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For ranking aggregation in crowdsourcing task, the key issue is how to select the optimal working group with a given number of workers to optimize the performance of their aggregation. Performance prediction for ranking aggregation can solve this issue effectively. However, the performance prediction effect for ranking aggregation varies greatly due to the different influencing factors selected. Although questions on why and how data fusion methods perform well have been thoroughly discussed in the past, there is a lack of insight about how to select influencing factors to predict the performance and how much can be improved of.

In this paper, performance prediction of multivariable linear regression based on the optimal influencing factors for ranking aggregation in crowdsourcing task is studied. An influencing factor optimization selection method based on stepwise regression (IFOS-SR) is proposed to screen the optimal influencing factors. A working group selection model based on the optimal influencing factors is built to select the optimal working group with a given number of workers.

The proposed approach can identify the optimal influencing factors of ranking aggregation, predict the aggregation performance more accurately than the state-of-the-art methods and select the optimal working group with a given number of workers.

To find out under which condition data fusion method may lead to performance improvement for ranking aggregation in crowdsourcing task, the optimal influencing factors are identified by the IFOS-SR method. This paper presents an analysis of the behavior of the linear combination method and the CombSUM method based on the optimal influencing factors, and optimizes the task assignment with a given number of workers by the optimal working group selection method.

This paper aims to explore the ambiguity and limitations of measuring firm-level multinationality (FLM) using theoretical and empirical comparisons of existing methods. The paper puts forward a list of five key aspects that collectively serve as a tool for researchers to select the most appropriate method for future research and as a basis for the future development of methods.

Firstly, the author reviews existing methods of measuring FLM and consolidates findings into five key aspects. Secondly, the author uses the aspects to compare existing methods theoretically, and subsequently, the author groups them into three distinct streams. Thirdly, the author compares existing methods across a sample of the 35 largest European MNEs by sales in 2020 to identify and demonstrate the ambiguity and limitations of these methods.

The author identifies the five key aspects of measuring FLM: framework, aggregation, segmentation, metrics and indicators. Using empirical comparison, the author empirically confirms the limitations highlighted in the literature and shows the differences and inconsistencies among methods, which cause confusion rather than clarity in the extant literature. Additionally, the author emphasises that three distinct streams further drive the debate on the regional/global nature and present further limitations of methods not mentioned in the literature to date.

This paper provides the most comprehensive review of the existing literature on FLM, resulting in five novel aspects of measuring FLM. The analysis of a sample of 35 European firms demonstrates and identifies the ambiguity and limitations of FLM-measuring methods.

The purpose of this paper is to study the correlation between different topographies and the reaction of Ulva Linza fouling species.

In this research, topographies with a different method, such as hot embossing and hot pulling, were achieved, and biological analyses were done with macroalgae Ulva Linza cells. The effect of topography via local binding geometry (honeycomb size gradients) and Wenzel roughness on the settling of Ulva microorganisms was tested.

As a result, Ulva spores confirmed different reactions to a similar set of tapered microstructures that was in agreement with the results on distinct honeycombs. The local binding geometry and the Wenzel roughness factor “r” were dominant on settling of Ulva Linza spores.

The reaction of an organism at the interface of vehicles’ substrate is powerfully affected by surface topographies.

The best embedment occurred on structures with bigger sizes than Ulva Linza’s spores. The density of settled spores was proportional to Wenzel roughness and the spores favour to attach to “kink sites” positions.

Unfortunately, unpleasant aggregation of marine biofouling on marine vehicles’ surfaces, generate terrific difficulties in the relevant industry.

There was a sharp relationship between Wenzel roughness and settle of Ulva Linza spores. The local binding geometry and the Wenzel roughness factor “r” were dominant on settling of Ulva Linza spores.

