Search Results (332)

Ferrocene (Fc) is a redox-active organometallic scaffold whose unique electronic properties, stability, and modularity have enabled a broad range of catalytic and sensing applications. This review critically examines recent advances in Fc-based systems for hydrogen evolution and carbon dioxide (CO₂) conversion, encompassing electrochemical, photochemical, and thermochemical strategies. Fc serves diverse functions: it operates as a reversible redox mediator, an electron reservoir, a ligand framework, and a structural modulator. Each role contributes differently to enhancing catalytic performance, improving selectivity, or increasing operational stability. We highlight how Fc integration facilitates proton-coupled electron transfer in hydrogen evolution, supports selective CO₂ reduction in molecular and hybrid catalysts, and promotes efficient CO₂ fixation and capture within functionalised frameworks. Emerging applications in electrosynthetic organic transformations are also discussed. Together, these findings position Fc as a foundational motif for designing future electrocatalytic and carbon management platforms. Full article

Functional segregation in brain networks refers to the division of specialized cognitive functions across distinct regions, enabling efficient and dedicated information processing. This paper explores the significance of functional segregation in shaping brain network architecture, highlighting methodologies such as modularity and local efficiency that quantify the degree of specialization and intra-regional communication. We examine how these metrics reveal the presence of specialized modules underpinning various cognitive processes and behaviors and discuss the implications of disruptions in functional segregation in neurological and psychiatric disorders. Our findings underscore the fact that understanding functional segregation is crucial for elucidating normal brain function, identifying biomarkers, and developing therapeutic interventions. Overall, functional segregation is a fundamental principle governing brain organization, and ongoing research into its mechanisms promises to advance our comprehension of the brain’s complex architecture and its impact on human health. Full article

Simulation-based development is a structured approach that uses formal models to design and test system behavior before building the actual system. The Internet of Things (IoT) connects physical devices equipped with sensors and software to collect and exchange data. Cyber-Physical Systems (CPSs) integrate computing directly into physical processes to enable real-time control. This paper reviews the Discrete-Event System Specification (DEVS) formalism and explores how it can serve as a unified framework for designing, simulating, and implementing systems that combine IoT and CPS—referred to as the Internet of Cyber-Things (IoCT). Through case studies that include home automation, solar energy monitoring, conflict management, and swarm robotics, the paper reviews how DEVS enables construction of modular, scalable, and reusable models. The role of the System Entity Structure (SES) is also discussed, highlighting its contribution in organizing models and generating alternative system configurations. With this background as basis, the paper evaluates whether DEVS provides the necessary modeling power and continuity across stages to support the development of complex IoCT systems. The paper concludes that DEVS offers a robust and flexible foundation for developing IoCT systems, supporting both expressiveness and seamless transition from design to real-world deployment. Full article

OoC systems employing human cells mirror the functionality of human organs and faithfully simulate their physiological microfluidic environment. Despite the potential of OoC technology in emulating tissue complexity, a significant gap persists in the continuous real-time monitoring of cellular behaviors and their responses to external stimuli, arising from the lack of biosensors integrated onto OoC microfluidic platforms. Addressing this limitation constitutes the primary objective of this study. By developing and incorporating biosensors onto a modular integrated OoC platform, we aim to enable the monitoring of changes taking place in the cellular environment under various stimuli in real time. An in-series modular integration of a biosensor array into an OoC platform is demonstrated herein, along with its potential to sustain human cell proliferation and accommodate the detection of IL-6, as an example of a mediator protein secreted as part of the immune response to inflammation. The implementation of commercially fabricated PCB components also addresses the issue of cost efficiency and manufacturing scaling-up of sensor-integrated OoCs. This advancement will not only enhance the accuracy and reliability of preclinical studies, but also pave the way for improved drug development and disease treatment. Full article

The growing demand for sustainable materials has driven significant interest in composites reinforced with organic fibers, due to their mechanical performance, availability, and reduced environmental impact. This study investigates the mechanical behavior of two composite configurations: a cross-woven fabric and a sandwich-type panel, both made from totora (Schoenoplectus californicus) and low-density polyethylene–aluminum (LDPE–Al). Our experimental results show that the cross-woven variant achieved higher impact resistance (2.51 J), tensile strength (5.82 MPa), and greater deformation capacity (6.76%), making it more suitable for applications requiring energy absorption and flexibility, such as interior cladding and modular furniture. In contrast, the sandwich configuration exhibited superior stiffness (910 MPa), favoring structural panels and low-load roofing uses. This research distinguishes itself by integrating biodegradable totora fibers with recycled LDPE–Al to fabricate sustainable construction components, advancing circular economy principles while addressing limitations in previous composite formulations through improved mechanical balance and application-specific performance. Full article

