Prototype System Research Articles

Wet Conformation of Prion-Like Domain and Intimate Correlation of Hydration and Conformational Fluctuations.

Proteins with prion-like domains (PLDs) are involved in neurodegeneration-associated aggregation and are prevalent in liquid-like membrane-less organelles. These PLDs contain amyloidogenic stretches but can maintain dynamic disordered conformations, even in the condensed phase. However, the molecular mechanism underlying such intricate conformational properties of PLDs remains elusive. Here we employed molecular dynamics simulations to investigate the conformational properties of a prototypical PLD system (i.e., FUS PLD). According to our simulation results, PLD adopts a wet collapsed conformation, wherein most residues maintain sufficient hydration with the abundance of internal water. These internal water molecules can rapidly exchange between the protein interior and the bulk, enabling intensive coupling of the entire protein with its hydration environment. The dynamic exchange of water molecules is intimately correlated to the overall conformational fluctuations of PLD. Furthermore, the abundance of dynamic internal water suppresses the formation of aggregation-prone ordered structures. These results collectively elucidate the crucial role of internal water in sustaining the dynamic disordered conformation of the PLD and inhibiting its aggregation propensity.

Transforming Wikipedia text into audio through machine learning

Purpose The purpose of this study is to navigate the process of transforming Wikipedia articles into audio files for library readers. The system provides a feasible manner of listening to Wikipedia content, accommodating diverse learning preferences and enlarging knowledge in education society. Design/methodology/approach This framework has been constructed using the Python programming languages in the Linux operating platform. Application programming interface and Google text-to-speech (TTS) are required as additional software packages to design this prototype system. Transform any Wikipedia pages into audio files through Wikitrola for libraries and information centers. Wikipedia articles are directly transformed into audio, as these integrate the content seamlessly for the user experience. The whole system has been designed and configured on the basis of machine learning to provide dynamic services among the readers. Findings The viewer could use the machine learning system to turn Wikipedia articles into audio files, allowing them to listen to Wikipedia content in audio format. This would make information more accessible and adaptable to diverse learning modes, allowing written content to be engaged in novel and visionary ways. Originality/value The insightful observation in connection with the paper is that it shows how to convert text-based material into audio through the Google TTS and machine learning Python programming and finally incorporate them in Wikipedia articles. A harmonious system of information dissemination and technical education is established. This approach shows the effectiveness of imagination and the use of programming tools to enhance learning and knowledge-seeking processes.

ViCubeLab-An Integrated Platform Using VR to Visualise and Analyse Road Traffic Conditions

The main contribution of this paper is to introduce a framework for integrating Machine Learning (ML), Human, and Virtual Reality (VR) into one platform to promote a collaborative visualisation environment that can assist in better analysis and improve the human-machine teaming capability. This platform was demonstrated using a case study in ana-lysing road traffic conditions. The ‘Ab-normal Machine Learning Road Traffic Detection in VR (AbnMLRTD-VR)’ prototype system was developed to assist the human analyst. The proposed system has two main integrative components: a data-driven ML model and a 3D real-time visualisation in a VR environment. An unsupervised ML model was built using real traffic data. The AbnMLRTD-VR system highlights the outliers in the road sections in actual road contexts of a road traffic network. This gives the human analyst a 3D real-time immersive visualisation in a VR environment to evaluate road conditions. The AbnMLRTD-VR system demonstrated that it could help minimise the need for human pre-labelling of the data. It enables the visualisation of the road traffic conditions more meaningfully and to understand the context of the road traffic conditions of road sections at any given time.

DEVELOPMENT OF A SYSTEM WITH SUSTAINABLE SUPPLEMENTAL HEATING: AN ALTERNATIVE TO COMBAT WATER SCARCITY

The scarcity of water resources has led researchers to investigate and develop mechanisms for the good use of saline and brackish waters to produce drinking water. In Brazil, the water crisis has always been a reality. The objective was to carry out desalination through a prototyped system with sustainable supplementary heating, to help supply drinking water to residents who face precarious living conditions. The research is aimed at the Bailique Archipelago and Sucuriju District, located in the extreme east of the State of Amapá and bathed by the Atlantic Ocean. It presents a quali-quantitative analysis, as it involves the construction of the prototype and temperature and physical-chemical tests of water samples. Regarding the results, desalination was carried out using a prototype, with subsequent analysis of the physical-chemical conditions of the interstitial waters to correlate with the potability parameters. The supplementary heating positioned on the sides of the system converged solar rays towards the greenhouse that contains salt water inside, and a device on the glass slope collected the condensed water. The 50cm x 50cm model desalinated 890mL and the 25cm x 25cm model desalinated 190mL of water in one day, reaching a maximum temperature of 91 ºC.

