Dimensions Of Complexity Research Articles

Technology Focus: Hydraulic Fracturing Modeling (November 2022)

Hydraulic fracture stimulation is proven to be the economic way to develop tight unconventional reservoirs. From an operational perspective, hydraulic stimulation is well established and widely applied to most unconventional plays. There is still room for improvement, however, to increase the efficiency and deepen the understanding of the detailed mechanism in the process. Particularly in the domain of fracture simulation, significant effort has been devoted to enhance model complexity and accuracy. Recent simulation studies have covered sophisticated mechanisms such as hydraulic fractures—natural fracture interaction, proppant transportation and settlement, casing friction, new government equations of fracture propagation, and fracture roughness. Fully coupled fracture modeling work flows with stress variation, solid transportation, and fluid flow become the mainstream, while model complexity and budget tradeoff remains challenging. Interestingly, fracture modeling has been applied beyond hydrocarbon production. New applications include cutting injection, coal-seam-gas production, and recently evolving enhanced geothermal systems (EGS). Both industrial and academic researchers have made progress on EGS by introducing another dimension of complexity in that thermal effects alter hydraulic fracture modeling behavior. With the advancement of powerful measurement techniques in the past decade, the industry has seen novel ways to calibrate fracture models. Other than microseismicity and pumping-schedule history matching, data collected from downhole tools such as distributed temperature and acoustic sensing and fiber-optic sensors have broader applications and show promising results. By leveraging computational power and machine-learning approaches, scaled-up modeling on the field level energizes the study of well interference, the depletion effect, and frac-hit-damage mechanisms. Recommended additional reading at OnePetro: www.onepetro.org SPE 205896 - Offshore Cuttings Reinjection Well-Performance Diagnostics and Fractured Domain Mapping Using Injection Data Analytics and Hydraulic Fracturing Simulation, Verified Through 4D Seismic and Wireline Logging by Franz Marketz, Sakhalin Energy Investment Company, et al. URTeC 2021-5414 - Modeling of Distributed Strain Sensing and Distributed Acoustic Sensing Incorporating Hydraulic and Natural Fractures Interaction by Kildare George Ramos Gurjao, Texas A&M University, et al. URTeC 2021-5526 - Efficient Modeling of Enhanced Geothermal System With 3D Complex Hydraulic and Natural Fractures by Hongbing Xie, Sim Tech, et al.

Trajectory Tracking Design for a Swarm of Autonomous Mobile Robots: A Nonlinear Adaptive Optimal Approach

This research presents a nonlinear adaptive optimal control approach to the trajectory tracking problem of a swarm of autonomous mobile robots. Mathematically, finding an analytical adaptive control solution that meets the H2 performance index for the trajectory tracking problem when controlling a swarm of autonomous mobile robots is an almost impossible task, due to the great complexity and high dimensions of the dynamics. For deriving an analytical adaptive control law for this tracking problem, a particular formulation for the trajectory tracking error dynamics between a swarm of autonomous mobile robots and the desired trajectory is made via a filter link. Based on this prior analysis of the trajectory tracking error dynamics, a closed-form adaptive control law is analytically derived from a high-dimensional nonlinear partial differential equation, which is equivalent to solving the trajectory tracking problem of a swarm of autonomous mobile robots with respect to an H2 performance index. This delivered adaptive nonlinear control solution offers the advantages of a simple control structure and good energy-saving performance. From the trajectory tracking verification, this proposed control approach possesses satisfactory trajectory tracking performance for a swarm of autonomous mobile robots, even under the effects of huge modeling uncertainties.

