Modular Level Research Articles

Promoting Transfer of Learning Using the ‘Window of Opportunity’ in Emergency Nursing Education

Emergency nurses in training struggled to transfer their classroom learning into practice regarding traumatic brain injury (TBI). Consequently, secondary brain injury could occur. In this conceptual paper, we combined Tanner’s model of clinical judgement in nursing, the conceptual framework for educational design at the modular level, and knowledge levels to devise a strategy to promote the transfer of learning among emergency nursing students regarding the management of TBIs. Through a guided process, the students first learned the process of analytical clinical reasoning and practiced the process in class and then under supervision in the ED. Finally, they demonstrated their competence and transferal of their knowledge into a realworld environment during a summative assessment task.

Utilizing Extensive-Form Games for Energy-aware Production Plan Adaptation in Modular Skill-based Production Systems

Modular production systems provide an important basis to react more flexibly to changing and more individual production requirements. A challenge hereby is an appropriate production planning and scheduling that considers the capabilities and the degrees of freedom of these systems, which can be al-gorithmically optimized for their relevance. A great importance is also given to the aspect of energy-efficient production, which can be included in the planning as well as in its execution on the modular level. The main objective of the paper is to show the relevance of the adaptability of production plans within modular production systems from an energetic point of view, with special focus on electrical energy. It is considered on how modular production systems can be modeled, based on the current state of the art and from the perspective of the capability- and skill-based approach for energy-aware skill usage. Furthermore, it is examined how pre-optimized scheduling for production systems can be adapted by production modules. For the energy-related optimization an extensive-form game model is provided. The scheduler and the production modules are considered as players and strategies of the modules are selected based on their modeled capabilities by using their best response under energy-related aspects. The results of a simulation with prototypical implementation for energy-optimized adaptation, based on extensive-form games, and possible application for unforeseen event handling are presented and discussed.

Harmonics Mitigation Using MMC Based UPFC and Particle Swarm Optimization

The application of non-linear loads in the power electronic device causes serious harmonic issues in the power system since it has the intrinsic property of retrieving harmonic current and reactive power from Alternating Current (AC) supply that leads to voltage instability. To maintain a reliable power flow in the power system, an innovative Unified Power Flow Converter (UPFC) is utilized in this proposed approach. The conventional series converter is replaced with the Modular Multilevel Converter (MMC) that improves the power handling capability and achieves higher modular level with minimized distortions. The shunt compensator assists in minimizing the voltage fluctuations and maximizing the voltage stability under different load constraints. The Direct Quadrature (DQ) theory is utilized in this study to separate the harmonic components by generating reference frame current. The Proportional Integral (PI) controller aids in maintaining the direct current potential difference in the constant mode whereas the Pulse Width Modulation (PWM) generator helps in delivering optimized output to the MMC. The gain parameters of PI controller are optimized with the aid of employing Particle Swarm Optimization (PSO). The entire work is validated using MATLAB simulink and the obtained outcomes proves that this new UPFC is highly beneficial in minimizing the distortions with reduced Total Harmonic Distortion (THD) of 2.21%.

ForestTaxator: A tool for detection and approximation of cross-sectional area of trees in a cloud of 3D points

In this paper we propose a novel software, named ForestTaxator, supporting terrestrial laser scanning data processing, which for dendrometric tree analysis can be divided into two main processes: tree detection in the point cloud and development of three-dimensional models of individual trees. The usage of genetic algorithms to solve the problem of tree detection in 3D point cloud and its cross-sectional area approximation with ellipse-based model is also presented. The detection and approximation algorithms are proposed and tested using various variants of genetic algorithms. The work proves that the genetic algorithms work very well: the obtained results are consistent with the reference data to a large extent, and the time of genetic calculations is very short. The attractiveness of the presented software is due to the fact that it provides all necessary functionalities used in the forest inventory field. The software is written in C# and runs on the .NET Core platform, which ensures its full portability between Windows, MacOS and Linux. It provides a number of interfaces thus ensuring a high level of modularity. The software and its code are made freely available.

