Search Results (860)

The textile industry is progressively shifting towards more sustainable solutions, particularly in the field of printing technologies. This study reports the development and evaluation of water-based pigment inks formulated with bio-based pigments derived from intermediates produced via bacterial fermentation. Two pigments—indigo (blue) and quinacridone (red)—were incorporated into ink formulations and applied on cotton and polyester fabrics through valve-jet inkjet printing (ChromoJet). The physical properties of the inks were analyzed to ensure compatibility with the equipment, and printed fabrics were assessed as to their color fastness to washing, rubbing, artificial weathering, and artificial light. The results highlight the good performance of the bio-based inks, with excellent light and weathering fastness and satisfactory wash and rub resistance. The effect of different pre-treatments, including a biopolymer and a synthetic binder, was also investigated. Notably, the biopolymer pre-treatment enhanced pigment fixation on cotton, while the synthetic binder improved wash fastness on polyester. These findings support the integration of biotechnologically sourced pigments into eco-friendly textile digital printing workflows. Full article

Background: Breast cancer, the most prevalent malignancy among women worldwide, exhibits significant heterogeneity, particularly in the tumor microenvironment (TME), which poses challenges for treatment. Spatial transcriptomics (ST) has emerged as a transformative technology, enabling gene expression analysis while preserving tissue spatial architecture. This provides unprecedented insights into tumor heterogeneity, cellular interactions, and disease mechanisms, offering a powerful tool for advancing breast cancer research and therapy. This review aims to synthesize the applications of ST in breast cancer research, focusing on its role in decoding tumor heterogeneity, characterizing the TME, elucidating progression and metastasis dynamics, and predicting therapeutic responses. We also explore how ST can bridge molecular profiling with clinical translation to enhance precision therapy. The key scientific concepts of review included the following: We summarize the technological advancements in ST, including imaging-based and sequencing-based methods, and their applications in breast cancer. Key findings highlight how ST resolves spatial heterogeneity across molecular subtypes and histological variants. ST reveals the dynamic interplay between tumor cells, immune cells, and stromal components, uncovering mechanisms of immune evasion, metabolic reprogramming, and therapeutic resistance. Additionally, ST identifies spatial prognostic markers and predicts responses to chemotherapy, targeted therapy, and immunotherapy. We propose that ST serves as a hub for integrating multi-omics data, offering a roadmap for precision oncology and personalized treatment strategies in breast cancer. Full article

This study addresses the problem of agricultural waste utilization and nutrition for older adults by developing a food product based on a circular design approach. Pineapple core was used to produce a clean-label dietary powder without chemical or enzymatic treatment, relying on repeated rinsing and hot-air drying. The development process followed a structured analysis of physical, chemical, and sensory properties. The powder contained 83.46 g/100 g dietary fiber, 0° Brix sugar, pH 4.72, low water activity (aw < 0.45), and no detectable heavy metals or microbial contamination. Sensory evaluation by expert panelists confirmed that the product was acceptable in appearance, aroma, and texture, particularly for older adults. These results demonstrate the feasibility and safety of valorizing agri-waste into functional ingredients. The process was guided by the Transformative Circular Product Blueprint, which integrates clean-label processing, IoT-enabled solar drying, and decentralized production. This model supports traceability, low energy use, and adaptation at the community scale. This study contributes to sustainable food innovation and aligns with Sustainable Development Goals (SDGs) 3 (Good Health and Well-being), 9 (Industry, Innovation and Infrastructure), and 12 (Responsible Consumption and Production). Full article

This paper introduces a unified and systematic design methodology for robotic systems that is generalizable across a wide range of applications. It integrates rigorous mathematical formalisms such as kinematics, dynamics, control theory, and optimization with advanced simulation tools, ensuring that each design decision is grounded in provable theory. The approach defines clear phases, including mathematical modeling, virtual prototyping, parameter optimization, and theoretical validation. Each phase builds on the previous one to reduce unforeseen integration issues. Spanning from conceptualization to deployment, it offers a blueprint for developing mathematically valid and robust robotic solutions while streamlining the transition from design intent to functional prototype. By standardizing the design workflow, this framework reduces development time and cost, improves reproducibility across projects, and enhances collaboration among multidisciplinary teams. Such a generalized approach is essential in today’s fast-evolving robotics landscape where rapid innovation and cross-domain applicability demand flexible yet reliable methodologies. Moreover, it provides a common language and set of benchmarks that both novice and experienced engineers can use to evaluate performance, facilitate knowledge transfer, and future-proof systems against emerging application requirements. Full article

