Gridded Fields Research Articles

Algorithm optimization based on intelligent management of computer electrical equipment: A comprehensive method for PT power monitoring and remote fault indicator

In response to the accuracy limitations and response delays in current power and fault detection of electromechanical equipment, long short-term memory (LSTM) network algorithm was applied for intelligent optimization and management. Firstly, voltage and power data were collected through a potential transformer (PT), and wavelet transform (WT) was applied to remove noise. Fast Fourier transform (FFT) was utilized to extract key features. Secondly, a multi-layer long short-term memory network was designed, and back propagation algorithms and time series power data were used for LSTM network training to analyze abnormal fluctuations and trends in power data in real-time, and adjust threshold settings. Then, combining the model output and historical fault data, a fault mode knowledge base was established. Potential faults were determined through pattern matching, and signals were sent out through remote indicators. Finally, the algorithm model was evaluated. The research results showed that the weighted response time of voltage drop faults was shortened by 3.3%, and the information entropy values of nine experiments were distributed from 5.11 to 5.28. The diagnostic accuracy for frequency drift was improved by 11.1%. The comprehensive algorithm model used can effectively improve the accuracy of power monitoring and response speed. This study optimizes power monitoring and fault diagnosis by applying the LSTM network algorithm, addressing the limitations of existing methods in terms of real-time performance and accuracy. It effectively enhances the fault prediction capability and response speed of power systems, offering significant application value in the fields of smart grids and equipment management.

Preface

Focusing on the latest research development and progress in the fields of power system engineering and smart grid, the 2024 International Conference on Power System Engineering and Smart Grid (PSESG 2024) was held in Beijing, China from August 16th to 18th, 2024. It was presided over by North China University of Technology (NCUT), with the full support of many universities, research institutes and enterprises at home and abroad. PSESG 2024 was created to build a high-quality academic forum to exchange and share successful experiences of innovation in the domains of power system engineering and smart grid, and establish an effective cooperation platform for researchers and scholars from universities, research institutes and enterprises all over the world. Calling for papers on such topics as Transmission and Distribution System and Equipment, Electrical and Energy Circuits and Systems, Power System Planning and Dispatch, Power System Security Defense and Restoration Control, Smart Grid Deployment, Smart Grid and Distributed Energy, etc., this conference attracted about 80 scientists, engineers, practitioners and graduate students worldwide to exchange their current technical application experience and research results, so as to promote the development of relevant fields and industries. The conference took place for three days, among which day two could be deemed the most critical. On behalf of the organizer, Professor Jinghua Zhou (North China University of Technology), first made an opening speech, introducing NCUT and its cross-disciplinary characteristics of energy storage to the experts and scholars attending the conference, and expressing his sincere welcome to the attendees. And next is the keynote speech part performed by several renown experts, including Professor Shunli Wang (Inner Mongolia University of Technology), Professor Zechun Hu (Tsinghua University), Professor Jinghua Zhou (North China University of Technology), Professor Yu Wang (Chongqing University), and Associate Professor Chenye Wu (The Chinese University of Hong Kong). Their speeches not only covered the latest advances in power system engineering and smart grid, but also explored the innovative application of energy storage technology and the trend of the digital transformation of power systems, providing participants with valuable academic insights. Then, the oral and poster presentation mainly presented by young scholars were held to showcase their research achievements. They talked about the technical principles, operational characteristics, optimization methods, as well as challenges and solutions in practical applications of power system and smart grid from different perspectives, stimulating the spark of thought collision and promoting the deepening of academic cooperation. Discussion and Question and Answer happened throughout the meeting. We would like to thank all the authors and speakers for their hard work and wonderful contributions, and all the experts and scholars for their attention and support to this conference. We believe that with our joint efforts, research and application in the fields of power system and smart grid will achieve more remarkable results and make greater contributions to the cause of sustainable development of mankind. Finally, we look forward to meeting again in the next International Conference on Power System Engineering and Smart Grid to discuss the future development path of power system and smart grid. List of Committee Member is available in this Pdf.

