Ideal Framework Research Articles

Modeling the evolution of representative dislocation structures under high thermo-mechanical conditions during Additive Manufacturing of Alloys

Mesoscale simulations of discrete defects in metals provide an ideal framework to investigate the micro-scale mechanisms governing the plastic deformation under high thermal and mechanical loading conditions. To bridge size and time-scale while limiting computational effort, typically the concept of representative volume elements (RVEs) is employed. This approach considers the microstructure evolution in a volume that is representative of the overall material behavior. However, in settings with complex thermal and mechanical loading histories careful consideration of the impact of modeling constraints in terms of time scale and simulation domain on predicted results is required. We address the representation of heterogeneous dislocation structure formation in simulation volumes using the example of residual stress formation during cool-down of laser powder-bed fusion (LPBF) of AISI 316L stainless steel. This is achieved by a series of large-scale three-dimensional discrete dislocation dynamics (DDD) simulations assisted by thermo-mechanical finite element modeling of the LPBF process. Our results show that insufficient size of periodic simulation domains can result in dislocation patterns that reflect the boundaries of the primary cell. More pronounced dislocation interaction observed for larger domains highlight the significance of simulation domain constraints for predicting mechanical properties. We formulate criteria that characterize representative volume elements by capturing the conformity of the dislocation structure to the bulk material. This work provides a basis for future investigations of heterogeneous microstructure formation in mesoscale simulations of bulk material behavior.

Synergies and trade-offs among ecosystems functions and services for three types of lake-edge wetlands

Wetlands are the world’s most important providers of ecosystem functions and services (EFS) and the most threatened ecosystems. Systematic conservation planning strategies are urgently needed to identify efficient strategies that optimize EFS provisioning in wetlands. Evaluating synergies and trade-offs among EFS indicators provides an ideal framework, as they highlight the challenges faced by conservationists. However, associations between indicators often vary across region, scale, and ecosystem type. In this study, we compared the provisioning of eight EFS of three types of wetlands to evaluate the influence of indicator choice and aggregation on synergies and trade–offs. We quantified 25 EFS indicators in 37 lake-edge wetlands consisting of 12 peatlands, 8 alder swamps, and 17 ash swamps. We analyzed the synergies and trade-offs among wetland types and among EFS indicators, as well as the general EFS provisioning patterns of each type using cosine similarities and multivariate analysis. We showed that wetland type strongly influences the strength and direction of associations, with peatlands and ash swamps showing opposing patterns. While some EFS categories are less sensitive to indicator choice and aggregation, others, such as biodiversity, show important trade-offs. Our results revealed that synergies and trade-offs are strongly influenced by indicator choice and that protecting a diversity of wetland types is necessary to support multiple EFS categories.

Uterus Transplantation as a Surgical Innovation

Uterus transplantation (UTx) research has been introduced in several countries, with trials in Sweden and the United States producing successful outcomes. The growing interest in developing UTx trials in other countries, such as Spain, the Netherlands, Japan, and Australia, raises important questions regarding the ethics of surgical innovation research in the field of UTx. This paper examines the current state of UTx in the context of the surgical innovation paradigm and IDEAL framework and discusses the ethical challenges faced by those considering the introduction of new trials. We argue that UTx remains an experimental procedure at a relatively early stage of the IDEAL framework, especially in the context of de novo trials, where protocols are likely to deviate from those used previously and where researchers are likely to have limited experience of UTx. We conclude that countries considering the introduction of UTx trials should build on the strengths of the reported outcomes to consolidate the evidence base and shed light on the uncertainties of the procedure. Authorities responsible for the ethical governance of UTx trials are advised to draw on the ethical framework used in the oversight of surgical innovation.

