Modular Solution Research Articles

Integrating multiple genomic imaging data for the study of lung metastasis in sarcomas using multi-dimensional constrained joint non-negative matrix factorization

Integrative analysis of histopathology images and genomic data enables the discovery of potential biomarkers and multimodal association patterns. However, few studies have established effective association models for complex diseases, such as sarcoma, by combining histopathological images with multiple genetic variation data. Here, we present an integrative multiple genomic imaging framework called multi-dimensional constrained joint non-negative matrix factorization (MDJNMF) to identify modules related to lung metastasis of sarcomas based on sample-matched whole-solid image, DNA methylation, and copy number variation features. Three types of feature matrices were projected onto a common feature space, in which heterogeneous variables with large coefficients in the same projected direction form a common module. The correlation between image features and genetic variation features is used as network-regularized constraints to improve the module accuracy. Sparsity and orthogonal constraints are utilized to achieve the modular sparse solution. Multi-level analysis indicates that our method effectively discovers biologically functional modules associated with sarcoma or lung metastasis. The representative module reveals a significant correlation between image features and genetic variation features and excavates potential diagnostic biomarkers. In summary, the proposed method provides new clues for identifying association patterns and biomarkers using multiple types of data sources for other diseases.

Retail 4.0 – Digital Customer and Retailer Feedback to Garment Development

Digital technologies open new possibilities ranging from integrated virtual product development to the direct involvement of customers and retailers through apps and collaborative virtual reality (VR) or augmented reality (AR) solutions. This requires end-to-end, flexible and modular solutions, especially in the retail environment.

Efficient Word Size Modular Arithmetic

Modular multiplication is used in a wide range of applications. Most of the existing modular multiplication algorithms in the literature often focus on large size moduli. However, those large moduli oriented modular multiplication solutions are also used to implement modular arithmetic for applications requiring modular arithmetic on moduli of size inferior to a word size i.e., 32/64bits. As it happens, a large majority of applications are using word size modular arithmetic. In this work, we propose a new modular multiplication designed to be computed on one word size only. For word size moduli, in a large majority of instances, our solution outperforms other existing solutions including generalist solutions like Montgomery's and Barrett's modular multiplication as well as classes of moduli like Mersenne, Pseudo-Mersenne, Montgomery-Friendly and Generalized Mersenne.

Quad-Active-Bridge Converter With Current Balancing Coupled Inductor for SST Application

In this article, a current balancing coupled inductor (CBCL) based asymmetrically configured quad-active-bridge (QAB) converter is proposed. The QAB converter provides high-frequency isolation for a dc-dc stage of a solid state transformer (SST), as well as provides a modular solution to the SST medium-voltage (MV) dc link. The proposed CBCL ensures current balancing among/three active H-bridges without using any dedicated controller and without expensive sensors when transferring equal power from MV to low-voltage side. The proposed CBCL is the integration of three inductors of the active H-bridges required for power transfer and zero-voltage-switching and does not require an extra magnetic volume. Experimental results are presented to validate the performance.

An intelligent management system for aquaponics

Abstract Population rise, climate change, soil degradation, water scarcity, and food security require efficient and sustainable food production. Aquaponics is a highly efficient way of farming and is becoming increasingly popular. However, large scale aquaponics still lack stability, standardization and proof of economical profitability. The EU-INAPRO project helps to overcome these limitations by introducing digitization, enhanced technology, and developing standardized modular scalable solutions and demonstrating the viability of large aquaponics. INAPRO is based on an innovation a double water recirculation system (DRAPS), one for fish, and the other one for crops. In DRAPS, optimum conditions can be set up individually for fish and crops to increase productivity of both. Moreover, the integration of digital technologies and data management in the aquaculture production and processing systems will enable full traceability and transparency in the processes, increasing consumers’ trust in aquaculture products. In this paper, the innovations and the digitization approach will be introduced and explained and the key benefits of the system will be emphasized.

