Modular Development Research Articles

Design and optimization strategy of electricity marketing information system supported by cloud computing platform

This paper provides an in-depth discussion on the comprehensive requirements analysis, design implementation, algorithm optimization, and experimental evaluation of an electric power marketing information system, aiming to build a modern information system that is efficient, secure, and user-friendly. In the requirements analysis phase, the importance of business process optimization, data management analysis, security compliance, system integration and scalability is emphasized, while the diversified needs of end customers are considered. For the design and implementation part, the system architecture is based on microservices and cloud-native technologies to ensure high performance and security; and modularized development is achieved through Spring Boot, Vue.js and other technology stacks. For algorithm optimization, LSTM is used for power demand prediction and anomaly detection by combining integrated learning and self-encoder, which improves the prediction accuracy and anomaly identification capability. Experimental evaluation shows that the system demonstrates good performance, security and scalability in cloud computing environment, and the cost-effectiveness is significantly better than traditional deployment.

Embedding automated function performance benchmarking, profiling and resource usage categorization in function as a service DevOps pipelines

Function as a Service has enabled a modular development, automated elasticity and cloud-native implementation of applications. The break down into functions can lead to a more fine grained performance profiling of application parts. Embedding this process into the function development lifecycle can give significant benefits such as information per development version, optimized function sizing as well as enhanced function annotation with relation to its resource usage needs, exploited by FaaS providers for optimally co-allocating function executions in an effort to minimize overheads from their concurrent execution. In this paper, a function performance pipeline is created in order to directly analyze the performance of developed functions. The pipeline applies function benchmarking through load generation, profiling of resource usage of that function and categorization of the function relatively to other previous functions run in the framework. A two-step load generation is applied for enhanced validity of trace collection. Information is collected from both the function-side performance view (FaaS platform wait time, initialization and execution duration) as well as the underlying container platform (function level execution traces for CPU, memory, filesystem and network usage). The categorization stage clusters available function trace metrics based on the acquired resource profiles and extracts low/medium/high centroids for each resource metric. These are then used to classify and annotate each new function at the relevant level of activity per resource metric. We have applied the function benchmarking and categorization on 4 example functions (fibonacci, list, sort, and fileRW ones), while testing the effect of the two-stage load generation. The latter portrays an improvement on the profiling acquisition ranging from 1.23x to 45x times across the collected resource metrics. Different function inputs are also applied in order to observe their outcome on the categorization process itself. Finally, 8 different function co-allocation scenarios are applied based on different function categories that indicate concurrency overheads ranging from 15 % to 552 % performance degradation depending on the type of category selected for co-placement.

Modular Development of Enzyme-Activatable Proteolysis Targeting Chimeras for Selective Protein Degradation and Cancer Targeting.

As an emerging therapeutic modality, proteolysis targeting chimeras (PROTACs) indiscriminately degrade proteins in both healthy and diseased cells, posing a risk of on-target off-site toxicity in normal tissues. Herein, we present the modular development of enzyme-activatable PROTACs, which utilize enzyme-recognition moieties to block protein degradation activities and can be specifically activated by elevated enzymes in cancer cells to enable cell-selective protein degradation and cancer targeting. We identified the methylene alkoxy carbamate (MAC) unit as an optimal self-immolative linker, possessing high stability and release efficiency for conjugating enzyme-recognition moieties with PROTACs. Leveraging the MAC linker, we developed a series of enzyme-activatable PROTACs, harnessing distinct enzymes for cancer-cell-selective protein degradation. Significantly, we introduced the first dual-enzyme-activatable PROTAC that requires the presence of two cancer-associated enzymes for activation, demonstrating highly selective protein degradation in cancer cells over nonmalignant cells, potent in vivo antitumor efficacy, and no off-tumor toxicity to normal tissues. The broad applicability of enzyme-activatable PROTACs was further demonstrated by caging other PROTACs via the MAC linker to target different proteins and E3 ligases. Our work underscores the substantial potential of enzyme-activatable PROTACs in overcoming the off-site toxicity associated with conventional PROTACs and offers new opportunities for targeted cancer treatment.

What is modular fashion: Towards A Common Definition

Modular garments are defined as clothing items that can be dis/assembled into multiple parts without having to sew individual parts together. Instead, different parts (e.g., sleeves, collar) can be zipped or buttoned on to the main body. Modular garments have increasingly gained attention as a sustainable design strategy, as they (modular garments) have the potential to extend the lifetime of garments by exchanging modules and thus, also revolutionise business models and the way consumers shop. Although modular garments have gained increased attention, there is currently a lack of what ‘modular garments’ constitute, and this may differ from modular fashion.This paper provides a definition of modular garments and modular fashion by reviewing the origin and development of the term modularity. Although examples illustrating the concepts of modular garments and modular fashion are exclusive to modular outerwear, this paper provides a conceptual foundation for future research on modular garments and modular fashion.

