Dimensions Of Complexity Research Articles

Measuring complexity in mega construction projects: fuzzy comprehensive evaluation and grey relational analysis

PurposeMega construction projects (MCPs), characterized by their vast scale, numerous stakeholders and complex management, often face significant uncertainties and challenges. While existing research has explored the complexity of MCPs, it predominantly focuses on qualitative analysis and lacks systematic quantitative measurement methods. Therefore, this study aims to construct a complexity measurement model for MCPs using fuzzy comprehensive evaluation and grey relational analysis.Design/methodology/approachThis study first constructs a complexity measurement framework through a systematic literature review, covering six dimensions of technical complexity, organizational complexity, goal complexity, environmental complexity, cultural complexity and information complexity and comprising 30 influencing factors. Secondly, a fuzzy evaluation matrix for complexity is constructed using a generalized bell-shaped membership function to effectively handle the fuzziness and uncertainty in the assessment. Subsequently, grey relational analysis is used to calculate the relational degree of each complexity factor, identifying their weights in the overall complexity. Finally, the weighted comprehensive evaluation results of project complexity are derived by combining the fuzzy evaluation results with the grey relational degrees.FindingsTo validate the model’s effectiveness, the 2020 Xi’an Silk Road International Conference Center construction project is used as a case study. The results indicate that the overall complexity level of the project is moderate, with goal complexity being the highest, followed by organizational complexity, environmental complexity, technical complexity, cultural complexity and informational complexity. The empirical analysis demonstrates that the model can accurately reflect the variations across different dimensions of MCP complexity and can be effectively applied in real-world projects.Originality/valueThis study systematically integrates research on MCPs complexity, establishing a multidimensional complexity measurement framework that addresses the limitations of previous studies focusing on partial dimensions. Moreover, the proposed quantitative measurement model combines fuzzy comprehensive evaluation and grey relational analysis, enhancing the accuracy and objectivity of complexity measurement while minimizing subjective bias. Lastly, the model has broad applicability and can be used in MCPs across different countries and regions, providing a scientific and effective basis for identifying and managing MCP complexity.

Abstract 4113951: Calcification Alters Luminal Surface Complexity in Cadaveric Left Anterior Descending Arteries

Background: Understanding the intricacies of luminal surface topographic changes in pathologic coronary arteries may yield insight not only on the efficacy of existing clinical interventions (e.g. impact of surface changes on stent anchoring), but also on the subsequent hemodynamic changes and pathophysiology of thrombus formation. In this study, we aim to detect and quantify luminal surface alterations induced by the presence of calcification in cadaveric left anterior descending arteries (LADs) using a novel approach–surface metrology. Methods: LADs (n=10) were harvested from cadaveric hearts using a systematic dissection approach, scanned using the Bruker Skyscan 1173 microCT scanner, and then underwent threshold-based image segmentation on Dragonfly by Object Research Systems to quantify the total calcium volume present throughout the vessel. Each specimen was then splayed open and up to 15 scans were performed at 20X magnification using the Sensofar S Neox optical profiler. Each scan subsequently underwent surface metrologic scale-sensitive fractal analyses in SensoMap 10. Results: Extent of calcification was standardized and expressed using the following ratio: total calcium volume by total vessel length (TC/VL). We considered a TC/VL ratio of 0-0.25 as low calcification and 0.26-0.5 as high calcification. Interestingly, our findings demonstrated statistically significant increases in smooth-rough crossover (SRC) and scale of maximum complexity (Smfc) with positive correlation, but reductions in fractal complexity (Lsfc) and fractal dimension (Dls) with negative correlation, in highly calcified compared to lowly calcified LADs. Conclusions: Our data suggests that the presence of calcium in the walls of LADs may translate to increased roughness in luminal surfaces generally, but smoothness over calcified plaques, locally. Our proposed mechanism is increased endothelial tension directly over areas of calcification, thus reducing surface complexity. Future studies will involve modeling blood flow using smooth particle hemodynamics (SPH) based on complete luminal and surface topographical geometries to evaluate the impact of these surface changes on hemodynamics.

