Average Method Research Articles

Dynamic Behavior Analysis of Journal Bearings Lubricated by Non-Newtonian Fluid During Start-Up

Abstract The dynamic behavior of journal bearing lubricated by non-Newtonian fluid (conforms to the Carreau model) during start-up is investigated. A mixed lubrication model consisting of a GT (Greenwood-Tripp) contact equation and transient modified average Reynolds equation is proposed. The transient modified average Reynolds equation is established with the average flow method. The effect of non-Newtonian lubricant, surface roughness, external load, relative clearance, and accelerating time on the dynamic behavior of journal bearings during start-up is analyzed. The results show that increasing the relaxation time, external load, and relative clearance, as well as decreasing the power-law exponent, composite root-mean-square roughness and accelerating time can reduce the contact time and facilitate the rapid formation of hydrodynamic lubrication during start-up.

Cross-ViT based benign and malignant classification of pulmonary nodules.

The benign and malignant discrimination of pulmonary nodules plays a very important role in diagnosing the extent of lung cancer lesions. There are many methods using Convolutional neural network (CNN) for benign and malignant classification of pulmonary nodules, but traditional CNN models focus more on the local features of pulmonary nodules and lack the extraction of global features of pulmonary nodules. To solve this problem, a Cross fusion attention ViT (Cross-ViT) network that fuses local features extracted by CNN and global features extracted by Transformer is proposed. The network first extracts different features independently through two branches and then performs feature fusion through the Cross fusion attention module. Cross-ViT can effectively capture and process both local and global information of lung nodules, which improves the accuracy of classifying the benign and malignant nature of pulmonary nodules. Experimental validation was performed on the LUNA16 dataset, and the accuracy, precision, recall and F1 score reached 91.04%, 91.42%, 92.45% and 91.92%, respectively, and the accuracy, precision, recall and F1 score with SENet as CNN branch reached 92.43%, 94.27%, 91.68% and 92.96%, respectively. The results show that the accuracy, precision, recall and F1 score of the proposed method are 0.3%, 0.11%, 4.52% and 3.03% higher than those of the average optimal method, respectively, and the performance of Cross-ViT network for benign and malignant classification is better than most classification methods.

ANALISIS PERBANDINGAN METODE PERAMALAN (FORECASTING) PENJUALAN MOTOR LISTRIK PADA CV SANTOSA ABADI MOTOR PELAIHARI

The aim of this study is to identify the most accurate and effective forecasting method for CV. Santosa Abadi Motor Pelaihari in predicting the sales of electric motor products for the upcoming period. This research is a descriptive quantitative study utilizing secondary data, consisting of electric motor product sales data from 2023 through January-June 2024, obtained from the Administration department of CV Santosa Abadi Motor Pelaihari. The analysis involves comparing three forecasting methods: Trend Moment, Semi Average, and Least Square, and evaluating the accuracy of each method using the Mean Absolute Percentage Error (MAPE). The results indicate that the Trend Moment method yields a forecasting error percentage of 71%, the Semi Average method results in 23%, and the Least Square method shows 28%. Based on these findings, it can be concluded that the Semi Average method is more suitable for forecasting electric motor sales at CV Santosa Abadi Motor Pelaihari.

Hybrid event-based anti-bump finite-time H∞ control for cyber-physical switched systems under denial-of-service attacks

This paper aims to investigate the hybrid event-based anti-bump finite-time H∞ control method for cyber-physical switched systems under Denial of Service (DoS) attacks. By gaining insights into the characteristics of DoS attacks, a novel hybrid event-triggered scheme (HETS) is proposed. This scheme effectively reduces network transmission load and accurately captures the network service denial behavior caused by attack interference. In the context of complex network environments with multiple switching characteristics, this study addresses the challenges posed by HETS, transmission delays, and DoS attacks on the input signals of controllers. To tackle these challenges, we utilize an asynchronous switching strategy, along with bumpless transfer constraints and a zero-order hold mechanism. The objective is to design sustainable input asynchronous control signals while ensuring that the control scheme exhibits bumpless transfer (BT) characteristics and achieves H∞ performance. By employing the asynchronous switching strategy and the admissible edge-dependent average dwell time method (AED-ADT), a dual-mode Lyapunov function is devised to establish finite-time stability criteria and provide a design approach for constructing anti-bump H∞ controllers. Finally, two examples illustrate the applicability and effectiveness of the method.