Development of a urea-bond cleavage reaction induced by nitric oxide for fluorescence imaging.

Nitric oxide (NO) is a multifunctional signalling molecule with indispensable roles in physiological processes, but its abnormal production is implicated in various disease conditions. Detecting NO is crucial for interrogating its biological roles. Although many o-phenylenediamine-based fluorescent probes have been developed, only a small fraction has been employed in vivo. Moreover, these probes largely require direct modifications of the fluorophore backbones to render NO responsiveness, which restricts the general applicability of o-phenylenediamine-based probe designs to other types of fluorophores. Here, we report the rational development, optimization, and application of a NO-induced urea-bond cleavage reaction for selective fluorescence detection and imaging of NO in living systems. Through rational design and extensive screening, we identified a 2-aminophenylurea-derived functionality that can react with NO through N-nitrosation, acyltriazole formation, and hydrolysis to induce the cleavage of the urea bond and release of the amino-containing coumarin fluorophore. By caging different amino-containing fluorophore scaffolds with the 2-aminophenylurea-derived functionality, we modularly developed a series of NO fluorescent probes with different excitation and emission profiles for the detection of NO in aqueous solutions and live cells. Among these probes, the near-infrared probe has been demonstrated to enable in vivo fluorescence visualization of elevated endogenous levels of NO in a murine inflammation model. Overall, this study provides a NO-induced urea-bond cleavage reaction and establishes the utility of this reaction for the general and modular development of NO fluorescent probes, thus opening new opportunities for studying and manipulating NO in biological systems.

Modular development of organelle-targeting fluorescent probes for imaging formaldehyde in live cells.

Formaldehyde (FA) is endogenously generated via fundamental biological processes in living systems. Aberrant FA homeostasis in subcellular microenvironments is implicated in numerous pathological conditions. Fluorescent probes for detecting FA in specific organelles are thus of great research interest. Herein, we present a modular strategy to construct diverse organelle-targeting FA probes by incorporating selective organelle-targeting moieties into the scaffold of a 1,8-naphthalimide-derived FA fluorescent probe. These probes react with FA through the 2-aza-Cope arrangement and exhibit highly selective fluorescence increases for detecting FA in aqueous solutions. Moreover, these organelle-targeting probes, i.e., FFP551-Nuc, FFP551-ER, FFP551-Mito, and FFP551-Lyso, allow selective localization and imaging of FA in the nucleus, endoplasmic reticulum, mitochondria, and lysosomes of live mammalian cells, respectively. Furthermore, FFP551-Nuc has been successfully employed to monitor changes of endogenous FA levels in the nucleus of live mammalian cells. Overall, these probes should represent new imaging tools for studying the biology and pathology associated with FA in different intracellular compartments.

Neural-symbolic BDI-Agent as a Multi-Context System: A case study with negotiating agent

Intelligent systems are already being deployed in several fields (e.g., medicine, education, automation, or legal), providing relevant tools to assist in our daily tasks. It is claimed that the next step of AI is the integration of connectionist and symbolic methods. Connectionist methods embody knowledge by assigning numerical conductivities or weights to the connections inside a network of nodes. Symbolic methods work by carrying on a sequence of logic-like reasoning steps over a set of symbols consisting of language-like representations. Combining the strengths of connectionist and symbolic methods can allow the development of robust intelligent systems. We propose a neural-symbolic BDI-agent based Multi-Context Systems (MCS) model to integrate these two methods. Neural-symbolic area explores the effective integration of connectionist and symbolic methods, more precisely, learning and reasoning. MCSs allow the representation of information exchange among heterogeneous sources. BDI-agents offer robust and flexible behavior, rapid and modular development, intelligibility, and verifiability. In our work, MCSs represent the symbolic method and Neural Networks the connectionist method. We present a case study of how the proposed agent can be implemented using the Sigon framework. Sigon facilitates the development of MCS agents using a programming language-like paradigm. In this case study, we employed a trained MultiLayer Perceptron (MLP) neural network, enabling us to mitigate the necessity of modeling some hand-crafted rules in symbolic systems. We performed two experiments, in which the main goal was to analyze the impact of using a neural network during the agent’s decision-making. In the first experiment, we compare the proposed agent with 136 negotiating agents participating in the Automated Negotiating Agents Competition (ANAC) available on the GENIUS framework. In the second experiment, we compare our work with one similar to ours. The results show that our agent can adapt and achieve high utility functions for different situations. However, it is necessary to consider the computational cost of using the NN’s output in every reasoning cycle. The contributions of this paper are: (i) - integration of connectionist methods as part of the agent’s decision-making, enabling the development of agents in a modular and flexible fashion; (ii) - custom sensors to handle different data types; (iii) - practical implementation of the proposed integration method; and (iv) - experiments of the proposed integration method, focusing on mitigating the necessity of hard-coded and hand-crafted rules.

