Choice Selection Research Articles

Clear aligner therapy practice among orthodontists in the United Kingdom and the Republic of Ireland: A cross-sectional survey of the British Orthodontic Society membership.

To investigate clear aligner therapy (CAT) practice among orthodontists in the British Orthodontic Society (BOS). A cross-sectional online survey. An electronic survey was distributed to members of the BOS in 2022. The survey comprised questions regarding respondent demographics, general use of CAT, the choice of proprietary CAT appliances, CAT planning, case selection, treatment protocols and orthodontist-reported CAT problems. Overall, there were 233 (19.5%) respondants with the majority (n = 121, 53.1%) being female. Most respondents reported practising in England (n = 171, 74.7%). The majority (n = 177, 77.3%) indicated that they used CAT in their practice, with 48.1% (n = 81) treating 1-20 patients with CAT annually. The most frequently prescribed CAT system was Invisalign (n = 138, 81.2%). One to three changes to the initial digital treatment plan were made by 72.9% (n = 121) with final tooth positions being the most common reason for adjustment (64.4%). Most (n = 97, 60.3%) rarely or never performed premolar extractions with CAT. Of the respondents, 23 12.7%) reported that they always or mostly used a remote monitoring system in conjunction with CAT, with a wide range of aligner change protocols reported. The median number of months required to complete non-extraction CAT reported by the respondents was 12. Most respondents (n = 77, 51.7%) did not feel that CAT provides superior outcomes compared with fixed appliance therapy. CAT practice varied widely among the surveyed orthodontists. A predilection for the use of Invisalign and utility in less severe cases was noted.

RenderBench: The CPU Rendering Benchmark Suite Based on Microarchitecture-Independent Characteristics

This research addresses the issue of evaluating CPU rendering performance by introducing the innovative benchmark test suite construction method RenderBench. This method combines CPU microarchitecture features with rendering task characteristics to comprehensively assess CPU performance across various rendering tasks. Adhering to principles of representativeness and comprehensiveness, the constructed benchmark test suite encompasses diverse rendering tasks and scenarios, ensuring accurate capture of CPU performance features. Through data sampling and in-depth analysis, this study focuses on the role of microarchitecture-independent features in rendering programs, including instruction-level parallelism, instruction mix, branch prediction capability, register dependency distance, data flow stride, and memory reuse distance. The research findings reveal significant variations in rendering programs across these features. For instance, in terms of instruction-level parallelism, rendering programs demonstrate a high level of ILP (instruction-level parallelism), with an average value of 5.70 for ILP256, surpassing benchmarks such as Mibench and NAS Parallel Benchmark. Furthermore, in aspects such as instruction mix, branch prediction capability, register dependency distance, data flow stride, and memory reuse distance, rendering programs exhibit distinct characteristics. Through the application of the RenderBench method, a scalable and highly representative benchmark test suite was constructed, facilitating an in-depth exploration of CPU performance bottlenecks in rendering tasks. By delving into microarchitecture-independent features, this study provides profound insights into rendering program performance, offering valuable guidance for optimizing CPU rendering performance. The application of ensemble learning models, such as random forest, XGBoost, and ExtraTrees, reveals the significant influence of features like floating-point computation, memory access patterns, and register usage on CPU rendering program performance. These insights not only offer robust guidance for performance optimization but also underscore the importance of feature selection and algorithm choice. In summary, the results of feature importance ranking in this study provide beneficial directions and deep insights for the optimization and enhancement of CPU rendering program performance. These findings are poised to exert a positive impact on future research and development endeavors.

