Set Framework Research Articles

Strategies to reduce antimicrobials in livestock and aquaculture, and their impact under field conditions: a structured scoping literature review.

Antimicrobial resistance is a pandemic problem, causing substantial health and economic burdens. Antimicrobials are extensively used in livestock and aquaculture, exacerbating this global threat. Fostering the prudent use of antimicrobials will safeguard animal and human health. A lack of knowledge about alternatives to replace antimicrobials, and their effectiveness under field conditions, hampers changes in farming practices. This work aimed to understand the impact of strategies to reduce antimicrobial usage (AMU) in livestock and aquaculture, under field conditions, using a structured scoping literature review. The Extension for Scoping Reviews of the Preferred Reporting Items for Systematic Reviews and Meta-Analysis guidelines (PRISMA-ScR) were followed and the Patient, Intervention, Comparison, Outcome, Time and Setting (PICOTS) framework used. Articles were identified from CAB Abstracts, MEDLINE and Scopus. A total of 7505 unique research articles were identified, 926 of which were eligible for full-text assessment; 203 articles were included in data extraction. Given heterogeneity across articles in the way alternatives to antimicrobials or interventions against their usage were described, there was a need to standardize these by grouping them in categories. There were differences in the impacts of the strategies between and within species; this highlights the absence of a 'one-size-fits-all' solution. Nevertheless, some options seem more promising than others, as their impacts were consistently equivalent or positive when compared with animal performance using antimicrobials. This was particularly the case for bioactive protein and peptides, and feed/water management. The outcomes of this work provide data to inform cost-effectiveness assessments of strategies to reduce AMU.

Multi-Attribute Decision-Making Based on Picture Fuzzy Einstein Operator and The TOPSIS Method

Picture Fuzzy Sets (PFSs) denote the extension of conventional fuzzy sets, which capture a broader spectrum of human opinions, encompassing responses such as acceptance, neutrality, rejection, and hesitation. This wider range of responses cannot be accurately accommodated within fuzzy sets as well as intuitionistic fuzzy sets framework. In the realm of Multiple Attribute Group Decision-Making (MAGDM) methods, attributes frequently exhibit conflicts, uncertainties, imprecisions, as well as a lack of commensurability. To tackle the complexities inherent in MAGDM, the Technique for Order of Preference by Similarity to the Ideal Solution (TOPSIS) method has demonstrated its effectiveness. This method is employed in a compromise ranking approach founded on aggregation functions that showcase closeness to the reference points. This study's goal is to instigate a fresh approach to aggregation, referred to as the Picture Fuzzy Einstein Weighted Averaging Distance-based TOPSIS (PFEWAD-TOPSIS) method. To validate the effectiveness of this method in addressing MAGDM problems, a detailed example is conducted.

Basis Set Generation and Optimization in the NISQ Era with Quiqbox.jl.

In the noisy intermediate-scale quantum era, ab initio computation of electronic structure problems has become one of the major benchmarks for identifying the boundary between classical and quantum computational power. Basis sets play a key role in the electronic structure methods implemented on both classical and quantum devices. To investigate the consequences of single-particle basis sets, we propose a framework for more customizable basis set generation and optimization. This framework allows composite basis sets to go beyond typical basis set frameworks, such as atomic basis sets, by introducing the concept of mixed-contracted Gaussian-type orbitals. These basis set generations set the stage for more flexible variational optimization of basis set parameters. To realize this framework, we have developed an open-source software package named "Quiqbox" in the Julia programming language. We demonstrate various examples of using Quiqbox for basis set optimization and generation, ranging from optimizing atomic basis sets on the Hartree-Fock level, preparing the initial state for variational quantum eigensolver computation, and constructing basis sets with completely delocalized orbitals. We also include various benchmarks of Quiqbox for basis set optimization and ab initial electronic structure computation.

Cumulative risk assessment with pesticides in the framework of MRL setting.