Identifying and developing the roles of private business actors in international regime complexes

Abstract Business actors play increasingly important roles in global governance and international regulation. This paper considers how regime complexity influences the roles of businesses and impacts opportunities for business influence on international regulatory regimes. We conducted a scoping literature review of 243 articles from the International Regime Complexity (IRC) theory literature to explore if and how complexity affects the roles of businesses and their influence on international regulation. We found that complexity presents opportunities for businesses to regime shift and exploit knowledge asymmetry in order to influence international regulation. Further, IRC theory illustrates how the roles of businesses interact and leverage one another in order to create better opportunities for influence in specific international regulatory regimes. This paper contributes to IRC theory by building on the existing non-state actor discussions and offering specific theorization of business behavior, thus starting to bridge the gap between the empirical and theoretical understanding. Second, it contributes to existing discussions in business and politics literature by developing existing knowledge on the roles of businesses in global governance to better reflect the added dimension of complexity.

Hard X-ray Photoelectron Spectroscopy Probing Fe Segregation during the Oxygen Evolution Reaction.

NiFe electrocatalysts are among the most active phases for water splitting with regard to the alkaline oxygen evolution reaction (OER). The interplay between Ni and Fe, both at the surface and in the subsurface of the catalyst, is crucial to understanding such outstanding properties and remains a subject of debate. Various phenomena, ranging from the formation of oxides/(oxy)hydroxides to the associated segregation of certain species, occur during the electrochemical reactions and add another dimension of complexity that hinders the rational design of electrodes for water splitting. In this work, we have developed the procedure for the quantification of chemical depth profiling by XPS/HAXPES measurements and applied it to two NiFe electrodes with different porosities. The main outcome of this study is related to the surface reconstruction of the electrodes during the OER, followed at two different depths by means of X-ray photoelectron spectroscopy. We find that Fe initially segregates at the surface when exposed to ambient conditions, resulting in the formation of an inactive FeOx phase. In addition, the porosity of the catalyst plays a significant role in the segregation process and thus in the performance of the electrode. In particular, the higher porosity of the nanostructured sample is responsible for a more pronounced diffusion of Fe from the subsurface to the surface with a more effective suppression of the activity of the Ni1-xFexOOH phase. These results highlight the importance of the fact that the chemical state of the surface of a multielement system is a snapshot in time, dependent on both external parameters, such as the applied potential and the adjacent electrolyte, and the underlying bulk properties accessible with HAXPES.

Examining the Relationship Between Project Complexity and Project Success: The Moderating Role of Project Management Competencies in Ghana’s Construction Sector

ABSTRACT This study seeks to examine the relationship between project complexity and project success in construction projects in Ghana while assessing the moderating role of project management competencies (PMC). Using a quantitative cross-section research design, data was collected from 141 respondents in organizations that undertake construction projects in Ghana. Responses were analyzed using IBM SPSS (v.23.0) and Smart PLS 3.0. The results established a negative relationship between Technological, Organizational, and Environmental complexity and project success. Technological complexity was seen as the dimension of complexity that significantly affects the success outcomes of projects. PMC’s moderating influence was insignificant. The findings suggest that, for projects to have an increased success rate, organizations must thoroughly investigate the kind of project complexity expected to be present in the project, and this will inform decisions on the caliber of skills/competencies needed for a successful execution of the project.

Mind map for cross-reading the glossary of urban morphology and soundscape research

Urban morphological research has a long-established and systematic method of analysis that provides a clear and shared research framework. Since the late 1990s, the International Seminar on Urban Form (ISUF) has compiled a glossary of urban morphology for the agenda of urban researchers. The soundscape literature emerged in the late 1960s and early 1970s. Since then, there have been numerous studies dealing with the sonic dimension of urban complexity from different perspectives. In order to create an overarching framework, attempts have been made since the early 1970s to define the principles and processes of soundscape research. There is a comprehensive glossary created by the Wold Soundscape Project Team (WSP) in 1978 which has been of great importance for further soundscape studies. This paper argues that there is a link between urban morphology research and soundscape research. A cross-reading between the two glossaries therefore has the potential to offer an alternative reading of urban research by revealing the commonalities or differences in the sonic-morphological conceptual schemes.