How remote sensing choices influence ecosystem services monitoring and evaluation results of ecological restoration interventions

Large-scale ecological restorations are recognized worldwide as an effective strategy to combat environmental degradation and promote sustainability. Remote sensing (RS) imagery, such as obtained from Landsat and Sentinel-2 satellites, can provide spatial, spectral, and temporal information on ecosystem service supply to support monitoring and evaluation of restoration interventions. However, because of the abundance of satellite data and methodological analysis options, choices in data selection and processing options need to be made. This study explored the effect of RS choices on the evaluation of changes in ecosystem services as a result of ecological restoration interventions. Using the ecosystem service of forage provision for wildlife as an example, we used a before-after-control-impact (BACI) analysis to compare how the following choices affected restoration evaluation outcomes: a) different number of control pixels; b) different spatial distribution of control pixels; c) intra-annual image selection; and d) different reference periods. In addition, e) we evaluated the effect of using two different satellite sensor types, using the ecosystem service ‘erosion prevention’ as an example. We explored the effect of these five choices for restoration sites in the Baviaanskloof, South Africa. Results showed that the choice of intra-annual image selection, and the reference period describing the ‘before state’ had a strong effect on the outcomes, often leading to opposite BACI evaluation results. BACI results were less sensitive to choices related to the number of control points in the evaluation. The impact of methodological choices on the BACI outcomes was greater for the less degraded areas of our study site. Satellite sensor choice resulted in similar temporal trajectories of estimated supply. We demonstrated that RS choices have a strong effect on the evaluation results of restoration interventions. Therefore, we recommend that documenting the key RS choices results is essential when communicating restoration evaluation results in order to properly understand, manage and adapt restoration initiatives.

Analysis of the Factors Affecting Bank Selection Decisions in Mogadishu, Somalia

Purpose: The purpose of this article is to analysis of the factors affecting bank selection decisions in Mogadishu, Somalia.
 
 Theoretical framework: This study is to address several limitations in this study by providing extensive evidence on the determinants of the bank selection choice in Mogadishu, Somalia, and better inform bank executives and authorities about the types of services customers find most appropriate for their bank needs.
 
 Design/methodology/approach: The methodology for the study is based on data collected from one hundred twenty customers who were selected as a sample size from the population of bank customers. Since it is impossible to find out the exact number of the entire customers for time-consuming and expensive reasons, the researcher chose the sample.
 
 Findings: The results showed they have a positive influence on the bank selection decision of consumers living in Mogadishu – Somalia. Elements used under the investigation include the Provision of bank services, the convenience of the bank, the reputation and image of the bank, the financial gain of the bank, people's influence, and information availability of the bank.
 
 Research, Practical & Social implications: The results will indicate that customers' primary motivation for selecting a bank is the security of their funds, and better enlighten bank executives and authorities about the types of services that customers find most suitable for their bank requirements.
 
 Originality/value: Original Research article

History and Evolution of Hip Design to Decrease Failure.

The evolution of fracture fixation designs over the past 100 years is coupled with incredible advances in materials and manufacturing technology. Failures are unavoidable and should be evaluated from the perspective of refining our technology and technique to improve patient outcomes. Failure after intramedullary fixation of hip fractures is multifactorial with contributions from an unsuccessful surgery, some preventable, some unavoidable, and rarely, design and manufacturing errors. However, whenever implants are disseminated to large population groups with variable individual anatomy, physiology, and associated comorbidities, unexpected and unpredicted weaknesses in the design and surgical technique will be discovered. Surgeons have multiple choices for nail selection with specific and critical technical requirements. The surgeon should evaluate each patient individually and determine the treatment based on patient anatomy and morphology of the fracture. The surgical approach should be modified over time after deciding how best to improve surgical treatment based on the current scientific studies.

Adjusting counting-based secret-sharing via personalized passwords and email-authentic reliability

