Exploratory Application Research Articles

How to evaluate the reduction effect of the park on PM2.5? Exploratory application of the maximum and cumulative perspective

Urban parks have been widely proved to be effective in reducing particulate matter pollution, but there is still a knowledge gap in quantitatively evaluating their reduction effects. The purpose of this study is to develop a new method to quantify the reduction effect of PM2.5 in urban parks through high-precision spatio-temporal monitoring experiments in 22 typical urban parks in Shenyang, China, so as to fill this gap. In this study, the cubic polynomial function model was used for the first time to establish the relationship curve between PM2.5 concentration inside and outside the park at different distances. The results showed that the park PM2.5 reduction magnitude and distance were about 5.04–10.14 ug/m3 and 149.47–150.19 m, respectively. Partial correlation analysis revealed that the relationship between the reduction evaluation indexes and the environmental factors had time heterogeneity. The park's internal characteristics and surrounding building environment was the key factor affecting the park PM2.5 reduction effect. In addition, parks smaller than 4.71 hm2 demonstrated better PM2.5 reduction efficiency. In conclusion, this study provides a new quantitative approach to evaluating the park PM2.5 reduction effect and offers data-driven insights for optimizing park planning to enhance the permeability of these effects beyond park boundaries.

Network analyses of emotion components: an exploratory application to the component process model of emotion

Emotion is an episode involving changes in multiple components, specifically subjective feelings, physiological arousal, expressivity, and action tendencies, all these driven by appraisal processes. However, very few attempts have been made to comprehensively model emotion episodes from this full componential perspective, given the statistical and methodological complexity involved. Recently, network analyses have been proposed in the field of emotion and cognition as an innovative theoretical and statistical framework able to integrate several properties of emotions. We therefore addressed the call for more multi-componential evidence by modeling the network of a comprehensive list of emotion components drawn from the Component Process Model of Emotion. Five-hundred students were confronted with mildly ambiguous scenarios from everyday life, and reported on their situational appraisals and emotion responses. Network analyses were applied to the emotion components related to a positive and a negative scenario to explore 1) how the components organize themselves into networks and dimensions; 2) which components are the most central within networks and dimensions; and 3) the patterns of components relation between and within dimensions. A three-dimensional solution emerged in both scenarios. Additionally, some appraisals and responses appeared to be differentially relevant and related to each other in both scenarios, highlighting the importance of context in shaping the strength of emotion component relations. Overall, we enriched the field of affective science by exploring the connections between emotion components in three novel ways: by using network analyses, by integrating them into a multi-componential framework, and by providing context to our emotion components. Our results can also potentially inform applied research, where understanding the interconnections and the centrality of components could aid the personalization of interventions.

Mētis: a framework for understanding tactics of resistance

ABSTRACT Teachers’ negative experiences of high-stakes accountability have been documented extensively, but the ways in which teachers are able to engage in tactics of resistance in response are less well known. This is most especially true in terms of the subtle, covert forms of resistance which occur through the practice of teachers’ everyday working lives. This paper contributes through a novel exploratory application of ‘Mētis’, a framework for understanding tactics of resistance derived from the thought of Detienne and Vernant (1978). When Mētis is harnessed alongside de Certeau’s (1984) notion of the practice of everyday life, it provides an innovative means of exploring the complex, elusive and intricate ways in which teachers pursue tactics of resistance within a context of high-stakes accountability. By so doing, it demonstrates how teachers are able to evade or resist high-stakes accountability in order to pursue their own educational goals. Thus, the article also adds to an understanding of the gap between policy formulation and its enactment. This initial exploratory study contributes to the literature by offering a transferable conceptual approach which is recommended for broader use while identifying some important considerations which attend the empirical application of Mētis.

