Search Results (170)

This study is part of the FAIST research project—Agile, Intelligent, Sustainable and Technological Factory, coordinated by the Footwear Technology Centre of Portugal (CTCP), which aims to develop an innovative production process through the creation of a sustainable footwear model fully adapted to the user’s foot anatomy and personalized according to individual aesthetic preferences. Within this context, the need emerged to design an online platform with an interface capable of effectively addressing user needs throughout all stages of the personalization process, from the foot scanning to the aesthetic personalization of the model, while ensuring an efficient, intuitive, and pleasant navigation experience. Thus, this work aims to demonstrate how the design process of a footwear personalization platform, across its different phases, can contribute to the revitalization of the Portuguese footwear industry, as well as to describe its effectiveness, with the goal of being potentially adapted and implemented in similar contexts. The adopted methodology was based on the principles of Design Thinking, an approach centered on user needs. The development of the platform involved the creation of personas, the definition of the information architecture, the development of wireframes and workflows, and the execution of usability tests using the System Usability Scale (SUS). The results demonstrate a high success rate, validating the proposed solution with users and confirming the suitability of the applied methodologies. Full article

The purpose of this study is to develop and validate a System Dynamics (SD) model that illustrates how Artificial Intelligence (AI), including generative AI, alters project lifecycle behavior under a hybrid agile–predictive governance approach. The study method uses SD model to operationalize the PMBOK performance domains as an interconnected system of stocks, flows, and feedback loops. These constructs and their interaction represent delivery progress, stakeholder engagement, team capacity, measurement accuracy, governance alignment, and uncertainty exposure. Planning effectiveness is treated as an emergent performance indicator arising from the interaction of the planning-related feedback structures. The proposed model embeds AI levers for planning, risk, measurement, stakeholder sensing, and team support. A calibrated baseline model representing conventional project dynamics was validated in two ways. First it was validated structurally against PMBOK guidance and the SD literature. Secondly, it was validated behaviorally against stylized project trajectories. The AI-augmented variant was then simulated under identical initial conditions to assess marginal effects. Across multiple scenarios, AI integration reduced peak uncertainty exposure by up to 33%. Also, the AI-augmented system showed reduced planning effort by 15%, and improved monitoring and risk sensing by accelerating feedback and reducing delays by 25%. AI also improved measurement accuracy trajectories and accelerated cumulative delivery while lowering volatility in work completion rates. Governance coherence and development approach alignment improved, while stakeholder engagement and team capacity showed smaller changes. The results demonstrate that AI primarily acts as an enabler that strengthens high-impact feedback loops in planning, monitoring, and risk sensing within a hybrid methodology. AI also delineates boundaries where managerial judgment and cultural change remain critical for effective framework validation. Full article

Objectives: Using high-performance liquid chromatography (HPLC), we developed and validated an in vitro assay for the quantitative determination of beta-site amyloid precursor protein cleaving enzyme 1 (BACE1) activity, supplementing limited current methodologies to assess the efficacy of BACE1 inhibitor compounds. A hexa-histidine tagged peptide substrate of BACE1 was used as the analyte for the determination of in vitro BACE1 activity; it was validated according to ICH guidelines. Methods: The HPLC analysis was performed on the Agilent 1290 Series Infinity II UHPLC System equipped with a Phenomenex Kinetex EVO C18 (100 × 3 mm) 5 µm column. The method was developed using a gradient programme comprising 10% aqueous acetonitrile (0.02 M TFA) to 30% aqueous acetonitrile (0.02 M TFA) for 5 min at a flow rate of 0.6 mL/min. Results: The method showed linearity over the range of 14.92 to 72 µM with ${\mathrm{r}}^2=0.9997$. The accuracy of the method in terms of mean recovery ranged between 96.62 and 98.38%. The %RSD for intra- and inter-day precision was less than 5%. Two commercial inhibitors, AZD3839 and OM99-2, were used to evaluate the performance of the method at their respective IC₅₀, resulting in inhibition of 53.46 and 50.74%, respectively. The described method addresses the void for a practical and cheap alternative to quantitatively determine the activity of BACE1 compared to current commercially available detection assays. Conclusions: We have successfully developed an HPLC method to measure the inhibitory function of two commercial inhibitors of BACE1, indicating the suitability of the method for the identification and characterisation of novel BACE1 inhibitors. Full article