A novel thyristor‐based hybrid DC circuit breaker with short arcing capability

AbstractMulti‐terminal flexible high‐voltage direct current (HVDC) transmission and DC grid technology are developing rapidly. The hybrid DC circuit breaker (HCB) has broad application prospects in the field of HVDC transmission and DC power grid. Nevertheless, existing HCBs have one or more following blemishes: arc suppression scheme of mechanical switch, high cost and unreliable adaptive reclosure. In this paper, a modified thyristor‐based hybrid DC circuit breaker with short arcing capacity (SA‐HCB) is introduced, which consists of the main branch, the fault handling branch and the pre‐charging branch. Besides, the modified SA‐HCB has the inherent capability of adaptive reclosing. The advantages of thyristors are fully used in the SA‐HCB, such as low cost, low conduction voltage drop, and natural shutdown. The detailed structure diagram and mathematical model of the SA‐HCB are introduced, and the operation processes of the fault current transfer are also analysed. Later, a design method for parameters of the proposed SA‐HCB is provided. Then, a single‐ended equivalent system is built in PSCAD/EMTDC to verify the accuracy of the theoretical analysis and parameter design. Subsequently, based on four‐terminal simulation, the economy of the SA‐HCB is verified by comparing with the topology proposed by ABB. Finally, the small‐scale experiment of the proposed SA‐HCB is introduced.

Smart grid and blockchain: Bibliometric analysis

The interest in smart grids and new technologies is growing around the world. Countries are investing in the development of new technologies that will help achieve environmental goals, energy supply efficiency, improve energy efficiency and increase consumer involvement in the energy generation. One of such technology is a blockchain. It is believed that the blockchain, combined with a smart grid, provides an opportunity to integrate the activities of all stakeholders, including: generators, distributors and consumers of electricity. The aim of the article is to identify the key research areas discussed by the researchers of both the smart grid and the blockchain issues. An analysis of the Scopus database from 2015 to 2023 was conducted. Using a created bibliometric query, a systematic literature review was conducted. 476 scientific publications relating to the issues addressed were identified. Using the VOSviewer software, a bibliometric analysis was performed using the author’s keywords. The bibliometric maps obtained allowed for the identification of key research areas. The article also presents potential future directions of scientific considerations, which should be focused on the issue of green smart grid and green blockchain. The results presented in the article can inspire researchers looking for research gaps or describing the current state of knowledge in the field of the smart grid and the blockchain issues.

The role of volume averaging effects, beam hardening, and phantom scatter in dosimetry of grid therapy

Objective. Current reference dosimetry methods for spatially fractionated radiation therapy (SFRT) assume a negligible beam quality change, perturbation, or volume-averaging correction factor. Therefore, the aim of this work was to investigate the impact of the grid collimators on the dosimetric characteristics of a 6 MV photon beam. A detector-specific correction factor,kQgrid, Qmsr fgrid,fmsr, was proposed. Several dosimeters were evaluated for their ability to measure both reference dose and grid output factors (GOFs).Approach. A Monte Carlo model of a grid collimator was created to study the change in the depth dose characteristics with the grid collimator. The impact of the collimator on the percent depth dose (PDD), electron contamination, and average photon energy was investigated. ThekQgrid, Qmsr fgrid,fmsrcorrection factors were calculated for two reference-class micro ion chambers. The reference dose and GOFs were measured with a grid collimator using six ion chambers, two silicon diodes, and a diamond detector.Main results.The PDD in the presence of the grid was observed to be steeper compared to the open field. The average photon energy increased from 1.33 MeV to 1.74 MeV with the presence of the grid collimator. The dose contribution by scattered photons was significantly higher at deeper regions for the open field compared to the grid field. ThekQgrid, Qmsr fgrid,fmsrcorrection was calculated to be <0.5%. The reference dose for all detectors, except for the CC13 and CC04 chambers, was within 1% of each other. The CC13 under-responded up to 3.2% due to volume-averaging effects. The GOFs calculated for all detectors, except Razor and A16, were within 1% of each other.Significance. The phantom scatter dictates the change in the PDD with the presence of the grid. The micro ion chambers exhibit negligiblekQgrid, Qmsr fgrid,fmsrcorrection. All detectors, except the CC13 ion chamber, were found to be suitable for SFRT reference dosimetry.

Enhancing domain-specific text generation for power grid maintenance with P2FT.

