Open Platform Research Articles

Benchmarking for platform-aggregated manufacturing service collaboration: Methodology and implementation

In light of the global economic downturn and the intricate division of labor in manufacturing, the imperative for advanced manufacturing services and Manufacturing Service Collaboration (MSC) has escalated significantly. As manufacturing services gravitate towards aggregation on manufacturing service platforms, platform-aggregated MSC has emerged as an inevitable and compelling trend, capturing the attention of researchers worldwide. However, despite the existence of numerous frameworks, models, operational mechanisms, and algorithms proposed for the platform-aggregated MSC, drawing comparisons between these studies remains a complex endeavor. To address this predicament, this article proposes and explores a novel benchmarking methodology for platform-aggregated MSC. By employing complex network theory, a comprehensive model of platform-aggregated MSC is constructed and supplemented with corresponding methodologies for data generation and the configuration of optimization algorithms. Moreover, pertinent performance evaluation metrics are scrutinized to assess their applicability in the context of platform-aggregated MSC. The article culminates with the execution of a series of platform operation experiments designed to test the effectiveness and practicality of the proposed benchmarking system, thereby contributing to the ongoing evolution of the MSC domain.

APPS: Authentication-enabled privacy protection scheme for secure data transfer in Internet of Things

The Internet of Things (IoT) is an emerging field that encompasses several heterogeneous devices and smart objects that are integrated with the network. In open platforms, these objects are deployed to present advanced services in numerous applications. Innumerable security-sensitive data is generated by the IoT device and therefore, the security of these devices is an important task. This work formulates a secure data transfer technique in IoT, named Authentication enabled Privacy Protection (APPS) scheme for resource-constrained IoT devices. The proposed scheme demonstrates resilience against various attacks; such as resisting reply attacks, device anonymity, untracebility, session key establishment, quantum attacks and resisting MITM attacks. For the privacy protection scheme, the secured data transfer is initiated between the entities, like IoT devices, servers, and registration centers, by using various phases, namely registration phase, key generation phase, data encryption, authentication, verification, and data retrieval phase. Here, a mathematical model is designed for protecting data privacy using hashing, encryption, secret keys, etc. Finally, performance of proposed APPS model is analyzed; wherein the outcomes reveal that the proposed APPS model attained the maximum detection rate of 0.85, minimal memory usage of 0.497MB, and minimal computational time of 112. 79 sec and minimal turnaround time 131.91 sec.

Enhancing physically-based flood forecasts through fusion of long short-term memory neural network with unscented Kalman filter

Accurate flood forecasts are vital for reservoir operation and flood prevention. The unscented Kalman filter (UKF) excels in improving physically-based models flood forecasting but depends on precise noise estimation, posing challenges in complex uncertainty quantification. In this study, we propose a novel approach, the deep fusion of a long short-term memory (LSTM) neural network and unscented Kalman filter (LSTM-UKF), to enhance flood forecasts by adaptively updating hydrological model states. The LSTM efficiently learns noise-related information from the estimation of Kalman Gain, obviating the need for intricate uncertainty recognition and approximation. For comparative analysis, the LSTM-UKF and UKF filters are constructed to correct Xinanjiang (XAJ) hydrological model states. Both filters utilize streamflow observations to update the XAJ model states and facilitate its flood forecasting performance. Comprehensive evaluations were conducted using 3-hourly meteor-hydrological sequences from the Jianxi and Tianyi basins in China, focusing on the accuracy, stability, and reliability of multi-step-ahead flood forecasts. Results indicate that the LSTM-UKF performs superior to the UKF, with maximal advancements in Nash-Sutcliffe efficiency coefficient (NSE) by 9.1%, maximal reduction in root mean square error (RMSE) by 18.7%, and maximal decrease in mean absolute error (MAE) by 22.6%. Additionally, the LSTM-UKF exhibits better robustness and stability in non-stationary flood events. The proposed LSTM-UKF mitigates the over-reliance on noise estimates, reducing systematic biases and error accumulation in state estimates and enhancing hydrological model generalizability in operational flood prevention platforms and systems.

