Scheduled Basis Research Articles

Study on runoff forecasting and error correction driven by atmosphere–ocean-land dataset

Accurate runoff forecasting results can not only provide an important basis for flood control scheduling, but also provide scientific support for water resources optimization, which promotes the maximization of the overall benefits of the basin. To further explore the inherent mechanisms of the atmosphere–ocean-land factors driving runoff changes, this study proposes the factors dimension reduction and interpretation framework based on Pearson, eXtreme Gradient Boosting and SHapley Additive exPlanations (P-XGBoost-SHAP). Base on this, the Gaussian Process Regression (GPR), Long Short-Term Memory neural network (LSTM) and Support Vector Machine (SVM) models are used to construct the atmospheric-ocean-land data-driven runoff prediction model. Meanwhile, for the runoff prediction residuals, this paper proposes an error multi-step correction framework based on ensemble empirical mode decomposition and autoregressive model (EEMD-AR). The case study of Lianghekou hydrological station shows that the factor dimension reduction and interpretation framework can greatly reduce the input dimension of the model, and explain the factors globally and locally by using SHAP Value. Compared with the traditional Random Forest (RF) dimension reduction method, it shows higher prediction accuracy. The Nash-Sutcliffe efficiency coefficient (NSE) can be increased to about 0.93, which is 4.91 % and 1.97 % higher than the series–parallel coupling (AR-Parallel) and empirical mode decomposition-autoregressive (EMD-AR) correction methods, respectively. The accuracy of the runoff forecasting prediction is improved while reducing the input dimension of the model.

Complications of acute myocardial infarction in women over 50 years of age in a coronary unit

Background: Cardiovascular diseases are the main cause of mortality in women around the world. Erroneously for many years it was considered a disease of men and therefore they were the most studied in terms of risk factors, symptoms, treatments and complications. Ischemic heart disease mainly affects postmenopausal women because by decreasing the estrogen load, cardiovascular risk increases. Therefore, we will study the characteristics of acute myocardial infarction in women over 50 years of age to learn about this highly prevalent disease from another gender. Material and methods: For this study we will carry out an observational, retrospective, cross-sectional and analytical investigation. At the Instituto Cardiovascular de Buenos Aires, collecting information from the medical records of female patients over 50 years of age with acute myocardial infarction in the period May-July 2022.Results: Of 22 patients analyzed in this study, 19 of them successfully survived the cardiovascular event and only 3 had fatal consequences. These patients, who were between the ages of 53 and 94 (with almost 50% of them between the ages of 70 and 80) had comorbidities at the time of the coronary event. 72% had high blood pressure, while only 18% had diabetes. In the interview we found that 77% of them led a sedentary life, 36% were smokers, and 22% had suffered a previous myocardial infarction. When performing the admission electrocardiogram, 36% revealed ST-segment elevation while 63% presented ST-segment depression. The treatment of choice in 36% of the patients was transluminal coronary angioplasty within the first hour of admission, while 63% received the same treatment on a scheduled basis in the following days. Conclusion: Based on what was analyzed in this study, transluminal coronary angioplasty is the treatment with the highest success rate for acute-stage infarction, because it is a minimally invasive procedure and has a low mortality rate. Sometimes when performed together with the placement of a stent, the percentage of recurrence decreases since the artery is kept patent. Its main objective is to restore blood flow to the heart tissue with the intention of preventing a more complex procedure

The Application of Deep Learning in Sports Competition Data Prediction

In order to predict sports competition data, the author needs to implement the structure and related processes of the relevant competition victory and defeat prediction system, and specifically introduce and plan the implementation of each functional module. The data collection and storage module adopts Alibaba Cloud servers and combines Python to remotely and automatically collect data on a scheduled basis, according to the actual situation of game wins and losses, data cleaning and filtering are carried out, and multiple encoding forms are used to vectorize the data in order to find the best model. The data is divided according to the standard training and testing sets, and multiple classifiers are used for model training and saved locally for direct use next time; Test the above model using the training set; Compare the advantages and disadvantages of each vectorized encoding and classifier based on the final performance evaluation module. Based on the relevant experimental results, a detailed analysis was conducted to compare the advantages and disadvantages of each model, proving that introducing word vectors (word embeddings) into the competition data analysis system is worthwhile. We have obtained an excellent performance prediction model with a highest accuracy P of 0.825, a recall R of 0.729, and a corresponding F1 value of 0.774. For a prediction model that only knows the initial lineup allocation as a prerequisite, this already has sufficient practical guidance significance.