Industrialized construction faces persistent fragmentation challenges that negatively impact project performance. Although there is consensus on the importance of integration, its effective adoption in industrialized construction, particularly for modular timber building companies, remains underexplored. This study examines how intra-organization fragmentation and stakeholder integration influence project performance through a combined empirical case study and theoretical simulation analysis. This study adopted a computational modeling strategy based on the Virtual Design Team (VDT) approach to simulate the organizational structure and process in a real-world industrialized timber building company. The VDT’s baseline results reveal significant rework from inadequate early integration among specialties and functional departments, increasing the project schedule. A revised scenario introduces horizontal and vertical integration by co-locating design and manufacturing team members and reducing the decision-making level. These adjustments substantially reduced rework in design phases and shortened the project duration below the original plan. The critical role of early collaborative involvement of all disciplines emphasizes that integrated organizational structures and processes are essential for ensuring reliable project outcomes in industrialized timber building companies. This research provides empirically grounded insights highlighting the strategic importance of integration in industrialized construction and establishes a validated modeling basis to guide practical interventions and future research on integration-driven improvements. Full article

The management of municipal solid waste remains a critical environmental and energy challenge across the European Union (EU), where a significant portion of waste still ends up in landfills, generating landfill gas (LFG) rich in methane and harmful impurities. In Latvia, despite national strategies to enhance circularity, untreated LFG is underutilized due to inadequate purification infrastructure, particularly in meeting biomethane standards. This study addressed this gap by proposing and evaluating an innovative, multistep LFG purification system tailored to Latvian conditions, with the aim of enabling the broader use of LFG for energy cogeneration and potentially biomethane injection. The research objective was to design, describe, and preliminarily assess a pilot-scale LFG purification prototype suitable for deployment at Latvia’s largest landfill facility—Landfill A. The methodological approach combined chemical composition analysis of LFG, technical site assessments, and engineering modelling of a five-step purification system, including desulfurization, cooling and moisture removal, siloxane filtration, pumping stabilization, and activated carbon treatment. The system was designed for a nominal gas flow rate of 1500 m³/h and developed with modular scalability in mind. The results showed that raw LFG from Landfill A contains high concentrations of hydrogen sulfide, siloxanes, and volatile organic compounds (VOCs), far exceeding permissible thresholds for biomethane applications. The designed prototype demonstrated the technical feasibility of reducing hydrogen sulfide (H₂S) concentrations to <7 mg/m³ and siloxanes to ≤0.3 mg/m³, thus aligning the purified gas with EU biomethane quality requirements. Infrastructure assessments confirmed that existing electricity, water, and sewage capacities at Landfill A are sufficient to support the system’s operation. The implications of this research suggest that properly engineered LFG purification systems can transform landfills from passive waste sinks into active energy resources, aligning with the EU Green Deal goals and enhancing local energy resilience. It is recommended that further validation be carried out through long-term pilot operation, economic analysis of gas recovery profitability, and adaptation of the system for integration with national gas grids. The prototype provides a transferable model for other Baltic and Eastern European contexts, where LFG remains an underexploited asset for sustainable energy transitions. Full article

Plastic waste accumulation presents critical environmental challenges demanding innovative circular economy solutions. This study developed a comprehensive machine learning framework to systematically identify optimal enzyme combinations for polyester depolymerization. We integrated kinetic parameters from the BRENDA database with sequence-derived features and network topology metrics to train ensemble classifiers predicting enzyme-substrate relationships. A multi-objective optimization algorithm evaluated enzyme combinations across four criteria: prediction confidence, substrate coverage, operational compatibility, and functional diversity. The ensemble classifier achieved 86.3% accuracy across six polymer families, significantly outperforming individual models. Network analysis revealed a modular organization with hub enzymes exhibiting broad substrate specificity. Multi-objective optimization identified 156 Pareto-optimal enzyme combinations, with top-ranked pairs achieving composite scores exceeding 0.89. The Cutinase–PETase combination demonstrated exceptional complementarity (score: $0.875\pm 0.008$), combining complete substrate coverage with high catalytic efficiency. Validation against experimental benchmarks confirmed enhanced depolymerization rates for recommended enzyme cocktails. This framework provides a systematic approach for enzyme prioritization in plastic valorization, advancing biological recycling technologies through data-driven biocatalyst selection while identifying key economic barriers requiring technological innovation. Full article