Patient´s perception of spatial complexity in emergency spaces of a hospital in China: a method for stakeholders to improve design and management

ABSTRACT The focus of medical facility users has shifted from prioritizing epidemic prevention to prioritizing experience after COVID-19, particularly in the emergency department (ED) of a large-scale Chinese hospital. The current visual elements of the emergency department provide a negative perception to the patients who visit the department. The study aims to optimize the ED's spatial design by understanding how visual elements affect patients' perception of spatial complexity. The methodology involved recreating a patient's typical path through the ED using data from the hospital information system (HIS), capturing images at 70 location nodes, and calculating the fractal dimensions of these images using the box-counting method. Visual elements were categorized into eight groups, and their fractal dimensions and proportions within the images were analyzed. A breakpoint graph was developed to track changes in spatial complexity perception as patients move through the ED. Statistical analysis, including correlation and one-way ANOVA, revealed that spatial attributes and lighting conditions significantly impact spatial complexity. From the results, grouping analysis was conducted on the visual elements of the horizontal surfaces, vertical surfaces, ceiling, furniture, and vegetation. Further optimization was proposed. In conclusion, this study highlights the importance of visual elements in emergency spaces, directly influencing users' perception of spatial complexity. The relationship between spatial complexity and fractal dimension is further emphasized. Stakeholders and designers can use the findings of this study as references to make informed decisions to improve the user's experience in the healthcare-built environment.

Circular supply chains as complex adaptive ecosystems: A simulation-based approach

Circular Supply Chains (CSCs) are self-sustained ecosystems designed to pursue Circular Economy principles. Compared to linear supply chains, they involve new classes of stakeholders performing circular activities and multiple resource streams (e.g. virgin materials, by-products, end-of-life products, waste) moving forward and back the original and different supply chains. Therefore, CSCs are characterized by increasing complexity that call for proper coordination mechanisms. In this regard, we propose a theoretical framework, based on the theory of Complex Adaptive Systems, capturing the static and dynamic dimensions of CSC complexity which characterize the archetype of a CSC and then we develop a novel agent-based model to simulate the effect of competition and collaboration mechanisms on CSC performance across different CSC archetypes. The results of numerical simulations suggest which mechanism to adopt for the coordination of certain CSC archetypes: while competition is always detrimental, local collaboration is effective for CSCs with closed loop structures, and global collaboration is instead required in presence of open and hybrid loop structures.

Mapping the associations among task complexity, emotionality, and linguistic complexity in L2 writing

This study explored the relationship between task complexity, textual emotionality, and linguistic complexity in second language (L2) writing. Fifty-eight L2 English learners performed simple and complex versions of an argumentative writing task manipulated along with resource-­directing variables. The essays were first analysed for textual emotionality with sentiment analysis and then examined using a set of lexical and syntactic complexity and cohesiveness indices. Regarding emotionality, the complex task elicited more positive and negative emotions than the simple task. Further, the complex task led to greater lexical complexity and cohesiveness than the simple task, but the two tasks were not statistically different from each other in terms of syntactic complexity features. Varied patterns of correlations were observed between emotionality metrics and indices of different dimensions of linguistic complexity in the two tasks. Our findings focusing on the intersections of cognitive, socio-emotive, and linguistic components suggest that different task complexity manipulations could generate varying cognitive demands, affecting L2 written production. These findings provide theoretical and pedagogical implications for TBLT researchers and L2 writing practitioners.