The effects of different interruption conditions on mental workload: an experimental study based on multimodal measurements

Interruptions in the working environment cause extra mental workload for the operators, and this phenomenon has garnered significant research attention. This study designed four interruption conditions based on the perceptual and cognitive perspectives of human information processing, using a 2(perceptual primary task and cognitive primary task)*2(perceptual interruption task and cognitive interruption task) factorial design. Multimodal measurement methods were used to evaluate mental workload in different interruption conditions. The results show that when the primary task and the interruption task are different load types, they generate a higher mental workload than the same load type. It can be attributed to the fact that perceptual tasks and cognitive tasks increase mental workload during switching. In addition, based on the multimodal index data, the prediction model of interruption recovery delay time and the classification model of interruption conditions are established, which provides a basis for rational scheduling of work and preventing mental overload.

A new short-term wind power prediction methodology based on linear and nonlinear hybrid models

Fast and accurate wind power prediction is of great significance for grid planning. However, wind power dataset tends to be highly stochastic and volatile, while showing more stable seasonal and cyclical changes, which is a linear and nonlinear superposition features, and it is difficult to use a single linear or nonlinear model to make accurate predictions. Considering these essential features of wind power data, a short-term wind power prediction method based on linear and nonlinear hybrid models is proposed in this paper. First, the complete ensemble empirical mode decomposition (CEEMD) is used to decompose and reconstruct the wind speed and direction in the original dataset to reduce the volatility. Then, to capture the linear features in the dataset, the autoregressive integrated moving average model with exogenous input variables (AIRMAX) is used for linear modeling, and the sparrow search algorithm optimized gated recurrent unit (SSA-GRU) is used to model the nonlinear features. At the same time, to improve the efficiency of model training, federated learning (FL) is utilized for distributed model training. Finally, the ARIMAX and SSA-GRU models are weighted and summed the prediction results to gain the final prediction results using equal weighted averaging (EWA), minimum sum of squares error (MSSE), and maximum grey relational analysis (MGRA), which are commonly used to compute the weights of the combined models, respectively. The wind power dataset from a wind farm in northwest China from January 1, 2022 to October 31, 2022 are used for experiments and analysis, and the results show that the root mean square error (RMSE), R-square (R2), and accuracy (ACC) of the proposed method are 11.944, 0.987, and 0.613, respectively, which are better than all the compared models. The main contribution of this paper is twofold, on the one hand, it indirectly proves that the wind power dataset is formed by the superposition of linear and nonlinear features, and on the other hand, it puts forward a theoretical model and research paradigm for predicting short-term wind power and presents a scientific basis for grid scheduling.

Implementasi Konten Marketing Pada Akun Instagram @Kampunginggrisbandung Tahun 2024

Instagram is still one of the popular social media choices in Indonesia for marketing activities. This can be seen from the number of Instagram users in Indonesia which reaches hundreds of millions of people. Based on data from Napoleon Cat, there are 109.33 million Instagram users in Indonesia as of April 2023. English Village is a company operating in the education sector by offering various English language course programs such as Speaking Class, IELTS and TOEFL Preparation as well as bootcamp programs. English Village uses Instagram social media for marketing purposes, but in the midst of increasingly massive marketing competition for English language courses, the Instagram account used by the English Village for promotion is faced with a big challenge, because engagement from followers on Instagram is still relatively low and inconsistent at all times. type of content, even though the marketing division team always uploads various types of content on a scheduled basis. Based on the research conducted by the author, it can be concluded that planning, implementing marketing content that is right and interesting and consistent in carrying out regular evaluations will greatly influence customer engagement so that it can influence buying interest for customers and potential customers.

