Search Results (107)

This research addresses the challenges of proactive scheduling and routing in manufacturing systems governed by the Material Requirement Planning (MRP) method. Such systems often face capacity constraints, difficulties in resource balancing, and limited traceability of component requirements. The lack of seamless integration between customer orders and production tasks, combined with the manual and time-consuming nature of schedule adjustments, highlights the need for an automated and optimized scheduling method. We propose a novel optimization-based approach that leverages mixed-integer linear programming (MILP) combined with a proprietary procedure for reducing the size of the modeled problem to generate feasible and/or optimal production schedules. The model incorporates dynamic routing, partial resource utilization, limited additional resources (e.g., tools, workers), technological breaks, and time quantization. Key results include determining order feasibility, identifying unfulfilled order components, minimizing costs, shortening deadlines, and assessing feasibility in the absence of available resources. By automating the generation of data from MRP/ERP systems, constructing an optimization model, and exporting the results back to the MRP/ERP structure, this method improves decision-making and competes with expensive Advanced Planning and Scheduling (APS) systems. The proposed innovation solution—the integration of MILP-based optimization with the proprietary PT (data transformation) and PR (model-size reduction) procedures—not only increases operational efficiency but also enables demand source tracking and offers a scalable and economical alternative for modern production environments. Experimental results demonstrate significant reductions in production costs (up to 25%) and lead times (more than 50%). Full article

The built environment plays a critical role in achieving climate neutrality, yet the construction sector continues to contribute significantly to carbon emissions and resource depletion. This study evaluates the experimental phase of the New European Bauhaus Academy (NEBA) Alliance pilot project, which aims to support sustainable transformation in the built environment through the integration of circular economy principles, adaptive reuse, and nature-based solutions. Conducted at the Lodz University of Technology, the pilot study involved interdisciplinary modules combining Building Information Modeling (BIM), urban regeneration strategies, and sustainable material use. A mixed-methods approach was employed, including structured surveys and qualitative analysis of student projects, to assess the effectiveness of these interventions. The results indicate that the pilot project successfully enhanced the participants’ understanding of sustainable design practices and their application in real-world architectural and urban contexts. Participants demonstrated increased competence in using digital tools for low-carbon design and in proposing regenerative solutions for existing urban fabric. The findings suggest that targeted, design-led initiatives can contribute meaningfully to the transformation of the built environment, aligning with the goals of the European Green Deal and the New European Bauhaus. This study offers a replicable model for embedding sustainability into professional practice through applied, context-sensitive strategies. Full article

This study explores how the sequence and timing of Industry 4.0 technology adoption affect sustainable innovation in manufacturing firms. Using longitudinal data from the State Society of Industrial Participations, we track the adoption patterns of eight technologies, including industrial IoT, cloud computing, RFID, machine learning, robotics, additive manufacturing, autonomous robots, and generative AI. Sequence analysis reveals five distinct adoption profiles: data-centric foundations, automation pioneers, holistic integrators, cautious adopters, and product-centric innovators. Our results show that these adoption pathways differentially impact sustainability outcomes such as circular material innovation, energy transition, operational eco-efficiency, and emissions reduction. Mediation analysis indicates that data orchestration capabilities significantly enhance resource productivity in holistic integrators, generative design competencies accelerate biomaterial innovation in product-centric innovators, and cyber-physical integration reduces lifecycle emissions in automation pioneers. By highlighting how temporal complementarities among technologies shape sustainability performance, this research advances dynamic capabilities theory and emphasizes the path-dependent nature of sustainable innovation. The findings provide practical guidance for firms to align digital transformation with sustainability objectives and offer policymakers insights into designing timely support mechanisms for industrial transitions. This work bridges innovation timing with ecological modernization, contributing a new understanding of capability development for sustainable value creation. Full article

Generative AI is an emerging tool in higher education; however, its connection with transversal competencies, as well as their sustainable adoption, remains underexplored. The study aims to analyze the scientific and conceptual development of generative artificial intelligence in higher education to identify the most relevant transversal competencies, strategic processes for its sustainable implementation, and global trends in academic production. A systematic literature review (PRISMA) was conducted on the Web of Science, Scopus, and PubMed, analyzing 35 studies for narrative synthesis and 897 publications for bibliometric analysis. The transversal competencies identified were: Academic Integrity, Critical Thinking, Innovation, Ethics, Creativity, Communication, Collaboration, AI Literacy, Responsibility, Digital Literacy, AI Ethics, Autonomous Learning, Self-Regulation, Flexibility, and Leadership. The conceptual framework connotes the interdisciplinary nature and five key processes were identified to achieve the sustainable integration of Generative AI in higher education oriented to the development of transversal competencies: (1) critical and ethical appropriation, (2) institutional management of technological infrastructure, (3) faculty development, (4) curricular transformation, and (5) pedagogical innovation. On bibliometric behavior, scientific articles predominate, with few systematic reviews. China leads in publication volume, and social sciences are the most prominent area. It is concluded that generative artificial intelligence is key to the development of transversal competencies if it is adopted from a critical, ethical, and pedagogically intentional approach. Its implications and future projections in the field of higher education are discussed. Full article