Efficient synthesis of novel 2-deoxy-C-nucleosides containing oxa and thiadiazole derivatives and their biological activity

Five membered oxa- and thiadiazole derivatives have shown significant biological activity due to their unique bioisosteric properties. Herein, we describe an efficient synthetic approach leading to several novel C-nucleosides containing an oxadiazole or thiadiazole ring system. This work provides for the first-time ability to assemble β-C-nucleosides in a facile manner offering an ideal framework for the possible development of new antiviral and antitumor drugs. All new C-nucleosides were tested for their activity against TBEV and SARS-CoV-2. Two of the synthesized compounds exerted mild anti-SARS-CoV-2 activity, as evidenced by the decrease in viral titers by <1 log10 PFU/ml compared with controls. Mechanism for the formation of 5-substituted 1,3,4-thiadiazole ring is proposed and a structure-activity relationship established with these C-nucleosides.

Ultrathin porous Fe2O3@C nanosheets: Novel preparation strategy and high lithium storage

Low conductivity and large volume change are the two major factors that limit the performance of Fe2O3 for lithium-ion battery (LIB) anodes. Two-dimensional (2D) porous Fe2O3/C nanocomposites hold great potential for solving the above problems, because their characteristic nanostructures can provide ideal frameworks for rapid, durable and high lithium storage. In spite of high performance, the preparation of the 2D porous Fe2O3/C composites is usually complex, remaining challenging. Herein, a novel strategy is demonstrated to prepare conformal carbon coated ultrathin porous Fe2O3 nanosheets (Fe2O3@C) and hence the above problems are addressed efficaciously. With the advantages of conformal carbon coating, well-defined ultrathin nanosheet and porous structure, the Fe2O3@C shows enhanced conductivity, fast kinetics, superior durability and high capacity as LIB anodes, therefore exhibiting outstanding electrochemical performance with 991.5 and 594.5 mA h g−1 after 200 and 550 cycles at 200 and 1000 mA g−1, respectively. The performance of the Fe2O3@C anode in a full battery is also evaluated, and hence a beneficial attempt to pave the way for its practicality is performed. The novel preparation strategy and outstanding electrochemical performance of the Fe2O3@C may provide a new insight into design and fabrication of other metal oxides/carbon composites anodes with advanced performance.

Tissue Engineering: Current Technology for Facial Reconstruction.

Facial reconstruction is a complex surgical process that requires intricate three-dimensional (3D) concepts for optimal functional and aesthetic outcomes. Conventional reconstruction of structural facial anomalies, such as those including cartilage or bony defects, typically rely on hand-carving autologous constructs harvested from a separate donor site, and shaping that cartilage or bone into a new structural framework. Tissue engineering has emerged in recent decades as a potential approach to mitigate the need for donor site morbidity while improving precision in the design of reconstructive construct. Computer-aided design and computer-aided manufacturing have allowed for a digital 3D workflow to digitally execute the planned reconstruction in virtual space. 3D printing and other manufacturing techniques can then be utilized to create custom-fabricated scaffolds and guides to improve the reconstructive efficiency. Tissue engineering can be paired with custom 3D-manufactured scaffolds to theoretically create an ideal framework for structural reconstruction. In the past decade, there have been several compelling preclinical studies demonstrating the capacity to induce chondrogenesis or osteogenesis in a custom scaffold. However, to date, these preclinical data have not yet translated into significant clinical experience. This translation has been hindered by a lack of consensus on the ideal materials and cellular progenitors to be utilized in these constructs and a lack of regulatory guidance and control to enable clinical application. In this review, we highlight the current state of tissue engineering in facial reconstruction and exciting potential for future applications as the field continues to advance.

