Open Platform Research Articles

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.

Exploring the Relationship Between E-Government, Transparency, and Citizen Trust in Government Services

This study investigates the relationship between e-government initiatives, transparency, and citizen trust in government services. By synthesizing existing literature and analyzing case studies from various countries, the research aims to understand how digital governance and transparency can influence public trust in governmental operations and services. E-government, which encompasses the use of digital platforms and technologies to deliver government services, is found to play a significant role in enhancing transparency and accessibility. The study reveals that e-government initiatives facilitate greater openness by providing citizens with easy access to information and services, thereby reducing opportunities for corruption and enhancing accountability. This increased transparency is shown to be crucial in fostering a more informed and engaged citizenry. Transparency in government is identified as a key factor in building citizen trust. The research highlights that when governments are open about their processes, decisions, and use of resources, citizens are more likely to perceive them as reliable and trustworthy. Transparency initiatives, such as open data platforms and online public consultations, allow for greater scrutiny and participation, which in turn can increase public confidence in government actions. Citizen trust in government services is essential for the effective functioning of a democratic society. The study indicates that trust is significantly influenced by the perceived transparency and accessibility of government services. When citizens trust that their government is transparent and accountable, they are more likely to engage with and utilize government services, leading to greater societal cohesion and cooperation. In conclusion, the interplay between e-government, transparency, and citizen trust is critical in shaping the effectiveness and credibility of government services. The study suggests that enhancing digital governance and transparency can lead to increased public trust and more effective government operations. Future research should explore the specific mechanisms through which e-government initiatives can further enhance transparency and trust in different socio-political contexts.

Teleoperated Driving with Virtual Twin Technology: A Simulator-Based Approach

This study introduces an innovative Teleoperated Driving (ToD) system integrated with virtual twin technology using the MORAI simulator. The system minimizes the need for extensive video data transmission by utilizing text-based vehicle information, significantly reducing the communication load. Key technical advancements include the use of high-precision GNSS devices for accurate vehicle location tracking, robust data communication via the MQTT protocol, and the implementation of the Ego Ghost mode in the MORAI simulator for precise vehicle simulation. The integration of these technologies enables efficient data transmission and enhanced system reliability, effectively mitigating issues such as communication blackouts and delays. Our findings demonstrate that this approach ensures stable and efficient operation, optimizing communication resource management and enhancing operational stability, which is crucial for scenarios requiring high video quality and real-time response. This research represents a significant advancement in ToD technology, establishing a precedent for integrating virtual twin systems to create more resource-efficient and reliable autonomous driving backup solutions. The virtual twin-based ToD system provides a robust platform for remote vehicle operation, ensuring safety and reliability in various driving conditions.

OPLEM: Open Platform for Local Energy Markets

Local Energy Markets (LEMs) have been proposed as an effective solution to coordinate distributed energy resources within emerging smart local energy systems. However, LEMs are still at an early stage of development and are not widely implemented yet. Extensible open-source software tools are needed to simulate different market designs and evaluate their feasibility and effectiveness. This paper presents OPLEM, an open-source Python package for modelling and testing LEM designs. It offers a modular and flexible framework to create and test market designs adapted for distribution networks. OPLEM integrates two distinct models—one for designing and simulating local energy markets, and another for network operation. Unlike existing tools, OPLEM platform offers a comprehensive solution that bridges the gap between market design and network operation assessment, enabling stakeholders to gain deeper insights into the interactions between market dynamics and grid performance. Additionally, the inclusion of participant and asset models further enhances the tool’s versatility, allowing for a holistic analysis. Thanks to its modular structure, OPLEM allows the development of customised LEM designs and initially incorporates four popular LEM designs: Time-of-Use pricing market, centralised dispatch with distribution locational marginal pricing, bilateral peer-to-peer market and auction market. By combining market design and network modelling functionalities, the tool empowers users to explore various scenarios and optimise market clearings to enhance both economic efficiency and grid reliability.

