Modular Level Research Articles

Optimal decisions of modularity, prices and return policy in a dual-channel supply chain under mass customization

Online customization is very popular among consumers in current e-commerce because of its flexible customization time and diverse product attributes. This paper establishes a dual-channel supply chain composed of one manufacturer and one retailer under mass customization. The manufacturer sells standard products offline via the retailer when providing modular customized products online. Two online distribution strategies of the manufacturer are considered: The manufacturer can choose direct or agency sales through the retailer’s online channel. Additionally, the return policy of modular customized products available online is explored. A Stackelberg game model is proposed under each return scenario (with and without online consumer returns) to derive the optimal solutions for different channel strategies. Then, the influence of different parameters on the optimal modularity level and product prices is investigated under each model. Through sensitivity analysis, we found that the cross-price sensitivity coefficient of dual channels has a considerably positive impact on optimal decisions. Furthermore, the manufacturer’s channel strategy and return policy decisions are examined. The result indicates that direct sales should be chosen if manufacturers’ online channel operating costs are lower than a certain threshold that increases with the revenue allocation ratio under agency sales. When the market demand of the product that is allowed to be returned is much more sensitive to the refund than its return quantity, a full refund policy should be offered for online customized products. These conclusions also apply to the return scenario where full refunds are provided for both standard and customized products.

Modular Link Level Simulator for the Physical Layer of Beyond 5G Wireless Communication Systems

AbstractThe low THz band is a promising candidate to enable data rates of up to 1 Tbit/s. To develop suitable communications systems, novel simulation approaches are needed that account for the specifics of the evolving technology. This article presents a modular link level simulator for the physical layer of beyond‐5G and 6G wireless communication systems in the THz range. The simulator, that is oriented toward the IEEE Std 802.15.3d‐2017 is contrasted to the state of the art of physical layer simulation tools. Its concept and basic building blocks are presented and the simulator is validated by channel simulations considering an AWGN channel model. Moreover, it is applied to a top‐of‐rack scenario in a wirelessly augmented data center. Different parameter sets are compared showing that a LOS condition and sufficient transmit power are a prerequisite in order to profit from the large bandwidth in the low THz range. The extensive data set of simulation results serves as input for future studies with higher layer simulation tools.

Brain functional network abnormalities in parkinson's disease with mild cognitive impairment.

Mild cognitive impairment in Parkinson's disease (PD-M) is related to a high risk of dementia. This study explored the whole-brain functional networks in early-stage PD-M. Forty-one patients with PD classified as cognitively normal (PD-N, n = 17) and PD-M (n = 24) and 24 demographically matched healthy controls (HC) underwent clinical and neuropsychological evaluations and resting-state functional magnetic resonance imaging. The global, regional, and modular topological characteristics were assessed in the brain functional networks, and their relationships to cognitive scores were tested. At the global level, PD-M and PD-N exhibited higher characteristic path length and lower clustering coefficient, local and global efficiency relative to HC. At the regional level, PD-M and PD-N showed lower nodal centrality in sensorimotor regions relative to HC. At the modular level, PD-M showed lower intramodular connectivity in default mode and cerebellum modules, and lower intermodular connectivity between default mode and frontoparietal modules than PD-N, correlated with Montreal Cognitive Assessment scores. Early-stage PD patients showed weaker small-worldization of brain networks. Modular connectivity alterations were mainly observed in patients with PD-M. These findings highlight the shared and distinct brain functional network dysfunctions in PD-M and PD-N, and yield insight into the neurobiology of cognitive decline in PD.

A Pencil Shaped 9-Level Multilevel Inverter With Voltage Boosting Ability: Configuration and Experimental Investigation

A comprehensive study of a minimized component pencil shaped (PS) 9-level inverter constructed with just two DC supplies is presented in this research. Many of the suggested low component multilevel inverters (MLIs) use DC supplies that are not being used appropriately along with additional conducting switches. Since this proposed MLI has a reduced quantity of power electronic switches, it is more efficient. The architecture may be expanded to a modular higher voltage level inverter, which uses less DC supplies and uses them correctly without the need of an extra H-bridge circuit. To determine their optimum capabilities, the proposed inverter parameters’ simplified formulas are constructed. Furthermore, the extended model of the proposed architecture is used to generate an optimum PSMLI design for lowering the total standing voltage (TSV) of the inverter. To demonstrate its advantages over recent MLIs of similar types, comparison studies are given to justify the proposed inverter. Through proper simulation and laboratory experiment, the MLI obtained a higher efficiency of 95.54%. On the other hand, the optimized 17-level version of PSMLI obtained total harmonic distortions (THD) of only 5.15% which successfully attained IEEE 519 standard performance.