The sustainable management of organic waste is a global priority due to its environmental impact and the increasing pressure on urban and rural systems, particularly in regions with limited technological infrastructure. This study introduces and validates a comprehensive methodology based on Digital Twins (DTs) to optimize production and service systems in organic waste management. The methodology includes contextual and propositional phases and is built on a modular three-layer architecture (physical, cloud, and virtual) that enables real-time monitoring, simulation, and feedback. It was validated through a field implementation in a composting facility in Cajamarca, Colombia. The results showed a 10% increase in composting efficiency and a monthly gain of 1200 kg of compost. A statistical analysis confirmed a significant increase in process efficiency (p < 0.001) and a reduction in performance variability (p < 0.01). The return on investment reached 18,957.6% using low-cost technology. These findings demonstrate the viability and adaptability of the proposed methodology for low-tech environments and support its potential for scaling in circular economy applications across waste management and agriculture. Full article

As key nodes in the energy transmission network, oil and gas pipeline stations are crucial in ensuring national energy security and stable economic development. The traditional construction mode of “on-site prefabrication and installation” has problems, such as low efficiency, high cost, and large quality fluctuations, which make it difficult to meet current construction needs. Factory prefabrication technology for pipelines has become a key path to solving industry pain points. This article focuses on the factory prefabrication technology of oil and gas station pipelines. By integrating key technologies, such as 3D modeling, automated welding, modular transportation, and intelligent detection, the visualization and digitization of station pipeline design are achieved, providing a basis for prefabrication and processing. They also improve welding quality and efficiency through automated welding technology and non-destructive testing technology. Through research on the planning and construction of prefabrication factories, construction organization and quality management, supply chain management, and information technology applications, real-time monitoring and information management of the construction process have been achieved. Case analysis shows that factory prefabrication can achieve a prefabrication rate of 70% for DN50–DN600 pipelines in the station, 80% for automated welding seams, a total construction period reduction of about 30%, a one-time welding qualification rate of over 96%, and a significant cost reduction, reflecting the significant advantages of factory prefabrication in terms of construction period, quality, and cost. Further research has clarified that factory prefabrication technology can effectively improve the efficiency, quality, and economic benefits of pipeline construction in oil and gas stations, promote the transformation of construction towards a high-efficiency, low-carbon, and sustainable direction, and provide support for the strategic goal of “One National Network”. Full article

Perfusable microvasculature is critical for advancing in vitro tissue models, particularly for neural applications where limited diffusion impairs organoid growth and fails to replicate neurovascular function. This study presents a versatile fabrication platform that integrates mesh-driven design, two-photon lithography (TPL), and modular interfacing to create multi-material, perfusable 3D microvasculatures. Various 2D and 3D capillary paths were test-printed using both polygonal and lattice support strategies. A double-layered capillary scaffold based on the Hilbert curve was used for comparative materials testing. Methods for printing rigid (OrmoComp), moderately stiff hydrogel (polyethylene glycol diacrylate, PEGDA 700), and soft elastomeric (photocurable polydimethylsiloxane, PDMS) materials were developed and evaluated. Cone support structures enabled high-fidelity printing of the softer materials. A compact heat-shrink tubing interface provided leak-free perfusion without bulky fittings. Physiologically relevant flow velocities and Dextran diffusion through the scaffold were successfully demonstrated. Cytocompatibility assays confirmed that all TPL-printed scaffold materials supported human neural stem cell viability. Among peripheral components, lids fabricated via fused deposition modeling designed to hold microfluidic needle adapters exhibited good biocompatibility, while those made using liquid crystal display-based photopolymerization showed significant cytotoxicity despite indirect exposure. Overall, this platform enables creation of multi-material microvascular systems facilitated by TPL technology for complex, 3D neurovascular modeling, blood–brain barrier studies, and integration into vascularized organ-on-chip applications. Full article