An integrated socio-cyber-physical system framework to assess responsible digitalisation in agriculture: A first application with Living Labs in Europe

CONTEXTIt is still an open question how to assess the contribution of digitalisation in agriculture to the United Nations' Sustainable Development Goals, and how digitalisation then can be done in a responsible way. A socio-cyber-physical system (SCPS) concept can help this analysis, but little experience exists with its operationalisation and application, and its integration with the Responsible Research and Innovation approach. OBJECTIVETo address this gap, this paper has a twofold purpose: a) operationalise the SCPS concept within an integrated assessment framework adaptable to multiple levels of analysis, contexts, and purposes (e.g. ex-ante, ongoing, ex post evaluation) to shed light on impacts of digitalisation in relation to SCPS entities, relationships, and activities; b) apply the designed framework in 21 multi-stakeholder platforms (Living Labs), which were established to explore needs and expectations in specific subjects relevant for European agriculture, forestry and rural areas. METHODSImpacts were assessed through interviews (158 respondents), focus groups (378 participants), online surveys (273 respondents), and other secondary data. RESULTS AND CONCLUSIONSThe findings indicate that the SCPS framework enables elucidating relationships between digital and broader sustainable development goals and needs, and can sharpen earlier assessments, going beyond a pessimistic or optimistic dichotomy associated to digitalisation by specifying effects and trade-offs in terms of enabling, disenabling, boosting and depleting impacts of digital agriculture. However, the framework being comprehensive and open to emerging socio-cyber-physical interactions, makes that Livings Labs doing participatory impact assessments struggled with the complexity and multiple dimensions of the topic. SIGNIFICANCEThe paper provides both conceptual and operational knowledge to set up impact evaluations of responsible digitalisation in agriculture and outline concepts that can help anticipating the consequences and trade-offs.

Deep neural learning based optimization for automated high performance antenna designs

The present paper introduces an optimization-oriented method here practiced for designing high performance single antennas in a fully automated environment. The proposed method comprises two sequential major steps. The first one devotes configuring the shape of antenna and determining the feeding point by employing the bottom-up optimization (BUO) method. In this algorithm, the number of microstrip transmission lines (TLs) used to model the radiator is increased consecutively and the shape of the antenna is revised up to finding the initial satisfying results. Secondly, for determining the best design parameters of the configured antenna shape in the first step (i.e., width and length of TLs), deep neural network (DNN) that is based on Thompson sampling efficient multi-objective optimization (TSEMO) is applied. The recommended optimization method is successfully attracted as a problem solver for designers to tackle the subject for antenna design such as the complexity and large dimensions of structures. Hence, the main advantage of the implemented optimization method in this article is to noticeably decrease the required designer’s involvement automatically generating valid layouts. For validating the suggested method, two wideband antennas are designed, prototyped and subjected to experiment. The first optimized antenna covers the frequency band 8.8–10.1 GHz (13.75 % bandwidth) characterized by a maximum gain of 7.13 dB while the second one covers the frequency band 11.3–13.16 GHz (15.2 %) which exhibits a maximum gain of 7.8 dB.

Development and application of 3D spatial metrics using point clouds for landscape visual quality assessment

Landscape visual quality (LVQ) relates to a landscape’s spatial, functional and visual structure at a given time, which can affect the senses and experience of the person using or viewing the landscape. A holistic LVQ assessment can be undertaken by using two complementary approaches. The first uses visual quality assessment tools to measure a person’s perceptive responses to landscapes; the second, the focus of our study, is based on the quantitative assessment of the physical attributes of landscapes. The physical attributes of landscapes refer to their spatial configuration and composition, and have conventionally been characterised using 2D or 2.5D metrics. The use of 3D attributes is an emerging field, but there is scant information on the range of 3D metrics that can describe the physical attributes of landscapes and which relate to the perception of landscape quality. This study develops 3D spatial metrics to describe landscapes' structures and spatial characteristics grounded on landscape visualization theory. These metrics are derived from point clouds and describe 3D attributes of volume and area, landscape diversity, shape, connectivity, colour, topography, and openness. The effectiveness of the 3D spatial metrics to quantify LVQ was verified using a set of urban landscapes in the high-rise, compact environment of our study sites. The results show that 3D spatial metrics can successfully detect the differences among landscapes in the dimensions of naturalness, complexity, coherence and visual scale. For example, landscapes with a random distribution and irregular shapes had high values in horizontal, vertical, and distance diversity (HVDD) and the shape index, respectively, indicating their high spatial and shape diversities. Our study highlights the potential of using 3D spatial metrics derived from point clouds for a more holistic and objective LVQ assessment.

