Search Results (100)

This article examines how smart cities can act as catalysts for enterprise development by integrating technological, infrastructural, governance and human capital dimensions into a coherent urban innovation ecosystem. Drawing on an extensive literature review, the study first conceptualizes smart cities as adaptive systems that combine physical infrastructure, digital data layers, and institutional frameworks, creating conditions for knowledge spillovers, entrepreneurial opportunities, and business model innovation. Empirically, the research is based on an expert survey conducted among 54 specialists from academia, business, and public administration, who assessed the importance of technological, infrastructural, governance, innovation ecosystem, and human capital factors for enterprise development in the context of smart cities. The results suggest that advanced digital technologies, smart infrastructure, open data, R&D support, startup programs and talent development are perceived by experts as key, mutually complementary drivers of firms’ innovation, efficiency, sustainable growth, and competitiveness, with notable differences between expert groups. On this basis, the study proposes a synthetic model of relationships and impact pathways linking smart city components with enterprise outcomes. The paper concludes with a discussion of the study’s limitations, related to the expert-based, country-specific, and perceptional character of the data, and outlines directions for further quantitative and qualitative research on the firm-level effects of smart city development. Full article

Physical manufacturing and research infrastructures are essential for advanced innovation but often remain inaccessible to SMEs, start-ups, and research institutions that cannot justify ownership of capital-intensive assets. This study examines whether platform-mediated infrastructure sharing can function as a sustainable open-innovation mechanism in advanced manufacturing. Using the SCIP/SYNPRO platform developed in the SYNERGY and IDEATION projects, an exploratory case-study design combines descriptive analysis of a registry of 290 infrastructure items across 11 countries with qualitative analysis of 23 documented access requests, interaction records, and pilot reports. The results show that the Provider–Taker model facilitates observable access-enabling interactions, including infrastructure publication, request submission, provider–taker communication, negotiation, and selected documented use, although it does not measure population-wide access outcomes. Sharing potential is uneven: modular and emerging technologies, especially VR/AR infrastructures, attract higher request intensity than production-integrated assets. Users and providers favour negotiated access, flexible pricing, operator support, and contractual clarification rather than standardised rental models. Qualitative evidence shows that value is created through access to otherwise unavailable equipment, postponed investment, experimentation, technology familiarisation, student training, capability development, and new inter-organisational research links. The findings indicate that infrastructure sharing can support more resource-efficient innovation but depends on discoverability, governance, trust, and support mechanisms to scale. Full article

Driven by renewable energy, the operating temperatures of alkaline water electrolyzers (AWEs) exhibit significant dynamic variations. Conventional control strategies rely on fixed startup parameters, causing dispatch plans to deviate from actual physical states, which leads to transient over-temperature or startup failures. To address this issue, this paper proposes a dual-layer optimization strategy for multi-electrolyzer systems based on temperature–power adaptation. First, a thermo-electro-hydrogen coupling model is established to quantitatively reveal the dynamic relationship among the initial temperature, startup power, and transition time. This relationship is utilized to construct a dynamic startup boundary, overcoming the limitations of traditional static constraints. Within the proposed framework, the upper layer utilizes a Mixed-Integer Linear Programming (MILP) model to formulate state-switching and baseline power allocation plans derived from short-term forecasts. Concurrently, the lower layer employs the Mongoose Optimization Algorithm (MOA) for real-time rolling optimization, enabling the system to actively perceive temperature variations and adaptively schedule power allocation. Simulations across typical seasonal scenarios validate the strategy’s superiority. In a typical spring scenario, compared to the traditional Daisy Chain and Rotation Control strategies, as well as the Equal Allocation strategy, the proposed approach reduces total startup time and energy consumption by 59.2% and 54.6%, respectively. Furthermore, it increases wind power accommodation rates by 17.7% and 14.2%, and total hydrogen production by 20.0% and 14.9%, respectively. These superior renewable energy utilization and production efficiencies are robustly maintained across typical seasonal scenarios. By actively perceiving actual temperatures for adaptive scheduling, the proposed strategy ultimately ensures synergy and reliability between the control strategy and actual operational constraints under fluctuating conditions. Full article