Mortality and hospitalizations fully attributable to alcohol use before versus during the COVID-19 pandemic in Canada.

The COVID-19 pandemic period was associated with increased alcohol consumption. We sought to estimate excess mortality and hospitalizations attributable to alcohol use in Canada between April 2020 and December 2022. Using data from the Canadian Vital Statistics Database and hospital Discharge Abstract Database (Jan. 2016 to Dec. 2022), we analyzed monthly mortality and hospitalization rates for conditions fully attributable to alcohol use in people in Canada aged 15 years and older. We estimated excess rates during the study period of April 2020 to December 2022 by comparing observed rates to expected rates, modelled using the autoregressive integrated moving average method, accounting for trends, seasonality, autocorrelation, and pandemic waves. Between April 2020 and December 2022, mortality fully attributable to alcohol in Canada increased by 17.6% (1600 excess deaths), and hospitalizations fully attributable to alcohol rose by 8.1% (7142 excess hospitalizations). Most increases occurred in the first 2 years, with deaths up about 24% and hospitalizations about 14%. Mortality rose by 55.4% in adults aged 25-44 years, 19.1% in those aged 45-64 years, and 2.6% in those aged 65 years and older, with similar increases among males (17.0%) and females (17.8%). Deaths rose by 11.7% in the highest income quintile, as compared with 17.0%-21.5% in the other quintiles. Excess hospitalizations were highest among people aged 15-24 years (20.3%) and 25-44 years (13.1%) and increased more for females (15.6%) than for males (5.7%). Regionally, mortality increased most in the Prairie provinces (Manitoba, Saskatchewan, and Alberta; 28.1%) and British Columbia (24.2%), whereas hospitalizations increased the most in the territories (Northwest Territories, Nunavut, and Yukon; 27.3%) and the Prairie provinces (14.6%). Alcoholic liver disease was the leading cause of excess mortality and hospitalizations, which increased by 22% and 23%, respectively. Mortality and hospitalizations fully attributable to alcohol increased substantially across different demographics and regions in Canada during the April 2020 to December 2022 period of the COVID-19 pandemic. A comprehensive approach to preventing and managing high-risk drinking, alcohol use disorder, and alcoholic liver disease in the aftermath of the pandemic should comprise both public health and clinical management interventions.

A Three-DimensionalImaging Method for Unmanned Aerial Vehicle-Borne SAR Based on Nested Difference Co-Arrays and Azimuth Multi-Snapshots

Due to its miniature size and single-pass nature, Unmanned Aerial Vehicle (UAV)-borne array synthetic aperture radar (SAR) is capable of obtaining three-dimensional (3D) electromagnetic scattering information with a low cost and high efficiency, making it widely applicable in various fields. However, the limited payload capacity of the UAV platform results in a limited number of array antennas and affects 3D resolution. This paper proposes a 3D imaging method for UAV-borne SAR based on nested difference co-arrays and azimuth multi-snapshots. We first designed an antenna arrangement based on nested arrays, generating a virtual antenna twice as long as the original one. Then, we used a difference co-array method for 3D imaging. The required multi-snapshot data were obtained through azimuth down-sampling, rather than traditional spatial averaging methods. Due to the slow flight of the UAV, this method could generate multiple SAR images without affecting the two-dimensional resolution. Based on simulations and real data verification, the proposed algorithm overcomes the problem of two-dimensional resolution decline caused by traditional spatial averaging methods and improves three-dimensional resolution ability, theoretically achieving half the Rayleigh resolution.