This abstract presents a report on the proposed work programme, focus on cumulative risk assessment (CRA) for chemical risks, specifically pesticide residues in food. While not a scientific publication, this technical report aims to provide insights without including the fellow's data to avoid publication restrictions. This report focuses on addressing the question concerning the trigger value to perform a prospective CRA in case of a new maximum residue level (MRL) setting. The 1,000 margin of exposure (MOE) threshold value was tested and compared to preliminary ANSES results. Alternative thresholds were calculated and explored. The EU-FORA fellow selected two cumulative assessment groups (CAGs) related to acute craniofacial alterations and chronic thyroid effects. The fellow performed exposure assessments, integrating effects data, French monitoring data, processing factors, agricultural uses, MRLs and extrapolations into Monte Carlo risk assessment (MCRA). Retrospective cumulative exposures using MCRA were conducted for children, adults and a vulnerable group of childbearing women based on the French survey INCA3, identifying background levels at P99.9. The fellow also performed prospective assessments with MCRA, analysing results at P99.9 to evaluate the adequacy of the 1,000 MOE threshold. Alternative thresholds are discussed and proposed.

A Socio-Ecological Approach to A Community-Based Health Promotion Intervention on the U.S.-Mexico Border: Insights and Lessons Learned During the COVID-19 Pandemic

The Healthy Families Healthy Kids Initiative (HFHKI) is a community-based initiative developed based on the socio-ecological model to address preventive health care needs in El Paso County, Texas, one of the most economically and health-challenged border communities in the United States. HFHKI’s three main goals are to increase access to experiential learning and health education, service delivery, and sustainable systems/linkages of care. These were accomplished through seven critical activities. We present the rationale, background, setting, and conceptual framework for the initiative, followed by the methods used to develop and assess the success of the activities and results of our project outcomes. We end with a discussion of lessons learned and future directions. We also share insights gained from our community health promotion intervention during the COVID-19 pandemic, which will enrich current conversations among applied social scientists. Overall, our project served 2,347 participants of all age ranges during our first year of project implementation across all 7 activities. We recommend the use of the socio-ecological model in designing, implementing, and improving health interventions aimed at enhancing family and community health, with each intervention tailored to the needs of different segments of the community. While the need to contain the COVID-19 virus amid the pandemic has created challenges in health promotion efforts, the need to help affected communities regain control of their health cannot be stressed enough. Our project can serve as an implementation framework for community-based projects on the U.S.-Mexico border.

An integrated SWARA-QFD under Fermatean fuzzy set approach to assess proactive risk mitigation strategies in recycling supply chain: Case study of plastic recycling industry

The role of the recycling supply chain in managing plastic waste problems has been emphasized by both academics and practitioners. Plastic recycling producers face various risks associated with supply chain processes that require proper management and proactive strategies to mitigate negative impacts on business performance. However, quantitative research on risk factors and corresponding proactive risk mitigation strategies in recycling supply chain has been limited. This study proposes a novel integrated approach using Stepwise Weight Assessment Ratio Analysis (SWARA) and Quality Function Deployment (QFD) under the Fermatean fuzzy set framework to fill this research gap. The study identifies eleven risk factors of recycling supply chain and eight corresponding proactive risk mitigation strategies through literature review and expert validation. The SWARA technique is employed to compute the relative importance weights of risk factors, and QFD is used to analyze the relationship between risk factors and corresponding proactive risk mitigation strategies. The proposed framework is applied to a recycling plastic packaging waste manufacturer in Thailand as a case study. The results demonstrate that the proposed framework can assist logistics and supply chain managers in the recycling plastic industry in making rational decisions to implement proactive risk mitigation strategies. Moreover, the framework can be applied to other waste recycling industries in a similar way.

Enhancing student conceptual understanding and critical thinking through SETS-based digital modules on environmental changes

The rapid advancements in science and technology have necessitated the adaptation of biology education to enhance students' understanding and critical thinking skills, aligning with the demands of the 21st century. The development of digital modules based on the Science, Environment, Technology, and Society (SETS) framework represents an innovative approach to facilitate students' learning by seamlessly integrating theories from science, environment, technology, and society. This research adopts the Research and Development methodology, employing the ADDIE development model. The digital module's development underwent validation by four experts, comprising two media experts and two material experts. The validation results yielded a 90% agreement from media experts and an 85% concurrence from material experts, indicating the suitability of the digital module on environmental change, grounded in the SETS framework, for educational use. The developed module was subjected to testing using a one-group pretest-posttest design, followed by the analysis of the obtained data through paired sample t-tests. The analysis results indicate a significant improvement in both conceptual understanding and critical thinking skills following the utilization of the module. Hence, this module is recommended for use in biology education, particularly in the context of environmental change materials.