Spatiotemporal and Environmental Completeness of eBird Data in Taiwan

Recent developments in citizen science have significantly enhanced the public availability of biodiversity data (Chandler et al. 2017, Kelling et al. 2019). Associated open data platforms, such as eBird, have become the major data source supporting scientific research and biodiversity conservation (Tsai et al. 2021, Meehan et al. 2022, Walker and Taylor 2017). However, the increasing availabililty of data does not resolve, but rather exacerbates, data bias issues (Escribano et al. 2019, La Sorte and Somveille 2020, Troia and McManamay 2016). While over 11.2 million bird observation records from Taiwan, including historical data spanning 40 years, are currently available on the eBird platform, their usefulness for characterizing the spatiotemporal patterns of bird communities remains unclear. To address this gap, we evaluated the data’s completeness in representing species richness across spatial and temporal scales and examined the representativeness of the data along several environmental gradients. Using the iNEXT package in R, we found an increase in data completeness with increasing spatial scales Fig. 1, with the mean proportion of bird species represented being increased from 14.0% at the 1 km scale to 68.0 and 82.9% at the 5 and 10 km scales, respectively, across Taiwan. We also found an exponential improvement of data completeness over time. At the 5 km scale, the represented species proportion increased from 6.78 and 32.14% in the 1980s and 1990s to 36.86 and 63.56% in the 2000s and 2010s Fig. 2, with the data in the last decade alone achieving similar completeness to the entire four-decade data. We also found that the areas with good data completeness (i.e., over 80%) adequately represented the full gradient of annual precipitation across Taiwan, but showed a bias toward areas at lower elevations and with lower forest coverage. These results indicate overall good data completeness of the eBird dataset for representing bird species richness in Taiwan. However, this study also highlights some data gaps, especially at finer scales, which need attention when using the dataset. This information is crucial for shaping future research designs and survey plans, aiming to enhance the applications of biodiversity data.

Integrative spatiotemporal modeling of biomolecular processes: application to the assembly of the Nuclear Pore Complex.

Dynamic processes involving biomolecules are essential for the function of the cell. Here, we introduce an integrative method for computing models of these processes based on multiple heterogeneous sources of information, including time-resolved experimental data and physical models of dynamic processes. We first compute integrative structure models at fixed time points and then optimally select and connect these snapshots into a series of trajectories that optimize the likelihood of both the snapshots and transitions between them. The method is demonstrated by application to the assembly process of the human Nuclear Pore Complex in the context of the reforming nuclear envelope during mitotic cell division, based on live-cell correlated electron tomography, bulk fluorescence correlation spectroscopy-calibrated quantitative live imaging, and a structural model of the fully-assembled Nuclear Pore Complex. Modeling of the assembly process improves the model precision over static integrative structure modeling alone. The method is applicable to a wide range of time-dependent systems in cell biology, and is available to the broader scientific community through an implementation in the open source Integrative Modeling Platform software.

Towards interworking of matter and oneM2M: Design and implementation of a matter–oneM2M Interworking Proxy Entity

The new smart home protocol, Matter, has garnered worldwide attention for its ability to tackle the most significant obstacle to fully unlock the potential of smart homes: interoperability. The increasing support for Matter among numerous vendors in their products is having a noticeable impact on interoperability at the device level. This work expands the reach of Matter to the IoT platform level by integrating it with an open and standardized platform, oneM2M. One of the key strengths of oneM2M is its ability to provide an open, common, and flexible interface at the platform level, facilitating seamless interconnection across various domains. This integration pave the way to provide Matter’s smart homes user with advanced intelligence services, including AI/ML applications, and emerging concepts like the metaverse. The proposed Interworking Proxy Entity (IPE) for Matter has been successfully implemented, and multiple scenarios have been tested using both commercial and experimental Matter devices within the oneM2M ACME and Mobius platforms. The results demonstrate seamless integration of Matter devices into the oneM2M platform, enabling other applications to interact with them solely through oneM2M standard interfaces. Additionally, the IPE proves to be practical for real-world applications with negligible overhead, offering mutual benefits for both Matter and oneM2M ecosystems.

