Original Estimates Research Articles

Use of generalized Gaussian cyclostationarity for blind deconvolution and its application to bearing fault diagnosis under non-Gaussian conditions

Blind deconvolution (BD) methods can extract fault signatures from noisy observations. Among all the BD methods, maximum second-order cyclostationarity blind deconvolution (CYCBD) is an effective method for extracting weak periodic impulses related to bearing faults. CYCBD is done by maximizing the second-order cyclostationarity of a signal through the indicator of second-order cyclostationarity (ICS2, which can be calculated using the squared envelope spectrum, SES). The effectiveness of the SES is under the assumption that signals are Gaussian distributed. However, SES, ICS2 and CYCBD can be ineffective when signals are corrupted by highly leptokurtic noise.A recent study separates the detection of non-stationarity from non-Gaussianity by releasing the hypothesis of Gaussianity to generalized Gaussianity. The indicator IGGCS/GGS can detect the cyclostationarity of a signal corrupted by highly impulsive noise. In addition, it can be approximated by the β-power envelope spectrum (PES). Inspired by the relationship between IGGCS/GGS and ICS2, a blind deconvolution method by maximizing the generalized Gaussian cyclostationarity (CYCBDβ) is proposed in this study. Compared with CYCBD, the robustness of CYCBDβ against strong non-Gaussian noise increases. However, the original shape parameter estimation method has two limitations, which introduces difficulties in using IGGCS/GGS as the criterion for BD. (1) The fault period must be known in advance. (2) The fault period should be an integer for the synchronous average. In addition, a serious problem of CYCBD is that the used cyclic frequencies must match fault frequencies. Otherwise, the performance of CYCBD can be severely compromised. To better incorporate the shape parameter estimation into the BD method, the GGCS model using fault frequencies for synchronous average is proposed. In addition, a fault frequencies estimation approach has been added to the generation of the weighting matrix of CYCBDβ. Benefiting from these improvements, the proposed CYCBDβ has two advantages compared with CYCBD: (1) the robustness of CYCBDβ against strong non-Gaussian noise is higher, and (2) CYCBDβ can extract fault features when fault frequencies are unknown. The effectiveness and robustness of the method are validated using simulations and real vibration datasets.

Correcting the 2019 survey abundance of Bering-Chukchi-Beaufort Seas bowhead whales for disturbance from powered skiffs

The 2019 ice-based survey of Bering-Chukchi-Beaufort Seas bowhead whales was challenged by missed survey effort, unusual ice conditions, and frequent use of motor-powered skiffs by hunters. All three of these likely led to downward bias in the abundance estimate of Givens et al. (2021). Data were collected about boat excursions during the survey period. Indices of short-term whale abundance at the survey perch and short-term boat noise disturbance were computed from the available data, where ‘short term’ refers to a few hours. A generalized additive model (GAM) was fit to the results, predicting short-term whale abundance as a smooth function of the boat noise disturbance index, after controlling for long-term variation in the whale passage rate over the course of the season. The fitted GAM was then used to predict passage with and without the presence of boat noise. The ratio of the integrals of these two predicted passage curves provided a correction factor which can be applied post hoc to the abundance estimate of Givens et al. (2021). Variance of this correction factor was estimated using two approaches, and found to be small. A wide array of sensitivity analyses was conducted to examine the robustness of the result to potential changes in methodology, and the correction factor was found to be quite stable. The estimated correction factor would inflate the original abundance estimate by about 12%, yielding a corrected abundance of 14,025 (CV=0.228). We recommend that this replace the original abundance estimate.

Estimating the undetected emergence of COVID-19 in the US.