The MoXFo initiative – study design: Considerations related to study design and methodology in exercise research for people with multiple sclerosis

Background: Exercise as a subset of physical activity is a cornerstone in the management of multiple sclerosis (MS) based on its pleiotropic effects, but continued progression of the field requires better future designs and methodologies. Objectives: This paper outlines the work of the ‘Study design and methodology’ group of the MoXFo (moving exercise research forward) initiative, and addresses critical aspects and future directions when defining the research question of interest, and subsequently, designing the study and exercise intervention in MS patients. Methods: The work is based on the formation of an international expert panel formed within the MoXFo initiative. We provide a structured and concise synthesis of exercise-specific MS research challenges and considerations when designing randomized controlled trials (RCTs). Results: Challenges and considerations are presented using the Patient population, Intervention, Comparator, Outcomes, Timing, Setting (PICOTS) framework, thereby forming a new and specific MS exercise PICOTS framework. Conclusion: We propose that researchers should carefully consider and align all elements of this MS exercise PICOTS framework when developing future research questions and study designs, ultimately improving the quality of new exercise studies in people with MS.

An Improved Intuitionistic Fuzzy Decision-Theoretic Rough Set Model and Its Application

The Decision-Theoretic Rough Set model stands as a compelling advancement in the realm of rough sets, offering a broader scope of applicability. This approach, deeply rooted in Bayesian theory, contributes significantly to delineating regions of minimal risk. Within the Decision-Theoretic Rough Set paradigm, the universal set undergoes a tripartite division, where distinct regions emerge and losses are intelligently distributed through the utilization of membership functions. This research endeavors to present an enhanced and more encompassing iteration of the Decision-Theoretic Rough Set framework. Our work culminates in the creation of the Generalized Intuitionistic Decision-Theoretic Rough Set (GI-DTRS), a fusion that melds the principles of Decision-Theoretic Rough Sets and intuitionistic fuzzy sets. Notably, this synthesis bridges the gaps that exist within the conventional approach. The innovation lies in the incorporation of an error function tailored to the hesitancy grade inherent in intuitionistic fuzzy sets. This integration harmonizes seamlessly with the contours of the membership function. Furthermore, our methodology deviates from established norms by constructing similarity classes based on similarity measures, as opposed to relying on equivalence classes. This shift holds particular relevance in the context of aggregating information systems, effectively circumventing the challenges associated with the process. To demonstrate the practical efficacy of our proposed approach, we delve into a concrete experiment within the information technology domain. Through this empirical exploration, the real-world utility of our approach becomes vividly apparent. Additionally, a comprehensive comparative analysis is undertaken, juxtaposing our approach against existing techniques for aggregation and decision modeling. The culmination of our efforts is a well-rounded article, punctuated by the insights, recommendations, and future directions delineated by the authors.

A novel (alpha, beta)-indiscernibility-assisted intuitionistic fuzzy-rough set model and its application to dimensionality reduction

An intuitionistic fuzzy (IF) set is an interesting extension of a fuzzy set (FS) that includes more accurate expression of the uncertainty in contrast to conventional FS theory. By considering positive, negative, and hesitancy degree concurrently, IF set appears as an effective notion to deal with the uncertainty included by both the judgement and identification. In this article, we propose an innovative IF rough set (IFRS) framework that integrates IF set and rough set (RS) with the concept of (alpha, beta)-indiscernibility, which incorporates two thresholds to avoid noise. In addition, we utilize the supporting proofs to defend the suggested framework. Thereafter, a feature selection method is presented by using the proposed framework, and an appropriate algorithm is provided to determine the reduct for the high-dimensional datasets. Moreover, a concrete illustration using a sample information system depicts the efficacy of the suggested idea. Then, the proposed method is applied on the benchmark datasets to produce the reduced datasets. Furthermore, performance measures of numerous machine learning techniques are evaluated over these reduced datasets. Finally, a comparative study with the recently discussed fuzzy rough set (FRS) and IFRS-assisted methods is presented to justify the superiority of the established technique.

