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Periodicals

Role of Nanomaterials in the Treatment of Wastewater: A Review

Water is an essential part of life and its availability is important for all living creatures. On the other side, the world is suffering from a major problem of drinking water. There are several gases, microorganisms and other toxins (chemicals and heavy metals) added into water during rain, flowing water, etc. which is responsible for water pollution. This review article describes various applications of nanomaterial in removing different types of impurities from polluted water. There are various kinds of nanomaterials, which carried huge potential to treat polluted water (containing metal toxin substance, different organic and inorganic impurities) very effectively due to their unique properties like greater surface area, able to work at low concentration, etc. The nanostructured catalytic membranes, nanosorbents and nanophotocatalyst based approaches to remove pollutants from wastewater are eco-friendly and efficient, but they require more energy, more investment in order to purify the wastewater. There are many challenges and issues of wastewater treatment. Some precautions are also required to keep away from ecological and health issues. New modern equipment for wastewater treatment should be flexible, low cost and efficient for the commercialization purpose.

Architecture & Planning• 2020• Multidisciplinary Digital Publishing Institute
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Periodicals

The Insight ToolKit image registration framework

Publicly available scientific resources help establish evaluation standards, provide a platform for teaching and improve reproducibility. Version 4 of the Insight ToolKit (ITK(4)) seeks to establish new standards in publicly available image registration methodology. ITK(4) makes several advances in comparison to previous versions of ITK. ITK(4) supports both multivariate images and objective functions; it also unifies high-dimensional (deformation field) and low-dimensional (affine) transformations with metrics that are reusable across transform types and with composite transforms that allow arbitrary series of geometric mappings to be chained together seamlessly. Metrics and optimizers take advantage of multi-core resources, when available. Furthermore, ITK(4) reduces the parameter optimization burden via principled heuristics that automatically set scaling across disparate parameter types (rotations vs. translations). A related approach also constrains steps sizes for gradient-based optimizers. The result is that tuning for different metrics and/or image pairs is rarely necessary allowing the researcher to more easily focus on design/comparison of registration strategies. In total, the ITK(4) contribution is intended as a structure to support reproducible research practices, will provide a more extensive foundation against which to evaluate new work in image registration and also enable application level programmers a broad suite of tools on which to build. Finally, we contextualize this work with a reference registration evaluation study with application to pediatric brain labeling.

Architecture & Planning• 2014• Frontiers Media
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Periodicals

IoT-Based Smart Irrigation Systems: An Overview on the Recent Trends on Sensors and IoT Systems for Irrigation in Precision Agriculture

Water management is paramount in countries with water scarcity. This also affects agriculture, as a large amount of water is dedicated to that use. The possible consequences of global warming lead to the consideration of creating water adaptation measures to ensure the availability of water for food production and consumption. Thus, studies aimed at saving water usage in the irrigation process have increased over the years. Typical commercial sensors for agriculture irrigation systems are very expensive, making it impossible for smaller farmers to implement this type of system. However, manufacturers are currently offering low-cost sensors that can be connected to nodes to implement affordable systems for irrigation management and agriculture monitoring. Due to the recent advances in IoT and WSN technologies that can be applied in the development of these systems, we present a survey aimed at summarizing the current state of the art regarding smart irrigation systems. We determine the parameters that are monitored in irrigation systems regarding water quantity and quality, soil characteristics and weather conditions. We provide an overview of the most utilized nodes and wireless technologies. Lastly, we will discuss the challenges and the best practices for the implementation of sensor-based irrigation systems.

Architecture & Planning• 2020• Multidisciplinary Digital Publishing Institute
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Periodicals

Default and Executive Network Coupling Supports Creative Idea Production

The role of attention in creative cognition remains controversial. Neuroimaging studies have reported activation of brain regions linked to both cognitive control and spontaneous imaginative processes, raising questions about how these regions interact to support creative thought. Using functional magnetic resonance imaging (fMRI), we explored this question by examining dynamic interactions between brain regions during a divergent thinking task. Multivariate pattern analysis revealed a distributed network associated with divergent thinking, including several core hubs of the default (posterior cingulate) and executive (dorsolateral prefrontal cortex) networks. The resting-state network affiliation of these regions was confirmed using data from an independent sample of participants. Graph theory analysis assessed global efficiency of the divergent thinking network, and network efficiency was found to increase as a function of individual differences in divergent thinking ability. Moreover, temporal connectivity analysis revealed increased coupling between default and salience network regions (bilateral insula) at the beginning of the task, followed by increased coupling between default and executive network regions at later stages. Such dynamic coupling suggests that divergent thinking involves cooperation between brain networks linked to cognitive control and spontaneous thought, which may reflect focused internal attention and the top-down control of spontaneous cognition during creative idea production.

