Optimal Interval Research Articles

Optimal confidence interval for the difference between proportions

Optimal confidence interval for the difference between proportions

ADVANTAGES AND FEATURES OF USING DIGITAL TWIN TECHNOLOGY FOR STATIONARY GAS ENGINES

At the moment, there is no mass alternative to power plants based on an internal combustion engine. Such power plants are fully mastered in production, they are mobile, not bulky, and do not depend on weather conditions (wind, current, etc.), as required by wind and hydroelectric power plants. The variety and availability of fuels is also a very big advantage in favour of internal combustion engines. On the other hand, engines are a source of heat, noise and chemical pollution, and by running on fossil fuels, they contribute to negative climate change on the planet, so the issue of improving the fuel, economic and environmental efficiency of vehicle engines is relevant. With the rapid development of information technology, it is now possible to create a perfect control system for stationary engines using digital twin technology, which in the future could take over the entire task of controlling the unit with minimal human intervention, eliminating the effect of the human factor and thereby increasing the life and efficiency of the unit. The goal is to use the forecasting system to assess the actual technical condition of the engine and, based on this data, set the optimal maintenance intervals, as well as prevent accidents that entail expensive repairs or even complete replacement of the unit. Such a system would be relevant for large, expensive stationary machines involved in any technological processes where the unit's downtime entails financial losses associated with the cessation of a particular production process. Representatives of this type of power plant are gas engines - drives for booster or downhole gas compressors, gas generators operating on landfill gas or biogas.

406. EXPLORING THE OPTIMAL TIMING OF MINIMALLY INVASIVE SURGERY FOR ESOPHAGEAL SQUAMOUS CELL CARCINOMA AFTER NEOADJUVANT CHEMORADIOTHERAPY

Abstract Background Neoadjuvant chemoradiotherapy has gradually become a first-line treatment for esophageal squamous cell carcinoma patients. After the completion of neoadjuvant chemoradiotherapy, patients usually undergo minimally invasive surgery to ensure complete removal of the tumor and lymphatic tissue. However, the optimal interval between neoadjuvant chemoradiotherapy and minimally invasive surgery is still an unknown factor, and whether it affects postoperative disease-free survival (DFS) and overall survival (OS) of patients remains unknown. Therefore, the main purpose of this study is to explore the specific impact of the interval between neoadjuvant chemoradiotherapy and minimally invasive surgery on patients, and to find the optimal interval time. Methods Patients who underwent neoadjuvant chemoradiotherapy and minimally invasive surgery from two institutions between October 2012 and September 2022 were included in this study. The time interval is defined as the days between the last time a patient receives neoadjuvant chemoradiotherapy and the execution of surgery. Determine the optimal interval truncation point using the surv_cutpoint function in the survminer package. The main research result is the OS and DFS of different interval groups. A multivariate Cox regression model was established to determine the main factors affecting Patient's OS. Construct a nomogram based on selected factors to meet clinical needs. Results 252 patients were included in the study. The average interval between patients undergoing neoadjuvant chemoradiotherapy and minimally invasive surgery is 59 days. The earliest interval for patients to undergo surgery is 28 days. Determine the optimal interval of 46 days based on the survminer package and divide the patients into two groups. The five-year OS rates for the short interval group and the long interval group were 82.6% and 61.0% (P = 0.023). The five-year DFS rates for the two groups were 67.6% and 48.2% (P=0.068). Multivariate Cox regression shows that the time interval, patient's sex, tumor differentiation, ypT, and ypN are the main factors affecting overall survival. Conclusions The interval between neoadjuvant chemoradiotherapy and minimally invasive surgery has a certain impact on patient survival, and it is recommended to undergo surgery between 28 and 46 days to improve patient prognosis and survival.

