Number Of Samples Research Articles

Computation Theory of Large-Scale Partially Coherent Imaging by the Modified Modal Expansion Method

The numerical calculation of partially coherent imaging involves a fourfold integral, which is numerically complex and impracticable to be calculated directly. The use of coherent-mode decomposition (CMD) can make this problem more manageable but finding the coherent-modes for complicated partial coherent fields (without already-known coherent-mode expansion) are rather computationally intensive. In this letter, a modified modal expansion method is proposed, which significantly reduces the requirement of computational resources. The propagation of partial coherence in imaging systems with extremely large sampling number could be handled by an ordinary computer. A comparison between the new method and the traditional method in terms of memory resource requirements and computational time consumption is also detailed in this article. We will also show that this method could deal with the anisoplanatic imaging cases while maintaining the same computational efficiency.

Pengaruh Kualitas Pelayanan Petugas Check-In Counter KNP Kokapura terhadap Kepuasan Penumpang Maskapai Wings Air di Bandar Udara Internasional Jendral Ahmad Yani Semarang

General Ahmad Yani Semarang International Airport (SRG) is an airport located in the city of Semarang. KPN Kokapura Ahmad Yani must pay attention to services and facilities in the Check-In Counter area because they can affect passenger satisfaction. The main objectives of this research are as follows: 1) to find out whether there is an influence on the service quality of KPN Kokapura Check-In Counter officers on passenger satisfaction with Wings Air at Jendral Ahmad Yani International Airport Semarang and 2) to find out how much influence the Service Quality of Check-In Officers has. KPN Kokapura Counter on Wings Air Passenger Satisfaction at Jendral Ahmad Yani International Airport, Semarang. This research method uses quantitative methods. The number of samples in this research was 100 respondents. The respondents in this research were Wings Air passengers at Ahmad Yani International Airport, Semarang. The sampling technique in this research uses a Non-Probability Sampling technique with a Purposive Sampling approach, namely a sampling technique based on certain criteria that will be used as samples. Data analysis techniques use data management and drawing conclusions. The research results show that the significant value is 0.000<0.05 and the calculated t value is 3.604>t table value 1.660, which means that there is a positive influence on the quality of the KPN Kokapura Check-In Counter service on the satisfaction of Wings Air airline passengers at Jendral Ahmad Yani International Airport, Semarang. Based on the R square (R2) value of 0.117. shows that the Check-In Counter officer's service quality variable has an effect of 11.7% on passenger satisfaction. Meanwhile, 88.7% is explained by variables other than the independent variables in the research.

Analysis of Compensation, Work Motivation and Work Discipline on the Morale of Health Workers at the RSUD Prof. Dr. M.A Hanafiah SM Batusangkar

The new disease that has attacked the world is called COVID-19. This disease attacks all aspects of life, be it health, economy, social and politics. This causes the morale of health workers to decrease due to many factors including: low compensation while the risks faced are high, so that health workers lack motivation and also the level of work discipline. The purpose of this study was to analyze the effect of compensation and morale, motivation and morale, work discipline and morale, as well as the effect of compensation, work motivation and work discipline on morale and formulate scenario planning that can be applied at the Prof. DR.M. A Hanafiah SM Batusangkar Hospital. The method used is non-probability sampling with purposive sampling technique. The analysis method uses SEM-PLS and scenario planning. The location of this research at Prof DR M.A Hanafiah Batusangakar Hospital was conducted from September to December 2023. The population was 450 health workers, while the number of samples in this study was 244 health workers. The results in this study found that compensation had an effect on morale, work motivation had an effect on morale, work discipline had an effect on morale and compensation, work motivation and work discipline had an effect on morale and the scenario that could be applied was scenario IV (four). Keywords: Compensation, Hospital, Work Discipline, Work Motivation, Work Morale

Data‐driven target localization using adaptive radar processing and convolutional neural networks