Dynamic Operations of a Mobile Charging Crowdsourcing Platform

This paper investigates the operation of a novel electric vehicles (EVs) charging service mode, that is, crowdsourced mobile charging service for EVs, whereby a crowdsourcing platform is established to arrange suppliers (crowdsourced chargers) to deliver charging service to customers’ electric vehicles (parked EVs) at low-battery levels. From the platform operator’s perspective, we aim to determine the optimal operation strategies for mobile charging crowdsourcing platforms to achieve specific objectives. A mathematical modeling framework is developed to capture the interactions among supply, demand, and service operations in the crowdsourced mobile charging market. To design an efficient solution method to solve the formulated model, we first analyze the model properties by rigorously proving that a crucial variable set for operating the mobile charging crowdsourcing system includes charging price, commission control, and period-specific aggregate demand control. Besides, we provide both an equivalent condition and a necessary condition for checking the feasibility of these crucial variables. On top of this, we construct a search tree according to the operation periods in a day to solve the optimal operation strategies, wherein a nondominated principle is adopted as an accelerating technique in the searching process. The solution obtained from the proposed solution algorithm is proved to be sufficiently close to the actual global optimal solutions of the formulated model up to the resolution of the discretization scheme adopted. Numerical examples provide evidence verifying the model’s validity and the solution method’s efficiency. Overall, the research outcome of this work can offer service operators structured and valuable guidelines for operating mobile charging crowdsourcing platforms. Funding: This work was supported by the Singapore Ministry of Education [Grant RG124/21]. Supplemental Material: The online appendices are available at https://doi.org/10.1287/trsc.2023.0126 .

Bibliometric Patent Review of Minimally Invasive Spine Surgery.

This study analyzes patents associated with minimally invasive spine surgery (MISS) found on the Lens open online platform. The goal of this research was to provide an overview of the most referenced patents in the field of MISS and to uncover patterns in the evolution and categorization of these patents. MISS has rapidly progressed, with a core focus on minimizing surgical damage, preserving the natural anatomy, and enabling swift recovery, all while achieving outcomes that rival traditional open surgery. While prior studies have primarily concentrated on MISS outcomes, the analysis of MISS patents has been limited. To conduct this study, we used the Lens platform to search for patents that included the terms "minimally invasive" and "spine" in their titles, abstracts, or claims. We then categorized these patents and identified the top 100 with the most forward citations. We further classified these patents into 4 categories: Spinal Stabilization Systems, Joint Implants or Procedures, Screw Delivery System or Method, and Access and Surgical Pathway Formation. Five hundred two MISS patents were identified initially, and 276 were retained following a screening process. Among the top 100 patents, the majority had active legal status. The largest category within the top 100 patents was Access and Surgical Pathway Formation, closely followed by Spinal Stabilization Systems and Joint Implants or Procedures. The smallest category was Screw Delivery System or Method. Notably, the majority of the top 100 patents had priority years falling between 2000 and 2009, indicating a moderate positive correlation between patent rank and priority year. Thus far, patents related to Access and Surgical Pathway Formation have laid the foundation for subsequent innovations in Spinal Stabilization Systems and Screw Technology. This study serves as a valuable resource for guiding future innovations in this rapidly evolving field.

Modeling collaborative data service provision around an open source platform under uncertainty with stochastic provision games

This paper uses concepts taken from Cooperative Game Theory to model the incentives to join forces among a group of agents involved in collaborative provision of a mobile app under uncertainty around an open source platform. Demand uncertainty leads the agents to reach a noncooperative equilibrium by offering low quality apps. This can be avoided by introducing a coordination scheme through a common platform that eliminates the effects of lack of information. Coordination is achieved by providing a revenue sharing scheme enforcing the stability of the collaboration but also defined in a “fair” way, depending on the importance of the resources that each provider supplies to the app. To this aim, we introduce the concept of Stochastic Provision Games. This coordination leads both to higher app quality and improved profitability for the participants.