Condition monitoring of wind turbines based on spatial-temporal feature aggregation networks

The existing supervisory control and data acquisition (SCADA) system continuously collects data from wind turbines (WTs), which provides a basis for condition monitoring (CM) of WTs. However, due to the complexity and high dimension and nonlinearity of data, effective modeling of spatial-temporal correlations among the data still becomes a primary challenge. In this paper, we propose a novel CM approach based on the multidirectional spatial-temporal feature aggregation networks (MSTFAN) to accurately evaluate the performance and hence diagnose the faults of the turbines. Firstly, the data collected from various sensors are formulated into graph-structured data at each timestamp. Spatial-temporal network constructed by combing a graph attention network (GAT) and a temporal convolutional network (TCN) is used to extract spatial-temporal features of the data. Then, a bi-directional long short-term memory (BiLSTM) neural network is adopted to further study long-term spatial-temporal dependency of the extracted features. Finally, the condition score is obtained and the streaming peaks over threshold (SPOT) is applied to determine the abnormal threshold for early warning of the fault occurrence. Experiments on datasets from real-world wind farms demonstrate that the proposed approach can detect the early abnormal situation of the WTs, and outperform other established methods.

What do complexity measures measure? Correlating and validating corpus-based measures of morphological complexity

AbstractWe present an analysis of eight measures used for quantifying morphological complexity of natural languages. The measures we study are corpus-based measures of morphological complexity with varying requirements for corpus annotation. We present similarities and differences between these measures visually and through correlation analyses, as well as their relation to the relevant typological variables. Our analysis focuses on whether these ‘measures’ are measures of the same underlying variable, or whether they measure more than one dimension of morphological complexity. Principal component analysis indicates that the first principal component explains 92.62 percent of the variation in eight measures, indicating a strong linear dependence between the complexity measures studied.

Formal Activation Through Complexity Characteristic

Formal developments were characterized by different concepts, one of which was complexity, which affects the architecture because of its association with ambiguity and differentiation. The research begins with the definition of the complexity property. Then, the study discussed complexity as a design characteristic in terms of its phenomena and touched on the dimensions of complexity in the residential environment. The research problem is a need for knowledge about the property of complexity in the forms of buildings and what is related to their design dimensions in residential environments. The goal of the research was to identify the important vocabulary in complexity characteristic of formal and urban activation, leading to the theoretical framework on the dimensions of complexity in residential environments and complexity in buildings. The research adopted the descriptive analytical method in the framework and applied it within the practical study. The results showed a dimension of complexity was achieved with a percentage of 89.83%, and a dimension of complexity in buildings with a percentage of 86.05%. The research concluded the complexity characteristic achieves the formal activation within the city, and it is linked to ambiguity within the architectural formations to achieve a contradictory architecture that generates multiple levels of meanings. أتصفت التطورات الشکلیة بمفاهیم مختلفة، أحد هذه المفاهیم هو التعقید، والذی یؤثر على نتاج العمارة لارتباطه بالغموض والتمایز والاختلاف والاسالیب التکنولوجیة. یبدأ البحث بتعریف خاصیة التعقید، ومن ثم مناقشة التعقید کخاصیة تصمیمیة من حیث ظواهره ومکوناته، وتطرق البحث الى ابعاد التعقید فی البیئة السکنیة والحضریة، تبلورت من خلالها المشکلة البحثیة "وجود حاجه معرفیة حول خاصیة التعقید فی اشکال المبانی وما یرتبط بأبعادها التصمیمیة فی البیئات السکنیة". اما هدف البحث تمثل بتحدید أهم المفردات فی خاصیة التعقید للتنشیط الشکلی والعمرانی، وصولا الى بناء اطار نظری عن ابعاد التعقید فی البیئات السکنیة والتعقید فی المبانی. اما فرضیة البحث فتمثلت تُحقق خاصیة التعقید التنشیط الشکلی والعمرانی للمبانی والاحیاء السکنیة ضمن المدینة. اعتمد البحث المنهج الوصفی التحلیلی فی الإطار النظری، وتطبیقها ضمن الدراسة العملیة. أظهرت النتائج تحقیق مفردة ابعاد التعقید نسبة 89.83%، ومفردة التعقید فی المبانی بنسبة 86.05%. توصل البحث الى ان خاصیة التعقید تحقق التنشیط الشکلی ضمن المدینة کونها ترتبط بالغموض الشکلی ضمن التکوینات المعماریة لتحقق عمارة متناقضة تولد مستویات متعددة من المعانی.