The increasing deployment of renewable energy sources is reshaping power systems and presenting new challenges for the integration of distributed generation and energy storage. Power converters have become essential to manage energy flows, coordinate storage systems, and maintain grid stability. This study presents a literature review following the PRISMA 2020 methodology, covering 71 peer-reviewed articles published between 2014 and 2024. The analysis organizes current research into five main areas: converter topologies, storage integration, grid interaction, advanced control strategies, and renewable energy applications. Recent developments include progress in multilevel and bidirectional converter designs, the use of wide-bandgap semiconductors (SiC, GaN), and the application of advanced control techniques such as model predictive control, fuzzy logic, and reinforcement learning. However, several challenges remain unresolved, including the lack of standardized validation protocols, limited implementation of modular and scalable converter solutions, and insufficient integration of hybrid storage technologies such as hydrogen and second-life batteries. Future efforts should focus on developing interoperable control platforms, extending field validation studies, and incorporating digital twins and AI-based supervisory systems to improve the reliability, efficiency, and scalability of converter-based energy storage solutions under high renewable energy scenarios. Full article

This paper introduces the Integrated Model for Sustainable Development (IMSD), a theory-driven governance framework that embeds Corporate Diversity Responsibility (CDR) into climate and energy policy to advance systemic equity, institutional resilience, and inclusive innovation. Grounded in Institutional Theory, the Resource-Based View (RBV), and Intersectionality Theory, IMSD unifies fragmented sustainability efforts across five pillars: Climate Sustainability, Social Sustainability (CDR), Governance Integration, Collaborative Partnerships, and Implementation and Monitoring. Aligned with SDGs 7, 10, and 13, IMSD operationalizes inclusive leadership, anticipatory adaptation, and equity-centered decision-making. It addresses the compounded climate vulnerabilities faced by women and marginalized groups in the Global South, integrating insights from Indigenous resilience and intersectional adaptation strategies. Unlike conventional CSR or ESG models, IMSD institutionalizes diversity as a strategic asset and governance principle. It transforms DEIB from symbolic compliance into a catalyst for ethical leadership, legitimacy, and performance in turbulent environments. The model’s modular structure supports cross-sector scalability, making it a practical tool for organizations seeking to align ESG mandates with climate justice and inclusive innovation. Future empirical validation of the IMSD framework across diverse governance settings will further strengthen its applicability and global relevance. IMSD represents a paradigm shift in sustainability governance—bridging climate action and social equity through theory-based leadership and systemic institutional transformation. Full article

This paper presents the design and implementation of a Model-based Predictive Control (MPC) strategy integrated within a modular multilayer architecture for a three-wheeled omnidirectional mobile robot, the Robotino 4 from Festo. The implemented architecture is organized into three hierarchical layers to support modularity and system scalability. The upper layer is responsible for trajectory planning. This planned trajectory is forwarded to the intermediate layer, where the MPC computes the optimal velocity commands to follow the reference path, taking into account the kinematic model and actuator constraints of the robot. Finally, these velocity commands are processed by the lower layer, which uses three independent PID controllers to regulate the individual wheel speeds. To evaluate the proposed control scheme, it was implemented in MATLAB R2024a using a lemniscate trajectory as the reference. The MPC problem was formulated as a quadratic optimization problem that considered the three states: the global position coordinates and orientation angle. The simulation included state estimation errors and motor dynamics, which were experimentally identified to closely match real-world behavior. The simulation and experimental results demonstrate the capability of the MPC to track the lemniscate trajectory efficiently. Notably, the close agreement between the simulated and experimental results validated the fidelity of the simulation model. In a real-world scenario, the MPC controller enabled simultaneous regulation of both the position and orientation, which offered a greater performance compared with approaches that assume a constant orientation. Full article

Background: The integration of Artificial Intelligence (AI) into Business Process Management Systems (BPMSs) has led to the emergence of AI-Augmented Business Process Management Systems (ABPMSs). These systems offer dynamic adaptation, real-time process optimization, and enhanced knowledge management capabilities. However, key challenges remain, particularly regarding explainability, user engagement, and behavioral integration. Methods: This study presents a novel framework that synergistically integrates the Socialization, Externalization, Combination, and Internalization knowledge model (SECI), Agile methods (specifically Scrum), and cutting-edge AI technologies, including explainable AI (XAI), process mining, and Robotic Process Automation (RPA). The framework enables the formalization, verification, and sharing of knowledge via a well-organized, user-friendly software platform and collaborative practices, especially Communities of Practice (CoPs). Results: The framework emphasizes situation-aware explainability, modular adoption, and continuous improvement to ensure effective human–AI collaboration. It provides theoretical and practical mechanisms for aligning AI capabilities with organizational knowledge management. Conclusions: The proposed framework facilitates the transition from traditional BPMSs to more sophisticated ABPMSs by leveraging structured methodologies and technologies. The approach enhances knowledge exchange and process evolution, supported by detailed modeling using BPMN 2.0. Full article