With the increasing number of mobile applications, efficiently recommending mobile applications to users has become a challenging problem. Although existing mobile application recommendation approaches based on user attributes and behaviors have achieved notable effectiveness, they overlook the diffusion patterns and interdependencies of topic-specific mobile applications among user groups. mobile applications among user groups. This paper aims to capture the diffusion patterns and interdependencies of mobile applications among user groups. To achieve this, a topic-aware neural network-based mobile application recommendation method, referred to as TN-MR, is proposed.

In this method, first, the user representations are enhanced by introducing a topic-aware attention layer, which captures both the topic context and the diffusion history context. Second, it exploits a time-decay mechanism to simulate changes in user interest. Multitopic user representations are aggregated by the time decay module to output the user representations of cascading representations under multiple topics. Finally, user scores that are likely to download the mobile application are predicted and ranked.

Experimental comparisons and analyses were conducted on the actual 360App data set, and the results demonstrate that the effectiveness of mobile application recommendations can be significantly improved by using TN-MR.

In this paper, the authors propose a mobile application recommendation method based on topic-aware attention networks. By capturing the diffusion patterns and dependencies of mobile applications, it effectively assists users in selecting their applications of interest from thousands of options, significantly improving the accuracy of mobile application recommendations.

Traditionally, life cycle assessment (LCA) has focused on environmental aspects, but integrating social aspects in LCA has gained traction among scholars and practitioners. This study aims to review key social life cycle assessment (SLCA) themes, namely, drivers and barriers of SLCA implementation, methodology and measurement metrics, classification of initiatives to improve SLCA and customer perspectives in SLCA.

A total of 148 scientific papers extracted from the Web of Science database were used and analyzed using bibliometric and content analysis.

The findings suggest that the existing research ignores several aspects of SCLA, which impedes positive growth in topical scholarship, and the study proposes a classification of SLCA research paths to enrich future research. This study contributes positively to SLCA by further developing this area, and as such, this research is a primer to gain deeper knowledge about the state-of-the-art in SLCA as well as to foresee its future scope and challenges.

The study provides an up-to-date review of extant research pertaining to SLCA.

This paper presents a review of the state of the art on the application of blockchain and smart contracts to the peer-review process of scientific papers. The paper seeks to analyse how the main characteristics of the existing blockchain solutions in this field to detect opportunities for the improvement of future applications.

A systematic review of the literature on the subject was carried out in three databases recognized by the research community (IEEE Xplore, Scopus and Web of Science) and the Frontiers in Blockchain journal. A total of 1,967 articles were initially found, and after the exclusion process, the 26 remaining articles were classified according to the following dimensions: System Type, Open Access, Review Type, Reviewer Incentive, Token Economy, Blockchain Access, Blockchain Identification, Blockchain Used, Paper Storage, Anonymity and Maturity of the solution.

Results show that the solutions are normally concerned on offering incentives to the reviewers' work (often monetary). Other common general preferences among the solutions are the adoption of open reviews, the use of Ethereum, the implementation of publishing ecosystems and the use of InterPlanetary File System to the storage of the papers.

There are currently no studies covering the main aspects of blockchain solutions in the field of scientific peer review. The present study provides an overall review of the topic, summarizing important information on the current research and helping new adopters to develop solutions grounded on the existing literature.

The Overall Equipment Effectiveness (OEE) is considered a standard for measuring equipment productivity in terms of efficiency. Still, Artificial Intelligence solutions are rarely used for analyzing OEE results and identifying corrective actions. Therefore, the approach proposed in this paper aims to provide a new rule-based Machine Learning (ML) framework for OEE enhancement and the selection of improvement actions.

Association Rules (ARs) are used as a rule-based ML method for extracting knowledge from huge data. First, the dominant loss class is identified and traditional methodologies are used with ARs for anomaly classification and prioritization. Once selected priority anomalies, a detailed analysis is conducted to investigate their influence on the OEE loss factors using ARs and Network Analysis (NA). Then, a Deming Cycle is used as a roadmap for applying the proposed methodology, testing and implementing proactive actions by monitoring the OEE variation.