Inclusive higher education requires educators who are not only willing to teach inclusively but who also have the skills to do so. This preliminary study offers a blueprint on how to examine the effectiveness of a three-day professional development program to strengthen the attitudes, self-efficacy, and inclusive didactics of educators. We propose a quasi-experimental design with pre-, post-, and follow-up measures, to measure the effect of the professional development program at three levels: attitudes (SACIE-R), self-efficacy (TEIP), and inclusive teaching practices (adapted Teaching Practices Questionnaire). The results, although preliminary, show a small but significant decrease in concerns toward inclusive education over time. Self-efficacy, on the other hand, showed a non-significant but consistent increase, especially at follow-up. In terms of teaching practices, significant improvements were observed in the teaching of basic skills, but not in dealing with diversity or differentiating for individual students. These preliminary findings seem to underline that short professional development programs, while contributing to increased confidence and certain didactic skills, are not sufficient to achieve lasting changes in attitudes and inclusive teaching strategies. This suggests that lasting impact likely requires structural follow-up, practical support, and strengthening the inclusive learning climate within higher education institutions. Full article

Introduction: The important factor in applying substances for inner ear therapy is the atraumatic execution, as well as effective concentration uniformly distributed in all regions of the cochlea within a reasonable time frame. This study investigates whether an additional cochlear opening (“second-hole technique”) can improve fluid distribution and reduce intracochlear pressure during dye delivery into the cochlear models and human petrous bone. Material and Methods: Three experimental setups were used: an uncoiled scala tympani model, a full-scale 3D-printed cochlear model, and a human petrous bone. In all cases, 1% methylene blue-stained saline was infused using a cochlear catheter (MED-EL, Innsbruck, Austria) through the round window. Intracochlear pressure was measured via fiberoptic pressure sensors inserted through a burr hole (artificial cochlear models) or at the lateral semicircular canal (human petrous bone). A second hole was made on the helicotrema in the inner ear models or at the oval window of the human petrous bone to examine the effect of a second hole on intracochlear pressure and fluid distribution. Dye distribution and intracochlear pressure were measured in 3D artificial models at two flow rates (0.2 and 0.4 mL/h). The intracochlear pressure were measured in the human petrous bone at a fixed rate (0.4 mL/h). Results: The use of a second hole significantly improved dye distribution in 3D models at both flow rates (p < 0.05) and led to earlier saturation-level distribution. Intracochlear pressure remained significantly lower and more stable in models with a second hole (p < 0.05). In human petrous bones, pressure fluctuation was reduced by the second hole, though pressure still increased over time. Conclusions: Using a second-hole technique leads to a faster, uniform level of dye distribution throughout the cochlear models, as well as a lower intracochlear pressure, which can be assumed to be an essential factor for hearing preservation during dye application. Full article

Metastases are the leading cause of cancer-related deaths. The spread of neoplasms involves multiple mechanisms, with metastatic tumors exhibiting molecular behaviors distinct from their primary counterparts. The key hallmarks of metastatic lesions include chromosomal instability, copy number alterations (CNAs), and a reduced degree of subclonality. Furthermore, metabolic adaptations such as enhanced glycogen synthesis and storage, as well as increased fatty acid oxidation (FAO), play a critical role in sustaining energy supply in metastases and contributing to chemoresistance. FAO promotes the infiltration of macrophages into the tumor, where they polarize to the M2 phenotype, which is associated with immune suppression and tissue remodeling. Additionally, the tumor microbiome and the action of cytotoxic drugs trigger neutrophil extravasation through inflammatory pathways. Chemoresistant neutrophils in the tumor microenvironment can suppress effector lymphocyte activation and facilitate the formation of neutrophil extracellular traps (NETs), which are linked to drug resistance. This article examines the genomic features of metastatic tumors, along with the metabolic and immunological dynamics within the metastatic tumor microenvironment, and their contribution to drug resistance. It also discusses the molecular mechanisms underlying resistance to chemotherapeutic agents commonly used in the treatment of metastatic cancer. Full article

ESG serves as a key metric for measuring corporate sustainability, but divergence among rating agencies has led to uncertainty in such an assessment. This investigation identifies ESG rating divergence as a critical catalyst for corporate digital transformation, establishing empirical analysis through a robust positive correlation between the heterogeneity in sustainability assessments and organizational digitalization intensity. Comprehensive robustness examinations and endogeneity controls substantiate the persistent significance of this relationship. Mechanistically, such divergence drives technological adaptation by restructuring the R&D team composition and elevating capital allocation toward innovative initiatives. Contextual heterogeneity manifests through amplified effects in firms with elevated analyst scrutiny and stringent internal governance, whereas pollution-intensive enterprises exhibit significant effect suppression. These findings collectively advance theoretical frameworks concerning ESG evaluation economics and digital transformation drivers, while furnishing actionable implementation blueprints for corporate digitization strategists. Full article

