Number Of Samples Research Articles

Pengaruh Quality of service dan Strategi Promotion Terhadap Kepuasan Pelanggan UMKM Kuliner di Badung

This research aims to analyze and test the influence between the variables studied in this research. The influences tested include the partial and simultaneous influence of quality service and promotion on customer satisfaction. The type of research used in this research is a quantitative approach. The number of samples in this research was 130 Waroeng Pan Yoga customers. The data collection method in this research used observation and questionnaires. The measurement scale used in this research is the Likert scale. The data analysis techniques in this research are descriptive analysis, classical assumption test, multiple linear regression analysis and hypothesis testing consisting of the f test, t test and determinant coefficient. The data in this study was processed using SPSS 21 for Windows. The results of this research show that partially the quality service and promotion variables have a positive and significant effect on customer satisfaction, which is shown by values ​​of 0.005 and 0.006, which means <0.05. Simultaneously, the quality service and promotion variables have a positive and significant influence on customer satisfaction, 0.000, which means <0.05. The suggestion in this research is for companies to pay attention to quality service and promotion factors to increase customer satisfaction.

The Effect of Marjoram Aromatherapy and Deep Breathing Techniques on Blood Pressure of Hypertensive Patients

Hypertension is a major cause and risk factor for death worldwide. Nonpharmacological therapy of hypertension to reduce blood pressure of hypertensive patients, one of which is marjoram aromatherapy and deep breathing techniques that have proven effective in hypertensive patients. Objective: The purpose of the study was to determine the effect of marjoram aromatherapy and deep breathing techniques on blood pressure of hypertensive patients. Methods: This type of research is a quasy experiment with a one group pretest-postest design without control group approach, the number of samples was 20 respondents using total sampling. The data collection technique used is observation through blood pressure measurement. Hypothesis analysis is normality test, One Way Repeated Measurement (ANOVA), and multiple linear regression. Results: There is an effect of marjoram aromatherapy and deep breathing techniques on blood pressure of hypertensive patients (p-value <0.05). Conclusion: There is an effect of marjoram aromatherapy and deep breathing techniques on the blood pressure of hypertensive patients. Marjoram aromatherapy interventions and deep breathing techniques can be socialized in hospitals and can be done independently at home to reduce blood pressure in people with hypertension.

Why millennials and gen Z are silently leaving their jobs? Unraveling the ‘quiet quitting’ trend

This paper investigates the phenomenon and factors that influence quiet quitting behavior that is happening in millennial and Z generation workers. Quiet quitting is employee behavior that chooses to work moderately according to the tasks that have been given. Several previous research results found that this behavior can be anticipated by increasing job satisfaction which is influenced by empowering leadership, organizational culture, and work overload. This research was designed with a survey method and using a quantitative approach. Data collection in this study used online questionnaires distributed to workers in the age range of millennial and Z generations. The data analysis technique used in this research is Structural Equation Modelling (SEM) with AMOS software. The number of samples in this study was 600 respondents. The respondents are dominated by workers on the islands of Java and Sumatra and the rest are from other regions in Indonesia. The findings of this research highlight the negative effect of job satisfaction towards quiet quitting behavior in millennial and Z workers. Another result of this study is that there is a positive effect of empowering leadership and organizational culture on job satisfaction, while work overload have a negative effect on job satisfaction. The research results are useful information for planning strategies to retain employees and provide their best performance at work. These findings are significant to measure the factors that influence job satisfaction of millennial and generation Z employees so that they do not quiet quitting or leaving the company.

Pengaruh Iklan dan Citra Merek Terhadap Keputusan Pembelian Produk Madu Wild Bee

This research aims to analyze advertising and brand image on purchasing decisions for wild bee honey products. This type of research is research with a quantitative approach. The population in this research is all Wild Bee Honey customers. Determining the sample in the study used probability sampling using the Slovin formula so that the number of samples in this study was 97 employees. The data analysis method in this research uses SPSS analysis tools through validity, reliability, classical assumption tests, namely normality test, multicollinearity test, heteroscedasticity test, multiple linear regression, t test, F test and correlation and determination coefficients. The results of this research show that (1) Advertising variables influence purchasing decisions, this is proven by the significance value of 0.000 < 0.05. 2. The Brand Image variable has a significant effect on purchasing decisions, this is proven by the significance value of 0.000 < 0.05. 3. Simultaneous test results show that the advertising and brand image variables influence the decision to purchase wild bee honey. This can be proven by the Fcount result of 113.280 or a significance value of 0.000 < 0.05.