FDA-approved machine learning algorithms in neuroradiology: A systematic review of the current evidence for approval

Over the past decade, machine learning (ML) and artificial intelligence (AI) have become increasingly prevalent in the medical field. In the United States, the Food and Drug Administration (FDA) is responsible for regulating AI algorithms as “medical devices” to ensure patient safety. However, recent work has shown that the FDA approval process may be deficient. In this study, we evaluate the evidence supporting FDA-approved neuroalgorithms, the subset of machine learning algorithms with applications in the central nervous system (CNS), through a systematic review of the primary literature. Articles covering the 53 FDA-approved algorithms with applications in the CNS published in PubMed, EMBASE, Google Scholar and Scopus between database inception and January 25, 2022 were queried. Initial searches identified 1505 studies, of which 92 articles met the criteria for extraction and inclusion. Studies were identified for 26 of the 53 neuroalgorithms, of which 10 algorithms had only a single peer-reviewed publication. Performance metrics were available for 15 algorithms, external validation studies were available for 24 algorithms, and studies exploring the use of algorithms in clinical practice were available for 7 algorithms. Papers studying the clinical utility of these algorithms focused on three domains: workflow efficiency, cost savings, and clinical outcomes. Our analysis suggests that there is a meaningful gap between the FDA approval of machine learning algorithms and their clinical utilization. There appears to be room for process improvement by implementation of the following recommendations: the provision of compelling evidence that algorithms perform as intended, mandating minimum sample sizes, reporting of a predefined set of performance metrics for all algorithms and clinical application of algorithms prior to widespread use. This work will serve as a baseline for future research into the ideal regulatory framework for AI applications worldwide.

Sibyls, destruction, and loss in the context of reproductive science

The sixth-century prologue to a collection of Sibylline Oracles introduces the anthology according to a pattern recognizable in prologues to a number of texts of the period. It begins by praising the oracles for the great value they have for readers. But then, it introduces a problem: the oracles have suffered from destruction, loss, and corruption at several points in their history. The prologue goes on to offer a solution: the creation of the very anthology of Sibylline Oracles being produced. The rhetorical function of such a prologue is clear: It ensures the value of Sibylline Oracles while simultaneously demonstrating the utility, or even necessity of the newly created collection. Of interest in this study is the way the prologue introduces and illustrates the problem of destruction, loss, and corruption. In no less than three different vignettes, the prologue shows how the oracles are especially resistant to preservation. And these instances are not alone. A discourse of sibylline loss circulated around the textual world of antiquity. This study argues that the discourse is best understood within the framework of ancient ideals concerning intergenerational transfers of knowledge from fathers to sons, and some ancient theories of reproductive science, which held that only men contained reproductive potential, while women were empty vessels or fallow fields waiting to be filled. The article concludes that sibyls are so frequently sites of loss and destruction of knowledge because, as women, they are believed to lack the capacity to reproduce themselves both intellectually and biologically.

Choosing the Best Design in Surgical Research.

Choosing the Best Design in Surgical Research.

Gamification and Controversial Heritage: Trainee Teachers’ Conceptions

Heritage education is configured as an ideal framework for the treatment of socio-environmental problems relevant to modern society. To this end, it is essential that teachers can develop proposals in the classroom that promote critical thinking and the eco-social education of their students, based on innovative and experiential methodologies. For this reason, initial teacher training must ensure that future teachers acquire these innovative tools. Thus, the aim of this work is to identify the conceptions of student teachers in initial teacher training regarding gamification as a teaching methodology to address controversial heritage. To achieve this, a questionnaire has been designed with questions about heritage, controversy, and educational gamification, which was given to 235 students (M = 60; F = 175) taking their primary education degrees at different Spanish universities. To guide the design of the research instrument and analyze the results, a system of categories was developed and the SPSS data processing program was used. The first results show that, even though students in initial teacher training think that they do have a predisposition to use gamification to work around controversial heritage in class, they lack the knowledge of methodological strategies and gamified educational activities, which suggests a dearth of training on these issues in the primary education degree courses.