Evaluation of Light-Induced Electroluminescence in Photovoltaic Field Applications

We present a performance analysis of the new measurement method Light-Induced Electroluminescence (LIEL) in a PV system. The LIEL method is applied to photovoltaic (PV) modules consisting of two PV module halves which are internally connected in parallel. Today’s main stream half-cell modules are constructed this way. To measure the electroluminescence of one half of the module, the other half is illuminated by an LED array. Our LIEL prototype system is a hood-based setup. It is equipped on one half with a LED array that is separated by a wall from the other half. This other half is observed by an InGaAs camera. We measure the impact of a LIEL hood misalignment to the electroluminescence intensity, the influence of the PV generator working point to the electroluminescence intensity and determine the measurement speed under realistic conditions. We show that the experimentally realized LIEL hood alignment to the PV modules is in 97.7% of the cases sufficient for acquiring high quality EL images. The LIEL system work with a switched off and on inverter. Under inverter on working condition the luminescence intensity is a function of the intensity of the sun. The effect of hood alignment and sun intensity on the luminescence intensity is successfully reproduced by an analogue electronic circuit simulation using LT Spice. The maximum measuring speed of a full module is in this study 12 s including the time for movement and alignment of the measurement hood from PV module to PV module

Design and Prototyping of a Novel Toroidal Magnet System for MOLLER Experiment at Jefferson Lab

Design and Prototyping of a Novel Toroidal Magnet System for MOLLER Experiment at Jefferson Lab

Readout studies for the HIKE main electromagnetic calorimeter

The High-Intensity Kaon Experiment (HIKE) is a proposed future kaon experiment designed for the CERN SPS accelerator. To cope with a planned increase of beam intensity by a factor of 4 with respect to the current NA62 experiment, the new Main Electromagnetic Calorimeter (MEC) has to provide a similar improvement in time resolution over the current Liquid Krypton calorimeter, down to ≈100ps for a 40GeV photon, while maintaining a comparable energy and spatial resolution. To achieve these goals, the planned detector consists of a shashlik calorimeter paired with high-resolution analog to digital converter for both energy deposit and arrival time estimates, operating at fast sample rates (∼1GHz).This contribution describes a prototype of the readout system using commercial “off-the-shelf” boards. In particular, we focused on feature extraction and tested it using a pulse generator. We included a data readout logic based on the TCP/IP protocol allows for easy deployment in beam tests.

Product innovation design approach driven by implicit relationship completion via patent knowledge graph

Product innovation design process involves a great deal of discrete engineering knowledge, limiting the ability of designers to quickly utilize this knowledge to support design innovation. Nowadays, innovation design based on knowledge graphs has enhanced the ability to explore design knowledge, improving the efficiency of knowledge retrieval. Previous studies have focused on mining more design knowledge to enrich the knowledge graph overlooks the implicit relationships with potential value among design knowledge, wasting design resources. To address these issues, an approach for product innovation design based on implicit knowledge relationship completion in the patent knowledge graph is proposed, which explores the implicit relationships between design knowledge to provide new knowledge satisfying design preferences and enhance the innovativeness of solutions. First, a requirements-function-structure-benefit (RFSB) knowledge ontology is constructed and extracted from the benefit knowledge of patents to build the knowledge graph. Second, an implicit relationship completion model based on the similarity of function or benefit entities explores the implicit relationships, replacing structure entities directly connected to similar function or benefit entities to generate new relationships and outputs novel ideas. Third, a scheme improvement process based on the co-occurrence frequency of requirement and structure knowledge supplements neglected design preferences. Final, a pipeline inspection robot case study is further employed to verify the proposed approach, and a patent knowledge graph assisted design solution prototype system is developed to assist in the utilization of innovative design knowledge. Evaluation results show the significant design potential of the proposed approach in inspiring innovative thinking and knowledge reuse.