Naturally Abscised Fruitlets as a Potential Breeding Resource for Early Spring Buildup of Medfly Populations in Temperate Regions

The Mediterranean fruit fly (medfly), Ceratitis capitata, is considered one of the most destructive fruit pests worldwide. Female medflies prefer to lay eggs in ripening, ripe, or overripe fruits. In deciduous orchards of temperate regions, fruitlets (juvenile fruits) of early cultivars could represent the earliest spring resource with a potential host function for medflies. However, little is known about the medfly female’s ability to ovipositing and complete breeding in fruitlets. To clarify the possible role of fruitlets, the suitability of key-host fruitlets for the oviposition of medflies and the further development of immature stages were examined in laboratory conditions and an attempt was made to assess the degree of infection of fruitlets collected in the field. Additionally, the development of the overwintering medfly population and the putative use of fruitlets as a resource for early population growth were simulated using the PESTonFARM model optimized to reflect specific features of medfly biology and behavior. Our laboratory data demonstrate that nectarine and pear fruitlets can support the full development of medfly offspring. However, no medfly pupae were obtained from a sample of 14,000 fruitlets collected at the farm. The simulation showed that the sample number needed to detect with at least 95% certainty rare cases of medfly pupae or adults emerging from infected fruitlets is several tens of thousands, which is several times more than the actual number of fruitlets used in our study. Our research suggests that the fruitlets of some cultivated fruit species can potentially constitute a viable food resource for the development of the earliest generation of spring medflies, with implications for medfly Integrated Pest Management (IPM).

Massively parallel analysis of single-molecule dynamics on next-generation sequencing chips.

Single-molecule techniques are ideally poised to characterize complex dynamics but are typically limited to investigating a small number of different samples. However, a large sequence or chemical space often needs to be explored to derive a comprehensive understanding of complex biological processes. Here we describe multiplexed single-molecule characterization at the library scale (MUSCLE), a method that combines single-molecule fluorescence microscopy with next-generation sequencing to enable highly multiplexed observations of complex dynamics. We comprehensively profiled the sequence dependence of DNA hairpin properties and Cas9-induced target DNA unwinding-rewinding dynamics. The ability to explore a large sequence space for Cas9 allowed us to identify a number of target sequences with unexpected behaviors. We envision that MUSCLE will enable the mechanistic exploration of many fundamental biological processes.

Analisis Mutu Pelayanan Kesehatan Masyarakat terhadap Kepuasan Pasien di Puskesmas Bukit Lawang Kabupaten Langkat Tahun 2023

The quality of health services is very important to measure how far the Community Health Center is in providing services to patients by looking at the number of BPJS patient visits at the Bukit Lawang Community Health Center, Langkat Regency, which only reached 82.00% in 2020, 77.9% in 2022 and 76.9% in 2023 compared to the target of 95%. Data from the Bukit Lawang Community Health Center, Langkat Regency for the period 2021-2023 showed fluctuations in the number of BPJS patient visits every month. The purpose of this study was to analyze the effect of the quality of public health services on patient satisfaction at the Bukit Lawang Health Center, Langkat Regency in 2023. The research design used a quantitative method with a cross-sectional study approach. The population in this study were all patients who visited the Bukit Lawang Health Center, Langkat Regency in 2023 with an average number of visits of 292 patients per month, the number of samples of 75 people was obtained using a purposive sampling technique. Data analysis used univariate, bivariate and multivariate analysis with logistic regression tests. The results of the study using the chi-square test showed that the variables reliability, assurance, tangible, empathy and responsiveness had a p-value <0.05. The results of the study using the logistic regression test showed that the variables reliability, empathy and responsiveness had a sig. <0.05 and the variables assurance and tangible had a sig.> 0.05. The conclusion of the study is that the variables that significantly affect patient satisfaction are the variables reliability, empathy and responsiveness and the variable that most significantly affects patient satisfaction is the variable reliability.