AbstractLeveraging the advanced functionalities of modern radio frequency (RF) modeling and simulation tools, specifically designed for adaptive radar processing applications, this paper presents a data‐driven approach to improve accuracy in radar target localization post adaptive radar detection. To this end, we generate a large number of radar returns by randomly placing targets of variable strengths in a predefined area, using RFView®, a high‐fidelity, site‐specific, RF modeling & simulation tool. We produce heatmap tensors from the radar returns, in range, azimuth [and Doppler], of the normalized adaptive matched filter (NAMF) test statistic. We then train a regression convolutional neural network (CNN) to estimate target locations from these heatmap tensors, and we compare the target localization accuracy of this approach with that of peak‐finding and local search methods. This empirical study shows that our regression CNN achieves a considerable improvement in target location estimation accuracy. The regression CNN offers significant gains and reasonable accuracy even at signal‐to‐clutter‐plus‐noise ratio (SCNR) regimes that are close to the breakdown threshold SCNR of the NAMF. We also study the robustness of our trained CNN to mismatches in the radar data, where the CNN is tested on heatmap tensors collected from areas that it was not trained on. We show that our CNN can be made robust to mismatches in the radar data through few‐shot learning, using a relatively small number of new training samples.

A Machine Learning Method to Retrieve Global Rainfall and Snowfall Rates From the Passive Microwave Observations of FY‐3E

AbstractPassive microwave radiometers onboard satellites rely on the received upwelling radiation to retrieve precipitation, which is a mixed signal from the surface, atmosphere and precipitation hydrometeors. Liquid precipitation droplets increase the upwelling radiation from the surface at lower frequencies, while ice particles cause a decrease in upwelling radiation at higher frequencies. The task of the retrieval algorithm is to identify the precipitation phase and to isolate the signal of precipitation from that of the surface. This study develops a machine learning method to retrieve rainfall and snowfall rates based on observations from the Microwave Hydrometer Sounder and Microwave Temperature Sounder onboard FY‐3E. Self‐organized mapping (SOM) is selected to classify the precipitation and underlying surface types, and an artificial neural network (ANN) is subsequently used to relate the brightness temperature to the precipitation rate for the clusters derived from the SOM. The half‐hour product of the Integrated Multi‐Satellite Retrieval for Global Precipitation Measurement (IMERG) is used to train the ANN. To address the issue that number of heavy precipitation samples are not enough for training, the simulation of radiative transfer for TOVS is used as a supplement to heavy rain samples. The SOM‐ANN algorithm outperforms the IMERG and Goddard profiling algorithm (GPROF) retrieval products in both rainfall and snowfall retrieval. Compared with the hourly observations at ∼4,400 stations during a 2‐year period, the root mean square errors of SOM‐ANN proposed here are 1.06 and 0.34 mm/hr for the rainfall and snowfall rates, which are better than those of IMERG (1.23 and 0.42 mm/hr) and GPROF (1.22 and 0.44 mm/hr).

High-fidelity, large-scale targeted profiling of microsatellites.

Microsatellites are highly mutable sequences that can serve as markers for relationships among individuals or cells within a population. The accuracy and resolution of reconstructing these relationships depends on the fidelity of microsatellite profiling and the number of microsatellites profiled. However, current methods for targeted profiling of microsatellites incur significant "stutter" artifacts that interfere with accurate genotyping, and sequencing costs preclude whole-genome microsatellite profiling of a large number of samples. We developed a novel method for accurate and cost-effective targeted profiling of a panel of > 150,000 microsatellites per sample, along with a computational tool for designing large-scale microsatellite panels. Our method addresses the greatest challenge for microsatellite profiling - "stutter" artifacts - with a low-temperature hybridization capture that significantly reduces these artifacts. We also developed a computational tool for accurate genotyping of the resulting microsatellite sequencing data that uses an ensemble approach integrating three microsatellite genotyping tools, which we optimize by analysis of de novo microsatellite mutations in human trios. Altogether, our suite of experimental and computational tools enables high-fidelity, large-scale profiling of microsatellites, which may find utility in diverse applications such as lineage tracing, population genetics, ecology, and forensics.

