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Found 6341 publications

Application of Digitalisation in Regulated Environments for Predictive Failure Modelling

John Cosgrove Samuel Carvalho Frank Doyle Zsolt Kovács

Publication Name: IFAC Papersonline

Publication Date: 2024-06-01

Volume: 58

Issue: 8

Page Range: 222-227

Description:

This paper explores the challenges of applying digitalization in regulated pharmaceutical manufacturing environments. A large range of complex equipment including pumps, valves and vessels may be associated with pharmaceutical batch production processes. Maintenance of such equipment are often based on reactive or preventative strategies which are not always effective and not completely successful in preventing costly downtime or scrap. This research examines how predictive maintenance Key Performance Indicators (KPIs) can be developed through data capture using non-intrusive sensors and their integration with production data derived from Programmable Logic Controllers (PLCs), Enterprise Resource Planning (ERP) systems, and Product Lifecycle Management (PLM) systems. The significance of regulation and the associated challenges in applying digitalization within such a highly regulated environment are also considered. This research aims to shed light on the potential benefits and challenges of implementing digital solutions for predictive maintenance in regulated manufacturing environments to contribute to the enhancement of operational efficiency and product quality while reducing costs due to outages.

Open Access: Yes

DOI: 10.1016/j.ifacol.2024.08.124

Novel Insights in Soil Mechanics: Integrating Experimental Investigation with Machine Learning for Unconfined Compression Parameter Prediction of Expansive Soil

Chuangxin Lyu Haidar Hosamo Hosamo Ammar N. Alnmr Mounzer Omran Alzawi Richard P. Ray

Publication Name: Applied Sciences Switzerland

Publication Date: 2024-06-01

Volume: 14

Issue: 11

Page Range: Unknown

Description:

This paper presents a novel application of machine learning models to clarify the intricate behaviors of expansive soils, focusing on the impact of sand content, saturation level, and dry density. Departing from conventional methods, this research utilizes a data-centric approach, employing a suite of sophisticated machine learning models to predict soil properties with remarkable precision. The inclusion of a 30% sand mixture is identified as a critical threshold for optimizing soil strength and stiffness, a finding that underscores the transformative potential of sand amendment in soil engineering. In a significant advancement, the study benchmarks the predictive power of several models including extreme gradient boosting (XGBoost), gradient boosting regression (GBR), random forest regression (RFR), decision tree regression (DTR), support vector regression (SVR), symbolic regression (SR), and artificial neural networks (ANNs and proposed ANN-GMDH). Symbolic regression equations have been developed to predict the elasticity modulus and unconfined compressive strength of the investigated expansive soil. Despite the complex behaviors of expansive soil, the trained models allow for optimally predicting the values of unconfined compressive parameters. As a result, this paper provides for the first time a reliable and simply applicable approach for estimating the unconfined compressive parameters of expansive soils. The proposed ANN-GMDH model emerges as the pre-eminent model, demonstrating exceptional accuracy with the best metrics. These results not only highlight the ANN’s superior performance but also mark this study as a groundbreaking endeavor in the application of machine learning to soil behavior prediction, setting a new benchmark in the field.

Open Access: Yes

DOI: 10.3390/app14114819

Trademark protection for faces? A comprehensive analysis on the benefits and drawbacks of trademarks and the right to facial image

No authors available

Publication Name: Journal of Intellectual Property, Information Technology and E-Commerce Law

Publication Date: 2024-06-01

Volume: 15

Issue: 1

Page Range: 88-99

Description:

The purpose of this paper is to present a comprehensive framework for the possibility of trademark protection for human faces. In the case law of the European Union Intellectual Property Office there are a few examples of trademarks, which consist of only photorealistic human faces. Private law protects the use of images; however, the trends of recent years demonstrate that trademarks could also have a role in such protection. The author aims to analyze the similarities and differences between trademark protection and personality rights in order to determine whether trademarks for faces are necessary or not. The over- arching analysis compares twelve aspects of the two ways in which the legal systems protect facial imagery, highlighting their various advantages and drawbacks. The comparison includes the following attributes: function of protection, scope of protection, territorial dimensions of protection, temporal dimensions of protection, conditions of protection, content of protection, limitations and exceptions, transferability of rights, enforcement of rights, requirement of use, termination of rights and costs.