Proposing and Prototyping an Extension to the Adapter Concept in the IEC 61499 Standard

Component-Design Architecture has been in demand based on the growing needs for modularity and flexibility in the automation industry. IEC 61499 standard, a component-based automation architecture, provides various tools and techniques for automation developers to accommodate the need for flexibility in automation sequences. However, the adapter concept, one of the significant features of the standard, remains untouched and undeveloped since its inclusion in the standard and lacks the utilization of its true potential. In this work, we enhance the adapter concept by proposing the addition of logic into them. This proposition advances the adapter technology and gives the automation standard more capabilities to support higher levels of modularization without the increase of applications complexity.

Hybrid MMC Redundancy Configuration Calculation Method Based on Heuristic Algorithm

Most of the existing redundant configuration methods of modular level converters (MMC) only consider the reliability of the MMC, and rarely consider the utilization of insulated gate bipolar transistors (IGBT). A heuristic algorithm-based calculation method for hybrid MMC redundancy configuration is proposed in order to better meet the engineering practicality. Firstly, the topology of the hybrid MMC is introduced, and the correlation between submodules is introduced into the calculation of its reliability. Secondly, the index of IGBT utilization is proposed to judge the economic advantage. The effects of correlation and service life on the reliability of the hybrid MMC and the IGBT utilization are analyzed. Then, heuristic factors are constructed with reliability and IGBT utilization, and the optimal redundancy configuration scheme of hybrid MMC is obtained by heuristic algorithm. Finally, based on the parameters of Kunliulong multi-terminal hybrid DC transmission project, the calculation is verified in Matlab and compared with the results of the difference method to illustrate the feasibility and superiority of the proposed redundancy configuration calculation method.

The individual‐based network structure of palm‐seed dispersers is explained by a rainforest gradient

How species interactions change in space and time is a major question in ecology. In tropical forests, plant individuals share mutualistic partners (pollinators or seed dispersers), yet we have little understanding of the factors affecting these individual interaction patterns. We used a seed dispersal individual‐based network describing interactions between individuals of a palm species and bird species to investigate how intrinsic and extrinsic characteristics of individual plants influence the network structure. We evaluated if average canopy height, number of fruits, distance to forest gap and habitat type influence the role of palm individuals in the network. From 102 palms, 62 individuals had their seeds dispersed at least once: 17 individual palms in the restinga, 15 in the lowland and 30 in the pre‐montane habitat. Twelve bird species were recorded dispersing Euterpe edulis seeds. No palm characteristics influenced interaction patterns in the network, characterized by the level of centrality of each palm. At the network level, modularity with qualitative data was reproduced by the null models which consider the variation in the number and distribution across interactions. Three of the seven identified modules were associated with a particular habitat. Indeed, habitat type explained 50% of network modularity. Habitat association with modularity was driven by differences in species composition across habitats. Palm individuals did not differ greatly in central positions, indicating that bird species are not selecting palm individuals by their characteristics. When using the weighted network, modularity level was higher than expected by the number of interactions, and frequency of interactions was positively correlated with canopy height. Our results suggest that the organization of this individual‐based network is mostly driven by habitat type. We hypothesize that extrinsic characteristics, such as habitat type, may affect the network organization of populations of sessile organisms with potentially unanticipated consequences to ecological and evolutionary dynamics.

Structuring Interaction Networks Between Epiphytic Bryophytes and Their Hosts in Yunnan, SW China

Ecological networks are commonly applied to depict general patterns of biotic interactions, which provide tools to understand the mechanism of community assembly. Commensal interactions between epiphytes and their hosts are a major component of species interactions in forest canopies; however, few studies have investigated species assemblage patterns and network structures of epiphyte–host interactions, particularly non-vascular epiphytes in different types of forest. To analyze the characteristics of network structures between epiphytes and their hosts, composition and distribution of epiphytic bryophytes were investigated from 138 host individuals using canopy cranes in a tropical lowland seasonal rain forest (TRF) and a subtropical montane moist evergreen broad-leaved forest (STF), in Southwest China. We structured binary networks between epiphytic bryophytes and their hosts in these two forests, which presented 329 interactions in the TRF and 545 interactions in the STF. Compared to TRF, the bryophyte–host plant networks were more nested but less modular in the STF. However, both forests generally exhibited a significantly nested structure with low levels of specialization and modularity. The relatively high nestedness may stabilize the ecological networks between epiphytic bryophytes and their hosts. Nevertheless, the low modularity in epiphyte–host networks could be attributed to the lack of co-evolutionary processes, and the low degree of specialization suggests that epiphytes are less likely to colonize specific host species. Vertical distribution of the bryophyte species showed structured modules in the tree basal and crown zones, probably attributing to the adaptation to microclimates within a host individual. This study highlights the nested structure of commensal interaction between epiphytic bryophytes and host trees, and provides a scientific basis to identify key host tree species for conservation and management of biodiversity in forest ecosystems.

