bluuebunny/crossref_metadata_2025 huggingface dataset:
Fractionation of flavonols and anthocyanins in winemaking residues using molecularly imprinted cellulose‐synthetic hybrid particles with pyridyl active surface
Authors: Gomes Catarina P. , Dias Rolando C. S. , Costa Mário Rui P. F. N.
Published: February 2024

Hybrid cellulose‐synthetic particles with surface active pyridyl moieties and molecularly imprinted cavities for quercetin were prepared via atom transfer radical polymerization. The functionalization of the materials with pyridyl groups was confirmed by FTIR and the SEM micrographs of the hybrid particles demonstrate a clear surface modification compared with the pristine cellulose. Competitive sorption/desorption testing of the imprinted and non‐imprinted particles with standard polyphenols show the achievement of an imprinting factor IF ∼8. Moreover, it was also confirmed the high retention capability of the hybrid materials for polyphenols, due to their strong binding with the surface pyridyl moieties, even when using hydroalcoholic solvents of high ethanol content (e.g., ethanol/water 80/20 v/v). The sorption capabilities of the synthesized materials for polyphenols were explored with the fractionation of flavonols and anthocyanins in winemaking residues. High concentration and enrichment factors were achieved for high‐added value compounds, namely five times for quercetin and 12 for quercetin‐3‐O‐glucuronide with a diatomaceous earth extract and up to 4 for flavonols in a grape pomace (e.g. myricetin and quercetin glucosides). This research demonstrates the feasibility for the combination between the development of engineered materials addressing sustainability with their application to the valorization of agro‐industrial wastes.
M‐PINN: A mesh‐based physics‐informed neural network for linear elastic problems in solid mechanics
Authors: Wang Lu , Liu Guangyan , Wang Guanglun , Zhang Kai
Published: February 2024

Physics‐informed neural networks (PINNs) have emerged as a promising approach for solving a wide range of numerical problems. Nevertheless, conventional PINNs frequently face challenges in model convergence and stability when optimizing complex loss functions containing complex gradients. In this study, a new mesh‐based PINN method, called M‐PINN, is proposed drawing the ideas of the finite element method (FEM). By partitioning the solution domain into several subdomains and incorporating finite element data distribution constraints to the prior estimates of the predicted data distribution of PINN on the solution domain, the M‐PINN approach effectively reduces the optimization difficulty of conventional PINNs. Moreover, it is sometimes difficult to directly obtain precise boundary conditions in some practical applications. This method can be used to solve PINN problems with unknown boundary conditions, thus having wider applicability. In this study, the efficiency of M‐PINN was demonstrated through a standard 2D linear elastic solid mechanics simulation experiment, and its applicability was investigated in depth. The results indicate that the M‐PINN method outperforms traditional PINN and exhibits superior applicability and convergence, especially in cases involving unknown boundary conditions.
Adherence to prescription proton pump inhibitor therapy amongst individuals diagnosed with Barrett's esophagus
Authors: Gaber Charles E. , Abdelaziz Abdullah I. , Sarker Jyotirmoy , Lund Jennifer L. , Dellon Evan S. , Cotton Cary C. , Eluri Swathi , Shaheen Nicholas J.
Published: February 2024

Introduction

In the United States, clinical guidelines recommend daily use of proton pump inhibitors (PPIs) amongst individuals diagnosed with Barrett's esophagus to decrease the risk of progression to dysplasia and neoplasia. Prior studies documenting adherence to PPIs in this population have not characterized heterogeneity in adherence patterns. Factors that may relate to adherence are incompletely described.

Methods

We used administrative claims data from the Merative MarketScan Commercial Claims and Encounters database to conduct a retrospective study of adherence to prescription PPIs. A cohort of individuals diagnosed with incident Barrett's esophagus between 2010 and 2019 was identified. Group‐based trajectory models were generated to detect longitudinal adherence subgroups.

Results

79 701 individuals with a new diagnosis of Barrett's esophagus were identified. The best fitting model detected five distinct adherence trajectory groups: consistently high (44% of the population), moderate decline (18%), slow decline (12%), rapid decline (10%), and decline‐then‐increase (16%). Compared to individuals starting PPIs, those already using PPIs were less likely to have a declining adherence pattern. Other factors associated with membership in a declining adherence group included (but were not limited to): female sex, having a past diagnosis of anxiety or depression, and having one or more emergency department visits in the past year.

