Scaffolds have a key role in the clinical success of tissue engineering for the regeneration of damaged tissues. Their bio-performance is often described as the extent to which they can provide an extracellular matrix-like environment for cells embedded where their function and growth can effectively continue. For this purpose, tissue engineering scaffolds should exhibit biodegradability, biocompatibility, bioactivity, delivery, and mechanical performance. The use of polymer coatings, especially poly(lactic-co-glycolic acid) (PLGA), on tissue engineering scaffolds has been found to be one of the most effective methods to improve the scaffold properties. This paper reviews the techniques used to coat tissue engineering scaffolds with PLGA and its effects on the mechanical characteristics, biodegradability, biocompatibility, Molecular delivery, and osteointegration of the scaffolds. It is concluded that apart from apatite-formation ability, all bio-functionalities can be tuned through PLGA coatings. This reflects the great potential of this modification approach to be used in tissue regeneration and therapeutic delivery applications.
Vacuum systems are an intrinsic part of any accelerator around the world: all particles circulate under vacuum. This lecture gives rudiments on the fundamentals of vacuum science such as units, ideal gas law, partial pressure, mean free path, flow of molecules, conductance, pumping speed, and outgassing. An overview of standard vacuum instruments for pressure measurement and pumping is presented. Finally, the specificities of accelerator vacuum systems are introduced, discussing some design, construction, installation, and commissioning aspects, as well as fundamentals of beam-vacuum interactions with synchrotron radiation and electron cloud.
Diego Firmenich, Leandro Antonelli, Bruno Pazos
et al.
User stories are one of the most widely used artifacts in the software industry to define functional requirements. In parallel, the use of high-fidelity mockups facilitates end-user participation in defining their needs. In this work, we explore how combining these techniques with large language models (LLMs) enables agile and automated generation of user stories from mockups. To this end, we present a case study that analyzes the ability of LLMs to extract user stories from high-fidelity mockups, both with and without the inclusion of a glossary of the Language Extended Lexicon (LEL) in the prompts. Our results demonstrate that incorporating the LEL significantly enhances the accuracy and suitability of the generated user stories. This approach represents a step forward in the integration of AI into requirements engineering, with the potential to improve communication between users and developers.
The high-precision intelligent control technology for hydraulic power machinery is of great significance in improving the automation level of the equipment manufacturing industry. This study addresses issues such as low precision and poor reliability in traditional hydraulic control systems by developing a high-precision control technology solution based on intelligent algorithms. The research adopts a control strategy combining adaptive PID and fuzzy neural networks, establishing a real-time monitoring and fault diagnosis system to achieve intelligent control of the system. Engineering applications show that this technology significantly improves equipment control precision and system stability, reduces equipment failure rates and operational costs, and enhances production efficiency and product quality, providing good economic benefits and application value for promotion.
Phased-array ultrasonic transducers using sol–gel composites face challenges in terms of polarization uniformity when using conventional corona poling. Pb(Zr, Ti)O<sub>3</sub> (PZT)/PZT composites with a thickness of 25 µm were fabricated on 3 mm thick titanium substrates, and the samples were poled by AC poling, DC poling, and corona discharge poling at RT. It was found that the polarization direction could be controlled by the voltage off-phase angle. When poling was performed with a voltage off-phase angle of 90°, applied voltage of 200 V (rms), 10 cycles, and frequency of 1 Hz, average values and standards of measured piezoelectric constant <i>d</i><sub>33</sub> of −35.1 ± 0.8 pC/N and ultrasonic sensitivity of 11.4 ± 0.1 dB were obtained. Furthermore, the AC-poled samples demonstrated smaller variations in <i>d</i><sub>33</sub> and ultrasonic sensitivity compared with the corona-poled samples, and higher values of <i>d</i><sub>33</sub> and ultrasonic sensitivity compared with the DC-poled samples, indicating the potential of AC poling for PZT/PZT sol–gel composites with large areas.
This study addresses the challenge of data scarcity in training reinforcement learning (RL) controllers for power system economic dispatch problems (EDP) by integrating Generative Adversarial Network (GAN)-generated synthetic data and transfer learning (TL). Traditional data collection for power systems may face limitations like privacy concerns hindering the performance of deep neural network-based controllers. To overcome this, a GAN-based framework is proposed to generate synthetic load demand data, preserving characteristics of real datasets. A TL technique is then employed to fine-tune a Twin Delayed Deep Deterministic Policy Gradient (TD3) agent, pretrained in a synthetic environment, into a target environment with real-world data. Experiments evaluate three GAN-generated datasets, including scenarios with mode collapse, and compare results against regression-based data generation methods. Key findings demonstrate that even low-quality synthetic data, when combined with TL, significantly enhances RL performance. For instance, a mode-collapsed GAN model reduced test operation cost by 54.7% and power unbalance by 89.9% compared to a baseline TD3 agent. This work highlights the potential of synthetic data augmentation and TL in data-scarce power system applications, offering a viable pathway to improve controller performance without additional real-world data collection.
