Grid Analysis Research Articles

Latin American formal consensus on the indications for the surgical treatment of massive and irreparable rotator cuff tears

ObjectivesTo implement the Formal Consensus Method among a group of experts in shoulder surgery in Latin America, in order to establish appropriate indications for the surgical treatment of massive and irreparable rotator cuff injuries. MethodsThe Formal Consensus Method was used to develop surgical treatment recommendations for massive and irreparable rotator cuff tears (MIRCT). Three independent groups of experts in shoulder surgery were confirmed. The steering group conducted a systematic literature review and constructed a voting matrix consisting of 348 clinical scenarios. The rating group, composed of 15 members, rated each scenario on two occasions: first anonymously and then during an in-person discussion meeting. The median and voting ranges were used to classify each scenario as inappropriate, uncertain, or appropriate for each surgical technique. Finally, the reading group, consisting of 10 surgeons, reviewed, evaluated and rated the recommendations derived from the detailed analysis of the voting grids. ResultsThe main finding of the study reveals a high percentage (70%) of clinical scenarios in which consensus was achieved regarding the appropriateness or inappropriateness of different surgical alternatives for the treatment of massive and irreparable rotator cuff injuries. Through a detailed analysis of the voting grids, a total of 20 recommendations were elaborated concerning the appropriateness of various surgical techniques in addressing irreparable rotator cuff tears. ConclusionsThe indications for the operative treatment of MIRCT were determined based on expert consensus and the best available evidence, they seek to provide guidance on the appropriateness of various surgical techniques for different clinical scenarios. Level of evidenceV.

An Automated Method for Container Counting in Satellite Images Based on Grid Analysis and Shadow Recognition

An Automated Method for Container Counting in Satellite Images Based on Grid Analysis and Shadow Recognition

Spatial differences and underlying mechanisms in electronic word of mouth in the foodservice industry: A case of Sanya, China.

Studying the electronic word-of-mouth (eWOM) in the foodservice industry can not only provide guidance for merchants, but also spatially optimize the urban foodservice industry, restaurants' location selection, and customers' purchasing decisions. In this study, taking Sanya city as the research object, using big data crawling technology to collect the directory and their attribute information of 2107 restaurants with more than 100 reviews. Kernel density analysis, grid analysis and the geographically weighted regression (GWR) model were applied to reveal the distribution characteristics and influencing factors of eWOM in the foodservice industry in Sanya, China. The main results are as follows. The foodservice industry in Sanya extends along the southern coastline and is characterized by little dispersion and agglomeration at the macro level. The overall eWOM score of the foodservice industry is low. Market popularity, restaurant rating, transportation conditions, and commercial development all have a positive impact on the eWOM of the foodservice industry. Population and price have both positive and negative effects and the public services has a nonsignificant impact on the eWOM. This study not only improves the theoretical understanding of the foodservice industry, but also provides a general reference for its development in other industries and cities.

Explosive cyclones occurred between 2010 and 2020 in the South Atlantic under the perspective of two detection schemes.

Under two detection schemes, this study analyzes one of the most destructive weather systems - the explosive cyclones - in the South Atlantic, from 2010 to 2020. Then, two methods are presented to study these systems: the Observational Method (OBSM) and the Automated Method (AUTM). The first uses visual analysis of the mean sea level pressure (mslp) fields and functions to identify the local minimums using the Grid Analysis and Display (GrADS) software. The second utilizes a function from OpenGrADS called mfhilo. It shows the local minimum in the grid using laplacian, magnitude, and percentile. Two shell algorithms for data manipulation are used for the AUTM: one to trace the cyclones' trajectories according to a previously defined fixed area and the other to separate them into explosives. The OBSM methodology showed 271 cases averaging 25 yearly and revealed important characteristics regarding the intensities. According to AUTM's methodology, from the 2705 ordinary cyclone cases identified, 299 are explosives. There is a clear seasonality pattern in the systems' distribution along South America, similar to OBSM, but more highlighted. In summer, they concentrate at high latitudes, while in winter and spring, they are assembled near southern Brazilian and Uruguayan coasts.

