Differential Evolution (DE) stands as a potent global optimization algorithm, renowned for its application in addressing a myriad of practical engineering issues. The efficacy of DE is profoundly influenced by its control parameters and mutation strategies. In light of this, we introduce a refined DE algorithm characterized by adaptive parameters and dual mutation strategies (APDSDE). APDSDE inaugurates an adaptive switching mechanism that alternates between two innovative mutation strategies: DE/current-to-pBest-w/1 and DE/current-to-Amean-w/1. Furthermore, a novel parameter adaptation technique rooted in cosine similarity is established, with the derivation of explicit calculation formulas for both the scaling factor weight and crossover rate weight. In pursuit of optimizing convergence speed whilst preserving population diversity, a sophisticated nonlinear population size reduction method is proposed. The robustness of each algorithm is rigorously evaluated against the CEC2017 benchmark functions, with empirical evidence underscoring the superior performance of APDSDE in comparison to a host of advanced DE variants.

A pervasive threat regarding human health, ecological balance, progress, and sustainability marks the current era. Many nations are grappling with the consequences of the overabundance of carbon emissions from a wide range of destructive human activities, which is the primary driver of air pollution, global warming, and warming. Thus, while some countries are squandering their riches, others are making great strides to keep the environment clean and green so that future generations may thrive. National governments and policymakers are now focusing a lot of energy on addressing the dangers posed by environmental concerns and the threat of climate change. A very contentious issue in recent years has been the link between environmental change and tourism and its vulnerability. This study focuses on the impact of fluctuating visitor numbers on greenhouse gas emissions, the primary gas responsible for the acceleration of global warming and other environmental changes. Therefore, we look at how the most visited countries\' carbon emissions have changed due to increased tourism. The ecological effects of tourism on a regional scale are investigated using a panel data analysis spanning the years 2001-2018 in China, including the top 80 countries. The best-modified assessment methodologies determine the overall, direct, and indirect impact of tourist spending on carbon emissions. The findings demonstrate that CO2 emissions might be reduced by environmental regulation, urbanization, and tourist revenue and that they could be increased through economic expansion, population, and tourism. Due to this distinction, tourists\' overall impact is much more harmful than their direct impact. In addition, a U-shape is formed by the direct effects of carbon emissions and a growing economy, and vice versa. Several factors impact environmental regulation, including population density, population growth, pollution, and GDP growth. Spending on infrastructure development and economic expansion also considerably mitigates the impacts of tourism and environmental alteration. The results reveal that a nation\'s emissions often rise with the expansion of its tourism industry. Still, they begin to decline after certain levels and show that the link between the two has important policy implications.

The aim of this study was to investigate to what extent fragments of the HEV genome could be used for accurate diagnostics and inference of viral population-scale processes. For this, we selected all the published whole genome sequences from the NCBI GenBank and trimmed them to various fragment lengths (ORF1,2,3, ORF1, ORF2, ORF3, 493 nt in ORF2 and 148 nt in ORF2). Each of the fragment lengths was used to infer the richness and diversity of the viral sequence types, typing accuracy, and potential use in phylodynamics. The results obtained from the different fragments were compared. We observed that, generally, the longer the nucleic acid fragment used in typing, the better the accuracy in predicting the viral subtype. However, the dominant HEV subtypes circulating in Europe were relatively well classified even by the 493 nt fragment, with false negative rates as low as 8 in 1000 typed sequences. Most fragments also give comparable results in analyses of population size, albeit with shorter fragments showing a broader 95 % highest posterior density interval and less obvious increase of the viral effective population size. The reconstructed phylogenies of a heterochronous subset indicated a good concordance between all the fragments, with the major clades following similar branching patterns. Furthermore, we have used the HEV sequence data from the Netherlands available in the HEVnet database as a case study for reconstruction of population size changes in the past decades. This data showed that molecular and epidemiological results are concordant and point to an increase in the viral effective population size underlying the observed increase in incidence of acute HEV infection cases. In the absence of whole genome sequencing data, the 493 bp fragment can be used for analyzing HEV strains currently circulating in Europe, as it is informative for describing short term population-scale processes.

