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Organic electrochemical transistors (OECTs) are ideal devices for translating biological signals into electrical readouts and have applications in bioelectronics, biosensing, and neuromorphic computing. Despite their potential, developing programmable and modular methods for living systems to interface with OECTs has proven challenging. Here we describe hybrid OECTs containing the model electroactive bacterium Shewanella oneidensis that enable the transduction of biological computations to electrical responses. Specifically, we fabricated planar p-type OECTs and demonstrated that channel de-doping is driven by extracellular electron transfer (EET) from S. oneidensis. Leveraging this mechanistic understanding and our ability to control EET flux via transcriptional regulation, we used plasmid-based Boolean logic gates to translate biological computation into current changes within the OECT. Finally, we demonstrated EET-driven changes to OECT synaptic plasticity. This work enables fundamental EET studies and OECT-based biosensing and biocomputing systems with genetically controllable and modular design elements. Interfacing living systems with electronics for biosensing and biocomputing applications is challenging. Here, Gao et al. present hybrid transistors with electroactive bacteria capable of extracellular electron transfer, enabling transduction of biological computations to electrical readouts. [ABSTRACT FROM AUTHOR]

To investigate water movement in environmental systems, stable water isotopes (2H and 18O) are commonly used tracers. Analyzing the isotopic composition of water bonded to substances like soil or plant tissue necessitates its extraction prior to analysis. One such method, direct vapor equilibration, is popular due to its cost-effectiveness and straightforward sample processing. However, sample analysis requires a significant manual labor, thereby limiting the number of samples that can be analyzed. This limitation is compounded by the fact that stored samples undergo isotopic composition changes over time, and in addition manual measurements may lack objectivity. To address these challenges, we have developed a device that automates the analysis process. Our vapor autosampler for vapour samples, named VapAuSa, features a modular design with up to 350 ports for direct vapor equilibration samples. These ports sequentially connect the prepared samples to the analyzer, enabling continuous automatic measurements. Within the accompanying software, measurement criteria can be specified, facilitating objective analysis. VapAuSa measurements have a similar uncertainty compared to manual analysis of identical samples (VapAuSa δ 2H = +/− 4.5 ‰ δ 18O = +/− 0.58 ‰ vs. manual δ 2H = +/− 5.7 ‰ δ 18O = +/− 0.37 ‰). However, the increased sample throughput minimizes storage-induced isotopic changes. Moreover, VapAuSa triples sample throughput per week while reducing direct labor time to just 10 % of manual processing. [ABSTRACT FROM AUTHOR]

Heating furnace is one of the important production equipment of petrochemical plant with high technical content and complex internal structure. With the large scale of petrochemical plants, the construction of heating furnace is developing from traditional bulk to modular. Taking the heating furnace of a certain refining unit as an example. Firstly, the heating furnace is divided into modules, and then the cost difference between modular and traditional bulk construction methods is compared and analyzed from three aspects of design, procurement and construction. It is found that the cost of modular construction saves 7. 8 million yuan, compared with the traditional bulk construction method, saving ratio 10. 18%. Moreover, modular construction shortens the construction period, reduces high-altitude operations, improves construction quality, and ensures the intrinsic safety of the heating furnace. The conclusion is that, under external conditions such as transportation and lifting, adopting a modular construction method for the heating furnace has significant promotional advantages. [ABSTRACT FROM AUTHOR]

Relating the macroscopic properties of protein-based materials to their underlying component microstructure is an outstanding challenge. Here, we exploit computational design to specify the size, flexibility, and valency of de novo protein building blocks, as well as the interaction dynamics between them, to investigate how molecular parameters govern the macroscopic viscoelasticity of the resultant protein hydrogels. We construct gel systems from pairs of symmetric protein homo-oligomers, each comprising 2, 5, 24, or 120 individual protein components, that are crosslinked either physically or covalently into idealized step-growth biopolymer networks. Through rheological assessment, we find that the covalent linkage of multifunctional precursors yields hydrogels whose viscoelasticity depends on the crosslink length between the constituent building blocks. In contrast, reversibly crosslinking the homo-oligomeric components with a computationally designed heterodimer results in viscoelastic biomaterials exhibiting fluid-like properties under rest and low shear, but solid-like behavior at higher frequencies. Exploiting the unique genetic encodability of these materials, we demonstrate the assembly of protein networks within living mammalian cells and show via fluorescence recovery after photobleaching (FRAP) that mechanical properties can be tuned intracellularly in a manner similar to formulations formed extracellularly. We anticipate that the ability to modularly construct and systematically program the viscoelastic properties of designer protein-based materials could have broad utility in biomedicine, with applications in tissue engineering, therapeutic delivery, and synthetic biology. [ABSTRACT FROM AUTHOR]

Motivation The volume of biomedical data generated each year is growing exponentially as high-throughput molecular, imaging and mHealth technologies expand. This rise in data volume has contributed to an increasing reliance on and demand for computational methods, and consequently to increased attention to software quality and data integrity. Results To simplify data verification in diverse data-processing pipelines, we created PipeVal, a light-weight, easy-to-use, extensible tool for file validation. It is open-source, easy to integrate with complex workflows, and modularized for extensibility for new file formats. PipeVal can be rapidly inserted into existing methods and pipelines to automatically validate and verify inputs and outputs. This can reduce wasted compute time attributed to file corruption or invalid file paths, and significantly improve the quality of data-intensive software. Availability and implementation PipeVal is an open-source Python package under the GPLv2 license and it is freely available at https://github.com/uclahs-cds/package-PipeVal. The docker image is available at: https://github.com/uclahs-cds/package-PipeVal/pkgs/container/pipeval. [ABSTRACT FROM AUTHOR]

We present our continuous efforts from a modeling and numerical viewpoint to develop a powerful and flexible mathematical and computational framework called Ocular Mathematical Virtual Simulator (OMVS). The OMVS aims to solve problems arising in biomechanics and hemodynamics within the human eye. We discuss our contribution towards improving the reliability and reproducibility of computational studies by performing a thorough validation of the numerical predictions against experimental data. The OMVS proved capable of simulating complex multiphysics and multiscale scenarios motivated by the study of glaucoma. Furthermore, its modular design allows the continuous integration of new models and methods as the research moves forward, and supports the utilization of the OMVS as a promising non‐invasive clinical investigation tool for personalized research in ophthalmology. [ABSTRACT FROM AUTHOR]

Mesh denoising is a fundamental yet challenging task. Most of the existing data‐driven methods only consider the zero‐order information (vertex location) and first‐order information (face normal). However, higher‐order geometric information (such as curvature) is more descriptive for the shape of the mesh. Therefore, in order to impose such high‐order information, this paper proposes a novel Curvature‐Driven Multi‐Stream Graph Convolutional Neural Network (CDMS‐Net) architecture. CDMS‐Net has three streams, including curvature stream, face normal stream and vertex stream, where the curvature stream focuses on the high‐order Gaussian curvature information. Moreover, CDMS‐Net proposes a novel block based on residual dense connections, which is used as the core component to extract geometric features from meshes. This innovative design improves the performance of feature‐preserving denoising. The plug‐and‐play modular design makes CDMS‐Net easy to be implemented. Multiple sets of ablation study are carried out to verify the rationality of the CDMS‐Net. Our method establishes new state‐of‐the‐art mesh denoising results on publicly available datasets. [ABSTRACT FROM AUTHOR]