This study presents a structured development and implementation process executed within the KAMAX manufacturing plant, leveraging a sophisticated technical workflow that integrates 3D scanning (via iPad Pro), the FataMorgana AR ecosystem, and Microsoft HoloLens 2 hardware. The goal is to practically show the possibilities of using the means of augmented reality in connection with specific hardware equipment, which helps in more agile management and functioning of a modern production company. A fundamental methodological advancement of this research is the deployment of a QR-code-based spatial synchronization protocol, which guarantees high-fidelity alignment during the superimposition of digital twins onto the physical production environment. Through a pilot initiative centered on the configuration of new manufacturing cells, the research empirically validates that AR-enhanced auditing substantially mitigates spatial design discrepancies. Specifically, the system excels at detecting physical interferences undetectable in conventional 2D blueprints, thereby streamlining the consultative and decision-making processes for organizational stakeholders during layout verification. These findings offer significant empirical evidence regarding the integration and interoperability of AR devices and IoT datasets within the broader Industry 4.0 paradigm. Full article

The creation of unified, open, secure, reliable, and agile data spaces is essential for collecting, storing, and sharing data in a standardized and accessible manner, promoting data reuse and addressing current interoperability limitations. In this context, this research presents a proof of concept for a unified agronomic data space based on the structured integration of heterogeneous open data sources. The central hypothesis is that the automated acquisition, preprocessing, and harmonization of publicly available agronomic data can significantly improve accessibility, usability, and interoperability for agricultural decision support applications. To this end, a comprehensive analysis of relevant open data sources was conducted, followed by the design and implementation of configurable algorithms for automated data downloading, cleaning, validation, and integration. The proposed approach explicitly addresses key challenges such as heterogeneous data formats, inconsistent spatial and temporal resolutions, missing values, and outlier detection. As a result, a unified access point was developed, providing reliable agronomic information, including (i) preprocessed climatological time series, (ii) crop and phytosanitary data, (iii) high-resolution aerial orthophotography, (iv) remote-sensing imagery, (v) pest-related information, and (vi) time series of major vegetation indices. The proof of concept was implemented for olive groves in the Andalusian region of Spain; however, the methodology is fully transferable to other crops, regions, and institutional contexts where comparable open data sources are available. The results demonstrate the potential of shared agronomic data spaces to enhance data reuse, support scalable analytics, and facilitate interoperable, data-driven agricultural management beyond the specific regional case study. Full article

Digital transformation offers significant potential to reshape supply chains; however, implementation efforts remain fragmented, technology-centric, and insufficiently aligned with strategic, organizational, and sustainability goals. Existing frameworks and maturity models tend to emphasize the technological dimension, offering limited guidance on how digital transformation should be integrated with people, processes, culture, and sustainability at the supply network level. Building on evidence synthesized through an umbrella review of the state of the art, this paper proposes the Agile and Sustainable Supply Network Compass, a holistic and actionable framework designed to support organizations in advancing toward agile and sustainable supply networks. The Compass incorporates three structural dimensions—Strategy, Processes, and Capabilities (related to digitalization and sustainability)—as foundational pillars for transformation. We hypothesize that an effective transformation requires the joint alignment of strategy, cross-functional processes, and capabilities, as well as the explicit identification of a reduced supply network, a focal firm, and its critical linkages. The results show that positioning agility and sustainability as shared strategic objectives at the supply network level enables coherent decision-making, targeted capability development and improved coordination across interconnected actors. Rather than prescribing specific technologies, the proposed framework provides a guiding methodological logic that explains how digitalization and sustainability can co-evolve within supply networks. This work contributes to both theory and practice by bridging conceptual gaps in the literature and establishing the groundwork for future maturity models and empirical applications. Full article

Modern software development fundamentally relies on agile methodologies and DevOps practices to facilitate accelerated software delivery. Continuous integration and continuous deployment CI/CD are among the most critical DevOps practices that require considerable attention to execute successfully. Therefore, this study proposes a multi-class XGBoost-based model to improve the performance of CI/CD failure prediction. The proposed model was trained and tested using the comprehensive TravisTorrent dataset, which contains extensive build information from several projects developed in various programming languages. The experimental results demonstrate that the proposed model achieves a statistically significant performance improvement of nearly 18% over SVM and the Random Forest models. Beyond the performance improvement, SHAP (SHapley Additive exPlanations) analysis was employed to explain the model’s decision-making process, revealing that the most influential features, ranked in descending order of importance, are build log status, build duration, build start time, the number of commits in the repository, and repository age. This interpretability enhances both the reliability and transparency of the proposed model. Full article

