Open Platform Research Articles

An Integrated CEEMDAN to Optimize Deep Long Short-Term Memory Model for Wind Speed Forecasting

Accurate wind speed forecasting is crucial for the efficient operation of renewable energy platforms, such as wind turbines, as it facilitates more effective management of power output and maintains grid reliability and stability. However, the inherent variability and intermittency of wind speed present significant challenges for achieving precise forecasts. To address these challenges, this study proposes a novel method based on Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN) and a deep learning-based Long Short-Term Memory (LSTM) network for wind speed forecasting. In the proposed method, CEEMDAN is utilized to decompose the original wind speed signal into different modes to capture the multiscale temporal properties and patterns of wind speeds. Subsequently, LSTM is employed to predict each subseries derived from the CEEMDAN process. These individual subseries predictions are then combined to generate the overall final forecast. The proposed method is validated using real-world wind speed data from Austria and Almeria. Experimental results indicate that the proposed method achieves minimal mean absolute percentage errors of 0.3285 and 0.1455, outperforming other popular models across multiple performance criteria.

An integrated analysis of consumer purchase decision-making on second-hand trading platforms: evidence from china

ABSTRACT In the context of circular economy and sustainable development, second-hand trading platforms are in the preliminary stages of development, revealing the consumer behavior of second-hand trading platform consumers has become a challenging topic. Previous studies have often used technology acceptance models to analyze consumers’ acceptance of electronic commerce platform technologies. However, these studies have not analyzed consumer purchase decision-making well, lacked affective explanations of the logic of consumer decisions, and lacked discussions of multiple stakeholders. Therefore, this study proposes a comprehensive framework model for consumer decision making on second-hand trading platforms. We analyzed consumer purchase decision process, identified three key dimensions eight factors. Through a combination of the ABC attitude model and the technology acceptance model, we sorted out a hypothetical model consisting of cognitive, affective, and behavioral. Then, we validated and analyzed the empirical results in China using the partial least squares structural equation technique. The findings underscore the significant impact of product and platform dimensions on consumer sentiment and behavior. Theoretically, this study advances the understanding of consumer decision-making in the context of second-hand platforms, particularly within the framework of the circular economy. Practically, it offers actionable insights for merchants and platform operators to enhance user experience and service quality, thereby supporting the sustainable growth of e-commerce.

Sex and gender reporting in UK emergency medicine trials from 2010 to 2023: a systematic review

BackgroundFemale participants are underrepresented in randomised control trials conducted in urgent care settings. Although sex and gender are frequently reported within demographic data, it is less common for primary outcomes...

Interstitial dual-mode ultrasound with a 3-mm MR-compatible catheter for image-guided HIFU and directional in-vitro tissue ablations.

Current interstitial techniques of tumor ablation face challenges that ultrasound technologies could meet. The ablation radius and directionality of the ultrasound beam could improve the efficiency and precision. Here, a 9-gauge MR-compatible dual-mode ultrasound catheter prototype was experimentally evaluated for Ultrasound Image-guided High Intensity Focused Ultrasound (USgHIFU) conformal ablations. The prototype consisted of 64 piezocomposite linear array elements and was driven by an open research programmable dual-mode ultrasound platform. After verifying the US-image guidance capabilities of the prototype, the HIFU output performances (dynamic focusing and HIFU intensities) were quantitatively characterized, together with the associated 3D HIFU-induced thermal heating in tissue phantoms (using MR thermometry). Finally, the ability to produce robustly HIFU-induced thermal ablations in in-vitro liver was studied experimentally and compared to numerical modeling. Investigations of several HIFU dynamic focusing allowed overcoming the challenges of miniaturizing the device: mono-focal focusing maximized deep energy deposition, while multi-focal strategies eliminated grating lobes. The linear-array design of the prototype made it possible to produce interstitial ultrasound images of tissue and tumor mimics in situ. Multi-focal pressure fields were generated without grating lobes and transducer surface intensities reached up to Isapa = 14 W·cm-2. Seventeen elementary thermal ablations were performed in vitro. Rotation of the catheter proved the directionality of ablation, sparing non-targeted tissue. This experimental proof of concept demonstrates the feasibility of treating volumes comparable to those of primary solid tumors with a miniaturized USgHIFU catheter whose dimensions are close to those of tools traditionally used in interventional radiology, while offering new functionalities.