Elastic founder graphs improved and enhanced

Indexing labeled graphs for pattern matching is a central challenge of pangenomics. Equi et al. (2022) [14] developed the Elastic Founder Graph (EFG) representing an alignment of m sequences of length n, drawn from alphabet Σ plus the special gap character: the paths spell the original sequences or their recombination. By enforcing the semi-repeat-free property, the EFG admits a polynomial-space index for linear-time pattern matching, breaking through the conditional lower bounds on indexing labeled graphs (Equi et al. [13]). In this work, we improve the space of the EFG index answering pattern matching queries in linear time, from linear in the length of all strings spelled by three consecutive node labels, to linear in the size of the edge labels. Then, we develop linear-time construction algorithms optimizing for different metrics: we improve the existing linearithmic construction algorithms to O(mn), by solving the novel exclusive ancestor set problem on trees; we propose, for the simplified gapless setting, an O(mn)-time solution minimizing the maximum block height, that we generalize by substituting block height with prefix-aware height. Finally, to show the versatility of the framework, we develop a BWT-based EFG index and study how to encode and perform document listing queries on a set of paths of the graphs, reporting which paths present a given pattern as a substring. We propose the EFG framework as an improved and enhanced version of the framework for the gapless setting, along with construction methods that are valid in any setting concerned with the segmentation of aligned sequences.

A data-driven decision support framework for DEA target setting: an explainable AI approach

The intention of target setting for Decision-Making Units (DMUs) in Data Envelopment Analysis (DEA) is to perform better than their peers or reach a reference efficiency level. However, most of the time, the logic behind the target setting is based on mathematical models, which are not achievable in practice. Besides, these models are based on decreasing/increasing inputs/outputs that might not be feasible based on DMU's potential in the real world. We propose a data-driven decision support framework to set actionable and feasible targets based on vital inputs-outputs for target setting. To do so, DMUs are classified in their corresponding Efficiency Frontier (EF) levels based on multiple EFs approach and a machine learning classifier. Then, the vital inputs-outputs are determined using an Explainable Artificial Intelligence (XAI) method. Finally, a Multi-Objective Counterfactual Explanation is developed based on DEA (MOCE-DEA) to lead DMU in reaching the reference EF by adjusting actionable and feasible inputs-outputs. We studied Iranian hospitals to evaluate the proposed framework and presented two cases to demonstrate its mechanism. The results show that the performance of the DMUs is improved to reach the reference EF for studied cases. Then, a validation was conducted with the primal DEA model to show the robust improvement of DMUs after adjusting their original value based on the generated solutions by the proposed framework. It demonstrates that the adjusted values can also improve DMUs' performance in the primal DEA model.

Factors influencing travellers’ decision-making behaviour towards online travel purchases: a fuzzy-DEMATEL approach

ABSTRACT The growth of technology and easy access to the internet have led to an unparalleled boom in online booking. The uncertainty surrounding online travel purchases, however, still makes traveller’s decision-making process very stressful. Hence, this study aims to identify and classify different factors that drive travellers’ decision-making and actual behaviour. This study seeks to fill the void by systematically reviewing and synthesizing the online travel literature. The study utilizes the fuzzy-DEMATEL approach to identify, classify and analyse various factors to provide a comprehensive framework for understanding sets of factors driving travellers’ decisions and their actual behaviour. The findings reveal that travellers’ online travel purchasing behaviour is influenced most by their attitude, while social influence also emerged as a significant factor. This study provides key insights for academics, researchers and practitioners by identifying major factors and sub-factors, along with their causal interactions. This study is novel in its approach as it significantly contributes to existing knowledge by synthesizing the literature and employing the fuzzy-DEMATEL technique in categorizing the key factors that influence travellers’ decision-making and actual purchase.

