Selection Framework Research Articles

A Framework for Selection of High-Yielding and Drought-tolerant Genotypes of Barley: Applying Yield-Based Indices and Multi-index Selection Models

A Framework for Selection of High-Yielding and Drought-tolerant Genotypes of Barley: Applying Yield-Based Indices and Multi-index Selection Models

Explainable AI for CHO cell culture media optimization and prediction of critical quality attribute

Cell culture media play a critical role in cell growth and propagation by providing a substrate; media components can also modulate the critical quality attributes (CQAs). However, the inherent complexity of the cell culture media makes unraveling the impact of the various media components on cell growth and CQAs non-trivial. In this study, we demonstrate an end-to-end machine learning framework for media component selection and prediction of CQAs. The preliminary dataset for feature selection was generated by performing CHO-GS (-/-) cell culture in media formulations with varying metal ion concentrations. Acidic and basic charge variant composition of the innovator product (24.97 ± 0.54% acidic and 11.41 ± 1.44% basic) was chosen as the target variable to evaluate the media formulations. Pearson’s correlation coefficient and random forest-based techniques were used for feature ranking and feature selection for the prediction of acidic and basic charge variants. Furthermore, a global interpretation analysis using SHapley Additive exPlanations was utilized to select optimal features by evaluating the contributions of each feature in the extracted vectors. Finally, the medium combinations were predicted by employing fifteen different regression models and utilizing a grid search and random search cross-validation for hyperparameter optimization. Experimental results demonstrate that Fe and Zn significantly impact the charge variant profile. This study aims to offer insights that are pertinent to both innovators seeking to establish a complete pipeline for media development and optimization and biosimilar-based manufacturers who strive to demonstrate the analytical and functional biosimilarity of their products to the innovator.Key points• Developed a framework for optimizing media components and prediction of CQA.• SHAP enhances global interpretability, aiding informed decision-making.• Fifteen regression models were employed to predict medium combinations.

Framework for Understanding Factors Influencing Clothing Selection

Clothing selection is a product of social construction that defines what people choose to wear. The choice of what, how, and when to select what individuals, wear is based on the perception that surrounds their reality. For the purpose of this research work, factors influencing clothing selection have been organized into a framework to facilitate easier understanding. The factors influencing clothing selection have been arranged into a logical system of concepts grouped into four categories: Social/Psychological, Economic, Environmental, and Physiological Factors. A detailed deconstruction of each of these factors has been further conceptualized into smaller concepts and discussed in a nuanced context. Under the Social/Psychological factors are concepts such as culture, gender, occupation, personality, religion, socio-status, and fashion. Economic factors evaluated are concepts such as the Economic well-being of a Nation (GDP), the Economic status of individuals, Economic environment (availability of technology and raw materials and buying behavior). Environmental factors such as Location (Urban/Rural), Climatic conditions, protection from extreme weather conditions, and unconventional places like deep seas, and space were examined. Lastly, the physiological factors such as age, health condition, and figure were also evaluated. Developing a conceptual framework for understanding clothing selection is apt in filling the gap in Literature since most research works on factors influencing clothing selection may not have been organized into a framework for clothing selection, whether intrinsically or extrinsically motivated.

TENNER: intrusion detection models for industrial networks based on ensemble learning

In the pursuit of discerning patterns within computer network attacks, the utilization of Machine Learning and Deep Learning algorithms has been prevalent for crafting detection models based on extensive network traffic datasets. Furthermore, enhancing detection efficacy is feasible by applying cluster learning techniques, wherein multiple Machine Learning models collaborate to yield detection outcomes. Nevertheless, it is imperative to discern the optimal features within the dataset for training the intrusion detection model. In the present study, we proffer a novel framework for feature selection and intrusion detection within industrial networks, employing Ensemble Learning to achieve commendable performance in terms of both high predictive accuracy and efficient learning duration. The outcomes evince that the proposed model exhibits an accuracy of 99.93%, with a mere one h and 34 min required for comprehensive training. In contrast, a model trained without the framework presented in this paper attains an accuracy of 99.94%, necessitating an extensive training period of 156 h. Notably, the detection model derived from the proposed solution demonstrates superior results in prediction time, accomplishing predictions within 0.0009 seconds, compared to the alternative model which requires 0.0076 seconds for predictions.