Public data openness and firm innovation strategies

ABSTRACT This paper investigates the impact of urban-level public data open platforms on corporate innovation strategies by using them as a quasi-natural experiment and employing the difference-in-differences (DID) method. The findings indicate that public data openness significantly promotes incremental innovation rather than breakthrough innovation, with economic and public service data playing a particularly influential role. Moreover, the preference for incremental innovation by enterprises reduces innovation quality, but this issue can be mitigated by introducing industry competition and strengthening intellectual property protection.

Microservices architecture to enable an open platform for realizing zero defects in cyber-physical manufacturing

Abstract The market’s demand for high-quality products necessitates innovative manufacturing approaches that emphasize flexibility, adaptability, and the reduction of defects. Traditional systems are currently evolving towards embracing I4.0 technologies, including data collection, processing, analytics and digital twin, aiming for zero-defect manufacturing quality. This paper introduces an open platform, compliant with RAMI4.0 standards, designed to improve manufacturing quality. The platform integrates data using Asset Administration Shells with microservices adaptation for data ingestion and advanced analytics. Additionally, it incorporates Non-Destructive Inspection tools, demonstrating a seamless integration of measurement and quality assurance. This study details the architectural specification and validation of the openZDM platform, employing microservices to ensure flexibility, modularity and scalability, aligning with the RAMI4.0 model. The platform was validated through deployment in an automotive assembly line, highlighting its effectiveness in integrating inspection scenarios and early defect detection tools. The architecture employs a choreographed approach to manage loosely coupled microservices, enabling efficient data lifecycle management from collection through analytics to visualization.

Another Evidence to not Employ Customized Masked Hardware

As a well-studied countermeasure against side-channel analysis attacks, there is a general interest in applying masking to different cryptographic functions executed on different platforms. On the one hand, despite their high performance, masked hardware implementations are dedicated to specific algorithms, making them inflexible. On the other hand, applying masking on software involves serious challenges including significant overhead in terms of efficiency and difficulties to maintain theoretical security guarantees in practice. As a result, a line of research has been devoted to enable masked operations in flexible platforms (i.e., microprocessors) by including some masked modules in their hardware, hence a combination of flexibility and performance. In such scenarios, RISC-V is a natural choice as hardware can be adjusted to the extended instruction set. One such attempt presented at CHES 2021 is known as SCARV, which extends the Instruction Set Architecture (ISA) of a RISC-V core with a rich number of first-order masked operations on both Boolean and arithmetic masked operands. In this work, we conduct a comprehensive analysis of SCARV. Instead of relying on empirical measurements to demonstrate security, we utilize tool-assisted evaluations. Through these evaluations, we identified a couple of design flaws that lead to leakage in the masked implementations hosted by the corresponding processor. These flaws are primarily due to the lack of composability of cascaded components. While heuristic and ad-hoc design principles can result in secure, small, and efficient designs, they lack formal security proofs, which may lead to security flaws, like those we report here. Consequently, this work serves as a motivation for using composable masked modules and tool-assisted evaluations when constructing complex circuits.

CTGAN-ENN: a tabular GAN-based hybrid sampling method for imbalanced and overlapped data in customer churn prediction

Class imbalance is one of many problems of customer churn datasets. One of the common problems is class overlap, where the data have a similar instance between classes. The prediction task of customer churn becomes more challenging when there is class overlap in the data training. In this research, we suggested a hybrid method based on tabular GANs, called CTGAN-ENN, to address class overlap and imbalanced data in datasets of customers that churn. We used five different customer churn datasets from an open platform. CTGAN is a tabular GAN-based oversampling to address class imbalance but has a class overlap problem. We combined CTGAN with the ENN under-sampling technique to overcome the class overlap. CTGAN-ENN reduced the number of class overlaps by each feature in all datasets. We investigated how effective CTGAN-ENN is in each machine learning technique. Based on our experiments, CTGAN-ENN achieved satisfactory results in KNN, GBM, XGB and LGB machine learning performance for customer churn predictions. We compared CTGAN-ENN with common over-sampling and hybrid sampling methods, and CTGAN-ENN achieved outperform results compared with other sampling methods and algorithm-level methods with cost-sensitive learning in several machine learning algorithms. We provide a time consumption algorithm between CTGAN and CTGAN-ENN. CTGAN-ENN achieved less time consumption than CTGAN. Our research work provides a new framework to handle customer churn prediction problems with several types of imbalanced datasets and can be useful in real-world data from customer churn prediction.