A two-stage electricity consumption forecasting method integrated hybrid algorithms and multiple factors

Electricity consumption forecasting provides a reference basis for electricity scheduling, it has become a research hotspot in the field of electric power. The fluctuation of electricity consumption is influenced by multiple factors, which has strong randomness and are strongly disturbed by noise. Single algorithms cannot ensure the stability and accuracy of electricity consumption forecasting. To address this problem, a two-stage framework for electricity consumption forecasting is proposed in this paper. In the first stage, multi-forecasting algorithms are considered to eliminate noise and forecast the electricity consumption trend. A hybrid model is constructed, which consists of three parts: Ensemble Empirical Mode Decomposition (EEMD), Gated Recurrent Unit (GRU) and Gradient Boosting Regression Tree (GBRT). The EEMD method is employed to decompose the electricity consumption signal into different frequency components. GRU and GBRT are adopted to forecast high-frequency and low-frequency components respectively. In the second stage, multidimensional meteorological factors are considered to further improve the accuracy of the electricity consumption forecasting. A structure for feature extraction is constructed to reduce the dimension of meteorological factors. The experimental results indicate that the two-stage framework model outperforms mainstream measurement models by an average reduction of 35.7% and 43.2% in MAE and RMSE.

Do multisource data matter for NGP prediction? Evidence from the G-LSTM model

Precisely predicting natural gas prices (NGPs) is important because it can provide the necessary decision-making basis for energy scheduling, planning and control. However, NGPs are affected by many factors and exhibit the characteristics of nonlinearity and randomness, which makes accurate predictions challenging. Therefore, in this paper, the information gain of multisource data and the global optimization ability of the gray wolf algorithm are used to build a multifactor-driven NGP hybrid forecasting model to improve the prediction performance. First, the emotional tendency and readability of news text are extracted and calculated by using VADER and textstat tools, respectively. Then the network search index is filtered and integrated by using the correlation coefficient method and the CRITIC method to form alternative variables of multisource data (news and search index). Second, the gray wolf optimization algorithm is used to find and determine the best key parameter group in long short-term memory model. Finally, the spot price of natural gas in Henry Hub from March 1, 2012 to February 28, 2022 is selected as the prediction object, and multi-scenario numerical experiments are carried out to verify the effectiveness of the proposed model. The ablation experiment results show that the information gain brought by multisource data can effectively improve the prediction effect of NGPs. Furthermore, the proposed model has the best prediction performance in different scenarios and can be regarded as a promising prediction tool.

Efforts To Optimize Loading and Unloading Equipment

The purpose of this study is to determine the efforts made to optimize loading and unloading equipment to facilitate loading and unloading in MV. Isa Golden. The method used in this study is qualitative descriptive. The results of the study include efforts made to optimize the maintenance of loading and unloading equipment by planning loading and unloading maintenance on a scheduled basis by following the maintenance checklist that has been determined by the ship management, evaluating at safety meetings or deck meetings to provide solutions to problems if found and provide answers or solutions to each problem, Make coordination in parts of the organization in dividing 2 (two) groups in loading and unloading equipment maintenance activities or other maintenance so that it can run well following the job description. Mualim I is obliged to compile crane spare parts to make it easier to supervise spare parts that are not available. View lists of equipment and spare parts that expire and make replacements if there is equipment that expires or is used by making and submitting a list of spare parts requests to the company