In high-frequency, stable signal generation, the Colpitts-type crystal oscillator circuit stands out for its low phase noise and long-term frequency stability, offering a reliable solution, especially for communication systems and precision measuring instruments. In this study, equivalent circuit models of the Colpitts-type crystal oscillator were created, mathematical relationships were obtained, and theoretical analyses were performed. Theoretical models were examined in a simulation environment using OrCAD/PSpice^® and NI Multisim^® software; numerical analyses were performed with MATLAB R2025b to support the system outputs. Simulation results were validated with experimental data obtained in the laboratory, demonstrating a high agreement between theoretical, simulation, and experimental findings. The results show that the developed models successfully represent the physical system in terms of oscillation startup time, transient behavior, and oscillation band settling times. Furthermore, solutions to practical problems such as startup instabilities and load effects led to the conclusion that the circuit operates reliably and repeatably. Full article

Heat pipe cooled reactors rely on heat pipes for passive heat transfer and exhibit high reliability and compactness. Therefore, they are considered candidate nuclear reactor systems for future deep space exploration missions. To enable a deeper investigation of heat pipe reactor systems, particularly the transient response characteristics of the core, a transient coupled analysis framework is developed based on the multi-physics coupling code MOOSE. This framework includes the core heat transfer module, point kinetics module, heat pipe module, and Stirling engine module. A novel strategy that allows two distinct heat pipe models to be simultaneously invoked within a single simulation in MOOSE is developed. All modules are developed within the MOOSE framework and do not rely on any external programs. The heat pipe module is validated using experimental data from heat pipe startup and operation tests within the maximum relative error of only 0.45%. The entire coupled framework is validated against the KRUSTY operational experiments and is compared with other multi-physics models, demonstrating higher accuracy within the maximum relative error of only 13.7% in core load variation conditions. Meanwhile, transient coupled analyses of the KRUSTY reactor are performed to evaluate its safety performance under accident conditions. In the hypothetical positive reactivity step insertion accident and heat pipe failure accidents, the KRUSTY core exhibits excellent safety performance. And the mechanism of heat pipe power redistribution following heat pipe failure is examined in detail. Full article

Food waste prevention and reduction are some of the important initiatives to improve the environmental sustainability of food systems. The global agenda of the United Nations provides a framework of targets and actions against food waste to which the European Union (EU), within the “Farm to Fork” strategy, aims to contribute. In this context, evaluating the impacts of food prevention measures is of great importance for supporting policies. This LCA analyzes the impact of classic lasagna from cradle to grave, through a generic food case study, prepared by food shops in Bologna (Northern Italy). Four scenarios are simulated, comparing the impacts of some end-of-life alternatives for the management of leftover lasagna (landfilling, composting, and redistribution with the digital application of the circular start-up “Squiseat”) versus the ideal scenario where no leftover lasagna is assumed. The results show that the preparation of classic lasagna generates non-negligible impacts on the analyzed LCA categories due to some of its ingredients, such as Bolognese sauce and Parmigiano Reggiano, and their associated production processes. For this reason, it is important to prevent classic lasagna leftovers from being wasted. The comparison of the four scenarios shows that redistribution is the scenario with the lowest impacts in all the investigated impact categories, including global warming (6.24 kg CO₂ eq./kg of lasagna). The impacts are also lower than the ideal scenario due to the assumption of more sustainable means of transport. Normalization of characterized results confirms that Global Warming (GW) is only one of the most relevant impact categories in the life cycle of classic lasagna. The results have practical implications for raising awareness concerning the impacts of food production throughout the whole life cycle and the need for preserving the value of food by avoiding waste. Moreover, this study also shows that a reduction in the impact is a shared outcome that could be achieved by the joint efforts of all the stakeholders involved in the life cycle of food. In this regard, urban centers are confirmed to be important hubs of circular and more sustainable innovation. Finally, the LCA enriches the current research by investigating redistribution through the relationship of the food shop–virtual intermediate–consumer. So far, the prevalent focus of the LCA research allows us to assess the redistribution of collected surplus food from retailers and its delivery to the consumers by means of physical intermediaries and related infrastructures (e.g., food hubs, food banks, and food emporiums). Full article