Optimization and Innovation of the Operation Model of Mobile Social E-commerce under AI Empowerment: Taking the Female Community Platform “Little Red Book” as an Example

This article takes the female community platform “Little Red Book” as an example to explore the optimization and innovation of mobile community e-commerce operation mode under Artificial Intelligence (AI) empowerment. Firstly, the relevant concepts were defined, and then the unique attributes of mobile community e-commerce were analyzed. As a typical representative of mobile community e-commerce, Little Red Book introduces the background and characteristics of its platform, analyzes its mobile community operation mode, and focuses on exploring how to establish a mobile community e-commerce platform and effective operation mode under the empowerment of AI technology, to provide some reference and inspiration for the development and operation of Little Red Book and other e-commerce platform enterprises.

Variable frequency constant pressure water supply system based on Siemens S-1200

Abstract This paper presents the design of an efficient, cost-effective, and intelligent variable frequency water supply control system. The system consists of a PLC, frequency converters, water pumps, pressure sensors, and other components to form a closed-loop control system. It enables closed-loop control of water pressure, ensuring a dynamic balance between water consumption and water supply. A user-friendly HMI is designed based on a software platform for easy operation by management personnel. It allows for seamless switching of the system’s operating modes and the running status of water pumps. Real-time display of pump operation and monitoring of system performance are also provided. Compared to traditional water supply methods, this system exhibits superior reliability, speed, and performance. It is capable of meeting the daily water consumption needs of residents in high-rise buildings.

An Empirical Study of Sustainable Development Model for Recreational Fishery Based on Multiple Perspectives

ABSTRACTTo analyze the sustainable development of recreational fisheries, this study constructs an evaluation model by employing resource‐based theory, service‐dominant logic, and consumer value cocreation. Data are collected using a questionnaire survey of offshore platform operators, government officials, and scholars with expertise in tourism, leisure, and recreation. Furthermore, the decision‐making trial and evaluation laboratory (DEMATEL) and DEMATEL‐based analytic network process (DANP) methods are adopted to evaluate the causal relationships between the dimensions and criteria and their magnitudes of influences. The results show that recreational fishery operators in Penghu believe that improving the evaluation dimension of service experience should have the highest priority, while officials and scholars consider marine environmental value as the most important for sustainable development. The evaluation model and analysis results will be provided to Penghu offshore platform operators to improve the service experience items provided by recreational fishery through designing high‐value recreational fishery experiential activities.

Unveiling Shopee App Continuance Use: Insights from Malaysian Shoppers

The popularity and acceptance of mobile shopping apps have surged widely due to mobile technology advancement and the COVID-19 outbreak. Retaining customers would be challenging for all mobile shopping app platform operators, particularly in the post-Covid era. However, research on the continuance of mobile shopping apps must be addressed. Hence, the Shopee mobile shopping app was adopted as a case study to examine antecedent factors of continuance intention leading to its continuance use among consumers. This quantitative study was conducted in Klang Valley, Malaysia, and data was collected via a self-administered Google Form. The study included 386 respondents and was analysed using Partial Least Squares Structural Equation Model (PLS-SEM). Customer loyalty, the success quality factors of the information system, incentives, trust, and satisfaction positively affect Shopee's mobile shopping app's continuance intention. Furthermore, it is found that continuance intention does indeed have a crucial effect on continuance use. However, the study reported that perceived enjoyment did not influence continuance intention. The objective of this research is achieved as it manages to generate valuable knowledge on continuance intention and use. This paves the way for more effective development of sustainable user retention strategies.

Prediction of Ground Subsidence Induced by Groundwater Mining Using Three-Dimensional Variable-Parameter Fully Coupled Simulation

In order to predict the ground settlement in a scientific, intuitive, and simple way, based on the theory of Bio-consolidation, a three-dimensional fluid-solid coupled numerical calculation programme FGS-3D for ground settlement was compiled by using the Fortran 95 language, and a front-end operation platform was developed by using Microsoft VisualBasic, so that a three-dimensional variable-parameter fully coupled viscoelastic-plastic model of ground settlement was constructed using the city of Yancheng as an example, and the development of ground settlement and horizontal displacement changes from 2021 to 2030 were predicted. The results show that the three-dimensional fully coupled finite-element numerical model of building load, groundwater seepage, and soil deformation established by the above computer development program can directly create a hydrogeological conceptual model of groundwater mining and predict ground settlement, so as to achieve the visualisation of the three-dimensional seepage of groundwater and the fully coupled simulation of ground subsidence in the whole process of groundwater mining. Under the joint action of construction load and groundwater mining, the water level of the aquifer in Yancheng City rises by 1.26 m on average in the main groundwater mining area of the group III pressurised aquifer, forming two smaller landing funnels, and the lowest water level of the two landing funnels is −15 m.

