Maintenance Costs Research Articles

The Russian mission in Istanbul in the mid-1740s: everyday life

In the article, the author examines the daily activities of the Russian embassy in Istanbul during the mid-18th century. The eastern orientation of Russian diplomatic efforts during the era of Peter the Great and his successors represents a relatively understudied aspect of the foreign policy of the Russian Empire. Furthermore, the focus of researchers is understandably on political issues, while the everyday reality of embassy staff – including the actual number of personnel, their individual personalities, maintenance costs, material culture and food – is overlooked. The Archive of the Foreign Policy of the Russian Empire contains a wealth of hitherto unpublished documents that shed light on a number of subjects from the late summer and autumn of 1745. Following the unexpected passing of the Russian resident (the head of the embassy), Alexey Veshniakov, on 29 July 1745, his deputies, who had assumed the role of senior representatives of the Russian Mission, were concerned about the potential allegations of misappropriation of state-owned assets. It compelled them to meticulously catalogue all items stored within the mission, in addition to maintaining comprehensive records of the daily expenditure incurred for food supplies for a total staff complement of 42 individuals, including the family and domestic personnel of the late resident (16 persons). It would be fair to say that such matters are rarely addressed in diplomatic missives. However, the exceptional circumstances following the demise of Alexey Veshniakov provided scholars with the opportunity to reconstruct certain aspects of the daily life of the Russian colony in Istanbul.

A comparison between the ocean and offshore photovoltaic production system into microgrids: benefits and limits

The current work offers a detailed comparison of the advantages and disadvantages of microgrids concerning the developments of photovoltaic (PV) production installed near the shore and those installed offshore. As demand for renewable energy increases, integrating offshore and marine photovoltaic systems offers a promising approach to increase energy production while minimizing land use. This study explores the inherent advantages of offshore photovoltaic systems, including higher energy production due to the cooling effect of water, reduced reliance on land, and the ability to tap into sustained marine solar resources. On the other hand, this paper also addresses challenges associated with these systems, such as: B. Increased installation complexity, vulnerability to harsh ocean conditions, and potential impacts on marine ecosystems. The results in this paper show good performance for both offshore and floating PV systems, except that the offshore PV system excels over the other system by 3.13% in energy production. Moreover, the difference in the annual efficiency of the two PV systems reached 0.55%. These values are considered low because both systems are installed in water, given that both systems benefit from lower temperature and solar irradiation values. Nevertheless, these two systems equally present their own unique challenges including, but not limited to, operational and maintenance cost increase, effect on marine ecology and the technical hindrances on installation and grid interconnectivity. The aim of this review is to disentangle the achievements made regarding the current state of the art in floating photovoltaic technologies. When dealing with performance metrics, two solutions are examined in order to demonstrate the feasibility of providing the energy needs in an ecological way.

RASTE MITRA: AUTOMATED POTHOLES DETECTION AND FILLING SYSTEM

Potholes are one of the major issues on the roads because they cause damage to vehicles, lead to accidents, and incur higher maintenance costs. “Raste Mitra” in contrast is a comprehensive solution as it performs automatic pothole detection and classification, as well as repairs. The system employs a Raspberry Pi for controlling the functions, YOLOv5 for detecting potholes in movement, and ultrasonic sensors for object detection. The technique is sustainable due to the use of a solar powered battery management system and autonomous and self-filling technique eliminates need of labor. Additionally, the use of cloud notifications allows the responsible guards to be informed on occurrences of potholes in the dangerous state enabling them to take measures in time. The technologies adopted in “Raste Mitra” contribute to advancing road safety management through better maintenance systems and promotes the management of roads in a sustainable way. The paper discusses the processes used, hardware used, and the scope of the project on road maintenance. Key Words: Automated pothole detection, YOLOv5, Raspberry Pi, road safety, sustainable infrastructure, ultrasonic sensors, cloud-based notifications.