Architecture & Planning• 2015• Nature Portfolio
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Periodicals

Federated Learning: A Survey on Enabling Technologies, Protocols, and Applications

This paper provides a comprehensive study of Federated Learning (FL) with an emphasis on enabling software and hardware platforms, protocols, real-life applications and use-cases. FL can be applicable to multiple domains but applying it to different industries has its own set of obstacles. FL is known as collaborative learning, where algorithm(s) get trained across multiple devices or servers with decentralized data samples without having to exchange the actual data. This approach is radically different from other more established techniques such as getting the data samples uploaded to servers or having data in some form of distributed infrastructure. FL on the other hand generates more robust models without sharing data, leading to privacy-preserved solutions with higher security and access privileges to data. This paper starts by providing an overview of FL. Then, it gives an overview of technical details that pertain to FL enabling technologies, protocols, and applications. Compared to other survey papers in the field, our objective is to provide a more thorough summary of the most relevant protocols, platforms, and real-life use-cases of FL to enable data scientists to build better privacy-preserving solutions for industries in critical need of FL. We also provide an overview of key challenges presented in the recent literature and provide a summary of related research work. Moreover, we explore both the challenges and advantages of FL and present detailed service use-cases to illustrate how different architectures and protocols that use FL can fit together to deliver desired results.

Architecture & Planning• 2020• Institute of Electrical and Electronics Engineers
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Periodicals

Redox potential (Eh) and pH as drivers of soil/plant/microorganism systems: a transdisciplinary overview pointing to integrative opportunities for agronomy

Background Oxidation-reduction and acid–base reactions are essential for the maintenance of all living organisms. However, redox potential (Eh) has received little attention in agronomy, unlike pH, which is regarded as a master variable. Agronomists are probably depriving themselves of a key factor in crop and soil science which could be a useful integrative tool. Scope This paper reviews the existing literature on Eh in various disciplines connected to agronomy, whether associated or not with pH, and then integrates this knowledge within a composite framework. Conclusions This transdisciplinary review offers evidence that Eh and pH are respectively and jointly major drivers of soil/plant/microorganism systems. Information on the roles of Eh and pH in plant and microorganism physiology and in soil genesis converges to form an operational framework for further studies of soil/plant/microorganism functioning. This framework is based on the hypothesis that plants physiologically function within a specific internal Eh-pH range and that, along with microorganisms, they alter Eh and pH in the rhizosphere to ensure homeostasis at the cell level. This new perspective could help in bridging several disciplines related to agronomy, and across micro and macro-scales. It should help to improve cropping systems design and management, in conventional, organic, and conservation agriculture.

Architecture & Planning• 2012• Springer Science+Business Media
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Periodicals

The Path to Smart Farming: Innovations and Opportunities in Precision Agriculture

Precision agriculture employs cutting-edge technologies to increase agricultural productivity while reducing adverse impacts on the environment. Precision agriculture is a farming approach that uses advanced technology and data analysis to maximize crop yields, cut waste, and increase productivity. It is a potential strategy for tackling some of the major issues confronting contemporary agriculture, such as feeding a growing world population while reducing environmental effects. This review article examines some of the latest recent advances in precision agriculture, including the Internet of Things (IoT) and how to make use of big data. This review article aims to provide an overview of the recent innovations, challenges, and future prospects of precision agriculture and smart farming. It presents an analysis of the current state of precision agriculture, including the most recent innovations in technology, such as drones, sensors, and machine learning. The article also discusses some of the main challenges faced by precision agriculture, including data management, technology adoption, and cost-effectiveness.

Architecture & Planning• 2023• Multidisciplinary Digital Publishing Institute
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Periodicals

Adult mental health disorders and their age at onset

The study of age at onset of mental health disorders is technically and conceptually difficult. It is important to consider these age distributions in order to understand causes and mechanisms of illness and to intervene at an appropriate juncture for primary and secondary prevention. This article reviews some of the approaches to studying age at onset, sets out the evidence to support the assertion that adult mental disorders begin in adolescence, and finds that perhaps half of all adult mental health disorders have begun by the teenage years. The paper then discusses whether this fits what is known about the developmental neurobiology of the brain and introduces the implications for mental health services.