Steering model identification and control design of autonomous ship: a complete experimental study

ABSTRACT Steering ship models are important for the study of autonomous ship manoeuverability and design of ship motion control system. It is always a difficult task to find the mathematical model by first principle as it needs prior knowledge of hydrodynamic derivatives. The input–output-based system identification theory can be used to establish system mathematical models. A solution is offered by developing a Wi-Fi-based self-propelled, autonomous system for a ship model with Internet of Things (IoT) capabilities to perform manoeuvering and seakeeping tests in indoor environment without any complex mechanical structure, viz. following bridge. The developed autonomous on-board system equipped with main computer, suitable electronics, sensors, data acquisition system and Wi-Fi-based communication system. The developed system offers a cost effective, modular and portable solution to perform hydrodynamic studies of different hull form without incorporating major changes in the system. The use of IoT makes the data accessible to a naval architecture in real-time to analyse the motion response of the ship in different wave conditions and enables to implement the digital twin to simulate the real field scenario. Input–output-based model identification experiments such as turning circle and zig-zag tests are conducted to estimate the first-order steering model parameters and is further extended to design and implementation of a classical proportional–derivative-based steering control. The design is described in this paper with details of implementation on a demonstration oceanographic coastal research vessel. It illustrates the excellent communication between shore station computer and the on-board system on a wire-free model with robust control and exhibiting all the motion behaviour and dynamic effects. Experiments performed in wave basin in different wave conditions validate the efficacy of the proffered method.

Automorphic Schwarzian equations and integrals of weight 2 forms

In this paper, we investigate the non-modular solutions to the Schwarz differential equation \(\{f,\tau \}=sE_4(\tau )\) where \(E_4(\tau )\) is the weight 4 Eisenstein series and s is a complex parameter. In particular, we provide explicit solutions for each \(s=2\pi ^2(n/6)^2\) with \(n\equiv 1\mod 12\). These solutions are obtained as integrals of meromorphic weight 2 modular forms. As a consequence, we find explicit solutions to the differential equation \(\ \displaystyle y''+\frac{\pi ^2n^2}{36}\,E_4\,y=0\) for each \(n\equiv 1\mod 12\) generalizing the work of Hurwitz and Klein on the case \(n=1\). Our investigation relies on the theory of equivariant functions on the complex upper half-plane. This paper supplements a previous work where we determine all the parameters s for which the above Schwarzian equation has a modular solution.

The INUS Platform: A Modular Solution for Object Detection and Tracking from UAVs and Terrestrial Surveillance Assets

Situational awareness is a critical aspect of the decision-making process in emergency response and civil protection and requires the availability of up-to-date information on the current situation. In this context, the related research should not only encompass developing innovative single solutions for (real-time) data collection, but also on the aspect of transforming data into information so that the latter can be considered as a basis for action and decision making. Unmanned systems (UxV) as data acquisition platforms and autonomous or semi-autonomous measurement instruments have become attractive for many applications in emergency operations. This paper proposes a multipurpose situational awareness platform by exploiting advanced on-board processing capabilities and efficient computer vision, image processing, and machine learning techniques. The main pillars of the proposed platform are: (1) a modular architecture that exploits unmanned aerial vehicle (UAV) and terrestrial assets; (2) deployment of on-board data capturing and processing; (3) provision of geolocalized object detection and tracking events; and (4) a user-friendly operational interface for standalone deployment and seamless integration with external systems. Experimental results are provided using RGB and thermal video datasets and applying novel object detection and tracking algorithms. The results show the utility and the potential of the proposed platform, and future directions for extension and optimization are presented.

Peptide-targeted dendrimeric prodrugs of 5-aminolevulinic acid: A novel approach towards enhanced accumulation of protoporphyrin IX for photodynamic therapy