Counting-based secret-sharing (CBSS) is a current cybersecurity technique that provide applicable authentication for multi-participants collaborative trust access control utilization. The CBSS originally assigns its main confidential target key as secret random automatically selected to generate all participants private shares fulfilling trap-door function. CBSS is normally not allowing to entertain user preference in personalizing the passwords causing severe remembrance reliability difficulty. This research addressed this retention personalized challenge by adjusting the CBSS procedure commencing its process by user password selection choice, as for secret shares extraction to generate the target-key, securing the cybersecurity collaborative system. We proposed several algorithms for user-preference secret shares combination based on text and images alternatives, further verified by personal email convenience authentication. The proposals tested this adjusted CBSS reliability comparing overall security, systems number of shares possibilities, and variations functional time complexity as well as PSNR comparing to others for proper validation, noting its novel relevance attraction. The analysis remarked very interesting contribution comparison commented highest randomization secrecy of 95% for proposed model A passwords followed by 39%, 21%, 19% and 5%, for other different images-based passwords models B, C, D, and E, respectively. The work remarked attractive contribution compared to other works providing applicable CBSS real-life current utilization, opening door for promising progressive advancements to come.

Exploring Household Wood Preferences among Consumers in Coimbatore

Aims: This study aims to comprehensively investigate and analyze the factors that shape consumer preferences for various types of wood in the context of household applications in Coimbatore. By delving into the intricate dynamics that guide wood selection for domestic purposes, the research seeks to identify the underlying drivers that influence consumer choices. Through a meticulous examination of factors such as material versatility, eco-friendliness, aesthetic appeal, and material uniqueness, the study aims to provide a nuanced understanding of the key determinants that significantly impact the preferences of consumers when it comes to selecting specific wood varieties for furniture, decor, and other household uses. By shedding light on these preferences, the research aims to contribute valuable insights that can aid businesses, manufacturers, and policymakers in tailoring their products and strategies to better align with the evolving demands and preferences of consumers in the Coimbatore region.
 Study Design: Exploratory research design.
 Place and Duration of Study: The present study was conducted in the year 2023 during June and July
 Methodology: The data were collected from 120 consumers in Coimbatore city using snowball sampling and Convenience sampling will be used to select the respondents for the present study with the help of a well-structured interview schedule used to scale the factors. To analyze the data using factor analysis to identify the main factors that influence consumers’ preference for wood such as availability, price, durability, appearance, eco-friendliness, and personal preference.
 Results: Factors analysis provides that material excellence factors which comprise four factors versatile and modifiable, eco-friendly, aesthetic appeal, and uniqueness of material with a variance of 63.770 percent were the most influenced factors for preference for wood.
 Conclusion: In conclusion, this study employed factor analysis to discern the key determinants shaping consumers' wood preferences. The analysis revealed that material excellence, encompassing factors related to versatility, eco-friendliness, aesthetic appeal, and material uniqueness, accounted for a significant variance of 63.770 percent in influencing wood preference. These findings emphasize the paramount importance of considering factors beyond traditional attributes like availability, price, and durability, and highlight the significance of holistic material qualities in shaping individuals' choices in wood selection.

EFFECTS OF DIFFERENT CLIMATE TYPES ON COLOR CHANGE OF WOOD MATERIAL USED OUTDOOR

Field tests are important for evaluating how wood performs in real-world conditions and making informed choices for material selection. These tests help assess wood's durability, strength, decay processes, and resistance against harmful organisms. Furthermore, it helps users make more informed decisions about the color of wood and understand the importance of color changes depending on the place and time of use. Because weather conditions are asignificant factor that influences the color of wood. Wood that is exposed to prolonged sunlight, moisture, and rain may experience fading, darkening, or staining in its color. In this study, heartwood, sapwood, and CCB impregnated sapwood samples of Scotchpine, spruce, beech and alder were exposed to the soil contact (hazard class 4) according to EN252 for 3years in Trabzon, Muğla, Çanakkale, and Elazığ provinces of Turkey with completely different climatic conditions from each other. Color parameters and color change values were evaluatedusing L*, a*, b*and ∆E* of the samples collected from test sites. The most significant color change was observed at Scotchpine in Çanakkale province. Greater color changes were observed in theheartwood of coniferous species.Impregnated samples showed the least color change.