BACKGROUND: Population size, prevalence, and incidence are essential metrics that influence public health programming and policy. However, stakeholders are frequently tasked with setting performance targets, reporting global indicators, and designing policies based on multiple (often incongruous) estimates of these variables, and they often do so in the absence of a formal, transparent framework for reaching a consensus estimate.
OBJECTIVE: This study aims to describe a model to synthesize multiple study estimates while incorporating stakeholder knowledge, introduce an R Shiny app to implement the model, and demonstrate the model and app using real data.
METHODS: In this study, we developed a Bayesian hierarchical model to synthesize multiple study estimates that allow the user to incorporate the quality of each estimate as a confidence score. The model was implemented as a user-friendly R Shiny app aimed at practitioners of population size estimation. The underlying Bayesian model was programmed in Stan for efficient sampling and computation.
RESULTS: The app was demonstrated using biobehavioral survey-based population size estimates (and accompanying confidence scores) of female sex workers and men who have sex with men from 3 survey locations in a country in sub-Saharan Africa. The consensus results incorporating confidence scores are compared with the case where they are absent, and the results with confidence scores are shown to perform better according to an app-supplied metric for unaccounted-for variation.
CONCLUSIONS: The utility of the triangulator model, including the incorporation of confidence scores, as a user-friendly app is demonstrated using a use case example. Our results offer empirical evidence of the model\'s effectiveness in producing an accurate consensus estimate and emphasize the significant impact that the accessible model and app offer for public health. It offers a solution to the long-standing problem of synthesizing multiple estimates, potentially leading to more informed and evidence-based decision-making processes. The Triangulator has broad utility and flexibility to be adapted and used in various other contexts and regions to address similar challenges.

BACKGROUND: HIV surveillance among key populations is a priority in all epidemic settings. Female sex workers (FSWs) globally as well as in Rwanda are disproportionately affected by the HIV epidemic; hence, the Rwanda HIV and AIDS National Strategic Plan (2018-2024) has adopted regular surveillance of population size estimation (PSE) of FSWs every 2-3 years.
OBJECTIVE: We aimed at estimating, for the fourth time, the population size of street- and venue-based FSWs and sexually exploited minors aged ≥15 years in Rwanda.
METHODS: In August 2022, the 3-source capture-recapture method was used to estimate the population size of FSWs and sexually exploited minors in Rwanda. The field work took 3 weeks to complete, with each capture occasion lasting for a week. The sample size for each capture was calculated using shinyrecap with inputs drawn from previously conducted estimation exercises. In each capture round, a stratified multistage sampling process was used, with administrative provinces as strata and FSW hotspots as the primary sampling unit. Different unique objects were distributed to FSWs in each capture round; acceptance of the unique object was marked as successful capture. Sampled FSWs for the subsequent capture occasions were asked if they had received the previously distributed unique object in order to determine recaptures. Statistical analysis was performed in R (version 4.0.5), and Bayesian Model Averaging was performed to produce the final PSE with a 95% credibility set (CS).
RESULTS: We sampled 1766, 1848, and 1865 FSWs and sexually exploited minors in each capture round. There were 169 recaptures strictly between captures 1 and 2, 210 recaptures exclusively between captures 2 and 3, and 65 recaptures between captures 1 and 3 only. In all 3 captures, 61 FSWs were captured. The median PSE of street- and venue-based FSWs and sexually exploited minors in Rwanda was 37,647 (95% CS 31,873-43,354), corresponding to 1.1% (95% CI 0.9%-1.3%) of the total adult females in the general population. Relative to the adult females in the general population, the western and northern provinces ranked first and second with a higher concentration of FSWs, respectively. The cities of Kigali and eastern province ranked third and fourth, respectively. The southern province was identified as having a low concentration of FSWs.
CONCLUSIONS: We provide, for the first time, both the national and provincial level population size estimate of street- and venue-based FSWs in Rwanda. Compared with the previous 2 rounds of FSW PSEs at the national level, we observed differences in the street- and venue-based FSW population size in Rwanda. Our study might not have considered FSWs who do not want anyone to know they are FSWs due to several reasons, leading to a possible underestimation of the true PSE.

The spotted lanternfly is an invasive pest for which we lack individual movement data due in part to the difficulty posed by individual identification. We developed a computer-aided method to identify individual adult spotted lanternfly using wing spot patterns from photos processed in the software I3S and demonstrated the method\'s accuracy with lab and field validations. Based on 176 individuals in the lab, we showed that digitizing the spots of one wing allowed a 100% reliable individual identification. The errors due to user input and the variation in the angle of the image were largely negligible compared to inter-individual variations. We applied this method in the context of a mark-recapture experiment to assess the feasibility of this method in the field. We initially identified a total of 84 unique spotted lanternflies, 31 of which were recaptured after four hours along with 49 new individuals. We established that the analysis of recaptures can possibly be automated based on scores and may not require systematic visual pairwise comparison. The demonstration of the effectiveness of this method on relatively small sample sizes makes it a promising tool for field experimentation as well as lab manipulations. Once validated on larger datasets and in different contexts, it will provide ample opportunity to collect useful data on spotted lanternfly ecology that can greatly inform management.