The Influence of Earnings Quality and Dividend Policy on Company Value

Company value is an important aspect that needs to be considered It highlights the company's performance, which can shape investors' perceptions of the business. The aim of the research is to provide empirical evidence regarding the influence of earnings quality and dividend policy on company value. Company size and leverage are used as control variables in this research. The research uses PBV as a proxy for company value, DACC as a proxy for earnings quality, DPR as a proxy for dividend policy, total assets as a proxy for company size and DER as a proxy for leverage. This research was conducted on companies listed on the Indonesia Stock Exchange (BEI) in 2012-2022. The number of samples taken was 6325 observation samples using the non-probability sampling method, especially using the sampling technique, namely purposive sampling. Data collection was carried out using non-participant observation. The analysis technique used is multiple linear regression analysis technique. The results of this research indicate that earnings quality has no effect on company value and dividend policy has a positive effect on company value.

Detection of Anxiety-Based Epileptic Seizures in EEG Signals Using Fuzzy Features and Parrot Optimization-Tuned LSTM.

In humans, epilepsy is diagnosed through electroencephalography (EEG) signals. Epileptic seizures (ESs) arise due to anxiety. The detection of anxiety-based seizures is challenging for radiologists, and there is a limited availability of anxiety-based EEG signals. Data augmentation methods are required to increase the number of novel samples. An epileptic seizure arises due to anxiety, which manifests as variations in EEG signal patterns consisting of changes in the size and shape of the signal. In this study, anxiety EEG signals were synthesized by applying data augmentation methods such as random data augmentation (RDA) to existing epileptic seizure signals from the Bonn EEG dataset. The data-augmented anxiety seizure signals were processed using three algorithms-(i) fuzzy C-means-particle swarm optimization-long short-term memory (FCM-PS-LSTM), (ii) particle swarm optimization-long short-term memory (PS-LSTM), and (iii) parrot optimization LSTM (PO-LSTM)-for the detection of anxiety ESs via EEG signals. The predicted accuracies of detecting ESs through EEG signals using the proposed algorithms-namely, (i) FCM-PS-LSTM, (ii) PS-LSTM, and (iii) PO-LSTM-were about 98%, 98.5%, and 96%, respectively.

Physics-informed reinforcement learning for probabilistic wind power forecasting under extreme events

With wind power penetration increases, accurate and reliable wind power forecasting is becoming gradually critical, and data driven model is a promising solution to implement this task. However, limited by deficient data samples under extreme conditions, scarcity of feature measurements fails to meet the number of training samples required, making the forecasting model exhibits low adaptability. This paper proposes a physics-informed reinforcement learning based method for probabilistic forecasting. Analytical physical expression of wind power output under extreme event is established to construct the error evaluation function for abrupt feature. Deep deterministic policy gradient-based quantile fitting model is then proposed, with abrupt feature embedded as the auxiliary input data for neural network. On this basis, parameter training technique under small data set is proposed to deal with the effect of extreme conditions on correction process of network. It extracts key historical transitions from experience replay pool to establish effective training samples, and model parameters are updated through the small-batch learning strategy to minimize long-term error feedback. Test result on the practical cold wave event of wind farms in China shows effectiveness of the proposed method.

Multi-task metric learning for optical performance monitoring

In our experiments, applying few shot metric learning for optical performance monitoring (OPM), we set the dataset as 16-way-6-shot. Modulation format identification (MFI) was utilized as a classification task, and optical signal-to-noise ratio (OSNR) estimation was used as a regression task for joint analysis. Multi-task metric learning (MML) used the adaptive weights to balance the weights of the three metric functions, six modulation formats (QPSK, 8QAM, 16QAM, 32QAM, 64QAM, 128QAM) are classified correctly with 100 % accuracy after 200 epochs. Furthermore, the lowest mean square error (MSE) of OSNR is 0.431 dB. Then, Ablation experiments compute the corresponding similarity (SIM) for each metric function show that the MSE of MML, SIMLocal+Cosine, SIMCosine+Point, SIMLocal+Point, single-task metric learning (SML) and adaptive multi-task learning (AMTL) is 0.431 dB, 0.572 dB, 0.569 dB, 0.567 dB, 0.637 dB, 1.319 dB, respectively. The proposed model achieves the highest accuracy in MFI and the lowest MSE in OSNR estimation. Finally, when comparing the various metric functions while altering the transmission distance of the optical fiber, it was observed that MML stayed within an acceptable range between 200 km and 800 km. This shows that our algorithm requires only a small number of training samples to create a reasonably good model, offering a new approach to solving problems that arise in optical performance monitoring.