Shoreline Retreat in California: Taking a Step Back

Lester, C.; Griggs, G.; Patsch, K., and Anderson, R., 2022. Shoreline retreat in California: Taking a step back. Journal of Coastal Research, 38(6), 1207–1230. Coconut Creek (Florida), ISSN 0749-0208. Adapting to long-term sea-level rise has emerged as perhaps the most significant coastal management challenge of the 21st century. But this challenge has become fraught with controversy in California, especially around the idea of managed retreat. This article reframes the current adversarial and highly politicized dichotomous controversy about managed retreat with an overview of California's actual experience managing coastal shoreline hazards over the last half-century. The review shows that managed retreat in California is more than an either-or fight about private beachfront residential property. The article reviews a range of cases in which development or development potential has been moved back from the shoreline. The cases stand in contrast to the overly simplified debate about managed retreat and show that retreat has been happening on California's coast for decades. The discussion identifies the complexity and multiple dimensions of shoreline adaptation over long periods and concludes with some observations in hopes of shedding light on and opening more reasonable discussion of pathways to shoreline retreat and resilient communities.

Evaluation Method of Writing Fluency Based on Machine Learning Method

In light of society's rapid advancement, more and more people worldwide are placing importance on education. There are several domains in China where the importance of writing exceeds the importance of reading, listening, or speaking. It has been shown that many Chinese students commit grammar problems if they are writing an article. Several researchers attempted to determine students' writing talents in terms of amount and complexity, on the one side, and then also focused on identifying conclusions on the accuracy, the organization of ideas, and the barriers to fluent writing via qualitative data gathering approaches. This research uses a machine learning technique to measure students' writing fluency. Writing fluency capabilities can be predicted using a novel adaptive generative adversarial network-based deep support vector machine (AGAN-DSVM) technique. The trace-oriented approach can be used to examine the features like accuracy, syntactic complexity, and organization of ideas aspects. The prediction rate of lexical complexity and sentence complexity of our proposed method achieves 90 and 95%, respectively. Plots created with origin's graphing tool display the results of a comparison between the proposed approach and several other ways already in use. The proposed method is evaluated and compared using several different metrics, including the accuracy dimension, syntactic complexity dimension, organization of ideas dimension, distributions of the mistakes in the text, lexical complexity, sentence complexity, essay particularities, and comparison of accuracy, F1 score, and syntactic complexity.

A Comparative Study of Four Nonlinear Dynamic Characteristics in Feature Extraction of Bearing Fault Diagnosis

To effectively extract the fault features of bearing signals in complex environments, four common nonlinear dynamic characteristics are used to analyze and compare the simulated signals and measured bearing fault signals in this paper, including Lempel-Ziv complexity (LZC), dispersion entropy (DE), Lyapunov exponent (LE), and fractal dimension (FD). Three groups of simulation experiments were carried out, and the simulation results show that all nonlinear dynamic characteristics can reflect the complexity change trend of MIX signal; compared with LZC and LE, DE and FD can better reflect the frequency change of amplitude modulated trend of chirped signal; only DE can reflect the amplitude change trend of amplitude modulated chirped signal. The experiments of feature extraction and classification are carried out for bearing fault signals. It concluded that compared with LZC, LE, and DE, FD has better stability and classification performance with the minimum standard deviation and the highest recognition rate.

