Conceptual Examples Research Articles

A novel meshless method for numerical simulation of fractured-vuggy reservoirs

This paper proposes a novel meshless numerical simulation method for fractured-vuggy reservoirs. Initially, model discretization involves extracting fracture and vug feature nodes along the contours of the reservoir bodies and in alignment with fracture orientations, tailored to the characteristics of the fractured-vuggy oil deposit. Subsequently, a connection element model is established within the influence domain. Based on the seepage equations of the connection system, a computational method for the parameters of the connection elements (connection transmissibility and connection volume) is defined. In addition, a sequential method is employed to solve for the pressure and saturation, achieving rapid prediction of the reservoir's production dynamics. On this basis, an automatic history matching algorithm is utilized for real-time fitting of oil–water dynamic indicators, inverting the characteristic parameters of the connection element model, and quantitatively characterizing the injection–production connection elements. The research findings indicate that the method is capable of rapidly fitting and predicting the dynamics of oil reservoir production. Furthermore, conceptual model examples have substantiated that it achieves comparable computational efficiency and superior computational accuracy when compared to the traditional finite difference (volume) method under the same nodal conditions. Additionally, the node arrangement for fractures and vugs is comparatively flexible, and it can ensure the integrity of the flow paths with a limited number of nodes to enhance predictive accuracy. Therefore, this method can effectively meet the rapid prediction requirements for fractured-vuggy reservoirs and also provides a novel meshless computational approach for the numerical simulation of such reservoirs.

Augmented Reality (AR) as a Visualization Tool for a Missile Launch: a Conceptual Example Using the GROM Man-Portable Air-Defense System

The article presents the technology of augmented reality (AR) and its application in military sector. The concept of utilizing AR in the training of GROM system operators is introduced. The necessary software and hardware for implementing this concept are discussed. Furthermore, the requirements that must be met by the device for the effective execution of the project are presented. Additionally, the anticipated benefits resulting from the implementation of this solution in the training process of the Polish Armed Forces are discussed.

Design and implementation of a universal converter for microgrid applications using approximate dynamic programming and artificial neural networks

This paper introduces a novel design for a universal DC-DC and DC-AC converter tailored for DC/AC microgrid applications using Approximate Dynamic Programming and Artificial Neural Networks (ADP-ANN). The proposed converter is engineered to operate efficiently with both low-power battery and single-phase AC supply, utilizing identical side terminals and switches for both chopper and inverter configurations. This innovation reduces component redundancy and enhances operational versatility. The converter's design emphasizes minimal switch usage while ensuring efficient conversion to meet diverse load requirements from battery or AC sources. A conceptual example illustrates the design's principles, and comprehensive analyses compare the converter's performance across various operational modes. A test bench model, rated at 3000W, demonstrates the converter's efficacy in all five operational modes with AC/DC inputs. Experimental results confirm the system's robustness and adaptability, leveraging ADP-ANN for optimal performance. The paper concludes by outlining potential applications, including microgrids, electric vehicles, and renewable energy systems, highlighting the converter's key advantages such as reduced complexity, increased efficiency, and broad applicability.

All that glitters is not gold: An interpretive framework for diagnostic test evaluation using Ascaris lumbricoides as a conceptual example.

All that glitters is not gold: An interpretive framework for diagnostic test evaluation using Ascaris lumbricoides as a conceptual example.

Oxygen-deficient NiCo2O4 porous nanowire for superior electrosynthesis of ammonia coupling with valorization of ethylene glycol

Electrocatalytic nitrate reduction (NO3RR) is emerging as promising pathway for ammonia (NH3) synthesis. The development of NO3RR-based coupling systems are vital for the energy-saving electrosynthesis of high-added chemicals, but the efficient bifunctional electrocatalysts are lacking. Herein, we take oxygen-deficient NiCo2O4 (Vo-NiCo2O4) porous nanowire as a conceptual example to study its potential application in the co-production of NH3 and formate by ethylene glycol oxidation reaction (EGOR) assisted NO3RR technology. Benefiting from the unique porous structure, abundant oxygen vacancies, active dual metal centers, and the improved reaction kinetics, the Vo-NiCo2O4/NF displays excellent bifunctional performance and can be applied for NO3RR||EGOR electrolyzer by the membrane electrode assembly (MEA). Specifically, the electrolyzer achieves a high current density of 100 mA cm−2 a low voltage of 1.53 V and delivers high Faradaic efficiency of formate (over 94.2 %) and NH3 (over 96.3 %) at 1.4 V, along with superior catalytic stability over 120 h. In-situ characterizations confirm the main active Co-O species and the formation of formate for EGOR, as well as the NHO pathway with the appearance of –NH2OH key intermediate for NO3RR. Our work offers a promising strategy for co-upcycling production of valuable chemicals.