The Interaction between the Investment Behavior of Transnational Corporations and the International Political and Economic System

With the development of economic globalization and world modularization, more and more economic activities transcend national boundaries, making the connection between the organic economic whole formed through foreign trade, capital flow, technology transfer and other activities become closer. Transnational Corporations (TNCs) come into being in this context, making the global convergence trend of the economies of the world in production, distribution, exchange and consumption links more obvious. Because TNCs usually conduct economic activities in two or more countries, their strong economic power will inevitably affect the economic, political, cultural and social life of the country. On the one hand, the investment and business behavior of TNCs will have a series of effects on the local economy of the host country, and in some economic fields, TNCs can even interfere with the political order of the host country. On the other hand, in the process of globalization, transnational corporations themselves will also be affected by the international political and economic situation.

Digital twin for a robot assembly line: Architecture and implementation

As an enabling technology and means to practice the concept of intelligent manufacturing, digital twin has attracted extensive attention from both industry and academia. In view of the lack of concrete implementation methods of digital twin robot assembly lines, and the complex implementation process, and long development cycle using existing methods, the implementation of digital twin robot assembly lines is a significant research problem. In view of this, this paper proposes an architecture as well as an implementation method of a digital twin system for a robot assembly line. Taking a robot assembly production line as an example, a digital twin architecture and modular development approach are proposed, and three key technologies for implementing the digital twin system, namely virtual model construction, real-time data acquisition and accurate virtual-real mapping, are elaborated. The architecture and implementation method can quickly and effectively facilitate building a digital twin system of the robot assembly line, and realizing real-time monitoring and intelligent analysis of all elements of the physical robot production line.

Robot joint module based on universal joint configuration driven by SMA wires

Modular robots can adapt to different production needs and working conditions. However, the complexity of structures and the choice of driving modes limit the development of modularization. In this paper, a robot joint module with a universal joint configuration driven by shape memory alloy (SMA) wires is proposed, which has 2-DOFs. For lightweight, the main body of the module is made of polylactic acid materials. SMA wires are used as the actuator due to the advantages of large output displacement, high mass-to-energy ratio, and low activation voltage. Adopting antagonistically arranged SMA wires and bias springs combination method increases the recovery speed and driving angles. A locking device is creatively proposed to avoid the influence of self-biasing spring compression. The detailed kinematic model of the module is established. A proportional integral differential controller is used to precisely control module movements, and the attitude angle sensor is used as the monitoring device. Through the maximum rotation angles and trajectory tracking movement experiments, the module is tested comprehensively, and the results show that the module has good movement characteristics and control precision. In addition, analyzing the tracking performance of the module in the time domain shows that the module has a relatively fast tracking speed. This module is beneficial to the research of configurations, actuators, and controls of modular robots.

Microservice reference architecture design: A multi‐case study

AbstractMicroservice architecture (MSA) is an architectural style that is designed to support the modular development of software systems within a particular domain. It is characterized by the use of small, independently deployable services, which can be developed and deployed autonomously. The benefits of MSA include improved scalability, fault‐tolerance, and ease of deployment and maintenance. However, developing MSA for a specific domain can be challenging and requires a thorough consideration of various concerns such as service boundaries, communication protocols, and security. To support the easy development and guidance of an MSA for practitioners, we present a reference architecture design for MSA. The reference architecture has been designed after a comprehensive domain analysis of MSA in the literature and the MSAs of key vendors. This analysis has been used to identify best practices and common patterns that can be applied to the development of MSA. Additionally, relevant architecture viewpoints have been selected to model the corresponding architecture views, providing a clear and comprehensive understanding of the architecture. To validate the proposed reference architecture, a multi‐case study research approach has been used. In this approach, two industrial case studies have been used to demonstrate the practical applicability of the proposed reference architecture. The results of these case studies have shown that the reference architecture can be used to effectively guide the development of MSA in real‐world scenarios.