The digitization of operation and maintenance in the intelligent power grid equipment relies on a diverse array of information for smart decision-making. In the domain of intelligent decision generation, proficiency is contingent upon extensive learning from copious amounts of text. This necessitates not only robust processing capabilities but also a high level of specialization. In addressing situations where authorization is lacking, pre-trained language models (PLMs) have already provided ideas when confronted with specialized domains or tasks. In consideration of the complexity of textual content in the field of the power grid, which encompasses a multitude of specialized knowledge and involves an abundance of proprietary terminology, we have undertaken an exploration of pre-trained model specialization using the power grid domain as an example, specifically for the task of generating maintenance strategies. A two-stage fine-tuning approach (P2FT) is employed, utilizing a large-scale pre-training model specifically designed for natural language processing. The efficacy and practical value of this method were evaluated through multiple metrics, juxtaposed with other advanced approaches involving low-parameter or parameter-free fine-tuning methods. Through a meticulous analysis and validation of experimental outcomes, we have corroborated the feasibility and practical application value of employing this approach for pre-trained model specialization. Additionally, it has furnished valuable guidance for text generation within both the Chinese language domain and the power grid domain.

Category boundaries modulate memory in a place-cell-like manner

Concepts describe how instances of the same kind are related, enabling the categorization and interpretation of new information.1,2 How concepts are represented is a longstanding question. Category boundaries have been considered defining features of concept representations, which can guide categorical inference,3 with fMRI evidence showing category-boundary signals in the hippocampus.4,5 The underlying neural mechanism remains unclear. The hippocampal-entorhinal system, known for its spatially tuned neurons that form cognitive maps of space,6,7 may support conceptual knowledge formation, with place cells encoding locations in conceptual space.4,8,9,10,11 Physical boundaries anchor spatial representations and boundary shifts affect place and grid fields,12,13,14,15,16 as well as human spatial memory,17,18,19 along manipulated dimensions. These place cell responses are likely driven by boundary vector cells, which respond to boundaries at specific allocentric distances and directions,20,21,22,23 the neural correlates of which have been identified in the subiculum and entorhinal cortex20,24,25. We hypothesize similar patterns of memory adaptations in response to shifting category boundaries. Our findings show that after category boundary shifts, participants' memory for category exemplars distorts along the changed dimension, mirroring place field deformations. We demonstrate that the boundary vector cell model of place cell firing best accounts for these distortions compared with alternative geometric explanations. Our study highlights a role of category boundaries in human cognition and establishes a new complementary link between hippocampal coding properties with respect to boundaries and human concept representation, bridging spatial and conceptual domains.

DeepMIP-Eocene-p1: multi-model dataset and interactive web application for Eocene climate research

Paleoclimate model simulations provide reference data to help interpret the geological record and offer a unique opportunity to evaluate the performance of current models under diverse boundary conditions. Here, we present a dataset of 35 climate model simulations of the warm early Eocene Climatic Optimum (EECO; ~ 50 million years ago) and corresponding preindustrial reference experiments. To streamline the use of the data, we apply standardised naming conventions and quality checks across eight modelling groups that have carried out coordinated simulations as part of the Deep-Time Model Intercomparison Project (DeepMIP). Gridded model fields can be downloaded from an online repository or accessed through a new web application that provides interactive data exploration. Local model data can be extracted in CSV format or visualised online for streamlined model-data comparisons. Additionally, processing and visualisation code templates may serve as a starting point for advanced analysis. The dataset and online platform aim to simplify accessing and handling complex data, prevent common processing issues, and facilitate the sharing of climate model data across disciplines.