ECG Signals Classification Using Overlapping Variables to Detect Atrial Fibrillation

In the present work a method for the detection of the cardiac pathology known as atrial fibrillation is proposed by calculating different information, statistics and other nonlinear measures over ECG signals. The original database contains records corresponding to patients who are diagnosed with this disease as well as healthy subjects. To formulate the dataset the Rényi permutation entropy, Fisher information measure, statistical complexity, Lyapunov exponent and fractal dimension were calculated, in order to determine how to combine this features to optimize the identification of the signals coming from ECG with the above mentioned cardiac pathology. With the aim to improve the results obtained in previous studies, a classification method based upon decision trees algorithms is implemented. Later a Montecarlo simulation of one thousand trials is performed with a seventy percent randomly selected from the dataset dedicated to train the classifier and the remaining thirty percent reserved to test in every trial. The quality of the classification is assessed through the computation of the area under the receiver operation characteristic curve (ROC), the F1-score and other classical performance metrics, such as the balanced accuracy, sensitivity, specificity, positive and negative predicted values. The results show that the incorporation of all these features to the dataset when are employed to train the classifier in the training task produces the best classification, in such a way that the largest quality parameter is achieved.

A universal approach for multi-model schema inference

The variety feature of Big Data, represented by multi-model data, has brought a new dimension of complexity to all aspects of data management. The need to process a set of distinct but interlinked data models is a challenging task. In this paper, we focus on the problem of inference of a schema, i.e., the description of the structure of data. While several verified approaches exist in the single-model world, their application for multi-model data is not straightforward. We introduce an approach that ensures inference of a common schema of multi-model data capturing their specifics. It can infer local integrity constraints as well as intra- and inter-model references. Following the standard features of Big Data, it can cope with overlapping models, i.e., data redundancy, and it is designed to process efficiently significant amounts of data.To the best of our knowledge, ours is the first approach addressing schema inference in the world of multi-model databases.

No Hard Feelings? Leadership, Emotions, and Emotional Capacities

The global pandemic left the world with prevailing uncertainties, which leaders continue to navigate and adapt to in attempts to create a new sense of normalcy for their employees. This major shift in work elicits intense emotions (e.g., ambiguity, tension, fear) that leaders must manage both for themselves and their followers. Consequently, leaders must now, more than ever, pay attention to their own and their followers’ emotions. Leadership and emotions both inherently have interpersonal outcomes, thus, it is important to understand the psychological mechanisms that are at play and its relevant outcomes. In the present symposium, we advance the role of emotions in workplace relationships, namely the leader-follower relationship, in hopes to further develop academics’ and practitioners’ understanding of the interpersonal effects of emotions (gratitude, positive and negative affect) and emotional capacities (emotional intelligence) that may influence a more successful, healthier, and happier work environment. It is our hope that practitioners and scholars will recognize the importance of and take action to create healthier social interactions at work to ultimately improve workplace behaviors and well-being during the challenging transitions the workforce currently faces. Self, Follower, and Ability Ratings of Leader Emotional Intelligence and Follower Outcomes Presenter: Alisha Gupta; Virginia Commonwealth U. Presenter: Zhavit Levitats; Yale U. (Yale Center for Emotional Intelligence) Presenter: Alexander S. McKay; Virginia Commonwealth U. Presenter: Zorana Ivcevic; Yale U. (Yale Center for Emotional Intelligence) Presenter: Chris Alwine; - Presenter: Shengjie Lin; Yale U. (Yale Center for Emotional Intelligence) Unpacking Dimensional Complexity between Emotional Intelligence and Transformational Leadership Presenter: Matthew Crayne; U. at Albany, State U. of New York Presenter: Omer Kocak; Istanbul Medipol U. The Daily Impact of Leadership Interactions on Positive and Negative Affect Presenter: Keaton Allen Fletcher; Georgia Institute of Technology Presenter: Claire Burnett; Georgia Institute of Technology Presenter: Cooper Drose; Georgia Institute of Technology Presenter: Spencer Garcia; Georgia Institute of Technology Group Gratitude and Group Helping Behavior: The Role of Leader Entitlement and Servant Leadership Presenter: Michael Lance Frazier; Creighton U. Presenter: Steve Russell; Creighton U.