Porosity Prediction Based on Ensemble Learning for Feature Selection and an Optimized GRU Improved by the PSO Algorithm

Accurate and reliable prediction of porosity forms the foundational basis for evaluating reservoir quality, which is essential for the systematic deployment of oil and gas exploration and development plans. When data quality of samples is low, and critical model parameters are typically determined through subjective experience, resulting in diminished accuracy and reliability of porosity prediction methods utilizing gated recurrent units (GRU), a committee-voting ensemble learning (EL) method, and an enhanced particle swarm optimization (PSO) algorithm are proposed to optimize the GRU-based porosity prediction model. Initially, outliers are eliminated through box plots and the min–max normalization is applied to enhance data quality. To address issues related to model accuracy and high training costs arising from dimensional complexity, substantial noise, and redundant information in logging data, a committee-voting EL strategy based on four feature selection algorithms is introduced. Following data preprocessing, this approach is employed to identify logging parameters highly correlated with porosity, thereby furnishing the most pertinent data samples for the GRU model, mitigating constraints imposed by single-feature selection methods. Second, an improved PSO algorithm is suggested to tackle challenges associated with low convergence accuracy stemming from random population initialization, alongside the absence of global optimal solutions due to overly rapid particle movement during iteration. This algorithm uses a good-point set for population initialization and incorporates a compression factor to devise an adaptive velocity updating strategy, thereby enhancing search efficacy. The enhanced PSO algorithm’s superiority is substantiated through comparison with four alternative swarm intelligent algorithms across 10 benchmark test functions. Ultimately, optimal hyper-parameters for the GRU model are determined using the improved PSO algorithm, thereby minimizing the influence of human factors. Experimental findings based on approximately 15,000 logging data points from well A01 in an operational field validate that, relative to three other deep learning methodologies, the proposed model proficiently extracts spatiotemporal features from logging data, yielding enhanced accuracy in porosity prediction. The mean squared error on the test set was 7.19 × 10–6, the mean absolute error stood at 0.0082, and coefficient of determination reached 0.99, offering novel insights for predicting reservoir porosity.

Beta-band power classification of go/no-go arm-reaching responses in the human hippocampus

Objective. Can we classify movement execution and inhibition from hippocampal oscillations during arm-reaching tasks? Traditionally associated with memory encoding, spatial navigation, and motor sequence consolidation, the hippocampus has come under scrutiny for its potential role in movement processing. Stereotactic electroencephalography (SEEG) has provided a unique opportunity to study the neurophysiology of the human hippocampus during motor tasks. In this study, we assess the accuracy of discriminant functions, in combination with principal component analysis (PCA), in classifying between ‘Go’ and ‘No-go’ trials in a Go/No-go arm-reaching task. Approach. Our approach centers on capturing the modulation of beta-band (13–30 Hz) power from multiple SEEG contacts in the hippocampus and minimizing the dimensional complexity of channels and frequency bins. This study utilizes SEEG data from the human hippocampus of 10 participants diagnosed with epilepsy. Spectral power was computed during a ‘center-out’ Go/No-go arm-reaching task, where participants reached or withheld their hand based on a colored cue. PCA was used to reduce data dimension and isolate the highest-variance components within the beta band. The Silhouette score was employed to measure the quality of clustering between ‘Go’ and ‘No-go’ trials. The accuracy of five different discriminant functions was evaluated using cross-validation. Main results. The Diagonal-Quadratic model performed best of the 5 classification models, exhibiting the lowest error rate in all participants (median: 9.91%, average: 14.67%). PCA showed that the first two principal components collectively accounted for 54.83% of the total variance explained on average across all participants, ranging from 36.92% to 81.25% among participants. Significance. This study shows that PCA paired with a Diagonal-Quadratic model can be an effective method for classifying between Go/No-go trials from beta-band power in the hippocampus during arm-reaching responses. This emphasizes the significance of hippocampal beta-power modulation in motor control, unveiling its potential implications for brain–computer interface applications.