An Evidential Solar Irradiance Forecasting Method Using Multiple Sources of Information

In the context of global warming, renewable energy sources, particularly wind and solar power, have garnered increasing attention in recent decades. Accurate forecasting of the energy output in microgrids (MGs) is essential for optimizing energy management, reducing maintenance costs, and prolonging the lifespan of energy storage systems. This study proposes an innovative approach to solar irradiance forecasting based on the theory of belief functions, introducing a novel and flexible evidential method for short-to-medium-term predictions. The proposed machine learning model is designed to effectively handle missing data and make optimal use of available information. By integrating multiple predictive models, each focusing on different meteorological factors, the approach enhances forecasting accuracy. The Yager combination method and pignistic transformation are utilized to aggregate the individual models. Applied to a publicly available dataset, the method achieved promising results, with an average root mean square error (RMS) of 27.83 W/m2 calculated from eight distinct forecast days. This performance surpasses the best reported results of 30.21 W/m2 from recent comparable studies for one-day-ahead solar irradiance forecasting. Comparisons with deep learning-based methods, such as long short-term memory (LSTM) networks and recurrent neural networks (RNNs), demonstrate that the proposed approach is competitive with state-of-the-art techniques, delivering reliable predictions with significantly less training data. The full potential and limitations of the proposed approach are also discussed.

Analysis of ship electrical load management to reduce exhaust gas emissions in landing platform dock (LPD) vessel class

This paper investigates the impact of electrical load management on reducing exhaust gas emissions in Landing Platform Dock (LPD) class vessels. The study focuses on the implementation of Integrated Fully Electric Propulsion (IFEP) systems and Variable Speed Drives (VSD) to enhance energy efficiency and minimize environmental impact. The conventional Diesel Engine Propulsion system in LPD vessels often results in high fuel consumption and significant emissions, particularly under non-optimal operating conditions. By transitioning to an IFEP system and optimizing electrical load management using VSD, the study demonstrates a substantial reduction in carbon dioxide (CO₂), nitrogen oxides (NOₓ), and sulfur oxides (SOₓ) emissions. The results indicate a potential emission reduction of up to 40%, alongside a 20% improvement in overall energy efficiency. Additionally, the operational benefits of these technologies include increased flexibility, reduced maintenance costs, and extended equipment lifespan. The findings underscore the importance of adopting advanced propulsion and load management technologies in naval vessels to meet stricter environmental regulations and support sustainable maritime operations. This study provides a framework for future research and practical applications in expanding these technologies across different ship types and operational scenarios.

Crack Propagation and Fatigue Life Analysis of Wind Turbine Blades

This dissertation investigates the fatigue and fracture behaviour of wind turbine blades exposed to varying wind speeds and load intensities, aiming to enhance their durability and operational lifespan in sustainable energy systems. The increasing reliance on wind energy underscores the need for durable blades that can withstand cyclic loads and extreme environmental conditions, as fatigue and fracture failures significantly impact maintenance costs and reliability. This research specifically examines fatigue life and fracture initiation at wind speeds of 15 m/s and 60 m/s, analysing the effects of increased load intensities (30 MPa and 62 MPa). Finite Element Analysis (FEA) through ANSYS is employed to model fatigue and fracture dynamics, incorporating fatigue life prediction using S-N curves, stress analysis with von Mises stress, and crack propagation simulation via fracture mechanics principles, such as Paris’ Law. Results reveal a notable reduction in fatigue life with increased loads, evidenced by a drop from 9e09 to 7.9e08 cycles at 15 m/s, with comparable fatigue cycle reductions at 60 m/s. Fracture analysis identifies critical crack initiation points in high-stress areas, and simulations indicate the progressive nature of crack propagation under cyclic loads. These findings underscore the importance of integrating fatigue and fracture assessments in the design and maintenance strategies of wind turbine blades to enhance resilience and support the sustainable advancement of wind energy systems.

IMPROVING THE EFFICIENCY OF ENERGY COMPANIES BY OPTIMISING THE DISTRIBUTION OF COSTS FOR MAINTENANCE AND REPAIR OF ELECTRICAL EQUIPMENT