Fairness in Machine Learning Meets with Equity in Healthcare

With the growing utilization of machine learning in healthcare, there is increasing potential to enhance healthcare outcomes. However, this also brings the risk of perpetuating biases in data and model design that can harm certain demographic groups based on factors such as age, gender, and race. This study proposes an artificial intelligence framework, grounded in software engineering principles, for identifying and mitigating biases in data and models while ensuring fairness in healthcare settings. A case study is presented to demonstrate how systematic biases in data can lead to amplified biases in model predictions, and machine learning methods are suggested to pre-vent such biases. Future research aims to test and validate the proposed ML framework in real-world clinical settings to evaluate its impact on promoting health equity.

Room-aware portable Auditory Augmented Reality: Real-time spatial audio generation with geometric data analysis

This study proposes an innovative and practical approach to spatial acoustic rendering for Auditory Augmented Reality (AAR) in small room usage. AAR aims to enhance user experiences by overlaying spatially appropriate sounds in the real environment, creating the illusion that virtual sounds coexist in the physical world. However, current technological constraints in hardware and AAR rendering present challenges in creating a truly immersive sensation of “being there.” To address these challenges, we present a dynamic processing framework for a typical mobile AAR setting. This framework combines a game-audio engine with 3-D visual computation both specialized for real-time processing, providing a plausible spatial impression with lightweight computation. The process involves capturing room information by the Light Detection And Ranging (LiDAR) sensor mounted on the device, followed by estimating its dominant acoustic materials. The generated data will be used to compute dynamic early reflections and late reverberation. Early reflections are calculated up to the fourth reflection using the image source method, considering the acoustic materials. Late reverberation is generated using the Feedback Delay Network (FDN), with room frequency characteristics and spatial directivity varying relative to the listener's position. All components will be represented in fifth-order Ambisonics and binauralized with head-tracking. Notably, this dynamic rendering method could be light enough to handle all processing on a single mobile device.

ANALYSIS OF QUALITY STANDARDS FOR DIDACTIC DESIGN OF TEXTBOOKS USING THE EXAMPLE OF A FABLE IN THIRD GRADE PRIMARY SCHOOL READERS

This paper analyzes quality standards for didactic design of textbooks, using the example of readers for the third grade, published by Klett, before and after curriculum changes resulting from the alteration of legal frameworks defined by the Regulation on the Curriculum for the Third Grade of Primary Education: 5/2019-6, 1/2020-1, 6/2020-1. By means of content analysis, the reader published by Klett and used in teaching until the 2020/21 school year will be analyzed, as well as the reader approved for use in accordance with the new recommendations, serving as a Serbian Language textbook in the Republic of Serbia from the 2020/21 school year. The analysis of the quality standards for didactic design of textbooks has been carried out according to the general quality standards for textbooks (Ivić et al., 2009) and legislative regulations (Law on Textbooks, Official Gazette RS, Educational Gazette 68/2015, 27/2018–13, and the Regulation on Quality Standards for Textbooks and Guidelines for Their Use, Official Gazette RS, Educational Gazette: 42/2016-11, 45/2018–40), which define the quality framework for textbooks and textbook sets. This analysis is conducted using the example of the fable “The Tortoise and the Hare” by Aesop, which is part of the curriculum. A comparative analysis of the realization of standards within the domain of didactic design will provide insight into the alignment of curriculum requirements and textbooks, both the previous and newly approved ones, using the fable as an example. Additionally, it will examine the encountered qualitative didactic adequacy of the reader as a component of the textbook set. This paper will highlight possibilities for enhancing the existing didactic-methodological apparatus that accompanies the fable, aiming to expedite the process of educational interpretation of the work.