The better application of robot technology in space unstructured scenes has become an important research topic. How to adapt to limited workspace and maintain high reliability sampling activities on unstructured surfaces with unknown geometric information and physical features poses high requirements for the implementation of automated sampling missions for extraterrestrial celestial bodies (ECBs). This work introduces a biomimetic adhesion sampler PBAS. It expects to achieve multisite core sampling activities on the unstructured surface of ECBs under astronaut assistance or unmanned automatic operation. Different from previous sampler, the system adopts novel modular structure design and has made improvements to the bionic adhesion mechanism, percussive and drilling mechanism, power transmission and sensing mechanism, and control mechanism. PBAS can firmly adhere to unstructured surfaces and efficiently obtain complete coring samples through penetration and drilling activities. It is compact and lightweight and has high portability, good reusability, excellent efficiency, as well as better performance in automatic sampling operations. Finally, to verify the effectiveness and reliability of the PBAS, a testing apparatus was designed. Relevant tests were conducted outdoors and in the laboratory. The experimental results indicate that PBAS can successfully adhere to surfaces with different characteristics and obtain coring samples with a length exceeding 50 mm. • The operation mode and design criteria of the PBAS system are provided. • Design details and mechanism model are analyzed. • Relevant experimental platform and environment are introdudced. • Preliminary filed test results are presented. • Some discussion and conclusion remarks are made. [ABSTRACT FROM AUTHOR]

Images obtained in low-light scenes are often accompanied by problems such as low visibility, blurred details, and color distortion, enhancing them can effectively improve the visual effect and provide favorable conditions for advanced visual tasks. In this study, we propose a Multi-Technology Fusion of Low-light Image Enhancement Network (MTIE-Net) that modularizes the enhancement task. MTIE-Net consists of a residual dense decomposition network (RDD-Net) based on Retinex theory, an encoder-decoder denoising network (EDD-Net), and a parallel mixed attention-based self-calibrated illumination enhancement network (PCE-Net). The low-light image is first decomposed by RDD-Net into a lighting map and reflectance map; EDD-Net is used to process noise in the reflectance map; Finally, the lighting map is fused with the denoised reflectance map as an input to PCE-Net, using the Fourier transform for illumination enhancement and detail recovery in the frequency domain. Numerous experimental results show that MTIE-Net outperforms the comparison methods in terms of image visual quality enhancement improvement, denoising, and detail recovery. The application in nighttime face detection also fully demonstrates its promise as a pre-processing means in practical applications. [ABSTRACT FROM AUTHOR]

To address the nonlinearities and external disturbances in unstructured and complex agricultural environments, this paper investigates an autonomous trajectory tracking control method for agricultural ground vehicles. Firstly, this paper presents the design and implementation of a lightweight, modular two-wheeled differential drive vehicle equipped with two drive wheels and two caster wheels. The vehicle comprises drive wheel modules, passive wheel modules, battery modules, a vehicle frame, a sensor system, and a control system. Secondly, a novel robust trajectory tracking method was proposed, utilizing an improved pure pursuit algorithm. Additionally, an Online Particle Swarm Optimization Continuously Tuned PID (OPSO-CTPID) controller was introduced to dynamically search for optimal control gains for the PID controller. Simulation results demonstrate the superiority of the improved pure pursuit algorithm and the OPSO-CTPID control strategy. To validate the performance, the vehicle was integrated with a seeding and fertilizing machine to realize autonomous wheat seeding in an agricultural environment. Experimental outcomes reveal that the vehicle of this study completed a seeding operation exceeding 1 km in distance. The proposed method can robustly and smoothly track the desired trajectory with an accuracy of less than 10 cm for the root mean square error (RMSE) of the curve and straight lines, given a suitable set of parameters, meeting the requirements of agricultural applications. The findings of this study hold significant reference value for subsequent research on trajectory tracking algorithms for ground-based agricultural robots. [ABSTRACT FROM AUTHOR]

Sea surface temperature (SST) is a key indicator of the global climate system and is directly related to marine and coastal ecosystems, weather conditions, and atmospheric events. Marine heat waves (MHWs), characterized by prolonged periods of high SST, affect significantly the oceanic water quality and thus, the local ecosystem, and marine and coastal activities. Given the anticipated increase of MHWs occurrences due to climate change, developing targeted strategies is needed to mitigate their impact. Accurate SST forecasting can significantly contribute to this cause and thus it comprises a crucial, yet challenging, task for the scientific community. Despite the wide variety of existing methods in the literature, the majority of them focus either on providing near-future SST forecasts (a few days until 1 month) or long-term predictions (decades to century) in climate scales based on hypothetical scenarios that need to be proven. In this work, we introduce a robust deep learning-based method for efficient SST forecasting of the interim future (1 year ahead) using high-resolution satellite-derived SST data. Our approach processes daily SST sequences lasting 1 year, along with five other relevant atmospheric variables, to predict the corresponding daily SST timeseries for the subsequent year. The novel method was deployed to accurately forecast SST over the northeastern Mediterranean Seas (Aegean, Ionian, Cretan Seas: AICS). Utilizing the effectiveness of well-established deep learning architectures, our method can provide accurate spatiotemporal predictions for multiple areas at once, without the need to be deployed separately at each sub-region. The modular design of the framework allows customization for different spatial and temporal resolutions according to use case requirements. The proposed model was trained and evaluated using available data from the AICS region over a 15-year time period (2008–2022). The results demonstrate the efficiency of our method in predicting SST variability, even for previously unseen data that are over 2 years in advance, in respect to the training set. The proposed methodology is a valuable tool that also can contribute to MHWs prediction. [ABSTRACT FROM AUTHOR]