Leisure reimagined: mapping the enablers of restorative servicescapes

ABSTRACT Servicescapes play a crucial role in the marketing strategies of contact-based service businesses, significantly influencing consumer experiences and well-being. Researchers have explored their potential impact on stress levels and attention restoration from various perspectives, including environmental psychology, perceived sensory dimensions and evidence-based design. This study builds upon existing knowledge by employing Interpretive Structural Modelling and MICMAC Analysis to identify the enablers that contribute to the restorative quality of a leisure servicescape. The current research reveals that enhancing servicescape restorativeness involves integrating elements such as refuge, culture, complexity, typicality, accessibility, familiarity, hygiene, space, calmness, legibility, biophilic designs and social dimension. Additionally, the investigation into the interrelationships among these factors highlights the pivotal roles of refuge and culture, influencing the efficacy of most other enablers. Servicescape typicality, however, relies heavily on the operationalization of other enablers. Notably, none of these enablers operates independently in promoting a restorative servicescape. Their interconnected associations underscore the importance for marketers in the leisure service industry to comprehend and leverage the interplay of these factors to achieve optimal results in enhancing the restorative property of their servicescapes. The study concludes by offering recommendations for marketers and policymakers to improve consumer and citizen well-being in a sustainable manner.

Accelerating Digital Transformation through MBSE, Multi‐physics Simulation and Digital Twin in Industry

AbstractIn the dynamic aviation industry, Aerospace Industries grapple with the intricate challenge of certifying products amidst integration requirements from aerospace manufacturers and airworthiness regulations imposed by aviation authorities. This paper underscores the indispensable role of ModelBased Systems Engineering (MBSE) in addressing the multiphase issues inherent in our product complexity. The first focal point is the certification process, where Safran SEATS navigates the complexities imposed by aerospace manufacturers and stringent airworthiness regulations. This intricate certification process demands the strategic deployment of MBSE to streamline and enhance procedural efficiency. The second challenge arises from the abstract nature of style and perceived quality requirements articulated by diverse customers. With the goal of exceeding customer expectations and creating a genuine “WOW” effect, MBSE becomes instrumental in aligning product development with diverse and often oriented customers' needs. The third dimension of complexity stems from the variability in emerging requirements, where innovations swiftly become industry standards. MBSE provides a structured approach to handle this variability, ensuring adaptability and positioning products at the forefront of innovation. Additionally, we dissect the influence of Model‐Based Design (MBD) on design processes, shedding light on its transformative effects. Introducing the concept of a digital twin, we explore its significance in the aerospace domain. Real‐world implementations of digital twin technology examined through concrete examples from aerospace projects. Finally, the presentation addresses uncertainties surrounding program efficiency, a critical consideration in the intensely competitive seat market. This paper unfolds a compelling industrial testimony, showcasing Safran SEATS' journey in overcoming the complexity of digital transformation in Aerospace Industries. It provides solutions to intricate challenges posed by certification, customers' requirements, evolving industry standards, and economic competitiveness, encapsulating the transformative impact of MBSE in our pursuit of excellence.

Radiative squeezed flow of magnetized Prandtl-Eyring fluid over a sensor surface under Soret effect

The central aim of this numerical analysis is to describe the effects of magnetic body force and thermal radiation on the boundary layer flow of Prandtl-Eyring fluid over a sensor surface suspended between two parallel plates. The considered fluid flow is two-dimensional unsteady electrically conducting and viscous incompressible in nature. A novel Soret effect is included in the concentration equation to reveal the significant features of mass diffusion mechanism under the influence of external squeezing mechanism. Surface permeable velocity concept is also included in the boundary conditions to describe the suction/injection process. However, squeezing flows find their abundant applications in bio-medicine, medical and bio-technology areas. Particularly, the micro-cantilevers with physical or chemical sensor surfaces are effectively used in the bio-medical applications for the finding of various hazardous or bio-warfare agents’, infections, hazardous virus, nuclear reactor cooling, polymer processing, pharmaceutical, blood flow, injection molding and many more. Thus, inspired by these practical applications of squeezing flows, the present problem is modeled based on the investigated geometry. The present physical problem gives the highly nonlinear coupled unsteady two-dimensional partial differential equations and which are not amenable to any of the direct methods. Owing to this reason, the appropriate similarity transformations are used to diminish the dimensional complexity of the emerged partial differential equations (PDEs). Thus, a robust Runge-Kutta -4th order scheme (RK-4) is used as a basic tool to obtain the similar solutions of the governing equations. The graphical visualization is used to analyze the computer generated numerical data in the boundary layer regime for the different set of physical parameters. It is observed that, the enhancing magnetic number ( 0.1 ≤ M ≤ 2.7 ) in the flow region 0 ≤ η ≤ 3 increases the velocity field and decreases the temperature and concentration fields. Increasing squeezed flow index ( 0.1 ≤ b ≤ 2.5 ) in the flow domain 0 ≤ η ≤ 3 decreased the fluid velocity, temperature and concentration fields. This result may be useful in sensor surface cooling process. Rising Prandtl-Eyring fluid parameters in the range 0 ≤ η ≤ 3 decreased the velocity field and enhanced the temperature and concentration profiles. Increasing Soret number ( 0.1 ≤ Sr ≤ 0.9 ) in the flow domain 0 ≤ η ≤ 3 enhances the concentration field. Enhancing magnetic and squeezed flow numbers amplifies the skin-friction coefficient on the sensor surface. Enhancing radiation parameter increases the heat transport rate. Increasing Soret number magnifies the concentration transport rate. Finally, it is explored that, the current similar results displaying good agreement with the formerly published results in the literature and this fact confirms the accuracy and guarantee of the RK-4 method and present similar results.