Preface

Against the backdrop of the rapidly evolving global energy transition, power, grid and energy storage technology are receiving unprecedented attention as key forces in promoting cleaner, smarter and more efficient energy sources. The Proceedings of the 2024 2nd International Conference on Power, Grid and Energy Storage (PGES 2024) brings together the latest research results and cutting-edge explorations of experts and scholars in the field of power energy from all over the world, demonstrating the vigorous vitality and unlimited potential of this field. The PGES 2024 took place virtually in Kunming, China from June 21st to 23rd, 2024. The conference was organized in order to provide an international academic forum for promoting research and other academic activities as well as developing research cooperation in field of power, grid and energy. It is worth mentioning that the number of international participants in this conference hit a record high. Experts and scholars from Canada, Malaysia, Italy, New Zealand, China, and many other countries and regions gathered together to contribute to the development of power, grid and energy. They brought cutting-edge technologies and research results in their respective fields, injecting new vitality and momentum into the conference. The conference fully demonstrated the characteristics of globalized exchanges. Participants from different countries and regions promoted international academic exchanges and cooperation by sharing their research achievements and experiences. At the same time, the conference also invited a number of internationally renowned experts and scholars to give keynote speeches, bringing participants cutting-edge academic views and profound insights. The keynote speakers include Hossam A. Gabbar (full Professor of Ontario Tech University, Canada), Tao Lin (Professor of Wuhan University, China), M A Hannan (Professor of Sunway University, Malaysia), Pierluigi Siano (Full Professor of University of Salerno, Italy), Udaya Madawala (Professor of The University of Auckland, New Zealand), as well as Guobing Zhou (Professor of North China Electric Power University, China). In addition, the conference also organized oral and poster presentations, which provided participants with more opportunities for in-depth exchanges and discussions. These activities not only helped to enhance the friendship and cooperation among the attendees, but also laid a solid foundation for promoting international exchanges and cooperation in the field of information education and artificial intelligence. The topics of papers collected and undergone rigorous peer review in the Proceedings of PGES 2024 can be classified as follows: Power Systems and Automation, Energy Storage Equipment and Systems, Smart Grid Information Engineering, Electrical Power Generation Technology, Power Transmission and Distribution, Communication and Control in The Power System, etc. We would like to express our sincere thanks and gratitude to all the keynote speakers, authors, committee members and other colleagues for the success of PGES 2024, which has given rise to this present volume of selected papers. We would also like to acknowledge the staff of Journal of Physics: Conference Series for their effective work to make this Proceedings published. The Committee of PGES 2024 List of Committee Member is available in this Pdf.

A 561-yr (1461-2022 CE) summer temperature reconstruction for Mid-Atlantic-Northeast USA shows connections to volcanic forcing and atmospheric circulation

Contextualizing current increases in Northern Hemisphere temperatures is precluded by the short instrumental record of the past ca. 120 years and the dearth of temperature-sensitive proxy records, particularly at lower latitudes south of <50 °N. We develop a network of 29 blue intensity chronologies derived from tree rings of Tsuga canadensis (L.) Carrière and Picea rubens Sarg. trees distributed across the Mid-Atlantic and Northeast USA (MANE)—a region underrepresented by multi-centennial temperature records. We use this network to reconstruct mean March-September air temperatures back to 1461 CE based on a model that explains 62% of the instrumental temperature variance from 1901−1976 CE. Since 1998 CE, MANE summer temperatures are consistently the warmest within the context of the past 561 years exceeding the 1951−1980 mean of +1.3 °C. Cool summers across MANE were frequently volcanically forced, with significant (p<0.05) temperature departures associated with 80% of the largest tropical (n=13) and extratropical (n=15) eruptions since 1461 CE. Yet, we find that more of the identified cool events in the record were likely unforced by volcanism and either related to stochastic variability or atmospheric circulation via significant associations (p<0.05) to regional, coastal sea-surface temperatures, 500-hpa geopotential height, and 300-hpa meridional and zonal wind vectors. Expanding the MANE network to the west and south and combining it with existing temperature-sensitive proxies across North America is an important next step toward producing a gridded temperature reconstruction field for North America.

Predictive grid coding in the medial entorhinal cortex.

The entorhinal cortex represents allocentric spatial geometry and egocentric speed and heading information required for spatial navigation. However, it remains unclear whether it contributes to the prediction of an animal's future location. We discovered grid cells in the medial entorhinal cortex (MEC) that have grid fields representing future locations during goal-directed behavior. These predictive grid cells represented prospective spatial information by shifting their grid fields against the direction of travel. Predictive grid cells discharged at the trough phases of the hippocampal CA1 theta oscillation and, together with other types of grid cells, organized sequences of the trajectory from the current to future positions across each theta cycle. Our results suggest that the MEC provides a predictive map that supports forward planning in spatial navigation.

Reimagining Teaching Style Through Renewable Energy Quality Assessment—A Focus on Power Technology and Equipment