An efficient Quasi-Affine Transformation Evolutionary algorithm with fixed dimension updating and its application in UAV 3D path planning

Quasi-Affine Transformation Evolutionary (QUATRE) algorithm is a kind of swarm-based collaborative optimization algorithm that solves the problem of a position deviation in a DE search by using the co-evolution matrix M instead of the cross-control parameter CR in the differential evolution algorithm (DE). However, QUATRE shares some of the same weaknesses as DE, such as premature convergence and search stagnation. Inspired by the artificial bee colony algorithm (ABC), we propose a new QUATRE algorithm to improve these problems that ranks all the individuals and evolves only the poorer half of the population. In an evolving population, individuals of different levels intersect with dimensions of different sizes to improve search efficiency and accuracy. In addition, we establish a better selection framework for the parent generation individuals and select more excellent parent individuals to complete the evolution for the individuals trapped in search stagnation. To verify the performance of the new QUATRE algorithm, we divide the comparison algorithm into three groups, including ABC variant group, DE variant group, and QUATRE variant group, and the CEC2014 test suite is used for the comparison. The experimental results show the new QUATRE algorithm performance is competitive. We also successfully apply the new QUATRE algorithm on the 3D path planning of UAV, and compared with the other famous algorithm performance it is still outstanding, which verifies the algorithm’s practicability.

Developing a Selection Framework for Zinc Ion-Based Biomaterial Design: Guided by the Biosafety Assessment of ZIF-8 and ZnO.

In recent years, nanomaterials have gained widespread use in the biomedical field, with ZIF-8 and ZnO emerging as promising candidates due to their remarkable performance in osteogenesis, angiogenesis, and antimicrobial therapy. However, before advancing these nanomaterials for clinical applications, it is imperative to evaluate their biocompatibility. In particular, comparing nanomaterials with similar biomedical functions is crucial for identifying the most suitable nanomaterials for further development and market entry. Our study aimed to compare the biocompatibility of nano-ZIF-8 and nano-ZnO under the same conditions. We found that nano-ZIF-8 exhibited lower toxicity both in vitro and in vivo compared to nano-ZnO. To gain insights into the underlying mechanisms responsible for this difference, we conducted further experiments to investigate lysosome damage, mitochondrial change, and the occurrence of ferroptosis. Additionally, we performed transcriptome sequencing to analyze the expression of relevant genes, thereby providing robust validation for our findings. In summary, our study highlighted the importance of evaluating nanomaterials with similar biomedical effects. Through this comparative study, we have not only shed light on the superior biocompatibility of nano-ZIF-8 over nano-ZnO, but also contributed valuable insights and methodological references for future material screening endeavors. Ultimately, our study served as a stepping stone toward the development of safer and more effective nanomaterials for various biomedical applications.

Semi-supervised multi-label feature selection based on dual dynamic graph

Traditional multi-label feature selection is usually performed under the condition of given label information, but nowadays, labeling multi-label data is a huge project, which is both time-consuming and labor-intensive, but if there is no label information condition, it will lead to poor feature selection, in order to solve this problem, this paper proposes a new semi-supervised multi-label feature selection method, i.e., semi-supervised multi-label feature selection algorithm based on dual dynamic graph. In this paper, a semi-supervised multi-label feature selection algorithm is proposed by constructing a dual dynamic graph. First, the method selects the most discriminative features for dimensionality reduction through the feature selection method of least squares regression, combined with the redundancy penalty of highly correlated features. Second, the label information is added to the construction of sample matrix similarity to learn the similarity. A semi-supervised multi-label feature selection framework is constructed by designing iterative updates of dual dynamic graphs to learn more accurate pseudo-label matrices to guide feature selection. Finally, the paper validates the above model using the alternating iteration optimization algorithm and verifies the effectiveness of the algorithm through experiments.

2024 European Heart Rhythm Association/Heart Rhythm Society/Asia Pacific Heart Rhythm Society/Latin American Heart Rhythm Society expert consensus statement on catheter and surgical ablation of atrial fibrillation.