With society’s progression, the rapid advancements in science and technology, and the continual economic growth, a heightened emphasis on health and sports is observed. Sports footwear plays an indispensable role in various sporting activities, and, with the progress in science and technology, these products are continually evolving to cater to diverse requirements. Nevertheless, this evolution has led to the manufacturing and disposal of substantial quantities of sports footwear, thereby adversely impacting the environment. Our Common Future advocates for sustainable development that fulfills the present generation’s needs without jeopardizing those of the future generations. In the contemporary context, global concerns regarding sustainability have made it imperative to tackle the environmental pollution stemming from sports footwear across its life cycle. The research focuses on sports footwear and employs a modular design approach to investigate sustainable solutions for sports shoes from a sustainability perspective.In this examination of existing sustainable and modular shoes, the article aimed to elucidate their strengths and limitations concerning sustainability. Sustainable footwear of the present excels in various aspects, including the utilization of recycled materials, reduction of environmental impact, increased adaptability, and the consideration of social equity concerns. Nevertheless, the use of adhesives in assembly processes impedes recycling efforts and inflates disposal costs. In the meanwhile, modular sports footwear often lacks sustainability considerations. To address these sustainability challenges, the study embraced a comprehensive approach that places significant emphasis on full life-cycle sustainability, modular design, and the enhancement of recovery and recycling processes. The article approached the formulation of a sustainable design strategy for modular footwear from a systematic perspective. It outlined a threefold approach to constructing a sustainable modular sports footwear system. Firstly, it is necessary to create a modular system with detachable structures, tailored to user needs and sustainability. Employing the system-function-structure layering model, the article analyzed the structure and function of sports shoes to provide the foundation for module segmentation, and establish a connection with user needs. The article gathered user needs through desktop research, questionnaires, and interviews, providing the basis for module segmentation and subsequent design. In terms of structure, each module was designed without the use of adhesive, facilitating individual disassembly, thereby enhancing production efficiency and recycling ease. Secondly, material selection guided shall be by life cycle and recycling design principles, and it is necessary to prioritize natural materials with minimal environmental impact and recyclable materials. Thirdly, it is necessary to enhance the end-to-end service system design and business chain to improve the user experience throughout the entire process. From a product and service system design perspective, the article created a systematic process that spans from purchase to recycling and replacement. Finally, from the four design elements of structural division, user demand, material selection and system service, the specific design practice was verified. It is found that by applying the proposed design strategy to the specific sports footwear design, the improved solution exhibits enhanced sustainability, with an improved overall evaluation of the design’s ability to better meet user needs while striving for sustainability. Considering that it offers improved repairability but relatively weak durability, subsequent solutions can focus on further optimizing and strengthening the durability to meet evolving user demands.The modular design approach offers a novel avenue for sustainable footwear design, addressing key innovation challenges in the field. It effectively reduces the consumption of non-renewable resources, and mitigates environmental pollution by employing natural and biodegradable materials. The modular structure facilitates easy assembly and recycling, enhances production efficiency, and allows for personalized customization. Compared to traditional sports shoes, sustainable modular sports footwear caters to individual preferences while emphasizing environmental friendliness and recyclability. It preserves the performance characteristics of sports footwear and provides valuable insights for the sustainable design of sports shoes. [ABSTRACT FROM AUTHOR]

Utilizing Arduino development boards for learning microcontroller circuits is a prevalent practice across various educational levels. Nevertheless, the literature offers limited insights into the impact of these boards on student performance and attitudes. Therefore, this paper aims to investigate the performance of 58 university students in learning microcontroller circuits with modular boards designed for Arduino through a series of 4 exercises. Specifically, students' performance is assessed through pre-tests and post-tests, in three learning units: (a) microcontroller, (b) coding, and (c) circuit. Additionally, the study captures students' attitudes and measures their perceived usability of modular boards. For this purpose, the students completed a specially designed attitude questionnaire and the system usability scale (SUS) questionnaire. Statistical analysis is conducted using t-tests, ANOVA, and ANCOVA, along with bootstrapping. The findings reveal statistically significant differences between pre-tests and post-tests in all cases. Among the three learning units, the use of modular boards appears to have the most significant impact on coding. Based on students' responses, the SUS results indicate that modular boards appear to be a quite usable approach for teaching microcontrollers. Finally, students generally express positive attitudes toward modular boards. [ABSTRACT FROM AUTHOR]

In this paper, a hybrid strategy is proposed for an innovative 15-level inverter method for the grid-connected photovoltaic (PV) system. The proposed technique is combined with the Zebra Optimization Algorithm (ZOA) plus the spiking neural network (SNN) and is called the ZOASNN method. The major goals of the ZOASNN approach are to fulfill the power demand of load, lessen harmonics, and improve the PV system power regulation or maximal energy conversion. The multilevel inverter (MLI) is used to operate at symmetrical and asymmetrical configurations aimed at utilizing reduced power components. The proposed ZOASNN controller develops the operating modes of two-generation methods to determine the converter switching states for this purpose. Using this control method, load demands are optimally fulfilled while external disturbances and fluctuations in system parameters are minimized. The proposed strategy is implemented in MATLAB and its performance is estimated with other algorithms, such as the Grasshopper Optimization Algorithm (GOA), the Random Forest Algorithm (RFA), and the Cuckoo Search Algorithm (CSA). The proposed method shows high efficiency, low total harmonics distortion (THD), and lower cost than other existing methods. [ABSTRACT FROM AUTHOR]

Trends towards product differentiation, fast changing market conditions, and shorter product lifecycles demand flexible production solutions for the chemical industry. Modular production systems promise to provide the required capacities as needed in a short period of time. However, the potential of modular production systems can only be fully utilized if they are embedded in modular and thus flexible logistics processes. For this reason, standardized logistic modules need to be defined. Process standardization is the starting point for reducing the number of variants and minimizing the number modules. Thus, in this paper a reference process model is developed. The standardized processes defined in this model are then used to form modules by applying a method from cluster analysis. [ABSTRACT FROM AUTHOR]

The concept of a modular difference set was originally motivated by the cognate notion of modular Hadamard matrices, which have been researched extensively. We initiate the study of the repetition-parameter set in a modular difference set, and we relate the repetition-parameter set to integer partitions and Diophantine equations. By example, we show how a computational study of integer partitions can improve the upper bound on the size of such repetition-parameter set. All previously known examples of modular difference sets in a direct product of groups are concerned with a product of just two groups. We present new constructions of modular difference sets in a direct product of n groups. These new constructions suggest that the size of the repetition-parameter set is intimately related to the group's structure. A generalization of difference sets, partial difference sets, and relative difference sets, modular difference sets have been used to construct both modular symmetric designs and equiangular tight frames in finite fields. [ABSTRACT FROM AUTHOR]

Being able to perform modular design of artificial transcription factors is useful in bioengineering and synthetic biology, particularly in the development of biosensors and therapeutics. This study aimed to develop a two-fragment transcription factor system by splitting a lactose repressor (LacI). To fragment LacI, we screened potential split positions from transposon-based insertional libraries that we generated to identify those positions that did not disturb the intrinsic activity of LacI. The interaction of protein tags fused with fragments induces the reassembly of LacI and recovers the isopropyl-β-D-thiogalactoside-dependent regulatory function. The split LacI-based biosensor was implemented on an in vitro platform using a cell-free protein expression system to facilitate accurate analytical studies with high reproducibility. This versatile platform holds great potential to realize the rapid and simple detection of protein–protein interactions in cell-free systems; thus, it can be further extended to disease diagnosis, particularly at the point-of-care. [ABSTRACT FROM AUTHOR]