Integrating people with hearing disabilities into schools is one of the biggest problems that Latin American societies face. Mexican Sign Language (MSL) is the main language and culture of the deaf community in Mexico. However, its use in formal education is still limited by structural inequalities, a lack of qualified interpreters, and a lack of technology that can support personalized instruction. This study outlines the conceptualization and development of a mobile application designed as an adaptive assistive technology for learning MSL, utilizing a combination of computer vision techniques, deep learning algorithms, and personalized pedagogical interaction. The suggested system uses convolutional neural networks (CNNs) and pose-estimation models to recognize hand gestures in real time with 95.7% accuracy. It then gives the learner instant feedback by changing the difficulty level. A dynamic learning engine automatically changes the level of difficulty based on how well the learner is doing, which helps them learn signs and phrases over time. The Scrum agile methodology was used during the development process. This meant that educators, linguists, and members of the deaf community all worked together to design the product. Early tests show that sign recognition accuracy and indicators of user engagement and motivation show favorable performance and are at appropriate levels. This proposal aims to enhance inclusive digital ecosystems and foster linguistic equity in Mexican education through scalable, mobile, and culturally relevant technologies, in addition to its technical contributions. Full article

This paper proposes a hybrid collaborative framework to optimize technology selection in Small and Medium-sized Enterprises (SMEs) by integrating machine learning (ML) predictions with Planning Poker, consensus-based estimation technique used in agile software development. Addressing known challenges such as cognitive bias, resource constraints, and the need for inclusive decision-making, the proposed model combines data-driven suitability analysis with stakeholder-driven consensus. ML generates quantitative, criterion-wise suitability scores based on historical SME data, providing transparent baselines for evaluation. Stakeholders independently assess candidate technologies using Planning Poker, and their consensus is blended with ML predictions through a flexible weighting mechanism. An illustrative case study on CRM tool selection illustrates the framework’s practical advantages: improved decision accuracy, transparency, and greater stakeholder engagement. The methodology is iterative, allowing for continuous learning and adaptation as new data emerges. This dual approach ensures that technology adoption decisions in SMEs are both empirically validated and contextually robust, offering a significant improvement over traditional, siloed methods. Full article

This research focuses on ontology-driven conversational agents (CAs) that harness large language models (LLMs) and their mediating role in performing collective tasks and facilitating knowledge-sharing capabilities among multiple healthcare stakeholders. The research addresses how CAs can promote a therapeutic working alliance and foster trustful human–AI collaboration between emergency department (ED) stakeholders, thereby supporting collaborative tasks with healthcare professionals (HPs). The research contributes to developing a service-oriented human–AI collaborative framework (SHAICF) to promote co-creation and collaborative learning among patients, CAs, and HPs, and improve information flow procedures within the ED. The research incorporates agile heavy-weight ontology engineering methodology (OEM) rooted in the design science research method (DSRM) to construct an ontological metadata model (PEDology), which underpins the development of semantic artifacts. A customized OEM is used to address the issues mentioned earlier. The shared ontological model framework helps developers to build AI-based information systems (ISs) integrated with LLMs’ capabilities to comprehend, interpret, and respond to complex healthcare queries by leveraging the structured knowledge embedded within ontologies such as PEDology. As a result, LLMs facilitate on-demand health-related services regarding patients and HPs and assist in improving information provision, quality care, and patient workflows within the ED. Full article

Uncertainties and changes significantly shape the path of the design process, requiring situation-specific strategies and methods. Literature and practice highlight the implementation of agility as a means for companies to achieve competitive advantages in a dynamic development environment through more robust costumer integration and improved responsiveness. From a design science perspective, a key challenge remains the development of a theoretical model that explains how agility can be operationalized to realize benefits such as enhanced adaptability. Drawing on a literature review and six empirical studies on agile development of mechatronic systems in the German-speaking context, we propose a novel conceptualization of agility. Using systems thinking, we conceptualize agility as a construct and establish its relationship to agility as an attribute. Thus, the article provides a new methodological perspective on agility by explicitly linking its structural elements to established outcome perspectives from multiple domains. This work advances the methodological understanding of agility and identifies future research directions for the development of mechatronic systems, aiming to enrich the theory for its utilization. Full article

This study develops and psychometrically validates the Neuroplasticity Literacy in Working Life Scale (NLWLS), designed to evaluate employees’ engagement in enrichment activities and deliberate cognitive renewal practices. Based on a theoretical framework, neuroplasticity literacy is conceptualized through two behavioral dimensions: Enrichment Behaviors (EB) and Deliberate Cognitive Renewal (DCR). The scale was developed via a two-stage process involving expert evaluation, pilot testing, exploratory factor analysis, and confirmatory factor analysis using robust maximum likelihood estimation. Findings from two independent samples (n = 120; n = 164) consistently support the two-factor structure, demonstrating high internal consistency, strong convergent and discriminant validity, and satisfactory model fit indices. The NLWLS offers a methodologically rigorous instrument for measuring neuroplasticity-related behaviors at work, contributing to understanding employees’ cognitive renewal capacity, learning agility, and sustainable learning outcomes. These results support the integration of neuroscience-based behavioral indicators into organizational learning research and provide a theoretical–practical foundation for future studies. Full article