In the last three decades, ablation of atrial fibrillation (AF) has become an evidence-based safe and efficacious treatment for managing the most common cardiac arrhythmia. In 2007, the first joint expert consensus document was issued, guiding healthcare professionals involved in catheter or surgical AF ablation. Mounting research evidence and technological advances have resulted in a rapidly changing landscape in the field of catheter and surgical AF ablation, thus stressing the need for regularly updated versions of this partnership which were issued in 2012 and 2017. Seven years after the last consensus, an updated document was considered necessary to define a contemporary framework for selection and management of patients considered for or undergoing catheter or surgical AF ablation. This consensus is a joint effort from collaborating cardiac electrophysiology societies, namely the European Heart Rhythm Association, the Heart Rhythm Society (HRS), the Asia Pacific HRS, and the Latin American HRS.

Intelligent device recognition of internet of things based on machine learning

With the rapid popularization and application of Internet of Things technology, smart devices have become an indispensable part of people's daily lives. Therefore, it is crucial to accurately identify these devices as their numbers continue to grow. The research aimed to introduce a lightweight method for identifying Internet of Things devices based on network flow characteristics and scheduling algorithms. This can improve device identification accuracy while maintaining high efficiency. The research constructed a comprehensive optimization algorithm selection framework to achieve performance optimization in different scenarios. This framework took into account many factors, including network traffic characteristics, device identification requirements, and system efficiency, to ensure flexible adaptation in different scenarios and optimize overall performance. Research results showed that the proposed system had an accuracy of 96.8 % at 1-minute intervals, which increased to 99.7 % at 10-minute intervals, and reached 99.9 % at both 30-minute and 60-minute intervals. In 100 experiments, the research method improved the accuracy by an average of 1.5 % compared with the baseline. In fingerprint recognition, the overall accuracy of the long short-term memory network exceeded 90 %, with an area under the curve of 0.99. Most devices had an accuracy of over 95 % in recognition, and the recall rate remained around 90 %, the effectiveness of the method proposed in the study was further verified. The method proposed in the study not only improved the accuracy and efficiency of device recognition, but also provided powerful solutions for the field of network security. This provides useful guidance for research and practical applications in related fields.

Multi-criteria decision framework for catalyst selection: Production of formic acid as a circular liquid organic hydrogen carrier in the hydrogen economy

This study aims to use the multi-criteria decision analysis (MCDA) for the selection of suitable catalysts for the production of formic acid (FA), which serves as a circular liquid H2 carrier for the H2-carbon cycle. TOPSIS, VIKOR, ARAS, COPRAS, and PROMETHEE analytical decision-making methods were used to establish priority rankings for each method. With the exception of the top two ranks, the rankings differed across the methods. Therefore, we proposed an alternative ranking for the catalyst selection in the FA production, where all the methods’ results were given equal priority. By aggregating the outcomes from the decision methods and sorting them in ascending order, a final prioritized ranking was established. Our results showed that the proposed MCDA method was effective in identifying the most suitable catalyst for the FA production as a circular LOHC in the H2-carbon cycle. The model determined that Pd nanoparticle-based activated carbon-supported catalyst exhibited high activity, selectivity, and stability, while also being cost-effective. The findings of this study might contribute to the development of more efficient H2 storage systems based on FA.

A matrix norm-based Pythagorean fuzzy metric and its application in MEREC-SWARA-VIKOR framework for solar panel selection

Due to the escalating electricity demand from industrialization and modernization, it is imperative to explore clean and sustainable energy sources. Solar energy has become a feasible solution for this surging demand. Solar panels, vital components of solar power systems, are crucial in converting sunlight into electricity. However, selecting the most suitable solar panel is a complex task involving subjective and measurable parameters involving uncertainties. To address the uncertainties and ambiguities in real-life problems, the theory of Pythagorean fuzzy sets with distance measures has emerged as a flexible and superior tool. Nevertheless, many existing distance functions often fail to meet necessary metric conditions, leading to inaccurate and unreasonable outcomes. These limitations are pointed out by many literature. In addition, we observe a serious drawback of parameter dependency of some existing measures. This paper proposes a novel metric for Pythagorean fuzzy sets using the matrix norm to overcome these drawbacks. Additionally, we discuss various mathematical properties and provide geometrical representations for the proposed distance measure. To demonstrate the superiority of the proposed measure, we conduct a comparative study against existing distance measures. Furthermore, we develop a Pythagorean fuzzy-method based on the removal effects of criteria-stepwise weight assessment ratio analysis-vlse kriterijumska optimizacija kompromisno resenje (PF-MEREC-SWARA-VIKOR) method for selecting solar panel systems. Our approach incorporates the MEREC method for objective criteria, utilizes the SWARA method to calculate subjective weights, and employs the VIKOR method in the Pythagorean fuzzy context to determine the preference order of alternatives. We conduct sensitivity analysis and comparative studies with existing developed methods to validate our method. Highlight the effectiveness and advantages of our novel approach in selecting solar panels. Finally, exploring the future research directions and challenges associated with the proposed methodology.