Reliable delineation of Bacillus cytotoxicus from other members of the Bacillus cereus group by MALDI-TOF MS – An extensive validation study

Bacillus cytotoxicus is classified as a thermotolerant member of the Bacillus cereus group, producing the diarrhoea causing cytotoxin K1. This species came into the focus of food microbiologists when the type strain was isolated as the cause of a fatal food-borne outbreak. However, knowledge of the role of B. cytotoxicus as a food-borne microbe and data on its prevalence in food is still limited today. One reason for this is the lack of an easy to perform method of identification at the species level other than PCR, due to the highly similar phenotype of many B. cereus group representatives. Matrix-assisted laser desorption ionization time of flight mass spectrometry (MALDI-TOF MS) is widely used for the identification of microorganism species. This study describes the formal validation of a MALDI-TOF MS system for a reliable differentiation between B. cytotoxicus and other members of the B. cereus group. An extensive number of well-supported individual spectra from B. cytotoxicus, several other species of the B. cereus group, and 652 other microorganism species in a taxonomically wide range served as data sets. These were used to perform the validation for two database compilations: the commercial MALDI-Biotyper version K, and an extended compilation, including reference spectra from an open collection of spectra created by several users. Using both database combinations, the obtained values fulfil the formal criteria for the targeted identification of B. cytotoxicus in accordance with a national guideline for the validation of MALDI-TOF MS parameters in food analysis, with significant improvements due to the expanded database. By using 183 single spectra from 170 isolates, created in eleven different laboratories, the number of samples significantly exceeded the sample sizes for validation currently specified for ISO 16140–4:2020 for the validation of identification methods in food microbiology by single laboratories. Therefore, the method is suitable for routine application in an accredited laboratory context.

Contrastive prototype network with prototype augmentation for few-shot classification

In recent years, metric-based meta-learning methods have received widespread attention because of their effectiveness in solving few-shot classification problems. However, the scarcity of data frequently results in suboptimal embeddings, causing a discrepancy between anticipated class prototypes and those derived from the support set. These problems severely limit the generalizability of such methods, necessitating further development of Few-Shot Learning (FSL). In this study, we propose the Contrastive Prototype Network (CPN) consisting of three components: (1) Contrastive learning proposed as an auxiliary path to reduce the distance between homogeneous samples and amplify the differences between heterogeneous samples, thereby enhancing the effectiveness and quality of embeddings; (2) A pseudo-prototype strategy proposed to address the bias in prototypes, whereby the pseudo prototypes generated using query set samples are integrated with the initial prototypes to obtain more representative prototypes; (3) A new data augmentation technique, mixupPatch, introduced to alleviate the issue of insufficient data samples, whereby enhanced images are generated by blending the images and labels from different samples, to increase the number of samples. Extensive experiments and ablation studies conducted on five datasets demonstrated that CPN achieves robust results against recent solutions.

Multi-objective robust optimization design framework for low-pollution emission burners

The optimal design of low-pollution emission burners plays an important role in controlling pollutant emissions of industrial equipment, and is crucial for the sustainable development of the national economy and environmental protection. However, many uncertain factors challenge the optimal design of low-pollution emission burners. The Latin hypercube sampling (LHS) method was used to obtain sampling data representing the distribution of the uncertain variable. The training dataset was obtained using the turbulent combustion coupling model. A high-precision sparse polynomial chaos expansion (PCE) model was constructed by the degree-adaptive scheme and least angle regression (LAR) algorithm. Furthermore, the Legendre polynomial is introduced to establish a continuous robust optimization model. The model is carried out by the non-dominated sorting genetic algorithm II (NSGA-II). The results show that the excess air coefficient of 1.227 is optimal. Compared with the excess air coefficient of 1.20 under the discrete robust optimization, the optimal coefficient can further reduce pollutant emissions and bring strong robustness to the ethylene cracking furnace. It has also been proven that the continuous robust optimization scheme improves the optimization granularity. Compared with discrete robust optimization, this method reduces the number of samples by 66.7 %.