Aqueous alginate/MXene inks for 3D printable biomedical devices

Electrochemically responsive hydrogel networks have been obtained using printable inks made of a biopolymer, alginate (Alg), and an inorganic 2D material, MXene (titanium carbide, Ti3C2Tx) nanosheets. While MXene offers an electrically conductive pathway for electron transfer and Alg provides an interconnected framework for ion diffusion, the printed nanocomposite results, after gelation, in an extended active interface for redox reactions, being an ideal framework to design and construct flexible devices for biomedical applications. In this work, after characterization, we demonstrate that hydrogels obtained by cross-linking printed Alg/MXene inks exhibit great potential for bioelectronics. More specifically, we prove that flexible Alg/MXene hydrogels act as self-supported electroactive electrodes for the electrochemical detection of bioanalytes, such as dopamine, with a performance similar to that achieved using more sophisticated electrodes, as for example those containing conducting polymers and electrocatalytic gold nanoparticles. In addition, Alg/MXene hydrogels have been successfully used to regulate the release of a previously loaded broad spectrum antibiotic (chloramphenicol) by electrical stimulation.

Relocation trends determined by increasing risks in Eastern Europe: An ANP-TOPSIS approach

BACKGROUND: The COVID-19 pandemic has changed companies’ perspective on relocation and brought uncertainty into people’s lives. Uncertainty, a decisive factor in today’s global environment, requires new research about human resources and companies. The COVID-19 pandemic has caused an unexpected need for change within organizations, especially in terms of human resources management, creating a complex and challenging environment that interferes with business continuity, forcing employees to cope with this challenging situation. OBJECTIVE: The article aims identifying the changes generated by the COVID 19 pandemic in the relocation of businesses from the European Union, assessing the relocation trends of companies around the world in European countries in this volatile macroeconomic environment. The sustainability of companies, the way they can overcome the crises generated by the pandemic depend mainly on economic, social, financial, political factors and human resource involved in the relocation process. Studying the influence of the pandemic on relocation decision contributes to better management of crises in the future and to reducing risks. METHODS: The study proposes an integrated ANP-TOPSIS (Analytic Network Process- Technique for Order of Preference by Similarity to Ideal Solution) for ordering preference according to the ideal solution framework. Priority should be given to solutions that consider the interactions between factors involved in decision-making. The proposed model will increase the efficiency of the transfer decision-making process and help managers choose solutions effectively based on their importance and impact on the company and the human resources involved. RESULTS: The synthesis of the indicators and methods used, in addition to the factors that affect relocation, complements the specialized literature. The results showed a shift in business relocation options from east to west, demonstrating the current trend in the relocation issue associated with the COVID-19 virus. Eastern European countries are no longer as attractive for companies relocating compared to the pre-pandemic period. The countries with more stable economies, characterized by lower risks, seem to become more attractive to companies that relocate their facilities. CONCLUSIONS: The strategic positioning of the firm, its growth or adaptation to the present environment, and its geographic focus are fundamental components of a company’s migration. The selection of an ideal site is a research problem; not only to find a place where firms will have access to qualified human resources, to lower their costs, to be close to raw materials or the market, but also to prevent associated relocation hazards. According to research, today’s reality necessitates a risk-focused strategy.

A distributed delay based controller for simultaneous periodic disturbance rejection and input-delay compensation

The paper presents a controller design for systems suffering from multi-harmonic periodic disturbance and substantial input time-delay. It forms an alternative approach to Repetitive Control where the goal is to stabilize a close-loop that encapsulates an explicit time-delay model of the periodic signal. The proposed controller design is based on the Internal Model Control (IMC) framework, and it consists of the inverse system model and a tuneable distributed delay with an overall length related to the period of the disturbance. The properness of the controller can be ensured by utilizing a low-pass filter, however, such a component is shown to be unnecessary when the relative order of the system model is one. This fact makes the alternative approach especially suitable for systems approximated by a first-order model with input time-delay, leading to a straightforward controller design thanks to its simple structure and attainable conditions. Stability of the configuration is guaranteed by an ideal IMC framework. For further performance and robustness requirements for the non-ideal case the tuning of the controller is posed as a weighted-H∞ optimization problem where frequency-, spectral- and time-domain requirements are formulated as constraints. The overall control design is experimentally verified on a laboratory setup that has high-order dynamics approximated by a first-order model with input delay.