A systematic approach towards mastering complexity through hybrid assembly planning

In the face of increasingly agile product development, new challenges arise for assembly planning due to growing complexity. Within this paper, we show that the complexity-dependent combination of agile methods and traditionally plan-driven assembly planning procedures holds great potential for mastering planning complexity. Thus, a methodology towards hybrid assembly planning is elucidated with a focus on selecting dominant planning methods for specific planning scopes within this paper. Based on a detailed model of assembly planning as a system, the complexity requirements of different planning modules are determined. Therefore, an integrative complexity measure is developed to quantitatively operationalise complexity in a quasistatic-structural and a dynamic-functional complexity dimension. In order to identify dominant planning methods, the complexity potentials of agile and plan-driven methods are examined in an Analytic Hierarchy Process expert study with 22 participants. Within a decision model, dominant planning methods are selected for specific planning modules by matching the quantified complexity requirements with the complexity potentials of agile and plan-driven methods. Based on the systematic method selection, dominant planning methods for specific planning scopes can be tailored into a project-specific hybrid process framework. The approach to hybrid assembly planning enables optimised planning results through the improved mastery of complexity.

Pen-and-Paper versus Computer-Mediated Writing Modality as a New Dimension of Task Complexity

In this paper we make a proposal that writing modality (pen-and-paper versus computer-based writing can be conceptualized as a cognitive task complexity factor. To lay ground for this theoretical proposal, we first review previous adaptations of cognitive task-based models to second language (L2) writing. We then compare pen-and-paper and computer-based writing modalities in terms of their general characteristics, outline the main tenets of multidisciplinary theoretical models which attribute learning and performance-related importance to writing modality, and review the available empirical evidence. From this we draw theoretical and empirical justification for our conceptualization of writing modality as a task complexity dimension. After outlining our conceptual view, we proceed with the review of the methods which could be used to independently assess cognitive load in paper and computer-written L2 tasks. In the conclusion, implications and suggestions for future research are provided.

Beyond effectiveness evaluation: Contributing to the discussion on complexity of digital health interventions with examples from cancer care.

Digital health interventions (DHIs) have become essential complementary solutions in health care to enhance support and communication at a distance, with evidence of improving patient outcomes. Improving clinical outcomes is a major determinant of success in any health intervention, influencing its funding, development, adoption and implementation in real-world practice. In this article we explore our experiences of developing and testing DHIs to identify and discuss complexity challenges along their intervention research lifecycle. Informed by the case study research approach, we selected three individual DHIs aimed at satisfying the supportive and educational needs of people living with cancer. The Care Expert, the Digi-Do and the Gatapp were underpinned on different complexity frameworks i.e., the Medical Research Council framework and the Non-adoption, Abandonment, Scale-up, Spread and Sustainability framework. This variance on the methodological underpinning was expected to prompt a multifaceted discussion on the complexity dimensions endorsed by each of the frameworks. Our discussion endorses the adoption of mixed-methods research designs, to gather the perspectives of stakeholders and end-users, as well as pragmatic evaluation approaches that value effectiveness outcomes as much as process outcomes. Furthermore, the dissemination and sustainability agenda of DHIs needs to be considered from early-stage development with the inclusion of a business model. This business plan should be worked in partnership with healthcare services, regulatory bodies and industry, aiming to assure the management of the DHI throughout time.