Digitalisation of chemical processes as graphs and applications of modular decomposition to process design and analysis

The digital transformation of chemical processes requires methods that support the modelling and analysis tools suitable to decompose complex systems into manageable components at all stages of process development, starting with conceptual design and analysis. This paper presents examples on how the digitalisation of chemical processes as graphs can facilitate their study and analysis for developing alternative process designs. In a process graph, inputs, outputs and unit operations are represented as nodes and streams are edges which can be weighted using process variables such as flow rate. Once a process is represented as a graph, community detection algorithms can be applied to identify process modules and obtain their modularity. The novel approach presented is based on modular decompositions to identify highly related unit operations in a flowsheet which can be analysed for insightful directions to improve a chemical process. The approach showed to be helpful in analysing a biofuel production process by adopting a combined economic and greenhouse gas emissions analysis at a modular level for hot spot analysis and identification of opportunities for decarbonisation. In an application to process integration, CO2 capture and utilisation networks are analysed in terms of its modularity and efficiency in processing CO2. In both cases, the improved designs had higher modularity values suggesting a correlation between modularity and technical performance which needs to be investigated further. Finally, prospective applications of modularity and modular decomposition approaches to realise several digitalisation tasks are presented.

Structural network alterations in patients with nasopharyngeal carcinoma after radiotherapy: A 1-year longitudinal study.

This longitudinal study explored the changed patterns of structural brain network after radiotherapy (RT) in patients with nasopharyngeal carcinoma (NPC). Diffusion tensor imaging (DTI) data were gathered from 35 patients with NPC at four time points: before RT (baseline), 0∼3 (acute), 6 (early delayed), and 12 months (late-delayed) after RT. The graph theory was used to characterize the dynamic topological properties after RT and the significant changes were detected over time at the global, regional and modular levels. Significantly altered regional metrics (nodal efficiency and degree centrality) were distributed in the prefrontal, temporal, parietal, frontal, and subcortical regions. The module, that exhibited a significantly altered within-module connectivity, had a high overlap with the default mode network (DMN). In addition, the global, regional and modular metrics showed a tendency of progressive decrease at the acute and early delayed stages, and a partial/full recovery at the late-delayed stage. This changed pattern illustrated that the radiation-induced brain damage began at the acute reaction stage and were aggravated at the early-delayed stage, and then partially recovered at the late-delayed stage. Furthermore, the spearman's correlations between the abnormal nodal metrics and temporal dose were calculated and high correlations were found at the temporal (MTG.R and HES.L), subcortical (INS.R), prefrontal (ORBinf.L and ACG.L), and parietal (IPL.R) indicating that these regions were more sensitive to dose and should be mainly considered in radiotherapy treatment plan.

Between an n-ary and an n + 1-ary near-unanimity term

We devise a condition strictly between the existence of an n-ary and an n + 1-ary near-unanimity term. We evaluate exactly the distributivity and modularity levels implied by such a condition.

DEVELOPING A NEW TECHNIQUE FOR DETERMINING SERVICE MODULARITY LEVEL

This paper addresses the existing methodological gap in assessing service modularity level. The study uses a qualitative approach and develops a technique to quickly and cost-effectively determine service modularity level. The theoretical framework builds on the principles of measurement theory, evaluation theory, and multicriteria analysis (MCA). To test the developed technique, an exploratory case study was conducted focusing on three service companies in Lithuania. Data were collected by interviewing key informants using the proposed instrument. The collected data were analysed and linked to the developed multidimensional scale for measuring the service modularity level. The obtained results support the view that qualitative evaluation can be an alternative for calculating modularity indices in the field of service modularity. The paper provides a practical tool for service modularity assessment for service industry practitioners to accelerate decision-making. The study contributes to the field of service modularity by delivering insights into assessing service modularity level. These insights are particularly important because the literature on measuring modularity level in services has been quite scarce.

Longitudinal alterations of cortical structural-functional coupling in temporal lobe epilepsy.

To investigate the longitudinal alterations of cortical structural-functional coupling (SF coupling) in patients with temporal lobe epilepsy (TLE) over a 2-year follow-up, thereby exploring the neuropathophysiological mechanisms of TLE. Twenty-eight TLE patients and 42 age- and gender-matched healthy controls (HCs) were recruited. We used resting-state functional MRI and diffusion-weighted imaging to estimate and compare SF coupling at the multiscale network level (whole-brain, modular, and regional levels). Then, we analyzed the relationships between the spatial patterns of SF coupling, the principal functional connectivity (FC) gradient, and the functional participation coefficient (PC). Finally, we related regional SF coupling changes between baseline and follow-up to the expression of regional TLE-specific genes. Compared with HCs, TLE patients showed higher baseline SF couplings within the whole-brain, limbic, and default-mode modules. SF couplings within visual and dorsal attention modules were increased at follow-up compared to baseline. In all three groups, the spatial patterns of SF coupling aligned with the principal FC gradient and the functional PC. The longitudinal change in regional SF coupling in TLE patients was significantly positively correlated with the expression of the CUX2 gene. Aberrant SF coupling was revealed in TLE and related to macroscale cortical hierarchies, functional segregation, and TLE-specific gene expression; these data help increase our understanding of the neuropathophysiological mechanisms underlying TLE.