Exploring Ethical Space in land use planning: a case study of the Upper Columbia, British Columbia

In 2019, British Columbia (BC) adopted Bill 41: The Declaration on the Rights of Indigenous Peoples Act (DRIPA). DRIPA committed BC to developing a new planning process, modernized land use planning, that involves ethical collaboration with Indigenous Peoples. Although ethical decision-making in planning theory has emerged in academic discourse, planning practitioners are missing clear frameworks to implement theory in practice. Ethical Space, a conceptual approach used to balance power between Indigenous and non-Indigenous people, may prove to be a promising implementation framework. This paper offers an exploratory application of Ethical Space for land use planning in Upper Columbia, a region in expressed need of modernized land use planning efforts. Research methods include semi-structured interviews and document analysis. Findings present recommendations for Upper Columbia governments to begin Ethical Space in land use planning. Key insights are transferable to planners with an interest in ethical collaborations between multiple governance structures.

The mediating effect of BIM application on the link between organizational support and BIM user satisfaction

ABSTRACT Building information modeling (BIM) is considered to be the most promising technological innovation in the construction industry, and its widespread application is an inevitable trend. The satisfaction level of employees using BIM serves as a crucial indicator of the success of BIM implementation. Therefore, it is imperative to enhance users’ satisfaction. While some researchers have explored the impact of organizational support, there has been a dearth of in-depth studies on the specific mechanisms underlying its influence. Previous research has identified both emotional and instrumental dimensions of organizational support, however, the specific mechanisms through which these two forms of support affect BIM user satisfaction have not been thoroughly explored. This study investigates the effects of organizational support on BIM user satisfaction in construction companies and proposes the mediating role of exploitative and exploratory applications in their relationship from the perspective of dual innovation theory. Drawing data from 151 valid questionnaires, regression analysis is conducted to examine the relationship between organizational support and user satisfaction. Additionally, the Partial Least Squares Structural Equation Model (PLS-SEM) is established to verify the mediating role of explorative and exploitive applications. The findings show that: (1) instrumental support and emotional support both increase BIM user satisfaction; (2) both exploitative and exploratory applications mediate the effect of emotional support on BIM user satisfaction; (3) only exploratory application mediates between instrumental support and BIM user satisfaction. These findings explain the mechanism through which organizational support influences BIM user satisfaction and offer insight for managers to improve the satisfaction level.

Exploratory Application of Climate Change Analysis and Assessment Tools from Mainstream Institutions

Exploratory Application of Climate Change Analysis and Assessment Tools from Mainstream Institutions

Exploratory application of a cannulation model in recently weaned pigs to monitor longitudinal changes in the enteric microbiome across varied porcine reproductive and respiratory syndrome virus (PRRSV) infection statuses.

The enteric microbiome and its possible modulation to improve feed conversion or vaccine efficacy is gaining more attention in pigs. Weaning pigs from their dam, along with many routine procedures, is stressful. A better understanding of the impact of this process on the microbiome may be important for improving pig production. The objective of this study was to develop a weaner pig cannulation model, thus allowing ileum content collection from the same pig over time for 16S rRNA sequencing under different porcine reproductive and respiratory syndrome virus (PRRSV) infection statuses. A total of 15 3-week-old pigs underwent abdominal surgery and were fitted with an ileum cannula, with ileum contents collected over time. In this pilot study, treatment groups included a NEG-CONTROL group (no vaccination, no PRRSV challenge), a POS-CONTROL group (no vaccination, challenged with PRRSV), a VAC-PRRSV group (vaccinated, challenged with PRRSV), a VAC-PRO-PRRSV group (vaccinated, supplemented with a probiotic, challenged with PRRSV), and a VAC-ANTI-PRRSV group (vaccinated, administered an antibiotic, challenged with PRRSV). We assessed the microbiome over time and measured anti-PRRSV serum antibodies, PRRSV load in serum and nasal samples, and the severity of lung lesions. Vaccination was protective against PRRSV challenge, irrespective of other treatments. All vaccinated pigs mounted an immune response to PRRSV within 1 week after vaccination. A discernible impact of treatment on the diversity, structure, and taxonomic abundance of the enteric microbiome among the groups was not observed. Instead, significant influences on the ileum microbiome were observed in relation to time and treatment. The cannulation model described in this pilot study has the potential to be useful in studying the impact of weaning, vaccination, disease challenge, and antimicrobial administration on the enteric microbiome and its impact on pig health and production. Remarkably, despite the cannulation procedures, all vaccinated pigs exhibited robust immune responses and remained protected against PRRSV challenge, as evidenced by the development of anti-PRRSV serum antibodies and viral shedding data.