The process industry (PI) plays a vital role in the global economy and faces mounting pressure to enhance sustainability, operational agility, and resource efficiency amid tightening regulatory and market demands. Although artificial intelligence (AI) has been explored in this domain for decades, its adoption in industrial practice remains limited. Recently, machine learning (ML) has gained momentum, particularly when integrated with core PI systems such as process control, instrumentation, quality management, and enterprise platforms. Among ML techniques, reinforcement learning (RL) has emerged as a promising approach to tackle complex operational challenges. In contrast to conventional data-driven methods that focus on prediction or classification, RL directly addresses sequential decision making under uncertainty, a defining characteristic of dynamic process operations. Given RL’s growing relevance, this study conducts a systematic literature review to evaluate its current applications in the PI, assess methodological developments, and identify barriers to broader industrial adoption. The review follows the PRISMA methodology, a structured framework for identifying, screening, and selecting relevant publications. This approach ensures alignment with a clearly defined research question and minimizes bias, focusing on studies that demonstrate meaningful industrial applications of RL. The findings reveal that RL is transitioning from a theoretical construct to a practical tool, particularly in the chemical sector and for tasks such as process control and scheduling. Methodological maturity is improving, with algorithm selection increasingly tailored to problem-specific requirements and a trend toward hybrid models that integrate RL with established control strategies. However, most implementations remain confined to simulated environments, underscoring the need for real-world deployment, safety assurances, and improved interpretability. Overall, RL exhibits the potential to serve as a foundational component of next-generation smart manufacturing systems. Full article

Organizations increasingly rely on Agile software development to navigate the complexities of digital transformation. Agile emphasizes flexibility, empowerment, and emergent design, yet large-scale initiatives often extend beyond single teams to include multiple subsidiaries, business units, and regulatory stakeholders. In such contexts, team-level mechanisms such as retrospectives, backlog refinement, and planning events may prove insufficient to achieve alignment across diverse perspectives, organizational boundaries, and compliance requirements. To address this limitation, this paper introduces a complementary framework and a supporting software tool that enable systematic cross-stakeholder alignment. Rather than replacing Agile practices, the framework enhances them by capturing heterogeneous stakeholder views, surfacing tacit knowledge, and systematically reconciling differences into a shared alignment artifact. The methodology combines individual Functional Resonance Analysis Method (FRAM)-based process modeling, iterative harmonization, and an evidence-supported selection mechanism driven by quantifiable performance indicators, all operationalized through a prototype tool. The approach was evaluated in a real industrial case study within the regulated gaming sector, involving practitioners from both a parent company and a subsidiary. The results show that the methodology effectively revealed misalignments among stakeholders’ respective views of the development process, supported structured negotiation to reconcile these differences, and produced a consolidated process model that improved transparency and alignment across organizational boundaries. The study demonstrates the practical viability of the methodology and its value as a complementary mechanism that strengthens Agile ways of working in complex, multi-stakeholder environments. Full article

This study investigates the impact of digital transformation on a Brazilian post-harvest engineering company. The manuscript examines how digital technologies impact performance and competitive advantages, providing actionable insights for practitioners. The methodology is a twofold case study. First, it describes the company’s technology development process, examining the journey from initial implementations to its current state over the past ten years. Then, it focuses on the recommendations for future advancements, provided by a leading technology research institute located in Germany. Data collection involved observation, interviews (personnel, clients, experts), document analysis, and facility tours. The findings include qualitative (grain quality, agility) and quantitative impacts (EBITDA increase, 84% storage efficiency). Barriers included mechanical adaptation and costs. Opportunities related by BIBA include advanced technologies (such as AI and digital twins), aligning with a proposed six-level digital maturity model for post-harvest systems. Regarding practical implications, the findings emphasize the need for a strategic vision for digital technology adoption in the post-harvest industry, which is crucial for addressing labor shortages, reducing losses, and promoting sustainability, with potential annual gains of $700 million. The main novelty introduced by this study is a novel, empirically derived six-level digital maturity model. It provides comprehensive qualitative/quantitative impact analysis, highlighting advanced technologies for industry challenges. Full article