The Functionality of Global Quality Management in the Dynamic and Global Environment of Mergers, Acquisitions, and Strategic Partnerships - Adoption of a Systematic Process Concept

<i>Purpose: </i>To explore the functionality of the quality management processes at the global level and understand its complexity and multiple dimensions. <i>Design/methodology/approach: </i>The researchers use a novel systems perspective to explore international standards ISO 9001/9004 capacity to encompass this complexity and analyze the stages by which global multinational organizations develop. In-depth interviews with experts (CEOs, quality managers, and global managers) from 18 international firms were some of the qualitative methods used in the research, along with content analysis, observations, and other methods. The study addresses the philosophy of global quality management and emphasizes the need for inter-organizational coordination and process management. <i>Findings: </i>The results of the qualitative tools showed that the existing definitions in ISO 9004/9001 have made it challenging to address the level of process complexity in international organizations. The main recommendations of the study are the following key points for expanding and refining the existing process concept for a global reality: 1) adopting a systems approach for analyzing the development of a global quality function in complex, multinational companies. 2) extending the classic process approach to open, multi-interface systems oriented towards mutual strategic, operational, and marketing processes.<i> Originality/value: </i>In this article, we suggest an innovative, integrated systems approach to multinational quality management based on shifting from the traditional emphasis on separate operations and manufacturing sites toward a comprehensive perspective that considers the interdependence and coordination among locations. This novel analysis and mapping process offers a comprehensive view of coordinating interdependence between elements comprising an international global process system. It expands the ‎classical approach and provides a basis for ‎developing a global, networked QMS. The current study might be useful for organizations that implement quality management systems in multinational companies. The study is part of vast academic research on international strategy in the global competitive landscape.

Higher analogues of discrete topological complexity

In this paper, we introduce the nth discrete topological complexity and study its properties such as its relation with simplicial Lusternik–Schnirelmann category and how the higher dimensions of discrete topological complexity relate with each other. Moreover, we find a lower bound of n-th discrete topological complexity which is given by the nth usual topological complexity of the geometric realisation of that complex. Furthermore, we give an example for the strict case of that lower bound.

Multimodal Learning for Mapping the Genotype-Phenotype Dynamics.

How complex phenotypes emerge from intricate gene expression patterns is a fundamental question in biology. Quantitative characterization of this relationship, however, is challenging due to the vast combinatorial possibilities and dynamic interplay between genotype and phenotype landscapes. Integrating high-content genotyping approaches such as single-cell RNA sequencing and advanced learning methods such as language models offers an opportunity for dissecting this complex relationship. Here, we present a computational integrated genetics framework designed to analyze and interpret the high-dimensional landscape of genotypes and their associated phenotypes simultaneously. We applied this approach to develop a multimodal foundation model to explore the genotype-phenotype relationship manifold for human transcriptomics at the cellular level. Analyzing this joint manifold showed a refined resolution of cellular heterogeneity, enhanced precision in phenotype annotating, and uncovered potential cross-tissue biomarkers that are undetectable through conventional gene expression analysis alone. Moreover, our results revealed that the gene networks are characterized by scale-free patterns and show context-dependent gene-gene interactions, both of which result in significant variations in the topology of the gene network, particularly evident during aging. Finally, utilizing contextualized embeddings, we investigated gene polyfunctionality which illustrates the multifaceted roles that genes play in different biological processes, and demonstrated that for VWF gene in endothelial cells. Overall, this study advances our understanding of the dynamic interplay between gene expression and phenotypic manifestation and demonstrates the potential of integrated genetics in uncovering new dimensions of cellular function and complexity.