Integrating a PABX Telephone System Learning Tool into Telecommunications Technology Curricula

This study aims to develop an operational Private Automatic Branch Exchange (PABX) telephone system to be used as a laboratory tool for students in the Bachelor of Science in Electronics and Communications Technology (BSECT) program at the University of Science and Technology of Southern Philippines (USTP). The PABX trainer is expected to enhance students' occupational preparation for potential employment in the telecommunications and networking industries. Through the operational platform, students' cognitive, manipulative, and programming skills related to PABX can be assessed. A telecommunications course is a core component of the BSECT curriculum, familiarizing students with corporate communications platforms like PABX. The presence of physical PABX equipment is crucial for quality instruction aligned with the Commission on Higher Education's (CHED) standards and the university's accreditation requirements. While PABX has been widely used in industry, incorporating its technology into the BSECT curriculum remains necessary. This study aims to innovate pedagogical tools that establish students' PABX-related skill sets and potentially lead to further investigations and system integrations with other ICT technologies. The study yielded positive affirmation of the content, technical, and instructional qualities, and its overall acceptability relative to the learning tool. There are also significant improvements in the student’s cognitive skills, manipulative skills, and programming skills set out of using the PABX learning tool in laboratory activities.

Enhanced lithium‐ion battery state‐of‐charge estimation for Electric Vehicles using the AOA‐DNN approach

AbstractElectric vehicles (EVs) battery management systems (BMSs) rely on exact state of charge (SoC) estimations to guarantee efficient and safe operation. Lithium‐ion batteries (LIBs) are favored for EVs due to their extended lifespan, high energy density, and minimal self‐discharge and high voltage. To address these issues, this research propose a LIB SoC prediction based on an actual BMS in EVs. The main objective is improving SoC of LIB. The proposed hybrid strategy is the combined performance of both the dynamic neural networks (DNN) and arithmetic optimization algorithm (AOA). Commonly it is named as DNN‐AOA technique. The SoC of Lithium‐ion batteries are predicted using the DNN approach. The proposed AOA is used to optimize the weight parameter of DNN to enhance prediction accuracy and reliability. By then, the operational MATLAB platform has adopted the proposed framework, and existing procedures are used to compute its execution. The proposed method demonstrates superior existing like Bayesian network (DBN), random vector functional link neural network (RVFLNN) and Gaussian progress regression (GPR). The proposed method yields a lower error value of 0.1 and a higher accuracy value of 98% compared with other existing methods.

Enhancing Manufacturing Excellence with Digital-Twin-Enabled Operational Monitoring and Intelligent Scheduling

This research examines the potential of digital twin (DT) technology for reformation within China’s traditional solid-wood-panel processing industry, which currently suffers from production inefficiencies and the slow adoption of digital technology. The research centers around developing a digital twin system, elucidating improvements in manufacturing efficiency, waste management, process simulation, and real-time monitoring. These capabilities facilitate immediate problem solving and offer transparency in the process. The digital twin system is comprised of physical, transport, virtual, and application layers, employing a MySQL database and using the Open Platform Communications Unified Architecture (OPC UA) protocol for communication. The application of this system has led to heightened production efficiency and better material use in the solid-wood-panel manufacturing line. Integrating the dynamic selection adaptive genetic algorithm (DSAGA) into the virtual layer drives the system’s efficiency forward. This evolved approach has allowed for an enhancement of 8.93% in the scheduling efficiency of DSAGA compared to traditional genetic algorithms (GAs), thereby contributing to increased system productivity. Real-time mapping and an advanced simulation interface have strengthened the system’s monitoring aspect. These additions enrich data visualization, leading to better comprehension and a holistic process view. This research has ignited improvements in solid-wood-panel production, illustrating the tangible benefits and representing progress in incorporating digital technology into traditional industries. This research sets a path for transforming these industries into smart manufacturing by effectively bridging the gap between physical production and digital monitoring. Furthermore, the adjustability of this approach extends beyond solid-wood-panel production, indicating the capability to expedite movement towards intelligent production in various other manufacturing sectors.