Open Access: No

DOI: DOI not available

Power in the supply chain: a state-of-the-art literature review and propositions from the perspective of gender differences

Gilberto Marquez-Illescas Heba F. Zaher

Publication Name: Journal of Business and Industrial Marketing

Publication Date: 2024-05-30

Volume: 39

Issue: 6

Page Range: 1282-1310

Description:

Purpose: This paper aims to examine the existing literature on firms’ power through the lens of the supply chain and highlights some gaps that could be covered by future research. Design/methodology/approach: This study uses a systematic framework-based review combining the insights of the antecedents, decisions and outcomes (ADO) and theories, contexts and methods (TCM) frameworks. The review was carried out using a sample of 108 articles published between 1984 and 2022 in 25 prestigious journals. Findings: The ADO framework maps out the state of the art of the antecedents of power (i.e. sources and types of firm power), the decision to use power and the effect that exercising power over other firms may have on firm performance and the quality of inter-firm relationships. In addition, this framework highlights factors that mediate or moderate the decision to exercise power and the factors that mediate or moderate the outcomes of exercising power or power asymmetry. The TCM framework provides insights into the theories, contexts (i.e. countries, industries, level of analysis and sources of data) and methods used by the existing literature. The content analysis using the aforementioned frameworks provides the basis to elaborate propositions for future research on power in the supply chain from the perspective of gender differences. Research limitations/implications: This systematic literature review offers a comprehensive guide for researchers to understand the antecedents, decisions and outcomes of firm power in the supply chain, as well as the TCM used in the literature. The content analysis using frameworks provides a road map to investigate the proposed factors that might moderate the decision to exercise power and the outcome of exercising power or power asymmetry from the perspective of gender differences. In addition, based on content analysis, the authors make propositions about TCM that could be applied in future research. Practical implications: From a practical perspective, this systematic literature review may help managers to better understand the sources and consequences of their firm’s power. This would allow managers to make better decisions when negotiating with their supply chain parties, which could potentially lead to better performance for their firms and the whole supply chain. Originality/value: To the best of the authors’ knowledge, this study is the first to conduct a comprehensive systematic literature review of the different dimensions of firms’ power in the supply chain.

Open Access: Yes

DOI: 10.1108/JBIM-10-2022-0484

The dilemma of water, food, and greener energy nexus: A novel context of COP27 for G20 economies

Waldemar Tarczyński Grzegorz Mentel Kang He Irum Shahzadi Salahuddin Khan

Publication Name: Land Degradation and Development

Publication Date: 2024-05-30

Volume: 35

Issue: 9

Page Range: 2993-3006

Description:

In the contemporary world, achieving sustainable food production has become an urgent task for the international community and policymakers due to the rapidly growing social challenges of mankind. Sustainable food production practices aid countries in adapting to the challenges posed by climate change, thereby ensuring a better and more sustainable future for all. This study examines the impact of land use, energy efficiency (ENE), water productivity (WP), renewable energy consumption (REC), and gross domestic product (GDP) on sustainable food production in G20 nations over the period of 1998–2020. We use quantile regression approaches to capture potential heterogeneity across various food value-added distribution quantiles. The results show that arable land, WP, GDP, ENE, and REC are important factors affecting food value added in G20 nations. However, the nature of the relationship varies across different quantiles, suggesting heterogeneity in the relationships. The results show that ENE, renewable energies, and GDP are positively related to food production. However, arable land and WP are negatively related to food production. The findings can assist policymakers and stakeholders in making informed decisions to increase value added in the agricultural sector while promoting resilience and sustainability.