As SARS-CoV-2 emerged as a global threat in early 2020, China enacted rapid and strict lockdown orders to prevent introductions and suppress transmission. In contrast, the United States federal government did not enact national orders. State and local authorities were left to make rapid decisions based on limited case data and scientific information to protect their communities. To support local decision making in early 2020, we developed a model for estimating the probability of an undetected COVID-19 epidemic (epidemic risk) in each US county based on the epidemiological characteristics of the virus and the number of confirmed and suspected cases. As a retrospective analysis we included county-specific reproduction numbers and found that counties with only a single reported case by March 16, 2020 had a mean epidemic risk of 71% (95% CI: 52-83%), implying COVID-19 was already spreading widely by the first detected case. By that date, 15% of US counties covering 63% of the population had reported at least one case and had epidemic risk greater than 50%. We find that a 10% increase in model estimated epidemic risk for March 16 yields a 0.53 (95% CI: 0.49-0.58) increase in the log odds that the county reported at least two additional cases in the following week. The original epidemic risk estimates made on March 16, 2020 that assumed all counties had an effective reproduction number of 3.0 are highly correlated with our retrospective estimates (r = 0.99; p<0.001) but are less predictive of subsequent case increases (AIC difference of 93.3 and 100% weight in favor of the retrospective risk estimates). Given the low rates of testing and reporting early in the pandemic, taking action upon the detection of just one or a few cases may be prudent.

Internal consistency and structural validity of the nomophobia questionnaire (NMP-Q) and its translations: A systematic review with meta-analysis

BackgroundA psychological condition known as NOMOPHOBIA causes individuals to fear losing ability to use or reach their mobile phones. The NMP-Q (nomophobia questionnaire) is a commonly used survey for assessing symptoms related to nomophobia. Materials and methodsWe performed a meta-analysis using reliability generalization (RG) on the NMP-Q. Thirteen studies involving 15,929 participants have reported original reliability estimates of the NMP-Q determined through a comprehensive and methodical examination of the available literature. ResultsFor the total scores, the pooled internal consistency reliability was 0.93 [0.91; 0.95] and for the subscales it ranged from 0.83 to 0.91. Specifically, 0.91 [0.88; 0.93], 0.84 [0.80; 0.88], 0.83 [0.78; 0.88, and 0.83 [0.80; 0.85] for the subscales. Subscale 1 = not being able to communicate; subscale 2 = losing connectedness; subscale 3 = not being able to access information; and subscale 4 = giving up convenience”, respectively. All reported effect sizes are Cronbach's alphas. Structural validity supported a solution of four-factors. ConclusionsNMP-Q has an excellent internal consistency. Structural validity of four factors appears to be vigorous in fitting NMP-Q items. Our recommendation is to require future studies using NMP-Q to provide a reliability estimate based on their own data.

Cost-effectiveness of second-line ipilimumab for metastatic melanoma: A real-world population-based cohort study of resource utilization.

The efficacy-effectiveness gap between randomized trial and real-world evidence regarding the clinical benefit of ipilimumab for metastatic melanoma (MM) has been well characterized by previous literature, consistent with initial concerns raised by health technology assessment agencies (HTAs). As these differences can significantly impact cost-effectiveness, it is critical to assess the real-world cost-effectiveness of second-line ipilimumab versus non-ipilimumab treatments for MM. This was a population-based retrospective cohort study of patients who received second-line non-ipilimumab therapies between 2008 and 2012 versus ipilimumab treatment between 2012 and 2015 (after public reimbursement) for MM in Ontario. Using a 5-year time horizon, censor-adjusted and discounted (1.5%) costs (from the public payer's perspective in Canadian dollars) and effectiveness were used to calculate incremental cost-effectiveness ratios (ICERs) in life-years gained (LYGs) and quality-adjusted life years (QALYs), with bootstrapping to capture uncertainty. Varying the discount rate and reducing the price of ipilimumab were done as sensitivity analyses. In total, 329 MM were identified (Treated: 189; Controls: 140). Ipilimumab was associated with an incremental effectiveness of 0.59 LYG, incremental cost of $91,233, and ICER of $153,778/LYG. ICERs were not sensitive to discounting rate. Adjusting for quality of life using utility weights resulted in an ICER of $225,885/QALY, confirming the original HTA estimate prior to public reimbursement. Reducing the price of ipilimumab by 100% resulted in an ICER of $111,728/QALY. Despite its clinical benefit, ipilimumab as second-line monotherapy for MM patients is not cost-effective in the real world as projected by HTA under conventional willingness-to-pay thresholds.