Service product codesign with digital platform operators based on hierarchical interactive optimization

This paper proposes a novel approach to address the evolving landscape of service product design, particularly in collaboration with digital platform operators. By formulating a nonlinear hierarchical interactive optimization model (HIO) based on bilevel programming (BLP), the paper tackles the complexities of joint decision-making between service providers and digital platforms. At the upper level, the model optimizes digital platform selection, product attribute configuration, and pricing to maximize provider profit, while the lower level determines platform participation in codesign to maximize their own profits. Furthermore, a codesign incentive mechanism, encompassing revenue-sharing proportions and platform promotion efforts, is designed to incentivize collaboration.Applied to the tourism industry, the HIO model is solved using a nested genetic algorithm (NGA), allowing for sensitivity analyses on platform contribution and promotion efforts. Results indicate that increased platform participation in codesign may raise configuration complexity and costs for the service agency, potentially impacting profitability. Additionally, higher platform promotion efforts can drive profit growth for both parties, supporting long-term development. Ultimately, the HIO model demonstrates superior performance compared to traditional two-stage method (TSM), underscoring its applicability and effectiveness in addressing codesign challenges between service agencies and digital platforms.

Automation of customizable library preparation for next-generation sequencing into an open microfluidic platform

Next-generation sequencing (NGS) is becoming more relevant for medical diagnostics, especially for using cell-free DNA to monitor response to therapy in cancer management, as high sensitivity of NGS enables detection of rare events. Sequencing Library preparation is a time-consuming and complex process, and large-scale liquid handlers are often used for automation. However, for smaller labs and low-to-medium throughput samples, these liquid handlers are expensive and need experts for handling. This work presents a proof-of-concept for library preparation on a commercially available and open lab-on-a-chip platform, which provides an alternative automation for low-to-medium throughput requirements. It covers common library preparation steps optimized to a microfluidic environment that include customizable PCR for target enrichment, end-repair, adapter ligation, nucleic acid purification via magnetic beads, and an integrated quantification step. The functionality of the cartridge is demonstrated with reference cfDNA containing different allelic frequencies of seven known mutations. Processing the samples in the cartridge reveals highly comparable results to manual processing (Pearson r = 0.94) based on amplicon sequencing. Summarized, the proposed automated lab-on-a-chip workflow for customizable library preparation could further pave the way for NGS to evolve from a technology used for research purposes to one that is applied in routine cancer management.

Analyzing the NetEase CloudMusics Marketing Campaign

The Internet's innovation has had a profound impact on NetEase CloudMusic's development and marketing strategy adjustments. This paper examines the 2017 "Music Review Train" campaign, which NetEase CloudMusic and Hangzhou Metro launched, with the goal of identifying the strategic factors that led to its success. Based on the marketing mix theory of 4Is, this paper applies the campaign as a case and employs relevant data and literature. This paper found that the success of the campaign was primarily due to the application of an interesting strategy by selecting interesting music reviews as advertising content. This paper suggests exploring the interests of different audience groups in the future and collaborating with popular film and television intellectual property (IP). The second reason is the application of an interaction strategy, which allows for offline and online interaction with users. It is suggested that in the future, emphasis can be placed on the operation of new media platforms and the opening of offline shops to offer an interactive experience. This paper hoped to provide a reference for upgrading the marketing strategy of NetEase CloudMusic to improve the communication effect and brand image and thus contribute to the brands future development.

Retraction Note: Unsupervised machine learning and image recognition model application in English part-of-speech feature learning under the open platform environment

Retraction Note: Unsupervised machine learning and image recognition model application in English part-of-speech feature learning under the open platform environment

FAIRsoft-a practical implementation of FAIR principles for research software.