Strategic Management Model of Network Organization Based on Artificial Intelligence and Modular Enterprise Big Data

Modularization has been a research hotspot in recent years. Among these, there are two issues that many scholars pay attention to. First, we must know what factors cause modularization, and secondly, what impact modularization has brought to enterprises. The era of information technology background makes enterprises have to deal with more and more complex information. The integration and application of internal knowledge resources also force enterprises to adjust their structure, so that knowledge can spread and create value within the enterprise more effectively. This paper, based on artificial intelligence and modular enterprise big data, constructs a matching model of corporate strategy and performance management mode, determines four different matching methods based on corporate strategy and performance management mode, and puts forward corresponding assumptions. This article selects the participants of EMBA and MBA courses offered by several universities as the survey subjects. Most of them are enterprise managers, have many years of management practical experience, have a good understanding of the basic situation of the enterprise, and can truly understand the scale and make a choice based on the actual content. In this paper, a strategic management model based on artificial intelligence and modular enterprise big data construction proves that paired sample T test can be found, the data analysis is based on the various indicator scores of the enterprise modularity level, and the resulting P value is less than 0.05 as a significant difference, which proves that these two indicators are too quantitative. The company has not done relevant statistics, so it is difficult to make a practical answer. Through comparison and analysis, it is more likely to find the gap between the company’s financial strategy and its competitors, and then it is possible to make targeted improvements and upgrades.

RES-NOVA sensitivity to core-collapse and failed core-collapse supernova neutrinos

RES-NOVA is a new proposed experiment for the investigation of astrophysical neutrino sources with archaeological Pb-based cryogenic detectors. RES-NOVA will exploit Coherent Elastic neutrino-Nucleus Scattering (CEνNS) as detection channel, thus it will be equally sensitive to all neutrino flavors produced by Supernovae (SNe). RES-NOVA with only a total active volume of (60 cm)3 and an energy threshold of 1 keV will probe the entire Milky Way Galaxy for (failed) core-collapse SNe with > 3 σ detection significance. The high detector modularity makes RES-NOVA ideal also for reconstructing the main parameters (e.g. average neutrino energy, star binding energy) of SNe occurring in our vicinity, without deterioration of the detector performance caused by the high neutrino interaction rate. For the first time, distances <3 kpc can be surveyed, similarly to the ones where all known past galactic SNe happened. We discuss the RES-NOVA potential, accounting for a realistic setup, considering the detector geometry, modularity and background level in the region of interest.We report on the RES-NOVA background model and on the sensitivity to SN neutrinos as a function of the distance travelled by neutrinos.

Sustainable text summarization over mobile devices: An energy-aware approach

Due to the omnipresence of hand-held mobile devices, people nowadays are running many applications in such devices to fulfill their daily life requirements. However, due to the limited energy (battery power) of mobile hand-held devices, the energy consumption of such applications determines their feasibility of running in such mobile devices for a long term basis. One such important application is the summarization of text information. Although almost all the existing summarization approaches to date are designed to run on high-end servers or cloud platforms aiming to optimize only the summary quality, there are many applications nowadays, e.g., summarizing data in crisis scenarios or summarizing privacy-sensitive data which demands the functionality of on-device computationally light-weight text summarization to generate effective summaries, while keeping in mind the limited battery power of the device. This paper is the first of its kind where we design energy-efficient summarization algorithms for mobile devices. First, we provide a methodology to systematically measure the energy consumption characteristics of various classical extractive summarization techniques at a modular level by analyzing the algorithmic constructs. Through this process, energy-hungry modules are identified under different configurations and environmental parameters, like input data type, dataset size, device type, among others. Next, based on the observations, we develop four classes of energy-efficient hybrid summarization approaches. Extensive experiments show that the hybrid summarization approaches, when applied on various datasets of varying size and type, can save up to 90% energy, with 5–40% degradation in the summary quality with respect to the high-performing base summarization approaches.