Image Text Extraction and Natural Language Processing of Unstructured Data from Medical Reports

This study presents an integrated approach for automatically extracting and structuring information from medical reports, captured as scanned documents or photographs, through a combination of image recognition and natural language processing (NLP) techniques like named entity recognition (NER). The primary aim was to develop an adaptive model for efficient text extraction from medical report images. This involved utilizing a genetic algorithm (GA) to fine-tune optical character recognition (OCR) hyperparameters, ensuring maximal text extraction length, followed by NER processing to categorize the extracted information into required entities, adjusting parameters if entities were not correctly extracted based on manual annotations. Despite the diverse formats of medical report images in the dataset, all in Russian, this serves as a conceptual example of information extraction (IE) that can be easily extended to other languages.

One size does not fit all: Three tools and approaches for soundscape simulations

We present a reflection on three prototypes of real-time interactive soundscape simulators aimed at supporting participatory urban sound planning and interventions. These prototypes were developed as part of the Sounds in the City cross-sectoral partnership through an iterative process involving various stakeholders. Each prototype enables different ways to manipulate soundscapes through tailored interfaces and audio/visual outputs, targeting different types of users. The first version is audio-only and uses live music tools for Ambisonics spatialization, with limited environmental modeling for co-design exercises with urban and sound professionals. The second version builds on the idea of the first and adds acoustic modeling. It was used to assess the impact of sound installations in public spaces through research-creation involving sound artists and residents. The last version utilizes (desktop or head-mounted) virtual reality with binaural rendering, immersing the user in an audio-visual city to raise sound awareness and support urban soundscape design. We emphasize that there is no one-size-fits-all tool. Rather, we highlight how different tools are needed for different auralization tasks and target user groups. These tools are presented through examples of early-stage conceptualization, educational components, creative processes, laboratory-based soundscape assessments, and both individual and participatory design sessions.

Probabilistic measures for biological adaptation and resilience.

This paper introduces an approach to quantifying ecological resilience in biological systems, particularly focusing on noisy systems responding to episodic disturbances with sudden adaptations. Incorporating concepts from nonequilibrium statistical mechanics, we propose a measure termed "ecological resilience through adaptation," specifically tailored to noisy, forced systems that undergo physiological adaptation in the face of stressful environmental changes. Randomness plays a key role, accounting for model uncertainty and the inherent variability in the dynamical response among components of biological systems. Our measure of resilience is rooted in the probabilistic description of states within these systems and is defined in terms of the dynamics of the ensemble average of a model-specific observable quantifying success or well-being. Our approach utilizes stochastic linear response theory to compute how the expected success of a system, originally in statistical equilibrium, dynamically changes in response to a environmental perturbation and a subsequent adaptation. The resulting mathematical derivations allow for the estimation of resilience in terms of ensemble averages of simulated or experimental data. Finally, through a simple but clear conceptual example, we illustrate how our resilience measure can be interpreted and compared to other existing frameworks in the literature. The methodology is general but inspired by applications in plant systems, with the potential for broader application to complex biological processes.

Interpretation issues with "genomic vulnerability" arise from conceptual issues in local adaptation and maladaptation.

As climate change causes the environment to shift away from the local optimum that populations have adapted to, fitness declines are predicted to occur. Recently, methods known as genomic offsets (GOs) have become a popular tool to predict population responses to climate change from landscape genomic data. Populations with a high GO have been interpreted to have a high "genomic vulnerability" to climate change. GOs are often implicitly interpreted as a fitness offset, or a change in fitness of an individual or population in a new environment compared to a reference. However, there are several different types of fitness offset that can be calculated, and the appropriate choice depends on the management goals. This study uses hypothetical and empirical data to explore situations in which different types of fitness offsets may or may not be correlated with each other or with a GO. The examples reveal that even when GOs predict fitness offsets in a common garden experiment, this does not necessarily validate their ability to predict fitness offsets to environmental change. Conceptual examples are also used to show how a large GO can arise under a positive fitness offset, and thus cannot be interpreted as a population vulnerability. These issues can be resolved with robust validation experiments that can evaluate which fitness offsets are correlated with GOs.