A New Index of Perceived Job Quality in 116 Countries: Associations with Working Hours and Other National Characteristics

The main purpose of this study was to create a global index of perceived job quality that assesses individuals’ perceptions of enjoyment, meaning, and engagement at work, as well as freedom of choice in job selection. The study also explored the correlation between weekly working hours and perceived job quality. A sample of 121,207 individuals from 116 countries was used, sourced from the Gallup World Poll. Additionally, variables from other sources were incorporated to establish the nomological net of the new index. Perceived job quality was highest in South and North America, while it was lowest in East Asia, the Middle East, and North Africa. Perceived job quality was weakly associated with cultural characteristics at the national level, while it was more strongly associated with experienced positive affect, psychosocial well-being, and optimism. No evidence was found that countries with higher levels of wealth have higher average levels of perceived job quality. The number of hours worked per week was not significantly related to perceived job quality at the national level. Working hours were found to be longer in collectivist, hierarchical, and less free countries, as well as in countries where work is valued over leisure. Weekly working hours was largely unrelated to economic indicators at the national level.

:Male Choice, Female Competition, and Female Ornaments in Sexual Selection

:<i>Male Choice, Female Competition, and Female Ornaments in Sexual Selection</i>

Generalized Wardrop Equilibrium for Charging Station Selection and Route Choice of Electric Vehicles in Joint Power Distribution and Transportation Networks

This paper presents the equilibrium analysis of a game composed of heterogeneous electric vehicles (EVs) and a power distribution system operator (DSO) as the players, and charging station operators (CSOs) and a transportation network operator (TNO) as coordinators. Each EV tries to pick a charging station as its destination and a route to get there at the same time. However, the traffic and electrical load congestion on the roads and charging stations lead to the interdependencies between the optimal decisions of EVs. CSOs and the TNO need to apply some tolling to control such congestion. On the other hand, the pricing at charging stations depends on real-time distributional locational marginal pricing, which is determined by the DSO after solving the optimal power flow over the power distribution network. This paper also takes into account the local and the coupling/infrastructure constraints of EVs, transportation and distribution networks. This problem is modeled as a generalized aggregative game, and then a decentralized learning method is proposed to obtain an equilibrium point of the game, which is known as variational generalized Wardrop equilibrium. The existence of such an equilibrium point and the convergence of the proposed algorithm to it are proven. We undertake numerical studies on the Savannah city model and the IEEE 33-bus distribution network and investigate the impact of various characteristics on demand and prices.

Using knowledge graphs and contrastive learning for detecting APT Malware on Endpoint systems

Advanced persistent threat (APT) attacking campaigns have been a common method for cyber-attackers to attack and exploit end-user computers (workstations) in recent years. In this study, to enhance the effectiveness of the APT malware detection, a combination of deep graph networks and contrastive learning is proposed. The idea is that several deep graph networks such as Graph Convolution Networks (GCN), Graph Isomorphism Networks (GIN), are combined with some popular contrastive learning models like N-pair Loss, Contrastive Loss, and Triplet Loss, in order to optimize the process of APT malware detection and classification in endpoint workstations. The proposed approach consists of three main phases as follows. First, the behaviors of APT malware are collected and represented as graphs. Second, GIN and GCN networks are used to extract feature vectors from the graphs of APT malware. Finally, different contrastive learning models, i.e. N-pair Loss, Contrastive Loss, and Triplet Loss are applied to determine which feature vectors belong to APT malware, and which ones belong to normal files. This combination of deep graph networks and contrastive learning algorithm is a novel approach, that not only enhances the ability to accurately detect APT malware but also reduces false alarms for normal behaviors. The experimental results demonstrate that the proposed model, whose effectiveness ranges from 88% to 94% across all performance metrics, is not only scientifically effective but also practically significant. Additionally, the results show that the combination of GIN and N-pair Loss performs better than other combined models. This provides a base malware detection system with flexible parameter selection and mathematical model choices for optimal real-world applications.