Radar-Based Precipitation Nowcasting Based on Improved U-Net Model

Rainfall nowcasting is the basis of extreme rainfall monitoring, flood prevention, and water resource scheduling. Based on the structural features of the U-Net model, we proposed the Double Recurrent Residual Attention Gates U-Net (DR2A-UNet) deep-learning model to carry out radar echo extrapolation. The model was trained with mean square error (MSE) and balanced mean square error (BMSE) as loss functions, respectively. The dynamic Z-R relationship was applied for quantitative rainfall estimation. The reference U-Net model, U-Net++, and the ConvLSTM were used as control experiments to carry out radar echo extrapolation. The results showed that the model trained by BMSE had better extrapolation. For 1 h lead time, the rainfall nowcasted by each model could reflect the actual rainfall process. DR2A-UNet performed significantly better than other models for intense rainfall, with a higher extrapolation accuracy for echo intensity and variability processes. At the 2 h lead time, the nowcast accuracy of each model was significantly reduced, but the echo extrapolation and rainfall nowcasting of DR2A-UNet were better.

Application of Data Mining in Disease Clustering at Klinik Keluarga

The purpose of this study is to provide recommendations for counseling materials to assist the Promkes and Marketing Coordinator at the Klinik Keluarga in determining appropriate health counseling materials in an area. Counseling is conducted on a scheduled basis to a predetermined area. However, many of the materials presented are not in accordance with the majority of diseases suffered by the community in the area. This causes clinic services to be not optimal due to lack of awareness of a disease in the region. Data Mining with Clustering method and K-Means algorithm is used in this study to determine the pattern of disease distribution based on its characteristics from the data sources used, consisting of visit data, parent disease data, disease category data, sub-district data, and village data. The evaluation results show that the disease clustering process produces an average accuracy of 0.263. These results show that data mining with the clustering method can help the Promkes and Marketing Coordinator at the Family Clinic in determining the right counseling material for the community.

Estimating the composition ratios of network services carried in mixed traffic

Network performance management (NPM) and quality of service (QoS) guarantee are the key concerns of network operators and service providers/consumers, respectively. The basis for traffic scheduling is one of the vital elements guiding the implementation of both NPM and QoS assurance. Although traffic identification technologies can theoretically be applied to providing basis for traffic scheduling, the computational complexity caused by the flow-by-flow processing makes them unsuitable for actual large-scale high-throughput network scenarios. In this work, we introduce a new scheme to provide a basis for service-oriented NPM and QoS guarantee. The proposed scheme treats the mixed traffic as a whole and estimates the composition ratios of network services carried in mixed traffic, which makes traffic scheduling for NPM and QoS more reasonable and flexible. To estimate the composition ratios, the observed mixed traffic is expressed as a bipartite graph according to the communication relationship. Then, the bipartite graph is further projected to a multi-dimensional hyper-image based on node coding, random walking and embedding mapping. By this way, we convert the service composition ratio estimation into a problem similar to image classification and enable it to be solved by general machine learning methods like convolutional neural networks. The proposed scheme facilitates batch traffic scheduling and can reorganize the distribution of compositions of network services for each link based on given management strategies. Its computational complexity is significantly lower than that of flow-by-flow processing methods. We validate the proposed solution via real network data and evaluate its performance with some benchmark methods based on traffic identification. The experimental results show that the proposed solution can accurately infer the service composition ratios of mixed traffic with low computational complexity.

Kimura's Disease Finding on Ocular Adnexal Mass

Introduction: Kimura's disease is an unknown chronic lymphoproliferative inflammatory disease affecting the skin, soft tissues, and lymph nodes. Until September 2020, only 200 cases of Kimura's disease were reported worldwide, however, their exact incidence is unknown. Here, we are interested in reporting a patient with Kimura's disease of the ocular adnexa due to its rarity and to enhance the knowledge of ophthalmologists about confirming the diagnosis of Kimura's disease. Case Presentation: A 40-year-old female presented with a chief complaint of a mass on her left nasal orbit for the last year. The mass gradually increased for six months, and it was painless. The systemic laboratory workup revealed eosinophilia and increased serum IgE. Contrast CT-Scan revealed a benign tumor, suspect dermoid cyst. She underwent surgery, and the histopathologic showed Kimura's disease. The patient was followed up on a scheduled basis, and there was no recurrence during four months of monitoring. Conclusions: A clinical, systemic laboratory, and histopathological examination is required to confirm the diagnosis of Kimura's ocular adnexa disease and determine the best therapy for the patient due to the high recurrence rate. Combining surgical excision with postoperative radiation is recommended as the most effective treatment in terms of controlling the residual lesion and minimizing the recurrence rate while causing the fewest side effects. Eosinophil screening regularly is advised to evaluate the recurrence rate.