The technological advancements in sensory systems and robotics over the past decade have facilitated the innovation of centralized systems for optimizing resource utilization and monitoring efficiency in inspection applications. This paper presents a novel system designed for gas concentration sensing in environments by implementing a modular artificial nose (emulating the inhalation and exhalation process) equipped with a strategically designed air capture centralization system based on computational fluid dynamics analysis (CFD). The system incorporates three gas identification sensors distributed within the artificial nose, and their information is processed in real-time through embedded systems. The artificial nose is hardware–software integrated with a quadruped robot capable of traversing the environment to collect samples, maximizing coverage area through its mobility and locomotion capabilities. This integration provides a comprehensive perspective on gas distribution in a specific area, enabling the efficient detection of substances in the surrounding environment. The robotic platform employs a graphical interface for real-time gas concentration data map visualization. System integration is achieved using the Robot Operating System (ROS), leveraging its modularity and flexibility advantages. This innovative robotic approach offers a promising solution for enhanced environmental inspection and monitoring applications. [ABSTRACT FROM AUTHOR]

Nowadays, ontologies are backbone of Semantic Web. Several domains use ontologies as knowledge models. As their number is constantly increasing, designers are opting to reuse some of those that exist to build new ones. When it is impossible to reuse a part depending on its organization, they import the whole ontology and this makes the manipulation cumbersome especially if the ontology has large concepts. Therefore, segmenting ontologies into partitions, if they are not yet, becomes a constant challenge for designers. This paper presents an approach to modularize ontology using hidden Markov model. Ontology triples are extracted within ontology through SPARQL queries and labelled with integers. The labelled triples constituted a Markov chain where ontology concepts are states and ontology relationships are symbols. This set is used to initialize HMM parameters such as states transition probabilities and symbols observation probabilities matrix and initial states probabilities vector. The transition probabilities matrix of HMM is then used as input of K-Means algorithm to generated modules of ontology concepts. This approach does not handled ontology axioms, which characterize heavy ontologies, and only lightweight ontologies are considered. Experiment on eighteen ontologies, obtained modules satisfied ontology modularization criteria such as independence, non-redundancy, correctness and completeness. [ABSTRACT FROM AUTHOR]

Integrated circuits suffer severe deterioration due to single-event upsets (SEUs) in irradiated environments. Spin-transfer torque magnetic random-access memory (STT-MRAM) appears to be a promising candidate for next-generation memory as it shows promising properties, such as non-volatility, speed, and unlimited endurance. One of the important merits of STT-MRAM is its radiation hardness, thanks to its core component, a magnetic tunnel junction (MTJ), being capable of good function in an irradiated environment. This property makes MRAM attractive for space and nuclear technology applications. In this paper, a novel radiation-hardened triple modular redundancy (TMR) design for anti-radiation reinforcement is proposed based on the utilization of STT-MTJ devices. Simulation results demonstrate the radiation-hardened performance of the design. This shows improvements in the design's robustness against ionizing radiation. [ABSTRACT FROM AUTHOR]

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Insects can navigate in cluttered spaces and perform challenging functions such as pollination and collective object transport. By exploiting scaling laws and bioinspired designs, insect‐scale micro‐aerial‐vehicles (MAVs) have demonstrated impressive flight capabilities such as in‐flight collision resilience and acrobatic maneuvers. However, existing subgram MAVs are difficult to design, construct, and repair. Coupled with challenges in robot sensing and control, existing subgram MAVs have not achieved insect‐like swarm flight, which limits potential studies of swarm behaviors and future applications such as collective sensing. Herein, a new design and fabrication method is developed to substantially improve the fabrication scalability of subgram MAVs. Based on a small set of design parameters, an automated algorithm generates the laser cut files of microrobotic components. To reduce fabrication and assembly time, stereolithographic 3D printing is used for making static components such as the airframe and connectors. The modular design enables straightforward assembly and repair, which reduces the overall fabrication time by over 2 times. Owing to the ease of fabrication and good reliability, two subgram MAVs demonstrate controlled hovering flight and coordinated lifting of an object. This result lays the foundation for future robotic studies of collective insect flight. [ABSTRACT FROM AUTHOR]

This study presents the neutronic design of a small modular long-life Pressurized Water Reactor (PWR) using thorium carbide fuel with 233U fissile material. The target optimization for this study is a reactor designed to operate for 20 years, with excess reactivity throughout the reactor operational cycle consistently below 1.00 % dk/k. The analysis involves dividing the reactor core into three fuel regions with 233U enrichment levels ranging from 3 % to 8 %, with a 1 % difference for each fuel region. To achieve optimum conditions, 231Pa was randomly added to the fuel. The fuel volume fraction in this design varied from 30 % to 65 %, with a 5 % incremental variation. Power level variations are also studied within the 300–500 MWth with increments of 50 MWth. Calculations were performed using the Standard Reactor Analysis Code (SRAC) program with the PIJ and CITATION modules for cell and core calculations utilizing JENDL-4.0 nuclide data. Neutronic calculations indicate that the fuel with a 60 % volume fraction achieves optimum conditions at a power level of 300 MWth. The best performance was observed with a fuel volume fraction of 65 %, reaching optimum conditions across power levels ranging from 300 to 500 MWth. For the fuel with the best performance, the power density distributions for low and high power levels follow the same pattern radially and axially. The power peaking factor (PPF) for all fuel configurations approaching the optimum conditions remains below two, a safe limit for the PWR. Other neutronic safety parameters, such as the Doppler coefficient and void fraction coefficient, also stay within the safe limits for the PWR, with both values remaining negative throughout the reactor operational cycle. [ABSTRACT FROM AUTHOR]

Background: The COVID-19 pandemic revealed a need for better collaboration among research, care, and management in Germany as well as globally. Initially, there was a high demand for broad data collection across Germany, but as the pandemic evolved, localized data became increasingly necessary. Customized dashboards and tools were rapidly developed to provide timely and accurate information. In Saxony, the DISPENSE project was created to predict short-term hospital bed capacity demands, and while it was successful, continuous adjustments and the initial monolithic system architecture of the application made it difficult to customize and scale. Methods: To analyze the current state of the DISPENSE tool, we conducted an in-depth analysis of the data processing steps and identified data flows underlying users' metrics and dashboards. We also conducted a workshop to understand the different views and constraints of specific user groups, and brought together and clustered the information according to content-related service areas to determine functionality-related service groups. Based on this analysis, we developed a concept for the system architecture, modularized the main services by assigning specialized applications and integrated them into the existing system, allowing for self-service reporting and evaluation of the expert groups' needs. Results: We analyzed the applications' dataflow and identified specific user groups. The functionalities of the monolithic application were divided into specific service groups for data processing, data storage, predictions, content visualization, and user management. After composition and implementation, we evaluated the new system architecture against the initial requirements by enabling self-service reporting to the users. Discussion: By modularizing the monolithic application and creating a more flexible system, the challenges of rapidly changing requirements, growing need for information, and high administrative efforts were addressed. Conclusion: We demonstrated an improved adaptation towards the needs of various user groups, increased efficiency, and reduced burden on administrators, while also enabling self-service functionalities and specialization of single applications on individual service groups. [ABSTRACT FROM AUTHOR]