A multi-criteria framework for electric vehicle charging location selection using double hierarchy preferences and unknown weights

The adverse effects of traditional vehicles on the environment increase the demand for clean vehicles, such as electric vehicles (EVs). The correct positioning of the charging points for such vehicles certainly promotes the acceptance and spread of EVs. Indeed, selecting optimal locations for electric vehicle charging stations (EVCS) is crucial for shaping a sustainable future. This study introduces an integrated methodology under a double hierarchy linguistic context with a criteria importance through an inter-criteria correlation (CRITIC) technique for experts' reliability determination, an attitudinal Cronbach's method for criteria weight estimation, and a novel multi-criteria technique considering the compromise ranking of alternatives from distance to ideal solution (CRADIS) formulation for optimal EVCS location selection. Based on the results, criteria such as service capability, ecological impact, land cost, and traffic density are the most crucial, with Manapparai, India, as the optimal location for a new EVCS construction. Further, a crucial finding is that the social dimension is substantial compared to the economy and environment dimensions for EVCS location selection. The novelty of the paper is that (i) uncertainty and expression of choices in natural language form for locations for EVCS are modeled effectively using a double hierarchy structure, (ii) experts' weights are obtained methodically by considering hesitation and interactions among experts, (iii) interdependencies among criteria and importance of experts are considered during criteria weight determination, and (iv) locations are ranked by not only considering criteria type but also resembles closely to the human-centric decision process. A detailed sensitivity analysis is further conducted to prove the proposed approach's effectiveness and stability. In the context of sustainable transportation, the work could contribute to the relevant literature through a powerful decision-making tool.

A closed‐loop representative day selection framework for generation and transmission expansion planning with demand response

AbstractIn power systems with a high proportion of renewable energy resources (RES), the inherent stochasticity and volatility of RES necessitate careful consideration in power system planning. Scenario analysis is commonly employed to address the stochastic nature in power system planning. Existing studies generally adopt an open‐loop structure, where representative days are selected first and planning decisions are subsequently made. However, this method may not accurately represent the operating status of a system owing to changes in the power generation structure during the planning process. To address this limitation, this paper introduces a closed‐loop framework for representative day selection within the context of generation and transmission expansion planning (G&TEP), incorporating demand response (DR). The framework comprises three layers: representative day selection, planning decisions, and long‐term operational simulation. Initially, an approach for selecting representative days is proposed by combining the clustering and optimization‐based methods. Subsequently, a G&TEP model that incorporates DR is presented in the second layer. Lastly, the framework encompasses a three‐layer closed‐loop structure, enabling dynamic adjustments and enhancements to the representative day selection process to ensure optimality. Case studies on the reliability and operational test system of a power grid with large‐scale renewable integration (XJTU‐ROTS) demonstrate the effectiveness of our proposed framework.

EAG-RS: A Novel Explainability-Guided ROI-Selection Framework for ASD Diagnosis via Inter-Regional Relation Learning.

Deep learning models based on resting-state functional magnetic resonance imaging (rs-fMRI) have been widely used to diagnose brain diseases, particularly autism spectrum disorder (ASD). Existing studies have leveraged the functional connectivity (FC) of rs-fMRI, achieving notable classification performance. However, they have significant limitations, including the lack of adequate information while using linear low-order FC as inputs to the model, not considering individual characteristics (i.e., different symptoms or varying stages of severity) among patients with ASD, and the non-explainability of the decision process. To cover these limitations, we propose a novel explainability-guided region of interest (ROI) selection (EAG-RS) framework that identifies non-linear high-order functional associations among brain regions by leveraging an explainable artificial intelligence technique and selects class-discriminative regions for brain disease identification. The proposed framework includes three steps: (i) inter-regional relation learning to estimate non-linear relations through random seed-based network masking, (ii) explainable connection-wise relevance score estimation to explore high-order relations between functional connections, and (iii) non-linear high-order FC-based diagnosis-informative ROI selection and classifier learning to identify ASD. We validated the effectiveness of our proposed method by conducting experiments using the Autism Brain Imaging Database Exchange (ABIDE) dataset, demonstrating that the proposed method outperforms other comparative methods in terms of various evaluation metrics. Furthermore, we qualitatively analyzed the selected ROIs and identified ASD subtypes linked to previous neuroscientific studies.