Unveiling the Sway of Marketing Mix on Student Decisions: A Study on Higher Learning Institutions

&#x0D; &#x0D; &#x0D; &#x0D; &#x0D; &#x0D; This research delves into the pivotal process of student decision-making within higher learning institutions, focusing on the dynamic interplay of marketing mix services encompassing product, price, promotion, and place. Through an analysis of alumni and current student cohorts at a prominent university, this study empirically demonstrates the profound influence exerted by product quality, strategic promotion, and favorable institutional positioning on students' selection choices. Notably, while price emerges as a comparatively weaker determinant, the collective impact of product, place, and promotion proves instrumental in shaping prospective students' decisions. These findings illuminate the complex landscape of student preferences, offering valuable insights for crafting targeted marketing strategies that effectively guide students' educational trajectories.&#x0D; Highlight:&#x0D; &#x0D; Influence of Marketing Mix Dynamics: This study examines the interaction of product, price, promotion, and place in shaping student decisions within higher learning institutions, revealing their collective impact on choice.&#x0D; Empirical Insights: Analysis of alumni and current students from a renowned university highlights the significant sway of product quality, strategic promotion, and favorable institutional positioning on student selection choices.&#x0D; Role of Price in Decision-making: While price emerges as a less potent factor, the study underscores the pivotal role played by the interplay between product, place, and promotion in molding the educational preferences of prospective students.&#x0D; &#x0D; Keyword: Student Decision-Making, Higher Learning Institutions, Marketing Mix Services, Product Quality, Strategic Promotion&#x0D; &#x0D; &#x0D; &#x0D; &#x0D; &#x0D;

Genome-wide association study as a powerful tool for dissecting competitive traits in legumes.

Legumes are extremely valuable because of their high protein content and several other nutritional components. The major challenge lies in maintaining the quantity and quality of protein and other nutritional compounds in view of climate change conditions. The global need for plant-based proteins has increased the demand for seeds with a high protein content that includes essential amino acids. Genome-wide association studies (GWAS) have evolved as a standard approach in agricultural genetics for examining such intricate characters. Recent development in machine learning methods shows promising applications for dimensionality reduction, which is a major challenge in GWAS. With the advancement in biotechnology, sequencing, and bioinformatics tools, estimation of linkage disequilibrium (LD) based associations between a genome-wide collection of single-nucleotide polymorphisms (SNPs) and desired phenotypic traits has become accessible. The markers from GWAS could be utilized for genomic selection (GS) to predict superior lines by calculating genomic estimated breeding values (GEBVs). For prediction accuracy, an assortment of statistical models could be utilized, such as ridge regression best linear unbiased prediction (rrBLUP), genomic best linear unbiased predictor (gBLUP), Bayesian, and random forest (RF). Both naturally diverse germplasm panels and family-based breeding populations can be used for association mapping based on the nature of the breeding system (inbred or outbred) in the plant species. MAGIC, MCILs, RIAILs, NAM, and ROAM are being used for association mapping in several crops. Several modifications of NAM, such as doubled haploid NAM (DH-NAM), backcross NAM (BC-NAM), and advanced backcross NAM (AB-NAM), have also been used in crops like rice, wheat, maize, barley mustard, etc. for reliable marker-trait associations (MTAs), phenotyping accuracy is equally important as genotyping. Highthroughput genotyping, phenomics, and computational techniques have advanced during the past few years, making it possible to explore such enormous datasets. Each population has unique virtues and flaws at the genomics and phenomics levels, which will be covered in more detail in this review study. The current investigation includes utilizing elite breeding lines as association mapping population, optimizing the choice of GWAS selection, population size, and hurdles in phenotyping, and statistical methods which will analyze competitive traits in legume breeding.