At present, there is a high emergencies probability in the electric power system as an electrical equipment failures result caused primarily by the objectively existing ageing and its service life significant exhaustion. Emergencies arising from electrical equipment failures lead to the dynamic stability violation of power systems and power system load nodes by voltage, cascading accidents and, as a result, technological processes disruption of consumer enterprises, which is accompanied by significant losses. One of the ways to prevent sudden failures or minimise the risk of their occurrence is to ensure rational and efficient operation management and reduce the operating equipment cost without risking a decrease in the power system reliability. To solve the optimal distribution problems of power company operating costs, taking into account the objectively existing limiting factors, a generalised balance sheet model of cost allocation by electrical equipment groups and units is proposed, which is a total costs function of an operating factors number. Mathematical models for calculating the electrical equipment share participation coefficients in the costs formation for its repair and maintenance for each of the identified factors have been obtained. A linguistic mathematical model for determining generalised estimates of the electrical equipment units and groups significance degree in the repair and maintenance costs distribution formation, taking into account the main operational factors, has been proposed. An algorithm and software for modelling the modes of the electric power system with an electrical connections scheme of electric networks with a 35-330 kV voltage have been developed to assess the operation risk index and power company repair and maintenance costs distribution. The estimated power company repair and maintenance costs distribution according to the current technical equipment characteristics and power system subsystem modes was performed.

A unified multi‐class anomaly detection model based on reverse distillation

AbstractThe multi‐class single‐model detection paradigm is a prevalent design for industrial anomaly detection, exhibiting suitability for varying industrial classes and dynamic, flexible production scenarios. This approach not only enhances model adaptability but also minimizes maintenance costs. However, the current popular methods are susceptible to the ‘copying shortcut’ phenomenon, which constrains their performance on benchmark datasets. To overcome this limitation, this article proposes a multi‐class anomaly detection model: UniRD, based on the reverse distillation method. This model creates an image corruptor that expands the dataset and generates ‘normal‐corrupt’ image pairs. During the training process, their correspondence is used to optimize the reverse distillation. This process greatly exploits and releases the potential of the student decoder. Furthermore, a teacher feature adaptation module is devised to enhance the compatibility between the pre‐trained model and the anomaly detection task. This has the effect of reducing the discrepancy between teacher and student features while ensuring the consistency of normal sample features. The comprehensive evaluation results in two mainstream datasets, MVTec and VisA, and demonstrates that the proposed method exhibits improvement in all indicators compared to benchmark methods. The proposed method attains the state‐of‐the‐art, substantiating its effectiveness and advancement.

Rationalizing mAb Candidate Screening Using a Single Holistic Developability Parameter.

A framework for the rational selection of a minimal suite of nondegenerate developability assays (DAs) that maximize insight into candidate developability or storage stability is lacking. To address this, we subjected nine formulation:mAbs to 12 mechanistically distinct DAs together with measurement of their accelerated and long-term storage stability. We show that it is possible to identify a reduced set of key variables from this suite of DAs by using orthogonal statistical methods. We exemplify our approach by predicting the rank formulation:mAb degradation rate at 25 °C (determined over 6 months) using just five DAs that can be measured in less than 1 day, spanning a range of physicochemical features. Implementing such approaches focuses on resources, thus increasing sustainability and decreasing development costs.

A Miniaturized and Ultra-Low-Power Wireless Multi-Parameter Monitoring System with Self-Powered Ability for Aircraft Smart Skin

The aircraft smart skin (ASS) with structural health monitoring capabilities is a promising technology. It enables the real-time acquisition of the aircraft’s structural health status and service environment, thereby improving the performance of the aircraft and ensuring the safety of its operation, which in turn reduces maintenance costs. In this paper, a miniaturized and ultra-low-power wireless multi-parameter monitoring system (WMPMS) for ASS is developed, which is capable of monitoring multiple parameters of an aircraft, including random impact events, vibration, temperature, humidity, and air pressure. The system adopts an all-digital monitoring method and a low-power operating mechanism, and it is integrated into a low-power hardware design. In addition, considering the airborne resources limitations, an energy self-supply module based on a thermoelectric generator (TEG) is developed to continuously power the system during flight. Based on the above design, the system has a size of only 45 mm × 50 mm × 30 mm and an average power consumption of just 7.59 mW. Through experimental validation, the system has excellent performance in multi-parameter monitoring and operating power consumption, and it can realize the self-supply of energy.