Re-thinking Architecture Education: Conceptualising Curriculum Through the Lens of 21st Century Graduate Attributes

There has been a paradigm shift in architecture education to embrace the 21st century attributes for graduates. While attributes such as problem solving, critical and creative thinking, interpersonal and intrapersonal skills, and technological as well as global awareness have been central to the curriculum design in architectural education, this paper posits the need to critically examine the emerging 21st century attributes, and how they can be embedded into architecture education. In recent years, it is clear that these attributes have been designed and executed into design and other-related student projects through either extra-curricular platforms or at modular level, however there is a lack of exploration of these attributes as structured and intentional design at curriculum level. Using design research in education as the methodology for study, this paper explores the design thinking for architecture education that embraces these attributes. This paper justifies and argues for the need to re-think architectural education in relation to these attributes and offers a conceptual thinking, framework and principles for curriculum design.

Evolving Modularity in Soft Robots Through an Embodied and Self-Organizing Neural Controller.

Modularity is a desirable property for embodied agents, as it could foster their suitability to different domains by disassembling them into transferable modules that can be reassembled differently. We focus on a class of embodied agents known as voxel-based soft robots (VSRs). They are aggregations of elastic blocks of soft material; as such, their morphologies are intrinsically modular. Nevertheless, controllers used until now for VSRs act as abstract, disembodied processing units: Disassembling such controllers for the purpose of module transferability is a challenging problem. Thus, the full potential of modularity for VSRs still remains untapped. In this work, we propose a novel self-organizing, embodied neural controller for VSRs. We optimize it for a given task and morphology by means of evolutionary computation: While evolving, the controller spreads across the VSR morphology in a way that permits emergence of modularity. We experimentally investigate whether such a controller (i) is effective and (ii) allows tuning of its degree of modularity, and with what kind of impact. To this end, we consider the task of locomotion on rugged terrains and evolve controllers for two morphologies. Our experiments confirm that our self-organizing, embodied controller is indeed effective. Moreover, by mimicking the structural modularity observed in biological neural networks, different levels of modularity can be achieved. Our findings suggest that the self-organization of modularity could be the basis for an automatic pipeline for assembling, disassembling, and reassembling embodied agents.

A Multi-Objective Co-Design Optimization Framework for Grid-Connected Hybrid Battery Energy Storage Systems: Optimal Sizing and Selection of Technology

This paper develops a multi-objective co-design optimization framework for the optimal sizing and selection of battery and power electronics in hybrid battery energy storage systems (HBESSs) connected to the grid. The co-design optimization approach is crucial for such a complex system with coupled subcomponents. To this end, a nondominated sorting genetic algorithm (NSGA-II) is used for optimal sizing and selection of technologies in the design of the HBESS, considering design parameters such as cost, efficiency, and lifetime. The interoperable framework is applied considering three first-life battery cells and one second-life battery cell for forming two independent battery packs as a hybrid battery unit and considers two power conversion architectures for interfacing the hybrid battery unit to the grid with different power stages and levels of modularity. Finally, the globally best HBESS system obtained as the output of the framework is made up of LTO first-life and LFP second-life cells and enables a total cost of ownership (TCO) reduction of 29.6% compared to the baseline.

Co‐located and space‐shared multiple‐input multiple‐output antenna module and its applications in 12 × 12 multiple‐input multiple‐output systems

AbstractIn this study, we developed a co‐located and space‐shared multiple‐input multiple‐output (MIMO) antenna module with a modular design and high integration level. The proposed antenna pair includes a half‐wavelength loop antenna and a dipole‐type antenna printed on the front and back sides of a compact modular board. Owing to their modal orthogonality, these two independent antenna elements are highly self‐isolated and free of additional decoupling components, even though they are assembled at the same location and within the same space. Thus, the proposed antenna is attractive in 5G MIMO systems. Furthermore, the proposed co‐located and space‐shared MIMO antenna module was employed in a 5G smartphone to verify their radiation and diversity performances. A 12 × 12 MIMO antenna system was simulated and fabricated using the proposed module. Based on the results, the proposed module can be employed in large‐scale MIMO antenna systems for current and future terminal devices owing to its high integration, compactness, simple implementation, and inherent isolation.