Genetic diversity is the raw material of evolution, yet the reasons why it varies among species remain poorly understood. While studies at deeper phylogenetic scales point to the influence of life history traits on genetic diversity, it appears to be more affected by population size but less predictable at shallower scales. We used proxies for population size, mutation rate, direct selection, and linked selection to test factors affecting genetic diversity within a diverse assemblage of Neotropical salamanders, which vary widely for these traits. We estimated genetic diversity of noncoding loci using ddRADseq and coding loci using RNAseq for an assemblage of Neotropical salamanders distributed from northern Mexico to Costa Rica. Using ddRADseq loci, we found no significant association with genetic diversity, while for RNAseq data we found that environmental heterogeneity and proxies of population size predict a substantial portion of the variance in genetic diversity across species. Our results indicate that diversity of coding loci may be more predictable than that of noncoding loci, which appears to be mostly unpredictable at shallower phylogenetic scales. Our results suggest that coding loci may be more appropriate for genetic diversity estimates used in conservation planning because of the lack of any association between the variables we used and genetic diversity of noncoding loci.

Capture-recapture (CRC) surveys are used to estimate the size of a population whose members cannot be enumerated directly. CRC surveys have been used to estimate the number of Coronavirus Disease 2019 (COVID-19) infections, people who use drugs, sex workers, conflict casualties, and trafficking victims. When k-capture samples are obtained, counts of unit captures in subsets of samples are represented naturally by a 2k contingency table in which one element-the number of individuals appearing in none of the samples-remains unobserved. In the absence of additional assumptions, the population size is not identifiable (i.e., point identified). Stringent assumptions about the dependence between samples are often used to achieve point identification. However, real-world CRC surveys often use convenience samples in which the assumed dependence cannot be guaranteed, and population size estimates under these assumptions may lack empirical credibility. In this work, we apply the theory of partial identification to show that weak assumptions or qualitative knowledge about the nature of dependence between samples can be used to characterize a nontrivial confidence set for the true population size. We construct confidence sets under bounds on pairwise capture probabilities using two methods: test inversion bootstrap confidence intervals and profile likelihood confidence intervals. Simulation results demonstrate well-calibrated confidence sets for each method. In an extensive real-world study, we apply the new methodology to the problem of using heterogeneous survey data to estimate the number of people who inject drugs in Brussels, Belgium.

We estimated the current size and dynamics of the wolf population in Tuscany and investigated the trends and demographic drivers of population changes. Estimates were obtained by two different approaches: (i) mixed-technique field monitoring (from 2014 to 2016) that found the minimum observed pack number and estimated population size, and (ii) an individual-based model (run by Vortex software v. 10.3.8.0) with demographic inputs derived from a local intensive study area and historic data on population size. Field monitoring showed a minimum population size of 558 wolves (SE = 12.005) in 2016, with a density of 2.74 individuals/100 km2. The population model described an increasing trend with an average annual rate of increase λ = 1.075 (SE = 0.014), an estimated population size of about 882 individuals (SE = 9.397) in 2016, and a density of 4.29 wolves/100 km2. Previously published estimates of wolf population were as low as 56.2% compared to our field monitoring estimation and 34.6% in comparison to our model estimation. We conducted sensitivity tests to analyze the key parameters driving population changes based on juvenile and adult mortality rates, female breeding success, and litter size. Mortality rates played a major role in determining intrinsic growth rate changes, with adult mortality accounting for 62.5% of the total variance explained by the four parameters. Juvenile mortality was responsible for 35.8% of the variance, while female breeding success and litter size had weak or negligible effects. We concluded that reliable estimates of population abundance and a deeper understanding of the role of different demographic parameters in determining population dynamics are crucial to define and carry out appropriate conservation and management strategies to address human-wildlife conflicts.

The western honey bee, Apis mellifera, lives worldwide in approximately 102 million managed hives but also wild throughout much of its native and introduced range. Despite the global importance of A. mellifera as a crop pollinator, wild colonies have received comparatively little attention in the scientific literature and basic information regarding their density and abundance is scattered. Here, we review 40 studies that have quantified wild colony density directly (n = 33) or indirectly using genetic markers (n = 7) and analyse data from 41 locations worldwide to identify factors that influence wild colony density. We also compare the density of wild and managed colonies at a regional scale using data on managed colonies from the Food and Agriculture Organization (FAO). Wild colony densities varied from 0.1 to 24.2/km2 and were significantly lower in Europe (average of 0.26/km2) than in Northern America (1.4/km2), Oceania (4.4/km2), Latin America (6.7/km2) and Africa (6.8/km2). Regional differences were not significant after controlling for both temperature and survey area, suggesting that cooler climates and larger survey areas may be responsible for the low densities reported in Europe. Managed colony densities were 2.2/km2 in Asia, 1.2/km2 in Europe, 0.2/km2, in Northern America, 0.2/km2 in Oceania, 0.5/km2 in Latin America and 1/km2 in Africa. Wild colony densities exceeded those of managed colonies in all regions except Europe and Asia. Overall, there were estimated to be between two and three times as many wild colonies as managed worldwide. More wild colony surveys, particularly in Asia and South America, are needed to assess the relative density of wild and managed colonies at smaller spatial scales.