Control Design for Electronic Voltage Stabilizer

Vietnam's electrical infrastructure has undergone notable advancements in recent times. Nevertheless, concerns pertaining to under-voltage, over-voltage, and voltage fluctuations persist in rural, mountainous, and industrial regions. This results in the inefficient operation of electrical equipment, disruptions in industrial production processes, data loss, and equipment damage. This paper puts forth a novel control design methodology for electronic voltage stabilizers, with the objective of mitigating the detrimental effects of grid voltage fluctuations on electrical apparatus. A compact and cost-effective AC-AC converter is proposed as a means of regulating the compensation voltage. The paper puts forth the use of the state-space averaging method for system modeling and proposes the combination of feedback and feed-forward controllers to achieve high accuracy and rapid response times. Furthermore, the moving average method is proposed for measuring the Root Mean Square (RMS) voltage, which significantly enhances the control response speed and accuracy. A 10 kVA single-phase electronic voltage stabilizer was constructed and tested in a laboratory setting under a range of grid voltage conditions, from 150V to 290V, and with varying loads. The results demonstrate that the electronic voltage stabilizer is effective in maintaining the load voltage within the desired range. The maximum response time recorded was found to be half a cycle of the grid voltage in the simulation and one cycle in the experiments, which is a significantly faster response time than that of similar designs. Furthermore, the low total harmonic distortion provides additional confirmation of the effectiveness of the designed electronic voltage stabilizer.

FGA-YOLO: A one-stage and high-precision detector designed for fine-grained aircraft recognition

Aircraft recognition in remote sensing images (RSIs) plays a pivotal role in various applications, such as disaster relief, airport monitoring. However, various aircraft types exhibit subtle inter-class variations, and the same model can display diverse visual characteristics under varying imaging conditions, presenting a significant challenge for the fine-grained aircraft recognition. This paper proposes an efficient detection model called FGA-YOLO. Firstly, we introduce a triple feature fusion module based on feature selection (STFF) to aggregate feature maps from three different levels, thereby constructing an efficient feature pyramid network that enables the network to capture salient information hidden across different levels of feature maps. Furthermore, we design a global multi-scale gather-and-distribute module (GMGD) to inject global information into the network, enhancing the extraction of the objects’ key discriminative features. Finally, we develop EMA Slide Loss by employing exponential moving average (EMA) method to mitigate the imbalance between easy and hard samples in aircraft recognition. Extensive experiments on two aircraft recognition datasets (i.e., MAR20 and FAIR1M) demonstrate that our proposed method can significantly improve the accuracy of fine-grained aircraft recognition. Moreover, experiments on embedded devices demonstrate that the proposed FGA-YOLO is also friendly for lightweight deployment.

Simplified Multireference Coupled-Cluster Methods: Hybrid Approaches With Averaged Coupled Pair Theories.

We define an approximation to the internally contracted multireference coupled-cluster method with single and double excitations by a hybrid approach. The rationale is to treat the external pair energy contributions by the coupled-cluster method, which provides accurate results for a large part of the correlation energy while being tractable as the involved pair cluster operators commute. For the internal and semi-internal contributions, for which the coupled-cluster part becomes involved due to non-commuting operators, a linearized approach based on the coupled-electron pair approximation (CEPA) is used. For the latter, the CEPA(0) method, the averaged coupled pair functional (ACPF), the averaged quadratic coupled-cluster (AQCC) method, and the averaged CEPA method are tested. We test the methods concerning size consistency, potential energy curves for C2, N2, CN, and O3 and for the singlet-triplet splitting of ortho-, meta-, and para-benzynes. Our results show that AQCC provides the most accurate results and stable performance. The main drawback of the method is that it shows small violations of size consistency.

Leveraging LSTM Predictions for Enhanced Portfolio Allocation with Markowitz Mean-Variance Optimization

This research investigates the application of Long Short-Term Memory (LSTM) networks for predicting expected returns and integrating these predictions into the Markowitz Mean-Variance Optimization (MVO) framework. The study utilized historical data from eight Indonesian stocks: BBCA, BBRI, TLKM, EXCL, UNVR, ICBP, ASII, and SMGR. The dataset covered the period from 2018 to 2024. The LSTM model was employed to predict cumulative returns over a 90-day horizon, and its performance was compared to the Exponentially Weighted Moving Average (EWMA) method. The findings indicate that LSTM achieved lower Root Mean Squared Error (RMSE) than EWMA for four stocks (BBCA, BBRI, UNVR, ICBP), while EWMA demonstrated better performance for the remaining four stocks. MVO results revealed that LSTM-based predictions achieved an average return of 4.285%, surpassing EWMA's 1.856% but falling short of the 12.298% obtained using actual returns. These results highlight the potential of LSTM models to enhance portfolio allocation strategies.