Governance for Sustainable Social Enterprise Performance: Moving Beyond Control Mechanisms

One of the prominent challenges of social enterprises is the inability to sustain their performance. In recent years, there have been instances where social enterprises have faced issues related to mismanagement, and these incidents have been associated with lack of effective governance. Therefore, this qualitative study engaged semi-structured interviews with representatives of social enterprises in Malaysia in assessing and examining the ideal governance framework that would suit the needs of social enterprise to attain sustainable performance. At first glance, the findings suggest that governance is achieved through effective control mechanisms and accountability practices. However, the respondents had also shared some insights that would suggest a forward-looking approach to social enterprise governance that prioritises sustainability, innovation, and social impact, rather than just control and compliance. Additionally, another substance being highlighted by the respondents that is closely related with social enterprise governance is about building relationships and trust. Keywords: governance, sustainability, control mechanisms, social enterprise, performance

Local–Global Decompositions for Conditional Microstructure Generation

Conditional microstructure generation tools offer an important, inexpensive pathway to constructing statistically diverse datasets for Integrated Computational Materials Engineering and Materials Informatics efforts. To provide this utility in practice, an ideal generative framework must be able to efficiently, systematically, and robustly generate microstructures corresponding to selected spatial statistics (e.g., 1- and 2-point statistics) while also producing realistic local features (e.g., shapes and sizes of individual phase constituents). Because of the austerity of these requirements, generative frameworks often target either statistical conditioning or visual realism, but not both. In this paper, we propose to bridge these two approaches by approximating a microstructure’s generating process (i.e., its stochastic microstructure function) using a two layer semi-directed probabilistic graphical model. The first layer – a Gaussian Random Field (GRF) – provides direct control of the 1- and 2-point statistics. The second layer – a Score Based Generative Deep Learning model – postprocesses GRF predictions to refine local features while preserving global patterning. To understand and evaluate our proposed framework, we apply it to generate statistically equivalent N-phase microstructures from experimental references, including a 2-Phase Nickel-based Super Alloy and a 3-phase α−β Titanium alloy. Through these two case studies, we demonstrate that our framework successfully matches both lower-order (1- and 2-point statistics) and several salient higher-order statistics. Additionally, we briefly explore the capacity of these models to extrapolate outside of their training data by varying the input 2-point statistics. We discuss the value of this ability towards systematically generating diverse microstructure datasets.

Tolerance-based granular methods: Foundations and applications in natural language processing

Natural Language processing (NLP) derives its roots from artificial intelligence and computational linguistics. The proliferation of large-scale web corpora and social media data as well as advances in machine learning and deep learning have led to practical applications in diverse NLP areas such as machine translation, information extraction, named entity recognition (NER), text summarization and sentiment analysis. Named-entity recognition (NER), is a sub task of information extraction that seeks to discover and categorize specific entities such as nouns or relations in unstructured text. In this paper, we present a review of the foundations three tolerance-based granular computing methods (rough sets, fuzzy-rough sets and near sets) for representing structured (documents) and unstructured (linguistic entities) text. Applications of these methods are presented via semi-supervised and supervised learning algorithms in labelling relational facts from web corpora and sentiment classification (non-topic based text). The performance of the three presented algorithms is discussed in terms of bench marked datasets and algorithms. We make the case that tolerance relations provide an ideal framework for studying the concept of similarity for text-based applications. The aim of our work is to demonstrate that approximation structures viewed through the prism of tolerance have a great deal of fluidity and integrate conceptual structures at different levels of granularity thereby facilitating learning in the presented NLP applications.

Evaluating the Development Status of Fluorescence-Guided Surgery (FGS) in Pediatric Surgery Using the Idea, Development, Exploration, Assessment, and Long-Term Study (IDEAL) Framework.