Multi-metric comparison of machine learning imputation methods with application to breast cancer survival

Handling missing data in clinical prognostic studies is an essential yet challenging task. This study aimed to provide a comprehensive assessment of the effectiveness and reliability of different machine learning (ML) imputation methods across various analytical perspectives. Specifically, it focused on three distinct classes of performance metrics used to evaluate ML imputation methods: post-imputation bias of regression estimates, post-imputation predictive accuracy, and substantive model-free metrics. As an illustration, we applied data from a real-world breast cancer survival study. This comprehensive approach aimed to provide a thorough assessment of the effectiveness and reliability of ML imputation methods across various analytical perspectives. A simulated dataset with 30% Missing At Random (MAR) values was used. A number of single imputation (SI) methods - specifically KNN, missMDA, CART, missForest, missRanger, missCforest - and multiple imputation (MI) methods - specifically miceCART and miceRF - were evaluated. The performance metrics used were Gower’s distance, estimation bias, empirical standard error, coverage rate, length of confidence interval, predictive accuracy, proportion of falsely classified (PFC), normalized root mean squared error (NRMSE), AUC, and C-index scores. The analysis revealed that in terms of Gower’s distance, CART and missForest were the most accurate, while missMDA and CART excelled for binary covariates; missForest and miceCART were superior for continuous covariates. When assessing bias and accuracy in regression estimates, miceCART and miceRF exhibited the least bias. Overall, the various imputation methods demonstrated greater efficiency than complete-case analysis (CCA), with MICE methods providing optimal confidence interval coverage. In terms of predictive accuracy for Cox models, missMDA and missForest had superior AUC and C-index scores. Despite offering better predictive accuracy, the study found that SI methods introduced more bias into the regression coefficients compared to MI methods. This study underlines the importance of selecting appropriate imputation methods based on study goals and data types in time-to-event research. The varying effectiveness of methods across the different performance metrics studied highlights the value of using advanced machine learning algorithms within a multiple imputation framework to enhance research integrity and the robustness of findings.

Benefit evaluation of HVAC and HVDC for offshore wind power transmission system under the multidimensional index

With the development of offshore wind power transmission technology and the emergence of new dimensions in deep-sea wind power scenarios, some indexes in the existing evaluation system cannot meet the comprehensive benefit evaluation of multiple offshore wind power transmission schemes in new operating scenarios, making the identification of the optimal economic distance of different schemes vague. Therefore, this paper integrates multiple parameters into the total life cycle cost and levelized cost of energy indexes for traditional and new transmission schemes in deep-sea scenarios, considers power generation income, environmental and carbon index, and proposes a multidimensional evaluation method for offshore wind power transmission systems, and analyzes the sensitivity of the constructed indicators. Then, using actual geographic information data, the established parameter fusion multidimensional evaluation method is used to analyze the technical and economic ranges of four offshore transmission schemes. The results show that the optimal economic critical interval of active commutation type high voltage direct current transmission system (CSC) is 98 km, which is about 23 % higher than that of voltage source converter based high voltage direct current transmission system (VSC). Therefore, CSC is more suitable for deep offshore wind power transmission than VSC and has better comprehensive technical and economic performance.

Impact of self-expanding metal stents on long-term survival outcomes as a bridge to surgery in patients with colon cancer obstruction: Current state and future prospects.

Since self-expanding metal stents (SEMS) were first introduced in acute colon cancer obstruction, the increased rate of primary anastomosis and improved quality of life following SEMS placement have been clearly shown. However, it was demonstrated that SEMS are associated with higher recurrence rates. Although several trials have shown that overall and disease-free survival in patients following SEMS placement is similar with patients undergoing emergency surgery, obstruction and a high incidence of recurrence imposed many concerns. The optimal time interval from SEMS to surgery is still a matter of debate. Some studies have recommended a time interval of ~2 weeks between SEMS insertion and elective surgery. A prolonged interval of time from SEMS insertion to elective surgery and the administration of neoadjuvant chemotherapy (NAC) has been proposed. SEMS-NAC might have advantages for improving the surgical and long-term survival outcomes of patients with acute colon cancer obstruction, which is an optional approach in the management of acute colon cancer obstruction.