Exploring geospatial digital twins: a novel panorama-based method with enhanced representation of virtual geographic scenes in Virtual Reality (VR)

An important step in implementing geospatial digital twins is to enhance the expressiveness of virtual geographical scenes for the physical world. However, the existing virtual geographical scenes cannot quickly express the dynamically changing geographic environment for remote users due to the inefficient handling of modeling processes, user perception, and remote sharing. The research analysed the concept and characteristics of geospatial digital twins, and constructed the virtual geographical scene ontology, based on which we developed geographical spatiotemporal semantic rules and designed a dynamic annotation algorithm to enhance the representation of virtual geographical scenes. Finally, we investigated a real-time transmission method of panoramic video based on 5 G and used immersive virtual reality (IVR) to realize the user experience of remote immersion in geographical scenes. We selected a specific geographic environment containing multiple typical geographic entities to develop three prototype systems for experimental analyses. The results showed that the proposed method enabled users to view the virtual geographical scene on a VR device. The average latency for this process was 14.72 seconds. Compared with the virtual geographical scenes constructed by traditional methods, the experiments showed the proposed method advantageous in comprehensiveness, timeliness, and photorealism and abilities to enhance the user’s geographical scene perception.

A home-based tele-rehabilitation exercise system for patients after knee replacement surgery

BackgroundKnee arthritis is a destructive disorder that affects the knee joints and causes pain and reduced mobility. Drug treatments, weight loss, and exercise control the symptoms of the disease, but these methods only delay the disease process and eventually, knee joint replacement surgery will be needed. After surgery, with the help of a proper physiotherapy program, full recovery takes an average of 6–12 months. However, currently, there is no similar tool to facilitate this process in Iran.ObjectiveThe purpose of this research is to design and develop the prototype of a rehabilitation system for patients after knee replacement surgery, which provides patients with information and appropriate physiotherapy programs.MethodsThis study was development-applied and was done in three stages. In the first stage, the needs and content of education and therapeutic exercises were prepared in the form of a checklist, and then the necessity of each item was checked in the evaluation sessions. In the second stage, the prototype of the system was developed using Adobe XD software and based on the requirements approved from the previous stage. In the third stage, the usability of the program was analyzed from the point of view of experts using the exploratory evaluation method with Nielsen’s 13 principles of usability.ResultsAt first, the system requirements were extracted and prepared in two checklists of content (51 exercises) and capabilities (60 items). Then after a survey of experts based on the Delphi technique, content information (43 exercises) and functional and non-functional requirements (53 items) were obtained. A survey of experts helped to finalize the information elements, categorize them, and prepare the final version of the checklists. Based on this, the system requirements were classified into 11 categories, and the training content was classified into 3 training periods. Finally, the design and development of the system was done. This system has admin, physician, and patient user interfaces. The result of usability showed that this system is efficient and there are only a few problems in the feature of helping users to detect and recover from errors.ConclusionIt seems necessary to develop a system based on the principles of usability by therapists and rehabilitation specialists to train and monitor the remote rehabilitation process of patients after knee joint replacement at home. And the importance of involving stakeholders in the design and development of remote rehabilitation systems is not hidden from anyone. Kara system has all of the above.

Design of image classification system for fabric inspection process using Raspberry Pi

This research is designed as a prototype of defect inspection system on fabric production using machine learning-based image processing technology using the open source Google teachable machine application integrated with Raspberry Pi-3B. The prototype of fabric defect inspection system is built by utilizing two rollers that function as a fabric roll house before and after the inspection process. On both rollers, a fabric is stretched to be inspected, so that from a roll of fabric with a certain length, it can be seen how many defects occur on the fabric. The inspection system is carried out using a web camera with a certain level of lighting connected to a raspberry pi as a control device. Raspberry Pi functions as an image processing device and fabric rolling motor controller. In addition to the category of fabric in good condition, this system classifies into two categories of defects, namely slap defects and sparse defects. The test results show that this system has an average frame per second (FPS) of 4.85, an average inference time of 181.1 ms, with an accuracy of image classification results of 98.4 %.

Automatic compliance checking of BIM models against quality standards based on ontology technology

Many countries have issued standards to stipulate the quality of BIM models. However, due to the complexity of BIM models and the textual expression of the standards, checking the quality of BIM models against the standards manually is challenging and time-consuming. In order to solve the problem, this paper presents an ontology-based approach for the automatic compliance checking of BIM models against the standards. Taking the Chinese standard as an example, it is represented in OWL to obtain an ontology, and SWRL is used for representing the checking rules. Then, an inference process is established to fulfill the checking. Finally, a prototype system is developed to validate the approach. It is concluded that the approach can be applied to check the quality of BIM models in IFC format automatically. This research contributes to the development of automatic compliance checking systems for the quality of BIM models against real standards.