Architecture & Planning• 2013• Cambridge University Press
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Periodicals

Applications and Challenges of Implementing Artificial Intelligence in Medical Education: Integrative Review

BACKGROUND: Since the advent of artificial intelligence (AI) in 1955, the applications of AI have increased over the years within a rapidly changing digital landscape where public expectations are on the rise, fed by social media, industry leaders, and medical practitioners. However, there has been little interest in AI in medical education until the last two decades, with only a recent increase in the number of publications and citations in the field. To our knowledge, thus far, a limited number of articles have discussed or reviewed the current use of AI in medical education. OBJECTIVE: This study aims to review the current applications of AI in medical education as well as the challenges of implementing AI in medical education. METHODS: Medline (Ovid), EBSCOhost Education Resources Information Center (ERIC) and Education Source, and Web of Science were searched with explicit inclusion and exclusion criteria. Full text of the selected articles was analyzed using the Extension of Technology Acceptance Model and the Diffusions of Innovations theory. Data were subsequently pooled together and analyzed quantitatively. RESULTS: A total of 37 articles were identified. Three primary uses of AI in medical education were identified: learning support (n=32), assessment of students' learning (n=4), and curriculum review (n=1). The main reasons for use of AI are its ability to provide feedback and a guided learning pathway and to decrease costs. Subgroup analysis revealed that medical undergraduates are the primary target audience for AI use. In addition, 34 articles described the challenges of AI implementation in medical education; two main reasons were identified: difficulty in assessing the effectiveness of AI in medical education and technical challenges while developing AI applications. CONCLUSIONS: The primary use of AI in medical education was for learning support mainly due to its ability to provide individualized feedback. Little emphasis was placed on curriculum review and assessment of students' learning due to the lack of digitalization and sensitive nature of examinations, respectively. Big data manipulation also warrants the need to ensure data integrity. Methodological improvements are required to increase AI adoption by addressing the technical difficulties of creating an AI application and using novel methods to assess the effectiveness of AI. To better integrate AI into the medical profession, measures should be taken to introduce AI into the medical school curriculum for medical professionals to better understand AI algorithms and maximize its use.

Architecture & Planning• 2019• JMIR Publications
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Periodicals

Patterns of engagement in authentic online learning environments

The use of authentic activities within online learning environments has been shown to have many benefits for learners in online units and courses. There has been renewed interest in the role of student activities within course units, as constructivist philosophy and advances in technology impact on educational design and practice. Courses based on these principles have been used successfully across a wide variety of discipline areas. In spite of the growing evidence of the success of authentic learning environments, they are not without their problems. In this paper we discuss patterns of engagement that have emerged from our own research on authentic learning tasks, in particular, the initial reluctance to willingly immerse in learning scenarios that some students experience, and the need for the suspension of disbelief before engaging in the task. The paper proposes ten characteristics of authentic activities, based on educational theory and research, which have been used as criteria for the selection of existing online units or courses for in depth investigation. The paper includes a short review of the literature, a description of the research and some preliminary findings and identification of issues related to the necessity for students to willingly suspend disbelief in order to fully engage in learning scenarios based on authentic tasks.

Architecture & Planning• 2003• Australasian Society for Computers in Learning in Tertiary Education
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Periodicals

Norepinephrine ignites local hotspots of neuronal excitation: How arousal amplifies selectivity in perception and memory

Emotional arousal enhances perception and memory of high-priority information but impairs processing of other information. Here, we propose that, under arousal, local glutamate levels signal the current strength of a representation and interact with norepinephrine (NE) to enhance high priority representations and out-compete or suppress lower priority representations. In our "glutamate amplifies noradrenergic effects" (GANE) model, high glutamate at the site of prioritized representations increases local NE release from the locus coeruleus (LC) to generate "NE hotspots." At these NE hotspots, local glutamate and NE release are mutually enhancing and amplify activation of prioritized representations. In contrast, arousal-induced LC activity inhibits less active representations via two mechanisms: 1) Where there are hotspots, lateral inhibition is amplified; 2) Where no hotspots emerge, NE levels are only high enough to activate low-threshold inhibitory adrenoreceptors. Thus, LC activation promotes a few hotspots of excitation in the context of widespread suppression, enhancing high priority representations while suppressing the rest. Hotspots also help synchronize oscillations across neural ensembles transmitting high-priority information. Furthermore, brain structures that detect stimulus priority interact with phasic NE release to preferentially route such information through large-scale functional brain networks. A surge of NE before, during, or after encoding enhances synaptic plasticity at NE hotspots, triggering local protein synthesis processes that enhance selective memory consolidation. Together, these noradrenergic mechanisms promote selective attention and memory under arousal. GANE not only reconciles apparently contradictory findings in the emotion-cognition literature but also extends previous influential theories of LC neuromodulation by proposing specific mechanisms for how LC-NE activity increases neural gain.

