Hasil untuk "Electrical engineering. Electronics. Nuclear engineering"

Ayyappan M, Muthiah A

In this paper, melting and solidification characteristics of composite PCM filled inside the shell and tube heat exchanger were investigated experimentally. ZnO nanoparticles (NPs) were synthesized using the sol–gel method. Myristic acid (MA) considered as the pure PCM and ZnO NPs serving as the supporting material. The morphology and crystal structure of ZnO particles were analyzed using Field Emission Scanning Electron Microscopy (FESEM) and x-ray Diffraction (XRD) techniques. ZnO nanoparticles at concentrations of 0.1, 0.3, and 0.5 wt% were individually dispersed in myristic acid to evaluate the heat transfer characteristics of nanocomposite phase change materials (NCPCMs) through phase change processes. Differential Scanning Calorimetry (DSC) analyses were used to assess the phase change behavior of PCM and nanocomposite PCMs in liquid and solid states. The phase change characteristics of the Myristic acid and nanocomposite PCMs were probed with regard to heat exchanger studies. The results show significant time savings, with a 68.04% reduction in complete melting time and a 42.73% reduction in solidification time when using 0.5 wt% ZnO NPs at a mass flow rate of 5 l min ^−1 . Furthermore, incorporating ZnO NPs at concentrations of 0.1, 0.3, and 0.5 wt% enhanced the thermal conductivity of the NCPCMs by 36.41%, 62.96%, and 82.71%, respectively, compared to pure MA.

Mutasem Z. Bani‐Fwaz, Adnan, Ishtiaque Mahmood

Different engineering systems acquired high energy fluids to cope with the problem of heat transfer in which the Nanofluids are progressive fluids that strengthen their roots in the modern arena owing to their improved thermal characteristics. These applicable at large‐scale but not restricted to engineering systems ranging from automotive industry, medical arena, electronics, heating of buildings, chemotherapy, medication, nuclear reactors, heat exchangers, aerodynamics, electrical, and chemical engineering. Many of the engineering systems exhibit the flow over spinning sphere. The core focus of this research is to analyze a heat transfer fluid model comprising penta‐composites thermal conductivity (PCT) [Al2O3, CuO, TiO2, Ag, and CdTe] saturated in the host solvent C12H26‐C15H32. The penta‐hybrid nanofluid (PHNF) model first time reported in this study, and it will add values in industry and play the role of umbrella over the traditional nanofluid classes. To investigate the actual facts regarding the heat transport in penta‐nanofluid, RK technique is adopted and achieved remarkable results. The penta‐composites observed as catalysis in the formation of penta‐nanofluid which boosts internal heat capability and is dominant over common nano and ternary nanofluids. Moreover, the shear stresses and surface heat trends are optimum for penta‐nanofluid than nano and ternary nanofluid.

H. Alshraideh, M. Abdelgawad

Biprateep Dey, David Zhao, Brett H Andrews et al.

Key science questions, such as galaxy distance estimation and weather forecasting, often require knowing the full predictive distribution of a target variable Y given complex inputs X . Despite recent advances in machine learning and physics-based models, it remains challenging to assess whether an initial model is calibrated for all x , and when needed, to reshape the densities of y toward ‘instance-wise’ calibration. This paper introduces the local amortized diagnostics and reshaping of conditional densities (LADaR) framework and proposes a new computationally efficient algorithm ( Cal-PIT ) that produces interpretable local diagnostics and provides a mechanism for adjusting conditional density estimates (CDEs). Cal-PIT learns a single interpretable local probability–probability map from calibration data that identifies where and how the initial model is miscalibrated across feature space, which can be used to morph CDEs such that they are well-calibrated. We illustrate the LADaR framework on synthetic examples, including probabilistic forecasting from image sequences, akin to predicting storm wind speed from satellite imagery. Our main science application involves estimating the probability density functions of galaxy distances given photometric data, where Cal-PIT achieves better instance-wise calibration than all 11 other literature methods in a benchmark data challenge, demonstrating its utility for next-generation cosmological analyzes ^9 .