154 Leveraging Implementation Science Competencies to Establish a D&I Science Core

OBJECTIVES/GOALS: Clinical and Translational Science Award (CTSA) hubs are launching D&I Science cores to provide resources and services to promote the translation of clinical evidence into practice. We developed a D&I Science Core strategic plan reflecting our research community’s needs by assessing Implementation Science (IS) competencies. METHODS/STUDY POPULATION: The Tufts CTSI D&I Science Core was launched in early 2023. To design services that meet research community needs, we conducted a survey and key informant interviews based on Padek etal.’s list of Implementation Science (IS) competencies. The competencies are organized into four domains (Definition, Background, and Rationale; Theory and Approaches; Design & Analysis; and Practice-Based Considerations) and categorized by expertise level (beginner, intermediate, advanced). Participants who had attended or expressed interest in a D&I interest group were asked via an email survey to rate their level of confidence in completing selected IS-related research activities, about their experience with IS research or practice, and the types of resources, services and training they desired. RESULTS/ANTICIPATED RESULTS: Twenty researchers (20/65, 31%) submitted survey responses and six researchers participated in in-depth interviews. Survey respondents felt most confident in engaging stakeholders in IS research and least confident selecting a model or framework for a study. Results suggest that researcher capacity building is needed to: • Understand IS models and frameworks and their approaches, strengths, and limitations • Select and use models and frameworks in studies • Assemble IS teams and prepare grant proposals Suggestions for resources, services, and training, include: • Customized education to address diverse needs, knowledge levels, and learning styles • Promotion of D&I Core consultations and grant support services • Sharing of successful proposals to help researchers learn how to apply IS methods DISCUSSION/SIGNIFICANCE: A strategic workplan for the D&I Science Core was developed and implemented to address the findings. Initial emphasis is on developing easily accessible resources and timely consultations for investigators new to IS needing to apply these methods in current grant proposals, while also providing training resources for deeper skill building.

Novel item selection strategies for cognitive diagnostic computerized adaptive testing: A heuristic search framework.

The computerized adaptive form of cognitive diagnostic testing, CD-CAT, has gained increasing attention in the domain of personalized measurements for its ability to categorize individual mastery status of fine-grained attributes more accurately and efficiently through administering items tailored to one's ability progressively. How to select the next item based on previous response(s) is crucial for the success of CD-CAT. Previous item selection strategies for CD-CAT have often followed a greedy or semi-greedy approach, which makes it difficult to strike a balance between diagnostic performance and item bank utilization. To address this issue, this study takes a graph perspective and transforms the item selection problem in CD-CAT into a path-searching problem, in which paths refer to possible test construction and nodes refer to individual items. A heuristic function is defined to predict the prospect of a path, indicating how well the corresponding test can diagnose the current examinee. Two search mechanisms with different biases towards item exposure control are proposed to approximate the optimal path with the best prospect. The first unused item on the resulting path is selected as the next item. The above components compose a novel CD-CAT item selection framework based on heuristic search. Simulation studies are conducted under a variety of conditions regarding bank designs, bank-quality conditions, and testing scenarios. The results are compared with different types of classic item selection strategies in CD-CAT, showing that the proposed framework can enhance bank utilization at a smaller cost of diagnostic performance.

How territoriality and sociality influence the habitat selection and movements of a large carnivore.