Exploring Product Modularity in Residential Building Areas

This study explores how the logic of product modularity could be useful for the design of complete residential building areas. Previous research has noted that product modularity is usually only applicable if a ‘full modularization’ approach is pursued (i.e., an approach with completely defined modules). This is challenging in Engineer To Order production strategies. Therefore, an approach towards partial product modularity is sought instead. In this approach, the modules are lesser defined to allow flexibility following, e.g., architectural design freedom, as well as per project-specific requirements posed in house-building projects. This study identifies nine (9) ‘modules’ which are denominated as functional spaces. By explaining how unique project requirements affect functional spaces, some integral elements of house-building are detailed. By evaluating the functional spaces in regards to the level of predefinition, as well as the level of relationship, their level of modularity is explored. The usefulness of partial modularity for house-building is suggested to be for coordination of design work and support tools that aides design work. This study suggests that partial modularity can be a feasible approach towards modularity without the need for countermeasures in terms of increasing product predefinition.

Optimal decision in MC supply chain with overconfident retailer based on the newsvendor model

In this paper, we analyze the optimal order-quantity decisions in a supply chain with mass customization (MC) manufacturer and overconfident retailers. First, we consider a newsvendor model in which an unbiased retailer sells mass customized products. The retailer needs to make order quantity decisions before the selling season. Meanwhile, the supplier is a mass customization manufacturer and implements modular production. The supply process is uncertain, as the real quantity the retailer received is the order quantity multiplied by a random yield rate. Second, two overconfident models are considered and theorems are proposed. In the first model, the behavioral bias of overconfidence only affects the retailer’s judgment of variance of market demand. In the second model, the behavior bias of overconfidence affects not only the retailer’s cognition of the variance of market demand, but also his cognition of the expectation of market demand. In addition, the relationship between the optimal decisions and the modularity level is obtained. Finally, we provide managerial insights for the decision makers of the retailers and the manufacturers on order quantity and modularity level, respectively.

Elucidating transcriptomic profiles from single-cell RNA sequencing data using nature-inspired compressed sensing.

Gene-expression profiling can define the cell state and gene-expression pattern of cells at the genetic level in a high-throughput manner. With the development of transcriptome techniques, processing high-dimensional genetic data has become a major challenge in expression profiling. Thanks to the recent widespread use of matrix decomposition methods in bioinformatics, a computational framework based on compressed sensing was adopted to reduce dimensionality. However, compressed sensing requires an optimization strategy to learn the modular dictionaries and activity levels from the low-dimensional random composite measurements to reconstruct the high-dimensional gene-expression data. Considering this, here we introduce and compare four compressed sensing frameworks coming from nature-inspired optimization algorithms (CSCS, ABCCS, BACS and FACS) to improve the quality of the decompression process. Several experiments establish that the three proposed methods outperform benchmark methods on nine different datasets, especially the FACS method. We illustrate therefore, the robustness and convergence of FACS in various aspects; notably, time complexity and parameter analyses highlight properties of our proposed FACS. Furthermore, differential gene-expression analysis, cell-type clustering, gene ontology enrichment and pathology analysis are conducted, which bring novel insights into cell-type identification and characterization mechanisms from different perspectives. All algorithms are written in Python and available at https://github.com/Philyzh8/Nature-inspired-CS.

Platform Governance Design in Platform Ecosystems: Implications for Complementors’ Multihoming Decision

Extant platform research focuses on how platform owners’ governance behaviors directly affect complementors. This study explicates the multilateral interdependence among different groups of producers within a platform ecosystem. We theorize about how platform owners’ governance design may create frictions between platform providers and complementors. While open governance grants greater autonomy to platform providers, it also cultivates a more complex ecosystem for complementors. Since ecosystem complexity raises the cost of product customization, complementors will be less willing to port an existing complement to a more complex ecosystem, that is, less likely to multihome. The negative effect is weakened as the complementor has greater experience with the destination ecosystem or when the complement exhibits a greater level of modularity. Our analysis of newly launched apps in Apple’s iOS and Google’s Android smartphone ecosystems finds supportive evidence. We discuss implications for the burgeoning literature on platform ecosystems and complementors.

A Modular Tide Level Prediction Method Based on a NARX Neural Network

Tide variations are affected not only by periodic movement of celestial bodies but also by time-varying interference from the external environment. To improve the accuracy of tide prediction, a modular tide level prediction model (HA-NARX) is proposed. This model divides tide data into two parts: astronomical tide data affected by celestial tide-generating forces and nonastronomical tide data affected by various environmental factors. Final tide prediction results are obtained using a nonlinear autoregressive exogenous model (NARX) neural network combined with harmonic analysis (HA) data. To verify the feasibility of the model, tide data under different climatic and geographical conditions are used to simulate the prediction of tide levels, and the results are compared with those of traditional HA, the genetic algorithm-back propagation (GA-BP) neural network and the wavelet neural network (WNN). The results show that the greater the influence of meteorological factors on tides, the more obvious is the improvement in accuracy and stability of HA-NARX prediction results compared to traditional models, with the highest prediction accuracy improvement of 234%. The proposed model not only has a simple structure but can also effectively improve the stability and accuracy of tide prediction.