Chao Wang, Jun-Feng Hao, He Huang et al.

Traffic flow forecasting is a critical and essential technology in the field of Intelligent Transportation Systems (ITS), as it plays a pivotal role in optimizing traffic management, improving road safety, and enhancing the overall efficiency of transportation networks. However, current research neglects the relationships between the local and global traffic flow data. Additionally, the predefined static graph structure fails to adequately capture the dynamic spatial features of traffic flow. To address the these challenges, this paper proposes a Dual-Channel Dynamic Gated Spatio-Temporal graph network (DC-DGST) for traffic flow prediction. We consider hourly slices as the local feature and daily slices to be the global feature of traffic flow. The DC-DGST framework employs a dual-channel structure to capture spatiotemporal dependencies between global and local features. It transforms the predefined static graph into a dynamic graph, enabling the establishment of connections between input data and historical information. Furthermore, we design gated spatio-temporal blocks based on residual structures within the spatio-temporal module. Specifically, we utilize Graph Gated Neural Networks (GGNNs) to learn and integrate both static and dynamic graphs, while Transformer encoders are used to capture long-range dependencies in the temporal sequence. We conducted a series of experiments on four publicly available benchmark datasets: PEMS03, PEMS04, PEMS07, and PEMS08. The results demonstrate that our model significantly outperforms baseline models. Moreover, the dual-channel structure effectively captures the correlation between local and global traffic flow features, while the dynamic graph enhances the model’s overall performance.

A. Yu. Mikhaylov

The article presents an in-depth analysis of the formation and development of the capital market (stocks and bonds segment) in China. The factors determining the valuation of shares that are in circulation are highlighted. While the first factor identifies an assessment based on the company’s financial statements, the second factor considers current market conditions and investor sentiment. Examining historical data, it is interesting to note that from 2000 to 2006, the total market capitalization of stocks remained stable and amounted to a substantial 4 trillion yuan. However, in 2007 There was a significant shift when the market capitalization grew significantly and reached as much as 30 trillion yuan. This sudden growth can be explained by various reasons, such as increased investor confidence, favorable economic conditions and the introduction of progressive financial regulation. In addition, it is important to note that in the same year, the Shanghai Composite index (a key indicator of the Chinese stock market) reached an unprecedented value of 6,123.04 points. This indicator demonstrated the stability and potential of the Chinese capital market, and attracted both domestic and international investors. Thus, the analysis presented in the article reveals the intricacies of the Chinese capital market and the mechanisms of its assessment. This analysis establishes the importance of both book value and market value in determining the valuation of outstanding shares. In addition, historical trends have highlighted the resilience and vulnerability of the market to external shocks, as evidenced by significant fluctuations in market capitalization. In general, this study helps to understand the Chinese capital market and its evolution over time.

Valdemar Ramon Farre Guijarro, Jose David Vega Sanchez, Martha Cecilia Paredes Paredes et al.

The 5th-Generation New Radio (5G-NR) network have been widely deployed around the world in the frequency range 1/range 2 bands. Once specific frequency bands within these ranges can vary across different countries and regions due to regulatory differences, it should be carried out radio network planning to evaluate the 5G coverage considering the particularities of different locations. In this regard, this paper performs a throughly analysis of the following methods for modeling wireless channel propagation in Quito, Ecuador: 3rd Generation Partnership Project, Knife Edge Diffraction (KED), ASTER and Dominant Path model (DPM). Specifically, we focus on KED, ASTER, and DPM for <inline-formula> <tex-math notation="LaTeX">$3.5/28$ </tex-math></inline-formula>-GHz bands to determine the propagation models in three-Dimensional urban macro scenarios. In the radio network planning, the multiple-input multiple-output array antennas, <inline-formula> <tex-math notation="LaTeX">$2\times 2/ 4\times 4$ </tex-math></inline-formula> configuration radiation patterns are deployed using WINPROP tool and <inline-formula> <tex-math notation="LaTeX">$64\times 64$ </tex-math></inline-formula> array configuration with the ATOLL tool. 5G frequency specifications, path-loss, influence of diffraction, reflection, blocking, and fading between transmitter and receiver have been considered for scenarios of interest, such as dense urban and urban in Quito, by using fixed wireless access applications and Vehicular-to-Everything (V2X) communications. In addition, data rates, throughput, and the quality metrics of the received reference signal, i.e., the signal-to-noise plus interference ratio, the reference signal received quality, the reference signal received power, and the received signal strength indicator, are also assessed for each propagation model. Finally, we provide useful insights into propagation models and design usage rules for the bands mentioned in 5G networks for Quito city.