Optimizing Defibrillator Deployment with Bus-Mounted Automated External Defibrillator

Objectives Early defibrillation with an automated external defibrillator (AED) can effectively improve the survival rate of patients with out-of-hospital cardiac arrest (OHCA). Placing AEDs in public locations can reduce the defibrillation response interval from collapse to defibrillation. Most public AEDs are currently placed in a stationary way (S-AED) with limited coverage area. Bus mounted AED (B-AED) can be delivered directly to the demand point. Although B-AEDs are only available during bus operating hours, they provide greater coverage area. When the number of available AEDs is insufficient, better coverage may be achieved by placing a portion of AEDs as B-AEDs. Our purpose is developing a model to determine the optimal locations of B-AEDs and S-AEDs with a predetermined number of available AEDs. The goal is to maximize the total coverage level of all demand points. Methods We proposed a joint location model to place B-AEDs and S-AEDs based on the p-median problem (JPMP). Using data from Chang’an District, Xi’an City, China, we determined the optimal AED deployment. The performance of JPMP was compared with several other models. The coverage results of JPMP are analyzed in details, including the quantity assignment, coverage level, and geographical location of B-AEDs and S-AEDs. The impact of the bus departure intervals on coverage was also discussed. Results The use of B-AEDs results in an average 98.43% increase in the number of covered demand points, and an average 74.05% increase in total coverage level. In optimal AED deployment, B-AEDs coverage follows an inverted U-shaped curve with increasing number of available AEDs. It begins to decrease when all demand points during the operating hours are covered. With a constant number of available AEDs, the total coverage level increases and then decreases as the bus departure interval increases. The larger the number of available AEDs, the smaller the optimal departure interval. Conclusions With a given number of available AEDs, combinational deployment of B-AEDs and S-AEDs significantly improves the coverage level. B-AEDs are recommended when AEDs are insufficient. If more AEDs are available, better coverage can be obtained with reasonable location of S-AEDs and B-AEDs.

Paradigm Shift in Sports Science: The IOT-Enhanced Agility-Pad for Athletic Performance Evaluation

This paper explores the development and implementation of an agility monitoring system, aiming to provide real-time assessment of an athlete's agility. The need for such technology to facilitate instant and remote agility assessment has become increasingly apparent in the field of sports science. Unfortunately, progress in this area has been relatively slow until the emergence of IoT (Internet of Things) technology, which presents a promising avenue for athlete agility monitoring through telemetering. The core of this innovative system consists of an agility pad and a real-time monitoring module, with communication facilitated by LabVIEW software. Our approach involves rigorous testing, including unit testing of the agility pad, which serves as an MQTT publisher, the server, which functions as an MQTT broker, and the monitoring module, acting as an MQTT subscriber. In addition to these technical aspects, this article showcases various outcomes of our research. These include the designs of two activity diagrams, a comprehensive conceptual framework for agility monitoring, the database structure, communication designs for the agility pad, a wiring schematic, a 3D model of the agility pad, and the corresponding wiring configuration. We have successfully constructed two sets of agility pads, each integrated with an MCU system (ESP8266) and a dedicated database architecture designed for agile monitoring. One significant achievement of our work is the identification of the optimal delay interval required on the agility pad to prevent data loss on the MQTT broker. Furthermore, we have effectively developed a mechanism for detecting and addressing abnormal data during each testing phase, which contributes to the robustness and reliability of the system. This paper not only highlights the innovative technology behind the IOT-Infused Agility-Pad but also underscores its potential to revolutionize the assessment of athletic performance, offering coaches and athletes real-time insights into agility progress.

CML-399 CARDINAL: A Phase 1, Multicenter, Open-Label, Dose-Escalation and Dose-Optimization Study to Evaluate the Safety, Tolerability, Pharmacokinetics, and Efficacy of TERN-701 in Chronic Myeloid Leukemia