Assessment of corrosion attack morphology on reinforced concrete structures exposed to chlorides

AbstractChloride‐induced corrosion of the reinforcement steel is the main degradation mechanism for reinforced concrete, considerably affecting the load and deformation capacity of structures. Despite its importance, there is limited knowledge about the morphology of corrosion attacks and accordingly, relatively crude assumptions are generally made when considering corrosion in structural analyses. Improved understanding could reduce maintenance costs and extend the service life of structures. This study presents a systematic documentation and analysis of more than 600 corrosion attack morphologies, both from observations on actual engineering structures and from laboratory specimens (X‐ray computed tomography data from previous studies). Findings reveal that corrosion attacks—regardless of age, concrete type, or whether occurring in structures or laboratory specimens—without exception grow laterally and longitudinally along the reinforcement bar, rather than in depth. This shallow morphology deviates from the conventional description of pitting corrosion in the literature. Therefore, we propose using the term ‘corrosion attack’ instead of ‘corrosion pit’ for better differentiation. We discuss different mechanism that may lead to this growth pattern. Moreover, implications for the consideration of corrosion in structural analysis are assessed. A comprehensive database of the documented corrosion attacks is provided to enhance existing service life models, offering substantial data for probabilistic modeling of input parameters.

Adoption challenges of sensing technologies in construction project management in sub-Saharan African countries

PurposeThe use of technology has increased in the industrial sector over the years. This has propelled the construction industry to follow suit by enhancing construction technology to experience the associated benefits. However, technology adoption in the construction environment has not been smoothly implemented. This article aimed to determine the obstacles to adopting sensing technology in managing construction projects in three major sub-Saharan African nations.Design/methodology/approachThe study adopted a quantitative approach using structured questionnaires. The questionnaire link was sent to construction professionals in Nigeria, Ghana and South Africa, of which 120 were responded. Data received were analysed using the Statistical Package for Special Science (SPSS) to generate mean scores and the Kruskal–Wallis test and component analysis.FindingsThe results show that construction professionals face herculean tasks when adopting sensing technology in construction project management. Some prominent challenges are initial cost, lack of proper information technology (IT) infrastructure, power supply, lack of well-trained staff, maintenance cost, operating cost, cost of training, technology immaturity and resistance to change. The results also revealed that adoption challenges differ in these three countries, which may be attributable to the economic development level.Practical implicationsDespite the importance of sensing devices in construction industries, the study has revealed that Sub-Saharan African countries face enormous challenges in adopting them in project management and execution. This situation may hinder the industry’s ability to benefit from this technology in terms of efficiency, effectiveness and safety management, which may ultimately affect their cash flows.Originality/valueThe study provides a valuable understanding of the challenges encountered in implementing sensing technology in the construction sector in major countries within sub-Saharan Africa. These findings will enable the countries examined and other African countries to become cognizant of the challenges and implement strategies to overcome them, thereby enhancing performance in the construction industry.

Bi-Level Interturn Short-Circuit Fault Monitoring for Wind Turbine Generators With Benchmark Dataset Development

Abstract Flourished wind energy market pushes the latest wind turbines (WTs) to further and harsher inland and offshore environment. Increased operation and maintenance cost calls for more reliable and cost effective condition monitoring systems. In this article, a bi-level condition monitoring framework for interturn short-circuit faults (ITSCFs) in WT generators is proposed. A benchmark dataset, consisting of 75 ITSCF scenarios and generator current signals of a specific WT, has been created and made publicly available on Zenodo. The data are simulated at a rate of 4 kHz. Based on the time and frequency features extracted from data processing, machine learning-based severity estimation and faulty phase identification modules can provide valuable diagnostic information for wind farm operators. Specifically, the performance of long short-term memory (LSTM) networks, gated recurrent unit (GRU) networks, and convolutional neural networks (CNNs) are analyzed and compared for severity estimation and faulty phase identification. For test-bed experimental reference, various numbers of scenarios for training the models are analyzed. Numerical experiments demonstrate the computational efficiency and robust denoising capability of the CNN algorithm. The GRU network, however, achieves the highest accuracy. The overall system performance improves significantly, from 87.76% with 16 training scenarios to 99.95% with 52 training scenarios, when tested on a set containing all 76 scenarios from an unforeseen period.