Architecture & Planning• 2015• Cambridge University Press
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A Review of Wireless Sensor Technologies and Applications in Agriculture and Food Industry: State of the Art and Current Trends

The aim of the present paper is to review the technical and scientific state of the art of wireless sensor technologies and standards for wireless communications in the Agri-Food sector. These technologies are very promising in several fields such as environmental monitoring, precision agriculture, cold chain control or traceability. The paper focuses on WSN (Wireless Sensor Networks) and RFID (Radio Frequency Identification), presenting the different systems available, recent developments and examples of applications, including ZigBee based WSN and passive, semi-passive and active RFID. Future trends of wireless communications in agriculture and food industry are also discussed.

Architecture & Planning• 2009• Multidisciplinary Digital Publishing Institute
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Periodicals

Redefining Radiology: A Review of Artificial Intelligence Integration in Medical Imaging

This comprehensive review unfolds a detailed narrative of Artificial Intelligence (AI) making its foray into radiology, a move that is catalysing transformational shifts in the healthcare landscape. It traces the evolution of radiology, from the initial discovery of X-rays to the application of machine learning and deep learning in modern medical image analysis. The primary focus of this review is to shed light on AI applications in radiology, elucidating their seminal roles in image segmentation, computer-aided diagnosis, predictive analytics, and workflow optimisation. A spotlight is cast on the profound impact of AI on diagnostic processes, personalised medicine, and clinical workflows, with empirical evidence derived from a series of case studies across multiple medical disciplines. However, the integration of AI in radiology is not devoid of challenges. The review ventures into the labyrinth of obstacles that are inherent to AI-driven radiology-data quality, the 'black box' enigma, infrastructural and technical complexities, as well as ethical implications. Peering into the future, the review contends that the road ahead for AI in radiology is paved with promising opportunities. It advocates for continuous research, embracing avant-garde imaging technologies, and fostering robust collaborations between radiologists and AI developers. The conclusion underlines the role of AI as a catalyst for change in radiology, a stance that is firmly rooted in sustained innovation, dynamic partnerships, and a steadfast commitment to ethical responsibility.

Architecture & Planning• 2023• Multidisciplinary Digital Publishing Institute
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Periodicals

Circuit Breaker

In the realm of microservices architecture, ensuring reliability and resilience is paramount due to the distributed nature and interdependencies of services. A key pattern employed to enhance system robustness is the circuit breaker. When a microservice experiences failures, the circuit breaker pattern detects these faults and transitions between three states: closed, open, and half-open. During this period, requests are automatically redirected or fail fast, preventing additional strain on the troubled service. Once the service stabilizes, the circuit breaker transitions to the half-open state, where it allows a limited number of test requests to verify recovery. If these succeed, the circuit breaker closes, restoring normal operation. This abstract explores the implementation, benefits, and challenges of circuit breakers in microservices, emphasizing their role in maintaining high availability and robust performance in modern distributed systems.

Architecture & Planning• 2024
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Periodicals

Drought and Salinity Stress Responses and Microbe-Induced Tolerance in Plants

Drought and salinity are among the most important environmental factors that hampered agricultural productivity worldwide. Both stresses can induce several morphological, physiological, biochemical, and metabolic alterations through various mechanisms, eventually influencing plant growth, development, and productivity. The responses of plants to these stress conditions are highly complex and depend on other factors, such as the species and genotype, plant age and size, the rate of progression as well as the intensity and duration of the stresses. These factors have a strong effect on plant response and define whether mitigation processes related to acclimation will occur or not. In this review, we summarize how drought and salinity extensively affect plant growth in agriculture ecosystems. In particular, we focus on the morphological, physiological, biochemical, and metabolic responses of plants to these stresses. Moreover, we discuss mechanisms underlying plant-microbe interactions that confer abiotic stress tolerance.