Open Access: Yes

DOI: 10.1002/ldr.5110

Administrative Law in Hungary

Gábor Hulkó

Publication Name: Comparative Administrative Law Perspectives from Central and Eastern Europe

Publication Date: 2024-05-20

Volume: Unknown

Issue: Unknown

Page Range: 36-67

Description:

No description provided

Open Access: Yes

DOI: 10.4324/9781003458135-2

Using dispersion models at microscale to assess long-term air pollution in urban hot spots: A FAIRMODE joint intercomparison exercise for a case study in Antwerp

S. Janssen J. L. Pérez-Camanyo László Környei J. L. Santiago V. Rodrigues G. Sousa Santos P. Thunis E. Rivas C. Cuvelier I. Sakellaris M. G. Villani Akos Kovacs B. Liszkai F. Martín N. Reiminger X. Jurado D. Barbero J. L. Pérez-Camanyo R. Jackson Ákos Kovács J. Sousa J. Bartzis R. San José G. Tinarelli J. Stocker Zoltán Horváth F. Russo

Publication Name: Science of the Total Environment

Publication Date: 2024-05-15

Volume: 925

Issue: Unknown

Page Range: Unknown

Description:

In the framework of the Forum for Air Quality Modelling in Europe (FAIRMODE), a modelling intercomparison exercise for computing NO2 long-term average concentrations in urban districts with a very high spatial resolution was carried out. This exercise was undertaken for a district of Antwerp (Belgium). Air quality data includes data recorded in air quality monitoring stations and 73 passive samplers deployed during one-month period in 2016. The modelling domain was 800 × 800 m2. Nine modelling teams participated in this exercise providing results from fifteen different modelling applications based on different kinds of model approaches (CFD – Computational Fluid Dynamics-, Lagrangian, Gaussian, and Artificial Intelligence). Some approaches consisted of models running the complete one-month period on an hourly basis, but most others used a scenario approach, which relies on simulations of scenarios representative of wind conditions combined with post-processing to retrieve a one-month average of NO2 concentrations. The objective of this study is to evaluate what type of modelling system is better suited to get a good estimate of long-term averages in complex urban districts. This is very important for air quality assessment under the European ambient air quality directives. The time evolution of NO2 hourly concentrations during a day of relative high pollution was rather well estimated by all models. Relative to high resolution spatial distribution of one-month NO2 averaged concentrations, Gaussian models were not able to give detailed information, unless they include building data and street-canyon parameterizations. The models that account for complex urban geometries (i.e. CFD, Lagrangian, and AI models) appear to provide better estimates of the spatial distribution of one-month NO2 averages concentrations in the urban canopy. Approaches based on steady CFD-RANS (Reynolds Averaged Navier Stokes) model simulations of meteorological scenarios seem to provide good results with similar quality to those obtained with an unsteady one-month period CFD-RANS simulations.

Open Access: Yes

DOI: 10.1016/j.scitotenv.2024.171761

Wasserstein distance for OWA operators

Lucian Coroianu Istvan A. Harmati Robert Fuller

Publication Name: Fuzzy Sets and Systems

Publication Date: 2024-05-15

Volume: 484

Issue: Unknown

Page Range: Unknown

Description:

We suggest a distance measure for OWA operators. First we associate an OWA operator with a unique regular increasing monotone quantifier and then define the distance between two OWA operators as the Wasserstein-1 distance between their associated quantifiers.

Open Access: Yes

DOI: 10.1016/j.fss.2024.108931

Harnessing Blockchain to Transform Healthcare Data Management: A Comprehensive Research Agenda

Horst Treiblmaier Alex Norta Anjum Khurshid Abderahman Rejeb Mike Gault Jim Poteet Suresh Sivagnanam

Publication Name: Blockchain in Healthcare Today

Publication Date: 2024-05-02

Volume: 7

Issue: 1

Page Range: Unknown

Description:

Properly managing healthcare data is a complex endeavor that must balance the requirements and interests of many stakeholders. In this paper, we present the findings from a panel discussion with healthcare professionals and academics, who elaborate on the current situation in healthcare data management as well as the future role that blockchain could play in this sector. Based on the findings of this panel, we structure the research field of healthcare data management and provide numerous avenues for future research. The outcome is a framework that highlights the important role of healthcare data and puts them into context. From a patient’s perspective, we specifically elaborate on trust and privacy as well as the expected benefits. Additionally, four important data aspects are identified: integrity, security, interoperability, and, finally, sharing and transfer. We also outline the importance of current problems and derive several relevant and timely research questions that build the foundation of a research agenda for blockchain-driven innovation in health-care data management. In summary, the framework will inform practitioners of blockchain’s potential in healthcare and structure the area for researchers, who are called upon to investigate the respective topics in greater detail.

Open Access: Yes

DOI: 10.30953/bhty.v7.301