Background/Objectives: Although technology has progressed and novel dosage forms have been developed, tablets are still the most used form of medication. However, the present manufacturing methods of these oral solid dosage forms offer limited capacity for personalized treatment and adaptable dosing. Personalized therapy, with a few exceptions, is not yet a part of routine clinical practice. Drug printing could be a possible approach to increase the use of personalized therapy. The aim of this work was to investigate the role of surface tension and the viscosity of inks in the formation of the printing pattern and to investigate how the porosity of substrate tablets influences the behavior of inks on the surface. Methods: Spray-dried mannitol served as a binder and filler, while magnesium stearate functioned as a lubricant in the preparation of substrate tablets. Brilliant Blue dye was a model “drug”. The ink formulation was applied to the substrates in three varying quantities. Results: Increasing the viscosity enhanced the drug content, potentially improving printing speed and pattern accuracy. However, it negatively impacted the dosing accuracy due to nozzle clogging and prolonged drying time. Viscosity had a significantly higher impact on the ink behavior than surface tension. Lowering the surface tension improved the dosing accuracy and reduced the drying time but resulted in smaller drop sizes and decreases in pattern accuracy. Reducing the substrate porosity led to longer drying times and diminished pattern accuracy. Conclusions: A target surface tension of around 30 mN/m is suggested for inkjet printing. It is necessary to further investigate the applicability of the technology with solutions of inks with high viscosity and low surface tension, including the API. Full article

The development of interoperable and reusable information models is a key challenge for digitalization in manufacturing domains with heterogeneous and complex process chains. Ensuring seamless data exchange requires the standardization of both data syntax and semantics, while maintaining compatibility with existing industry standards. This paper presents a methodology for deriving standardizable and generalizable OPC UA information models tailored to domains with high process variability and interdisciplinary requirements. The methodology integrates system analysis, parameter mapping, and the development of modular submodels, supported by expert input and validation. It emphasizes the reuse and extension of existing OPC UA Companion Specifications to reduce complexity, avoid redundancy, and enable long-term standardization. The approach is exemplified by its application to battery cell production, an emerging manufacturing domain combining process and mechanical engineering with continuous and discrete processes. Its high degree of heterogeneity and lack of domain-specific standards pose significant challenges for model development. Through iterative expert workshops and structured model validation, a dedicated and transferable OPC UA framework is created. The resulting layered model structure combines a cross-industry standard with newly developed, process-aware model elements. This enables both broad applicability and the depth required for complex production environments, while supporting use cases such as traceability, regulatory reporting (e.g., EU Battery Passport), and process optimization. The resulting model improves interoperability, transparency, and data integration, offering a scalable blueprint for other complex manufacturing sectors. Full article

Scaffold architecture plays a significant role in regulating cellular and microbial interactions in tissue engineering applications. This study evaluates the performance of 3D-printed poly (lactic acid) (PLA) scaffolds with varying porosity levels (20%, 40%, 60%, 80%, and 100%) in mechanical strength, supporting human skin fibroblast (HSF) viability and reducing bacterial colonization of Gram-positive bacteria (Staphylococcus epidermidis, Staphylococcus aureus), and Gram-negative bacteria (Pseudomonas aeruginosa, Escherichia coli). The maximum tensile strength (28 MPa) was achieved in the 100% dense scaffold. Increasing porosity drastically decreased tensile strength, where 80% PLA scaffold possessed 16 MPa strength. At greater levels of porosity (60% and 40%), tensile strengths greatly decrease (8 MPa and 4 MPa), while ductility increases, especially at high porosity levels. HSF viability, assessed using the AlamarBlue assay, showed a time-dependent increase in cell proliferation, with the highest viability observed on scaffolds with 60% and 80% porosity. SEM imaging confirmed strong cell adhesion on the 80% porous scaffold, indicating that intermediate-to-high porosity enhances cell attachment and metabolic activity. In contrast, bacterial adhesion showed species-specific responses to scaffold porosity. S. epidermidis and E. coli exhibited a progressive increase in adherence with porosity, peaking at 100%. P. aeruginosa showed maximum adhesion at 80%, suggesting a porosity “sweet spot” that favors its colonization. S. aureus adhered most strongly to scaffolds with intermediate porosities (40–60%) and significantly less at 100% porosity. The current study provides insights into scaffold design considerations, emphasizing the need for optimized scaffold architecture that balances regenerative potential with infection control in tissue engineering applications. Full article