Organizational Technology Policies and the Growth of Micro and Small Enterprises in Nairobi City County, Kenya

This study sought to establish the influence of establish the influence of organizational technology policies on the Growth of Micro and Small Enterprises in Nairobi, Kenya and; To determine the moderating effect of Entrepreneurial Orientation on organizational technology policies and the Growth of Micro and Small Enterprises in Nairobi, Kenya. The study was grounded on the Resource Based theory. The study adopted a descriptive research design since the design enabled the researcher to generalize the findings to a larger population of MSEs. In addition, this design enabled the researcher describe the characteristics of the population being studied as they exist at present hence minimizing biasness and maximizing the reliability of the evidence collected. This study was guided by positivism philosophy. The target population for this study comprised of all the 1835 Owners or managers of the MSEs registered with Nairobi City County and have operated for more than five years. Stratified and Simple Random sampling techniques was used to select a total of 184 respondents for the study. The study found out that organizational technology policies and organizational technology policies were the key variable that significantly influencing the Growth of Micro and Small Enterprises in Nairobi County, Kenya. Specifically, MSEs Staff were largely found to be un-trained especially on Emerging Technologies and lacked motivation to adopt and use technologies. In addition, study found out that there was low adoption on use of social media platform for business operation. The study recommends that the Management of MSEs needs to make deliberate efforts and invest on training especially on Emerging Technologies such as e-commerce, communication, e-production, e-procurement among others. This will ensure that the MSEs staffs are well equipped with the necessary skills and knowledge needed to effectively utilize the emerging technologies in the market. Further, the management of the MSEs needs to be motivated to adopt and use technologies and innovation. Motivation many be inform of incentives and rewards that motivates users to adopt and use technology in their operations. The is also need to invest in a strong and robust social media platform that are easy to use for most users. Social media networks can be harnessed to boast in product and services marketing and sourcing of market information among other.

Wikimedia Australia and first nations metadata: utilising the ATSILIRN protocols to create culturally appropriate description and access

ABSTRACT Protocols have been utilised as a tool to build dialogue on the management of Indigenous knowledges in a library and archive context. This paper discusses how the Aboriginal and Torres Strait Islander Protocols for Libraries, Archives and Information Services (ATSILIRN [n.d.] Protocols for Libraries Archives and Information Services. http://atsilirn.aiatsis.gov.au/protocols.php) could guide Wikimedia projects and Wikipedia and Wikidata editors to better describe Aboriginal and Torres Strait Islander content in a self-determined and culturally appropriate manner. Focussing specifically on questions of Description and Classification (Protocol 5), the paper examines the limitations of current metadata and Wikipedia practices and opportunities for embedding and being led by Aboriginal and Torres Strait Islander worldviews and principles. Overall, the article contributes to further understanding the challenges and opportunities of including Indigenous knowledges and perspectives in open access platforms.

Public data accessibility and corporate maturity mismatch: Evidence from China

As the importance of public data to the real economy continues to be recognized, this study explores public data accessibility as a determinant of corporate maturity mismatch between investment and financing. Exploiting the staggered establishment of public data open platforms (PDOPs) across different cities in China, we observe a significant reduction in local firms' maturity mismatch following the establishment of PDOPs. The results remain robust even after conducting a battery of robustness tests, including placebo tests, matching analysis, as well as controlling for the influence of other major concurrent reforms and utilizing alternative measures for corporate maturity mismatch. Mechanism analysis indicates that the establishment of PDOPs contributes to an increase in the scale of long-term loans and an extension of the debt maturity structure. This suggests that enhanced public data accessibility effectively strengthens banks' information discernment capabilities. Cross-sectional analysis indicates that the effects are more pronounced in samples of private enterprises, companies with high levels of earnings management, fewer analysts covering, lower collateral values, and a higher proportion of local subsidiaries. Additionally, the economic consequence analysis reveals that the establishment of PDOPs ultimately helps to reduce corporate default risk. Finally, this study finds no evidence of potential spatial spillover effects from the establishment of PDOPs. This study offers a new perspective on understanding changes in corporate maturity mismatch, providing significant policy implications for preventing and mitigating financial risks.

Design and Deployment of ML in CRM to Identify Leads

ABSTRACT In today’s era, organizations are increasingly prioritizing process automation to optimize efficiency and drive sales. One area where Machine Learning (ML) techniques can be particularly valuable is in automating tasks such as lead classification for sales. In Jupyter Notebooks, logistic regression was utilized to design and to train a model to accurately predict whether a lead will convert into a client or not. Then, in Azure Machine Learning Studio which is a Machine Learning Operations platform (MLOps), the Two-Class Logistic Regression algorithm was used to design a pipeline, train a model, and deploy a web service, which is consumed by Salesforce system through an Apex code. The web service receives the variables of a particular lead record and then returns the prediction as a numeric ranking. By leveraging these ML techniques, firm’s resources can strategically be focused for maximum effectiveness. Overall, our work involves a C# windows application to extract CRM marketing interactions, leveraging the power of ML, a logistic regression model in AML and Apex code. This approach enables us to drive efficiency, enhance sales outcomes, and allocate resources more effectively.