중국 웹소설 수출 관련 연구의 문제점 및 제언: 한·중·일 웹소설의 키워드 공유 사례를 중심으로

Research on the overseas export of Chinese web novels can be broadly divided into two streams. The first stream analyzes that the contents and platform operating systems accumulated by Chinese web novels in China are excellent enough to be directly applied abroad. The second stream suggests that Chinese web novels are being embraced as the novels of ‘Internet humans’ that transcending nationality, due to their Internet-based nature. However, the research that garners more attention from the Chinese side is the former. Even studies that refer to Chinese web novels as the novels of ‘Internet humans’ often premise the ‘superiority’ of Chinese web novels. This paper organizes the history of the overseas export of Chinese web novels and the context of related research, identifying that the ‘superiority’ of ‘Chinese contents’ or ‘Chinese systems’ created in mainland China is frequently presented as a factor in the growth of the overseas market for Chinese web novels. Furthermore, this paper argues that the growth of Chinese web novels in overseas markets is not merely due to the ‘superiority’ of Chinese contents or systems. Instead, it is attributed to the inherent characteristics of web novels, which candidly expose the desires of the users. This is evidenced by the overlap between the keywords of Chinese web novels and those of Korean and Japanese web novels.

Creating anatomically-derived, standardized, customizable, and three-dimensional printable head caps for functional neuroimaging.

Consistent and accurate probe placement is a crucial step towards enhancing the reproducibility of longitudinal and group-based functional neuroimaging studies. While the selection of headgear is central to these efforts, there does not currently exist a standardized design that can accommodate diverse probe configurations and experimental procedures. We aim to provide the community with an open-source software pipeline for conveniently creating low-cost, 3-D printable neuroimaging head caps with anatomically significant landmarks integrated into the structure of the cap. We utilize our advanced 3-D head mesh generation toolbox and 10-20 head landmark calculations to quickly convert a subject's anatomical scan or an atlas into a 3-D printable head cap model. The 3-D modeling environment of the open-source Blender platform permits advanced mesh processing features to customize the cap. The design process is streamlined into a Blender add-on named "NeuroCaptain". Using the intuitive user interface, we create various head cap models using brain atlases, and share those with the community. The resulting mesh-based head cap designs are readily 3-D printable using off-the-shelf printers and filaments while accurately preserving the head topology and landmarks. The methods developed in this work result in a widely accessible tool for community members to design, customize and fabricate caps that incorporate anatomically derived landmarks. This not only permits personalized head cap designs to achieve improved accuracy, but also offers an open platform for the community to propose standardizable head caps to facilitate multi-centered data collection and sharing.

User-generated accountability: Public participation in algorithmic governance on YouTube

Despite opaque automated systems and few formal channels for participation, YouTubers navigate algorithmic governance on the platform through a strategy we call user-generated accountability: the generation of publicity via content creation to reveal failures, oversights, or harmful policies. Through an analysis of 250 videos, we identify common strategies, concerns, and targets of accountability. Creators primarily upload vlogs that acknowledge the platform’s positive aspects, even as they express concern with YouTube’s policies, automated enforcement systems, poor communication practices, and discrimination against certain creators or content. In publicizing critiques of platform operations, videos enroll creators and audiences as active stakeholders in platform governance that can coordinate actions to draw the company’s attention to matters of concern. We argue that user-generated accountability practices offer a productive starting point for understanding how platform governance disputes come to be and how systems might be shaped or rebuilt to better serve the needs of competing stakeholders.