BACKGROUND: Traditional 3D dental segmentation methods usually utilize predefined spatial geometric features, such as curvature and normal vectors, as the reference information for tooth segmentation. OBJECTIVE: To propose an algorithm for complex 3D dental segmentation and deeply explore the correlation between segmentation results and application scenarios. METHODS: A 3D dental segmentation algorithm based on dual stream extraction of structural features and spatial features was established, and the modular design of split flow was used to avoid feature confusion. Among them, the attention mechanism on the structural feature flow was used to capture the finegrained semantic information required for tooth segmentation, and the Tran Net based on the spatial feature flow was used to ensure the robustness of the model to complex tooth and jaw segmentation. This algorithm verified its effectiveness and reliability based on clinical datasets including healthy dental jaws and complex dental jaws such as missing teeth, malocclusion and dentition crowding. The segmentation performance of the model was measured in terms of overall accuracy, mean intersection over union, and directional cut discrepancy. RESULTS AND CONCLUSION: The overall segmentation accuracy of this algorithm in the clinical data set is 97.08%, and the segmentation effect is superior to that of other competitive methods from the qualitative and quantitative perspectives. It is verified that the structural feature flow designed in this paper can extract more precise local details of tooth shape from coordinate and normal information by constructing an attention aggregation mechanism, and the spatial feature flow designed in this paper can ensure the robustness of the model to complex teeth such as missing teeth, dislocated teeth, and crowded dentition by constructing a transformation network (Tran Net). Therefore, this tooth segmentation algorithm is highly reliable for clinicians' practical reference. [ABSTRACT FROM AUTHOR]

Robust and accurate three-dimensional localization is essential for personal navigation, emergency rescue, and worker monitoring in indoor environments. For localization technology to be employed in various applications, it is necessary to reduce infrastructure dependence and limit the maximum error bound. This study aims to accurately estimate the location of various people using smartphones in a building with a cloud platform-based localization system. The proposed technology is modularized in a hierarchical structure to sequentially estimate the floor and location. This system comprises four localization modules: course level detection, fine level detection (FLD), fine location tracking (FLT), and level change detection (LCD). Each module operates organically according to the current user status. The position estimation range is defined as a total of three phases, and an appropriate location estimation module suitable for the corresponding phase operates to estimate the user's location gradually and precisely. When the user's floor is determined by an FLD, the two-dimensional position of the user is estimated by an FLT module that tracks the user's position by comparing the received signal strength indicator vector sequence and radio map. Also, LCD recognizes the user's floor change and converts the user's phase. To verify the proposed technology, various experiments were conducted in a six-story building, and an average accuracy of less than 2 m was obtained. [ABSTRACT FROM AUTHOR]

Implantable cell replacement therapies promise to completely restore the function of neural structures, possibly changing how we currently perceive the onset of neurodegenerative diseases. One of the major clinical hurdles for the routine implementation of stem cell therapies is poor cell retention and survival, demanding the need to better understand these mechanisms while providing precise and scalable approaches to monitor these cell-based therapies in both pre-clinical and clinical scenarios. This poses significant multidisciplinary challenges regarding planning, defining the methodology and requirements, prototyping and different stages of testing. Aiming toward an optogenetic neural stem cell implant controlled by a smart wireless electronic frontend, we show how an iterative development methodology coupled with a modular design philosophy can mitigate some of these challenges. In this study, we present a miniaturized, wireless-controlled, modular multisensor platform with fully interfaced electronics featuring three different modules: an impedance analyzer, a potentiostat and an optical stimulator. We show the application of the platform for electrical impedance spectroscopy-based cell monitoring, optical stimulation to induce dopamine release from optogenetically modified neurons and a potentiostat for cyclic voltammetry and amperometric detection of dopamine release. The multisensor platform is designed to be used as an opto-electric headstage for future in vivo animal experiments. [ABSTRACT FROM AUTHOR]

Herein – with the optimization goals of a lightweight, large-span structure; modularization; and heavy loading – a tapered-section truss module and standard truss module are integrated and assembled, yielding a new design for an all-carbon-fiber-reinforced polymer (CFRP) truss structure. The design structure fully considers the mechanical characteristics of the truss and the requirements of standardized production efficiency, integrating the "fish keel" structural design method to give it a hollow structure style. By fabricating two 1:1 truss structures (A-Truss and B-Truss) with a length of 20.4 m, the conceptual design, modular design method, and integrated assembly technology of the truss structure were effectively verified; further, experimental research on the strength performance of the mold-clave-forming plate-type joint and the axial compression behavior of large-span full CFRP truss structure is carried out. The failure position of the truss structure is determined, and an enhancement measure for the key connection joint between the truss modules is proposed. Based on specific application scenarios, it is clear that the truss meets the design requirements in terms of bearing capacity and deformation, and can be used as the main bearing structure of large aircraft and aerospace assembly platforms. [ABSTRACT FROM AUTHOR]

The perpetual changes affecting manufacturing systems caused by highly fluctuating demand and customer requirements make quality management of manufacturing systems more complex. In this context, the implementation of Reconfigurable Manufacturing Systems (RMS) offers decision-makers an efficient solution to cope with these changes. However, these systems require a large number of modifications and changes handled by workforces who must have an adequate level of qualification and skills. The aim of this paper is to propose a workforce qualification framework in RMS to ensure the skills needed for performing the activities. First, new framework is proposed to define the main phases of workforce qualification in a system requiring several changes. Second, new approach is proposed for workforce skills management and the building of training modules linked to the proposed qualification phases, using a modular design. This work provides decision-makers with a solution for the qualification and management of workforce skills in RMS. [ABSTRACT FROM AUTHOR]

Password authentication is readily cracked, and hackers may simply predict the patterns. Faceprint plays a significant role, making it more difficult for hackers to match it up. A customizable app that takes the faceprint as an input, compares it to the current database, and analyses facial attributes to obtain a more exact face pattern to eliminate errors is created. The strategy comprises some modular and efficient algorithms like Siamese Neural Network (twin neural network) for accurate one-shot image recognition. Dlib is utilized for facial landmark detection. Then, using Retina Face for both one face and multiface detection in a crowd. And a pixel wise face localization method, Retina face to perform tasks such as face detection,2D face alignment and 3D face reconstruction simultaneously for accurate precision.The technology leverages a modular design to reduce the need for separate facial recognition systems. It is easily adaptable to all locations by just changing a few lines of code to meet the needs. With the advent of facial recognition technology, the technique will assist in quickly adapting to the changes required by diverse sectors. [ABSTRACT FROM AUTHOR]

This research paper explores the role of code analyzer that takes the help of tokenization in its working. To assure code quality and maintainability, code analysis is essential in software development. In the context of the Java programming language, this paper provides a creative approach to code analysis utilizing tokenization. Our code analyzer allows for an accurate assessment of code structure, dependencies, and patterns by tokenizing source code into understandable units. The suggested analyzer, which uses token-based methodologies, aids in the correct identification of possible defects, and performance bottlenecks. It provides vital information for developers to improve code readability, modularization, and best practices adherence. The experimental findings show that the tokenization-based technique is successful and efficient in increasing overall code quality and maintainability. [ABSTRACT FROM AUTHOR]