Comparison of Regression Methods to Predict the First Spike Latency in Response to an External Stimulus in Intracellular Recordings for Cerebellar Cells.

The significance of intracellular recording in neurophysiology is emphasized in this article, with considering the functions of neurons, particularly the role of first spike latency in response to external stimuli. The study employs advanced machine learning techniques to predict first spike latency from whole cell patch recording data. Experiments were conducted on Control (Salin) and Experiment (Harmaline) groups, generating a dataset for developing predictive models. Because the dataset has a limited number of samples, we utilized models that are effective with small datasets. Among different groups of regression models (linear, ensemble, and tree models), the ensemble models, specifically the LGB method, can achieve better performance. The results demonstrate accurate prediction of first spike latency, with an average mean squared error of 0.0002 and mean absolute error of 0.01 in 10-fold cross-validation. The research suggests the potential of machine learning in forecasting the first spike latency, allowing reliable estimation without the need for extensive animal testing. This intelligent predictive system facilitates efficient analysis of first spike latency changes in both healthy and unhealthy brain cells, streamlining experimentation and providing more detailed insights into the captured signals.

Genotype Representation Graphs: Enabling Efficient Analysis of Biobank-Scale Data.

Computational analysis of a large number of genomes requires a data structure that can represent the dataset compactly while also enabling efficient operations on variants and samples. Current practice is to store large-scale genetic polymorphism data using tabular data structures and file formats, where rows and columns represent samples and genetic variants. However, encoding genetic data in such formats has become unsustainable. For example, the UK Biobank polymorphism data of 200,000 phased whole genomes has exceeded 350 terabytes (TB) in Variant Call Format (VCF), cumbersome and inefficient to work with. To mitigate the computational burden, we introduce the Genotype Representation Graph (GRG), an extremely compact data structure to losslessly present phased whole-genome polymorphisms. A GRG is a fully connected hierarchical graph that exploits variant-sharing across samples, leveraging ideas inspired by Ancestral Recombination Graphs. Capturing variant-sharing in a multitree structure compresses biobank-scale human data to the point where it can fit in a typical server's RAM (5-26 gigabytes (GB) per chromosome), and enables graph-traversal algorithms to trivially reuse computed values, both of which can significantly reduce computation time. We have developed a command-line tool and a library usable via both C++ and Python for constructing and processing GRG files which scales to a million whole genomes. It takes 160GB disk space to encode the information in 200,000 UK Biobank phased whole genomes as a GRG, more than 13 times smaller than the size of compressed VCF. We show that summaries of genetic variants such as allele frequency and association effect can be computed on GRG via graph traversal that runs significantly faster than all tested alternatives, including vcf.gz, PLINK BED, tree sequence, XSI, and Savvy. Furthermore, GRG is particularly suitable for doing repeated calculations and interactive data analysis. We anticipate that GRG-based algorithms will improve the scalability of various types of computation and generally lower the cost of analyzing large genomic datasets.

Research on insulator image segmentation and defect recognition technology based on U‐Net and YOLOv7

AbstractThis study focuses on aerial images in power line inspection, using a small sample size and concentrating on accurately segmenting insulators in images and identifying potential “self‐explode” defects through deep learning methods. The research process consists of four key steps: image segmentation of insulators, identification of small connected regions, data augmentation of original samples, and detection of insulator defects using the YOLO v7 model. In this paper, due to the small sample size, sample expansion is considered first. A sliding window approach is adopted to crop images, increasing the number of training samples. Subsequently, the U‐Net neural network model for semantic segmentation is used to train insulator images, thereby generating preliminary mask images of insulators. Then, through connected region area filtering techniques, smaller connected regions are removed to eliminate small speckles in the predicted mask images, obtaining more accurate insulator mask images. The evaluation metric for image recognition, the dice coefficient, is 93.67%. To target the identification of insulator defects, 35 images with insulator defects from the original samples are augmented. These images are input into the YOLO v7 network for further training, ultimately achieving effective detection of insulator “self‐explode” defects.