The Quality of Implementation of Academic Supervision in Vocational Secondary Schools in Teaching Factory Based Learning

This research aimed to get an idea of how academic supervision activities are implemented in learning at SMK Negeri 2 Business and Management, Jayapura based on a teaching factory in the Marketing Department. The resource persons in this research were the school principal, teaching factory field coordinator teacher majoring in marketing, the deputy principal for the curriculum department, and female students majoring in marketing. The method used in this research is a descriptive qualitative approach, through observation, in-depth interviews, and documentation studies. The data obtained was analyzed using data reduction, data presentation, and conclusion. The results obtained from this research are that supervision activities have been carried out through class visits and work practice activities which have been carried out even though they have not been carried out on a scheduled basis and have only taken the form of monitoring. Apart from that, there is an assessment of student performance which the coordinator provides, there are efforts to develop learning programs by dividing block schedules and adjusting the curriculum according to industry needs, and there is feedback provided as input for improvements including transparency of profits from Esemkamart as well as monitoring of facilities and infrastructure, although there needs to be continuous improvement and more awareness regarding the implementation of occupational safety and health when carrying out the teaching factory learning process at SMK Negeri 2 Business and Management, Jayapura.

Peran Guru PAI dalam Mengatai Kenakalan Siswa Sekolah Menengah Pertama

Junior high school students are entering a transitional age or puberty which causes them to be in a state of needing recognition and searching for identity so that they are very vulnerable to environmental influences. This causes the risk of juvenile delinquency. PAI (islamic religion) teachers have a very important role to provide religious and spiritual guidance for students to build character and morals. This research was conducted at SMPN 5 Sidoarjo. The method used is qualitative with a naturalistic positivistic approach with data collection techniques through interviews, documentation and literature review. The results of this study are that the form of juvenile delinquency in SMPN 5 Sidoarjo students is classified as low delinquency. PAI teachers have contributed and played a major role in handling student delinquency, namely carrying out spiritual guidance programs on a scheduled basis. The supporting factors are students with Islamic religion being the majority and the inhibiting factors are carelessness in supervision and differences in student character.

Gerakan Bersama Aksi Bergizi Untuk Mencegah Stunting Di SMK Islam Farmasi Sumbawa

This service aims to provide information through the joint movement of nutritious action to prevent Stunting at SMK Islam pharmacy Sumbawa. The results of this activity are expected to be able to raise awareness early on to the community, especially teenagers of advanced school age about the importance of practicing healthy living behaviors and taking blood-adding tablets regularly and on a scheduled basis.

PENERAPAN SENAM TERHADAP KUALITAS TIDUR IBU HAMIL TRIMESTER III DI PUSKESMAS TANJUNG BALAI KARIMUN

According to the World Health Organization (WHO), the global prevalence of insomnia, which is a sleep disorder in pregnant women worldwide, is 41.8%. Pregnant women who have difficulty getting enough sleep can make the condition of pregnant women decrease, their concentration decreases, their bodies feel sore, they tire easily, they are not enthusiastic about working and they tend to be more emotional. Pregnancy exercise is one of the activities in health services during pregnancy. The aim of the study was to provide midwifery care to pregnant women for Mrs. E at 32 weeks of gestation with sleep pattern disturbance problems in her third trimester of pregnancy, by doing pregnancy exercises. This report method is a case study, data collection techniques with interviews, observation and documentation. Pregnancy exercise is carried out at least twice a week for 30 minutes. Pregnancy exercise will have a relaxing effect on pregnant women which can affect the improvement of sleep quality for pregnant women, thus causing pregnant women to have good sleep quality. For mothers, it is expected that physical exercise during pregnancy will provide many benefits, especially if pregnancy exercise is carried out regularly and on a scheduled basis, the effects of the pregnancy exercise will be felt on health, especially during pregnancy.