Discussion

Using an exploratory method, we detected heterogeneity in adherence to prescription PPIs. Less than half of individuals were classified into the consistently high adherence group, suggesting that many individuals with Barrett's esophagus receive inadequate pharmacologic therapy.
Emulsion‐based monoliths with a solid‐gel phase‐transition and a solid–solid phase‐transition for latent heat storage
Authors: Jiang Yu , Li Boyuan , Xu Zhiguang , Zhang Tao , Zhao Yan
Published: February 2024

Emulsion‐based, phase change material‐encapsulated monoliths are promising for latent heat storage. However, such monoliths usually exhibit a solid–liquid phase‐transition and the one‐only phase‐transition might hinder the monoliths for more potential applications. Herein, we report the synthesis of emulsion‐based monoliths with a solid‐gel phase‐transition and a solid–solid phase‐transition for latent heat storage. The solid‐gel phase‐transition was from melting and crystallization of octadecane‐in‐crosslinked poly(octadecyl acrylate) that was formed via radical polymerization of the dispersed phase within oil‐in‐water emulsions, and the solid–solid phase‐transition was from melting and crystallization of the crosslinked polyethylene oxide blocks that were synthesized via the interface‐initiated radical polymerization of the continuous phase. The resulting emulsion‐based monoliths showed negligible mass loss during the heating at 60 °C for 8 h, and exhibited two different crystallization (heat release) temperatures and melting (heat storage) temperatures, corresponding to the solid‐gel transition and the solid–solid transition. Moreover, the monoliths possessed quite high heat density (up to 136.4 J/g), and the heat density did not deteriorate over 20 heating–cooling cycles, showing high reusability. The extremely low leakage, double phase‐transitions and the high reusability make the monoliths to be a good candidate for latent heat storage.
Assessing the performance of group‐based trajectory modeling method to discover different patterns of medication adherence
Authors: Diop Awa , Gupta Alind , Mueller Sabrina , Dron Louis , Harari Ofir , Berringer Heather , Kalatharan Vinusha , Park Jay J. H. , Mésidor Miceline , Talbot Denis
Published: February 2024

It is well known that medication adherence is critical to patient outcomes and can decrease patient mortality. The Pharmacy Quality Alliance (PQA) has recognized and identified medication adherence as an important indicator of medication‐use quality. Hence, there is a need to use the right methods to assess medication adherence. The PQA has endorsed the proportion of days covered (PDC) as the primary method of measuring adherence. Although easy to calculate, the PDC has however several drawbacks as a method of measuring adherence. PDC is a deterministic approach that cannot capture the complexity of a dynamic phenomenon. Group‐based trajectory modeling (GBTM) is increasingly proposed as an alternative to capture heterogeneity in medication adherence. The main goal of this paper is to demonstrate, through a simulation study, the ability of GBTM to capture treatment adherence when compared to its deterministic PDC analogue and to the nonparametric longitudinal K‐means. A time‐varying treatment was generated as a quadratic function of time, baseline, and time‐varying covariates. Three trajectory models are considered combining a cat's cradle effect, and a rainbow effect. The performance of GBTM was compared to the PDC and longitudinal K‐means using the absolute bias, the variance, the c‐statistics, the relative bias, and the relative variance. For all explored scenarios, we find that GBTM performed better in capturing different patterns of medication adherence with lower relative bias and variance even under model misspecification than PDC and longitudinal K‐means.
Practitioners' expectations on automated release note generation techniques
Authors: Sumana Nath Sristy , Roy Banani
Published: February 2024

The software development life cycle relies heavily on the software release note, a crucial document. Various practitioners, including project managers and clients, benefit from release notes as they provide an overview of the latest software release. However, the manual generation of release notes is a time‐consuming and stressful task. Researchers have recently proposed automated techniques to generate release notes, saving developers' time and enhancing their understanding of software projects. Unfortunately, the adoption of these tools in practice remains limited. To address this gap, we have taken steps to understand the expectations and requirements of practitioners regarding release note generation techniques before implementing new automated approaches. Consequently, our approach involves two main stages: First, we conduct a comprehensive review of the relevant literature and analyze existing release notes from GitHub repositories to gain insights into the current practices. Second, we conduct an online survey study to gather input from practitioners and understand their expectations regarding release notes. We have reviewed 16 papers related to release notes and explored 3347 release notes from 21 GitHub repositories. Our analysis revealed key artifacts present in release note contents, including issues (29%), pull requests (32%), commits (19%), and common vulnerabilities and exposures (CVE) issues (6%). Additionally, we conducted a survey study involving 32 professionals to understand the essential information that should be included in release notes based on users' roles. For instance, project managers were more interested in learning about new features rather than less critical bug fixes. Furthermore, we identified gaps in existing systems and essential factors to consider when implementing release notes techniques in software engineering. The insights gained from our study can guide future research directions and assist practitioners in generating release notes with relevant content, thus improving the overall quality of documentation in software development.
Limits on Ti Element Transfer in Submerged Arc Welding: Thermochemical Analysis
Authors: Coetsee Theresa , De Bruin Frederik
Published: February 2024