INTENSITAS TERPAAN INFORMASI REGULASI PEMERINTAH TENTANG PERUBAHAN DURASI KARANTINA DI INDONESIA PADA MEDIA ELEKTRONIK TERHADAP MINAT BERWISATA KAUM MILLENIAL KE LUAR NEGERI

As long as Indonesia was exposed with Covid-19 pandemic, government regulations regarding quarantine changes had adjusted several times. The frequency in which the information is published or reported in electronic media has an influence on the interest of millennials to visit abroad. This study aims to identify the effect of the intensity of exposure to government regulatory information regarding quarantine changes, especially in electronic media, on millennial travel interest abroad. The method used is a questionnaire distributed to 38 people (millennials) with purposive sampling technique. Data analysis using SEM (Structural Equation Modeling) in which the type used is Smart PLS. The relationship between exposure to government regulatory information on changes in quarantine duration (X) and millennial travel interest abroad (Y) is significant with a T-Statistic of 8,214 (&gt; 1.96). The original sample estimate value is positive, which is 0.653 which indicates that the direction of the relationship between exposure to government regulatory information on changes in quarantine duration (X) millennial travel interest abroad (Y) is positive. In other words, there is a positive impact between exposure to information on changes in quarantine and millennials’ interest in traveling abroad.

Some considerations of measuring temperature sensitivity in thermal ecology

Some considerations of measuring temperature sensitivity in thermal ecology

Design of a High-Order Kalman Filter for State and Measurement of A Class of Nonlinear Systems Based on Kronecker Product Augmented Dimension

A high-order Kalman filter for full-dimensional variables is proposed for a class of dynamic systems whose state model and measurement model are both nonlinear. The filter requires Taylor expansion of the system equations, and then performs Kronecker product operation on the linear part in the Taylor expansion. Finally, a linear dynamic model is achieved based on the full-dimensional vector formed by the state variables and the high-order dimension expansion variables. After designing the filter, the Kalman filter for the original state variables estimation was selected through the projection operator. The excellent performance of the novel filter is analyzed from the aspects of the information utilization of the state estimation value and the size of the state estimation error covariance matrix. The numerical verification is carried out by computer simulation.

Long-term shifts in otolith age interpretations

Multi-decadal time series using existing age data are increasingly being used to test important issues in ecology. Over the long term however, shifts in age determination protocol are almost unavoidable. This study uses an 86-yr time series of Icelandic cod (Gadus morhua) otolith samples (n = 906) to rigorously test the assumption of inter-year comparability of age interpretation across decadal changes in otolith preparation, viewing protocol and age reader. Comparison of the original age estimates with re-ageing of the same otoliths using modern preparation and interpretation protocols and a single age reader revealed a consistent bias, with the original readings over-estimating ages relative to modern. The extent of the bias was minimal at age 8 (0.18 yr), increasing to an average divergence of about 2 yr after age 14. The ageing bias was linked most strongly to the individual ager, whereas the transition from unsectioned (cracked or whole) to sectioned otoliths markedly improved precision. The implications of this study are encouraging, in that they suggest that old data sets incorporating historic age determinations based on specific methods may well be adequate for many purposes.

A self-adaptive local diversity-preserving NSGA-II for the multidisciplinary design optimization of a miniaturized supersonic rocket