A sentiment analysis is a Natural Language Processing (NLP) task that identifies the opinion or emotional tone of documents such as customer reviews, either at the general or detailed level. Improving domain-specific models is important, as it provides smaller and better-suited models that can be implemented by entities that own textual data. This paper presents a deep learning model trained on Portuguese restaurant reviews using recurrent and self-attention mechanisms, which have consistently delivered strong results in prior research studies. Designing an effective model involves numerous hyperparameters and architectural choices. To address this complexity, a discrete genetic algorithm was used to find an optimal configuration, selecting the layer types, placement of self-attention, dropout rate, and model dimensions and shape. A key outcome of this study was that the optimization process produced a model that is competitive with a Bidirectional Encoder Representation from Transformers (BERT) model retrained for Portuguese, which was used as the baseline. The proposed model achieved an area under the curve of 92.1% and F1-score of 75.4%, demonstrating that a small, optimized model can compete and even outperform larger state-of-the-art models. Moreover, this work helps address the scarcity of NLP resources for Portuguese, and highlights the potential of customized architectures over generic solutions. Full article

The large growth in the global population requires new solutions for the control of harmful insects that compete for our food. Changing regulatory requirements and public perception, together with the continuous evolution of resistance to conventional insecticides, also require, in addition to innovative molecules with different modes of action, new non-chemical control strategies that can help maintain efficient integrated pest management programs. The last 30 years have inaugurated a new era characterised by the discovery of new mechanisms of action and new chemical families. Although European programs also promote a green deal in the crop protection sector, the existing thorough regulations slow down its spread and the adoption of new products. In light of these changes, this review will describe in more detail the dynamics of discovery and registration of new conventional insecticides and the difficulties that the agrochemical industries encounter. Subsequently, the different innovative control strategies alternative to conventional insecticides based on natural substances of different origin, entomopathogenic microorganisms, semiochemical and semiophysical compounds, and classical and augmentative biological control will be described. The advantages of these green strategies will be illustrated and also the constrains to their diffusion and commercialisation. Finally, the main biotechnological discoveries will be described, from transgenic plants to symbiotic control, classical genetic control, and, more recently, control based on insect genomic transformation or on RNAi. These new biotechnologies can revolutionise the sector despite some constrains related to the regulatory restrictions present in different countries. Full article

The rapid advancement of artificial intelligence (AI) and digital transformation is reshaping labor markets, emphasizing creativity as a core competency in entrepreneurship education. Large Language Models (LLMs) provide personalized learning experiences through natural language processing (NLP), enhancing real-time feedback and problem-solving skills. However, research on how LLMs foster creativity in entrepreneurship education remains limited. This study analyzed the technical characteristics and educational impact of LLMs, focusing on their applications in entrepreneurship education and their role in fostering creativity-driven learning environments. Specifically, it explores the educational effects of LLMs, their integration into entrepreneurship education, and the ways in which they enhance learners’ creative thinking. A systematic literature review using the PRISMA methodology was conducted to analyze existing studies. Findings suggest that LLMs improve self-efficacy, cognitive engagement, and creative problem-solving, supporting entrepreneurship education in areas such as business model development, market analysis, and multicultural communication. Despite these benefits, concerns remain regarding over-reliance, ethical risks, and the need for critical thinking frameworks. This study proposes a hybrid model integrating LLMs with traditional pedagogies to maximize creativity. Future research should explore long-term effects, cross-cultural applications, and ethical challenges to ensure responsible implementation. Full article

This study explores how design thinking (DT) for sustainable futures fosters higher education (HE) students’ sustainability competencies. By analysing the DT process of two teams of HE students co-designing digital educational technologies to address sustainability challenges (namely, children’s nature connection and their engagements with fast fashion), we identify how sustainability competencies defined by the GreenComp framework emerge temporally, across the DT phases, and which DT practices foster or hinder their development. Our findings identify three specific DT practices—and three ways to enact those practices effectively—that unlock the transformative potential of DT and enable HE students to embody sustainability values, embrace complexity in sustainability, and envision sustainable futures. Our work contributes to the field of Education for Sustainability (EfS) by demonstrating how design-based learning can promote challenge-centred, collaborative sustainability learning within HE. Drawing on our findings, we also raise the need for new pedagogical interventions that can strengthen the emergence of sustainability competencies in the process of DT. Full article