Blockchain Technology for tracking and tracing containers: model and conception

The maritime industry has increasingly integrated advanced technologies such as AI, Blockchain, Big Data, and IoT, transforming traditional port operations into smart facilities aimed at enhancing global trade competitiveness. A particular focus has been on improving tracking and tracing services, with Blockchain technology emerging as pivotal for ensuring data integrity, transparency, and traceability across supply chains. This article proposes a blockchain-based tracking and tracing system model tailored for monitoring containers in Moroccan ports. Utilizing the Unified Modeling Language (UML), the model seeks to optimize resource allocation and boost stakeholder satisfaction through detailed diagrams and functional data requirements depiction. Despite challenges such as IoT terminal platform connectivity and operator resitance, successful implementation was achieved, establishing a foundational framework for a comprehensive container monitoring system. This model provides valuable insights for supply chain professionals and scholars interested in item tracking, aiming to integrate Blockchain with technologies like RFID, GPS, RTLS, QR Codes, BLE, and IoT sensors to enhance port operation efficiency and container management effectiveness. By leveraging these integrated technologies, ports can further improve operational efficiency and ensure accurate traceability of containers throughout the supply chain, contributing to overall trade facilitation and economic growth..

Exploring Innovation Success Factors and Strategies for B2B Platforms in the Manufacturing Sector in Germany

Objective: Business-to-business (B2B) platforms in the manufacturing sector have a profound impact on the digital transformation of small and medium-sized enterprises (SMEs). However, there is still a lack of research on this topic. The primary objective of this study is to explore the existing landscape of B2B platforms and thoroughly assess the benefits and challenges for companies using them, in order begin to fill the identified research gap. This paper presents a comprehensive investigation of B2B platforms in the German manufacturing sector and their profound influence on the digital transformation of SMEs. Methods: Within the scope of our in-depth analysis of the current state of B2B platforms, a meticulous review of relevant literature is conducted following the approach of Brocke et al. and a comprehensive analysis of both industry-specific and cross-industry best practices on B2B platforms is performed. In addition, the research includes in-depth interviews with platform providers, partners, and academic experts to enhance the depth of understanding. We identify the factors that contribute to their adoption as well as those that hinder their integration, providing the basis for successful innovation. The viability of the concept is assessed through a comprehensive evaluation methodology involving platform operators. Results: By providing an in-depth assessment of the benefits and challenges faced by companies using B2B platforms, this work contributes to filling existing research gaps. It identifies factors that contribute to their adoption and those that hinder their integration, thus laying the foundation for successful innovation. Finally, it proposes forward-looking guidelines that will enable SMEs to capitalize on the transformative capabilities of these platforms in the future. Conclusion: The findings of this study show that SMEs can reap numerous benefits from B2B platforms, such as increades efficiency, optimal use of resources, and access to previously untapped markets. However, certain barriers, including the high costs associated with implementation and a prevailing lack of trust among platform users, serve as obstacles to their widespread adoption. The results of this research provide important insights into the current state of B2B platforms and their potential to drive digital transformation. This research advances academic understanding in this area, and provides valuable knowledge for companies seeking to navigate the landscape of B2B platforms and their role in driving digital transformation as a foundation for innovation.

Inclusive Practices in the Classroom and Workplace

We are very pleased to announce that our journal JEICOM has now reached a new and exciting milestone that will help us increase our impact factor in open access platforms and beyond. To this end we are now hosted by the Open Access Publishing Association (OAPA), a new and exciting platform, which aims to challenge the current model of publishing; an aim that we share dearly as editors-in-chief of JEICOM. Since the creation of our journal in 2019, we aim to provide a free and open platform to academics, researchers, professionals, to share knowledge, communicate and exchange via research dialogue and high-quality publications. It is now time for us to join forces with a new partner, join a larger academic community, which will help us grow from within and with others, together.