Analysis and Dynamic Stabilization of a HighVoltage VSC-MTDC Grid with DC Power Flow Controller Connected to Extremely Weak AC Systems

Analysis and Dynamic Stabilization of a HighVoltage VSC-MTDC Grid with DC Power Flow Controller Connected to Extremely Weak AC Systems

Evidential Semantic Lane-Grid for Unknown Space Analysis and High-Level Representation of Dynamic Occupancy Grids

Evidential Semantic Lane-Grid for Unknown Space Analysis and High-Level Representation of Dynamic Occupancy Grids

A Versatile Surrogate Model of the Power Distribution Grid Described by a Large Number of Parameters

This paper aims to present a general-purpose Surrogate Model for the probabilistic analysis of power distribution grids with a large number of input parameters. The distinctive feature of the novel technique is the employment of the partial derivatives of output variables versus input parameters to tame the “curse of dimensionality” problem exhibited by prior surrogate model calculation techniques. The second important feature of the proposed Surrogate Model method is that it does not require any a priori assumption about the nature or statistical distribution of the input parameters. In fact, it can be applied whenever design parameters are deterministic variables as well as when they are uncertain and represented by continuous and/or discrete random variables. Relevant applications presented in the paper refer to the probabilistic analysis of the distribution grid in the presence of a large number of photovoltaic sources and electric vehicle charging stations.

Multidimensional data processing and tensor analysis for large-scale power grids in a parallel computing framework

Abstract Grid data is compressed and stored without processing. There are problems of large compression errors and long running time, which affect the compression and storage effect. Therefore, this paper proposes a method for storing grid data using parallel computing frameworks. While drawing on the task scheduling strategy of MapReduce distributed computing framework, combined with the current situation of centralized deployment of data in the information system of the State Grid Corporation, an efficient tensor-based parallel computing method is proposed. The massive multivariate and heterogeneous smart grid data is compressed using the Tucker decomposition method for big data compression of smart grids. This method’s line resistance and reactance dynamic parameters have average values of 0.033 and 0.520, respectively, which are very close to the actual values. The method used in this paper has a high degree of accuracy in estimating resistance dynamic parameters. It possesses certain practical application performance. By predicting the daily average load of the North China Power Grid, the accuracy of the prediction data of the gridded load analysis is close to the actual value, indicating the superiority of the grid multidimensional data processing method designed in this paper.

PPG based Noninvasive Blood Glucose Monitoring using Multi-view Attention and Cascaded BiLSTM Hierarchical Feature Fusion Approach.

Diabetes is a chronic disease with exponential growth and poses significant challenges to global healthcare. Regular blood glucose (BG) monitoring is key for avoiding diabetic complications. Traditional BG measurement techniques are invasive and minimally invasive, causing pain, discomfort, cost, and infection risks. To address these issues, we developed a noninvasive BG monitoring approach on photoplethysmography (PPG) signals using multi-view attention and cascaded BiLSTM hierarchical feature fusion approach. Firstly, we implemented a convolutional multi-view attention block to extract the temporal features through adaptive contextual information aggregation. Secondly, we built a cascaded BiLSTM network to efficiently extract the fine-grained features through bidirectional learning. Finally, we developed a hierarchical feature fusion with bilinear polling through cross-layer interaction to obtain higher-order features for BG monitoring. For validation, we conducted comprehensive experimentation on up to 6 days of PPG and BG data from 21 participants. The proposed approach showed competitive results compared to existing approaches by RMSE of 1.67 mmol/L and MARD of 17.88%. Additionally, the clinical accuracy using Clarke error grid (CEG) analysis showed 98.80% of BG values in Zone A+B. Therefore, the proposed approach offers a favorable solution in diabetes management by noninvasively monitoring the BG levels.

Techno-Economic and Sensitivity Analysis of Grid Connected Perovskite PV-Wind Systems

Techno-Economic and Sensitivity Analysis of Grid Connected Perovskite PV-Wind Systems

Accuracy of a Non-Invasive Home Glucose Monitor for Measurement of Blood Glucose.