The influence of performance in training on self-confidence of wheelchair athletes with coach-athlete intimacy as a moderating variable

This study aims to analyse the self-confidence of wheelchair basketball, tennis, badminton, and archery athletes. This study uses a moderated regression analysis (MRA) approach, namely performance in training as independent variable, self-confidence as a dependent variable, and coach-athlete intimacy as a moderator. Sampling was done using total sampling at the open tournament championship. The wheelchair athletes involved were 20 basketball athletes, 15 tennis athletes, 15 badminton athletes, and 15 archery athletes age 29.98±2.4 with 11.37±1.2 years of training. The highest experience of the competition is that 37 athletes have participated at the national and international level. This study was conducted for six months from early June to November 2023. The data were collected using questionnaires that were confirmed and given before the match and semi-structured interviews to strengthen the discussion. The instrument consists of 57 items, and an Aiken V validity value of 0.863 and Cronbach’s Alpha value of 0.708 were obtained. The results showed that performance in training influenced self-confidence (sig. 0.003 or p<0.05) and (R2 = 12.8%). Meanwhile, the coach-athlete intimacy can positively moderate the performance of the coach-athlete with self-confidence (sig. 0.000 or p<0.05) and (R2 = 47.2%). The results of this study suggest the involvement of government agencies to foster, develop, and facilitate all individuals with special needs. In addition, it is necessary to increase the competence of coaches through special training or by opening a coach study program for coaching people with disability at the university. Further research should consider the number of samples involved and how to collect data with a research design adapted to the circumstances but can produce accurate data. Keywords: Training Performance, Self-Confidence, Coach-Athlete Intimacy, Wheelchair

Transformational Leadership Style, Transactional Leadership Style and Job Satisfaction

This study aimed to determine and analyze the effect of transformational and transactional leadership styles on job satisfaction at PT. Bank Rakyat Indonesia Regional Office Jayapura. The population in this study were employees of at PT. Bank Rakyat Indonesia Regional Office Jayapura, totaling 120 employees. The number of samples used was 55 people, determined based on the Slovin formula. The data source used is primary data, namely data collected directly by researchers from respondents using questionnaire instruments. The data analysis method that will be carried out consists of descriptive statistical analysis, validity test, reliability test, normality test, heteroscedasticity test, multicollinearity test, hypothesis testing through multiple linear regression analysis, partial test, simultaneous test, and determination coefficient test. The results of this study indicate that partially Transformational and Transactional Leadership Styles have a positive and significant effect on employee. Meanwhile, the Transactional Leadership Style has a dominant influence on the job satisfaction.

Study on Rapid Simulation of the Pre-Cooling Process of a Large LNG Storage Tank with the Consideration of Digital Twin Requirements

The pre-cooling of a large LNG storage tank involves complex phenomena such as heat transfer, low-temperature flow, gas displacement, and vaporization. The whole pre-cooling process could take up to 50 h. For large-scale, full-capacity storage tanks, it is particularly important to accurately control the pre-cooling temperature. Digital twin technology can characterize and predict the full life cycle parameters from the beginning of pre-cooling development to the end and even the appearance of damage in real time. The construction of a digital twin platform requires a large number of data samples in order to predict the operating state of the device. Therefore, a simulation method with high computational efficiency for the pre-cooling process of LNG tanks is of great importance. In this paper, the mixture model and discrete phase model (DPM) are applied to simulate the pre-cooling process of a large LNG full-capacity tank. Following Euler–Lagrange, the DPM greatly simplifies the solution process. Compared with the experimental results, the maximum error of the DPM simulation results is less than 11%. Such a highly efficient simulation method for the large LNG full-capacity storage tank can make it possible to build the digital twin platform that needs hundreds of data model samples.