Analysis of power dispatching decisions with energy storage systems using the optimal probability distribution model of renewable energy

Effectively managing the inherent unpredictability and fluctuation attributes of renewable energy sources within scheduling systems has emerged as a critical matter demanding immediate attention. The incorporation of energy storage technology offers notable advantages by mitigating fluctuations in wind power generation and curtailing peak shaving costs in scheduling systems through economical utilization of energy storage. This study employs the adjustment of energy storage’s reserve capacity function to modify the economic gains and losses arising from fixed spinning reserve. This approach tailors the reserve capacity function by formulating the uncertainty inherent in wind power generation. The paper presents an enhanced selection method for calculating optimal bandwidth within the framework of least squares approximation probability density function scenarios. The fusion of least squares approximation and optimal bandwidth computation permits precise refinement of bandwidth in non-parametric kernel density estimation approaches. Consequently, the study puts forth a comprehensive model for coordinated scheduling and regulation of a multi-scale energy storage power system. Finally, the stability and economy of the proposed method in the scheduling operation process were verified through simulation, and the research results can provide a theoretical basis for future uncertain wind power scheduling.

Development of Character Education in the PAUD Curriculum through Getting Used to Simplified Kromo Javanese at the SELASIH Banyuwangi Kindergarten

This research aims to determine the character cultivation program for early childhood through familiarization with simple Javanese Kromo language for early childhood at Selasih Kindergarten, Grajagan Village, District. Purwoharjo, Banyuwangi Regency. In practice, this habituation program is carried out on a scheduled or incidental basis every day. In this research, the method used is a qualitative method through general steps, namely problem identification, planning, implementation, reporting and follow-up. Overall, the results of this research show that the simple Javanese Kromo language familiarization program at the SELASIH Purwoharjo Banyuwangi Kindergarten can form students' attitudes of character and love of the homeland through the introduction and habituation of local culture, one of which is simple Javanese language culture for PAUD children. The character that is formed is language, polite attitudes and behavior towards friends, teachers and parents as well as love for one's culture. In its implementation, support from teachers, teaching programs, and parents is the key to success in implementing this habituation activity program. Because PAUD children are great imitators, PAUD teachers must be models who are able to teach character values regarding language and polite and polite behavior in daily learning activities.

Morbidity associated with emergency surgery versus scheduled surgery in patients with placenta accreta spectrum.

This study aims to evaluate the clinical outcomes of surgical management for placenta accreta spectrum in a Latin American reference hospital specializing in this condition. The evaluation involves a comparison between surgeries performed on an emergent and scheduled basis. A retrospective cohort study was conducted on patients with placenta accreta spectrum who underwent surgery between January 2011 and November 2021 at a hospital in Colombia, using data from the institutional PAS registry. The study included patients with intraoperative and/or histological confirmation of PAS, regardless of prenatal suspicion. Clinical outcomes were compared between patients who had emergent surgeries and those who had scheduled surgeries. Descriptive analysis involved summary measures and the Shapiro-Wilk test for quantitative variables, with comparisons made using Pearson's Chi-squared test and the Wilcoxon rank sum test, applying a significance level of 5%. A total of 113 patients were included, 84 (74.3%) of them underwent scheduled surgery, and 29 (25.6%) underwent emergency surgery. The emergency surgery group required more transfusions (72.4% vs 48.8%, p=0.047). Patients with intraoperative diagnosis of placenta accreta spectrum (21 women, 19.5%) had a greater volume of blood loss than patients taken into surgery with known presence of placenta accreta spectrum (3500 ml, IQR 1700 - 4000 vs 1700 ml, IQR 1195-2135. p <0.001). Patients with placenta accreta spectrum undergoing emergency surgery require transfusions more frequently than those undergoing scheduled surgery.