RFI detection with spiking neural networks

Abstract Detecting and mitigating radio frequency interference (RFI) is critical for enabling and maximising the scientific output of radio telescopes. The emergence of machine learning (ML) methods capable of handling large datasets has led to their application in radio astronomy, particularly in RFI detection. Spiking neural networks (SNNs), inspired by biological systems, are well suited for processing spatio-temporal data. This study introduces the first exploratory application of SNNs to an astronomical data processing task, specifically RFI detection. We adapt the nearest latent neighbours (NLNs) algorithm and auto-encoder architecture proposed by previous authors to SNN execution by direct ANN2SNN conversion, enabling simplified downstream RFI detection by sampling the naturally varying latent space from the internal spiking neurons. Our subsequent evaluation aims to determine whether SNNs are viable for future RFI detection schemes. We evaluate detection performance with the simulated HERA telescope and hand-labelled LOFAR observation dataset the original authors provided. We additionally evaluate detection performance with a new MeerKAT-inspired simulation dataset that provides a technical challenge for machine-learnt RFI detection methods. This dataset focuses on satellite-based RFI, an increasingly important class of RFI and is an additional contribution. Our SNN approach remains competitive with the original NLN algorithm and AOFlagger in AUROC, AUPRC, and F1-scores for the HERA dataset but exhibits difficulty in the LOFAR and Tabascal datasets. However, our method maintains this accuracy while completely removing the compute and memory-intense latent sampling step found in NLN. This work demonstrates the viability of SNNs as a promising avenue for ML-based RFI detection in radio telescopes by establishing a minimal performance baseline on traditional and nascent satellite-based RFI sources and is the first work to our knowledge to apply SNNs in astronomy.

AI-Generated Images for Speech Pathology-An Exploratory Application to Aphasia Assessment and Intervention Materials.

Images are a core component of aphasia assessment and intervention that require significant resources to produce or source. Text-to-image generation is an Artificial Intelligence (AI) technology that has recently made significant advances and could be a source of low-cost, highly customizable images. The aim of this study was to explore the potential of AI image generation for use in aphasia by examining its efficiency and cost during generation of typical images. Two hundred targets (80 nouns, 80 verbs, and 40 sentences) were selected at random from existing aphasia assessments and treatment software. A widely known image generator, DALL-E 2, was given text prompts for each target. The success rate, number of prompts required, and costs were summarized across target categories (noun/verb/sentence) and compared to frequency and imageability. Of 200 targets, 189 (94.5%) successfully conveyed the key concept. The process took a mean of 2.3 min per target at a cost of $0.31 in U.S. dollars each. However, there were aesthetic flaws in many successful images that could impact their utility. Noun images were generated with the highest efficiency and accuracy, followed by verbs, while sentences were more challenging, particularly those with unusual scenes. Patterns of flaws and errors in image generation are discussed. The ability to rapidly generate low-cost, high-quality images using AI is likely to be a major contribution to aphasia assessment and treatment going forward, particularly as advances in this technology continue.

Exploratory examination of the scale structure of the Moorong Self-Efficacy Scale: Application of Rasch Measurement Theory