An integrated strategic framework for priority setting in the Australian vanilla industry

Global demand of vanilla, as a natural flavour, is consistently growing. Vanilla is an emerging crop with high potential for Australian growers due to its climatic suitability in some regions, and high domestic and global demand. In this paper, the priorities of Australian vanilla industry are recognised based on the current issues, challenges, and opportunities. We show the priorities and related activities as established through one-on-one interaction and group engagement with industry wide stakeholders to capture a varied range of views. Critical feedback from the industry included the need for structuring an industry association, arrangements that offer training for example on production, postharvest and product diversification, improvements to the linkages between stakeholders, and technical support from research and development agencies. The priorities were ranked by stakeholders according to their importance as follows: (a) capacity building, (b) harvest and postharvest protocols, (c) production procedures, and (d) selection of better clonal material. Addressing the industry’s needs and priorities will ensure its future development is sustainable and economically viable. Policymakers are encouraged to take into account these discerned priorities and the proposed strategies designed to confront the industry’s needs and challenges.

Lost in Transition: Missed Opportunities for the 4Ms to Support Post-Acute Care Transitions.

Thousands of health systems have adopted the 4Ms framework, a set of evidence-based practices specific to older adults, as part of the Age-Friendly Health Systems (AFHS) initiative. However, implementation efforts have largely been setting-specific and approaches to achieve continuity of the 4Ms during care transitions are nascent. Transitions from hospitals to skilled nursing facilities (SNFs) are one type of care transition that would greatly benefit from continuity of 4Ms practices. Drawing from the authors' insights and 5 exploratory interviews at 3 health systems that implemented the 4Ms in the inpatient setting, we describe a set of current-state challenges when trying to extend specific inpatient 4Ms practices (eg, deprescribing of high-risk medications) as well as the nuanced understanding of the individual's clinical trajectory developed during an inpatient stay. We also offer concrete opportunities, such as developing 4 Ms-centric discharge summary templates, to address the challenges. With the large investment in AFHS transformation and associated efforts to implement the 4Ms framework in all care settings used by older adults, it is critical to raise awareness of the specific obstacles to promoting continuity of successful 4Ms practices during care transitions and work to overcome them. Our insights from hospital-to-SNF transitions offer a starting point.

Likelihood function modelling of radar targets under high‐speed observation

AbstractThe likelihood ratio function of the target plays a crucial role in applications such as tracking before detection (TBD). In the framework of random finite sets, the performance of radar target TBD based on particle filters is limited by the performance of the likelihood ratio function. Traditional likelihood ratio functions only consider the likelihood ratio between the target and background noise, thus requiring a high signal‐to‐noise (SNR) ratio for the input signal. In scenarios where the observation time interval is extremely short, there exists a strong autocorrelation between the complex amplitudes of the radar target at different time instants. Based on this characteristic, an autoregressive model is utilized to establish the autocorrelation of the target complex amplitudes and update the target's complex amplitudes using a Kalman filter. The likelihood ratio is derived from the model and it is compared with the traditional likelihood ratio under pulse‐Doppler radar. This comparison aims to demonstrate the effectiveness of the proposed likelihood ratio. Due to the fact that this likelihood ratio only requires the extraction of the complex amplitudes of the target scattering centre, without the need to extract the energy around the scattering centre like traditional point spread function methods, the algorithm complexity is greatly reduced, enabling real‐time operation of the proposed method.

A Hilbertian projection method for constrained level set-based topology optimisation

We present an extension of the projection method proposed by Challis et al. (Int J Solids Struct 45(14–15):4130–4146, 2008) for constrained level set-based topology optimisation that harnesses the Hilbertian velocity extension-regularisation framework. Our Hilbertian projection method chooses a normal velocity for the level set function as a linear combination of (1) an orthogonal projection operator applied to the extended optimisation objective shape sensitivity and (2) a weighted sum of orthogonal basis functions for the extended constraint shape sensitivities. This combination aims for the best possible first-order improvement of the optimisation objective in addition to first-order improvement of the constraints. Our formulation utilising basis orthogonalisation naturally handles linearly dependent constraint shape sensitivities. Furthermore, use of the Hilbertian extension-regularisation framework ensures that the resulting normal velocity is extended away from the boundary and enriched with additional regularity. Our approach is generally applicable to any topology optimisation problem to be solved in the level set framework. We consider several benchmark constrained microstructure optimisation problems and demonstrate that our method is effective with little-to-no parameter tuning. We also find that our method performs well when compared to a Hilbertian sequential linear programming method.