Control engineering systems. Automatic machinery (General)
Jose A. Cabello-Mendez, Abraham Lopez-Miguel, Jose T. Perez-Quiroz
et al.
Concrete is the most important construction material, and improving its durability properties is a topic in constant development owing to the economic costs that the degradation of concrete implies. Different nanoparticles have been reported to improve concrete durability, although the positive results are not a generality. Among these nanomaterials, graphene oxide stands out as an option for improving concrete properties, such as its compressive strength, which could increase the useful life of concrete infrastructure. This study addresses the effects of graphene oxide on the durability properties of concrete, with the aim of obtaining data on the viability of graphene oxide as an additive in concrete. The incorporation of graphene oxide into concrete was carried out through graphene oxide suspensions that were incorporated into concrete mixtures with a high water/cement ratio. The characterization of concrete was done using non-destructive testing such as ultrasonic pulse velocity, electrical resistivity, porosity, capillary absorption, chloride ion permeability, and other characterization methods such as compressive strength, XPS, SEM, and EDS. Together, these tests provided an overview of the concrete durability properties that are improved, affected, or unchanged by the presence of graphene oxide. In this study, a chemical analysis was also carried out on concrete modified with graphene oxide. The results show that graphene oxide improves the compressive strength of concrete, but the effect on durability properties is negligible; however, there are indications that, in combination with other additives, improvements can be achieved, so it is advisable to continue with these studies.
Mechanical engineering and machinery, Engineering (General). Civil engineering (General)
Context: Jupyter Notebook has emerged as a versatile tool that transforms how researchers, developers, and data scientists conduct and communicate their work. As the adoption of Jupyter notebooks continues to rise, so does the interest from the software engineering research community in improving the software engineering practices for Jupyter notebooks. Objective: The purpose of this study is to analyze trends, gaps, and methodologies used in software engineering research on Jupyter notebooks. Method: We selected 146 relevant publications from the DBLP Computer Science Bibliography up to the end of 2024, following established systematic literature review guidelines. We explored publication trends, categorized them based on software engineering topics, and reported findings based on those topics. Results: The most popular venues for publishing software engineering research on Jupyter notebooks are related to human-computer interaction instead of traditional software engineering venues. Researchers have addressed a wide range of software engineering topics on notebooks, such as code reuse, readability, and execution environment. Although reusability is one of the research topics for Jupyter notebooks, only 64 of the 146 studies can be reused based on their provided URLs. Additionally, most replication packages are not hosted on permanent repositories for long-term availability and adherence to open science principles. Conclusion: Solutions specific to notebooks for software engineering issues, including testing, refactoring, and documentation, are underexplored. Future research opportunities exist in automatic testing frameworks, refactoring clones between notebooks, and generating group documentation for coherent code cells.
Background: Harnessing advanced computing for scientific discovery and technological innovation demands scientists and engineers well-versed in both domain science and computational science and engineering (CSE). However, few universities provide access to both integrated domain science/CSE cross-training and Top-500 High-Performance Computing (HPC) facilities. National laboratories offer internship opportunities capable of developing these skills. Purpose: This student presents an evaluation of federally-funded postgraduate internship outcomes at a national laboratory. This study seeks to answer three questions: 1) What computational skills, research skills, and professional skills do students improve through internships at the selected national laboratory. 2) Do students gain knowledge in domain science topics through their internships. 3) Do students' career interests change after these internships? Design/Method: We developed a survey and collected responses from past participants of five federally-funded internship programs and compare participant ratings of their prior experience to their internship experience. Findings: Our results indicate that participants improve CSE skills and domain science knowledge, and are more interested in working at national labs. Participants go on to degree programs and positions in relevant domain science topics after their internships. Conclusions: We show that national laboratory internships are an opportunity for students to build CSE skills that may not be available at all institutions. We also show a growth in domain science skills during their internships through direct exposure to research topics. The survey instrument and approach used may be adapted to other studies to measure the impact of postgraduate internships in multiple disciplines and internship settings.