ObjectiveDespite improvements in outcomes achieved with BCR::ABL1 active-site-targeting tyrosine kinase inhibitors (TKIs) for treatment of chronic-phase (CP) chronic myeloid leukemia (CML), many patients need to switch treatments due to disease progression, intolerance, or resistance. Because active-site TKIs utilize similar mechanisms of action, novel treatment options are needed for patients. TERN-701 is a highly selective, oral, investigational, allosteric BCR::ABL1 inhibitor specifically targeting the ABL myristoyl pocket. Preclinical data suggest that TERN-701 is highly potent against native BCR::ABL1 and most common BCR::ABL1 mutations, including T315I, acquired with use of second-generation active-site TKIs. CARDINAL (NCT06163430) is an open-label, global, 2-part, phase 1 study evaluating the safety, tolerability, pharmacokinetics (PK), and efficacy of TERN-701 in patients with previously treated CP-CML. MethodsEligible patients are ≥18 years old with an Eastern Cooperative Oncology Group performance status of 0–2, a confirmed diagnosis of BCR::ABL1-positive CP-CML, with or without the T315I mutation, and treatment failure/intolerance of ≥1 prior second-generation TKI (dasatinib, nilotinib, or bosutinib). Patients intolerant of asciminib without overt resistance are eligible. Patients with CML in accelerated or blast phase are ineligible. Patients will receive TERN-701 orally once daily in continuous 28-day cycles. Part 1 (dose escalation) has been initiated and will include ≈24–36 patients assigned to sequential dose-escalation cohorts, starting at 160 mg, and optional backfill cohorts; dose escalation will be guided by a Bayesian optimal interval algorithm. Primary endpoints of Part 1 include incidence of dose-limiting toxicities during the first treatment cycle and other measures of safety and tolerability; secondary endpoints include PK and efficacy measures, such as hematologic and molecular responses. Based upon the results of Part 1, ≥2 TERN-701 dose levels will be selected for further evaluation in Part 2. In Part 2 (dose expansion), ≈40 patients will be randomized to one of the selected dose levels. Part 2 primary endpoints will measure efficacy (hematologic response, molecular response, and best categorical shift in BCR::ABL1 transcript levels from baseline); secondary endpoints include safety, tolerability, and PK. The study is being conducted in the US, Europe, Australia, and South Korea.

A Spectral Detection Method Based on Integrated and Partition Modeling for Trace Copper in High-Concentration Zinc Solution.

In zinc smelting solution, because the concentration of zinc is too high, the spectral signals of trace copper are masked by the spectral signals of zinc, and their spectral signals overlap, which makes it difficult to detect the concentration of trace copper. To solve this problem, a spectrophotometric method based on integrated and partition modeling is proposed. Firstly, the derivative spectra based on continuous wavelet transform are used to preprocess the spectral signal and highlight the spectral peak of copper. Then, the interval partition modeling is used to select the optimal characteristic interval of copper according to the root mean square error of prediction, and the wavelength points of the absorbance matrix are selected by correlation-coefficient threshold to improve the sensitivity and linearity of copper ions. Finally, the partial least squares integrated modeling based on the Adaboost algorithm is established by using the selected wavelength to realize the concentration detection of trace copper in the zinc liquid. Comparing the proposed method with existing regression methods, the results showed that this method can not only reduce the complexity of wavelength screening, but can also ensure the stability of detection performance. The predicted root mean square error of copper was 0.0307, the correlation coefficient was 0.9978, and the average relative error of prediction was 3.14%, which effectively realized the detection of trace copper under the background of high-concentration zinc liquid.

Model predictive control based optimal operation of smart city

Rapid urbanization has resulted in a significant proportion of the world’s population residing in urban areas. Cities are undergoing a transition towards the designation of smart cities, which aims to facilitate the efficient management of electrical and heating energy systems derived from renewable energy sources (RES). This paper presents a smart city model that incorporates RES, battery, grid and a combined cooling heat and power (CCHP) system. The smart city model deals with cooling, heating, and electric energy and applies a simple model predictive control (MPC) based approach for optimal operation. MPC method can be operated for future fluctuations by optimizing at the control horizon (NC) while including the prediction horizon (NP) in the optimization interval. The NP and NC parameters have a significant impact on the results, and their selection is an important consideration. This study uses MATLAB simulation to validate the effectiveness of MPC-based operations with a simplified forecasting model in a smart city utilizing RES and CCHP systems. The proposed method is easy to implement and shows sufficient performance while avoiding model complexity. In addition, the impact of NC and NP parameters on performance is investigated. The results show that the proposed method performs better when NP is about 12 h.

Bioorthogonal Diels-Alder Click Chemistry-Based Pretargeted PET Imaging Strategy for Monitoring Programmed Death-Ligand 1 Expression.