Architecture & Planning• 2020• Frontiers Media
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Periodicals

Applications of Artificial Intelligence in Transport: An Overview

The rapid pace of developments in Artificial Intelligence (AI) is providing unprecedented opportunities to enhance the performance of different industries and businesses, including the transport sector. The innovations introduced by AI include highly advanced computational methods that mimic the way the human brain works. The application of AI in the transport field is aimed at overcoming the challenges of an increasing travel demand, CO2 emissions, safety concerns, and environmental degradation. In light of the availability of a huge amount of quantitative and qualitative data and AI in this digital age, addressing these concerns in a more efficient and effective fashion has become more plausible. Examples of AI methods that are finding their way to the transport field include Artificial Neural Networks (ANN), Genetic algorithms (GA), Simulated Annealing (SA), Artificial Immune system (AIS), Ant Colony Optimiser (ACO) and Bee Colony Optimization (BCO) and Fuzzy Logic Model (FLM) The successful application of AI requires a good understanding of the relationships between AI and data on one hand, and transportation system characteristics and variables on the other hand. Moreover, it is promising for transport authorities to determine the way to use these technologies to create a rapid improvement in relieving congestion, making travel time more reliable to their customers and improve the economics and productivity of their vital assets. This paper provides an overview of the AI techniques applied worldwide to address transportation problems mainly in traffic management, traffic safety, public transportation, and urban mobility. The overview concludes by addressing the challenges and limitations of AI applications in transport.

Architecture & Planning• 2019• Multidisciplinary Digital Publishing Institute
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Comprehensive Review of Artificial Neural Network Applications to Pattern Recognition

The era of artificial neural network (ANN) began with a simplified application in many fields and remarkable success in pattern recognition (PR) even in manufacturing industries. Although significant progress achieved and surveyed in addressing ANN application to PR challenges, nevertheless, some problems are yet to be resolved like whimsical orientation (the unknown path that cannot be accurately calculated due to its directional position). Other problem includes; object classification, location, scaling, neurons behavior analysis in hidden layers, rule, and template matching. Also, the lack of extant literature on the issues associated with ANN application to PR seems to slow down research focus and progress in the field. Hence, there is a need for state-of-the-art in neural networks application to PR to urgently address the above-highlights problems for more successes. The study furnishes readers with a clearer understanding of the current, and new trend in ANN models that effectively addresses PR challenges to enable research focus and topics. Similarly, the comprehensive review reveals the diverse areas of the success of ANN models and their application to PR. In evaluating the performance of ANN models, some statistical indicators for measuring the performance of the ANN model in many studies were adopted. Such as the use of mean absolute percentage error (MAPE), mean absolute error (MAE), root mean squared error (RMSE), and variance of absolute percentage error (VAPE). The result shows that the current ANN models such as GAN, SAE, DBN, RBM, RNN, RBFN, PNN, CNN, SLP, MLP, MLNN, Reservoir computing, and Transformer models are performing excellently in their application to PR tasks. Therefore, the study recommends the research focus on current models and the development of new models concurrently for more successes in the field.

Architecture & Planning• 2019• Institute of Electrical and Electronics Engineers
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A Review on Large Language Models: Architectures, Applications, Taxonomies, Open Issues and Challenges

Large Language Models (LLMs) recently demonstrated extraordinary capability, including natural language processing (NLP), language translation, text generation, question answering, etc. Moreover, LLMs are a new and essential part of computerized language processing, having the ability to understand complex verbal patterns and generate coherent and appropriate replies for the situation. Though this success of LLMs has prompted a substantial increase in research contributions, rapid growth has made it difficult to understand the overall impact of these improvements. Since a lot of new research on LLMs is coming out quickly, it is getting tough to get an overview of all of them in a short note. Consequently, the research community would benefit from a short but thorough review of the recent changes in this area. This article thoroughly overviews LLMs, including their history, architectures, transformers, resources, training methods, applications, impacts, challenges, etc. This paper begins by discussing the fundamental concepts of LLMs with its traditional pipeline of the LLMs training phase. It then provides an overview of the existing works, the history of LLMs, their evolution over time, the architecture of transformers in LLMs, the different resources of LLMs, and the different training methods that have been used to train them. It also demonstrated the datasets utilized in the studies. After that, the paper discusses the wide range of applications of LLMs, including biomedical and healthcare, education, social, business, and agriculture. It also illustrates how LLMs create an impact on society and shape the future of AI and how they can be used to solve real-world problems. Then it also explores open issues and challenges to deploying LLMs in real-world scenario. Our review paper aims to help practitioners, researchers, and experts thoroughly understand the evolution of LLMs, pre-trained architectures, applications, challenges, and future goals.