Quantum‐Noise‐Driven Generative Diffusion Models

AbstractGenerative models realized with Machine Learning (ML) techniques are powerful tools to infer complex and unknown data distributions from a finite number of training samples in order to produce new synthetic data. Diffusion Models (DMs) are an emerging framework that have recently overcome Generative Adversarial Networks (GANs) in creating high‐quality images. Here, is proposed and discussed the quantum generalization of DMs, i.e., three Quantum‐Noise‐Driven Generative Diffusion Models (QNDGDMs) that could be experimentally tested on real quantum systems. The idea is to harness unique quantum features, in particular the non‐trivial interplay among coherence, entanglement, and noise that the currently available noisy quantum processors do unavoidably suffer from, in order to overcome the main computational burdens of classical diffusion models during inference. Hence, the suggestion is to exploit quantum noise not as an issue to be detected and solved but instead as a beneficial key ingredient to generate complex probability distributions from which a quantum processor might sample more efficiently than a classical one. Three examples of the numerical simulations are also included for the proposed approaches. The results are expected to pave the way for new quantum‐inspired or quantum‐based generative diffusion algorithms addressing tasks as data generation with widespread real‐world applications.

Incidence, diagnosis, and management of orofacial pain among new patients receiving tertiary care in Thailand: A 6-year retrospective study comparing before and during the COVID-19 outbreak.

There is insufficient data on orofacial pain related to the COVID-19 outbreak in Thailand. To investigate the incidence, diagnosis and management of orofacial pain among new patients over the last 6 years, before and during the COVID-19 outbreak. Medical records from new patients who first visited Chulalongkorn Dental Hospital between 2017 and 2022 were retrospectively investigated. The sample size was determined, and proportional stratified random sampling was employed to distribute the sample number proportionally across each year. The hospital number of each patient was randomised using online software as a sampling strategy. A data collection form was developed and used to gather information from the digital data system. At the first visit, 770 out of 1359 patients (56.7/100 people, 95% CI: 54.0-59.3) reported orofacial pain defined as acute, chronic and unclassified pain. The most common chief complaints based on the AAOP classification were odontogenic pain (90.00%) and temporomandibular disorders (6.10%). The top three provisional modified ICD-10-TM diagnoses among these patients were pulp diseases (21.95%), impacted teeth (20.65%) and dental caries (9.09%). 81.87% of dental students' provisional diagnoses matched the final diagnoses given by dental specialists. Only 63.38% of orofacial pain patients were completely managed during the COVID-19 pandemic with common procedures being surgical removal, extraction and root canal therapy. Half of Thai dental patients seeking tertiary care at a university dental hospital reported orofacial pain, which was not impacted by the COVID-19 pandemic. In contrast, dysfunction-related problems in the orofacial area significantly increased during the pandemic.

PENGARUH GAYA KEPEMIMPINAN DAN LINGKUNGAN KERJA TERHADAP KINERJA PEGAWAI MELALUI MOTIVASI PADA SEKRETARIAT DPRD KOTA PROBOLINGGO

This study aims to find out the description of Leadership Style, Work Environment, Motivation and Performance of Probolinggo City DPRD Secretariat Employees and to determine the effect of Leadership Style, Work Environment, Motivation on the Performance of Probolinggo City DPRD Secretariat Employees. The sampling technique uses the nonprobability sampling technique using a saturated sampling method where the number of samples as much as the total population of the Probolinggo City DPRD Secretariat employees, amounting to 35 people. Data collection by distributing questionnaires offline to 35 respondents. This type of research is quantitative research using descriptive statistics with data analysis using the Partial Least Square (PLS) method. The tool used is the SmartPLS Version 4.0 program. The results of the study show that Leadership Style does not have a significant effect on Motivation, Work Environment does not have a significant effect on Motivation, Leadership Style has a significant effect on Performance, Work Environment does not has a significant effect on performance, motivation has no significant effect on performance, leadership style has no significant effect on performance through motivation and work environment has no significant effect on performance through motivation.

Sampling effects on background noise corrections

Background noise corrections are used to estimate true source levels from measured source levels. Use of a background correction involves determining the background noise from measured samples. Previous work explored the effect of sampling on the statistical background noise correction through the steadiness parameter. In this paper the effect of sampling on the statistical and legacy background noise corrections through the steadiness and source-background level difference parameters is investigated. Samples of background noise are drawn from a chi-square distribution, which has been shown to describe background noise arising from multi-path or multi-mode processes. Background mean and variance are determined from a specified number of background noise samples, and statistical distributions and confidence bands are formed. The distributions and confidence bands of the statistical and legacy background noise corrections are compared and are used to identify parameter ranges that promote accuracy of source levels obtained with background noise corrections. Specific limits on these parameters are recommended for the statistical background noise correction. The benefits and advantages of the statistical background noise correction are identified, and a path towards standardizing the statistical background noise correction is proposed.

