shaped laser scanning welding. Laser power, welding speed, scanning frequency, and scanning amplitude are set as the input parameters of the model, and welding depth (WD) as the output parameter of the model. The model can accurately reflect the nonlinear relationship between the main welding parameters and WD by validation. Moreover, the normalized root mean square error (NRMSE) of the welding depth is about 6.2%. On the whole, the proposed methodology and model can be employed for guiding the actual work in the main process parameters' preliminary selection and lay the foundation for the study of penetration morphology control of "∞"-shaped laser scanning welding.

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The proverbial phrase 'you can't get blood from a stone' is used to describe a task that is practically impossible regardless of how much force or effort is exerted. This phrase is well-suited to humanity's first crewed mission to Mars, which will likely be the most difficult and technologically challenging human endeavor ever undertaken. The high cost and significant time delay associated with delivering payloads to the Martian surface means that exploitation of resources in situ - including inorganic rock and dust (regolith), water deposits, and atmospheric gases - will be an important part of any crewed mission to the Red Planet. Yet there is one significant, but chronically overlooked, source of natural resources that will - by definition - also be available on any crewed mission to Mars: the crew themselves. In this work, we explore the use of human serum albumin (HSA) - a common protein obtained from blood plasma - as a binder for simulated Lunar and Martian regolith to produce so-called 'extraterrestrial regolith biocomposites (ERBs).' In essence, HSA produced by astronauts in vivo could be extracted on a semi-continuous basis and combined with Lunar or Martian regolith to 'get stone from blood', to rephrase the proverb. Employing a simple fabrication strategy, HSA-based ERBs were produced and displayed compressive strengths as high as 25.0 MPa. For comparison, standard concrete typically has a compressive strength ranging between 20 and 32 MPa. The incorporation of urea - which could be extracted from the urine, sweat, or tears of astronauts - could further increase the compressive strength by over 300% in some instances, with the best-performing formulation having an average compressive strength of 39.7 MPa. Furthermore, we demonstrate that HSA-ERBs have the potential to be 3D-printed, opening up an interesting potential avenue for extraterrestrial construction using human-derived feedstocks. The mechanism of adhesion was investigated and attributed to the dehydration-induced reorganization of the protein secondary structure into a densely hydrogen-bonded, supramolecular ß-sheet network - analogous to the cohesion mechanism of spider silk. For comparison, synthetic spider silk and bovine serum albumin (BSA) were also investigated as regolith binders - which could also feasibly be produced on a Martian colony with future advancements in biomanufacturing technology.