The effect of digital game-based language learning mobile application on the development of complexity, accuracy, and fluency in foreign language monologic oral production among Chinese learners of English as a foreign language

The study reported the effect of a digital game-based language learning (DGBLL) mobile application “Speaking English Fluently – An Automated Scoring Artificial Intelligent Tutoring System on Spoken English” on the complexity, accuracy, and fluency in foreign language (FL) monologic oral production among 31 second year Chinese university learners of English as a foreign language (EFL). The participants’ monologic oral production was measured in the first (week 1) and last week (week 21) of a semester using the same narrative picture description task. The oral production was audio-recorded and transcribed. Both the transcripts and audio-files were analyzed on the complexity, accuracy, and fluency dimensions. The complexity was measured using the number of Mean (M) words per T-unit, the accuracy dimension was measured using the number of repairs and errors per 100 words; and the fluency dimension was measured via speech rate (i.e., number of words per minute), and M length of pauses. Students were required to download the mobile application and followed the monological practice section twice a week for 30 minutes each time. Using paired sample t-tests, we found that even though the participants’ repair rate and speech rate remained unchanged, they produced more complex monologic speech, had significantly fewer errors, and reduced average length of pauses after 20 weeks treatment using the mobile application, demonstrating a positive effect of the DGBLL mobile application on FL learners’ monologic oral production.

Exploring local interpretability in dimensionality reduction: Analysis and use cases

Dimensionality reduction is a crucial area in artificial intelligence that enables the visualization and analysis of high-dimensional data. The main use of dimensionality reduction is to lower the dimensional complexity of data, improving the performance of machine learning models. Non-linear dimensionality reduction approaches, which provide higher quality representations than linear ones, lack interpretability, prohibiting their application in tasks requiring interpretability. This paper presents LXDR (Local eXplanation of Dimensionality Reduction), a local, model-agnostic technique that can be applied to any DR technique. LXDR trains linear models around a neighborhood of a specific instance and provides local interpretations using a variety of neighborhood generation techniques. Variations of the proposed technique are also introduced. The effectiveness of LXDR’s interpretations is evaluated by quantitative and qualitative experiments, as well as demonstrations of its practical implementation in diverse use cases. The experiments emphasize the importance of interpretability in dimensionality reduction and how LXDR reinforces it.

Pengaruh Inovasi Pada Aplikasi Access by KAI Terhadap Kualitas Pelayanan

Innovation in the Access by KAI application aims to provide better services to the public. However, this innovation encounters several issues such as limited technological access by the public, policy non-inclusivity, and lack of effectiveness in socialization efforts. Therefore, this study aims to investigate the impact of innovation in the Access by KAI application on service quality. Additionally, it aims to determine the effective contribution of innovation dimensions in the Access by KAI application to service quality. This research utilizes an associative method with a quantitative approach. Data was collected through observation and questionnaire results, while data analysis employed robust M-estimation regression. The results indicate that the quality of public service innovation is very high, and the quality of m-service is high. Furthermore, public service innovation obtains significant variable coefficient values at a significant level (α=0.05). This is indicated by the p-value of the public service innovation variable being 0.000<0.05, showing the influence of public service innovation on m-service quality in the Access by KAI application. The effective contribution of each dimension of public service innovation to m-service quality is as follows: relative advantage dimension 0.48%, compatibility dimension 29.03%, complexity dimension 14.22%, testability dimension 9.83%, and observability dimension 8.39%.