Extracting robust features against geometric transformations and adversarial perturbations remains a critical challenge in deep learning. Although capsule networks exhibit resilience through vector-encapsulated features and dynamic routing, they suffer from computational inefficiency due to iterative routing, dense matrix operations, and extra activation scalars. To address these limitations, we propose a method that integrates (1) compact vector-grouped neurons to eliminate activation scalars, (2) a non-iterative voting algorithm that preserves spatial relationships with reduced computation, and (3) efficient weight-sharing strategies that balance computational efficiency with generalizability. Our approach outperforms existing methods in image classification on CIFAR-10 and SVHN, achieving up to a 0.31% increase in accuracy with fewer parameters and lower FLOPs. Evaluations demonstrate superior performance over competing methods, with 0.31% accuracy gains on CIFAR-10/SVHN (with reduced parameters and FLOPs) and 1.93%/1.09% improvements in novel-view recognition on smallNORB. Under FGSM and BIM attacks, our method reduces attack success rates by 47.7% on CIFAR-10 and 32.4% on SVHN, confirming its enhanced robustness and efficiency. Future work will extend vexel representations to MLPs and RNNs and explore applications in computer graphics, natural language processing, and reinforcement learning. Full article

Chemical looping combustion (CLC), a promising technology employing oxygen carriers to realize cyclic oxygen transfer between reactors, represents a transformative approach to CO₂ capture with near-zero energy penalties. Among oxygen carriers, Fe-based materials have emerged as the predominant choice due to their cost-effectiveness, environmental compatibility, and robust performance. The reaction kinetics of oxygen carriers are crucial for both material development and the rational design of CLC systems. This comprehensive review synthesizes experimental and theoretical advances in kinetic characterization of Fe-based oxygen carriers, encompassing both natural and synthetic materials, while different models corresponding to specific reaction stages and their intrinsic relationships with microstructural transformations are systematically investigated. The kinetic characteristics across various reactor types and experimental conditions are analyzed. The differences between fixed bed thermogravimetric analysis and fluidized bed analysis are revealed, emphasizing the notable impacts of attrition on the kinetic parameters in fluidized beds. Furthermore, the effects of temperature and gas concentration on kinetic parameters are profoundly examined. Additionally, the significant performance variation of oxygen carriers due to their interaction with ash is highlighted, and the necessity of a quantitative analysis on the competing effects of ash is emphasized, providing actionable guidelines for advancing CLC technology using kinetics-informed material design and operational parameter optimization. Full article

This paper presents the innovative key knowledge breakthroughs achieved as one of the results of the BUILD2050 Erasmus+ project, focused on its contribution to advancing climate-resilient building engineering education and practice. In a recent work, the new methodologies applied in the BUILD2050 initiative were presented. This work discusses the incorporation of new knowledge in the courses and dissemination for the sustainability of the trans-European training experience. The challenge faced by the European Union for 2050 is achieving climate neutrality and decarbonization across all economic sectors, including the significantly impactful construction sector. To achieve this objective, it is necessary to develop technologies in an integrated way, following a holistic approach appropriately adapted to climatic conditions, cultural contexts, and natural resource availability through circular economy methodologies. To this end, it is necessary to develop innovative training methods with multidisciplinary content, incorporating a transnational perspective and scope, enabling continuous updating through learning cycles. These study cycles could be shorter and more complementary, allowing greater flexibility in knowledge acquisition while also enabling the creation of specialized training programs similar to those currently available. The BUILD2050 project has developed a transformative educational framework comprising eight comprehensive “Pilot Training” courses to address the critical challenge of integrating sustainability and circularity concepts into educational curricula at all levels, building engineering training and professional development. Addressing this gap is essential for transforming the construction sector and achieving global climate goals. The results of the BUILD2050 project demonstrate the potential of structured, trans-European training experiences to enhance professional competencies and support the transition to climate-neutral construction. Moving forward, widespread adoption and continuous dissemination of these educational advancements will be vital in ensuring a sustainable built environment by 2050. Full article

Globalization has transformed classrooms into culturally diverse environments, highlighting the critical need to prepare future teachers with skills to address these complexities. This study explores Intercultural Communicative Competence (ICC) as a fundamental skill in initial teacher training (ITT), aligning with Sustainable Development Goal (SDG) No. 4, which promotes inclusive, equitable and quality education. Through a narrative literature review, this research analyzes the main definitions and theoretical models of ICC, examining their relationship to gender, ethnicity and the teaching competencies of the faculty member. The findings highlight the lack of a unified consensus in the conceptualization of ICC, reflecting its dynamic and interdisciplinary nature. The results indicate that while gender does not significantly influence the development of ICC, ethnicity and migrant background are linked to a greater willingness to acquire it. In addition, the reflective and specialized training of the faculty member emerges as a decisive factor in the promotion of the ICC. These findings underscore the need to integrate ICC into initial teacher education plans to better prepare future teachers in multicultural contexts. By addressing equity and cultural diversity, the ICC supports educators in promoting respectful, effective and inclusive interactions, ultimately contributing to the achievement of SDG No. 4. This study enriches the discourse on teacher education and intercultural education by offering practical perspectives for connecting theory with practice in diverse educational contexts. Full article