To ensure the accuracy and reliability of subsequent analysis, research on electroencephalogram (EEG) signals typically requires preliminary processing of large datasets to eliminate noise and artifacts. Traditional batch processing methods require substantial hardware resources while lacking flexible automated workflows and user-friendly interactions. To address these challenges, we have implemented a modular batch processing platform for EEG (MBPPE) that offers both local execution and private deployment options to meet the demands of efficient signal processing from individuals to laboratories. We modularize the processing methods and organize them into pluggable multi-task batch processes, providing asynchronous processing solutions. In addition, we extend user functions by introducing plugins and promoting collaborative interaction through data sharing, access control, and comment communication. Simultaneously, interactive features are integrated into the visualization design, enabling users to process and analyze data more intuitively and naturally. Currently, the platform integrates several commonly used data preprocessing and analysis techniques, providing a novel solution for batch processing of EEG signals. [ABSTRACT FROM AUTHOR]

Featured Application: This paper presents a new concept of a modular system for the production and storage of energy in a bicycle at any speed, even below 9 km/h. This paper presents a new concept of a modular system for the production and storage of energy in a bicycle at any speed above 9 km/h. User-Centered Design methodology was applied to establish the design premises, and then each component of the modular system was selected, developed, and refined separately, carrying out all component integration (hub dynamo, USB charger, batteries, and solar panel) by means of a simple extension cable. Then, simulations were made with different software tools to create a design candidate. A new design of an integrated modular energy production–storage system was obtained, aiming to cover the needs of long-distance bikers and daily bike commuters. The designed system can charge its own batteries and power devices connected to the USB charger from a speed of 9 km/h. The system entails a modular integration solution that is not only cost-effective but also highly efficient. Its ergonomic design allows users to effortlessly replace batteries as and when needed. Current models on the market do not possess this integration. [ABSTRACT FROM AUTHOR]

Shell-and-tube designs with multi-pass fluid flow are not used for metal hydride hydrogen storage reactors. Here, the absorption performances of reactors having shell outer diameter [OD]: 114.3, 141.3 and 168.3 mm with 36, 60 and 92 tubes arranged in 3, 4 and 5 circular arrays housing 25 kg LaNi 5 at 20 bar hydrogen pressure, 10 LPM water entering at 30 °C are simulated. Considering the trade-off between tube volume and reaction duration, OD: 141.3 mm, 60 tubes offers a balanced performance with 90% absorption taking ∼320 s with four water passes (4-pass-scheme) vis-à-vis ∼380 s with one pass (1-pass-scheme). This design is scaled-up to house 100 kg LaNi 5 with four modules in series and parallel configurations. When in series, reactors take 320, 425, 545, and 665 s for 90% absorption; in parallel, each reactor takes 625 s. At higher water flow rate (40 LPM) series and parallel configurations perform similarly. [Display omitted] • Shell-and-tube MH reactors are designed and simulated for 1, 4 passes of water flow. • Trade-off exists between volume occupied by tubes and absorption performance. • Shell diameter and tube arrangement is varied to find balanced design. • 4 reactors connected in series or parallel were used together to increase capacity. • Series performs better at lower flow rates and similarly to parallel at higher. [ABSTRACT FROM AUTHOR]

Large-scale manufacturing of induced pluripotent stem cells (iPSCs) is essential for cell therapies and regenerative medicines. Yet, iPSCs form large cell aggregates in suspension bioreactors, resulting in insufficient nutrient supply and extra metabolic waste build-up for the cells located at the core. Since subtle changes in micro-environment can lead to a heterogeneous cell population, a novel Biological System-of-Systems (Bio-SoS) framework is proposed to model cell-to-cell interactions, spatial and metabolic heterogeneity, and cell response to micro-environmental variation. Building on stochastic metabolic reaction network, aggregation kinetics, and reaction-diffusion mechanisms, the Bio-SoS model characterizes causal interdependencies at individual cell, aggregate, and cell population levels. It has a modular design that enables data integration and improves predictions for different monolayer and aggregate culture processes. In addition, a variance decomposition analysis is derived to quantify the impact of factors (i.e., aggregate size) on cell product health and quality heterogeneity. To facilitate large-scale manufacturing of induced pluripotent stem cells, a Biological System-of-Systems (Bio-SoS) framework is proposed to model cell-to-cell interactions, spatial and metabolic heterogeneity, and cell response to micro-environmental variation. [ABSTRACT FROM AUTHOR]

This article summarizes principles and ideas from the emerging area of applying conditional computation methods to the design of neural networks. In particular, we focus on neural networks that can dynamically activate or de-activate parts of their computational graph conditionally on their input. Examples include the dynamic selection of, e.g., input tokens, layers (or sets of layers), and sub-modules inside each layer (e.g., channels in a convolutional filter). We first provide a general formalism to describe these techniques in an uniform way. Then, we introduce three notable implementations of these principles: mixture-of-experts (MoEs) networks, token selection mechanisms, and early-exit neural networks. The paper aims to provide a tutorial-like introduction to this growing field. To this end, we analyze the benefits of these modular designs in terms of efficiency, explainability, and transfer learning, with a focus on emerging applicative areas ranging from automated scientific discovery to semantic communication. [ABSTRACT FROM AUTHOR]

The sustainable development of the built environment is closely related to climate-neutral construction and minimum resources use. In this framework, deployable and reconfigurable building structures offer a promising solution while aiming at reduced self-weight with flexibility and structural efficiency. Nevertheless, commonly developed structural typologies often lead to increased structural weight, complexity, and energy-inefficient operation. The linkage-based systems considered in this work have minimum actuation components, are based on a modular design and the definition of a one-degree-of-freedom mechanism in each transformation step of a reconfiguration sequence from an initial to a target configuration. The effective crank–slider approach was applied on a class of planar eight-bar linkage aluminum structures of different typologies and geometrical characteristics of the members (i.e., simple and hybrid typologies), as well as of variable length. The considered planar structural mechanisms have an overall length of 12.0 m in their initial almost-flat configuration and a respective span of 6.0 m in their specific archlike target configurations. They are supported on a pivot joint on one end and a linear sliding block on the other end, which is associated to the external actuation. In addition, each intermediate joint is equipped with brakes. The simulation of the systems is based on kinematics and a comparative finite-element analysis. The study provides insight into the impact of various geometrical and typological aspects on the systems' behavior. The experimental implementation of the kinematics approach on a small-scale prototype in different typologies demonstrates its feasibility and highlights practical implementation issues. [ABSTRACT FROM AUTHOR]

Discrete states linked together by events can describe a series of classical video game scenarios. Therefore, the formal concept of automata seems appropriate for describing such games. However, as simple finite automata need to keep track of past events, they are less appropriate for complex designs. By contrast, push-down automata featuring a stack present better capabilities in this context. This paper discusses an enhanced version of the classic PAC-MAN game with push-down automata. The modular design of this game allows the extraction of discrete states, and its minimalist concept enables various approaches and new versions without sacrificing its original essence. Using push-down automata expanded the game with new features, allowing an enhanced game experience. The automaton controls all aspects of the game, ensuring a consistent gameplay. The presented project demonstrates the capabilities of push-down automata in the gaming industry and their potential in future game development projects. [ABSTRACT FROM AUTHOR]