Determinasi Lingkungan Kerja, Disiplin Kerja, Budaya Kerja dan Motivasi Kerja Terhadap Kinerja Karyawan dengan Kepuasan Kerja sebagai Variabel Intervening pada Divisi Sales PT. Link Net. Tbk

This research aims to analyze the influence of the work environment, work discipline, work culture and work motivation on employee performance with job satisfaction as an intervening variable in the Sales Division at PT. NetLink. Tbk. The research method used is a combined research method which is a research method between quantitative methods and qualitative methods to be used together in a research activity. The population in this study were employees of the Sales Division of PT. NetLink. Tbk which is spread throughout Indonesia with a total number of 960 people, while the number of samples in this study was 282 people, the questionnaire was distributed via the Google Form link with an estimated data collection for one month, after the data was collected it was then processed and analyzed further using using smartPLS 3.0 SEM (Partial Least Square-Structural Equation Modeling) software. The results of hypothesis testing on all independent variables show that work discipline, work culture, work motivation and work motivation have a significant effect on job satisfaction. Meanwhile, the work environment, job satisfaction, work discipline and work culture have no significant effect on employee performance. Furthermore, in this research, job satisfaction does not intervene in the influence of the work environment, work discipline, work culture and work motivation on employee performance.

Deep learning-based gap filling for near real-time seamless daily global sea surface salinity using satellite observations

Sea surface salinity (SSS) provides crucial information about ocean environments, influencing global hydrological cycles, thermohaline circulation, and climate change. Although L-band passive microwave radiometers have provided satellite-based SSS data, there are gaps due to the limited daily coverage of the sensors. This study proposes a U-Net-based spatial gap-filling model for global SSS using Soil Moisture Active Passive (SMAP) satellite data. The proposed model utilizes SSS swath data from the target and past days to generate daily global SSS maps with full coverage, incorporating only past temporal information. Additionally, bias-corrected data using gradient-boosted regression trees (GBRT) are employed to reduce inherent errors in the SMAP SSS data. We designed 24 schemes using data from the past 3, 5, and 7 days for both GBRT-corrected and original SMAP SSS data, with one to four times oversampling for low-salinity water, where the number of samples is significantly small. Validation results using masked-out pixels indicate that the gap-filling models with GBRT-corrected SSS data from the past 3 and 5 days and four times oversampling yielded the best performance, with root mean square errors (RMSEs) of 0.388 and 0.413 psu, respectively. Compared with the in situ Argo data for 2020, the RMSEs were 0.237 and 0.241 psu for the two models, respectively, significantly outperforming the SMAP Level 3 8-day SSS, which requires future data (RMSE of 0.456 psu). Notably, these models successfully filled gaps over coastal areas where low SSS (<30 psu) fluctuated due to freshwater discharge from inland areas. This study proposes an effective, novel gap-filling method for generating bias-corrected global daily SSS without delay, meeting operational needs. Moreover, the proposed gap-filling framework can be applied to other environmental swaths or even grid datasets.

Fine-Grained Few-Shot Image Classification Based on Feature Dual Reconstruction

Fine-grained few-shot image classification is a popular research area in deep learning. The main goal is to identify subcategories within a broader category using a limited number of samples. The challenge stems from the high intra-class variability and low inter-class variability of fine-grained images, which often hamper classification performance. To overcome this, we propose a fine-grained few-shot image classification algorithm based on bidirectional feature reconstruction. This algorithm introduces a Mixed Residual Attention Block (MRA Block), combining channel attention and window-based self-attention to capture local details in images. Additionally, the Dual Reconstruction Feature Fusion (DRFF) module is designed to enhance the model’s adaptability to both inter-class and intra-class variations by integrating features of different scales across layers. Cosine similarity networks are employed for similarity measurement, enabling precise predictions. The experiments demonstrate that the proposed method achieves classification accuracies of 96.99%, 98.53%, and 89.78% on the CUB-200-2011, Stanford Cars, and Stanford Dogs datasets, respectively, confirming the method’s efficacy in fine-grained classification tasks.