Applying the intervention Complexity Assessment Tool to brief interventions targeting long-term benzodiazepine receptor agonist use in primary care: Lessons learned

BackgroundBenzodiazepine receptor agonists (BZRAs) are often prescribed for long-term use. However, guidelines recommend limiting prescriptions to short-term use (< 4 weeks) to reduce the risk of adverse effects and dependence. A recent systematic review reported that brief interventions targeting long-term BZRA use in primary care (e.g., short consultations, written letters to patients) were effective in helping patients to discontinue BZRA medication. However, the complexity of these interventions has not been examined in detail. This study aimed to apply the intervention Complexity Assessment Tool for Systematic Reviews (iCAT_SR) to brief interventions targeting long-term BZRA use. MethodsTwo reviewers independently assessed the interventions using the six core iCAT_SR dimensions: organisational level/ category targeted, behaviour targeted, number of intervention components, degree of tailoring, skill level required by those delivering and receiving the intervention. The four optional iCAT_SR dimensions were applied where possible. A scoring system was using to calculate a complexity score for each intervention. Pearson’s correlations were used to assess the relationship between intervention complexity and effect size, as well as the relationship between intervention complexity and number of component behaviour change techniques (BCTs). Inter-rater reliability was calculated using Cohen’s Kappa coefficient.ResultsFour of the six core iCAT_SR dimensions were applied to the interventions with high inter-rater reliability (Cohen’s Kappa = 0.916). Application of the four optional dimensions was prevented by a lack of detail in study reports. Intervention complexity scores ranged from 8 to 11 (median: 11). There was no relationship detected between intervention complexity and either intervention effect size or number of component BCTs.ConclusionsThis study adds to the literature on worked examples of the practical application of the iCAT_SR. The findings highlight how more detailed reporting of interventions is needed in order to optimise the application of iCAT_SR and its potential to differentiate between interventions across the full range of complexity dimensions. Further work is needed to establish the validity of applying a scoring system to iCAT_SR assessments.

Revisiting Grammatical Complexity in L2 Writing via Exploratory Factor Analysis.

Since the 1990s, grammatical complexity has received substantial research attention in applied linguistics (Bulté and Housen, 2014). The representation of grammatical complexity has expanded in L2 writing with the application of diverse measures in empirical studies in the recent three decades (1991–2020). In response to this situation, we found it important to revisit grammatical complexity, and an exploratory factor analysis was applied to explore latent dimensions (i.e., factors) of grammatical complexity in L2 writing. We analyzed Lu’s (2011) 14 grammatical complexity measures in the L2 corpus of the British Academic Written English Corpus. We then proposed a four-factor model with “clausal subordination,” “phrasal construction”, “global length unit” and “others.” The four factors generally align with the types of grammatical complexity proposed in Lu (2011), but differences on six measures are also found. Noteworthy points were discussed to interpret the reasons behind the differences. Research implications are provided to show further research directions.

Word-formation complexity: a learner corpus-based study

This article explores the word-formation dimension of learner text complexity which indicates how skilful the non-native speakers are in using more and less complex - and varied - derivational constructions. In order to analyse the association between complexity and writing accuracy in word formation as well as interactive effects of task type, text register, and native language background, we examine the materials of the REALEC corpus of English essays written by university students with Russian L1. We present an approach to measure derivational complexity based on the classification of suffixes offered in Bauer and Nation (1993) and then compare the complexity results and the number of word formation errors annotated in the texts. Starting with the hypothesis that with increasing complexity the number of errors will decrease, we apply statistical analysis to examine the association between complexity and accuracy. We found, first, that the use of more advanced word-formation suffixes affects the number of errors in texts. Second, different levels of suffixes in the hierarchy affect derivation accuracy in different ways. In particular, the use of irregular derivational models is positively associated with the number of errors. Third, the type of examination task and expected format and register of writing should be taken into consideration. The hypothesis holds true for regular but infrequent advanced suffixal models used in more formal descriptive essays associated with an academic register. However, for less formal texts with lower academic register requirements, the hypothesis needs to be amended.