No, It Is Not All About Selective Exposure: Information Selection Strategies in Referendums

This article presents an in-depth understanding of information selection strategies in referendums. It builds on the theories of motivated reasoning, cognitive dissonance, information utility, and social identity to examine selective exposure in the context of referendums, and highlights how individuals select information, opting for either congruent and partisan (selective choice) or balanced and discrepant (enlightened choice) sources of information. Empirically, our study relies on two cross-sectional online surveys (N = 2,245; N = 735) that took place in a real referendum campaign on an Energy Act in Switzerland. We exposed respondents to different information contexts and measured whether they chose selectively or in an enlightened way. We conclude that individuals choose balanced or discrepant sources of information more frequently than the literature on selective exposure suggests. Moreover, attitude strength plays a twofold role: In a pre-campaign context, attitude strength motivates individuals’ enlightened choice; mid-campaign, however, it strengthens voters’ selective choice.

ChatGPT performance in the medical specialty exam: An observational study.

In our study, we aimed to evaluate the success of ChatGPT by determining its performance in the last 5 medical specialty exams (MSE) conducted and its ranking among the candidates of that year, and to determine its potential use in healthcare services. Publicly available MSE questions and answer keys from the last 5 years were scanned, a total of 1177 questions were included in the study, all questions were asked to the ChatGPT (OpenAI; San Francisco, CA) GPT-3.5 series, which is the March 23, 2023 version. The average score and rank that ChatGPT would receive if it had entered the exam that year were determined. Questions were categorized as short question group, long question group, single select multiple-choice questions, and multi-select multiple-choice questions. The lowest success proportion was determined as 54.3%, and the highest success proportion was 70.9% correct answer percentage. It achieved a sufficient result as 1787th out of 22,214 people in its most successful exam, and 4428th out of 21,476 participants in its least successful one. No statistically significant difference was found between the correct answers it gave to clinical and basic science questions (P: .66). ChatGPT statistically significantly answered a higher proportion of questions correctly in the short questions group compared to the long questions group (P = .03), and in the single select multiple choice questions group compared to the multi-select multiple choice questions group (P < .001). ChatGPT has been successful in the MSE, a challenging exam for doctors in our country. However, it is a fact that ChatGPT is still behind the expert in the field for now, and what will happen with program developments in the future is a matter of curiosity for all of us.

Association Mapping and Expression Analysis of the Genes Involved in the Wood Formation of Poplar.

Xylogenesis is a complex and sequential biosynthetic process controlled by polygenes. Deciphering the genetic architecture of this complex quantitative trait could provide valuable information for increasing wood biomass and improving its properties. Here, we performed genomic resequencing of 64 24-year-old trees (64 hybrids of section Aigeiros and their parents) grown in the same field and conducted full-sib family-based association analyses of two growth and six woody traits using GEMMA as a choice of association model selection. We identified 1342 significantly associated single nucleotide polymorphisms (SNPs), 673 located in the region upstream and downstream of 565 protein-encoding genes. The transcriptional regulation network of secondary cell wall (SCW) biosynthesis was further constructed based on the published data of poplar miRNA, transcriptome, and degradome. These provided a certain scientific basis for the in-depth understanding of the mechanism of poplar timber formation and the molecular-assisted breeding in the future.

Comprehensive analysis of codon usage pattern in Eisenia fetida and its correlation with gene expression

Every individual gene or genome follows the phenomenon of codon bias, where the usage among synonymous codons is not equal. The present study was conducted to understand the CUB in E. fetida and its impact on gene expression. The genes of E. fetida were G/Cbiased, where mean of C% was highest followed by G%, T%, and A% at the third codon position. ENC plot, Neutrality plot and Parity plot analysis revealed the codon usage pattern in E. fetida was largely influenced by natural selection. Further, it was observed that CUB had a substantial negative relationship with the gene expression levels (FPKM) at p < 0.05. Altogether, these findings indicate that translational efficiency and accuracy might have modulated the choice of codon selection in the studied earthworm genome.

Salting-Out Assisted Liquid-Liquid Microextraction and Reverse-Phase Chromatographic Quantification of Two Neonicotinoid Insecticides from Fruits and Vegetables.