While territoriality is one of the key mechanisms influencing carnivore space use, most studies quantify resource selection and movement in the absence of conspecific influence or territorial structure. Our analysis incorporated social information in a resource selection framework to investigate mechanisms of territoriality and intra-specific competition on the habitat selection of a large, social carnivore. We fit integrated step selection functions to 3-h GPS data from 12 collared African wild dog packs in the Okavango Delta and estimated selection coefficients using a conditional Poisson likelihood with random effects. Packs selected for their neighbors' 30-day boundary (defined as their 95% kernel density estimate) and for their own 90-day core (defined as their 50% kernel density estimate). Neighbors' 30-day boundary had a greater influence on resource selection than any habitat feature. Habitat selection differed when they were within versus beyond their neighbors' 30-day boundary. Pack size, pack tenure, pup presence, and seasonality all mediated how packs responded to neighbors' space use, and seasonal dynamics altered the strength of residency. While newly-formed packs and packs with pups avoided their neighbors' boundary, older packs and those without pups selected for it. Packs also selected for the boundary of larger neighboring packs more strongly than that of smaller ones. Social structure within packs has implications for how they interact with conspecifics, and therefore how they are distributed across the landscape. Future research should continue to investigate how territorial processes are mediated by social dynamics and, in turn, how territorial structure mediates resource selection and movement. These results could inform the development of a human-wildlife conflict (HWC) mitigation tool by co-opting the mechanisms of conspecific interactions to manage space use of endangered carnivores.

2024 European Heart Rhythm Association/Heart Rhythm Society/Asia Pacific Heart Rhythm Society/Latin American Heart Rhythm Society expert consensus statement on catheter and surgical ablation of atrial fibrillation.

In the last three decades, ablation of atrial fibrillation (AF) has become an evidence-based safe and efficacious treatment for managing the most common cardiac arrhythmia. In 2007, the first joint expert consensus document was issued, guiding healthcare professionals involved in catheter or surgical AF ablation. Mounting research evidence and technological advances have resulted in a rapidly changing landscape in the field of catheter and surgical AF ablation, thus stressing the need for regularly updated versions of this partnership which were issued in 2012 and 2017. Seven years after the last consensus, an updated document was considered necessary to define a contemporary framework for selection and management of patients considered for or undergoing catheter or surgical AF ablation. This consensus is a joint effort from collaborating cardiac electrophysiology societies, namely the European Heart Rhythm Association, the Heart Rhythm Society, the Asia Pacific Heart Rhythm Society, and the Latin American Heart Rhythm Society .

Online group streaming feature selection based on fuzzy neighborhood granular ball rough sets

Online group streaming feature selection holds significant research value in large-scale streaming data processing scenarios, and the related work based on rough set theory has attracted academic interest. Nevertheless, most relevant algorithms come with parameters, and performing a grid search for the optimal parameters reduces the efficiency. Moreover, the existing online group streaming feature selection frameworks cannot effectively fit the situation in practice. Focusing on these issues, this paper investigates an online group streaming feature selection method based on fuzzy neighborhood granular ball rough sets. First, Canopy clustering is introduced to granular ball computing, and the adaptive neighborhood of samples is generated based on the granular ball distribution. Second, we construct a fuzzy neighborhood granular ball rough set (FNGBRS) model and propose the integrated dependence degree to achieve maximal dependency and minimum classification error. Then, the purity of granular balls is considered as the weight of features, and some uncertainty measures based on FNGBRS are presented. Finally, we define a random factor to control the size of streaming groups and design an online group streaming feature selection algorithm. Comparative experimental results on sixteen public datasets demonstrate that the proposed algorithm exhibits superior and stable classification performance, coupled with increased efficiency from its parameter-free design.

Fuzzy Einstein-based decision-making model for the evaluation of site selection criteria of floating photovoltaic system

The site selection step is one of the milestones required to ensure the success of a renewable energy project. The present study proposes a novel framework for the suitable site selection of floating photovoltaic (FPV) systems by applying a robust Multi-Criteria Decision-Making (MCDM) method. A comprehensive literature review was performed to identify the exclusion and decision criteria. A questionnaire was prepared, and 30 experts from academia, industry, and non-governmental organizations answered it. The Fuzzy Einstein-based Logarithmic Additive Estimation of Weight Coefficients (LAAW) model was applied to assess the expert estimates and to determine the weights of 4 main and 41 sub-criteria. Based on the fuzzy model results, the main criteria were ranked in the following order of importance: economical, technical, environmental, and social and regulations. Although the number of decision criteria and the MCDM method significantly differ, the results of the present study are consistent with the findings of previous studies in the literature.