Analysis of the influence of CAR, NPF, FDR AND BOPO on the financial performance (ROA) of banking in Indonesia

This article analyzes the influence of CAR, NPF, FDR and BOPO on banking financial performance (ROA) in Indonesia, which is the aim of this research. The sample selection method uses a purposive sampling technique at Sharia Commercial Banks registered with Bank Indonesia (BI) for the 2017-2022 period. The sample in this study consisted of 10 companies with a total sample data of 58. The data analysis technique used was multiple linear regression with the help of SPSS version 25. The results of the study showed that the NPF variable had an effect on financial performance (ROA), while CAR, FDR and BOPO did not. influence on financial performance (ROA). The selection of research objects at Sharia Commercial Banks resulted in limitations in the number of samples in this research. Apart from that, the independent variables in this study can only explain 19.8% of the independent variables.

Pengaruh Brand Image Dan Kepuasan Terhadap Purchase Intention Pada Tiktok Shop (Studi Pada Generasi Zilenial)

The millennial generation currently has the convenience of accessing various kinds of applications, one of which is TikTok. TikTok provides online or online shopping features that allow anyone to make money through the features provided, namely the TikTok Shop, an online shopping feature offered by the TikTok platform. The purpose of this study is to find out and explain the influence of brand image and satisfaction on purchase intention at Tiktok Shop, and to know and explain the role of satisfaction in mediating the influence of brand image on purchase intention at Tiktok Shop. This research was conducted on tiktok users in Indonesia. The number of samples used in this study were 160 people. The analysis technique used is SEM-PLS. The results of this study indicate that brand image and satisfaction affect purchase intention, and satisfaction is able to mediate the effect of brand image on purchase intention.

Pengaruh Bauran Pemasaran 4D Terhadap Keputusan Pembelian Pengguna GoFood

Changes in technological advances and increased Internet penetration have led to changes in consumer purchases. The study aims to analyze “The impact of 4D marketing mix on GoFood consumer purchasing decisions in Surakarta”. The population in this study is the entire consumer in Surakarta City who has used the service of messaging between GoFood and has made a purchase. The study uses purposive sampling as a sample determination technique and the number of samples in this study is 100 respondents. Variables In this study there are 5 variables to be studied, namely the independent variables consisting of Disruption (X1), Digitalization (X2), De-Siloage (X3), and Diffusion (X4), as well as Purchase Decision (Y) as dependent variables. The results of this study show that the variables disruption(X1), de-siloage(X3) and diffusion(X4) have a positive and significant influence on the Purchasing Decision of GoFood Application Users in Surakarta, whereas the variable digitalization(X2) has a negative and significant impact on the Buying Decisions of Gofood Application users in Surakarta.

Pengaruh Implementasi Kampus Merdeka dan Persepsi Mahasiswa tentang MBKM terhadap Minat Mengikuti Program Merdeka Belajar Kampus Merdeka

This study aims to determine the factors that influence interest in participating in the Merdeka Belajar Kampus Merdeka program among students of Social Sciences Education. The method in this research uses descriptive quantitative with ex post facto and survey approaches. The population in this study were active students of Social Science Education class of 2020. The number of samples in this study totaled 153 students using a sampling technique stratified random sampling by using purposive sampling. Data collection using questionnaires and interviews. Data analysis uses multiple linear regression and multiple linear regression and is processed with the SPSS program. The results of this study indicate that there is an influence on the implementation of the independent campus and students perceptions of MBKM on the interest in participating in the Independent Learning Campus program among Social Sciences Education students with a determination level of 0.525 or 52.5% student interest is influenced by the implementation of an independent campus and students perceptions of MBKM, the remaining 47.5% is influenced by other factors not included in this model.