Hydraulic differences between flowers and leaves are driven primarily by pressure-volume traits and water loss.

Flowers are critical for successful reproduction and have been a major axis of diversification among angiosperms. As the frequency and severity of droughts are increasing globally, maintaining water balance of flowers is crucial for food security and other ecosystem services that rely on flowering. Yet remarkably little is known about the hydraulic strategies of flowers. We characterized hydraulic strategies of leaves and flowers of ten species by combining anatomical observations using light and scanning electron microscopy with measurements of hydraulic physiology (minimum diffusive conductance (g min) and pressure-volume (PV) curves parameters). We predicted that flowers would exhibit higher g min and higher hydraulic capacitance than leaves, which would be associated with differences in intervessel pit traits because of their different hydraulic strategies. We found that, compared to leaves, flowers exhibited: 1) higher g min, which was associated with higher hydraulic capacitance (C T); 2) lower variation in intervessel pit traits and differences in pit membrane area and pit aperture shape; and 3) independent coordination between intervessel pit traits and other anatomical and physiological traits; 4) independent evolution of most traits in flowers and leaves, resulting in 5) large differences in the regions of multivariate trait space occupied by flowers and leaves. Furthermore, across organs intervessel pit trait variation was orthogonal to variation in other anatomical and physiological traits, suggesting that pit traits represent an independent axis of variation that have as yet been unquantified in flowers. These results suggest that flowers, employ a drought-avoidant strategy of maintaining high capacitance that compensates for their higher g min to prevent excessive declines in water potentials. This drought-avoidant strategy may have relaxed selection on intervessel pit traits and allowed them to vary independently from other anatomical and physiological traits. Furthermore, the independent evolution of floral and foliar anatomical and physiological traits highlights their modular development despite being borne from the same apical meristem.

Modular development of deep potential for complex solid solutions

The multicomponent oxide solid solution is a versatile platform to tune the delicate balance between competing spin, charge, orbital, and lattice degrees of freedom for materials design and discovery. The development of compositionally complex oxides with superior functional properties has been largely empirical and serendipitous, in part due to the exceedingly complex chemistry and structure of solid solutions that span a range of length scales. The usage of classical molecular dynamics (MD), a powerful statistical method, in computer-aided materials design has not yet reached the same level of sophistication as that in computer-aided drug design because of the limited availability and accuracy of classical force fields for solids. Here, we introduce the strategy of ``modular development of deep potential'' (ModDP) that enables a systematic development and improvement of deep-neural-network-based model potential, termed as deep potential, for complex solid solutions with minimum human intervention. The converged training database associated with an end-member material is treated as an independent module and is reused to train the deep potential of solid solutions via a concurrent learning procedure. We apply ModDP to obtain classical force fields of two technologically important solid solutions, ${\mathrm{Pb}}_{x}{\mathrm{Sr}}_{1\ensuremath{-}x}{\mathrm{TiO}}_{3}$ and ${\mathrm{Hf}}_{x}{\mathrm{Zr}}_{1\ensuremath{-}x}{\mathrm{O}}_{2}$. For both materials' systems, a single model potential is capable of predicting various properties of solid solutions including temperature-driven and composition-driven phase transitions over a wide range of compositions. In particular, the deep potential of ${\mathrm{Pb}}_{x}{\mathrm{Sr}}_{1\ensuremath{-}x}{\mathrm{TiO}}_{3}$ reproduces a few known topological textures such as polar vortex lattice and electric dipole waves in ${\mathrm{PbTiO}}_{3}/{\mathrm{SrTiO}}_{3}$ superlattices, paving the way for MD investigations on the dynamics of topological structures in response to external stimuli. MD simulations of ${\mathrm{Hf}}_{x}{\mathrm{Zr}}_{1\ensuremath{-}x}{\mathrm{O}}_{2}$ reveal a substantial impact of composition variation on both the phase transition temperature and the nature of the high-temperature nonpolar phase.