The development of antibody tracers for positron emission tomography (PET) imaging enables real-time monitoring of tumor expression of programmed cell death ligand 1 (PD-L1) in vivo, aiming to facilitate the selection of immunotherapy responders. However, the slow pharmacokinetics of the antibodies in vivo limits their applications in PET imaging with commonly used radionuclides with short half-lives. In this study, we developed a pretargeted PET imaging strategy based on Diels-Alder (IEDDA) click chemistry to overcome these limitations. Atezolizumab and durvalumab, the most commonly used PD-L1 antibodies in cancer immunotherapy, were selected and compared in the development of the pretargeted PET imaging strategy. Fluorine-18-labeled derivatives of methyl tetrazine ([18F]Tz, [18F]PEG6-Tz, and [18F]PEG12-Tz) were tested in biodistribution and PET imaging of A549-PDL1 xenografts (PD-L1 positive) pretargeted with the trans-cyclooctene (TCO)-functionalized atezolizumab/durvalumab. The biodistribution and imaging results indicated that atezolizumab-TCO/[18F]PEG12-Tz was more suitable for pretargeted PET imaging strategy, and the optimal interval time was 48 h after atezolizumab-TCO administration, where the atezolizumab-TCO/[18F]PEG12-Tz pretargeted approach clearly delineated the A549-PDL1 tumor with a tumor-to-muscle ratio of 5.33, while the ratios are 3.39 and 2.39 for durvalumab/[18F]PEG12-Tz and mock-pretargeting controls, respectively. In conclusion, a pretargeted 18F-immuno-PET imaging technology was successfully established on atezolizumab. The high-contrast PET images of the A549-PDL1 tumor models demonstrate that the pretargeting strategy incorporating short-lived fluorine-18 is viable in identifying tumors with high PD-L1 expression, marking this strategy as a potential candidate for further clinical translation.

Intelligent algorithm selection for efficient update predictions in social media feeds

Efficiently synchronizing data with social media feeds while minimizing unnecessary requests presents a significant challenge in various fields. This paper investigates prediction algorithms for determining the optimal update intervals for Facebook and Twitter (now X) feeds, focusing on metrics such as delay (the time between a post’s publication and its retrieval) and requests per post. Variations in update intervals result in different algorithms producing varying results, making the selection of the most suitable algorithm for each feed crucial yet time-intensive. To address this, we propose three strategies for algorithm selection: baseline (applying a single algorithm to all feeds), optimum (identifying the best algorithm for each individual feed), and classification (selecting algorithms through a classification process based on each feed’s unique update patterns and context). Our strategies leverage various prediction algorithms, including static and adaptive algorithms, and inhomogeneous Poisson processes. We evaluate these strategies using real-world data from Facebook and Twitter, thoroughly assessing their performance in terms of delay and request efficiency. The findings demonstrate that the strategy Optimum effectively identifies the best algorithms for each feed, ensuring the highest prediction quality, though at a considerable computational cost. On the other hand, the strategy Classification offers superior runtime performance required to select algorithms. This research highlights the trade-offs between delay and request efficiency and presents a comprehensive solution for optimizing update predictions in social media feeds.

Fecal Immunochemical Test to Detect Colorectal Neoplasia in Lynch Syndrome: A Prospective Multicenter Study.

Colonoscopy surveillance for Lynch syndrome is burdensome and postcolonoscopy colorectal cancers (CRCs) still occur. The noninvasive fecal immunochemical test (FIT) might guide optimal colonoscopy intervals. Prospective, multicenter observational study in which individuals with Lynch syndrome performed a quantitative FIT before high-quality surveillance colonoscopy. Diagnostic performance of FIT at various thresholds ≤20 μg Hb/g feces was assessed for relevant neoplasia, including advanced neoplasia (CRC, advanced adenomas [AAs] and advanced serrated lesions [ASLs]) and non-advanced adenomas (NAAs). Of the 217 included individuals (59% female, median age 51 years), 4 had CRC, 5 AA, 4 ASL, and 57 NAA as most relevant neoplasia. The lowest FIT positivity threshold (2.5 μg Hb/g feces, 14% positivity rate) maximized detection: 4/4 CRCs, 4/5 AA, 1/4 ASL, and 9/57 NAA were detected, resulting in a sensitivity and negative predictive value of, respectively, 89% and 99% for CRC plus AA, 69% and 97% for advanced neoplasia, and 26% and 72% for all relevant neoplasia (91% specificity for all groups). At equal sensitivity and negative predictive value, specificity for advanced neoplasia optimized to 94% at threshold 4.1 μg/g. Per 100 FITs at threshold 4.1 μg/g, 11 individuals would test positive and thus proceed to colonoscopy, 2 individuals with advanced neoplasia would be missed and 3 individuals would need colonoscopy to detect 1 advanced neoplasia. FIT at thresholds ≤4.1 μg Hb/g feces may be a promising strategy to postpone colonoscopy in approximately 9 of 10 individuals with Lynch syndrome. Large validation studies that also provide gene variant-specific outcomes should be prioritized.