Photodynamic therapy (PDT) is a promising approach for the targeted treatment of cancer and various other human disorders. An effective, clinically approved approach in PDT involves the administration of 5-aminolevulinic acid (ALA) to generate elevated levels of the natural photosensitiser protoporphyrin IX (PpIX). The development of prodrugs of ALA is of considerable interest as a means to enhance the efficiency and cell selectivity of PpIX accumulation for PDT applications. In this work a novel peptide-targeted dendrimeric prodrug of 5-aminolevulinic acid (ALA) 13 was synthesised which displays nine copies of ALA on a core structure that is linked to a homing peptide for targeted delivery to a specific cancer cell type. The synthesis was accomplished effectively via a flexible, modular solid phase and solution phase route, using a combination of solid phase peptide synthesis and copper-catalysed azide-alkyne cycloaddition chemistry. The prodrug system shows a sustained and enhanced production of protoporphyrin IX (PpIX) in the MDA-MB-231 cell line that over-expresses the epidernal growth factor receptor (EGFR+) in comparison to equimolar ALA and the corresponding non-targeted ALA dendrimer (nine copies of ALA). This study provides a proof of concept for the development of a new generation of prodrugs for ALA-based photodynamic therapy that can deliver an enhanced ALA payload to specific tissue types.

Identifying and categorising knowledge reuse activities in electronic repositories

PurposeKnowledge reuse using electronic repositories, while increasingly important, requires more thorough analysis. Service modularity has been recently applied in services research but has not been integrated into knowledge reuse studies. The purpose of this paper is to draw on both service modularity and knowledge reuse to develop and validate a framework that categorises forms of packaged knowledge in an electronic repository.Design/methodology/approachDrawing on knowledge reuse and service modularity research, a model is proposed. The model is empirically tested using a case study research design.FindingsThis research highlighted the value of including both context and process as key dimensions when packaging service knowledge for reuse. This study identifies knowledge types present in modular solutions and how they were configured and reconfigured in the knowledge repository. This research identified five ways modularised services were leveraged. In addition to the traditional scale and stretch approaches, already present, but conflated, in the service literature, three other configurations were identified; shrink, separate and segment.Research limitations/implicationsThe findings are based on a single empirical case study which may limit the generalisability of the findings. There is a need for additional research to further validate the model in additional contexts.Practical implicationsThis study provides managers with empirical examples of how a modular repository was used in practice and outlines five ways of recombining contextual and processual elements to enable service codification and reuse. It has implications for how knowledge is decomposed and recombined in repositories, suggesting an explicit separation of context and process knowledge while developing modular elements within both.Originality/valueTo the best of the author’s knowledge, this is the first study that explicitly uses context and process as dimensions and draws on service modularity to understand types of knowledge reuse in electronic repositories. In doing so, it adds value by developing and validating a model that identifies five types of reuse.

A review of advantages and challenges of using engineered nanoparticles for waste and wastewater treatments

The continuous increase in engineered nanoparticles (ENPs) usage in environmental, industrial and agricultural applications is known to be associated with their further release to the environment. Recently, the presence of nanoparticles in wastewater and sewage systems has become one of the negotiable topics taking the attention of many scientists. ENPs can be evaluated as pollutants that must be eliminated from wastewater before being discharged to the aquatic and soil environment due to their potential toxicity risks. However, the supportive role of ENPs in the wastewater treatment process is discovered recently highlighting their possible usage in different treatment methods. ENPs can offer novel opportunities for the development of new generation water treatment systems, as they enable modular and multifunctional treatment solutions. This review paper focuses mainly on the effects of ENPs’ presence in wastewater and activated sludge. Besides, the paper discusses the beneficial usage and possible applications of ENPs in different treatment technologies enhancing the treatment process in detail.

Lumina: an adaptive, automated and extensible prototype for exploring, enriching and visualizing data

Given a tabular dataset which should be graphically represented, how could the current complex visualization pipeline be improved? Could we produce a more visually enriched final representation, while minimizing the user intervention? Most of the existing approaches lack in capacity to provide a simplified end-to-end solution and leave the intricate process of setting up the data connections to the user. Their results mainly depend on necessary user actions at every step of the visualization pipeline and fail to consider the data structural properties and the constantly rising volume of open and linked data. This work is motivated by the need of a flexible framework which will improve the user experience and interaction by simplifying the process and enhancing the result, capitalizing on the enrichment of the final visualization based on the semantic analysis of linked data. We propose Lumina, a visualization framework, which : (a) builds on structural data analytics and semantic analysis principles, (b) increases the explainability and expressiveness of the visualization leveraging open data and semantic enrichment, (c) minimizes user interventions at every step of the visualization pipeline and (d) fulfills the growing need for open-source, modular and self-hosted solutions. Using publicly available read-world datasets, we validate the adaptability of Lumina and demonstrate the effectiveness and practicality of our method, in comparison to other open source solutions.