This article delves into teaching style translation strategies from the perspective of power energy quality assessment, using power technology and equipment as specific cases, with the aim of enhancing the international dissemination efficiency of power energy professional knowledge in the education field. Through detailed data analysis, this article reveals the rapid pace of technological updates and equipment upgrades in the power energy industry, especially in the fields of smart grids and renewable energy. As of 2023, the proportion of non fossil energy generation installed capacity worldwide has approached 50%, indicating that the power industry is accelerating its transition to green and low-carbon. This article pays special attention to the complex terms and concepts involved in the teaching of power technology and equipment. Using the principle of adaptability in translation theory, it analyzes how to accurately convey the precise meanings of these terms in the translation process, ensuring that international students can accurately understand and master key knowledge points. By comparing traditional teaching methods with a new teaching model that incorporates elements of power energy quality assessment, the study found that the latter performs more outstandingly in improving students' practical and problem-solving abilities. Specifically, the accuracy of students in simulating power system fault handling has increased by about 30%. In addition, this article also utilizes big data analysis technology to comprehensively sort out and evaluate teaching resources in the field of power energy, and finds that high-quality teaching materials and case sharing are crucial for improving teaching effectiveness. Based on the above analysis, this article proposes a series of optimization suggestions for teaching style translation, in order to provide strong support for the international education of power energy majors and promote the exchange and progress of global power energy technology.

Preface

It is my honor to present the proceedings of the 2024 3rd International Conference on Smart Grid and Green Energy (ICSGGE 2024). ICSGGE 2024 is an event that brings together the world’s leading experts and scholars in the fields of smart grid and green energy, and is also an excellent platform for sharing the latest research results and discussing the application of cutting-edge technologies in related research domains. With the increasing severity of global climate change and energy crisis, smart grid and green energy, as the key means to solve the energy problem, have received extensive attention from the international community. In order to further promote technological innovation, academic exchanges and cooperation in the fields of smart grid and green energy, ICSGGE 2024 was held from April 19th to 21st, 2024 in Chengdu, China (virtual conference). ICSGGE 2024 brought together top scholars and researchers from all over the world, who shared their latest research results and practical experiences in the fields of smart grid and green energy in this conference. Such topics as The Role of Smart Grids to Drive Cities Towards Carbon Neutrality (by Prof. Steivan Defilla, Tianjin University, China), Role of Energy Conservation &amp; Management in Energy Transition (by Prof. Muhammad Asif, King Fahd University of Petroleum &amp; Minerals, Saudi Arabia), among others were discussed during the keynote speech part. Through exchanges and discussions, we have jointly explored the development trends and future research direction of smart grid and green energy, stimulated innovative thinking, and promoted academic exchanges and cooperation. This proceedings of ICSGGE 2024 contains excellent academic papers from all around the world, which not only cover the basic theories, teaching methods, and evaluation systems of smart grid and green energy, but also discuss in depth the applications and innovations in many fields. Topics include but are not limited to Smart Grid Security, Energy Transfer and Interactive Technology, Communication-Based Control and Protection, Photovoltaic Systems and Solar Energy Engineering, Power Electronics and Drives, Wind Energy Generation, etc. Each paper has been strictly reviewed and screened to ensure its academic value and practical application value. We would like to express my heartfelt thanks to all the experts and scholars, organizers, sponsors and participants who have contributed to and supported this conference. And we believe that all the experts and scholars have harvested fruitful results and inspired new inspirations in this event. Through our joint efforts and wisdom, the ICSGGE 2024 has become a grand event in the fields of smart grid and green energy, and made a new contribution to the promotion of electronic and energy engineering. The Committee of ICSGGE 2024 List of Committee Member is available in this Pdf.

Zeroth-order gradient tracking for decentralized learning with privacy guarantees

This paper proposes a differential privacy decentralized zeroth-order gradient tracking optimization (DP-DZOGT) algorithm for solving optimization problems of decentralized systems, where the gradient information of the function is unknown. To address the challenge of unknown gradient information, a one-point zeroth-order gradient estimator (OPZOGE) is constructed, which can estimate the gradient based on the function value and guide the update of decision variables. Additionally, to prevent privacy leakage of agents, random noise is introduced into both the state and the gradient of the agents, which effectively enhances the level of privacy protection. The linear convergence of the proposed DP-DZOGT under a fixed step size can be guaranteed. Moreover, it has been applied to the fields of smart grid (SG) and decentralized federated learning (DFL). Finally, the effectiveness of the algorithm is validated through three numerical simulations.