Fluorescence-guided surgery (FGS) is used in many pediatric subspecialties but there are currently no standard guidelines or outcome data. We aimed to assess the current status of FGS in pediatrics using the Idea, Development, Exploration, Assessment, and Long-term study (IDEAL) framework. Clinical papers on FGS in children published from January 2000 to December 2022 were systematically reviewed. The stage of research development was measured considering seven fields of application: biliary tree imaging, vascular perfusion for gastrointestinal procedures, lymphatic flow imaging, tumor resection, urogenital surgery, plastic surgery, and miscellaneous procedures. Fifty-nine articles were selected. For each field of application, the overall IDEAL stage was determined to be 2a for biliary tree imaging (10 publications, 102 cases), 1 for vascular perfusion for gastrointestinal procedures (8 publications, 28 cases), 1 for lymphatic flow imaging (12 publications, 33 cases), 2a for tumor resection (20 publications, 238 cases), 2a for urogenital surgery (9 publications, 197 cases), and 1-2a for plastic surgery (4 publications, 26 cases). One report did not belong to any categories. FGS in children is still in an early phase of adoption and development. We recommend using the IDEAL framework as a guide and suggest developing multicenter studies to define the standard guidelines, effectiveness, and outcomes.

Introduction of Minimally Invasive transCervical oEsophagectomy (MICE) according to the IDEAL framework.

Introduction of Minimally Invasive transCervical oEsophagectomy (MICE) according to the IDEAL framework.

Influence of the Lennard-Jones Combination Rules on the Simulated Properties of Organic Liquids at Optimal Force-Field Parametrization.

We recently introduced the CombiFF scheme [Oliveira et al., J. Chem. Theory Comput. 2020, 16, 7525], an approach for the automated refinement of force-field parameters against experimental condensed-phase data for large compound families. Using this scheme, once the time-consuming task of target-data selection and curation has been performed, the force-field optimization itself is both straightforward and fast. As a result, CombiFF provides an ideal framework for evaluating the influence of functional-form decisions on the accuracy of a force field at an optimal level of parametrization. We already used this approach to assess the effect of using an all-atom representation compared to united-atom representations in the force field [Oliveira et al., J. Chem. Theory Comput. 2022, 18, 6757]. Here, CombiFF is applied to assess the effect of three Lennard-Jones combination rules, geometric-mean (GM), Lorentz-Berthelot (LB), or Waldman-Hagler (WH), on the simulated properties of organic liquids. The comparison is performed in terms of the experimental liquid density ρliq, vaporization enthalpy ΔHvap, surface-tension coefficient γ, static relative dielectric permittivity ϵ, and self-diffusion coefficient D. The calibrations of the three force-field variants are carried out independently against 2044 experimental values for ρliq, and ΔHvap concerning 1516 compounds. The resulting root-mean-square deviations from experiment are 30.0, 26.9, and 36.7 kg m-3 for ρliq and 2.8, 2.8, and 2.9 kJ mol-1 for ΔHvap, when applying the GM, LB, and WH combination rules, respectively. In terms of these (and the other) properties, the three combination rules perform comparatively well, with the GM and LB results being more similar to each other and slightly more accurate compared to experiment. In contrast, the use of distinct combination rules for the parameter calibration and property calculation leads to much larger errors.

An optimal regulation of fluxes dictates microbial growth in and out of steady-state.

Effective coordination of cellular processes is critical to ensure the competitive growth of microbial organisms. Pivotal to this coordination is the appropriate partitioning of cellular resources between protein synthesis via translation and the metabolism needed to sustain it. Here, we extend a low-dimensional allocation model to describe the dynamic regulation of this resource partitioning. At the core of this regulation is the optimal coordination of metabolic and translational fluxes, mechanistically achieved via the perception of charged- and uncharged-tRNA turnover. An extensive comparison with ≈ 60 data sets from Escherichia coli establishes this regulatory mechanism's biological veracity and demonstrates that a remarkably wide range of growth phenomena in and out of steady state can be predicted with quantitative accuracy. This predictive power, achieved with only a few biological parameters, cements the preeminent importance of optimal flux regulation across conditions and establishes low-dimensional allocation models as an ideal physiological framework to interrogate the dynamics of growth, competition, and adaptation in complex and ever-changing environments.