Rapid implementation of a modular clinical trial informatics solution for COVID-19 research

Veterans Health Administration (VHA) services are most frequently used by patients 65 years and older, an age group that is disproportionally affected by COVID-19. Here we describe a modular Clinical Trial Informatics Solution (CTIS) that was rapidly developed and deployed to support a multi-hospital embedded pragmatic clinical trial in COVID-19 patients within the VHA. Our CTIS includes tools for patient eligibility screening, informed consent tracking, treatment randomization, EHR data transformation for reporting and interfaces for patient outcome and adverse event tracking. We hope our CTIS component descriptions and practical lessons learned will serve as a useful building block for others creating their own clinical trial tools and have made application and database code publicly available.

Modularization of joining elements in high variety manufacturing industries

Product variety is a growing trend of offering highly configurable products, but increases complexity costs throughout the entire product lifecycle. Modularization makes managing variety-induced complexity and ensuring profitable production possible. Unfortunately, the interfaces between modules represent joining domains and lack modularization and commonalization solutions. Meanwhile, joining element design finds increasingly more support from automation resulting in optimized solutions for individual product variants. Besides, continuous product development implies sequentially designed product variants that cause ambiguous and unnecessary design iterations for joining element design. This paper presents a five-step methodology to modularize joining elements in early product development, while balancing variety-induced complexity and production costs. It reduces complexity by commonalizing joining elements over product variants. The methodology unifies joining technologies, clusters joining locations and proposes an approach to increase modularity by balanced addition of joining elements.

Modular Hybrid Energy Solutions for Sustainable Development in the Pacific Region

Modular Hybrid Energy Solutions for Sustainable Development in the Pacific Region

Observations on modular product structures based on teaching product modularisation

This paper studies modular product structures based on teaching product modularisation in a university course. In product modularisation, one of the most important tasks is to identify the appropriate partitioning logic for the product portfolio based on the business environment describing why each product element should be thought of as its own module or not. Central to this are the product structuring principles that create value for the company and the customer’s variety requirements creating value for customers, as well as other requirements and boundary conditions set by the business environment for the chosen product. Based on the literature and empirical findings, product modularisation enables many benefits, especially in terms of reusing designs and supply chain elements, but modularisation as a product strategy is challenging to implement. It requires not only successful product development but also changes in company practices, such as delivery chain and new type of thinking and skills. In our university course for master’s degree students, product modularisation has been taught for several years with the help of an exercise that utilizes the wheel loader set of Lego Technics. The main content of the exercise has been mainly similar in different implementations in 2017 -2020. The article presents the content of the assignment and analyses what kind of modular solutions based on the same assignment topic the students have implemented in different years and how different solutions have met the requirements of the assignment. The result of the paper is an analysis of the key partitioning types, i.e. the typical ways of partitioning a product into modules, what the pros and cons of each partitioning type are and how they fit into different requirements.

Meta-Heuristic Approach for the Development of Alternative Process Plans in a Reconfigurable Production Environment

The need for automated production plans has evolved over the years due to internal and external drivers like developed products, new enhanced processes and machinery. Reconfigurable manufacturing systems focus on such needs at both production and process planning level. The age of Industry 4.0 focused on mass customization requires computer aided planning techniques that are able to cope with custom changes in products and explores intelligent algorithms for efficient scheduling solutions to reduce lead time. This problem has been categorized as NP-Hard in literature and is addressed by providing intelligent heuristics that focus on reducing machining time of the products at hand. However, as 70% of the lead time is consumed in non-value added tasks, it is fundamental to provide modular solutions that can reduce this time and handle part variety. To address the subject, this paper focuses on the generation of automated process plans for a single machine problem while focusing on reducing time lead time. Two evolutionary algorithms (EAs) have been proposed and compared to answer complex problem of process planning. A modified genetic algorithm (GA) has been proposed in addition to cuckoo search (CS) heuristic for this discrete problem. On testing with selected benchmark part ANC101, significant improvement was seen in terms of convergence with proposed EAs. Moreover, a novel Precedence Group Algorithm (PGA) is proposed to generate quality input for heuristics. The algorithm produces a set of initial population which significantly effects the performance of proposed heuristics. For the discrete constrained process planning problem, GA outperforms CS providing 10% more feasible scheduling options and three times lesser run time as compared to CS. The proposed technique is flexible and responsive in order to accommodate part variety, a necessary requirement for reconfigurable systems.