Modular ontology modeling

Reusing ontologies for new purposes, or adapting them to new use-cases, is frequently difficult. In our experiences, we have found this to be the case for several reasons: (i) differing representational granularity in ontologies and in use-cases, (ii) lacking conceptual clarity in potentially reusable ontologies, (iii) lack and difficulty of adherence to good modeling principles, and (iv) a lack of reuse emphasis and process support available in ontology engineering tooling. In order to address these concerns, we have developed the Modular Ontology Modeling (MOMo) methodology, and its supporting tooling infrastructure, CoModIDE (the Comprehensive Modular Ontology IDE – “commodity”). MOMo builds on the established eXtreme Design methodology, and like it emphasizes modular development and design pattern reuse; but crucially adds the extensive use of graphical schema diagrams, and tooling that support them, as vehicles for knowledge elicitation from experts. In this paper, we present the MOMo workflow in detail, and describe several useful resources for executing it. In particular, we provide a thorough and rigorous evaluation of CoModIDE in its role of supporting the MOMo methodology’s graphical modeling paradigm. We find that CoModIDE significantly improves approachability of such a paradigm, and that it displays a high usability.

Basicranial Modular Organization. A Study in the Araucanian Horse of Colombia.

The skull is divided into neurocranium and splanchnocranium, and its variation allows ecomorphological studies to learn about possible evolutionary and adaptive characteristics. The basicranial organization of the neurocranium and splanchnocranium modules was studied in a sample of 31 skulls from adult Araucanian horses by means of 2D geometric morphometric techniques. The neurocranium and splanchnocranium modules on the ventral aspect were analyzed separately using a set of 31 landmarks. The RV coefficient (the multivariate analog of a correlation) was estimated to analyze the independence of these two parts, as well as their morphological integration, using a two-block analysis of least squares. The study results confirm the modular development of the neurocranium and the splanchnocranium, the former being more stable than the latter as well as low morphological integration between the two. The development between both parties is structured in a modular way but allows relative independence. Now it would be interesting for future studies to add muscles (those that connect the cranial parts, but also the cervical), the hyoid apparatus, and the ossicles of the internal ear and the jaw and analyze if they behave as integrated modules between them. Since this research has been conducted at the subspecific breed level, it could be plausible that in other breeds, this integrative development was different.

Data-driven modelling of the functional level in model-based systems engineering – Optimization of module scopes in modular development

The modelling of the functional level of technical systems can be supported by the analysis of machine usage data. By creating an understanding of the actual use of provided functions of offered machine configurations, the definition of module scopes in modular development can be optimized. Characteristics of specific machine usage can be assigned to physical elements using model-based systems engineering. Analyses on a purely functional system level can thus be placed in context with each other via the connection to physical elements. Consequently, critical elements in the system design can be systematically uncovered.

Towards Modular Development of Reusable Language Components for Domain-Specific Modeling Languages in the MagicDraw and MontiCore Ecosystems.

Towards Modular Development of Reusable Language Components for Domain-Specific Modeling Languages in the MagicDraw and MontiCore Ecosystems.

“Guidelines for the classification and management of risk, for the evaluation of safety and for the monitoring of existing bridges”. An automatic calculation model of the seismic attention class optimized for the use of cloud computing services.

The Italian road network is complex due to the particular orography of the territories crossed, often characterized by areas of high seismicity. In addition, bridges and viaducts present a peculiar state of decay mainly due to their remote period of construction. The situational picture is also difficult to understand due to the absence or non-uniformity of infrastructural safety management systems (SMS). To fill this gap, the Italian Higher Council of Public Works has issued guidelines for risk management, that allow the classification, safety assessment and monitoring of existing bridges. The central focus of the new approach is the identification of the overall attention class, which allows to move from assessments on a territorial scale to safety assessments on a limited number of bridges. This work focuses on the development of a prototype for the automatic calculation of the seismic attention class that has been optimized for cloud computing services. In particular, a standardized procedure has been defined that guides the operator in the evaluation of the seismic attention class. The model also allows to carry out the validation of other software. Finally, the prototype created is easily extensible to other attention classes which are therefore manageable independently in the perspective of modular development of an open access and open source platform.

The Quality Elements of Flexible Learning: Basis for Localized Modular Development for Teachers and Students

The Quality Elements of Flexible Learning: Basis for Localized Modular Development for Teachers and Students

Modular and mobile facility location problems: A systematic review

Supply chain planning is based on the assumption of fixed production locations. This appears justified insofar as location decisions are usually strategic in nature and, traditionally, cannot be revised in the short term. Due to the development of new technologies and modularization in recent years, a completely new situation has emerged with the consideration of mobile and modular units whose locations can be changed in a short amount of time at a comparatively low cost. This has sparked interest in studying the (re)positioning of more-flexible networks. Therefore, we review and classify 125 contributions addressing the location problem with mobile and modular facilities. We provide a broad overview of their mathematical formulations, solution approaches, objectives, and main characteristics. Finally, we categorize case studies that have been presented in the literature and identify research trends for further developments in this field.