TiO2 inclusion formation in the weld pool is required to produce potent nucleation sites for acicular ferrite microstructure formation. Weld metal oxygen content must be limited to ensure acceptable materials properties, although sufficient oxygen is required for inclusion formation. Weld pool oxygen is sourced from the decomposition of flux oxides. Weld metal oxygen is controlled with flux chemistry formulation by CaF2 dilution of oxides. In conventional submerged arc welding (SAW), the flux is the source of weld metal Ti. Transfer of Ti from the slag appears to be limited to 400 parts per million (ppm). SAW modification by metal powder additions changes the element transfer reactions. Thermochemical analysis is applied to explain the limitations in Ti transfer from the slag, compared to improved reaction conditions for Ti element transfer in the aluminum‐assisted Ti alloying of weld metal. Low TiO2 activity due to low flux TiO2 content from CaF2 dilution and Ti loss from Ti‐fluoride gas formation limits Ti transfer from slag. Aluminum‐assisted alloying of the weld metal shifts the gas composition from Ti‐fluoride formation to Al‐fluoride formation and lowers the system partial oxygen pressure to increase the weld metal Ti content, with acceptable ppm O remaining in the weld metal.
Genetic Analysis of Accumulation of Amylose and Resistant Starch in Subtropical Maize Hybrids
Authors: Reddappa Shashidhar B. , Zunjare Rajkumar U. , Muthusamy Vignesh , Chhabra Rashmi , Talukder Zahirul A. , Maman Shalma , Bhatt Vinay , Kasana Ravindra Kumar , Pal Digvender , Kumar Rajesh , Mehta Brijesh K. , Pawar Godawari S. , Singh Ashok K. , Hossain Firoz
Published: February 2024

Amylose and resistant starch (RS) possess numerous industrial applications and health benefits. However, maize grains possess low amylose (<35%) and RS (<5%). To date, no information is available on the genetic variability of amylose and RS in subtropical maize. Here, 21 hybrids developed using half‐diallel mating design are evaluated for starch parameters at three locations. Analysis of variance (ANOVA) revealed a minor contribution for environments for amylose and RS. Amylose (22.2%–49.1%) and RS (1.02%–23.9%) showed wide variation among the hybrids. The experimental hybrids possessed significantly higher amylose (38.1%) and RS (20.9%) over the check hybrids (amylose: 29.1%, RS: 2.3%). PUSA‐AML‐H12, PUSA‐AML‐H18, and PUSA‐AML‐H16 are observed to be the most promising hybrids. Amylose and RS are positively correlated (r = 0.45), while total starch (TS) is negatively correlated with RS (r = −0.53). PMI‐AML‐147, PMI‐AML‐149, and PMI‐AML‐146 are the best general combiners for amylose and RS. Pusa‐AML‐H1, Pusa‐AML‐H12, and Pusa‐AML‐H5 emerged as the best specific combiners for amylose and RS. Both additive and nonadditive variances are important for amylose and RS. This is the first study of amylose and RS in a subtropical maize background, which could be significant in improving amylose and RS.
Amphibitic stoneflies (Plecoptera) are integrators of ecosystem processes in alluvial aquifers of gravel‐bed river floodplains
Authors: Stanford Jack A. , DelVecchia Amanda G. , Giersch J. Joseph , Malison Rachel L.
Published: February 2024

Over 50 years ago nymphs of the Plecoptera species, Paraperla frontalis Banks, 1906 (Plecoptera: Chloroperlidae), were shown to exist in a shallow floodplain aquifer of the Tobacco River, a gravel‐bed river in western Montana and later they were documented throughout the main stems of the Flathead River system. Nymphs are almost never found in surface waters, until they emerge on the river shorelines. As teneral adults, they mate and subsequently deposit fertilized eggs into the river. This novel life cycle is termed “amphibitic.” Over the years we and others have found P. wilsoni Ricker, 1965 (Plecoptera: Chloroperlidae), Kathroperla perdita Banks, 1920 (Plecoptera: Kathroperlidae), and five species of Isocapnia Banks, 1938 (Plecoptera: Capniidae), including long‐winged and brachypterous adults and wingless dwarfs (male and female), occupying amphibitic niches in the alluvial aquifers of rivers in Washington, Idaho, Colorado, Alaska and British Columbia. These stoneflies are remarkably tolerant of hypoxia which allows them to exist as abundant consumers in aquifer food webs subsidized by ancient methane. Indeed, stonefly tissues contain carbon that is up to 7000 years old, underscoring the existence of a strong interaction involving the uptake of labile carbon derived from methanogenic and methanotrophic process in aquifers. Details of life cycles, trophic relationships, distribution and abundance have been documented by a suite of studies on the Nyack Floodplain of the Middle Flathead River, Montana. In this paper we review the ecophysiology and ecology of these unique stoneflies in the context of their functional role in gravel‐bed river ecosystems.

This article is categorized under:

Water and Life > Nature of Freshwater Ecosystems

Water and Life > Conservation, Management, and Awareness
Overcoming challenges in cancer treatment: Nano‐enabled photodynamic therapy as a viable solution
Authors: Rajan Sheeja S. , Chandran Rahul , Abrahamse Heidi
Published: February 2024