The present monograph describes the salting-out assisted liquid-liquid microextraction (SALLME) and reverse-phase high-performance liquid chromatography with diode array detector (RP-HPLC-DAD) based quantification of two frequently applied neonicotinoid insecticides, i.e., acetamiprid (ACE) and imidacloprid (IMD), from selected tropical fruits (citrus and guava) and vegetables (tomato, okra and cauliflower). The SALLME conditions like pH, liquid/solid ratio and salt ratio (NaCl/Na2SO4) were varied over a wide range of conditions for the enhanced recovery of IMD and ACE from spiked quality control (low, high and medium) of fruits and vegetables. Meanwhile, RP-HPLC-DAD quantification of IMD and ACE was found to be linear over 1-100μg/L with the coefficient of determination (R2) value ≥0.997 and slope of the calibration curve (sensitivity) ≥2.3 × 104 peak area unit (μAu). The analysis of selected fruits and vegetables after 0 (1h), 1, 3, 7, 10 and 14days of foliar application revealed the presence of IMD and ACE residues in okra and guava higher than maximum residual limits set by the Codex Alimentarius Commission until the third day of spray. Overall, the SALLME-RP-HPLC-DAD was found to be a rapid, selective and expedient choice for the routine analysis of neonicotinoids in environmental and food commodities.

A Comprehensive Machine Learning Benchmark Study for Radiomics-Based Survival Analysis of CT Imaging Data in Patients With Hepatic Metastases of CRC.

Optimizing a machine learning (ML) pipeline for radiomics analysis involves numerous choices in data set composition, preprocessing, and model selection. Objective identification of the optimal setup is complicated by correlated features, interdependency structures, and a multitude of available ML algorithms. Therefore, we present a radiomics-based benchmarking framework to optimize a comprehensive ML pipeline for the prediction of overall survival. This study is conducted on an image set of patients with hepatic metastases of colorectal cancer, for which radiomics features of the whole liver and of metastases from computed tomography images were calculated. A mixed model approach was used to find the optimal pipeline configuration and to identify the added prognostic value of radiomics features. In this study, a large-scale ML benchmark pipeline consisting of preprocessing, feature selection, dimensionality reduction, hyperparameter optimization, and training of different models was developed for radiomics-based survival analysis. Portal-venous computed tomography imaging data from a previous prospective randomized trial evaluating radioembolization of liver metastases of colorectal cancer were quantitatively accessible through a radiomics approach. One thousand two hundred eighteen radiomics features of hepatic metastases and the whole liver were calculated, and 19 clinical parameters (age, sex, laboratory values, and treatment) were available for each patient. Three ML algorithms-a regression model with elastic net regularization (glmnet), a random survival forest (RSF), and a gradient tree-boosting technique (xgboost)-were evaluated for 5 combinations of clinical data, tumor radiomics, and whole-liver features. Hyperparameter optimization and model evaluation were optimized toward the performance metric integrated Brier score via nested cross-validation. To address dependency structures in the benchmark setup, a mixed-model approach was developed to compare ML and data configurations and to identify the best-performing model. Within our radiomics-based benchmark experiment, 60 ML pipeline variations were evaluated on clinical data and radiomics features from 491 patients. Descriptive analysis of the benchmark results showed a preference for RSF-based pipelines, especially for the combination of clinical data with radiomics features. This observation was supported by the quantitative analysis via a linear mixed model approach, computed to differentiate the effect of data sets and pipeline configurations on the resulting performance. This revealed the RSF pipelines to consistently perform similar or better than glmnet and xgboost. Further, for the RSF, there was no significantly better-performing pipeline composition regarding the sort of preprocessing or hyperparameter optimization. Our study introduces a benchmark framework for radiomics-based survival analysis, aimed at identifying the optimal settings with respect to different radiomics data sources and various ML pipeline variations, including preprocessing techniques and learning algorithms. A suitable analysis tool for the benchmark results is provided via a mixed model approach, which showed for our study on patients with intrahepatic liver metastases, that radiomics features captured the patients' clinical situation in a manner comparable to the provided information solely from clinical parameters. However, we did not observe a relevant additional prognostic value obtained by these radiomics features.