Tamer Eliyan, Ali Saeed Almuflih, Z. M. S. Elbarbary et al.

The new horizons for HVDC systems and their applications has intensified the research in HVDC protection systems. The HVDC circuit breakers (CB) has been the center of the protection systems with vacuum interrupters (VIs) showcasing a great potential for application as a part of HVDC-CBs. However, VIs still face the problem of arc-reignition with limited studies in this area. This paper tends to address this problem by investigating the impact of the parameters of VI and the shunt L-C branch upon the re-ignition occurrence during switching process. An HVDC testing system and VI-based HVDC-CB were modeled using ATP software. The testing included investigation of the impact of the rate of rise of dielectric strength (RRDS) of VI, the type of LC circuit either active or passive and its timing of injection. The results showed that active L-C circuits demanded a longer delay time before insertion to avoid re-ignitions and the increase in RRDS reduced the reignitions. The main contributions of this paper include; investigating the arc re-ignition in VI-based HVDC-CB; analyzing the combined effect of the VI and L-C parameters upon those reignitions and providing a co-relation between the re-ignition occurrence and these parameters. That co-relation is used to define the most suitable delay time for LC circuit to avoid re-ignitions. The co-relation shows that the delay time is inversely co-related to the RRDS and directly-correlated to active LC types with lower values for passive L-C. This was applied to the simulation results and showed agreement with that co-relation.

Al Amin Biswas

Nowadays, artificial intelligence (AI) has been utilized in several domains of the healthcare sector. Despite its effectiveness in healthcare settings, its massive adoption remains limited due to the transparency issue, which is considered a significant obstacle. To achieve the trust of end users, it is necessary to explain the AI models' output. Therefore, explainable AI (XAI) has become apparent as a potential solution by providing transparent explanations of the AI models' output. In this review paper, the primary aim is to review articles that are mainly related to machine learning (ML) or deep learning (DL) based human disease diagnoses, and the model's decision-making process is explained by XAI techniques. To do that, two journal databases (Scopus and the IEEE Xplore Digital Library) were thoroughly searched using a few predetermined relevant keywords. The PRISMA guidelines have been followed to determine the papers for the final analysis, where studies that did not meet the requirements were eliminated. Finally, 90 Q1 journal articles are selected for in-depth analysis, covering several XAI techniques. Then, the summarization of the several findings has been presented, and appropriate responses to the proposed research questions have been outlined. In addition, several challenges related to XAI in the case of human disease diagnosis and future research directions in this sector are presented.

Xizu Wang, D. Repaka, A. Suwardi et al.

Liquid metal gallium has been widely used in numerous fields, from nuclear engineering, catalysts, and energy storage to electronics owing to its remarkable thermal and electrical properties along with low viscosity and nontoxicity. Compared with high-temperature liquid metals, room-temperature liquid metals, such as gallium (Ga), are emerging as promising alternatives for fabricating advanced energy storage devices, such as phase change materials, by harvesting the advantageous properties of their liquid state maintained without external energy input. However, the thermal and electrical properties of liquid metals at the phase transition are rather poorly studied, limiting their practical applications. In this study, we reported on the physical properties of the solid–liquid phase transition of Ga using a custom-designed, solid–liquid electrical and thermal measurement system. We observed that the electrical conductivity of Ga progressively decreases with an increase in temperature. However, the Seebeck coefficient of Ga increases from 0.2 to 2.1 µV/K, and thermal conductivity from 7.6 to 33 W/(K∙m). These electrical and thermal properties of Ga at solid–liquid phase transition would be useful for practical applications.