Cancer presents a formidable challenge, necessitating innovative therapies that maximize effectiveness while minimizing harm to healthy tissues. Nanotechnology has emerged as a transformative force in cancer treatment, particularly through nano‐enabled photodynamic therapy (NE‐PDT), which leverages precise and targeted interventions. NE‐PDT capitalizes on photosensitizers activated by light to generate reactive oxygen species (ROS) that initiate apoptotic pathways in cancer cells. Nanoparticle enhancements optimize this process, improving drug delivery, selectivity, and ROS production within tumors. This review dissects NE‐PDT's mechanistic framework, showcasing its potential to harness apoptosis as a potent tool in cancer therapy. Furthermore, the review explores the synergy between NE‐PDT and complementary treatments like chemotherapy, immunotherapy, and targeted therapies, highlighting the potential to amplify apoptotic responses, enhance immune recognition of cancer cells, and inhibit resistance mechanisms. Preclinical and clinical advancements in NE‐PDT demonstrate its efficacy across various cancer types. Challenges in translating NE‐PDT into clinical practice are also addressed, emphasizing the need for optimizing nanoparticle design, refining dosimetry, and ensuring long‐term safety. Ultimately, NE‐PDT represents a promising approach in cancer therapy, utilizing the intricate mechanisms of apoptosis to address therapeutic hurdles. The review underscores the importance of understanding the interplay between nanoparticles, ROS generation, and apoptotic pathways, contributing to a deeper comprehension of cancer biology and novel therapeutic strategies. As interdisciplinary collaborations continue to thrive, NE‐PDT offers hope for effective and targeted cancer interventions, where apoptosis manipulation becomes central to conquering cancer.

This article is categorized under:

Therapeutic Approaches and Drug Discovery > Nanomedicine for Oncologic Disease
Rural NEETs: Individual Features, Challenges, and Opportunities
Authors: Simões Francisco
Published: February 2024

In this chapter, I present and discuss the main individual features of rural young people Not in Employment, nor in Education and Training (NEET) living in rural areas. These characteristics fit in the so-called individual system of the bioecological model. I start out by depicting rural NEETs educational background and gender differences within this subset of NEETs, due to their importance in shaping these young people’s pathways. Afterward, I elaborate on what is known to date about rural NEETs psychological profile, particularly regarding their cognitive skills, soft skills, and well-being. Finally, I will explore how these personal characteristics of rural NEETs may interact with emerging challenges and opportunities in rural communities located at other layers of the bioecological model. This chapter strongly relies on several contributions from the COST Action Rural NEET Youth Network, as well as on the most relevant international scholarship and policy reports focusing on rural communities’ development, youth employment, and school-to-work transition, to achieve an interdisciplinary understanding of rural NEETs individual traits and features.
Pathways for Young Farmers’ Entrepreneurship in Sustainable Rural Development
Authors: Mujčinović Alen , Bojnec Štefan , Nikolić Aleksandra , Tonković Anita Bušljeta , Gašparović Slaven , Lazereg Messaoud , Stojanović Anđelka , Bojadjieva Daniela
Published: February 2024

In this chapter we develop a theoretical-conceptual model on young farmers’ entrepreneurship in multi-functional, diverse and resilient sustainable rural development. Our aim is supported by policies fostering social and economic opportunities that target both rural youth and rural female entrepreneurship. The European Green Deal and associated targeted initiatives offer new avenues for agriculture, rural development, and social innovation aiming at vulnerable youth groups in rural communities such as rural young people Not in Employment, not in Education or Training (NEETs), or at setting up new, viable, and attractive businesses for overcoming negative representations about farming among rural younger generations. We identify and explain the obstacles and the policy opportunities for stronger rural youth entrepreneurship and their contribution to sustainable rural development. We make this by considering the concepts of sustainability and resilience associated with the multifunctionality and heterogeneity of rural areas in the context of the Industry 4.0 uprising. Altogether, these elements can determine young people’s level of involvement in the farming sector and their willingness to stay in rural areas, including among the most vulnerable ones. We also provide a set of research avenues to overcome traditional farming approaches and policy recommendations fostering entrepreneurship among rural young people.
A Participatory Modelling Approach to Agents in Industry Using AAS
Authors: Nikolova Nikoletta , Bouter Cornelis , Bekkum Michael van , Rongen Sjoerd , Wilterdink Robert
Published: February 2024

To develop interoperable and flexible systems, Industry 4.0 solutions need available models and standardization. There is not just a need for physical but also digital asset descriptions, which can be reused in different cases. Particularly, with newer complex system integration, there is an increase in implementations of agent-based solutions. Although the meta-language Asset Administration Shell aims to help with integration, it is still not mature enough and lacks sufficient methods and tooling. To support the aim for interoperability, we present three tools: an updated generic agent model, a methodology for AAS model creation, and a platform for model visualization and distribution.
Wearable Sensor-Based Human Activity Recognition for Worker Safety in Manufacturing Line
Authors: Suh Sungho , Rey Vitor Fortes , Lukowicz Paul
Published: February 2024