This study presents a transient mathematical model and its numerical solution for the moving-boundary problem related to substrate diffusion and reaction in enzymatic catalytic particles. The main focus is on bioreactor startup, where the initial substrate concentration inside the particles is zero, forming a dead core that shrinks over time and makes the catalytic effectiveness factor time-dependent. The substrate mass balance leads to a partial differential equation with a moving boundary, solved using the method of lines coupled with Newton’s method (MLN), implemented in Wolfram Mathematica (WM). The proposed approach was validated for zero- and first-order kinetics at steady state, whose analytical solutions are available. Compared to the method of orthogonal collocation on finite elements, the MLN offers advantages such as not requiring an initial concentration profile and simple implementation in WM. The results demonstrate that the proposed method provides accurate and physically consistent solutions, contributing to a better understanding of dead-core dynamics and supporting the design of heterogeneous bioreactors with immobilized enzymes. Full article

Filling operations in pressurised pipeline systems can trap air pockets, generating hazardous transient overpressures that threaten structural integrity and operational reliability. Evaluating these events using conventional hydraulic models can be computationally intensive, limiting design-space exploration of operational scenarios. This study presents a simulation-driven design-screening framework based on Monte Carlo simulation to evaluate and predict peak absolute pressures during pipeline start-up and filling operations. A total of 2000 transient scenarios were generated for a representative 1100 m pipeline system by varying key geometric and operational parameters, including diameter, friction factor, column lengths, slopes, and reservoir elevation. Twenty-eight machine learning regression models were trained to develop a physics-informed surrogate model capable of rapidly predicting pressure peaks within the defined parameter domain. The trilayered neural network achieved the highest predictive accuracy, with robust validation (RMSE = 10.95 m, R² = 0.99) and test performance (RMSE = 9.78 m, R² = 0.99). Screening results showed that nominal pressure thresholds of 61.18 m and 407.89 m were exceeded in 97.53% and 4.89% of the retained peak-forming scenarios (n = 1746), respectively. The proposed framework provides an efficient and reproducible surrogate-based design-screening approach for transient overpressure risk within the evaluated hydraulic domain. Full article

The Air-front Smart City (ASC) concept is proposed to address the stagnation of industries in developed countries and stimulate economic growth in developing countries while maintaining a higher quality of life for people and contributing to decarbonization and overall United Nations SDGs in an existing study. However, no studies have been conducted to assess ASC policies. Therefore, this study integrates the integrated accessibility index into the quality of life (QOL) and quality of business (QOB) evaluation models to assess the startup ecosystem in Aichi, Singapore, and Munich within the ASC concept. The study uses survey data conducted in Aichi to estimate monetary values of QOL and QOB component indicators, calculates the integrated accessibility indices, and estimates QOL and QOB. Furthermore, the study sets scenarios to assess the impacts of living and business urban policies in Aichi. Additionally, the study using Aichi parameters compares the startup ecosystem in Singapore and Munich. The result shows that the key drivers of startup attraction are corporate tax rate, economic growth, and safety; enhancing these indicators directly increases startups’ QOB, business partners, and residents’ QOL. It was found that QOB in Singapore is comparatively higher, whereas QOL is higher in Aichi. Full article

Deep-sea gear transmission systems encounter critical lubrication challenges arising from the synergistic coupling of extreme hydrostatic pressure and cryogenic temperatures. These environmental stressors induce exponential viscosity escalation in lubricants, precipitating severe fluidity degradation, elevated startup resistance, and lubrication starvation. Concurrently, seawater intrusion triggers lubricant emulsification, additive deactivation, and electrochemical corrosion at meshing interfaces, collectively escalating the risk of catastrophic lubrication failure and compromising long-term operational reliability. This study systematically elucidates the lubrication degradation mechanisms inherent to deep-sea environments and proposes targeted mitigation strategies. Through comprehensive characterization of deep-sea environmental parameters and their impact on lubricant rheological behavior, we critically evaluate the applicability and inherent limitations of conventional Thermal Elasto-Hydrodynamic Lubrication (TEHL) theory under extreme conditions. Our analysis reveals that established TEHL frameworks necessitate substantial modification to accurately capture pressure-viscosity-temperature coupling phenomena and seawater contamination kinetics. Meshing interface texturing, as an effective anti-friction and wear-mitigation strategy, is investigated to delineate its mechanistic pathways for enhancing lubricant film formation and tribological performance under starved lubrication regimes. Key findings demonstrate that optimized micro-texture architectures can effectively compensate for viscosity-induced fluidity deficits and attenuate the deleterious effects of seawater ingress. Critical knowledge gaps are identified, and future research trajectories are charted: (i) multiphysics coupling models integrating thermo-hydrodynamic, chemo-physical, and mechanical degradation processes; (ii) synergistic texture-coating design paradigms; (iii) high-pressure low-temperature experimental validation protocols; and (iv) engineering implementation frameworks for deep-sea gear transmission systems. This review establishes theoretical foundations and provides technical guidelines for robust lubrication design and long-term operational stability of deep-sea transmission equipment. Full article