A. Rashed, Walid Fawzy Zaky, Hazem M. El-Hageen et al.

Viktória Hideg, Emese Makó

In recent years an increasing number of cities and transport planning documents (such as Sustainable Urban Mobility Plan) aim to reduce car traffic and promote active modes of transport – walking and cycling. The development of active modes of transport is increasingly becoming a focus of urban planning. However, detailed information on the needs of pedestrians and aspects of the assessment of a pedestrian-friendly environment are usually not available. In most cases, the only indicator of the effectiveness of improvements is the modal split and the rate of pedestrians. An objective assessment method is needed to help identify areas that need to be developed for walking. The various planning regulations and legislation provide a framework for the design of pedestrian infrastructure, but many aspects that make public spaces attractive and pedestrian-friendly (green spaces, aesthetics, sense of safety, etc.) are not included in the regulations. This problem can be addressed by the walkability index, which can provide an objective, data-based measure of how pedestrian-friendly an area is. It can also be a tool for analysing and monitoring. It can show areas where walking conditions are inadequate and intervention is needed. Regularly carrying out the survey can also serve to analyse the impact of measures taken in the meantime. This article describes the methodology and application of the walkability index.

Lijuan LI, Beibei ZHANG, Zhaoda SUN

Under the background of the comprehensive implementation of the National Carbon Peak Carbon Neutralization Action, the superhard material industry energy consumption research was carried out. It aims to promote the superhard material industry to make new contributions to the green, low-carbon and high-quality development of the country. The energy consumption data of superhard material and product enterprises were summarized and analyzed, and the energy consumption of typical products in the inorganic non-metallic field was compared. The results show that from 2019 to 2021, the energy consumption per unit of gross industrial output value of superhard materials is 0.11, 0.10, and 0.08 tce/10 000¥; and that the energy consumption per unit of gross industrial output value of superhard materials products is 0.08, 0.07 and 0.05 tce/10 000¥ respectively. The reduction rate of energy consumption in superhard materials industry is far greater than the reduction percentage of national GDP energy consumption index. Compared with the typical products in the inorganic non-metallic field such as cement, ceramics and ordinary abrasives, the energy consumption in the superhard material industry is at a lower level. Finally, the paper makes suggestions on superhard industry policies and enterprise carbon reduction.

Dzakiya Ishmatul Ulya, Anang Kunaefi, Dwi Rolliawati et al.

QRIS, a mobile payment transaction system standardized by Bank Indonesia, has become the subject of extensive public discourse on Twitter. Employing VADER for sentiment analysis and LDA for topic modeling, this study aims to capture the nuanced perspectives of the Indonesian public toward QRIS. Our methodology includes real human validation for tweets that have been initially labeled by VADER. Our unique contributions lie in employing a mixed-methods approach for comprehensive sentiment and topic analysis, as well as making our dataset publicly available for future research. We achieve a sentiment labeling accuracy of 81.66%, uncovering that 67% of the sentiment towards QRIS is positive, 28.2% negative, and 4.17% neutral. Positive tweets mostly cover six dominant topics with a value of 0.488037, whereas negative sentiments are concentrated around three dominant topics with a   value of 0.383938. These findings not only affirm the generally positive public response towards QRIS but also highlight areas requiring attention for its continued success. Our study translates these insights into actionable recommendations, aiming to provide a multidimensional understanding that stakeholders can leverage for system enhancement. This study serves as a foundation for future works in sentiment analysis and public opinion mining related to financial technologies, particularly in the Indonesian context.

Qizhi Xie, Song Cui, Peilin Cheng et al.