Improving worker safety and productivity is of paramount importance in the manufacturing industry, driving the adoption of advanced sensing and control systems. This concern is particularly relevant within the framework of Industry 5.0. In this context, wearable sensors offer a promising solution by enabling continuous and unobtrusive monitoring of workers’ activities in the manufacturing line. This book chapter focuses on wearable sensor-based human activity recognition and its role in promoting worker safety in manufacturing environments. Specifically, we present a case study on wearable sensor-based worker activity recognition in a manufacturing line with a mobile robot. As wearable sensors comprise various sensor types, we investigate and compare sensor data fusion approaches using neural network models to effectively handle the multimodal sensor data. In addition, we introduce several deep learning-based techniques to improve the performance of human activity recognition. By harnessing wearable sensors for human activity recognition, this book chapter provides valuable insights into improving worker safety on the manufacturing line, aligning with the principles of the Industry 5.0 paradigm. The chapter sheds light on the potential of wearable sensor technologies and offers avenues for future research in this field.
Boosting AutoML and XAI in Manufacturing: AI Model Generation Framework
Authors: Barroso Marta , Hinjos Daniel , Martin Pablo A. , Gonzalez-Mallo Marta , Gimenez-Abalos Victor , Alvarez-Napagao Sergio
Published: February 2024

The adoption of AI in manufacturing enables numerous benefits that can significantly impact productivity, efficiency, and decision-making processes. AI algorithms can optimize production schedules, inventory management, and supply chain operations by analyzing historical data and producing demand forecasts. In spite of these benefits, some challenges such as integration, lack of data infrastructure and expertise, and resistance to change need to be addressed for the industry to successfully adopt AI. To overcome these issues, we introduce the AI Model Generation framework (AMG), able to automatically generate AI models that adjust to the user’s needs. More precisely, the model development process involves the execution of a whole chain of sub-processes, including data loading, automated data pre-processing, cost computation, automatic model hyperparameter tuning, training, inference, explainability generation, standardization, and containerization. We expect our approach to aid non-expert users into more effectively producing machine and deep learning algorithms and hyperparameter settings that are appropriate to solve their problems without sacrificing privacy and relying on third-party services and infrastructure as few as possible.
Anomaly Detection in Manufacturing
Authors: Scholz Jona , Holtkemper Maike , Graß Alexander , Beecks Christian
Published: February 2024

This chapter provides an introduction to common methods of anomaly detection, which is an important aspect of quality control in manufacturing. We give an overview of widely used statistical methods for detecting anomalies based on k-means, decision trees, and Support Vector Machines. In addition, we examine the more recent deep learning technique of autoencoders. We conclude our chapter with a case study from the EU project knowlEdge, where an autoencoder was utilized in order to detect anomalies in a manufacturing process of fuel tanks. Throughout the chapter, we emphasize the importance of humans-in-the-loop and provide an example of how AI can be used to improve manufacturing processes.
Process and Product Quality Optimization with Explainable Artificial Intelligence
Authors: Sesana Michele , Cavallaro Sara , Calabresi Mattia , Capaccioli Andrea , Napoletano Linda , Antonello Veronica , Grandi Fabio
Published: February 2024

In today’s rapidly evolving technological landscape, businesses across various industries face a critical challenge: maintaining and enhancing the quality of both their processes and the products they deliver. Traditionally, this task has been tackled through manual analysis, statistical methods, and domain expertise. However, with the advent of artificial intelligence (AI) and machine learning, new opportunities have emerged to revolutionize quality optimization. This chapter explores the process and product quality optimization in a real industrial use case with the help of explainable artificial intelligence (XAI) techniques. While AI algorithms have proven their effectiveness in improving quality, one of the longstanding barriers to their widespread adoption has been the lack of interpretability and transparency in their decision-making processes. XAI addresses this concern by enabling human stakeholders to understand and trust the outcomes of AI models, thereby empowering them to make informed decisions and take effective actions.
Examining the Adoption of Knowledge Graphs in the Manufacturing Industry: A Comprehensive Review
Authors: Martinez-Gil Jorge , Hoch Thomas , Pichler Mario , Heinzl Bernhard , Moser Bernhard , Kurniawan Kabul , Kiesling Elmar , Krause Franz
Published: February 2024

The integration of Knowledge Graphs (KGs) in the manufacturing industry can significantly enhance the efficiency and flexibility of production lines and improve product quality. By integrating and contextualizing information about devices, equipment, production resources, location, usage, and related data, KGs can be a powerful operational tool. Moreover, KGs can contribute to the intelligence of manufacturing processes by providing insights into the complex and competitive manufacturing landscape. This research work presents a comprehensive analysis of the current trends utilizing KG in the manufacturing sector. We provide an overview of the state of the art in KG applications in manufacturing and highlight the critical issues that need to be addressed to enable a successful implementation. Our research aims to contribute to advancing KG technology in manufacturing and realizing its full potential to enhance manufacturing operations and competitiveness.
Architecture of a Software Platform for Affordable Artificial Intelligence in Manufacturing
Authors: Cutrona Vincenzo , Landolfi Giuseppe , Alonso Rubén , Montini Elias , Falconi Andrea , Bettoni Andrea
Published: February 2024