P-1718. Use of Collaboration to Harmonize Antimicrobial Registry Measure (CHARM) to Assess Antibiotic Prescribing in Dental Practices

Abstract Background Dentists account for 10% of all antibiotic prescriptions in the United States. Limited data exist describing antibiotic use in dental practices; however, it appears that many antibiotics may not be warranted or consistent with treatment guidelines.1,2,3 Of particular concern is the unwarranted use of clindamycin. No standardized methods exist for gathering data and assessing the concordance of use related to antibiotic use in dental practices. Objectives Describe patterns of antibiotic prescribing in dental clinics within the CHARM network. Identify areas for stewardship intervention within dental practices. top 5 antibiotic prescription in dental clinics name of antibiotic, the number of prescriptions, percentages, and average duration Methods Dental practices were identified from 5 dental clinics that participate in CHARM. The CHARM database is a registry containing data collected from the electronic medical records of partnering health systems about outpatient antibiotic prescribing. Episodes of antibiotic prescribing were identified in dental clinics and extracted between January 2022 to June 2023. Data collected included the ICD-10 code, antibiotic, and regimen used. When a diagnosis was provided, the appropriateness of antibiotic selection was assessed in concordance with guidelines. The most common antibiotics and diagnoses were identified. Results 4,028 antibiotic prescriptions were written by 42 prescribers in 5 dental clinics. The most prescribed antibiotics were amoxicillin (n=2,400; 59.6%), penicillin (n=700;17.3%), and clindamycin (n=400;9.9%). The average length of therapy was 7.0, 8.2, and 6.9 days for amoxicillin, penicillin, and clindamycin, respectively. ICD-10 codes were recorded for 1,722 (43%) prescriptions. No indication/diagnosis was provided for 2,278 (57%) antibiotic prescriptions. While many prescriptions were not linked to any diagnosis (n=2,300; 57.1%), the most common diagnosis codes were general dental exam (n=1,400; 34.8%) and general dental infection (n=300; 7.4%). Conclusion For visits where the dentist prescribed antibiotics, clindamycin was prescribed about 10% of the time. Clindamycin is no longer recommended for use in most procedures that dentists manage.4 Reducing clindamycin use in dental practices represents a stewardship opportunity in dental practices. Disclosures Minji Sohn, PhD, Emergent BioSolutions, Inc.: Grant/Research Support

Utility of the average pixel intensity method for the detection of moderate-severe mitral regurgitation in comparison with the area of the regurgitant orifice

Abstract Background and purpose The "average pixel intensity" (API) method, which is applied to the continuous Doppler spectral signal in mitral regurgitation (MR), has been proposed as a new method for automated MR grading. The objective of this study is to compare the API method with the effective regurgitant orifice area (EROA) for the detection of moderate-severe or severe MR (grades III or IV/IV) using open-source software (ImageJ). Methods Prospective single-center study (n=267) including patients with different degrees of MI, with a subgroup with cardiac magnetic resonance (CRM, n=55). Of the total sample, API was measured in 148 patients, and EROA in 159. Significant MR by echocardiography was defined according to a multiparameter criterion (American Society of Echocardiography - ASE algorithm), and by CRM according to regurgitant volume (≥50 ml) or regurgitant fraction (≥40%). The association between quantitative parameters (API and EROA) and the diagnosis of moderate-severe MI according to each imaging technique (echocardiography vs CMR) was evaluated using ROC curves. Results The prevalence of significant MR was 34.1% by echocardiography, and 21.8% by CMR. In 24% of cases the MR mechanism was functional. The median [Q1-Q3] API was 112 [141-166] units, and EROA 0.17 [0.10-0.30] cm2. The correlation between API and EROA was low (r = 0.34; p < 0.01). For the diagnosis of moderate-severe MR, EROA performed better than API taking the ASE algorithm as a reference (AUC 0.89 vs 0.75; Figure 1A), but both parameters showed a similar performance when compared to the CMR criterion ( AUC 0.80 vs 0.75; Figure 1B). Conclusions The low correlation between the API method and EROA raises their potential complementarity in the diagnosis of MR. Regarding the ASE multiparametric criterion by echocardiography, the EROA is superior to the API (being a key element in this algorithm). However, both methods present a similar association with the diagnosis of moderate-severe MR by CMR, and the assessment of both parameters could improve the agreement between echocardiography and CMR.