Between Choice and Compulsion: The Dynamics of Bibliographic Reference Styles Preferred by Library and Information Science Professionals

The substantial surge in the publication of journal articles across diverse academic disciplines on open and online platforms globally has given rise to the challenge of effectively managing bibliographic references. Accordingly, the study will focus on reference styles in library and information science, particularly concerning their awareness, availability of online citation tools, standardization, and comprehensive overview of all commonly preferred styles among LIS professionals. The study explored the reference styles adopted by publishers publishing LIS journals indexed in prominent global databases such as SCOPUS, Web of Science, LISA (Proquest), LISTA, and UGC-CARE (India). The top ten LIS journals indexed in SCOPUS and their reference styles were identified. The preferences of reference styles including Indian Bibliographic Standard, preferred publishers, and the rationale behind their choices were found through a global survey conducted among LIS professionals. The result shows, among LIS professionals, APA style, followed by MLA style and Harvard style, were the most preferred reference styles. Elsevier, followed by Springer, Taylor & Francis, Emerald, and other were among the most preferred publishers. The Indian Bibliographic Standard is used less due to a lack of awareness and adaptability by journals and their publishers and the non-availability of automatic citation tools. This paper provides information to LIS professionals about reference styles used by leading LIS journals indexed in global databases published by different publishers to inform, promote, make early decisions, and enhance knowledge in preparing references or bibliographies for writing research papers or theses ahead of time.

An Assessment of the Urban Streetscape Using Multiscale Data and Semantic Segmentation in Jinan Old City, China

Urban street space is a significant component of urban public spaces and an important aspect of people’s perceptions of a city. Jinan Old City exemplifies the balance between the supply of and demand for green spaces in urban streets. The sense of comfort and the demand level of street spaces are measured via the space demand index. Open platform data, such as those from Baidu Maps and Amap, are evaluated using methods including ArcGIS network analysis and Segnet semantic segmentation. The results obtained from such evaluations indicate that, in terms of the green space supply, the overall level for Shangxin Street in Jinan is not high. Only 24% of the selected sites have an adequate green space supply. The level on Wenhua West Road is higher than that on Shangxin Street. The block on the western side of Shangxin Street has the highest green space demand, with a decreasing trend from west to east. There are several higher selection points in the middle section of Shangxin Street. The demand is lowest in the middle of Wenhua East Road. Shangxin Street’s demand is higher than that of Wenhua West Road. The supply and demand are highly matched on Wenhua West Road and poorly matched on Shangxin Street, with 44.12% of the area in the “low supply, high demand” quadrant. This study proposes targeted optimization strategies based on supply and demand, thereby providing research ideas and methods for urban renewal.

Enhancing Continuous Usage Intention in E-Commerce Marketplace Platforms: The Effects of Service Quality, Customer Satisfaction, and Trust

E-commerce marketplace platforms have evolved into integral digital intermediaries that shape online transactions in competitive environments. Companies continuously endeavor to improve e-service quality, customer satisfaction, and e-trust to gain a competitive advantage. This study aimed to identify the relationships between e-service quality, customer satisfaction, e-trust, and continuous usage intention in e-commerce marketplace platforms. Moreover, this study examined the roles of customer satisfaction and e-trust as mediators. We estimated nine hypothesized relationships using a structural equation modeling technique. Data from 311 users were used in the data analysis. The results are as follows: First, e-service quality significantly and positively affects customer satisfaction, e-trust, and continuous usage intention. Second, customer satisfaction has a significant and positive impact on e-trust and continuous usage intention. Third, e-trust has a significant and positive impact on continuous usage intention. Finally, both customer satisfaction and e-trust serve as significant mediating factors in the relationship between e-service quality and continuous usage intention. These insights hold strategic importance for e-commerce marketplace platform operators, allowing them to formulate service strategies and policies tailored to enhance user experience, foster trust, and drive continued usage, thereby strengthening their market position and ensuring sustained success.

A Unified Knowledge Model for Managing Smart City/IoT Platform Entities for Multitenant Scenarios

Smart city/IoT frameworks are becoming more complex for the needs regarding multi-tenancy, data streams, real-time event-driven processing, data, and visual analytics. The infrastructures also need to support multiple organizations and optimizations in terms of data, processes/services, and tools cross-exploited by multiple applications and developers. In this paper, we addressed these needs to provide platform operators and developers effective models and tools to: (i) identify the causes of problems and dysfunctions at their inception; (ii) identify references to data, processes, and APIs to add/develop new scenarios in the infrastructure, minimizing effort; (iii) monitor resources and the work performed by developers to exploit the complex multi-application platform. To this end, we developed a semantic unified knowledge model, UKM, and a number of tools for its implementation and exploitation. The UKM, with its inferences, allows to browse and extract information from complex relationships among entities. The proposed solution has been designed, implemented, and validated in the context of the open source Snap4City.org platform and applied in many geographical areas with 18 organizations, 40 cities, thousands of operators and developers, and free trials to keep platform complexity under control, as in the interconnected scenarios of the Herit-Data Interreg Project, which is a lighthouse project of the European Commission.