Patients with diabetes mellitus monitor their blood glucose at home with monitors that require a drop of blood or use a continuous glucose monitoring device that implants a small needle in the body. However, both cause discomfort to the patients which may inhibit them for regular blood glucose checks. Photoplethysmogram (PPG) sensing technology is an approach for non-invasive blood glucose measurement and PPG sensors can be used to predict hypoglycaemic episodes. InChcek is a PPG-based non-invasive glucose monitor. However, its accuracy has not been checked yet. Hence, this study aimed to evaluate the accuracy of InCheck, a non-invasive glucose monitor for the estimation of blood glucose. In a tertiary care hospital, patients who came for blood glucose estimation were tested for blood glucose non-invasively on the InCheck device and then by the laboratory method (glucose oxidase-peroxidase). These two readings were compared. We used International Organization for Standardization (ISO) 15197:2013 (95% of values should be within ± 15 mg/dL of reference reading if reference glucose <100 mg/dL or within ± 15% of reference reading if reference glucose ≥100 mg/dL and 99% of the values should be within zones A and B in consensus error grid), and Surveillance Error Grid for analyzing the accuracy. A total of 1223 samples were analyzed. There was a significant difference between the reference method glucose level (135 [Q1-Q3: 97 - 179] mg/dL) and monitor-measured glucose level (188.33 [Q1-Q3: 167.33-209.33] mg/dL) (P < 0.0001). A total of 18.5% of readings were following ISO 15197:2013 criteria and 67.25% of coordinates were within zone A and zone B of the consensus error grid. In the surveillance error grid analysis, about 29.4% of values were in the no-risk zone, 51.8% in slight risk, 18.6% in moderate risk, and 0.2% were in the severe risk zone. The accuracy of the InCheck device for the estimation of blood glucose by PPG signal is not following the recommended guidelines. Hence, further research is necessary for programming or redesigning the hardware and software for a better result from this optical sensor-based non-invasive home glucose monitor.

A personalized multitasking framework for real-time prediction of blood glucose levels in type 1 diabetes patients.

Real-time prediction of blood glucose levels (BGLs) in individuals with type 1 diabetes (T1D) presents considerable challenges. Accordingly, we present a personalized multitasking framework aimed to forecast blood glucose levels in patients. The patient data was initially categorized according to gender and age and subsequently utilized as input for a modified GRU network model, creating five prediction sub-models. The model hyperparameters were optimized and tuned after introducing the decay factor and incorporating the TCN network and attention mechanism into the GRU model. This step was undertaken to improve the capability of feature extraction. The Ohio T1DM clinical dataset was used to train and evaluate the performance of the proposed model. The metrics, including Root Mean Square Error (RMSE), Mean Absolute Error (MAE) and Clark Error Grid Analysis (EGA), were used to evaluate the performance. The results showed that the average RMSE and the MAE of the proposed model were 16.896 and 9.978 mg/dL, respectively, over the prediction horizon (PH) of 30 minutes. The average RMSE and the MAE were 28.881 and 19.347 mg/dL, respectively, over the PH of 60 min. The proposed model demonstrated excellent prediction accuracy. In addition, the EGA analysis showed that the proposed model accurately predicted 30-minute and 60-minute PH within zones A and B, demonstrating that the framework is clinically feasible. The proposed personalized multitask prediction model in this study offers robust assistance for clinical decision-making, playing a pivotal role in improving the outcomes of individuals with diabetes.

Real‐World Accuracy of a Continuous Glucose Monitoring System after Radiologic Exposure