Probability Density Evolution and Fractional PID Control of Magnetic Bearing-Rigid Rotor System Influenced by Multisource Stochastic Factors

Magnetic bearings are widely used in engineering for their remarkable advantages of low friction, no wear and high speed. In this paper, the dynamics and control of the magnetic bearing-rigid rotor system under the influence of multisource stochastic factors are studied. Firstly, a model of magnetic bearing-rigid rotor system driven by multiplicative noise and additive noise is established. Secondly, the stationary response probability density function (PDF) of the proposed magnetic bearing system is obtained by using the amplitude envelope stochastic average method. Then we analyze the influence of different system parameters and noise intensity on the magnetic bearing-rigid rotor system, and find that internal stochastic factors are the main factors affecting the stability of the bearing system. Finally, the fractional Proportional Integral Derivative (PID) control of the system is studied. Based on the numerical analysis, we verify the effectiveness of the control strategy and draw the conclusion that adjusting appropriately the parameters of fractional PID controller can make the magnetic bearing system control better.

Clustering of Districts Based on Infrastructure Indicators Using K-Means And Average Linkage Methods

Aceh Jaya is one of the districts in Aceh Province that has great potential in various sectors, such as agriculture, plantations, and tourism. In the Aceh Jaya District Development Plan (RPK) Book 2023-2026, it is stated that the unequal distribution of infrastructure development in Aceh Jaya District in 2023-2026 has resulted in low investment competitiveness and decreased economic performance. Because of these conditions, this study clusters regions in Aceh Jaya District based on infrastructure indicators so that groups of sub-districts are obtained based on the level of infrastructure development. The method used in this study is clustering analysis by comparing it with two clustering methods, namely K-means and average linkage, which are validated based on the silhouette coefficient value to see which cluster is the best. The results obtained are the Kaiser-Mayer-Olkin (KMO) value of 0.611, which indicates that the sample is representative of a total of eight variables; for the zscore value, there is no outlier data, which means that the data of the eight infrastructure variables can be used entirely. Meanwhile, the clustering results using the K-Means method resulted in 3 clusters, and with the Average Lingkage method resulted in 2 clusters. This means that Aceh Jaya District can be clustered into 2 clusters, as seen from the silhouette coefficient value of 0.736. The conclusion that can be drawn from this research is that infrastructure development has not been evenly distributed in the 9 existing sub-districts, so that strategies and priorities are needed in infrastructure development in Aceh Jaya Regency.

Redefining supplier selection in healthcare sector: a novel framework for supplier classification.