The inchworm in-pipe robot has the advantages of stable support, low walking resistance, and high flexibility. However, the gait motion of in-pipe robots relies on the precise coordination of three motors, which greatly increases the complexity of control. To solve this problem, an inchworm in-pipe robot based on a multi-cam combination is proposed. The robot needs only one motor to achieve active support and bidirectional crawling for the pipe wall, mainly used for detecting straight pipelines, such as the main drainage pipeline. In order to obtain the periodic motion law and characteristics of the inchworm in-pipe robot, structure design, constraint analysis, and dynamic simulation were carried out on the robot. Finally, the principle prototype was tested in the transparent pipe, the test results indicate that the robot can achieve bidirectional creep under a single motor drive, which can simplify the control of gait motion for inchworm in-pipe robots. The average displacement errors for horizontal walking and vertical walking are 2.0&#x0025; and 11.3&#x0025;, respectively, due to factors such as gravity. Therefore, the robot can achieve a more accurate step distance in the horizontal pipe.

Chenke He, Jizhong Zhu, Kwok Cheung et al.

An optimal planning method for an integrated energy system (IES) considering electric vehicles (EVs) swapping station (SS) and carbon capture power system (CCPS) is studied in this paper. Firstly, based on the analysis of swapping EV load, the model of SS is established. Then, we built the models of CCPS, and other energy supply and storage equipment in IES. Based on this, the annual comprehensive cost of planning is regarded as the optimization objective, a planning model of IES considering SS and CCPS is developed, and solved by CPLEX. Finally, an actual case was analyzed to verify the feasibility and effectiveness of the planning method proposed in this paper.

Y. Liu, S. Zhang, X. Lin et al.

A passive fast neutron multiplicity counting system has been developed in the School of Nuclear Science and Engineering, North China Electrical Power University, Beijing, China. The system mainly consists of 32 custom-made EJ-309 liquid scintillation detectors and a custom-made multichannel readout electronics. These detectors are symmetrically mounted on a patented spherical supporting structure. The simulation results show that the detection efficiency of the system is about 12% for 252Cf neutrons at an energy threshold of 100 keVee and not affected by the size and shape of the samples if their location and distribution within a radius of 10 cm in the system which is due to the symmetrical arrangement of these detectors. The experimental results further show that the system has a uniform detection efficiency of ~11% for 252Cf neutrons at an energy threshold of 100 keVee with a relative efficiency deviation of less than 5% as samples are located or distributed within a radius of 6 cm in the system. The readout electronics integrates two combined neutron-gamma discrimination circuits for the realization of pulse-shape and pulse-width discrimination algorithms and a FPGA(Field Programmable Gate Array)-based data acquisition system for picking out all neutron events and registering their global arriving time. By combining pulse-shape and pulse-width discrimination methods, the detection system can achieve a neutron-gamma discrimination performance with FOM>1 even at a lower energy threshold of 100 keVee. The spontaneous rates of seven different 252Cf sources measured with our system are within less than 5% of the results using a Plutonium Scrap Multiplicity Counter (PSMC). The system is going to be tested with Plutonium samples and the corresponding results will be reported on the conference.

Liangwei Zeng, Yongle Zhu, B. Malomed et al.

Liangwei Zeng, Yongle Zhu, Boris A. Malomed, 3 Dumitru Mihalache, Qing Wang, Hu Long, Yi Cai, Xiaowei Lu, and Jingzhen Li ∗ Shenzhen Key Laboratory of Micro-Nano Photonic Information Technology, Key Laboratory of Optoelectronic Devices and Systems of Ministry of Education and Guangdong Province, College of Physics and Optoelectronic Engineering, Shenzhen University, Shenzhen 518060, China Department of Physical Electronics, School of Electrical Engineering, Faculty of Engineering, and Center for Light-Matter Interaction, Tel Aviv University, P.O.B. 39040, Tel Aviv, Israel Instituto de Alta Investigación, Universidad de Tarapacá, Casilla 7D, Arica, Chile Horia Hulubei National Institute of Physics and Nuclear Engineering, Magurele, Bucharest, RO-077125, Romania College of Science, JiuJiang University, Jiujiang 334000, Jiangxi, China

Anita Mohanty, Subrat Kumar Mohanty, Bhagyalaxmi Jena et al.