Background. The increasing use of continuous glucose monitor (CGM) necessitates a review of variables that impact accuracy and interrupt use. Manufacturer recommendations include removing CGMs before diagnostic imaging, such as X‐ray and computed tomography (CT). Early removal and replacement of CGM components present financial, clinical, and psychosocial burdens to the wearer and interrupt optimal management of diabetes for pediatric patients who receive a total pancreatectomy with islet autotransplantation (TPIAT). The study’s aim was to evaluate the effect of scatter dose exposure during X‐ray or CT if the CGM remained intact but outside the field of view (FoV). Materials and Methods. Participants were followed through the first 3 months after TPIAT surgery, managed diabetes with an insulin pump and CGM, and were routinely exposed to diagnostic imaging. Participants’ CGMs were unshielded by a protective apron during any X‐ray or CT procedures for the duration of the study period, and the transmitter was collected after expiration or removal. Glucometer data was collected from hospital records and home glucometer downloads. Mixed models were used to analyze absolute differences between matched CGM and glucometer values, and Clarke error grid analyses (EGA) were performed. Scatter dose exposure was derived using anthropomorphic phantoms and calculated retrospectively. Results. A total of 14 patients (median 12.2 years, 64% female) received a median of five diagnostic imaging procedures with a median cumulative scatter dose of 559 µGy. The absolute difference between the CGM and glucometer values was not significantly associated with the cumulative scatter dose (p = 0.17) or time from TPIAT (p = 0.24) when analyzed in a mixed model. Regardless of scatter dose exposure, time from TPIAT, or glucometer, ≥98% of glucose values fell within zones A and B on EGA. Conclusion. Scatter dose exposure from diagnostic imaging did not affect the clinical accuracy of CGM values for the duration of transmitter use. Leaving CGM components in place when not in the FoV during diagnostic imaging successfully mitigated interruptions to use and undue burden or cost to participants.

GPU-based transient analysis of modern grids deploying a hybrid DDM algorithm

Solving a significant number of differential-algebraic equations is essential for conducting transient stability simulations on a complex and interconnected electrical power system. Because of the increasing size and intricacy of modern grids, traditional sequential simulation methods are becoming more computationally challenging and time-consuming to use for dynamic simulations. This research aims to examine parallelism techniques from an innovative algorithm viewpoint to the hardware level by utilizing multiple graphics processing units as the main computational tool designed to simulate the dynamic behaviour of power systems on a large scale. Our approach features a novel hybrid algorithm to improve the dynamic simulations of grids with significant renewable energy integration. The algorithm employs a Schwarz-based approach in its initial decomposition phase to isolate synchronous machines and renewable sources from the grid. This separation enhances the algorithm's ability to manage these components effectively. Subsequently, each subsystem undergoes further division into subdomains in the second stage, with separate computations carried out using the Schur-complement technique. The simulations were conducted on various test systems, with a maximum of 25,000 buses, 8,000 synchronous generators, and 256 PV farms all modelled in detail. We introduce a GPU-oriented preprocessing and vectorization parallelization method for the implementation of our two-stage algorithm. This approach provides the flexibility to tailor parallelization techniques, including nested for loops and kernel pragmas, to craft a hardware solution that enhances the performance of our dynamic domain decomposition algorithm. Simulation outcomes have verified that this approach can yield a notable 7.8x acceleration in speed when executed on an NVIDIA GeForce RTX 2070 SUPER GPU.

Research on urban three-dimensional greening design from the perspective of climate change—a case study of Beilin District, Xi’an, Shaanxi Province, China

Climate change is an important issue for cities today and in the future. At present, China has a large population and complex climate conditions, and cities are also vulnerable to the adverse effects of climate change (Tian, Environ Sustain Dev 6: 153-155 2020). Three-dimensional greening can not only improve the green space system of a city but also have a far-reaching impact on the ecology, image, and economic benefits of a city. Therefore, the study of urban three-dimensional greening is an effective means to deal with climate change strategies. By exploring the influence of traditional greening and three-dimensional greening on Local Climate in Beilin District of Xi’an, Shaanxi Province, the mechanism of three-dimensional greening on urban ecological environment was discussed, and the ecological theory, urban three-dimensional greening theory, and urban local climate zone (LCZ) were referred to. Based on the methods of national climate monitoring, ENVI-met simulation, and field independent measurement, this paper selected a research sample site in the east section of Jianshe Road, Beilin District, Xi’an City, Shaanxi Province, China, and applied ENVI-met software to simulate the thermal stress relationship among building exterior surfaces, plants, and air in the street; quantified the overall ecology of the area; and used measuring instruments. The influence of different types of greening in the base on the site temperature, humidity, CO2 (carbon dioxide) concentration, wind speed, and other climate factors data was, respectively, measured and analyzed. The grid analysis was used to compare the traditional greening and three-dimensional greening, then the numerical differences of each impact factor were sorted out, and the effect of three-dimensional greening on the improvement of urban ecological environment was discussed by analyzing the climate factors with greater impact. The results show that (1) three-dimensional greening plus traditional greening is the most beneficial mode; (2) in the same environment, according to the parameter of 1.5 m from the ground in the model environment, it can be seen that the temperature of the space treated with three-dimensional greening of buildings is reduced by 3.5–3.6 ℃ compared with the control group, the relative humidity is different by 7–8%, the CO2 concentration is reduced by about 5%, and the spatial wind speed is relatively small. (3) When the urban green coverage rate is more than 40%, the improvement of temperature is more obvious, if it reaches 50%, the cool phenomenon in summer can be fundamentally changed. From the perspective of human perception, the PMV index increased by 0.27 on average. This paper discusses and analyzes the three-dimensional greening of urban streets in Beilin District, Xi’an City, Shaanxi Province, China, and studies its influence on urban ecology to different degrees. The conclusions are as follows: Different types of greening have different degrees of influence on urban climate. Meanwhile, the experimental results of this paper show that in cities like Xi’an, Shaanxi Province, China, where summer is hot, adding three-dimensional greening to traditional street greening can significantly improve the environmental microclimate, which is an effective means to cope with climate change, improve the site environment, and stabilize the urban ecosystem.