Designing effective drug therapies requires balancing competing objectives, such as therapeutic efficacy, safety, and cost efficiency—a task that poses significant challenges for conventional optimization methods. To address this, we propose the multi-objective spider–wasp optimizer (MOSWO), a novel approach uniquely emulating the cooperative predation dynamics between spiders and wasps observed in nature. MOSWO integrates adaptive mechanisms for exploration and exploitation to resolve complex trade-offs in multiobjective drug design. Unlike existing approaches, the algorithm employs a dynamic population-partitioning strategy inspired by predator–prey interactions, enabling efficient Pareto frontier discovery. We validate MOSWO’s performance through extensive experiments on synthetic benchmarks and real-world case studies spanning antiviral and antibiotic therapies. Results demonstrate that MOSWO surpasses state-of-the-art methods (NSGA-II, MOEA/D, MOGWO, and MOPSO), achieving 11% higher hypervolume scores, 8% lower inverted generational distance scores, 9% higher spread scores, a 30% faster convergence, and superior robustness against noisy biological datasets. The framework’s adaptability to diverse therapeutic scenarios underscores its potential as a transformative tool for computational pharmacology. Full article

G-quadruplexes and i-motifs are four-stranded non-canonical structures of DNA. They exist in the cell, where they are implicated in the conformational regulation of cellular events, such as transcription, translation, DNA replication, telomere homeostasis, and genomic instability. Formation of the G-quadruplex and i-motif conformations in the genome is controlled by their competition with the pre-existing duplex. The fate of that competition depends upon the relative stabilities of the competing conformations, leading ultimately to a distribution of double helical, tetrahelical, and coiled conformations that coexist in dynamic equilibrium with each other. We previously developed a CD spectroscopy-based procedure to characterize the distribution of conformations adopted by equimolar mixtures of complementary G- and C-rich DNA strands from the promoter regions of the c-MYC, VEGF, and Bcl-2 oncogenes. In those bimolecular systems, duplex-to-tetraplex and duplex-to-coil transitions are accompanied by strand separation and an associated entropic cost. This situation is distinct from the pseudo-monomolecular nature of conformational transformations within the genome, where strand separation does not occur. To mimic better the situation in the genome, we here extend our studies to a monomolecular DNA construct—a hairpin—in which complementary G- and C-rich strands featuring sequences from the promoter region of the c-MYC oncogene are linked by a dT₁₁ loop. We used our CD-based procedure to quantify the distribution of conformational states sampled by the hairpin at pH 5.0 and 7.0 as a function of temperature and the concentration of KCl. The data were analyzed according to a thermodynamic model based on equilibria between the different conformational states to evaluate the thermodynamic properties of the duplex-to-coil, G-quadruplex-to-coil, and i-motif-to-coil transitions of the hairpin. The results have implications for the modulation of such transitions as a means of therapeutic intervention. Full article

This study investigates the simultaneous adsorption of Pb(II), Cu(II), Cd(II), and Zn(II) ions from aqueous solutions using Urtica dioica leaves (UDLs) modified with sulfuric acid, followed by heat treatment to enhance adsorptive properties. The heat treatment significantly increased the adsorbent’s specific surface area to 451.93 m²·g⁻¹. Batch adsorption experiments were performed to determine the influence of the contact time, pH of the aqueous solution, adsorbent dosage, temperature, and initial metal concentration on the adsorption efficiency. The material (modified UDLs) was characterized using X-ray diffraction (XRD), Fourier transform infrared spectroscopy (FT-IR), scanning electron microscope (SEM), and X-ray photoelectron spectroscopy (XPS). Maximum removal efficiencies were determined as 99.2%, 96.4%, 88.7%, and 83.6% for Pb(II), Cu(II), Cd(II), and Zn(II) ions, respectively. Adsorption isotherms and kinetics revealed that the process follows the Langmuir equation and pseudo-second-order models, indicating monolayer adsorption and chemisorption mechanisms. Furthermore, thermodynamic analysis indicated that the adsorption processes are spontaneous and endothermic in nature. The influence of competing ions on the adsorption of multiple heavy metals was also discussed. The results suggest that sulfuric acid and heat-treated Urtica dioica leaves can offer a promising, low-cost, and eco-friendly adsorbent for removing heavy metal ions from contaminated water. Full article