The fourth industrial revolution has driven companies of all sizes to embrace digitalization, recognizing the potential of AI technologies for data analysis and real-time decision-making. However, the adoption of AI by manufacturing SMEs faces challenges related to cost, accessibility, and the need for expertise. To address these challenges, this chapter introduces a groundbreaking platform developed as part of the EU H2020 KITT4SME project. The platform aims to democratize the adoption of AI tools by leveraging the “as-a-service” model, making them affordable and readily available for SMEs. It follows a five-step workflow (diagnose–compose–sense–intervene–evolve) to provide tailor-made AI solutions to SMEs. The distinctive functionality of the platform allows for the composition of AI components from a marketplace into a customized service offering for companies, filling a gap in existing AI platforms. The KITT4SME platform has been successfully applied in four use cases within the project and to 18 external demonstrators via Open Calls. This chapter presents one of the internal use cases to showcase the capabilities and benefits of the KITT4SME platform.
Conclusion: In Need of a Curricular Revolution
Authors: Gellman Mneesha
Published: February 2024

In the previous chapters, the textbook analysis showed what little progress US history textbooks have made in the last seventy years. White stories of victory and domination continue to disproportionally fill textbooks. While representation of non-White perspectives and more accurate information about BIPOC groups does increase in more recent textbooks, the same limited and narrow-minded tropes, events, and passivity still persist. So, what can be done? In the conclusion, I reiterate the link between culturally relevant curricula and youth wellbeing. Curricular reform is a beginning step that educators, policy-makers, and school districts could make to ensure their BIPOC students feel seen, heard, and valued as students and as people. Such a proposition is deeply political, but is necessary to correct past wrongs when it comes to misrepresentation in school curricula. Such reforms will move us toward providing a more inclusive environment for all students.
Artificial Intelligence for Digital Twin
Authors: Zhang Yan
Published: February 2024

Artificial intelligence (AI) is a promising technology that enables machines to learn from experience, adjust to environments, and perform humanlike tasks. Incorporating AI with digital twin (DT) makes DT modelling flexible and accurate, while improving the learning efficiency of AI agents. In this chapter, we present the framework of AI-empowered DT and discuss some key issues in the joint application of these two technologies. Then, we introduce the incorporation paradigms of three AI learning approaches with DT networks.
Blockchain for Digital Twin
Authors: Zhang Yan
Published: February 2024

Security and privacy are critical issues in digital twin edge networks. Blockchain, as a tamper-proof distributed database, is a promising solution for protecting data and edge resource sharing in digital twin edge networks. In this chapter, we first introduce the architecture of the blockchain-empowered digital twin edge network, to show the integration angles of the blockchain and digital twin techniques. Then, we show the block generation and consensus process in the developed blockchain-empowered digital twin edge network architecture.
Prompt Patterns for Agile Software Project Managers: First Results
Authors: Sainio Kari , Abrahamsson Pekka , Ahtee Tero
Published: February 2024

In the evolving field of Agile Project Management (APM), the role of the project manager is in transition. This paper identifies common ‘pain points’ in APM through a literature review and constructs a theoretical model to address them. The study introduces ‘Prompt Engineering’ as a novel approach to leverage artificial intelligence (AI), specifically ChatGPT, for mitigating these challenges. Empirical research evaluates ChatGPT's capabilities and reliability in managing various project tasks using engineered prompts. The findings suggest that while ChatGPT cannot fully replace human project managers, it excels in assisting, guiding, and automating specific tasks when guided by well-crafted prompts. As an outcome, prompt engineering patterns for project managers is proposed to facilitate the application of AI in agile settings. In this paper, we introduce patterns for requirements management, stakeholder and management teams and role clarification. The paper concludes that ChatGPT's knowledge is generally reliable but emphasizes the need for expert evaluation in critical areas.
Starting Collaborations Between SMEs and Researchers in Software Engineering
Authors: Rico Sergio , Dobslaw Felix , Öberg Lena-Maria
Published: February 2024

In software engineering research, academia-industry collaboration is predominantly understood as partnerships between academic institutions and large companies. Small and Medium-sized Enterprises (SMEs) are vital contributors to the industry, and they are numerous. Their unique preconditions and challenges differentiate their collaboration dynamics from larger corporations. We seek to identify guiding principles and practices for initiating collaborations between researchers and SMEs. Through a meta-synthesis approach drawn from two systematic literature reviews, we introduce a collaborative model canvas. This emphasizes the importance of SMEs’ business contexts and the relationships between researchers and SMEs. Our research offers insights for those looking to collaborate with SMEs, considering potential challenges and limitations.
Positive Customer Experience is Enhanced by Effective Agile Practices
Authors: Piiroinen Riina , Jormanainen Ilkka , Tukiainen Markku
Published: February 2024

This paper explores the connection between agile methods and digital customer experience, aiming to identify what are the hallmarks of a good agile way of working. The research is an exploratory case study consisting of interviews and analysis. In summary, the research suggests that the hallmarks of a good agile way of working are 1) breaking down tasks into sufficiently small pieces, 2) defining tasks precisely and releasing them to production evenly, 3) continuous improvement, and 4) good planning of sprints. These good agile operating methods can be seen in the development measures as a short lead time, a short time to export to production, low errors, and a high deployment frequency. According to the findings, these metrics are linked to the Net Promoter Score (NPS), a measure of customer experience. A team with sufficient technical capabilities team that utilizes agile operating methods is able to produce the desired things for customers at exactly the right time while constantly improving, so that the NPS is positive, and its direction is improving. On the other hand, the team’s bad operating methods are also visible in the NPS meter – in this case, the NPS fluctuates strongly. Teams can obtain insightful supplementary data about their own practices by keeping track of development measures.
Investigating Open Innovation Practices to Support Requirements Management in Software Ecosystems
Authors: Malcher Paulo , Viana Davi , Antonino Pablo Oliveira , dos Santos Rodrigo Pereira
Published: February 2024