Self-Blame Attribution: Concept Analysis and Application to HPV-Related Cancers.

The attribution of self-blame for a negative physical health outcome can lead to further deterioration of one's mental and spiritual state. Using Walker and Avant's eight-step model for concept analysis, self-blame attribution is explored following the diagnosis of a human papillomavirus-related cancer in women. Conceptual case examples provide application of the concept to patient situations. Spiritual assessment tools for nurses caring for those who evidence self-blame are provided.

Heterogeneous Cobalt Phthalocyanine/Sulfur‐Modified Hollow Carbon Sphere for Boosting CO2 Electroreduction and Zn–CO2 Batteries

AbstractTuning coordination environment of single‐atom catalysts (SACs) is widely applied for the electrochemical CO2 reduction reaction (CO2RR). However, the influence of the interaction between SACs and support on the catalytic performance are not comprehensive. Herein, taking cobalt phthalocyanine (CoPc) as a conceptual example, a hetero S‐atom doping strategy of hollow carbon sphere (S‐NHC) is put forward as a carrier, revealing the intrinsic relationship between microenvironment regulation and catalytic activity of single Co‐atom. The additional S doping may induce more carbon defect and increases the content of pyrrolic N in the S‐NHC sample, leading to stronger electronic interaction of heterogeneous CoPc/S‐NHC for boosting CO2RR performance. Remarkably, the catalyst achieves a highest CO selectivity of nearly 100% at −0.6 V, and the FECO are over 90% in a wide potential range from −0.4 to −0.8 V in a flow cell. The maximum power density is high as 2.68 mW cm−2 at 4.7 mA cm−2 for the Zn–CO2 battery with CoPc/S‐NHC cathode. In situ attenuated total reflection infrared (ATR‐IR) spectroscopy reveals the formation of key *COOH and *CO intermediates, while the calculation result confirm the optimized Gibbs free energy of these intermediates on CoPc/S‐NHC, suggesting the feasibility of the regulation strategy.

ADVANCING AGING EQUITY AMONG OLDER BOSTONIANS

Abstract More than five years after the City of Boston started its journey towards becoming an Age and Dementia Friendly community, the initiative and its collaborators have been working to ensure that the initiative and its many contributions are equally realized by all Bostonians. Drawing on the concept of aging equity--that all older adults, despite their life course histories, have equitable access to programs and services that help them age well. To achieve this means removing obstacles to accessing community features that support healthy aging, establishing social and civic engagement opportunities, ensuring safe environments, establishing access to healthcare, and disseminating knowledge of available supports and services. This presentation will describe the research and community engagement that led to the development of an aging equity conceptual framework and examples of how it is being operationalized in the City of Boston. For example, we will share the ways in which outreach and communication strategies have been altered to more equitable practices including efforts to build trust, engage in cultural translation of programs and services, and consider new channels of information sharing.

RESEARCH TO STRENGTHEN, INNOVATE, AND TRANSFORM AGE-FRIENDLY COMMUNITY PRACTICE

Abstract There is much research being conducted to better understand and advance age-friendly community practice. This symposium presents research from leading age-friendly researchers and practitioners across the United States. Drs. Black and Oh provide an analysis of the nation’s sectoral efforts based on progress reported by the age-friendly communities. Drs. Hernandez and Coyle will describe the research and community engagement that led to the development of an aging equity conceptual framework and examples of how it is being operationalized in the City of Boston. Drs. Greenfield and doctoral student Pope will present on a scoping review of studies in the U.S. and Canada on the range of ways in which the public sector participates in age-friendly community efforts. Drs. Coyle and Oh and doctoral students Gleason and Somerville present on a study that explored factors inhibiting communities from officially joining the age-friendly network. Dr. Perry reports on efforts to elevate the voice of older adults on social justice issues pertaining to aging in place in the domain of housing. Individual abstracts provide further detail on each study’s methods and findings.

Controlling biases in targeted plant removal experiments.