Objective: Exploratory application of the Rasch Measurement (RM) Model for evidence for reproducibility, conceptual/content validity, and structural validity of the Moorong Self-Efficacy Scale (MSES). Study Design: Secondary RM analysis of data collected in a randomized controlled trial comparing two exercise interventions for persons living with spinal cord injury (SCI). Setting: Community-dwelling persons living with SCI enrolled in an exercise study. Participants: Adults (n = 79) enrolled in the parent study had a traumatic SCI > 3 months prior, injury level C5 to T12. Interventions: Not applicable. Outcome measure: The original MSES is a 16-item measure of self-efficacy with a 7-level response scale for un/certainty which was developed for use with persons living with SCI. Results: We addressed item misfit, infrequent category endorsement, and category step disorder by removing two items and reorganizing the rating scale. Rating scale changes removed category 4 (Neutral), combined categories 1–3 (Very Uncertain, Somewhat Uncertain, and Uncertain) for all items, and further combined certainty categories for two items. Principal components analysis of the residuals indicated a possible second dimension with a first-contrast Eigenvalue of 2.4. However, the contrasted item groups had explained variance <10% and a dis-attenuated correlation = 0.92 indicating they measure the same underlying trait. The small sample size precluded examination of differential item functioning. Conclusions: Exploratory RM analysis of MSES produced a 14-item Rasch version which identified structural and content validity evidence concerns inherent in the original MSES. However, results could be biased by a small sample size and further study should examine the item content and rating scale structure with larger, more diverse samples of persons living with SCI.

MAP3D: An explorative approach for automatic mapping of real-world eye-tracking data on a virtual 3D model.

Mobile eye tracking helps to investigate real-world settings, in which participants can move freely. This enhances the studies' ecological validity but poses challenges for the analysis. Often, the 3D stimulus is reduced to a 2D image (reference view) and the fixations are manually mapped to this 2D image. This leads to a loss of information about the three-dimensionality of the stimulus. Using several reference images, from different perspectives, poses new problems, in particular concerning the mapping of fixations in the transition areas between two reference views. A newly developed approach (MAP3D) is presented that enables generating a 3D model and automatic mapping of fixations to this virtual 3D model of the stimulus. This avoids problems with the reduction to a 2D reference image and with transitions between images. The x, y and z coordinates of the fixations are available as a point cloud and as .csv output. First exploratory application and evaluation tests are promising: MAP3D offers innovative ways of post-hoc mapping fixation data on 3D stimuli with open-source software and thus provides cost-efficient new avenues for research.

Social support as a moderator between resilience and psychological distress among korean americans perceiving racial discrimination during COVID-19: An exploratory application of a moderated mediation model

This study examined how resilience is connected to psychological distress among Korean Americans (both U.S. born and foreign-born). Specifically, we explored the mediating role of perceived racial discrimination and the association between resilience and perceived racial discrimination moderated by different levels of social support. A structural equation model (a moderated mediation model) was employed with a sample of 781 participants. The data were obtained through online surveys for variables of interest. We employed this structural equation model to examine the association between variables using the bootstrapping method. We found that the indirect association of resilience with psychological distress through perceived racial discrimination was statistically significant. We also found that the mediating association of perceived racial discrimination was moderated by the extent of social support, especially when the level of social support was low and moderate. We discovered the roles of social support and perceived racial discrimination during the COVID-19 pandemic and the link between resilience and psychological distress for Korean Americans. The findings suggested that social services and culturally sensitive/responsive resilience-focused treatments (including a mindfulness program) alleviate heightened psychological distress among Asian Americans who experienced racial discrimination during the pandemic. Policy implications are also discussed.

Effects of Explicit Vocabulary Interventions for Preschoolers: An Exploratory Application of the Percent of Goal Obtained Effect Size Metric.

Systematic reviews of literature are routinely conducted to identify practices that are effective in addressing educational and clinical problems. One complication, however, is how best to combine data from both group experimental design (GED) studies and single-case experimental design (SCED) studies. Percent of Goal Obtained (PoGO) has been developed as a metric to express the size of the effect relative to the distance to a goal, which could have broad applicability. This study sought to augment this descriptive index with estimates of standard errors, which are needed to use PoGO as an effect size metric in meta-analyses of SCED and GED studies. This study investigated the application of PoGO and standard errors to both SCED and GED studies examining a common intervention approach used with a single population. Sixteen articles investigating explicit vocabulary instruction applied to pre-K and kindergarten students were identified. PoGO and standard errors were calculated for variations of explicit vocabulary interventions. Evaluated interventions included six studies using exclusively an SCED, nine studies using a GED, and one that used both. PoGO was calculated for each treatment condition when applicable (i.e., alternating treatments designs). Standard errors and confidence interval limits also were calculated. PoGO effect size values ranged from 14.4% to 93.6%. PoGO for single-case experiments was 49.2% with a standard error of 7.26, and for group experiments, it was 30.8% with a standard error of 3.71. Despite variation in the percentage of goal obtained across studies, the high degree of overlap in PoGO and standard errors between single-case and group experiments provides an indication that systematic reviews can apply this effect size metric to combine information obtained across experimental designs.