The paper presents the results of a study of linear wear of gas-flame and ion-plasma coatings of KNA-82 seals with an yttrium content of 0.5%, used in gas turbine engine assemblies, during physical modeling of their thermomechanical loading on small-sized samples. Tribotechnical tests were carried out in four stages, simulating the operating conditions of real gas turbine engines—from the first start-up with running-in of the coating cut-in areas to reaching a steady state with their service properties formed. The surface of the coatings was in contact with the ridges of triangular-shaped plates without heating (20 °C), at average heating (350–470 °C), after holding the samples at 1100 °C and average heating of 410–460 °C, and after grinding off the worn layer that had worn out after holding the samples at 1100 °C at average heating of 320–440 °C. Trends in the change in the linear ear of coatings and the formation of friction tracks caused by the uneven manifestation of the physical and mechanical properties of coatings, which are unevenly distributed throughout their body, were determined. It was found that both coatings tend to stabilize the wear process at certain mechanical pressures in the friction contact zone and only in the temperature range from 20 °C to 400 °C. These pressures range from 4 MPa to 6.7 MPa for gas-flame coatings and from 3 MPa to 4.2 MPa for ion-plasma coatings. It has been determined that within the depth range of 30–100 μm, the wear resistance (as assessed by linear wear) of ion-plasma coatings is higher than that of gas-flame coatings. This predetermines the fact that in the event of a catastrophic collision between the coatings and a blade, the geometry of the damage to the surface of the gas-flame coating will be greater than that of the ion-plasma coating. In the event of damage exceeding 75–100 μm in depth, both coatings become inoperable, since their wear characteristics are no longer maintained. This is indicated by a rapid decrease in their wear resistance under step loading. Moreover, the gas-flame coating is more prone to catastrophic failure than the ion-plasma coating. Full article

With the significant increase in the number of motor vehicles in plateau regions, the adaptability and reliability requirements of diesel engines operating under high-altitude and cold conditions have become increasingly critical. In this study, a one-dimensional transient simulation model of the overall engine lubrication system was developed based on a physical experimental prototype. The multiphysics-coupled lubrication system was numerically modeled and analyzed, with particular emphasis on elucidating the influence mechanisms of high-altitude and cold environments on the startup performance of diesel engine lubrication systems. System responses under different ambient pressures (0.88 bar, 0.92 bar, 0.96 bar, and standard atmospheric pressure) and oil temperatures (30 °C, 55 °C, and 100 °C) were systematically investigated. In addition, variations in the opening degree of the oil pump pressure relief valve (closed, 4%, 30%, 60%, and 100%) were incorporated to reveal the governing effects of high-altitude and cold environments on lubrication system startup behavior. The results indicate that under high-altitude and cold conditions, the decrease in oil temperature is the dominant factor and exerts the most significant influence on the steady-state oil pressure and flow rate of the lubrication system. Variations in ambient pressure lead only to an equivalent shift in absolute oil pressure, with negligible effects on relative oil pressure, steady-state flow rate, response time, or filling rate. However, a reduction in atmospheric pressure leads to a decrease in the peak oil flow rate at the outlet of the oil pump. The opening degree of the pressure relief valve exhibits a nonlinear influence on the startup performance of the lubrication system, and significantly decreases the oil filling rate. This study innovatively develops a lubrication system performance prediction model under high-altitude, low-pressure, and low-temperature conditions. Calibrated using vehicle road-test data, the model quantifies for the first time the relative contributions of the three key factors to start-up lubrication performance, thereby providing a clear decision-making framework and prioritized improvement directions for the reliability-oriented design and safety threshold calibration of lubrication systems in high-altitude diesel engines. Full article