IGBT Junction Temperature Estimation Based on External Parameters of Multiple Drive Devices in Practical Industrial Scenario

ABSTRACTThe insulated gate bipolar transistor (IGBT) is one of the most important power semiconductor devices in power electronics and is also prone to failure. High junction temperature and junction temperature fluctuation of IGBT are the main causes of IGBT module aging failure. The high‐precision monitoring of the junction temperature of the IGBT module is a prerequisite for IGBT life prediction, which is crucial for reducing maintenance costs and improving equipment reliability. Therefore, an IGBT junction temperature estimation method based on long short‐term memory (LSTM) neural network and sliding window estimation model is proposed and applied in practical industrial scenarios. This method uses the operating data of the motor drive device in the actual industrial application scenario as the training and test data set and uses the external operating parameters of the IGBT module to estimate the junction temperature of the IGBT module. Compared with the internal operating parameters of the IGBT module based on switching transient, the external operating parameters are easier to collect and process, and more suitable for practical application scenes. A sliding window estimation model is proposed to estimate the junction temperature of the IGBT module. Compared with the point‐to‐point estimation method, the sliding window estimation method can capture the influence of historical operation data better and has a higher capability of time series data estimation. The IGBT junction temperature estimation of sliding windows is realized by the LSTM neural network, which is more suitable for time series estimation in real industrial scenarios. The experimental results show that the estimation accuracy of the sliding window estimation method is better than that of the point‐to‐point estimation method, and the accuracy of the sliding window estimation method based on LSTM is better than that of the sliding window estimation method based on other machine learning models. It proves that the proposed method can better capture the dynamic process of the system and has higher estimation accuracy.

Ultra-Antifouling Liquid-Like Surfaces for Sustainable Viscous Water-in-Oil Emulsions Separation and Oil Recovery.

The demand for efficient separation techniques in industries dealing with high viscosity emulsions has surged due to their widespread applications in various scenarios, including emulsion-based drug delivery systems, the removal of emulsified impurities in formulations and oil spill remediation. However, membrane fouling is a major challenge for conventional separation methods, leading to decreased efficiency and increased maintenance costs. Herein, a novel approach is reported by constructing liquid-like surfaces with double anti-fouling structure, incorporating soft nanomicelles within a rigid, chemically cross-linked network for both anti-membrane-fouling and effective viscous water-in-oil emulsion separation. The coating significantly outperforms perfluorinated and commercial polytetrafluoroethylene (PVDF) membranes, effectively preventing the adhesion of viscous oils like crude oil and pump oil, and alleviating severe membrane fouling. For high-viscosity emulsions (97.3 cP and 52.8 cP), it maintains over 99% separation efficiency after 3h continuous use. Even after 15h immersion in strong acids, alkalis, salts, or organic solvents, its separation efficiency remains above 95%. In addition, thanks to the anti-membrane-fouling ability, this work achieved 6h continuous emulsion separation performance for the first time, demonstrating unparalleled long-term stability. Overall, this study offers valuable insights into the development of innovative coatings for efficient and eco-friendly separation of high-viscosity emulsions.

A Multi Objective Optimization Framework for Offshore Wind Farm Design In Deep Water Seas

Abstract Floating offshore wind energy will play a key role in the clean energy transition. The number of large-scale wind farm projects is growing in regions like Northern Europe, the East Coast of the United States, and the Mediterranean Sea. Offshore wind farms face fewer layout constraints, as they can be situated in vast, open sea areas. Turbines are often arranged in simple layouts, such as grid patterns, but this can cause significant annual energy production (AEP) losses due to wakerotor interaction. Increasing spacing can mitigate this effect, but it may not always be feasible due to marine space limitations or higher costs for cabling and maintenance. This paper introduces a multi-objective wind farm optimization framework using a genetic algorithm (NSGA II) to minimize costs and maximize AEP. The method is applied to two case studies in the Mediterranean Sea, assuming 15MW wind turbines. Case A is located off the coast of Civitavecchia, and Case Bin the Gulf of Squillace. AEP evaluation is performed with the open-source library FLORIS, while optimization is done using PyMoo. In Case A, the layout characterized by the lowest LCOE features 16 turbines, achieving an AEP of 709 GWh, an LCOE of 112.06 C/MWh, and wake losses of 2.6%. Meanwhile, in Case B, the layout with the lowest LCOE consists of 19 turbines, achieving an AEP of 1140 GWh, an LCOE of 80.82 C/MWh, and wake losses of 4%.