The miniaturized supersonic rocket or missile is an innovative concept most notably characterized by its small caliber, short length, and high flight speed. Tightly coupled design parameters and constrained objectives bring high complexity to launch safety assessments. The commonly used heuristic algorithm NSGA-II cannot address this complexity with its original density estimation strategy. In this paper, a multidisciplinary design optimization (MDO) based on a multidisciplinary feasible (MDF) architecture is established and simplified according to specific design requirements. The design system includes one coupled numerical model, two drivers, and six disciplines. As an optimizer for an MDO problem, multi-objective evolutionary algorithms (MOEAs) with heuristic features are very conducive to exploring the performance of complex rocket systems. The coupling relationship between the design variables of a small dual-chamber rocket is complex, and its optimal design is challenging to obtain empirically by traditional methods. Optimizing its propulsion performance and launch safety is imperative. For engineering designs, the convergence process of algorithms is often passive with a high degree of randomness, and the diversity of the final results is difficult to guarantee. This paper improves the Pareto front's diversity in designing a miniaturized supersonic rocket by using a self-adaptive local diversity-preserving NSGA-II (SLDNSGA-II) in an MDO framework with an active control strategy.

THE INFLUENCE OF INDIVIDUAL CHARACTERISTICS AND QUALITY OF WORK LIFE ON EMPLOYEE PERFORMANCE IN THE EMPLOYEE COOPERATIVE OF PASAR REBO HOSPITAL, EAST JAKARTA

This quantitative study aimed to investigate and analyze the impact of individual characteristics (X1) and the quality of work life (X2) on employee performance within the Employee Cooperative at Pasar Rebo Hospital, located in East Jakarta. The research focused on three main aspects: (1) the influence of individual characteristics on employee performance, (2) the influence of the quality of work life on employee performance, and (3) the influence of individual characteristics on the quality of work life within the Employee Cooperative of Pasar Rebo Hospital. The study encompassed a total of 50 employees from the Pasar Rebo East Jakarta Hospital Employee Cooperative, employing a Total Sampling technique due to the limited population size (less than 100 employees). Data collection involved a combination of observation and questionnaire surveys. The findings revealed that there exists a non-significant relationship between individual characteristics (X1) and employee performance (Y), as indicated by a T-statistic of 0.621 (&gt; 1.96). Despite the lack of statistical significance, the original sample estimate demonstrated a positive relationship between individual characteristics (X1) and employee performance (Y), with a value of 0.098. In contrast, the study revealed significant positive relationships between individual characteristics (X1) and the quality of work life (X2) (T-statistic = 4.813, &lt; 1.96) and between the quality of work life (X2) and employee performance (Y) (T-statistic = 6.143, &gt; 1.96). The original sample estimate values for both relationships were positive (0.471 and 0.570, respectively), highlighting the importance of fostering a positive quality of work life to enhance employee performance within the Employee Cooperative at Pasar Rebo Hospital.

Fast approximations of pseudo-observations in the context of right censoring and interval censoring.

In the context of right-censored and interval-censored data, we develop asymptotic formulas to compute pseudo-observations for the survival function and the restricted mean survival time (RMST). These formulas are based on the original estimators and do not involve computation of the jackknife estimators. For right-censored data, Von Mises expansions of the Kaplan-Meier estimator are used to derive the pseudo-observations. For interval-censored data, a general class of parametric models for the survival function is studied. An asymptotic representation of the pseudo-observations is derived involving the Hessian matrix and the score vector. Theoretical results that justify the use of pseudo-observations in regression are also derived. The formula is illustrated on the piecewise-constant-hazard model for the RMST. The proposed approximations are extremely accurate, even for small sample sizes, as illustrated by Monte Carlo simulations and real data. We also study the gain in terms of computation time, as compared to the original jackknife method, which can be substantial for a largedataset.

Sr-Pb isotope differences in pre- and post-burial human bone, teeth, and hair keratin: implications for isotope forensics.