Architecture & Planning• 2024• Institute of Electrical and Electronics Engineers
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Statistically optimized inversion algorithm for enhanced retrieval of aerosol properties from spectral multi-angle polarimetric satellite observations

Abstract. The proposed development is an attempt to enhance aerosol retrieval by emphasizing statistical optimization in inversion of advanced satellite observations. This optimization concept improves retrieval accuracy relying on the knowledge of measurement error distribution. Efficient application of such optimization requires pronounced data redundancy (excess of the measurements number over number of unknowns) that is not common in satellite observations. The POLDER imager on board the PARASOL micro-satellite registers spectral polarimetric characteristics of the reflected atmospheric radiation at up to 16 viewing directions over each observed pixel. The completeness of such observations is notably higher than for most currently operating passive satellite aerosol sensors. This provides an opportunity for profound utilization of statistical optimization principles in satellite data inversion. The proposed retrieval scheme is designed as statistically optimized multi-variable fitting of all available angular observations obtained by the POLDER sensor in the window spectral channels where absorption by gas is minimal. The total number of such observations by PARASOL always exceeds a hundred over each pixel and the statistical optimization concept promises to be efficient even if the algorithm retrieves several tens of aerosol parameters. Based on this idea, the proposed algorithm uses a large number of unknowns and is aimed at retrieval of extended set of parameters affecting measured radiation. The algorithm is designed to retrieve complete aerosol properties globally. Over land, the algorithm retrieves the parameters of underlying surface simultaneously with aerosol. In all situations, the approach is anticipated to achieve a robust retrieval of complete aerosol properties including information about aerosol particle sizes, shape, absorption and composition (refractive index). In order to achieve reliable retrieval from PARASOL observations even over very reflective desert surfaces, the algorithm was designed as simultaneous inversion of a large group of pixels within one or several images. Such multi-pixel retrieval regime takes advantage of known limitations on spatial and temporal variability in both aerosol and surface properties. Specifically the variations of the retrieved parameters horizontally from pixel-to-pixel and/or temporary from day-to-day are enforced to be smooth by additional a priori constraints. This concept is expected to provide satellite retrieval of higher consistency, because the retrieval over each single pixel will be benefiting from coincident aerosol information from neighboring pixels, as well, from the information about surface reflectance (over land) obtained in preceding and consequent observations over the same pixel. The paper provides in depth description of the proposed inversion concept, illustrates the algorithm performance by a series of numerical tests and presents the examples of preliminary retrieval results obtained from actual PARASOL observations. It should be noted that many aspects of the described algorithm design considerably benefited from experience accumulated in the preceding effort on developments of currently operating AERONET and PARASOL retrievals, as well as several core software components were inherited from those earlier algorithms.

Architecture & Planning• 2011• Copernicus Publications
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Understanding Variation in Managers' Ambidexterity: Investigating Direct and Interaction Effects of Formal Structural and Personal Coordination Mechanisms

Previous research focuses on firm and business unit level ambidexterity. Therefore, conceptual and empirically validated understanding about ambidexterity at the individual level of analysis is very scarce. This paper addresses this gap in the literature by investigating managers' ambidexterity, delivering three contributions to theory and empirical research on ambidexterity: first, by proposing three related characteristics of ambidextrous managers; second, by developing a model and associated hypotheses on both the direct and interaction effects of formal structural and personal coordination mechanisms on managers' ambidexterity; and third, by testing the hypotheses based on a sample of 716 business unit level and operational level managers. Findings regarding the formal structural mechanisms indicate that a manager's decision-making authority positively relates to this manager's ambidexterity, whereas formalization of a manager's tasks has no significant relationship with this manager's ambidexterity. Regarding the personal coordination mechanisms, findings indicate that both the participation of a manager in cross-functional interfaces and the connectedness of a manager to other organization members positively relate to this manager's ambidexterity. Furthermore, results show positive interaction effects between the formal structural and personal coordination mechanisms on managers' ambidexterity. The paper's theoretical contributions and empirical results increase our understanding about managers' ambidexterity and about how different types and combinations of coordination mechanisms relate to variation in managers' ambidexterity.

Architecture & Planning• 2009• Institute for Operations Research and the Management Sciences
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