Pengaruh Digital Marketing, Word Of Mouth Dan Brand Awerness Terhadap Minat Pembelian Produk BPR Sejahtera Batam

One of the banks in the city of Batam is known as PT. BPR Sejahtera Batam. A company that wants to survive in a competitive market needs help from customers. Banking customers need to have an interest in buying so that the products offered by the bank can run and operate. The main problem is the decreasing interest in purchasing BPR Sejahtera Batam products, which is known through the decrease in the number of active customers compared to the previous year. This is because digital marketing is not optimal, there is not enough discussion among the public and the ability of potential buyers to get to know BPR Sejahtera Batam is still weak. The aim of the research is to find out how much influence digital marketing, word of mouth and brand awareness partially and simultaneously have on interest in purchasing BPR Sejahtera Batam products. The population in this study were customers from BPR Sejahtera Batam KC Mitra Raya, totaling 199 customers based on the company's records of active customers in 2022. Simple random sampling technique was chosen as the sampling technique in the study reviewed. This research uses the Slovin formula to determine the number of samples with a sample size of 133 respondents. The results state that either partially or simultaneously digital marketing, word of mouth and brand awareness have a positive and significant influence on purchasing interest.

Prototype-wise self-knowledge distillation for few-shot segmentation

Few-shot segmentation was proposed to obtain segmentation results for a image with an unseen class by referring to a few labeled samples. However, due to the limited number of samples, many few-shot segmentation models suffer from poor generalization. Prototypical network-based few-shot segmentation still has issues with spatial inconsistency and prototype bias. Since the target class has different appearance in each image, some specific features in the prototypes generated from the support image and its mask do not accurately reflect the generalized features of the target class. To address the support prototype consistency issue, we put forward two modules: Data Augmentation Self-knowledge Distillation (DASKD) and Prototype-wise Regularization (PWR). The DASKD module focuses on enhancing spatial consistency by using data augmentation and self-knowledge distillation. Self-knowledge distillation helps the model acquire generalized features of the target class and learn hidden knowledge from the support images. The PWR module focuses on obtaining a more representative support prototype by conducting prototype-level loss to obtain support prototypes closer to the category center. Broad evaluation experiments on PASCAL-5i and COCO-20i demonstrate that our model outperforms the prior works on few-shot segmentation. Our approach surpasses the state of the art by 7.5% in PASCAL-5i and 4.2% in COCO-20i.

Pengaruh Pemberian Rebusan Jahe terhadap Penurunan Tekanan Darah pada Lansia Hipertensi di Wijaya Kusuma, Jakarta Barat

This study aims to determine the effect of giving ginger decoction to reducing blood pressure in elderly hypertensives. This study used a quasi-experimental design with a pre-post test without control group design. The population in this study were elderly people who suffer from hypertension in RT 07 Wijaya Kusuma, West Jakarta. The number of samples in this study were 22 people using the total sampling technique. The instruments in this study were 4gr ginger, 200cc water, digital scales, honey, knife, measuring cup, and digital tensimeter (automatic). The research results showed that the average systolic blood pressure at the first measurement (pre test) was 141.32 mmHg with a standard deviation of 10.242. In the last measurement (Post Test) the average systolic blood pressure was 132.59 mmHg with a standard deviation of 3.912 while the average diastolic blood pressure in the first measurement (Pre Test) was 94.27 mmHg with a standard deviation of 10.416 and in the last measurement (Post Test) was 81.55 mmHg with a standard deviation of 7.614. Statistical analysis using the paired t-test showed p value = 0.001 so it can be concluded that there is an effect of giving ginger decoction to reducing blood pressure in elderly hypertensives. Based on the results of the study, nurses are expected to be able to provide interventions to address hypertension problems by administering ginger decoction which can lower blood pressure on an ongoing basis without significant side effects and can be used as traditional medicine in patients with hypertension.