The isotopic signatures of human tissues can provide valuable information on geographic origin for medicolegal investigations involving unidentified persons. It is important to understand the impact of diagenetic processes on isotopic signatures, as alterations could result in incorrect estimation of geographic origin. This study examines alterations in isotope signatures of different tissues of five human body donors studied throughout decomposition at the Forensic Anthropology Research Facility (FARF), San Marcos, TX. Two body donors were buried, two were placed in open pits, and one was first allowed to naturally mummify and then buried. Remains were recovered after a period of 7-34months. The preplacement and post-recovery Sr-Pb isotope data of scalp hair, bone (iliac and tibia), and tooth enamel and dentine were compared. The hair samples record significant shifts in Sr-Pb isotope compositions, with hair keratin Pb isotope composition shifting towards the Pb signature of local soil samples. Hair keratin Sr isotope compositions were altered by the burial environment and possibly also by the lab sample cleaning method. The spongy iliac bone samples show inconsistencies in the recoverability of the preplacement Sr-Pb isotope signatures. The post-placement signatures of the buried donors show slight elevation over preplacement signatures. The post-placement signatures of donors placed in open pits are significantly elevated. The tibia and dental samples record the most consistent isotopic data with the least alteration. These more densely mineralised elements show good recoverability of the preplacement isotope signatures in burials and open pits and are thus deemed better targets for forensic investigative purposes.

The Influence of Training and Motivation on The Performance of Health Services Implementing Employees in Kramat Jati Hospital

This study aims to determine the effect of training and motivation on employee performance at Kramat Jati Hospital. This research method is quantitative with the instrument in the form of a questionnaire. The population in this study were all staff implementing health services at Kramat Jati Hospital as many as 221 employees. The sampling technique was using proportionate stratified random sampling technique or proportional random strata sampling with a total of 69 respondents. The data used in this study are primary data obtained through questionnaires and secondary data obtained from related agencies and journals to support this research. The instrument validity test uses Pearson correlation, while the reliability test uses Cronbach Alpha. The measuring instrument proved to be valid and reliable for the research instrument. Hypothesis testing in this study used the Partial Least Square (PLS) method. PLS is an analysis method with Structural Equation Modeling (SEM) based on variance. The results showed that: Training (X1) had a positive and significant effect on motivation with the original sample estimated value of 0.778 with a significance of 14.080. Second; Training (X1) has a positive but not significant effect on performance with the original sample estimate value of 0.080 with a significance of 0.831. Third; Motivation (X2) has a positive and significant effect on performance with the original sample estimated value of 0.840 with a significance of 11.124. So it can be concluded that in this study, training has a positive but not significant effect on performance and motivation has a positive and significant effect on performance Keywords: Training, Motivation, Employee performance

Occupancy-Based Energy Consumption Estimation Improvement through Deep Learning.

The energy consumed in buildings constitutes more than half of the total electricity consumption and is highly correlated with the number of occupants; therefore, it is necessary to use occupancy information in energy consumption analysis. However, the number of occupants may not be accurate owing to measurement errors caused by various factors, such as the locations of sensors or cameras and the communication environment. In this study, occupancy was measured using an object recognition camera, the number of people was additionally collected by manual aggregation, measurement error in occupancy count was analyzed, and the true count was estimated using a deep learning model. The energy consumption based on occupancy was predicted using the measured and estimated values. To this end, deep learning was used to predict energy consumption after analyzing the correlation between occupancy and energy consumption. Results showed that the performance of occupancy estimation was 1.9 based on RMSE, which is a 71.1% improvement compared to the original occupancy sensing. The RMSE of predicted energy consumption based on the estimated occupancy was 56.0, which is a 5.2% improvement compared to the original energy estimation.

Machine-Learning Approach to Predict Total Fabrication Duration of Industrial Pipe Spools

In industrial modular construction, components fabricated in the shop are assembled into modules before being transported to construction sites. This approach offers improved construction efficiency, higher quality products, and the opportunity to work in a controlled environment that offers more predictable schedules. A distinct category of modularized construction is that of piping structures where prefabricated segments of pipes, called pipe spools, are arranged and attached to a steel structure to speed up on-site assembly. Determining when a particular pipe spool is ready for delivery to the site is affected by many factors, including material availability, workforce performance and availability, shop loading conditions, and shop capacity. These factors combine to produce a high degree of uncertainty when estimating the fabrication duration. This paper investigates the improvement of delivery time prediction of pipe spools that are fabricated in a manufacturing facility. The study’s main contribution is to propose a data-driven machine-learning model that accurately predicts the total fabrication duration in a pipe spool fabrication facility. The developed model is expected to assist production managers to plan for the appropriate workforce and material delivery requirements. The proposed approach utilizes a combination of product composition and real-time loading status of the shop to develop and examine several tree-based machine-learning models. The approach was evaluated using records of 6,989 pipe spools. The overall prediction performance was improved by a reduction of 35% in the mean absolute percentage error compared to original heuristic estimates. The results also show a strong positive correlation of 0.8 between the actual and predicted values.