ВЕРИФИКАЦИЯ СЕЗОННЫХ АНСАМБЛЕВЫХ ПРОГНОЗОВ НА БАЗЕ МОДЕЛИ ЗЕМНОЙ СИСТЕМЫ INM-CM5

The issues related to the verification of ensemble seasonal forecasts obtained using a new technology implemented on the basis of the INM-CM5 Earth system model are considered. The forecast objects are global gridded (2.5° × 2.5°) fields of ensemble mean anomalies and probabilities of three gradations of the anomalies (below normal, normal, above normal) for each of the five characteristics: 500 hPa geopotential height (H500), mean sea level pressure (SLP), 850 hPa air temperature (T850), surface air temperature (T2m), and precipitation (Prec) for six periods of time averaging (at monthly intervals for the 1st, 2nd, 3rd, 4th months and at two seasonal intervals: season 1 (the 1st-3rd months) and season 2 (the 2nd-4th months)). It is noted that forecast skill scores vary greatly depending on a region, a season, and a meteorological parameter. The best results for all parameters were obtained for the tropics, where the main sources of long-term predictability of the atmosphere are located. In extratropical latitudes, the quality of forecasts, especially at the monthly intervals, decreases and approaches the level of climate forecasts. The skill of precipitation forecasts is significantly inferior to the quality of forecasts of other meteorological parameters. It is noted that the success in ensemble seasonal probabilistic and deterministic forecasting of the main meteorological elements with the INM-CM5 model, both on a global scale and in individual regions, is comparable with the skill scores of similar forecasts of the foreign meteorological centers participating in the LC MME-WMO project. The introduction of the new climate model into the scientific and operational practice of the Hydrometcenter of Russia/North Eurasia Climate Centre will contribute to improving the quality of monthly and seasonal forecasts and developing specialized climate services.

The Met Office Forecast Ocean Assimilation Model (FOAM) using a 1/12‐degree grid for global forecasts

AbstractThe Met Office Forecast Ocean Assimilation Model (FOAM) ocean–sea‐ice analysis and forecasting operational system has been using an ORCA tripolar grid with 1/4° horizontal grid spacing since December 2008. Surface boundary forcing is provided by numerical weather prediction fields from the operational global atmosphere Met Office Unified Model. We present results from a 2‐year simulation using a 1/12° global ocean–sea‐ice model configuration while keeping a 1/4° data assimilation (DA) set‐up. We also describe recent operational data assimilation enhancements that are included in our 1/4° control and 1/12° simulations: a new bias‐correction term for sea‐level anomaly assimilation and a revised pressure correction algorithm. The primary effect of the first is to decrease the mean and variability of sea‐level anomaly increments at high latitudes, whereas the second significantly reduces the vertical velocity standard deviation in the tropical Pacific. The level of improvement achieved with the higher resolution configuration is moderate but consistently satisfactory when measured using neighbourhood verification metrics that provide fairer quantitative comparisons between gridded model fields at different spatial resolutions than traditional root‐mean‐square metrics. A comparison of the eddy kinetic energy from each configuration and an observation‐based product highlights the regions where further system developments are most needed.

A Continuous Attractor Model with Realistic Neural and Synaptic Properties Quantitatively Reproduces Grid Cell Physiology.

Computational simulations with data-driven physiological detail can foster a deeper understanding of the neural mechanisms involved in cognition. Here, we utilize the wealth of cellular properties from Hippocampome.org to study neural mechanisms of spatial coding with a spiking continuous attractor network model of medial entorhinal cortex circuit activity. The primary goal is to investigate if adding such realistic constraints could produce firing patterns similar to those measured in real neurons. Biological characteristics included in the work are excitability, connectivity, and synaptic signaling of neuron types defined primarily by their axonal and dendritic morphologies. We investigate the spiking dynamics in specific neuron types and the synaptic activities between groups of neurons. Modeling the rodent hippocampal formation keeps the simulations to a computationally reasonable scale while also anchoring the parameters and results to experimental measurements. Our model generates grid cell activity that well matches the spacing, size, and firing rates of grid fields recorded in live behaving animals from both published datasets and new experiments performed for this study. Our simulations also recreate different scales of those properties, e.g., small and large, as found along the dorsoventral axis of the medial entorhinal cortex. Computational exploration of neuronal and synaptic model parameters reveals that a broad range of neural properties produce grid fields in the simulation. These results demonstrate that the continuous attractor network model of grid cells is compatible with a spiking neural network implementation sourcing data-driven biophysical and anatomical parameters from Hippocampome.org. The software (version 1.0) is released as open source to enable broad community reuse and encourage novel applications.

Dosimetric characterization for GRID collimator-based spatially fractionated radiation therapy: Dosimetric parameter acquisition and machine interchangeability investigation.