U-MET: Utility-Based Dose Optimization Approach for Multiple-Dose Randomized Trial Designs

The advent of dose optimization has led to a paradigm shift in oncology clinical trials to find the optimal biologic dose (OBD). Phase 1/2 trials with randomized doses can facilitate additional investigation of the identified OBD in a targeted population by incorporating safety and efficacy data. We propose to compare randomized doses by extending the Bayesian optimal interval phase 1/2 (BOIN12) approach, an OBD-finding design in which a utility is used to account for risk-benefit tradeoffs. Specifically, we used the BOIN12 standardized mean utilities in a hypothesis testing framework, in which frequentist and Bayesian inference were evaluated to compare the standardized mean utilities across doses and identify the OBD. We performed simulation studies and a hypothetical oncology study to compare the proposed BOIN12 utility-based extension (U-MET) design with an empirical design. The results demonstrated that the U-MET design has satisfactory operating characteristics for selecting the OBD. We recommend using the U-MET design as the primary dose comparison approach, or alternatively as supportive evidence, for optimal dose selection.

Fast algorithms of computing admissible intervals for discrete distributions with single parameter

It is of great interest to compute optimal exact confidence intervals for the success probability (p) in a binomial distribution, the number of subjects with a certain attribute (M) or the total number of subjects (N) in a hypergeometric distribution, and the mean λ of a Poisson distribution. In this paper, efficient algorithms are proposed to compute an admissible exact interval for each of the four parameters when the sample size (n) or the random observation X is large. The algorithms are utilized in four practical examples: evaluating the relationship between two diseases, certifying companies, estimating the proportion of drug users, and analyzing earthquake frequency. The intervals computed by the algorithms are shorter, and the calculations are faster, demonstrating the accuracy of the results and the time efficiency of the proposed algorithms.

Calibration and Validation of Simulation Parameters for Maize Straw Based on Discrete Element Method and Genetic Algorithm-Backpropagation.

There is a significant difference between the simulation effect and the actual effect in the design process of maize straw-breaking equipment due to the lack of accurate simulation model parameters in the breaking and processing of maize straw. This article used a combination of physical experiments, virtual simulation, and machine learning to calibrate the simulation parameters of maize straw. A bimodal-distribution discrete element model of maize straw was established based on the intrinsic and contact parameters measured via physical experiments. The significance analysis of the simulation parameters was conducted via the Plackett-Burman experiment. The Poisson ratio, shear modulus, and normal stiffness of the maize straw significantly impacted the peak compression force of the maize straw and steel plate. The steepest-climb test was carried out for the significance parameter, and the relative error between the peak compression force in the simulation test and the peak compression force in the physical test was used as the evaluation index. It was found that the optimal range intervals for the Poisson ratio, shear modulus, and normal stiffness of the maize straw were 0.32-0.36, 1.24 × 108-1.72 × 108 Pa, and 5.9 × 106-6.7 × 106 N/m3, respectively. Using the experimental data of the central composite design as the dataset, a GA-BP neural network prediction model for the peak compression force of maize straw was established, analyzed, and evaluated. The GA-BP prediction model's accuracy was verified via experiments. It was found that the ideal combination of parameters was a Poisson ratio of 0.357, a shear modulus of 1.511 × 108 Pa, and a normal stiffness of 6.285 × 106 N/m3 for the maize straw. The results provide a basis for analyzing the damage mechanism of maize straw during the grinding process.