Targeted removal experiments are a powerful tool to assess the effects of plant species or (functional) groups on ecosystem functions. However, removing plant biomass in itself can bias the observed responses. This bias is commonly addressed by waiting until ecosystem recovery, but this is inherently based on unverified proxies or anecdotal evidence. Statistical control methods are efficient, but restricted in scope by underlying assumptions. We propose accounting for such biases within the experimental design, using a gradient of biomass removal controls. We demonstrate the relevance of this design by presenting (1) conceptual examples of suspected biases and (2) how to observe and control for these biases. Using data from a mycorrhizal association-based removal experiment, we show that ignoring biomass removal biases (including by assuming ecosystem recovery) can lead to incorrect, or even contrary conclusions (e.g. false positive and false negative). Our gradient design can prevent such incorrect interpretations, regardless of whether aboveground biomass has fully recovered. Our approach provides more objective and quantitative insights, independently assessed for each variable, than using a proxy to assume ecosystem recovery. Our approach circumvents the strict statistical assumptions of, for example, ANCOVA and thus offers greater flexibility in data analysis.

Rethinking Pedagogical Use of Eye Trackers for Visual Problems with Eye Gaze Interpretation Tasks

Eye tracking technology enables the visualisation of a problem solver's eye movement while working on a problem. The eye movement of experts has been used to draw attention to expert problem solving processes in a bid to teach procedural skills to learners. Such affordances appear as eye movement modelling examples (EMME) in the literature. This work intends to further this line of work by suggesting how eye gaze data can not only guide attention but also scaffold learning through constructive engagement with the problem solving process of another human. Inferring the models’ problem solving process, be it that of an expert or novice, from their eye gaze display would require a learner to make interpretations that are rooted in the knowledge elements relevant to such problem solving. Such tasks, if designed properly, are expected to probe or foster a deeper understanding of a topic as their solutions would require not only following the expert gaze to learn a particular skill, but also interpreting the solution process as evident from the gaze pattern of an expert or even of a novice. This position paper presents a case for such tasks, which we call eye gaze interpretation (EGI) tasks. We start with the theoretical background of these tasks, followed by a conceptual example and representation to elucidate the concept of EGI tasks. Thereafter, we discuss design considerations and pedagogical affordances, using a domain-specific (chemistry) spectral graph problem. Finally, we explore the possibilities and constraints of EGI tasks in various fields that require visual representations for problem solving.

Synergy scenario for renewable energy production, CO2 and H2 storage in the Baltic offshore structure

CO2 Capture, Transport, Use and Storage (CCUS) is one of the core technologies to mitigate climate change. New techno-economic and techno-ecological concept of a synergy of CO2 geological storage (CGS), CO2 use, hydrogen (H2) production from different eco-friendly renewable energy recovery technologies and underground H2 storage (UHS), which we call here Geological Power Bank (Geo-PB), in Cambrian Deimena Formation sandstones in different compartments of the E6 structure offshore Latvia is presented for the first time. A five-phase circular economy concept of E6 geological structure energy and CO2 storage hub was developed in this study. The workflow is techno-ecological, eco-friendly, self-supporting, cost-competitive, and economically feasible. It consists of (1) CO2 transport by ships to the rig, (2) CO2 injection for CGS and CO2 Plume Geothermal technology (CPG), (3) H2 production, (4) Geo-PB, and (5) H2 transport by the same ships to the customers. The concept is supporting a win8 situation - innovative elements of techno-ecological synergy in one site: (1) CGS, (2) CPG, (3) solar energy, (4) wind energy, (5) sea currents energy, (6) H2 production (7) Geo-PB and (8) H2 transport to consumers. The proposed cycle is closed, demonstrating the principles of circular economy, which will increase the total efficiency of the concept. CGS and CPG are planned in the E6-A compartment of the E6 geological structure with an average CO2 storage capacity of 365 Mt in an optimistic approach and Geo-PB is planned in E6-B with an H2 storage capacity of 119 kt.The Baltic offshore scenario is ambitious and innovative, proposed new technologies, synergy with renewable energy (geothermal, solar, wind and sea current), large storage capacity, including CO2 storage and use captured by a CCUS clusters of emission sources from energy production, cement industry and bio-emissions from Estonia, Latvia and Lithuania. The concept aimed to decrease the artificial impact of climate change by avoiding CO2 emissions to the atmosphere and implementing circular economy principles. It will increase public and policymakers’ acceptance of new underground CO2 and energy storage technologies. The proposed synergy solution for CGS and energy storage projects will make such a business economically feasible and attractive for investors. Our study demonstrates a new era, the next generation of cost-competitive, self-supporting conceptual techno-ecological examples of a possible synergy of storage concepts with renewable energies combined using circular economy approaches.

The survival-incorporated median vs the median inthesurvivors or in the always-survivors: What arewemeasuring? and Why?