The Effect of Income Level on the Financial Behavior of Micro, Small and Medium Enterprises in Lubuklinggau City

This research was conducted to see The Effect of Income Level on the Financial Behavior Usaha Mikro Kecil dan Menengah di Kota Lubuklinggau. Sampling, using a purposive side. The number of samples was 98 respondents. The analysis tool used was SEM which was run with SmatPLS3.0 software. The results of this study are related to the theme of Influence of Income Level on the The Effect of Income Level on the Financial Behavior Usaha Mikro Kecil dan Menengah di Kota Lubuklinggau.The number of samples in this study used 98 respondents of Micro, Small and Medium Enterprises (MSMEs) in Lubuklinggau with the willingness of respondents to fill out questionnaires with valid data totaling 89 respondents. After testing, it can be concluded that: This study found that income levels had a positive effect on financial behavior. This was due to the results of testing the hypothesis which shows that the income level with financial behavior shows the path coefficient value of the original sample estimate of 0.451 with a significance below 5% which is indicated by the t-statistic value of 5.625 which is greater than the t-table value of 1.984. the P-Value is 0.000. P-value (0.000) &lt; α = 5 This study found that the level of income has a positive effect on financial behavior. This means that the higher the level of income, the better the financial behavior will be and this will have an impact on better financial management. This study made modifications to the data testing because there were several indicator results that did not meet the requirements so dropping or elimination had to be carried out on each variable indicator that did not meet the discriminant validity requirements. This research was useful for the academic environment and MSME managers in Lubuklinggau

Fractal Kinetic Implementation in Population Pharmacokinetic Modeling.

Compartment modeling is a widely accepted technique in the field of pharmacokinetic analysis. However, conventional compartment modeling is performed under a homogeneity assumption that is not a naturally occurring condition. Since the assumption lacks physiological considerations, the respective modeling approach has been questioned, as novel drugs are increasingly characterized by physiological or physical features. Alternative approaches have focused on fractal kinetics, but evaluations of their application are lacking. Thus, in this study, a simulation was performed to identify desirable fractal-kinetics applications in conventional modeling. Visible changes in the profiles were then investigated. Five cases of finalized population models were collected for implementation. For model diagnosis, the objective function value (OFV), Akaike's information criterion (AIC), and corrected Akaike's information criterion (AICc) were used as performance metrics, and the goodness of fit (GOF), visual predictive check (VPC), and normalized prediction distribution error (NPDE) were used as visual diagnostics. In most cases, model performance was enhanced by the fractal rate, as shown in a simulation study. The necessary parameters of the fractal rate in the model varied and were successfully estimated between 0 and 1. GOF, VPC, and NPDE diagnostics show that models with the fractal rate described the data well and were robust. In the simulation study, the fractal absorption process was, therefore, chosen for testing. In the estimation study, the rate application yielded improved performance and good prediction-observation agreement in early sampling points, and did not cause a large shift in the original estimation results. Thus, the fractal rate yielded explainable parameters by setting only the heterogeneity exponent, which reflects true physiological behavior well. This approach can be expected to provide useful insights in pharmacological decision making.