Rotating machinery is widely used in industrial applications, where its failure can lead to significant operational downtime and costly repairs. Traditional fault detection techniques, such as vibration analysis and statistical methods, are often insufficient for handling complex fault patterns and large datasets. This paper presents an exploration of machine learning (ML) algorithms for fault detection in rotating machinery. Specifically, it investigates how ML models can enhance predictive maintenance systems, detect anomalies, and classify faults in real-time. The paper reviews the application of various ML algorithms, including support vector machines (SVM), decision trees, neural networks, and ensemble methods. The benefits of using vibration signals, acoustic signals, and other sensor data for training ML models are discussed. Finally, case studies and future trends in the use of deep learning for fault detection in rotating machinery are also explored.
Tubagus Noor Rohmannudin, Sulistijono Sulistijono, Willy Deviet Kusuma
et al.
Corrosion is a decrease in metal quality caused by electrochemical reactions with the surrounding environment. Corrosion is destructive, dangerous, and can cause great losses. Corrosion cannot be stopped but its rate can be controlled. There are many methods that can be used to control the rate of corrosion, one of them is the sacrificial anode cathodic protection. Protection of anode cathodic sacrificial utilizes the principle of a galvanic cell where the metal will be protected or the cathode has a higher potential otherwise the metal will be sacrificed or the anode has a lower potential value. One of the standard practices used to design cathodic protection is DNV RP-B401. By carrying out tests according to the DNV RP-B401 standard, then it can be seen the value of the efficiency of the sacrificial anode working on cathode protection. The efficiency of the sacrificial anode needed to be known to measure the performance of the sacrificial anode in providing protection to the metal to be protected. This research was conducted to determine the effect of differences in electrolyte temperature on the efficiency values of aluminum alloy and Zinc alloy sacrificial anodes. In this study the feasibility test was carried out using electrolyte temperature variations, namely at temperatures of 20°C, 25°C, 30°C, and 35°C. the efficiency results obtained in aluminum alloys for temperatures of 20°C, 25°C, 30°C and 35°C were 88.6%, 80.66%, 76.55% and 63.23%. Then for the Zinc alloys with the same temperature variations were 92.95%, 90.39%, 88.15%, and 77.24%.
RFID (radio frequency identification) is a progressively adopted technology in today’s automated world. Wireless technologies have enabled contactless payments, tracking, identifying, and many more features in a system that can be introduced to build a smart environment. This work overviews the usage of the IoT (Internet of Things) platform for tracking passengers and enabling online payments through wireless sensors and RFID technology in Chennai Suburban Railways. The tracking system consists of an RFID reader that can locate and track passive as well as mobile objects attached with passive RFID tags. The proposed system incorporates the installation of RFID readers at every entrance and exit of the railway station, and every passenger carries their own RFID tags. This not only enables online payments for passengers but also helps the government in tracking the crowd for demand monitoring. The new methodology creates a digital workspace and enforces lawful safety regulations both for the administration and the consumers. A prototype of the proposed system is implemented in real-time to understand the workings of the system. Data collection is done through RFID tags that act as transit cards and an analysis for consumer demand is done using the DBSCAN (Density-Based Spatial Clustering of Application with Noise) algorithm with a Randomized KD-tree for the analysis of spatial and temporal patterns. A new algorithm, the iDBSCAN (improved Density-Based Spatial Clustering of Application with Noise) algorithm is proposed for faster performance on the datasets.
Control engineering systems. Automatic machinery (General), Automation
Tamer F. Megahed, Eid Abdelbaki Gouda, Diaa-Eldin A. Mansour
et al.
In this study, a novel magnetic gear design is introduced. Unlike conventional magnetic gears that can only achieve a single gear ratio using permanent magnetic poles, the proposed design incorporates electromagnetic coils that can adapt to various control strategies, resulting in a multiple gear ratio for the same machine design. We selected a gear system with five gear ratios to validate the new design. The performance of the proposed design was compared with that of the conventional magnetic gear. While permanent magnet poles offer high torque transmission with a small volume, they cannot provide different gear ratios for the same configuration. Therefore, this work suggests using a single-gear machine based on a fixed number of electromagnetic coils to achieve different gear ratios. This research outlines the design steps, simulation process, and detailed analysis. The results demonstrate the effectiveness of the proposed design strategy, which can be potentially applied to wind turbines, transportation, and other scenarios with comparable success.
The article elucidates the essence of the mechanisms of electronic and phonon friction in the coupling of samples (parts) using the methods of solid state physics.
It is shown that in the triboconjugation of samples made of metallic materials, the flow of fluctuation-electromagnetic and electron-phonon processes should be distinguished. Fluctuation-electromagnetic interactions have long-range effects, and electron-phonon interactions have short-range effects. Based on Lifshitz's fluctuation-electromagnetic theory, the force of friction in moving couplings of metal samples is substantiated, taking into account the frequency ratio in the atomic absorption spectrum and the plasma frequency. A formula for estimating the friction force was obtained, taking into account the dielectric function and the Clausius-Mossotti formula.