Modular Level Power Electronics (MLPE) Based Distributed PV System for Partial Shaded Conditions

Photovoltaic (PV) solar energy is a very promising renewable energy technology, as solar PV systems are less efficient because of climate conditions, temperature, and irradiance change. So, to resolve this problem, two PV topologies are used, i.e., centralized and distributed PV systems. The centralized technique is quicker than the distributed technique in terms of convergence speed and a faster power tracking approach. In the event of uniform irradiance, the centralized system also has the benefit of supplying superior energy, but in PS scenarios, a huge amount of energy is lost. However, the distributed approach requires current and voltage measurements at each panel, resulting in a massive data set. Nevertheless, in the event of shading circumstances, the distributed technique is highly effective because a modular level power electronics (MLPE) converter is used. While in a centralized PV system, there is only a single DC-DC converter for the whole PV system. In this research work, a DFO-based DC-DC converter is designed for modular level, with an ability to perform a rapid shutdown of the module under fire hazard conditions, troubleshooting, and monitoring of a module in a very efficient way. The robustness of the proposed MPPT DFO algorithm is tested with different techniques such as Cuckoo Search (CS), Fruit Fly Optimization (FFO), Particle swarm optimization (PSO), Incremental conductance (InC), and Perturb and observe(P&O) techniques. The proposed technique shows better results in terms of MPPT efficiency, dynamic responsiveness, and harmonics. Furthermore, the result of MLPE and the centralized system is verified by using the Helioscope with different inverter companies like SMA, Tigo, Enphase, Solar edge, and Huawei. The results prove that MLPE is a better option in the case of shading region for attaining the maximum power point.

Particle Swarm Optimization and Modular Multilevel Converter Communication in Electrical Applications with Machine Learning Algorithm.

As a result of their natural capacity to recover harmonic current and reactive power from alternating current sources, power electronic devices utilized in conjunction with nonlinear loads have the potential to generate significant harmonic problems within the power system when employed in this way. When this occurs, voltage instability occurs, which must be avoided in order to maintain the consistency and dependability of the power system's power flow. With this approach, the series controller has been replaced by a multilevel modular controller in order to improve power handling capability and achieve higher modular levels with minimal distortions. The shunt compensator is the most effective way to achieve an extremely protected energy system as well as righteous steadiness in electric potential difference under a variety of load constraints. The DQ thesis is employed in this proposed converter to separate the harmonic components by establishing reference frame current, which is accomplished by machine learning techniques. As part of the constant mode operation, the PI controller contributes to maintaining the direct current-potential difference, which is given to the PWM generator. Optimization of the values of K p and K i is accomplished by the use of particle swarm optimization (PSO). The construction of this power system simulation model has been made feasible by the use of time-fluctuating characteristics modeling and the MATLAB programming environment. The new (unified power flow controller) UPFC research that has been made available is persuasive in its capacity to reduce distortions and watt-less power components while simultaneously enhancing efficiency and reducing costs.

The Effects of Species Abundance, Spatial Distribution, and Phylogeny on a Plant-Ectomycorrhizal Fungal Network.

Plant and root fungal interactions are among the most important belowground ecological interactions, however, the mechanisms underlying pairwise interactions and network patterns of rhizosphere fungi and host plants remain unknown. We tested whether neutral process or spatial constraints individually or jointly best explained quantitative plant–ectomycorrhizal fungal network assembly in a subtropical forest in southern China. Results showed that the observed plant–ectomycorrhizal fungal network had low connectivity, high interaction evenness, and an intermediate level of specialization, with nestedness and modularity both greater than random expectation. Incorporating information on the relative abundance and spatial overlap of plants and fungi well predicted network nestedness and connectance, but not necessarily explained other network metrics such as specificity. Spatial overlap better predicted pairwise species interactions of plants and ectomycorrhizal fungi than species abundance or a combination of species abundance and spatial overlap. There was a significant phylogenetic signal on species degree and interaction strength for ectomycorrhizal fungal but not for plant species. Our study suggests that neutral processes (species abundance matching) and niche/dispersal-related processes (implied by spatial overlap and phylogeny) jointly drive the shaping of a plant-ectomycorrhizal fungal network.