Software ecosystems (SECO) affect requirements management when considering multiple actors (i.e., keystone, third-party developer, users) from different organizations using several communication channels such as issue trackers and forums. To deal with this scenario, professionals involved in requirements management in SECO have resorted to several open innovation (OI) practices. Our study aims to investigate OI practices applied to support requirements management in SECO. We conducted a field study based on interviews with 21 professionals involved in requirements management activities in SECO. We identified 10 OI practices to support requirements management in SECO and 14 communication channels to receive/provide requirements from/to external actors. OI practices identified in this study can help practitioners manage requirements in the SECO context in which they are engaged, making this process more informal, open, and collaborative.
Dynamic Capabilities for Sustainable Digital Transformation Amid Crisis: Insights from Law Firms in Emerging Economy
Authors: Adisa Mikhail O. , Ojikutu Gbadebo A. , Abdullai Larry , Oyedeji Shola , Porras Jari
Published: February 2024

Amidst the evolving crises and disruptions threatening firms’ competitiveness, businesses are faced with increased dynamism necessitated by technological development, digitalization, and sustainability requirements for survival and growth. This study delves into the intersection of dynamic capabilities (DC), digital transformation (DT), and sustainable resilience among law firms in developing countries. With Nigerian law firms as our case study, this research investigates the strategic integration of dynamic capabilities and digital transformation to foster long-term sustainability of law firms’ resilience during a crisis. Through empirical analysis and qualitative exploration, the study unveils obstacles ranging from digital resistance to technical constraints yet uncovers valuable insights from adopting innovative digital strategies that enhance operational resilience and contribute to driving positive economic, environmental, and social impact while ensuring long-term sustainability objectives. The study reaffirms the significance of dynamic capabilities for digital transformation and contributes to the broader discourse on how digital technology enables firms in emerging economies to maneuver disruptions during crises.
On Public Procurement of ICT Systems: Stakeholder Views and Emerging Tensions
Authors: Ghezzi Reetta , Mikkonen Tommi
Published: February 2024

The public sector is a significant consumer of ICT systems. In countries like Finland, where openness, objectivity, and fairness in public acquisitions are deemed essential, public ICT procurement is based on tenders initiated by public sector organizations. The tendering process is regulated by laws that aim to eliminate unfair advantages and provide all potential stakeholders with similar opportunities to participate. However, depending on the stakeholders’ perspectives, they may interpret the tendering process differently, leading to tensions among them. In this paper, we examine Finland’s public procurement of ICT systems using semi-structured interviews as our data collection method and analyze the results thematically. The interviewees include individuals familiar with tendering and acquisition processes in public organizations and those involved in delivering systems as vendors, representing two different perspectives on the tendering process. The results indicate that although there are significant differences in maturity among public sector organizations participating in procurement, several common themes emerged from nearly all the interviews. Furthermore, in light of contrasting views between public organizations and vendors, recurring tensions arise due to different interpretations of acquisition laws.
Who Does What? Evolving Division of Responsibilities in a B2B Platform
Authors: Vuolasto Jaakko
Published: February 2024

To remain vital, a digital platform ecosystem requires governance. In the extant literature a platform ecosystem typically has a single focal actor who is responsible for the governance. We conducted a case study in heavy industry to understand how the responsibilities of a focal actor in governing a business-to-business platform ecosystem are shared and how they change. We observe the division of responsibilities and their changes as configurations. We conclude that the focal actor’s responsibilities in a platform ecosystem are more multifaceted than the established view where a single actor has a stable set of responsibilities. The division of responsibilities in an ecosystem is subject to actor strategies and their positions in the supply chain. Thus, the strategic moves in an ecosystem are not made by a single actor but by multiple focal actors with multiple strategies.
Limit Theorems for the Cubic Mean-Field Ising Model
Authors: Contucci Pierluigi , Mingione Emanuele , Osabutey Godwin
Published: February 2024

We study a mean-field spin model with three- and two-body interactions. The equilibrium measure for large volumes is shown to have three pure states, the phases of the model. They include the two with opposite magnetization and an unpolarized one with zero magnetization, merging at the critical point. We prove that the central limit theorem holds for a suitably rescaled magnetization, while its violation with the typical quartic behavior appears at the critical point.
Falsch- und Desinformation in sozialen Medien: Ansätze zur Minimierung von Risiken in digitaler Kommunikation über Gesundheit
Authors: Schüz Benjamin , Jones Christopher
Published: February 2024

Insbesondere im Kontext der COVID-19-Pandemie sind Falsch- und Desinformationen in sozialen Medien zu einer Herausforderung für effektive Public-health-Maßnahmen geworden. Hier betrachten wir Einflussfaktoren auf das Glauben und Teilen von Falschinformationen auf individueller, sozialer und situativ-kontextueller Ebene und diskutieren Interventionsmöglichkeiten.