The electronic friction force was estimated using the "jelly" model and the generation of electron-hole pairs in the quantum perturbation theory of solid-state physics.
The mechanism of electronic friction was discovered based on the phenomenological theory of braking losses of slow ions in solids. The scheme of the model of the electronic friction mechanism is close to the Persson model, which connects the braking force with the electron scattering process. A refined formula for estimating the electronic friction force is proposed.
The strength of phonon friction is justified on the basis of structural effects that can be induced by the mechanism of breaking adhesive bonds, and perturbation theory. A formula was obtained for estimating the force of phonon friction, taking into account the frequency of phonons, the inverse decay time and the function of the two-dimensional Fourier image of the force of interaction between the atoms of the conjugated surface of the triboelement.
Cases of static and dynamic phonon friction are considered.
Electronic and phonon frictional forces are considered at the nanolevel. The Debye low-temperature approximation and refinement of the expressions for estimating the electronic and phonon friction forces are given, taking into account the type of interatomic potential
To address the challenges of delayed scheduling information, heavy reliance on manual labour, and low operational efficiency in traditional large-scale agricultural machinery operations, this study proposes a method for multi-agricultural machinery collaborative task assignment based on an improved genetic hybrid optimisation algorithm. The proposed method establishes a multi-agricultural machinery task allocation model by combining the path pre-planning of a simulated annealing algorithm and the static task allocation of a genetic algorithm. By sequentially fusing these two algorithms, their respective shortcomings can be overcome, and their advantages in global and local search can be utilised. Consequently, the search capability of the population is enhanced, leading to the discovery of more optimal solutions. Then, an adaptive crossover operator is constructed according to the task assignment model, considering the capacity, path cost, and time of agricultural machinery; two-segment coding and multi-population adaptive mutation are used to assign tasks to improve the diversity of the population and enhance the exploration ability of the population; and to improve the global optimisation ability of the hybrid algorithm, a 2-Opt local optimisation operator and an Circle modification algorithm are introduced. Finally, simulation experiments were conducted in MATLAB to evaluate the performance of the multi-agricultural machinery collaborative task assignment based on the improved genetic hybrid algorithm. The algorithm's capabilities were assessed through comparative analysis in the simulation trials. The results demonstrate that the developed hybrid algorithm can effectively reduce path costs, and the efficiency of the assignment outcomes surpasses that of the classical genetic algorithm. This approach proves particularly suitable for addressing large-scale task allocation problems.
Vibration fatigue characteristics are critical for rotating machinery components such as turbine rotor blades. Lattice structures are gaining popularity in engineering applications due to their unique ability to reduce weight and improve the mechanical properties. This study is an experimental investigation of octet-truss lattice structure utilization in turbine rotor blades for weight reduction and to improve vibration fatigue characteristics. One completely solid and three lattice infilled blades with variable strut thickness were manufactured via additive manufacturing. Both free and forced experimental vibration analyses were performed on the blades to investigate their modal and vibration fatigue characteristics. The blades were subjected to random vibration using a vibration shaker. The response was measured using a triaxial accelerometer in terms of vibration acceleration time histories in the X, Y, and Z directions. Results indicate a weight reduction of up to 24.91% and enhancement in the first natural frequency of up to 5.29% were achieved using lattice infilled blades. The fatigue life of the blades was investigated using three frequency domain approaches, namely, Lalanne, Dirlik and narrow band. The fatigue life results indicate that the 0.25 mm lattice blade exhibits the highest fatigue life, while the solid blade exhibits the lowest fatigue life of all four blades. The fatigue life of the 0.25 mm lattice blade was 1822-, 1802-, and 1819- fold higher compared to that of the solid blade, using the Lalanne, Dirlik, and narrow-band approaches, respectively. These results can serve as the first step towards the utilization of lattice structures in turbine blades, with thermal analysis as the next step. Therefore, apart from being light weight, the octet-truss lattice infilled blades exhibited superior vibration fatigue characteristics to vibration loads, thereby making them a potential replacement for solid blades in turbine rotors.
Standards such as ISO 13849 and ISO 12100 enable users to model safety related control elements with safety functions, according to a specified architecture and required performance level. In this direction, a novel Artificial Intelligence (AI)-based assistant is introduced in this paper to aid in determining the required performance level parameter by indulging the user in a dialog-based conversation regarding hazard scenarios. This will help inexperienced machinery safety personnel (e.g. mechanical engineer) to get an overview of the safety engineering aspects, before consulting with safety experts for risk assessment.