The Dark Side of Technological Modularity: Opportunistic Information Hiding During Interorganizational System Adoption

To drive competitive advantage in today’s fast-paced and disruptive business environment, firms are increasingly investing in the modularization of their technology infrastructure. In a rapidly changing and interconnected business environment where flexibility is key, modularity is often hailed as a foundational pillar of information technology systems of the future. For networked firms, modularity has been traditionally viewed as unambiguously beneficial because it allows for closer alignment with partner firms and also mitigates risk by lowering partner switching costs. However, we find that in interfirm networks undergoing technology transitions in the form of adoption of new interorganizational systems such as blockchains, modularity can also engender additional risks. Specifically, the early stages of IOS adoption are characterized by information asymmetries, and we find that high levels of technological modularity can render firms more susceptible to opportunistic information withholding by network partners. Our findings run counter to the traditional view of modularity as a capability that can improve the efficiency of IOS adoption, or as a governance mechanism that reduces risks associated with IOS adoption. As optimism and investments toward modularity grow, by identifying associated risks, our work cautions managers to adopt a more qualified view of this capability during technological transitions.

Rule-Based Classification Based on Ant Colony Optimization: A Comprehensive Review

The Ant Colony Optimization (ACO) algorithms have been well-studied by the Operations Research community for solving combinatorial optimization problems. A handful of researchers in the Data Science community have successfully implemented various ACO methodologies for rule-based classification. This family of ACO algorithms is referred to as AntMiner algorithms. Due to the flexibility of the framework, and the availability of alternative strategies at the modular level, a systematic review on the AntMiner algorithms can benefit the broader community of researchers and practitioners interested in highly interpretable classification techniques. In this paper, we provided a comprehensive review of each module of the AntMiner algorithms. Our motivation is to provide insight into the current practices and future research scope in the context of the rule-based classification. Our discussions address ACO methodologies, rule construction strategies, candidate selection metrics, rule quality evaluation functions, rule pruning strategies, methods to address continuous attributes, parameter selection, and experimental settings. This review also reports a summary of real-life implementations of the rule-based classifiers in diverse domains including medical, genetics, portfolio analysis, geographic information system (GIS), human-machine interaction (HMI), autonomous driving, ICT, quality, and reliability engineering. These implementations demonstrate the potential application domains that can be benefitted from the methodological contributions to the rule-based classification technique.

The Monetary and Non-Monetary Impacts of Prefabrication on Construction: The Effects of Product Modularity

Prefabrication is rapidly increasing in construction, and previous research has identified various impacts of prefabrication on projects. Modular product architecture is a great enabler for prefabrication; however, practitioners would benefit from more explicit knowledge on the impacts of prefabricated product types with different levels of product modularity. This study investigates the connection between the modularity level and the monetary and non-monetary impacts of prefabricated products. First, the literature on prefabrication and modularity is used to form three propositions which are related to product modularity and the benefits of prefabrication. The level of modularity is considered with two dimensions: the proportion of modules and the module description detail. Second, four prefabricated products are analyzed to test the propositions. The analysis revealed that (1) the level of modularity adopted in the product is directly proportional to the benefits. More specifically, (2) a higher proportion of modules in a project product contributes to higher cost-benefits. On the other hand, (3) prefabricated products with highly detailed module descriptions seem to lead to higher non-monetary benefits, such as better ergonomics and work satisfaction. The study reveals new empirical evidence on the relationship between product modularity and the benefits of prefabricated products. Cost-benefit analysis revealed that even though some prefabricated products could have higher direct costs, the total cost can still be lower than conventional construction when also considering the indirect benefits. Practitioners can utilize the findings when selecting modular and prefabricated products that best fulfil their project objectives.

Opportunities and challenges in machine learning-based newborn screening-A systematic literature review.

The development and continuous optimization of newborn screening (NBS) programs remains an important and challenging task due to the low prevalence of screened diseases and high sensitivity requirements for screening methods. Recently, different machine learning (ML) methods have been applied to support NBS. However, most studies only focus on single diseases or specific ML techniques making it difficult to draw conclusions on which methods are best to implement. Therefore, we performed a systematic literature review of peer‐reviewed publications on ML‐based NBS methods. Overall, 125 related papers, published in the past two decades, were collected for the study, and 17 met the inclusion criteria. We analyzed the opportunities and challenges of ML methods for NBS including data preprocessing, classification models and pattern recognition methods based on their underlying approaches, data requirements, interpretability on a modular level, and performance. In general, ML methods have the potential to reduce the false positive rate and identify so far unknown metabolic patterns within NBS data. Our analysis revealed, that, among the presented, logistic regression analysis and support vector machines seem to be valuable candidates for NBS. However, due to the variety of diseases and methods, a general recommendation for a single method in NBS is not possible. Instead, these methods should be further investigated and compared to other approaches in comprehensive studies as they show promising results in NBS applications.