Evaluation of a hybrid protocol using continuous glucose monitoring and point-of-care testing in non-critically ill patients in a community hospital.

Inpatient glycemic management typically involves use of point-of-care (POC) glucose measurements to inform insulin dosing decisions. This study evaluated a hybrid monitoring protocol using real-time continuous glucose monitoring (rtCGM) supplemented with POC testing at a community hospital. Adult inpatients receiving POC glucose testing were monitored using rtCGM in a telemetry unit. The hybrid monitoring protocol required a once-daily POC test but otherwise primarily relied on rtCGM values for insulin dosing decisions. Outcomes assessment included surveillance error grid (SEG) and Clarke Error Grid (CEG) analysis results, the mean absolute relative difference (MARD) for available rtCGM-POC value pairs before and after study protocol application, the number of POC tests avoided, and the number of hypoglycemic events involving a blood glucose value of <70 mg/dL identified by rtCGM and POC values. Data were collected from 30 inpatients (the mean age was 69.4 years, 77% were female, 80% had type 2 diabetes, and 37% were at-home insulin users). With the protocol applied, a total of 202 rtCGM-POC pairs produced a MARD of 12.5%. SEG analysis showed 2 pairs in the "moderate" risk category, with all other pairs in the "none" or "slight" risk categories. CEG analysis showed 99% of paired values to be in the clinically acceptable range. Six hypoglycemic events in 5 patients were resolved without incident. Three hundred three POC tests were avoided, a 60% reduction for the study duration. Use of a hybrid monitoring protocol of rtCGM and POC testing in a community hospital demonstrated sustained rtCGM accuracy and was found to reduce the frequency of POC testing to manage inpatient glycemia.

Smartphone-Based versus Non-Invasive Automatic Oscillometric Brachial Cuff Blood Pressure Measurements: A Prospective Method Comparison Volunteer Study.

Introduction: Mobile health diagnostics have demonstrated effectiveness in detecting and managing chronic diseases. This method comparison study aims to assess the accuracy and precision of the previously evaluated OptiBP™ technology over a four-week study period. This device uses optical signals recorded by placing a patient's fingertip on a smartphone's camera to estimate blood pressure (BP). Methods: In adult participants without cardiac arrhythmias and minimal interarm blood pressure difference (systolic arterial pressure (SAP) < 15 mmHg or diastolic arterial pressure (DAP) < 10 mmHg), three pairs of 30 s BP measurements with the OptiBP™ (test method) were simultaneously compared using three pairs of measurements with the non-invasive oscillometric brachial cuff (reference method) on the opposite arm over a period of four consecutive weeks at a rate of two measurements per week (one in the morning and one in the afternoon). The agreement of BP values between the two technologies was analyzed using Bland-Altman and error grid analyses. The performance of the smartphone application was investigated using the International Organization for Standardization (ISO) definitions, which require the bias ± standard deviation (SD) between two technologies to be lower than 5 ± 8 mmHg. Results: Among the 65 eligible volunteers, 53 participants had adequate OptiBP™ BP values. In 12 patients, no OptiBP™ BP could be measured due to inadequate signals. Only nine participants had known chronic arterial hypertension and 76% of those patients were treated. The mean bias ± SD between both technologies was -1.4 mmHg ± 10.1 mmHg for systolic arterial pressure (SAP), 0.2 mmHg ± 6.5 mmHg for diastolic arterial pressure (DAP) and -0.5 mmHg ± 6.9 mmHg for mean arterial pressure (MAP). Error grid analyses indicated that 100% of the pairs of BP measurements were located in zones A (no risk) and B (low risk). Conclusions: In a cohort of volunteers, we observed an acceptable agreement between BP values obtained with the OptiBPTM and those obtained with the reference method over a four-week period. The OptiBPTM fulfills the ISO standards for MAP and DAP (but not SAP). The error grid analyses showed that 100% measurements were located in risk zones A and B. Despite the need for some technological improvements, this application may become an important tool to measure BP in the future.