Motion Control of a Low-Cost Underwater Vehicle with Three-Position Cross Rudder

Underwater vehicles are widely employed as platforms for marine monitoring and operations, with rudders playing a crucial role in controlling their movements. Traditional underwater vehicles utilize servos to steer rudders, which results in high production and maintenance costs. To reduce the manufacturing costs of small underwater vehicles, this paper proposes a design featuring a three-position cross rudder driven by a binary-controlled electromagnet. Additionally, a novel virtual rudder angle controller is developed, which utilizes PWM frequency conversion to perform rudder control, ensuring motion performance comparable to that of conventional underwater vehicles. By establishing kinematic and dynamic models of a typical underwater vehicle (REMUS100), a corresponding virtual rudder angle control strategy is introduced. Based on the binary-controlled three-position cross rudder and virtual rudder angle controller, the effectiveness of the motion control method is verified by numerical simulation. The results confirm that the proposed virtual rudder angle controller successfully executes turning and spatial path-tracking tasks. The binary-controlled three-position cross rudder, together with the virtual rudder angle controller, offers a cost-effective solution for the practical application of small unmanned underwater vehicles, particularly for disposable underwater vehicles that do not require recovery.

Low cost of smart unilateral street lighting system with photovoltaic power plant using particle swarm optimization

The conventional street lighting system has played an important role in our modern life to keep the buildings and roads safe and active using constant light mode during all the night from the sunset to sunrise; however its high energy consumption, and maintenance cost are considered the huge challenges facing the country’s economy. The smart street light system is currently used as the best option to solve these problems. In our project a smart unilateral street light system was implemented using advanced technology in our experimental site in the south-west of Algeria. The prototype significantly decreased the consumed energy compared to that of the conventional street light system. A Standalone Photovoltaic Plant (SPP) is designed and sized to meet various energy requirements such as street light and photovoltaic pumping in order to isolate the whole system from the power grid. The Particle Swarm Optimization (PSO) algorithm is used to study the cost function to improve the economic feasibility.

Airborne soil-derived dust hazards in aviation

Abstract Airborne mineral dust poses a safety challenge for aviation. Several fatal accidents have happened in dust-laden air due to reduced visibility, strong gusty winds, and wind shear. Dust-induced icing also contributed at least to two fatal accidents. Furthermore, atmospheric dust has long- and short-term effects on aircraft operating condition due to corrosion and abrasion on the aircraft surfaces, and molten ingress deterioration of engine hot section components. The combined impact can increase operating and maintenance costs, and increase the overall cost of ownership. While the scientific community has started preparing and providing products based on atmospheric dust modeling and observation, there are still important data and information gaps in the fundamental science. These include (i) insufficient data which could be used to better understand the effects of dust on aircraft as well as on ground systems and operations (e.g., four-dimensional information of dust mineralogy, cost-benefit analysis of the impact of dust on aviation along flight routes), (ii) the identification of airborne dust monitoring and modeling products and services that could enable the flow of relevant information in commercial aviation and in decision-making workflows, and (iii) the underdeveloped, unclear, or absent role of dust hazards in regulations and operational procedures as well as in the training, skillset, and knowledge base of pilots. This review is aimed at both academic and aviation stakeholders, and presents the current state-of-the-art knowledge at the intersection of dust hazards, aviation safety, and impacts on flight operations and aircraft maintenance.

Librarians’ perceptions and use of open access resources for economic sustainability in selected university libraries in Kwara State, Nigeria

Purpose Open access (OA) resources are free to access and help in research and teaching/learning, allowing libraries to manage resources more sustainably and effectively. Hence, this study aims to examine use of OA resources for economic sustainability among librarians in selected university libraries in Kwara State, Nigeria. Design/methodology/approach This study adopts descriptive survey research design. Using total enumeration sampling technique, a sample of 46 professional librarians were drawn from the selected universities. Questionnaire was used for data collection, and descriptive statistics was used for data analysis. Findings The findings show that most of the librarians had high level of awareness about OA resources. Results demonstrate that majority of the librarians used PubMed Central bi-weekly. Findings show that the librarians used OA resources to support academic research, enhance library’s collection without additional cost and facilitate information access for users with limited financial resources. Results indicate that most of the librarians perceived that OA resources impact library’s operational cost and goal of economic sustainability. Practical implications This study highlights the need for targeted training and resource development to enhance the effective use of OA resources. Meanwhile, the consistent usage of OA resources suggests the importance of the resources to librarians, aligning with previous studies on frequent use of established OA repositories. Originality/value This study provides evidence on the relevance and impact of OA resources use on economic sustainability, demonstrating the importance of OA resources in knowledge dissemination and fostering innovation.