Flock Nocturnal Activity: Is There a Rotative Guard?

Animal activity during the night period is of enormous importance, since it represents approximately half of animals’ lives, and monitoring it during this period makes it possible to detect problems related to well-being and safety, and allows us to infer energy expenditure on the basis of their activity level. The present study analyzes a sheep activity dataset created during the night period to validate non-invasive techniques of monitoring that can be used to infer energy expenditure at night and to detect abnormal nocturnal activity. The study allowed us to detect cyclic changes in activity during the night period, which is composed of inactive and active periods, and to identify sheep lying positions. The analysis of the joint activity of the flock allowed us to perceive a time lag in the rest cycles, which consisted of periods of activity of ewes undone between elements of the flock. Although it does not allow us to identify the components of the period of inactivity, since the method used does not monitor brain activity, the results allow us to confirm the cyclical character of the nocturnal activity of sheep that has been reported in the literature, as well as their typical posture when lying down. Although this is an exploratory application with a very small number of animals, the similarity between the results obtained and the results documented in the existing literature, which have mostly been obtained using invasive methods, is encouraging, and suggests it is possible to rely on activity monitoring processes based on inertial sensors.

Exploratory application of an integrated topic-based curriculum in biochemistry experimental teaching.

Biochemistry, a complicated basic course in health sciences, plays a fundamental role in describing pathogenic mechanism of illness in molecular terms, and is required to be learned for all medical students. Due to various obstacles to biochemistry learning and teaching, there is a pressing issue of curriculum reform to arouse the student's enthusiasm in biochemistry learning. In this study, an integrated topic-based biochemistry training program (TBBTP) combining the traditional lectures, question-based learning and experimental practices, was introduced in biochemistry teaching. Its effectiveness was evaluated through examination and questionnaire analyses. Consequently, we found that TBBTP promoted the dissemination of biochemical knowledge via traditional lecture, designated learning issues and experimental practices, and acquisition of various skills through practical operation, presentation, and independent learning. It motivated students to study biochemistry with intense interest and enthusiasm. This study explored the feasibility of the topic-based biochemistry teaching as a supplement to biochemistry curriculum in medical education and as a mainstream pedagogy in biochemistry lab.

Exploratory application of machine learning methods on patient reported data in the development of supervised models for predicting outcomes

BackgroundPatient-reported outcome measurements (PROMs) are commonly used in clinical practice to support clinical decision making. However, few studies have investigated machine learning methods for predicting PROMs outcomes and thereby support clinical decision making.ObjectiveThis study investigates to what extent different machine learning methods, applied to two different PROMs datasets, can predict outcomes among patients with non-specific neck and/or low back pain.MethodsUsing two datasets consisting of PROMs from (1) care-seeking low back pain patients in primary care who participated in a randomized controlled trial, and (2) patients with neck and/or low back pain referred to multidisciplinary biopsychosocial rehabilitation, we present data science methods for data prepossessing and evaluate selected regression and classification methods for predicting patient outcomes.ResultsThe results show that there is a potential for machine learning to predict and classify PROMs. The prediction models based on baseline measurements perform well, and the number of predictors can be reduced, which is an advantage for implementation in decision support scenarios. The classification task shows that the dataset does not contain all necessary predictors for the care type classification. Overall, the work presents generalizable machine learning pipelines that can be adapted to other PROMs datasets.ConclusionThis study demonstrates the potential of PROMs in predicting short-term patient outcomes. Our results indicate that machine learning methods can be used to exploit the predictive value of PROMs and thereby support clinical decision making, given that the PROMs hold enough predictive power

A Qualitative Study of Data Management Development for Hybrid Organisation Application