This paper aims to delve into the critical aspect of supplier selection in the healthcare sector, emphasizing the significance of strategic sourcing in enhancing operational efficiency and quality of services. The primary aim is to develop a comprehensive framework for supplier evaluation that aligns with the unique requirements of hospitals, ultimately improving procurement processes and patient care outcomes. The study leverages the renowned Carter's 7 C model as a foundational framework for supplier assessment, supplemented by insights gathered from interviews with experts in the New Product Introduction, Purchasing and Procurement departments of a leading hospital in India. Data collection involved a combination of primary interactions through Google Forms and secondary data extraction from Purchase Inquisition Brochures and Hospital Information Systems. The weighted average method was employed to determine the significance of various criteria based on expert recommendations, ensuring a fair and consensus-driven evaluation process. Through the synthesis of expert inputs and data analysis, the study unveils a nuanced set of criteria weights that guide supplier selection decisions in the healthcare setting. The research highlights the dynamic nature of supplier evaluation criteria, underscoring the importance of tailoring assessments to suit the specific needs of each hospital. By emphasizing effective communication, technological advancements and collaborative supplier relationships, the study underscores the potential for strategic supplier selection to optimize supply chains, drive innovation and enhance patient care delivery in the healthcare sector. The framework for supplier selection in the healthcare sector has limitations, including limited generalizability, subjectivity in expert opinions and a static framework. The framework assumes the constant importance of selection criteria over time, which could be affected by dynamic changes in the industry. Its focus on the healthcare sector may require adaptation to other sectors. For hospitals, it also supports cost optimization by focusing on critical variables like cost efficiency and quality. For suppliers, the framework provides clear expectations, performance benchmarking and collaborative growth. It encourages performance benchmarking and encourages collaboration between the hospital and its supply chain partners. The framework is a replicable model that can be adapted by other hospitals or organizations to optimize supplier selection processes. It promotes standardization in supplier evaluation, fostering consistency across the sector. For patients, the framework improves care delivery by ensuring reliable access to high-quality supplies and services. This research intends to provide a novel framework for the classification of suppliers in the healthcare sector for operational excellence which enhances the strategic sourcing decision in the domain.

Using a Flexible Risk Priority Number Method to Reinforce Abilities of Imprecise Data Assessments of Risk Assessment Problems

Risk priority number (RPN) is the most commonly used failure risk ranking method among all risk assessment methods. However, the traditional RPN method not only cannot handle incomplete information and hesitation information (such as hesitation information between the s5=Low and s6=Moderate) provided by experts, but it also does not consider the objective weights of risk assessment factors. These reasons will lead to biased conclusions, causing decision makers to make wrong judgments. To address the limitations of the traditional RPN technique, the aim of this paper is to propose the flexible RPN assessment method under an uncertain environment. The flexible RPN assessment method is an extension of the traditional RPN technique. The flexible RPN method integrates the traditional RPN method and interval-valued 2-tuple weighted average method, and, simultaneously, considers subjective weights and objective weights. In the numerical verification, this study has adopted the example of stages of treatment planning for proton beam radiation therapy to verify the correctness and validity of the proposed flexible RPN technique. The numerical results show that the proposed flexible RPN technique not only handles the hesitation and incomplete information provided by experts but also considers the subjective and objective weights of risk assessment factors, providing more reasonable ranking results in the risk analysis.

Research on texture features classification of multilayered wood flooring using global image structure method

ABSTRACT The manual classification method based on grain batch in multilayer wood-flooring-production line is inefficient and has a high false detection rate. In this study, a new algorithm combining the global-image structure (GIST) feature extraction technique with the Adaboost strategy integrated support vector Machine (SVM) is proposed. After preprocessing the image acquired by the CCD linear array camera, 12 groups of Gabor filters with different scales and directions were used, and the feature vector of the image was compressed by the average pooling method. Then the local linear embedding (LLE) algorithm was used to further reduce the feature dimension. Finally, the Adaboost strategy was used to integrate 10 support vector machines (SVMs) as classifiers, and the particle swarm optimization algorithm (PSO) was used for the hyperparameters of each SVMs. The accuracy of this method was 98.93%, and the three main evaluation parameters of F1 Score, Precision and Recall reached 99%, 100% and 98%, respectively. The texture feature-classification method adopted in this study effectively solves challenges caused by abnormal surface colour and complex texture, achieving remarkable results in multilayer solid-wood composite-flooring-texture classification.