Argumentation frameworks (AFs) are a key formalism in AI research. Their semantics have been investigated in terms of principles, which define characteristic properties in order to deliver guidance for analyzing established and developing new semantics. Because of the simple structure of AFs, many desired properties hold almost trivially, at the same time hiding interesting concepts behind syntactic notions. We extend the principle-based approach to argumentation frameworks with collective attacks (SETAFs) and provide a comprehensive overview of common principles for their semantics. Our analysis shows that investigating principles based on decomposing the given SETAF (e.g. directionality or SCC-recursiveness) poses additional challenges in comparison to usual AFs. We introduce the notion of the reduct as well as the modularization principle for SETAFs which will prove beneficial for this kind of investigation. We then demonstrate how our findings can be utilized for incremental computation of extensions and show how we can use graph properties of the frameworks to speed up these algorithms. [ABSTRACT FROM AUTHOR]

The study is aimed to develop a hip joint wear simulator using a modular design approach to help experimentally monitor and control critical wear parameters to validate in-silico wear models. The proper control and application of wear parameters such as the range of motion, and the applied force values while estimating the lost material due to wear are essential for thorough analysis of wear phenomena for artificial joints. The simulator's dynamics were first modeled, then dynamic loading data was used to calculate the forces, which were further used for topology optimization to reduce the forces acting on each joint. The reduction of the link weights, connected to the actuators, intends to improve the quality of motion transferred to the femoral head. The modular design approach enables topology-optimized geometry, associated gravitational and dynamic forces, resulting in a cost-effective, energy-efficient product. Moreover, this design allows integration of the subject specific data by allowing different boundary conditions following the requirements of industry 5.0. Overall, the in-vitro motion stimulations of the hip-joint prosthesis and the modular design approach used in the study might help improve the accuracy and the effectiveness of wear simulations, which could lead into the development of better and longer-lasting joint prostheses for all. The subject-specific and society-based daily life data implemented as boundary conditions enable inclusion of the personalized effects. Next, with the results of the simulator, CEN Workshop Agreement (CWA) application is intended to cover the personalized effects for previously excluded populations, providing solution to inclusive design for all. [ABSTRACT FROM AUTHOR]

A fully functional prototype of a bullet‐shaped trans‐domain amphibious vehicle with the ability to perform reciprocating cross‐medium movements has been developed. Once the bullet‐shaped underwater vehicle completes its designated underwater tasks, upon reaching a suitable location for surface observation, it will transform into a bullet‐shaped coaxial counter‐propeller aircraft to carry out cross‐domain out‐of‐water and performing aerial flight missions. The successful transition of the vehicle into amphibious mode is attributed to the design of an innovative energy attitude multiplexing mechanism. Simultaneously, a rational and effective hybrid‐switching strategy has been proposed to enhance the reliability of the vehicle's amphibious mode transition. In view of the complex and changeable cross‐domain environment, we have adopted a modular design concept, breaking down the entire cross‐domain movement process into three key phases: air operation flight, transient cross‐domain, and underwater active sailing. Detailed theoretical analysis, algorithm design, and experimental research have been conducted for each phase. This paper proposes and expounds the key design principles, evaluates the performance indicators of key components, and weighs the matching of body dynamics and propulsion performance to ensure that the vehicle achieves the expected performance. Finally, the success of the test at different phases further proves that the vehicle has the ability to achieve complete cross‐domain operation. [ABSTRACT FROM AUTHOR]

In this paper, we study the association between the core American listed companies by analysing the stock data of the Standard and Poor's 500 Index. During the network analysis, we use a new correlation coefficient (Chatterjee in J Am Stat Assoc 116(536):1–21, 2020) to construct the directed association network and apply the directed spectral clustering on ratios of eigenvectors method (DSCORE) (Ji and Jin in Ann Appl Stat 10(4):1779–1812, 2016) for community detection. The obtained three communities are: "traditional" community, "intermediate" community, and "advanced" community respectively. We continue to analyse the entire directed association network and three communities by the node degree, and further study the companies of the central location of networks or associating within their own community or through the entire directed association network. Our results present a rational and particular community detection analysis of the financial market network. The microeconomic information hidden in stocks is successfully reflected in the associations between the American listed companies. The findings are also helpful to understand the United States market. [ABSTRACT FROM AUTHOR]

Integrating energy storage units (ESUs) into part of sub‐modules (SMs) enables the decoupling active power control for the modular multilevel matrix converter (M3C). The low frequency AC (LFAC) system based on M3C with symmetrically integrated energy storage (SI‐AM3C) can provide functions such as renewable power fluctuation smoothing and other power grid auxiliary services, which is attractive in engineering practices. This paper focuses on the design and control of SI‐AM3C. For the converter design, an evaluation method for the number of required active SMs (ASMs) is presented. On this basis, the influences of different system operating conditions on the ASM requirement are analyzed in case studies, and the effects of specific circulating injection strategies are also investigated. The control system targeted at decoupled AC side active power for SI‐AM3C is introduced. A modified inner‐arm capacitor voltage balance strategy is introduced to reduce the capacitor voltage deviation between ASMs and passive SMs (PSMs). The conclusions related to the presented estimation method and converter control strategy are verified by time‐domain simulations in PSCAD/EMTDC. [ABSTRACT FROM AUTHOR]

Soft grippers have good adaptability and flexibility for grasping irregular or fragile objects, and to further enhance their stiffness, soft grippers with variable stiffness have been developed. However, existing soft grippers with variable stiffness have the disadvantages of complex structure and poor interchangeability. Here, a soft gripper with modular variable stiffness is proposed that has flexible Velcro embedded in the bottom layer of the soft actuator and one side of the variable stiffness cavity respectively, and both the general and variable stiffness grasping modes are achieved by separation or combination. First of all, according to the neo-Hookean model and the assumption of constant curvature, a free bending model of the soft actuator is established and optimal structural parameters of the soft actuator are obtained by the Genetic Algorithm. Then, influence of the driving pressure on the soft actuator stiffness is investigated, and a mathematical model of the variable stiffness is established. Finally, correctness of the statics model and the stiffness model were verified by experiments. Experimental results indicate that the proposed soft gripper with modular variable stiffness structure has excellent adaptability and stability to different objects, outstanding load bearing capacity, and stiffness adjustment capability. [ABSTRACT FROM AUTHOR]

Purpose: The study aims to explore the conditional relationships between product modularization and new product development (NPD) efficiency. It is postulated that R&D outsourcing plays an important mediating role. Furthermore, the level of competency trust is considered an essential factor in moderating the indirect effect of product modularization on NPD efficiency via R&D outsourcing practices. Design/methodology/approach: Drawing on transaction cost economics theory, this study suggests a moderated mediation model that addresses how product modularization effectively promotes NPD efficiency via outsourcing practices. The hierarchical regression and PROCESS macro model were conducted to test the hypotheses based on survey data from 273 manufacturing firms in China. Findings: Product modularization enhances NPD efficiency directly and indirectly through the external collaboration of R&D outsourcing. Furthermore, the role of product modularization in R&D outsourcing practices is more effective when the competency trust in R&D outsourcing partners is high. Originality/value: By showing the critical role of external collaboration, this study provides valuable insights into how manufacturing firms utilize product modularization to achieve desired NPD performance more effectively. [ABSTRACT FROM AUTHOR]