Electrical Energy Consumption Interface in Modular Skill-Based Production Systems with the Asset Administration Shell

Modular production systems provide the opportunity to react more flexibly to customer-individual requirements resulting in smaller batch sizes. The implementation of capabilities in production systems leads to a skill-based production which manifests on the modular level. Electrical energy consumption can be analyzed more in detail if the consumption can be linked to the respective skill. The aim of this paper is to show the conception of an energy consumption interface in a modular skill-based production environment with the Asset Administration Shell. The current state of the art of modular skill-based production and modular energy consumption measurement with Infrastructure Nodes and Asset Administration Shell is introduced. The role of the modular level for an energy consumption interface and conceptional approaches of this interface to access and collect consumption data is presented on this basis. Furthermore, the prototypical implementation of the energy related Asset Administration Shell submodel with the BaSyx-Middleware is presented.

Distress propagation on production networks: Coarse-graining and modularity of linkages

Distress propagation occurs in connected networks, its rate and extent being dependent on network topology. To study this, we choose economic production networks as a paradigm. An economic network can be examined at many levels — linkages among individual agents (microscopic), among firms/sectors (mesoscopic) or among countries (macroscopic). New emergent dynamical properties appear at every level, so the granularity matters. For viral epidemics, even an individual node may act as an epicenter of distress and potentially affect the entire network. Economic networks, however, are known to be immune at the micro-levels and more prone to failure in the meso/macro-levels. We propose a dynamical interaction model to characterize the mechanism of distress propagation, across different modules of a network, initiated at different epicenters. Vulnerable modules often lead to large degrees of destabilization. We demonstrate our methodology using a unique empirical data-set of input–output linkages across 0.14 million firms in one administrative state of India, a developing economy. The network has multiple hub-and-spoke structures that exhibits moderate disassortativity, which varies with the level of coarse-graining. The novelty lies in characterizing the production network at different levels of granularity or modularity, and finding ‘too-big-to-fail’ modules supersede ‘too-central-to-fail’ modules in distress propagation.

Interaction Energy-Based Stability Analysis Method and Application in Grid-Tied Type-4 Wind Turbine Generator

Complex dynamic interaction between type-4 wind turbine generator (WTG) and ac grid causes subsynchronous oscillation (SSO). In view of this problem, this article proposes a stability analysis method based on interaction energy that can explicitly reveal how (positively or negatively) and to which degree the various interaction links between subsystems in modular state-space model change system stability level. Firstly, an energy model representation for modular state-space model of grid-tied type-4 WTG system, containing stored energy terms, dissipation energy terms, and interaction energy terms, is constructed and is proved to satisfy the condition of energy conservation. And then, according to Lyapunov’s 2nd stability principle, it reveals that the increase of interaction energy term with time is what causes the system to go unstable. Besides, it points out that the interaction energy term has one to one relationship with interaction link. Thus, the effects of different interaction links on system stability level can be identified by analyzing the variation trend of different interaction energy terms. Finally, hardware-in-loop tests with RTLAB verify that the dynamic interaction effects among different subsystems are quantified according to the value of interaction energy. Thus, the interaction links that have large impact on system stability are selected, which can provide theoretical support for future adding compensation branches between subsystems to suppress oscillation (i.e., supplementary compensation branch-based damping control).

Design of a household biogas digester using co-digested cassava, vegetable and fruit waste

Apart from solar photovoltaic and energy from wind turbine, biogas has also been identified as one of the alternatives for the replacement of fossil fuel at a modular level. With respect to the adoption of biogas for household application, extensive research has been conducted on the optimal techno-economic design of biogas digesters. However, available studies and case studies do not often provide simplified details that can be used for replicative studies. Hence, this study presents a simplified stepwise illustration of a household anaerobic digester for sustainable co-digestion of cassava, vegetable and fruit waste. Using a scenario-based approach, the digester was designed to produce biogas energy that will cater for five (5) families; each comprising eight (8) members. It is expected that the stepwise example presented in this study will help in expanding the frontier knowledge directed at presenting the optimal design of biogas digesters.