Spatial and Temporal Analysis of Earthquake Events in Kahramanmaraş on 6 February 2023

Earthquakes are one of the most destructive natural disasters that occur as a result of the energy resulting from the movements of the earth's crust spreading and shaking the earth's surface. Since it is not possible to predict when and where earthquakes will occur, it has become possible to make various inferences and take necessary precautions by analyzing previous earthquakes. Spatial and temporal analysis of natural disasters such as earthquakes with geographic information systems provides useful information to local and global decision-makers in measures to be taken and mitigation studies. The objective of this study is to demonstrate that the general orientation of stress accumulation on fault lines can be monitored by weighted average centre points with the use of Geographic Information Systems (GIS). Spatio-temporal analyses of two earthquakes with a magnitude of 7.7 Mw and 7.6 Mw in Kahramanmaraş and aftershocks were performed. Ten-week spatial distributions of the aftershocks were examined with the weighted average center method and it was determined that the earthquake intensity and average centers shifted towards the north. When the spatial distribution of the earthquake centers is examined with the help of standard deviation ellipses weekly, it has been determined that they show orientation along the Eastern Anatolian Fault System.

Autonomous Quality Control of High Spatiotemporal Resolution Automatic Weather Station Precipitation Data

How to prevent the influence of precipitation’s localized and sudden characteristics is the most formidable challenge in the quality control (QC) of precipitation observations. However, with sufficiently high spatiotemporal resolution in observational data, nuanced information can aid us in accurately distinguishing between intense, localized precipitation events, and anomalies in precipitation data. China has deployed over 70,000 automatic weather stations (AWSs) that provide high spatiotemporal resolution surface meteorological observations. This study developed a new method for performing QC of precipitation data based on the high spatiotemporal resolution characteristics of observations from surface AWSs in China. The proposed QC algorithm uses the cumulative average method to standardize the probability distribution characteristics of precipitation data and further uses the empirical orthogonal function (EOF) decomposition method to effectively identify the small-scale spatial structure of precipitation data. Leveraging the spatial correlation characteristics of precipitation, partitioned EOF detection with a 0.5° spatial coverage effectively minimizes the influence of local precipitation on quality control. Analysis of precipitation probability distribution reveals that reconstruction based on the first three EOF modes can accurately capture the organized structural features of precipitation within the detection area. Thereby, based on the randomness characteristics of the residuals, when the residual of a certain observation is greater than 2.5 times the standard deviation calculated from all residuals in the region, it can be determined that the data are erroneous. Although the quality control is primarily aimed at accumulated precipitation, the randomness of erroneous data indicates that 84 continuous instances of error data in accumulated precipitation can effectively trace back to erroneous hourly precipitation observations. This ultimately enables the QC of hourly precipitation data from surface AWSs. Analysis of the QC of precipitation data from 2530 AWSs in Jiangxi Province (China) revealed that the new method can effectively identify incorrect precipitation data under the conditions of extreme weather and complex terrain, with an average rejection rate of about 5%. The EOF-based QC method can accurately detect strong precipitation events resulting from small-scale weather disturbances, thereby preventing local heavy rainfall from being incorrectly classified as erroneous data. Comparison with the quality control results in the Tianqing System, an operational QC system of the China Meteorological Administration, revealed that the proposed method has advantages in handling extreme and scattered outliers, and that the precipitation observation data, following quality control procedures, exhibits enhanced similarity with the CMAPS merged precipitation data. The novel quality control approach not only elevates the average spatial correlation coefficient between the two datasets by 0.01 but also diminishes the root mean square error by 1 mm.

ARIMA Method Implementation for Electricity Demand Forecasting with MAPE Evaluation

Electricity demand forecasting is critical for efficient energy management and planning. This study focuses on the development and implementation of the Autoregressive Integrated Moving Average (ARIMA) method for forecasting electricity demand in South Sulawesi's power system. The evaluation of forecasting accuracy was conducted using the Mean Absolute Percentage Error (MAPE), which measures the percentage error between predicted and actual values. Two experiments were conducted with different ARIMA models: ARIMA(5,1,0) and ARIMA(2,0,1). Results showed that the ARIMA(5,1,0) model achieved a MAPE of 2.15%, while the ARIMA(2,0,1) model performed slightly better with a MAPE of 1.91%, indicating highly accurate predictions. The findings highlight the effectiveness of the ARIMA method in forecasting electricity demand, providing a reliable tool for energy providers to optimize resource allocation and enhance operational efficiency. Future research may explore integrating ARIMA with other advanced methods to further improve forecasting performance.