Patient-centric thresholding of Cobas® EGFR mutation Test v2 for surveillance of EGFR-mutated metastatic non-small cell lung cancer

Cobas EGFR mutation Test v2 was FDA-approved as qualitative liquid biopsy for actionable EGFR variants in non-small cell lung cancer (NSCLC). It generates semiquantitative index (SQI) values that correlate with mutant allele levels, but decision thresholds for clinical use in NSCLC surveillance are lacking. We conducted long-term ctDNA monitoring in 20 subjects with EGFR-mutated NSCLC; resulting in a 155 on-treatment samples. We defined optimal SQI intervals to predict/rule-out progression within 12 weeks from sampling and performed orthogonal calibration versus deep-sequencing and digital PCR. SQI showed significant diagnostic power (AUC 0.848, 95% CI 0.782–0.901). SQI below 5 (63% of samples) had 93% (95% CI 87–96%) NPV, while SQI above 10 (25% of samples) had 69% (95% CI 56–80%) PPV. Cobas EGFR showed perfect agreement with sequencing (Kappa 0.860; 95% CI 0.674–1.00) and digital PCR. SQI values strongly (r: 0.910, 95% 0.821–0.956) correlated to mutant allele concentrations with SQI of 5 and 10 corresponding to 6–9 (0.2–0.3%) and 64–105 (1.1–1.6%) mutant allele copies/mL (VAF) respectively. Our dual-threshold classifier of SQI 0/5/10 yielded informative results in 88% of blood draws with high NPV and good overall clinical utility for patient-centric surveillance of metastatic NSCLC.

Investigation of the Physico-Chemical and Mechanical Properties of Expanded Ceramsite Granules Made on the Basis of Coal Mining Waste

In this article, one of the main scientific directions was the search for ways of recycling coal mining waste to produce expanded clay granules. There are a number of scientific studies devoted to the use of various industrial wastes in the production of thermal insulation and fireproof expanded clay granules. The authors consider the production of granular porous aggregates based on pulverized fractions of igneous rocks—basalt, granite, and synertite, as well as man-made materials of various origins, to be promising. According to the results of the conducted studies, it was found that the optimal interval of the amount of waste in expanded clay was 4.0–6.0%, and the optimal firing temperature was 1150 °C with the production of samples with a bulk density of 0.337–0.348 t/m3 and with a compressive strength of 1.37–1.51 MPa under these conditions.

Optimal Allocation of Multi-Type Vaccines in a Two-Dose Vaccination Campaign for Epidemic Control: A Case Study of COVID-19

As a typical case of the optimal planning for the provision of restricted medical resources, widespread vaccination is considered an effective and sustainable way to prevent and control large-scale novel coronavirus disease 2019 (COVID-19) outbreaks. However, an initial supply shortage of vaccines is inevitable because of the narrow production and logistical capacity. This work focuses on the multi-type vaccine resource allocation problem in a two-dose vaccination campaign under limited supply. To address this issue, we extended an age-stratified susceptible, exposed, infectious, and recovered (SEIR) epidemiological model to incorporate a two-dose vaccination campaign involving multiple vaccine types to fully characterize the various stages of infection and vaccination. Afterward, we integrated the proposed epidemiological model into a nonlinear programming (NLP) model to determine the optimal allocation strategy under supply capacity and vaccine hesitancy constraints with the goal of minimizing the cumulative number of deaths due to the pandemic over the entire planning horizon. A case study based on real-world data from the initial mass vaccination campaign against COVID-19 in the Midlands, England, was taken to validate the applicability of our model. Then, we performed a comparative study to demonstrate the performance of the proposed method and conducted an extensive sensitivity analysis on critical model parameters. Our results indicate that prioritizing the allocation of vaccines to elderly persons is an effective strategy for reducing COVID-19-related fatalities. Furthermore, we found that vaccination alone will not be sufficient for epidemic control in the short term, and appropriate non-pharmacological interventions are still important for effective viral containment during the initial vaccine rollout. The results also showed that the relative efficacy of the first dose is a vital factor affecting the optimal interval between doses. It is always best to complete the two-dose vaccination schedule as soon as possible when the relative efficacy of the first dose is low. Conversely, delaying the second dose of a vaccine as long as possible to increase the proportion of the population vaccinated with a single dose tends to be more favorable when the relative efficacy of the first dose is high. Finally, our proposed model is general and easily extendable to the study of other infectious disease outbreaks and provides important implications for public health authorities seeking to develop effective vaccine allocation strategies for tackling possible future pandemics.