Many clinical studies evaluate the benefit of a treatment based on both survival and other continuous/ordinal clinical outcomes, such as quality of life scores. In these studies, when subjects die before the follow-up assessment, the clinical outcomes become undefined and are truncated by death. Treating outcomes as "missing" or "censored" due to death can be misleading for treatment effect evaluation. We show that if we use the median in the survivors or in the always-survivors as estimands to summarize clinical outcomes, we may conclude that a trade-off exists between the probability of survival and good clinical outcomes, even in settings where both the probability of survival and the probability of any good clinical outcome are better for one treatment. Therefore, we advocate not always treating death as a mechanism through which clinical outcomes are missing, but rather as part of the outcome measure. To account for the survival status, we describe the survival-incorporated median as an alternative summary measure for outcomes in the presence of death. The survival-incorporated median is the threshold such that 50% of the population is alive with an outcome above that threshold. Through conceptual examples and an application to a prostate cancer treatment study, we show that the survival-incorporated median provides a simple and useful summary measure to inform clinical practice.

Evaluating the complexity and falsifiability of psychological models.

Understanding model complexity is important for developing useful psychological models. One way to think about model complexity is in terms of the predictions a model makes and the ability of empirical evidence to falsify those predictions. We argue that existing measures of falsifiability have important limitations and develop a new measure. KL-delta uses Kullback-Leibler divergence to compare the prior predictive distributions of models to the data prior that formalizes knowledge about the plausibility of different experimental outcomes. Using introductory conceptual examples and applications with existing models and experiments, we show that KL-delta challenges widely held scientific intuitions about model complexity and falsifiability. In a psychophysics application, we show that hierarchical models with more parameters are often more falsifiable than the original nonhierarchical model. This counters the intuition that adding parameters always makes a model more complex. In a decision-making application, we show that a choice model incorporating response determinism can be harder to falsify than its special case of probability matching. This counters the intuition that if one model is a special case of another, the special case must be less complex. In a memory recall application, we show that using informative data priors based on the serial position curve allows KL-delta to distinguish models that otherwise would be indistinguishable. This shows the value in model evaluation of extending the notion of possible falsifiability, in which all data are considered equally likely, to the more general notion of plausible falsifiability, in which some data are more likely than others. (PsycInfo Database Record (c) 2023 APA, all rights reserved).

Collaborative Development: How Linguists and Aviation SMEs can Best Serve the Aviation Language Community

For the purposes of safety in aviation and in accordance with the ICAO recommendations, all pilots and controllers must follow a prescribed set of rules in order to communicate effectively within the worldwide aviation community. Therefore, they must be trained in their use of aviation language. It seems, after research conducted in this context, that the training provided solely by SMEs does not suffice as they do not draw attention to proper articulation and pronunciation, nor drill grammar and vocabulary sufficiently. On the other hand, language instructors deliver on such issues on a regular basis, though, not being experts in aviation themselves, may leave out important elements of aeronautical communication. Hence, the only way to deliver the training is the collaborative development of both specialists. We suggest here one model of such cooperation. The generation of sample language and conceptual examples to place the use of this structured communication in the proper context, should be initiated by the SME. However, language instructors should be available to participate in such exercise building sessions to continuously keep the linguistic goal of the exercise in clear focus, as they would be best suited to that role. Using such a model to develop outcome-based language exercises embodies several advantages over developing study material produced by only one half of this suggested collaborative team. This paper will seek to elaborate on the different models which may be used in such endeavours as well as narrow the focus to the model suggested above, with a more detailed explanation of this model, to include sample language lessons (or lesson plans) generated in this way.

Assessing critical materials demand in global energy transition scenarios based on the Dynamic Extraction and Recycling Input-Output framework (DYNERIO)

The energy transition process calls for striving interventions towards low-carbon technologies. While their deployment is expected to drive down greenhouse gases emissions, it is expected to bring along intense critical materials exploitation. This article presents a quantitative framework to assess the impact of energy transition pathways in terms of raw materials extraction, coupling Input-Output models with energy modelling methodologies, defined as Dynamic Extraction and Recycling Input-Output framework (DYNERIO). The framework is analytically formalized and tested through a conceptual two-regions example based on EXIOBASE dataset. DYNERIO is composed by three modules: an environmentally-extended Multi-Regional Input-Output model assesses the demand of energy carriers based on exogenously defined final demand of commodities in future scenarios; an energy optimization model quantifies the time-evolution of the technology capacity stocks needed to satisfy such demand; finally, a third module quantifies the amount of materials demanded, recycled and extracted by each region to manufacture the capacity requirements.