Prototype of Household Water Quality Monitoring System by Utilizing Internet of Things

This research aims to develop a household water quality monitoring system using the Internet of Things (IoT) by monitoring water quality parameters such as pH, Total Dissolved Solids (TDS), turbidity, and temperature in real time. The prototyping method in the development of this system allows continuous interaction between developers and users to meet the needs of the system. Data is collected through literature studies and observations to support system development. The results of this study are that every 1 minute, the sensor data is sent to the server, and then the sensor data will be displayed on the web platform; if the sensor data value is not at the predetermined value limit according to Permenkes number 2 of 2023, the displayed value will be red. The data collected by the sensor is sent to the server built with Node.js and stored in the PostgreSQL database. The monitoring results will also be displayed on a 20x4 I2C LCD. The system successfully monitors water quality parameters in real time, with results displayed on a web platform that is easily accessible to users. The implementation of IoT technology in this system provides an innovative solution for increasing public awareness of the importance of clean water quality. With this system, it is expected to create a healthier and safer household environment for families.

Decoupling beam power and beam energy on ASDEX Upgrade NBI with an in-situ variable extraction gap system

In early 2022 one source of ASDEX Upgrade’s (AUG) neutral beam injector 2 was equipped with a first-of-its-kind beam extraction grid system with in-situ variable extraction/acceleration gap that allows one to choose beam energy and beam power independently in a wide operational space, greatly enhancing experimental flexibility. The gap can be changed from one AUG discharge to the next. The extended operational space makes it possible to reduce beam energy and shine through while maintaining high heating power, or to reduce NBI power at high beam energy, e.g. to stay in L mode. Furthermore, the feature opens the door for advanced control of heat and torque deposition, such as to change torque and ion-to-electron heating ratio at constant power. The prototype system was successfully tested in 2022 and already found first applications in AUG’s physics programme. The remaining three sources of the same injector will also be equipped with variable gaps during the 2022–24 opening of AUG and installation of this new system on all sources of NBI 1 is also under discussion in order to exploit the full potential of the new feature.

A cloud–edge collaboration based quality-related hierarchical fault detection framework for large-scale manufacturing processes

Against the backdrop of the new-generation intelligent manufacturing and Industrial Internet of Things, manufacturing processes are evolving towards integration, large-scale operations, and complexity, and the requirements for process safety and product quality reliability are constantly improving. However, these processes are generally distinguished by multi-production sub-processes with cooperative coupling, and multi-hierarchical levels integrated automation systems, where any deviation or abnormality occurring within a sub-process or system level will lead to the extensive propagation and evolution of faults, making the comprehensive quality-related fault detection face increasing challenges in practice. In response, a novel cloud–edge collaboration-based quality-related hierarchical fault detection framework is constructed in this paper, which can facilitate interconnected communication between sub-processes and enhance collaborative interactions at different hierarchical levels to improve fault detection accuracy and reliability. First, two quality supervised training mechanisms based on minimal gated unit are introduced for capturing different scale dynamics features on the cloud side and edge side. These methods enable more adequate extraction of quality-related deep hidden features, particularly in the presence of nonlinearity and dynamics. Subsequently, a federated bi-directional knowledge distillation-based strategy is proposed, leveraging the concept of federated learning. Quality knowledge such as prediction and quality-related features from both the cloud side and edge side are integrated into the training steps of federated learning, thereby strengthening the interaction between multiple sub-processes and distinct levels. Furthermore, comprehensive quality-related fault detection strategies are formulated based on the quality-related features of the cloud side and each sub-process on the edge side, respectively. Finally, the proposed framework is deployed into a cloud–edge-device collaborative prototype system based on a real hot strip mill process to demonstrate its effectiveness and applicability.

Application of Piezoelectricity on Running Tracks: A Prototype for the Realization of Sustainable and Efficient Energy

Electrical energy has become a basic necessity today. As the population increases, the amount of electricity demand is also increasing. Therefore, many innovations are needed to meet these needs. One of the innovations in energy harvesting systems is the use of piezoelectricity. This research aims to create a piezoelectric-based running track prototype to produce environmentally friendly electrical energy. The arrangement of piezoelectric sensors used in this research is a parallel circuit. The research method used is the Research and Development method. The test results on the physical activities of walking, running, and jumping show that the prototype system that has been made has worked well and produces a fairly stable electric voltage even though it is still in a small amount. the difference in activity and body weight that presses the prototype produces different electric voltages. For walking activity, the prototype is able to produce a maximum voltage output of 0.83V, running activity has a maximum output of 5.83V and the maximum voltage output of jumping activity is 7.88V. The use of energy is done by storing the energy obtained from the piezoelectric in the battery, which can later be monitored directly by the voltage sensor, bluetooth module, HC-05, and RTC module the data will be sent to the cell phone so that the piezoelectric output voltage can be recorded and monitored. The electrical energy output generated by the piezoelectric is able to charge the battery and switch on the LED light at 2.5 V.