High voltage (HV) test environments require dependable visual status indicators to maintain operator safety; however, directly supplying these indicators from HV sources introduces substantial electrical and operational hazards. This work addresses these challenges through the design and implementation of a compact Flyback DC–DC converter that provides galvanic isolation and a stable low-power output specifically intended for LED-based safety beacons. While utilizing Discontinuous Conduction Mode (DCM) and valley-switching to minimize thermal stress, the primary innovation of this design lies in the rigorous optimization of the isolation barrier and PCB architecture to meet HV safety standards (such as IEC 60950-1) within a minimal physical footprint. Transformer parameters were determined using analytical design procedures and subsequently verified by circuit-level simulations, which confirmed correct DCM operation as well as rapid startup behavior without output overshoot. A two-layer PCB was designed in accordance with IPC-2221B standard, with particular emphasis on minimizing parasitic effects and thereby improving overall performance. Experimental characterization demonstrated stable output regulation and a strong correlation between measured and simulated waveforms. The proposed system enhances safety in HV laboratory settings while achieving a compact form factor and supporting a wide input voltage range. Full article

As an energy-saving fluid machinery component, the ejector holds significant potential for promoting energy conservation and sustainable transformation in aerospace. This review synthesizes recent progress, identifies persistent challenges, and outlines future directions for ejector technology in this field, addressing a gap in existing reviews. (1) In aero-engine systems, performance faces constraints from high-speed compression effects and flow losses. These systems require optimized design across a wide range of speeds. A mixed configuration incorporating a blade mixer achieved a 5~7% thrust increase under static conditions. (2) In high-altitude test facilities, transient start-up and flow instability under off-design conditions demand more precise models and control strategies. An alternative solution using a second throat exhaust diffuser reduced the start-up time by 50~70%. (3) In rocket-based combined cycle engines, development is limited by thermal choking, mode transition, and combustion-flow coupling issues. Optimization of the rocket layout and geometric throat increased the bypass ratio in ejector mode by 35% and improved the specific impulse by 12.5%. Future efforts should focus on constructing multi-physics coupling numerical simulation models for ejectors, analyzing unsteady flow behavior and thermal effects within ejectors, and developing performance optimization strategies based on intelligent control. These approaches are expected to enhance the engineering applicability and system efficiency of ejector technology in the aerospace field, which is increasingly focused on energy conservation and sustainable transformation. Full article

Grid-forming power converters play a foundational role in modern microgrids and inverter-dominated distribution systems by establishing voltage and frequency references during islanded or low-inertia operation. However, when subjected to nonlinear or impulsive impact-type loads, these converters often suffer from severe harmonic distortion and transient current overshoot, leading to waveform degradation and protection-triggered failures. While virtual impedance control has been widely adopted to mitigate these issues, conventional implementations rely on fixed or rule-based tuning heuristics that lack adaptivity and robustness under dynamic, uncertain conditions. This paper proposes a novel reinforcement learning-based framework for real-time virtual impedance scheduling in grid-forming converters, enabling simultaneous optimization of harmonic suppression and impact load resilience. The core of the methodology is a Soft Actor-Critic (SAC) agent that continuously adjusts the converter’s virtual impedance tensor—comprising dynamically tunable resistive, inductive, and capacitive elements—based on real-time observations of voltage harmonics, current derivatives, and historical impedance states. A physics-informed simulation environment is constructed, including nonlinear load models with dominant low-order harmonics and stochastic impact events emulating asynchronous motor startups. The system dynamics are modeled through a high-order nonlinear framework with embedded constraints on impedance smoothness, stability margins, and THD compliance. Extensive training and evaluation demonstrate that the learned impedance policy effectively reduces output voltage total harmonic distortion from over 8% to below 3.5%, while simultaneously limiting current overshoot during impact events by more than 60% compared to baseline methods. The learned controller adapts continuously without requiring explicit load classification or mode switching, and achieves strong generalization across unseen operating conditions. Pareto analysis further reveals the multi-objective trade-offs learned by the agent between waveform quality and transient mitigation. Full article