The purpose of this study is to characterize the dosimetric properties of a commercial brass GRID collimator for high energy photon beams including 15 and 10 MV. Then, the difference in dosimetric parameters of GRID beams among different energies and linacs was evaluated. A water tank scanning system was used to acquire the dosimetric parameters, including the percentage depth dose (PDD), beam profiles, peak to valley dose ratios (PVDRs), and output factors (OFs). The profiles at various depths were measured at 100cm source to surface distance (SSD), and field sizes of 10×10 cm2 and 20×20 cm2 on three linacs. The PVDRs and OFs were measured and compared with the treatment planning system (TPS) calculations. Compared with the open beam data, there were noticeable changes in PDDs of GRID fields across all the energies. The GRID fields demonstrated a maximal of 3mm shift in dmax (Truebeam STX, 15MV, 10×10 cm2). The PVDR decreased as beam energy increases. The difference in PVDRs between Trilogy and Truebeam STx using 6MV and 15MV was 1.5%±4.0% and 2.1%±4.3%, respectively. However, two Truebeam linacs demonstrated less than 2% difference in PVDRs. The OF of the GRID field was dependent on the energy and field size. The measured PDDs, PVDRs, and OFs agreed with the TPS calculations within 3% difference. The TPS calculations agreed with the measurements when using 1mm calculation resolution. The dosimetric characteristics of high-energy GRID fields, especially PVDR, significantly differ from those of low-energy GRID fields. Two Truebeam machines are interchangeable for GRID therapy, while a pronounced difference was observed between Truebeam and Trilogy. A series of empirical equations and reference look-up tables for GRID therapy can be generated to facilitate clinical applications.

Ozone, nitrogen dioxide, and PM2.5 estimation from observation-model machine learning fusion over S. Korea: Influence of observation density, chemical transport model resolution, and geostationary remotely sensed AOD

High-resolution multi-component estimates of ground-level air pollutants are necessary for assessing their impacts to human health, agriculture, and ecosystems. We demonstrate a high-resolution fusion and downscaling approach over South Korea for May 2016 and May 2021. Daily 1 km fine particulate matter (PM2.5), ozone (O3), and nitrogen dioxide (NO2) concentrations are calculated at ground level using a random forest machine learning (ML) algorithm, with predictors including reanalysis meteorology, satellite aerosol optical depth (AOD), and gridded surface fields from chemical transport models (CTM). The ML model is tested for May 2016, coinciding with the Korea-United States Air Quality Study (KORUS-AQ) intensive field campaign, and for May 2021, to allow incorporation of observations from the Geostationary Environment Monitoring Spectrometer (GEMS). In the tests for May 2016, the correlation coefficients (R) and root mean squared errors (RMSE) relative to withheld observations of daily-averaged pollutants in 10-fold cross-validation are promising: 0.93 (5.5 μg/m3), 0.90 (5.5 ppbv), and 0.95 (4.7 ppbv) for PM2.5, O3, and NO2, respectively. Relative performance is assessed for alternate choices of predictors: (a) 80-km global reanalysis Copernicus Atmosphere Monitoring Service (CAMS) vs. 4-km regional Weather Research and Forecasting model coupled with Chemistry (WRF-Chem); (b) AOD polar-orbiting Moderate Resolution Image Spectroradiometer (MODIS) Multi-Angle Implementation of Atmospheric Correction (MAIAC) vs. geostationary GEMS; and (c) variations in surface observation density. This study is among the very first to incorporate both CTM and GEMS AOD for building surface high resolution multiple air pollution predictions over South Korea.

Multi Electrode Differential Voltage Sensor Based on Electric Field Coupling

With the increasing demand for electricity, voltage measurement has been applied in various fields of the power grid, especially in low-voltage distribution networks and household appliances. However, in low-voltage distribution networks, traditional contact measurement cannot be used due to technical limitations such as the need to damage the insulation layer. Non contact voltage measurement is becoming the main voltage measurement method, A major technical challenge in non-contact measurement is the interference of external electric fields. In order to solve the above problems, this article develops a new type of non-contact low-voltage measurement equipment, which can overcome the interference of external electric fields on measurement and achieve high-precision non-contact voltage measurement. The experimental results indicate that the sensor designed in this paper has high measurement accuracy. The ratio error at power frequency is within ± 0.9%, and the phase error is within 3°. In addition, the sensor also has the advantages of low cost, small size, and convenient manufacturing.