The method proposed in this work has also been tested in an automotive company for framework validation and impact measuring. In particular, results highlighted that the rule-based ML methodology for OEE improvement addressed seven anomalies within a year through appropriate proactive actions: on average, each action has ensured an OEE gain of 5.4%.

The originality is related to the dual application of association rules in two different ways for extracting knowledge from the overall OEE. In particular, the co-occurrences of priority anomalies and their impact on asset Availability, Performance and Quality are investigated.

In the last two decades, risk reporting has followed a normative and calculative culture rather than the “materiality” of data. Although integrated reporting (IR) has become flooded with extra information, it does not adequately disseminate material information to stakeholders. In addition, the poor tone from the top diminishes creativity. This study aims to investigate how companies creatively address issues of the materiality of risk information in IR and how IR can be aligned with enterprise risk management.

Qualitative research was conducted via interviews with 50 chief risk officers and senior management executives in the Indian and UK insurance markets.

Overall, five institutions were observed to exhibit elements of being early adopters of institutional creativity. This confirmed the present study’s theoretical contribution of five divergent types of early adopters. The motivations for creativity are reflected in the resources available to these institutions.

To the best of the authors’ knowledge, this study provides a new insight into IR from internal mechanisms to deal with issue of materiality.

This paper aims to prepare a new donor–π–acceptor (D–π–A) and acceptor–π– D–π–A (A–π–D–π–A) phenothiazine (PTZ) in conjugation with vinyl isophorone (PTZ-1 and PTZ-2) were designed and their molecular shape, electrical structures and characteristics have been explored using the density functional theory (DFT). The results satisfactorily explain that the higher conjugative effect resulted in a smaller high occupied molecular orbital–lowest unoccupied molecular orbital gap (Eg). Both compounds show intramolecular charge transfer (ICT) transitions in the ultraviolet (UV)–visible range, with a bathochromic shift and higher absorption oscillator strength, as determined by DFT calculations.

The produced PTZ-1 and PTZ-2 sensors were characterized using various spectroscopic methods, including Fourier-transform infrared spectroscopy and nuclear magnetic resonance spectroscopy (¹H/¹³CNMR). UV–visible absorbance spectra of the generated D–π–A PTZ-1 and A–π–D–π–A PTZ-2 dyes were explored in different solvents of changeable polarities to illustrate positive solvatochromism correlated to intramolecular charge transfer.

The emission spectra of PTZ-1 and PTZ-2 showed strong solvent-dependent band intensity and wavelength. Stokes shifts were monitored to increase with the increase of the solvent polarity up to 4122 cm⁻¹ for the most polar solvent. Linear energy-solvation relationship was applied to inspect solvent-dependent Stokes shifting. Quantum yield (ф) of PTZ-1 and PTZ-2 was also explored. The maximum UV–visible absorbance wavelengths were detected at 417 and 419 nm, whereas the fluorescence intensity was monitored at 586 and 588 nm.

The PTZ-1 and PTZ-2 dyes leading to colorimetric and emission spectral changes together with a color shift from yellow to red.

This article investigates the practical implementation of the ecosystem approach in different branches of public management within an urban context. It explores how ecosystem thinking is introduced, disseminated and applied in a local government organization.

We utilize a qualitative case study methodology, relying on official documents and expert interviews. Our study focuses on the city of Espoo, Finland, which has actively embraced ecosystem thinking as a fundamental framework for its organizational development for almost a decade.

The case of Espoo highlights elements that have not been commonly attributed to the ecosystem approach in the public sector. These elements include (1) the significance of complementary services, (2) the existence of both collaborative and competitive relationships among actors in public service ecosystems and (3) the utilization of digital platforms for resource orchestration. Our study also emphasizes the need for an incremental adoption of ecosystem thinking in organizational contexts to enable its successful implementation.

The study provides valuable insights into the introduction and dissemination of ecosystem thinking in public management. It also further develops previously developed hypotheses regarding public service ecosystems.