Software plays a crucial and growing role in research. Unfortunately, the computational component in Life Sciences research is often challenging to reproduce and verify. It could be undocumented, opaque, contain unknown errors that affect the outcome, or be directly unavailable and impossible to use for others. These issues are detrimental to the overall quality of scientific research. One step to address this problem is the formulation of principles that research software in the domain should meet to ensure its quality and sustainability, resembling the FAIR (findable, accessible, interoperable, and reusable) data principles. We present here a comprehensive series of quantitative indicators based on a pragmatic interpretation of the FAIR Principles and their implementation on OpenEBench, ELIXIR's open platform providing both support for scientific benchmarking and an active observatory of quality-related features for Life Sciences research software. The results serve to understand the current practices around research software quality-related features and provide objective indications for improving them. Software metadata, from 11 different sources, collected, integrated, and analysed in the context of this manuscript are available at https://doi.org/10.5281/zenodo.7311067. Code used for software metadata retrieval and processing is available in the following repository: https://gitlab.bsc.es/inb/elixir/software-observatory/FAIRsoft_ETL.

Impact of contextual factors on the evaluation outcomes of national virtual simulation experimental teaching projects in biology and medicine

The number of applications for National Virtual Simulation Experimental Teaching Projects (NVSETPs) in China has seen a significant increase. Consequently, the influence of contextual factors and their potential personal connections on the evaluation results, whether for national or non-national NVSETPs, has become a prominent concern. In this study, we employed a modified back-chaining method using logistic regression to examine whether contextual factors in NVSETP applications could explain the evaluation outcomes. Our analysis was based on data available on the open platform of China's Ministry of Education (MOE). We identified several significant influencing factors, including the score on a five-point rating system, the number of clicks on the application page, school quality, school region, and the gender, title, and position of the applicants. Our results shed light on the impact of contextual factors on the evaluation results of NVSETPs in the fields of biology and medicine, using a modified back-chaining method. We conclude that enhancing the transparency of the assessment process and implementing standardized, detailed scoring guidelines for NVSETPs would mitigate the negative influence of contextual factors.

Digital Platforms in the Focus of National Law

The paper contains an overview of the research workshop Digital platforms: new environment for collaboration at the Institute of Legislation and Comparative Law under the Federal Government (ILCL) on 23 April 2024, and findings of the expert survey Digital platforms in the focus of national law. Presentations by participants were systematized by the authors of the overview around the most relevant subjects related to digital platform operations: general issues of digital platform regulation; digital platforms’ impact on human rights; digital platforms in public administration; digital platforms in private law, criminal law and specific branches. The workshop was attended by researchers representing the Institute of Legislation and Comparative Law, Kutafin Moscow State Law University, National Research University–Higher School of Economics, Moscow State University, Russian Academy of National Economy and Public Administration under the President of Russia, Plekhanov Russian University of Economics, Orenburg State University, Siberian Federal University, Gubkin Russian State University of Oil and Gas, Saint Petersburg University of the Interior Ministry, MTS Joint-Stock Company, etc. Legal themes under discussion included the legal nature of digital platforms; digital platforms’ impact on the economy, public administration, human rights and the underlying changes to their amount and content; associated risks; digital platforms and anti-trust regulation; networking effect of digital platforms; labor relations and platform employment; labor right protection and digital platforms; dangers of discrimination related to digital platforms.

HEROIC: a platform for remote collection of electroencephalographic data using consumer-grade brain wearables

The growing number of portable consumer-grade electroencephalography (EEG) wearables offers potential to track brain activity and neurological disease in real-world environments. However, accompanying open software tools to standardize custom recordings and help guide independent operation by users is lacking. To address this gap, we developed HEROIC, an open-source software that allows participants to remotely collect advanced EEG data without the aid of an expert technician. The aim of HEROIC is to provide an open software platform that can be coupled with consumer grade wearables to record EEG data during customized neurocognitive tasks outside of traditional research environments. This article contains a description of HEROIC’s implementation, how it can be used by researchers and a proof-of-concept demonstration highlighting the potential for HEROIC to be used as a scalable and low-cost EEG data collection tool. Specifically, we used HEROIC to guide healthy participants through standardized neurocognitive tasks and captured complex brain data including event-related potentials (ERPs) and powerband changes in participants’ homes. Our results demonstrate HEROIC’s capability to generate data precisely synchronized to presented stimuli, using a low-cost, remote protocol without reliance on an expert operator to administer sessions. Together, our software and its capabilities provide the first democratized and scalable platform for large-scale remote and longitudinal analysis of brain health and disease.