Auf individueller Ebene spielen Wissensdefizite, mangelnde Kompetenzen und emotionale Motivation eine Rolle. Menschen mit geringer Gesundheitskompetenz und bestimmten Überzeugungen sind anfälliger für Falschinformationen. Auf sozialer Ebene beeinflussen die Glaubwürdigkeit von Informationsquellen und soziale Normen das Teilen von Falschinformationen. Das Bedürfnis nach Status und Gruppenzugehörigkeit spielt ebenfalls eine Rolle. Auf kontextueller Ebene wirken Emotionen und die Wiederholung von Nachrichten auf das Glauben und Teilen von Falschinformationen.

Interventionen auf individueller Ebene beinhalten Maßnahmen zur Verbesserung von Wissen und Kompetenzen. Auf sozialer Ebene können soziale Prozesse und soziale Normen angesprochen werden, um das Teilen von Falschinformationen zu reduzieren. Auf kontextueller Ebene wird die Regulierung von sozialen Netzwerken als wichtiger Ansatzpunkt betrachtet.

Es wird darauf hingewiesen, dass soziale Ungleichheiten in der Exposition gegenüber Falschinformationen und im Umgang damit eine wichtige Rolle spielen. Es bleibt unklar, ob die Anfälligkeit für Falschinformationen eine generelle Tendenz oder kontextabhängig ist. Die Entwicklung von Interventionen muss daher vielfältige Einflussfaktoren berücksichtigen.
Doxorubicin concentrations in bone tumour-relevant tissues after bolus and continuous infusion: a randomized porcine microdialysis study
Authors: Jørgensen Andrea René , Bue Mats , Hanberg Pelle , Petersen Elisabeth Krogsgaard , Harlev Christina , Hansen Jakob , Baad-Hansen Thomas , Safwat Akmal , Stilling Maiken
Published: February 2024

Purpose

Doxorubicin is a widely used chemotherapeutic drug that can be administered intravenously as both a bolus infusion and a continuous infusion. The latter is believed to lower the risk of cardiotoxicity, which is a critical long-term complication of doxorubicin treatment. The local tissue concentrations of doxorubicin will be reflected in both treatment efficacy and toxicity, but very limited information is available. The aim of this study was to measure the concentration of doxorubicin after continuous and bolus infusion in tissue compartments around a typical location of a bone tumour.

Methods

Sixteen pigs (female, Danish Landrace, mean weight 77 kg) were randomized into two groups of eight. Both groups received an intravenous infusion of 150 mg doxorubicin; Group 1 received a bolus infusion (10–15 min) and Group 2 received a continuous infusion (6 h). Before infusion, microdialysis catheters were placed intravenously and in four bone tumour-relevant tissue compartments (cancellous bone, subcutaneous tissue, synovial fluid of the knee joint and muscle tissue). Sampling was done (n = 15) over 24 h, and venous blood samples were collected as a reference.

Results

Area under the concentration–time curve (AUC0–24 h) for plasma (total concentration) was significantly different between the two groups, while peak drug concentration (Cmax) was significantly higher in two compartments (plasma and synovial fluid of the knee joint) in Group 1 compared to Group 2. Overall, the unbound tissue concentrations were extremely low with values below 0.20 µg/mL.

Conclusion

The pharmacokinetic profile for doxorubicin in the investigated tissues is very similar when comparing bolus and 6 h continuous infusion.
Review on vegetation, landscape and climate changes in the Carpathian Basin during the Neolithic and Chalcolithic period
Authors: Magyari Enikő K. , Raczky Pál , Merkl Máté , Pálfi Ivett , Darabos Gabriella , Hajnalova Maria , Moskal-Hoyo Magdalena
Published: February 2024

The Neolithic and Copper Age (CA) of Hungary (6000–2800 cal bc) represents a meticulous construction of settlement structure, material culture, arable farming and herding techniques with at least one, but likely several reappearing population movements that brought in innovations and possibly contributed to the societal changes in this period. The last couple of decades witnessed a considerable progress in the study of concurrent vegetation, climate and landscape management changes particularly via the increased number of high-resolution pollen records, archaeobotanical and archaeological investigations, coupled with stable isotope analyses of the charred cereal assemblages. In this review we synthetize the results of these research projects and demonstrate that the Neolithic and CA landscapes of Hungary were characterised by mixed oak forest communities, and in the territory of Hungary thermophilous steppe oak forests were present in the lowland landscape that were the principal choice of early farmers represented by the Körös-Starčevo-Criş cultures. Climate modelling and climate reconstruction from these regions indicate higher than preindustrial summer mean temperatures and higher than modern summer rainfall. We demonstrate that Linear Pottery Culture was the first culture that technologically advanced to clear larger plots of land for crop cultivation purposes. The first large scale and landscape level clearance is discernible in the Hungarian pollen records in the Late Neolithic period, when population size likely reached its Neolithic maximum, both in the lowlands and the surrounding mid-mountains.