This study examines the role of AI tools in improving public communication via social media analysis. It reviews five of the top platforms—Google Cloud Natural Language, IBM Watson NLU, Hootsuite Insights, Talkwalker Analytics, and Sprout Social—to determine their accuracy in detecting sentiment, predicting trends, optimally timing content, and enhancing messaging engagement. Adopting a structured model approach and Partial Least Squares Structural Equation Modeling (PLS-SEM) via SMART PLS, this research uses 500 influencer posts from five Arab countries. The results demonstrate the impactful relationships between AI tool functions and communication outcomes: the utilization of text analysis tools significantly improved public engagement (β = 0.62, p = 0.001), trend forecasting tools improved strategic planning decisions (β = 0.74, p < 0.001), and timing optimization tools enhanced message efficacy (β = 0.59, p = 0.004). Beyond the technical dimensions, the study addresses urgent ethical considerations by outlining a five-principle ethical governance model that encourages transparency, fairness, privacy, human oversee of technologies, and institutional accountability considering data bias, algorithmic opacity, and over-reliance on automated solutions. The research adds a multidimensional framework for propelling AI into digital public communication in culturally sensitive and linguistically diverse environments and provides a blueprint for improving AI integration. Full article

Generative Artificial Intelligence (GenAI)-assisted Virtual Reality (VR) heritage serious game design constitutes a complex adaptive socio-technical system in which natural language prompts act as control levers shaping designers’ cognition and action. However, the systemic effects of prompt type on agency construction, decision boundaries, and process strategy remain unclear. Treating the design setting as adaptive, we captured real-time interactions by collecting think-aloud data from 48 novice designers. Nine prompt categories were extracted and their cognitive effects were systematically analyzed through the Repertory Grid Technique (RGT), principal component analysis (PCA), and Ward clustering. These analyses revealed three perception profiles: tool-based, collaborative, and mentor-like. Strategy coding of 321 prompt-aligned utterances showed cluster-specific differences in path length, first moves, looping, and branching. Tool-based prompts reinforced boundary control through short linear refinements; collaborative prompts sustained moderate iterative enquiry cycles; mentor-like prompts triggered divergent exploration via self-loops and frequent jumps. We therefore propose a stage-adaptive framework that deploys mentor-like prompts for ideation, collaborative prompts for mid-phase iteration, and tool-based prompts for final verification. This approach balances creativity with procedural efficiency and offers a reusable blueprint for integrating prompt-driven agency modelling into GenAI design workflows. Full article

Traditional project scheduling tools like Gantt charts struggle with dynamic adjustments and real-time optimization in complex construction projects, leading to inefficiencies and delays. This study addresses this challenge by proposing a dynamic optimization framework that integrates Building Information Modeling (BIM) and Artificial Intelligence (AI) to enhance schedule management. The framework comprises three layers: a data layer for collecting BIM and real-time site data, an analysis layer powered by AI algorithms for predictive analytics and optimization, and an application layer for visualizing progress and supporting decision-making. Through a case study on a large-scale water reservoir tunnel project in China, the framework demonstrated significant improvements in identifying schedule risks, optimizing resource allocation, and enabling real-time adjustments. Key innovations include a 4-in-1 Network Diagram Engine and a Blueprint Engine, which facilitate intuitive progress monitoring and automated task management. However, limitations in personnel skill matching, interface complexity, and mobile system performance were identified. This research advances the theoretical foundation of BIM-AI integration and provides practical insights for improving scheduling efficiency and project outcomes in the construction industry. Future work should focus on enhancing human resource management modules and refining system usability for broader adoption. Full article

The integration of artificial intelligence (AI) into education requires frameworks that are not only technically robust but also ethically and pedagogically grounded. This paper proposes the Generalist Education Artificial Intelligence (GEAI) framework—a conceptual blueprint designed to enable privacy-preserving, personalized, and multimodal AI-supported learning in educational contexts. GEAI features a Trusted Domain architecture that supports secure, voluntary multimodal data collection via multimedia registration devices (MM Devices), edge-based AI inference, and institutional data sovereignty. Drawing on principles from constructivist pedagogy and regulatory standards such as GDPR and FERPA, GEAI supports adaptive feedback, engagement monitoring, and learner-centered interaction while addressing key challenges in ethical data governance, transparency, and accountability. To bridge theory and application, we outline a staged validation roadmap informed by technical feasibility assessments and stakeholder input. This roadmap lays the foundation for future prototyping and responsible deployment in real-world educational settings, positioning GEAI as a forward-looking contribution to both AI system design and education policy alignment. Full article