Development of a blockchain-based embodied carbon estimator

PurposeCarbon management in the construction industry plays a vital role as carbon emissions have a significant impact on the environment. Current emphasis on reducing operational carbon through passive designs, zero carbon buildings and so forth has resulted in losing focus on embodied carbon (EC) reduction. Though there are various databases and tools to estimate EC in construction, these estimates are lacking in accuracy and consistency. A Blockchain-based Embodied Carbon (BEC) Estimator was developed as a solution to accurately estimate EC using a supply chain value addition concept as a methodology.Design/methodology/approachThis study focused on developing, testing and validating the blockchain-based prototype system identified as BEC Estimator. The system was developed using Hyperledger Fabric following a waterfall model. Case studies and an expert forum were used to test and validate BEC Estimator.FindingsThe system architecture, development process and the user interface of BEC Estimator are presented in this paper. The comparative evaluation with existing EC databases/tools and the expert forum validated the functioning of BEC Estimator and proved it to be an accurate, secure and trustworthy EC estimating system. SWOT (Strengths, Weaknesses, Opportunities, Threats) analysis identified the strengths and opportunities that will benefit the industry as well as the weaknesses and threats in the system that could be mitigated in future.Originality/valueBEC Estimator accurately accounts for EC additions happening at each supply chain node for any product that gets incorporated in a building, thereby facilitating EC-related decision-making for all relevant stakeholders.

Experimental Demonstration of a Solar Powered High Temperature Latent Heat Storage Prototype

The challenge of imbalances between renewable energy supply and grid demand underscores the significance of energy storage in microgrids. This research presents an empirical assessment of the operational capabilities of a full-scale Electrical Thermal Energy Storage (ETES) prototype system named Thermal Energy Storage Power On Demand (TES.POD®), in solar-abundant and harsh desert conditions. The system incorporates a high-temperature commercial-scale latent heat thermal energy storage, integrated with a Stirling engine. Over a continuous span of 10 days, from September 26th to October 6th, 2022, the input and output power, as well as the heat transfer fluid temperatures during charging and discharging were monitored to assess the power block and system efficiencies. Results from the experiments reveal that this prototype effectively maintains a near-constant electricity production rate of 10.5 ± 1 kW for a discharge duration of 13 hours. Average cycle efficiency stands at 23%, while power block efficiency reaches 25%. These findings collectively suggest the system's potential for applications involving long duration thermal energy storage.

The Development of a Prototype Solution for Detecting Wear and Tear in Pedestrian Crossings

Crosswalks play a fundamental role in road safety. However, over time, many suffer wear and tear that makes them difficult to see. This project presents a solution based on the use of computer vision techniques for identifying and classifying the level of wear on crosswalks. The proposed system uses a convolutional neural network (CNN) to analyze images of crosswalks, determining their wear status. The design includes a prototype system mounted on a vehicle, equipped with cameras and processing units to collect and analyze data in real time as the vehicle traverses traffic routes. The collected data are then transmitted to a web application for further analysis and reporting. The prototype was validated through extensive tests in a real urban environment, comparing its assessments with manual inspections conducted by experts. Results from these tests showed that the system could accurately classify crosswalk wear with a high degree of accuracy, demonstrating its potential for aiding maintenance authorities in efficiently prioritizing interventions.

Deep Learning Approaches for Power Prediction in Wind–Solar Tower Systems

Wind–solar towers are a relatively new method of capturing renewable energy from solar and wind power. Solar radiation is collected and heated air is forced to move through the tower. The thermal updraft propels a wind turbine to generate electricity. Furthermore, the top of the tower’s vortex generators produces a pressure differential, which intensifies the updraft. Data were gathered from a wind–solar tower system prototype developed and established at Kyushu University in Japan. Aiming to predict the power output of the system, while knowing a set of features, the data were evaluated and utilized to build a regression model. Sensitivity analysis guided the feature selection process. Several machine learning models were utilized in this study, and the most appropriate model was chosen based on prediction quality and temporal criteria. We started with a simple linear regression model but it was inaccurate. By adding some non-linearity through using polynomial regression of the second order, the accuracy increased considerably sufficiently. Moreover, deep neural networks were trained and tested to enhance the power prediction performance. These networks performed very well, having the most powerful prediction capabilities, with a coefficient of determination R2=0.99734 after hyper-parameter tuning. A 1-D convolutional neural network achieved less accuracy with R2=0.99647, but is still considered a competitive model. A reduced model was introduced trading off some accuracy (R2=0.9916) for significantly reduced data collection requirements and effort.