Multi-Objective Optimal Power Flow Calculation Considering Carbon Emission Intensity

In keeping with China’s dual carbon goals, optimal low-carbon power system dispatch has become a necessary component of the greening of the power system. However, typically, research considers only the economics of such efforts. Based on our power flow analysis of the power grid and the correlation properties of carbon emission flow, an optimal power flow calculation model targeting the total carbon emission rate of the power system’s power generation cost, active network loss, and load and network loss was constructed. Next, the NSGA-III algorithm was used to solve the model, and the decision was to coordinate and optimize the output schemes of various types of power plants, such as wind, water, and thermal. The modified IEEE39 node simulation system was built with Matlab software (MATLAB R2020b). The results of the calculation showed that, compared to the traditional method of determining the optimal power flow, the proposed method reduced the system carbon emissions by 20% while the power generation cost increased by less than 2%, which proves the effectiveness and practicability of the proposed method.

Investigation of iron ore potential in north-central Nigeria, using high-resolution aeromagnetic dataset and remote sensing approach

The instability of fuel prices in the international market has adversely affected the economy of Nigeria, which is a developing country that solely relies on crude oil. This has led the country to consider diversifying its economy into mineral resources. The consideration of iron ore as an important mineral for global infrastructure, steel production, and wealth creation has prompted this study to consider its potential within north-central Nigeria. Additionally, no geophysical reports considering the regional distribution of iron ore potential in both the Basement Complex and the sedimentary basins within north-central Nigeria have been previously carried out. This study employs an analytical hierarchy process (AHP) and weighted overlay to integrate the analyses of the high-resolution aeromagnetic data set (HRAMD) and Landsat 8 OLI/TIRS to assess the iron ore potential of the different geological terrain in north-central Nigeria. The ferrous mineral band ratio technique was adopted in processing the Landsat data, and several signal enhancement techniques were performed on the HRAMD, including the first vertical derivative (1VD), analytical signal, center for exploration targeting (CET) grid analysis, Euler deconvolution, and radially averaged power spectrum (RAPS). The shallowest magnetic sources and the high amplitude zones were delineated, and the dominant structural trend of the area was revealed to be WNW-ESE and NE-SW, and both orientations appeared to be favorable for mineralization. The apparent depth estimates of shallow and deep-seated magnetic structures obtained from Euler deconvolution ranged from 127.65 to 3540.64 m, whereas RAPS delineated a range of 285–3710 m. The analytical signal (AS) categorized the magnetism of the area into low, intermediate, and high magnetic zones. The higher magnetic zones were considered to be favorable for mineralization. The ferrous mineral ratio map reveals areas of higher reflectance, which depict zones of iron ore potential. The iron ore potential map was validated using known iron ore mines and deposits, which showed a perfect correlation, and new sites of probable iron ore mineralization within north-central Nigeria were revealed. This article will serve as a guide for stakeholders in the exploration of iron ore deposits.

Techno-Economic Feasibility Analysis of 143kW Solar Mini Grid for Rural Electrification in Gokule Village - A Case Study

Techno-Economic Feasibility Analysis of 143kW Solar Mini Grid for Rural Electrification in Gokule Village - A Case Study