A revolution of information technology through big data for data processing is greatly affecting organisations around the globe, specifically the education providers. Subsequently, there are increasing needs and demands for software, digitalisation, and processing power development for unlimited database development and applications. The study aims to determine how well-prepared UTMSPACE is in meeting the new data management planning requirement for developing the big data management application. This study is supplementary to the development of strategic management planning in digitalisation and data management. The study employs qualitative method for its exploratory and data processing application research. The findings will help to identify the weaknesses and strengths in preparing data management applications to achieve strategic management plan outcomes. The study suggests that the company must increase awareness on the importance of big-data management through data processing engines that provide a more organised and centralised data and information management. In addition, the results also share the type of data and information needed for prioritising the organisational strategies in implementing the lifelong learning programmes across different environments and sectors.

Microscale Discrete Element Model for Simulating Bridging Behavior of Fractured Glass Laminates

The bridging behavior between fragments is one vital factor in the post-fracture stage of glass laminates. A particle-based discrete element model was developed in this work to simulate the bridging behavior of fractured glass laminates. The model was based on the calibration of hyperelastic PVB material properties using soft-bond model and further validated with the experimental data as well as the observations from through-crack-tensile tests. In order to investigate the bridging performance of fractured glass laminates with multiple fragments under uniaxial tension, three factors encompassing the crack aligning, fragment number and adhesion conditions were considered to perform a parametric study. The results give an exploratory application of the developed discrete element models in the estimation of the post-fracture behaviors. It is found that the smaller fragment size can diminish the tension-stiffening effect in the non-aligned case which might generate an enhancing ratio of initial modulus up to nearly 3.6. The non-aligned cracks can also lead to a very high strength if the adhesion ability can be guaranteed. However, the delamination of fragments is expected to control the post-fracture strength of fractured glass laminates with PVB interlayer.

Exploratory application of a novel centre cross diaphragm method for the construction of large cross-section tunnels in soft ground

For tunnelling in urban areas, the current sequential excavation (SE) method usually increases the number of temporary drifts to achieve better control of surface settlements, which may prolong construction duration. In this case study, a four-drift SE method, termed the suspension-type centre cross diaphragm (S-CRD) method, was proposed and applied to the construction of a large cross-section tunnel in soft ground, where normally the six-drift double-side drift (DSD) method would be adopted. Field monitoring results and statistical data indicated that, in comparison with the six-drift DSD method, the four-drift S-CRD method achieved better control of the surface settlements while improving the efficiency of construction by 30%. The advantages of the S-CRD method, i.e., safe and rapid construction, were eminently clear.

Exploratory application of indocyanine green quantification in biliary atresia observational study

BackgroundIndocyanine green (ICG) is known to facilitate real-time imaging of the bile outflow during the Kasai procedure. This study explored more possibilities for applying ICG quantification in biliary atresia (BA). MethodsWe enrolled nine BA patients in this study. All patients received intravenous ICG injections before surgery and underwent Kasai operation. With a fluorescence imaging system (Nanjing Nuoyuan, REAL-IGS FLI-108) to observe the ICG and quantify the ICG fluorescence intensity (ICG-FI) changes of the hepatic portal fibrous tissue and the liver during the operation. As a short-term prognosis assessment, we monitored the postoperative ICG metabolism time in stool and used the jaundice-free (TBIL < 2 mg/dl) at 1–3 months after the operation. ResultsThe average age of the patients at the time of surgery was 73 days. There were no adverse reactions after ICG injection. All patients were observed ICG-FI increased after the dissection of hepatic portal fibrous tissue, and the ICG-FI of the liver decreased during the operation. They all received standardized treatment after surgery. Four patients completely metabolized ICG within about two weeks (no fluorescence detected in stool), and the other five were longer than two weeks. Five patients achieved a jaundice-free outcome in the short-term postoperatively, and the other four did not. ConclusionsIt is feasible to quantify ICG-FI in real-time to evaluate the anatomical degree of hepatic portal fibrous tissue in BA. The variations of ICG-FI in the liver and postoperative ICG metabolism time may be related to prognosis.