A Novel Matrix Transformation for Decoupled Control of Modular Multiphase PMSM Drives

When multiphase drives are used for specific applications, the modular solutions are preferred as they use consolidated power electronics technologies. The literature reports two modeling approaches for multiphase machines having a modular configuration of the stator winding. The first approach is the vector space decomposition (VSD) that models the energy conversion as for an equivalent three-phase machine. The main alternative to the VSD is the multistator (MS) modeling that emphasizes machine modularity in terms of torque production. Both approaches have advantages and disadvantages for multiphase machines with a modular structure. Therefore, this article aims to combine the VSD and MS approaches, defining a new matrix transformation and, hence, developing a new modeling approach for multiphase machines with a modular structure. The proposed transformation allows a decoupled and independent torque control of the sets composing the machine, preserving the torque regulation's modularity. Together with a new vector control scheme, it has been applied to a modular permanent magnet synchronous machine (PMSM) with a nonstandard spatial shift between windings. Experimental results are presented for a nine-phase PMSM prototype with a triple-three-phase stator winding configuration.

Particulate Matter Monitoring and Assessment through Internet of Things: a Health Information System for Enhanced Living Environments.

People spend most of their time inside buildings. Therefore, indoor air quality monitoring contributes to improve health and well-being. Several studies focus on the critical impact of particulate matter on residential air quality. In 2016, particulate matter caused 412 thousand premature deaths in 41 European countries. This paper presents the development of an affordable health information system for enhanced living environments. The authors propose a cost-effective, modular, scalable, and easy installation solution for particulate matter monitoring. The system is connected to ThingSpeak. It can be installed in any type of building. It requires only a power source and a Wi-Fi network with internet access. The main contribution of this paper is to present the detailed architecture and testing results. The particulate matter monitoring system was installed for one week in a domestic kitchen with an open fireplace. The results showed impact of the biomass burning on indoor air quality. The mean values per day ranged from: 10.53 to 50.62 μg/m3 for PM1.0, 15.35 to 69.37 μg/m3 for PM2.5, and 20.1 to 90.69 μg/m3 for PM10. The maximum values per hour were registered at 13:00: 72.14 μg/m3 for PM1.0, 99.70 μg/m3 for PM2.5, and 132.13 μg/m3 for PM10. Cost-effective sensors do not have the accuracy level of industrial equipment. Therefore, they should not be used for numerical and in-depth accurate characterization of the environment. Nevertheless, continuous particulate matter monitoring provides consistent data series for analysis of indoor air quality evolution.

Sustainability through reuse: a reconfigurable structural system for residential and office buildings

Current load-bearing systems for buildings rarely have a beneficial end of life. Modular design is a proven solution for revalorizing obsolete structures, but it hardly competes with conventional solutions: the range of future spatial configurations that the modules will accommodate is usually too limited to balance additional upfront costs due to necessary oversized elements and extra connections. Through a review of building demolition cases, this paper first presents motives, challenges and requirements for overcoming adverse end-of-life environmental impacts of building structures. Then a new structural system addressing the specified design constraints is introduced. The system is a highly versatile kit of slab and column elements. Contrary to existing modular solutions, its element dimensions do neither constrain the positioning of columns nor the shape of floor plans. Slab elements are stacked vertically to tune bending and shear stiffnesses locally and ensure serviceability requirements for a wide range of column and load cases layouts. All connections between elements are reversible and ready for reuse. Accordingly, the proposed structural system is well-suited for multiple service cycles and architectural needs, mitigating the detrimental effects that buildings have on the environment.