Abstract The delay in transportation of necessary items is due to traffic congestion throughout the world. This is a serious phenomenon which results in waste of time and fuel. The detection of road conditions and dissemination of traffic information efficiently and effectively is a big challenge to authorities. Recently, the technologies of vehicular ad hoc networks (VANETs) have been utilized and become an important part of the intelligent transportation system (ITS). For this existing problem, vehicle‐to‐vehicle (V2V) communication provides a means for cooperation and route management in transport networks. This paper proposed a novel congestion detection system based on the combination of k‐means clustering and analytical hierarchy process. In the simulation of urban mobility (SUMO) simulator, a transport network is created and parameters of vehicles facing congestion are taken to extract the key parameter by using the k‐means clustering technique and mathematical mean algorithm. This parameter is utilized in analytical hierarchy process to detect the highest priorities parameter and based on that the congestion is detected in particular lane. The result can be a better technique for congestion detection as it requires low installation cost and can be incorporate in vehicles for congestion avoidance which will alternatively improve the traffic flow.

Artem Dmytrovych Zubkov, Denys Dmytrovych Volkov, Vitalii Semenovych Didkovkyi

У даній статті розглянуто адаптацію та застосування генетичного алгоритму для знаходження параметрів моделі електродинамічного перетворювача. Розглянуто переваги та недоліки даного методу порівняно із класичними методом ідентифікації із застосуванням доданої маси. Представлено виведення функції пристосованості для оцінки ідентифікованих параметрів що може також бути використана для ідентифікації інших типів електроакустичних перетворювачів.  Було розглянуто теорію, що лежить в основі генетичних алгоритмів, і показано, як генетичні алгоритми працюють, збираючи найкращі рішення з невеликих структурних елементів, що володіють чудовими якостями. Далі було розібрано відмінності між генетичними і традиційними алгоритмами, в тому числі підтримку популяції рішень і використання генетичного уявлення рішень. Після цього було описано сильні сторони генетичних алгоритмів, що включають можливість глобальної оптимізації і застосовність до завдань зі складним математичним представленням або взагалі без представлення і стійкість до шуму. Також були освітлені недоліки: необхідність спеціальних визначень і налаштування гіперпараметрів, небезпеки передчасної збіжності. На закінчення перераховано ситуації, коли застосування генетичних алгоритмів може дати перевагу. Цей алгоритм не прив’язаний до конкретної інженерної чи наукової галузі, що робить його універсальним, рівною мірою він використовується і в генетиці і у комп’ютерних науках. За допомогою генетичного алгоритму було визначено параметри та порівняно їх з більш класичним для акустики методом доданої маси. Порівняльна таблиця у роботі ілюструє високу точність генетичного алгоритму у порівнянні з методом доданої маси. В ході роботи над практичною частиною, також щоб покращити поведінку моделі на частотах вищих за резонансну, було вирішено ускладнити модель електричної підсистеми перетворювача та увести додаткові параметри: паралельний опір та паралельну індуктивність. Ускладнена модель, як наслідок, почала краще відповідати виміряним значенням у всій частотній області, а отже є більш точною. Це є прикладом зручності використання генетичного алгоритму при переході від ідентифікації однієї моделі зі специфічними параметрами до іншої.  Результати даної роботи доводять, що використання генетичного алгоритму є доцільним для вирішення задач електроакустики адже його використання дозволяє швидко експериментувати  та ідентифікувати більш складні моделі для яких метод доданої маси не може бути застосованим. Також, у перспективі, генетичний алгоритм може бути застосований для ідентифікації моделей перетворювачів у часовій області, наприклад, нелінійних моделей електродинамічних перетворювачів або моделей у фазовому просторі, що є предметом майбутніх досліджень.