Educational technology is important because it helps today's teachers to integrate new technologies and tools into their classrooms. This allows a student to flip the notion of a classroom on his/her head by choosing when and where to learn. Video lectures, recordings, and digital resources make it possible for students to learn at their own pace. The walls of the classrooms are no longer a barrier as technology enables new ways of learning, communicating, and working collaboratively. Technology has also begun to change the roles of teachers and learners. This paper discusses the systematic five-step teaching-learning process for effective engagement in the class. These five steps - The Instructional Process, Modular Design, and Course Delivery, Creating a Lively classroom, Employing technology and Assessment are proven processes, and, an effective implementation of the same yielded very good results in terms of enhancement of the skills and overall development of the students' learning perspectives. Giving varied assignments to different groups based on their interest, had a spike in exhibiting their potential. Assessment through rubrics increased the clarity for students to perform and present their work effectively. This was implemented for the computer networks course for CSE branch students and Biomedical Instrumentation and Digital Health course for Biotechnology branch. The impact and results were amazing. There was an overall development of their knowledge and skills. [ABSTRACT FROM AUTHOR]

Modular architecture design can not only increase the product variety and thus make mass customization (MC) easier, but also improve interchangeability and then lower the remanufacturing cost. This paper models a remanufacturing MC supply chain with one manufacturer and one retailer. The manufacturer, as a Stackelberg game leader, designs and then sells the new and modular MC products via its brand retailer, and meanwhile the remanufacturing of used MC products would be carried out by either the manufacturer or the retailer. We study the optimal modular design for new MC products and the best remanufacturing mode for used MC products. One centralized and two decentralized decision models are formulated, and the equilibrium solutions of proposed models are compared and analyzed. Findings show that, compared to the retailer-remanufacturing mode, the manufacturer-remanufacturing mode can be superior in terms of the ability to cope with customer customization requirements (i.e., the modularity level), total supply chain profit, and consumer surplus. Moreover, both supply chain members and consumers would prefer the manufacturer-remanufacturing mode when the parameters meet certain conditions. We also extend the model to two scenarios: the remanufacturing is bounded by cores supply and the retailer acts a leader, so as to verify the robustness of main results. The above theoretical results are verified by numerical examples. [ABSTRACT FROM AUTHOR]

In this paper, a data-driven modeling method for precast concrete (PC) balcony components was proposed to solve the problems of low informatization and the difficult modeling of components at the design stage. Through the analysis of the characteristics of PC balcony components and the combination of modular design methods, the paper designed a data structure for the components and developed a data-driven modeling tool for PC balcony components that can realize the input of structural design data, automatically generating component models. First, this paper introduced the data-driven modeling concept and the modeling process. Second, the PC balcony components in common prefabricated residential projects were analyzed to identify their characteristics. By using a modular design approach, these components were divided and a module dataset was created based on the split modules. Consequently, a data structure for the prefabricated balcony component model was established, wherein both conventional parameters and adaptive parameters between modules were interrelated. Finally, the function of data-driven modeling was achieved by developing a modular design tool on the Revit platform using the C# programming language. The application conducted on a prefabricated building project demonstrated that the software tool and modeling method in this paper effectively improve the level of informatization and modeling efficiency of PC balcony components. The modular design approach was satisfied with the standardization and diversification requirements of balcony components, thereby offering insights for modeling other complex components. [ABSTRACT FROM AUTHOR]

Today, the leading cable production plants in many countries of the world manufacture single- and multi-module designs of fiber-optic cables (FOC) with different protective covers and the number of fibers. This creates a wide range of possible FOC models for different consumer (buyer) requirements. However, the lack of openness of prices for FOC for the consumer, in particular for the project organization, and the desire of the manufacturer to save on production creates a need for the development and research of a method for evaluating the effectiveness of FOC of a multi-module design. In the work, it is proposed to do this by analyzing a number of optical cable models according to parameters-criteria – the compactness coefficient and the economic efficiency coefficient of the FOC by diameter. Objective. To develop a method of evaluation the efficiency of fiber-optic cables models with multi-modular design based on mass-dimensional indicators, which will allow to quickly choose an appropriate model of the FOC with the given initial data. Method. A method of evaluating the efficiency of the FOC modular design has been developed and proposed. It is based on the comparison of cable models and the selection of the most appropriate of them at given initial data. The paper proposed and introduced parameters-criteria for this – the compactness coefficient υ and the efficiency coefficient according to the diameter of the cable E0 – which show the connection of the design characteristics of the FOC to a certain parameter of its structure. At the same time, the most effective model (design) of the FOC compared to the basic models in terms of technical conditions, both from the point of view of the manufacturer and the customer, consists in lower material costs for its production while simultaneously ensuring the specified requirements for the cable (first of all, number of fibers and mechanical strength). This will allow, at the stage of cable design, to make an appropriate choice of its model with given initial parameters and to develop such a design of the FOC, which will allow to minimize the dimensions (and therefore the material capacity and cost) of the model without losing its quantitative and qualitative characteristics. Results. The paper presents the results of the development and research of the method of evaluating the efficiency of the FOC with multi-module structure based on mass and dimensional indicators. For example, using the developed method, it is shown that it is possible to choose a FOC model with a diameter smaller by 10.9% and save 15.5% of the cable cost for each kilometer of the fiber optic communication line while ensuring the initial requirements for the cable. Conclusions. The scientific novelty of the work results is that, method of evaluation the efficiency of a modular FOC design has been first developed, which allows at the cable design stage to compare the model with the cable design according to the technical conditions (TC) and the appropriate choice of this model with the given initial parameters. The practical significance lies in the possibility of using this method to make an accelerated selection of the cable model at the stage of its design, while simultaneously providing the necessary capacity of the FOC with optical fibers and minimizing the cost of its materials and dimensions. [ABSTRACT FROM AUTHOR]

Cloud computing offers opportunities to increase productivity and reduce costs. Quickly adapting to changing needs is key to maintaining cloud applications. In traditional development, coordination is implemented in computational code. Although change impact analyses are studied, adjusting the implementation is time-consuming and error-prone when the coordination strategy changes. Exogenous coordination separates the implemented coordination and computational code to cope with this problem. This separation improves the reusability of components. Additionally, other applications with similar interaction patterns can reuse the coordination specification. The main contribution of this paper is to propose a methodology to develop and maintain cloud applications following the exogenous approach. To illustrate the idea, we introduce a new framework named OCCIwareBIP, which integrates JavaBIP — a framework for the exogenous coordination of concurrent Java components into OCCIware — a framework for designing cloud applications. We also leverage the coordination model to verify the deadlock-freedom of the cloud application. Finally, we present an application to show the ability of our approach to guarantee the safety and benefits of modularization in developing concurrent cloud applications. [ABSTRACT FROM AUTHOR]