Inferring the multiplicity of founder variants initiating HIV-1 infection: a systematic review and individual patient data meta-analysis

HIV-1 infections initiated by multiple founder variants are characterised by a higher viral load and a worse clinical prognosis than those initiated with single founder variants, yet little is known about the routes of exposure through which transmission of multiple founder variants is most probable. Here we used individual patient data to calculate the probability of multiple founders stratified by route of HIV exposure and study methodology. We conducted a systematic review and meta-analysis of studies that estimated founder variant multiplicity in HIV-1 infection, searching MEDLINE, Embase, and Global Health databases for papers published between Jan 1, 1990, and Sept 14, 2020. Eligible studies must have reported original estimates of founder variant multiplicity in people with acute or early HIV-1 infections, have clearly detailed the methods used, and reported the route of exposure. Studies were excluded if they reported data concerning people living with HIV-1 who had known or suspected superinfection, who were documented as having received pre-exposure prophylaxis, or if the transmitting partner was known to be receiving antiretroviral treatment. Individual patient data were collated from all studies, with authors contacted if these data were not publicly available. We applied logistic meta-regression to these data to estimate the probability that an HIV infection is initiated by multiple founder variants. We calculated a pooled estimate using a random effects model, subsequently stratifying this estimate across exposure routes in a univariable analysis. We then extended our model to adjust for different study methods in a multivariable analysis, recalculating estimates across the exposure routes. This study is registered with PROSPERO, CRD42020202672. We included 70 publications in our analysis, comprising 1657 individual patients. Our pooled estimate of the probability that an infection is initiated by multiple founder variants was 0·25 (95% CI 0·21-0·29), with moderate heterogeneity (Q=132·3, p<0·0001, I2=64·2%). Our multivariable analysis uncovered differences in the probability of multiple variant infection by exposure route. Relative to a baseline of male-to-female transmission, the predicted probability for female-to-male multiple variant transmission was significantly lower at 0·13 (95% CI 0·08-0·20), and the probabilities were significantly higher for transmissions in people who inject drugs (0·37 [0·24-0·53]) and men who have sex with men (0·30 [0·33-0·40]). There was no significant difference in the probability of multiple variant transmission between male-to-female transmission (0·21 [0·14-0·31]), post-partum transmission (0·18 [0·03-0·57]), pre-partum transmission (0·17 [0·08-0·33]), and intra-partum transmission (0·27 [0·14-0·45]). We identified that transmissions in people who inject drugs and men who have sex with men are significantly more likely to result in an infection initiated by multiple founder variants, and female-to-male infections are significantly less probable. Quantifying how the routes of HIV infection affect the transmission of multiple variants allows us to better understand how the evolution and epidemiology of HIV-1 determine clinical outcomes. Medical Research Council Precision Medicine Doctoral Training Programme and a European Research Council Starting Grant.

Orthogonal Msplit Estimation for Consequence Disaster Analysis

Nowadays, the data processing used for analyzing multifaceted disasters is based on technologies of mass observation acquisition. Terrestrial laser scanning is one of those technologies and enables the quick, non-invasive acquisition of information about an object after a disaster. This manuscript presents an improvement in the approach to the reconstruction and modeling of objects, based on data obtained by terrestrial laser scanning presented by the authors in previous work, as a method for the detection and dimensioning of the displacement of adjacent planes. The original Msplit estimation implemented in previous research papers has a specific limitation: the functional model must be selected very carefully in terms of the mathematical description of the estimated model and its data structure. As a result, using Msplit estimation on data from laser scanners is not a universal approach. The solution to this problem is the orthogonal Msplit estimation method proposed by the authors. The authors propose a new solution: the orthogonal Msplit estimation (OMsplit). The authors propose a modification of the existing method using orthogonal regression and the Nelder–Mead function as the minimization function. The implementation of orthogonal regression facilitates the avoidance of misfitting in cases of unfavorable data acquisition because the corrections are calculated perpendicularly to the estimated plane. The Nelder–Mead method was introduced to the orthogonal Msplit estimation due to it being more robust to the local minimum of the objective function than the LS method. To present the results, the authors simulated the data measurement of a retaining wall that was damaged after a disaster (violent storm) using a terrestrial laser scanner and their own software. The conducted research confirmed that the OMsplit estimation can be successfully used in the two-plane detection of terrestrial laser scanning data. It allows one to conduct the correct separation of the data set into two sets and to match the planes to the appropriate data set.