Number Of Samples Research Articles

Efficient strategy for topology optimization of stochastic viscoelastic damping structures

In this study, the dynamic topology optimization (TO) of stochastic viscoelastic damping structures (VDSs) is performed for the first time. However, the expensive computation cost seriously hinders the TO process. To address this problem, an efficient strategy is proposed. On the one hand, in the stochastic structural response analysis, a fully adaptive method based on direct probability integral method is presented to determine the number and locations of samples. Meanwhile, to improve the computational efficiency of structural response for each sample, a piecewise model-order reduction method based on Krylov subspace is adopted to generate the orthonormal basis and project the original large-scale system onto a low-order system. On the other hand, to overcome the optimization challenge arising from large number of design variables in the density-based topology method, a material-field series-expansion method is employed to describe the topology layout and significantly reduce the number of design variables. Moreover, the sensitivity of the optimization model is derived by the adjoint method and the method of moving asymptotes (MMA) is used to efficiently update the design variables. Some numerical examples comprehensively demonstrate the effectiveness and efficiency of the proposed strategy. The results indicate that the uncertainty of structural parameters greatly affect both the topology layout of VDS and vibration damping capacity.

Cost-effectiveness of sotorasib as a second-line treatment for non-small cell lung cancer with KRASG12C mutation in China and the US.

e20567 Background: Sotorasib was used for the treatment of adult patients with locally advanced or metastatic non-small cell lung cancer with KRASG12Cmutation who had received at least one systematic therapy, showing good antitumor activity and safety. However, it was unclear whether the high-cost sotorasib was cost-effective. Methods: A Markov model was developed to compare the lifetime cost and efficacy of sotorasib to treat non-small cell lung cancer with KRASG12C mutation from the CodeBreak 200 randomized placebo-controlled trial, which included 345 patients with previously treated non-small cell lung cancer with KRASG12C mutation. Incremental cost-effectiveness ratio(ICER), quality-adjusted life-year (QALY), incremental QALY were calculated for the 2 treatment strategies. One-way sensitivity analysis was used to investigate the factors that had a greater impact on the model results, and tornado diagram was used to verify the model stability, probabilistic sensitivity analysis was performed by Monte Carlo simulation, random factors were introduced into the model to simulate a certain number of large samples, and the results were presented as cost-effectiveness acceptable curves. Results: This economic evaluation of data from the CodeBreak 200 randomized trial. In China, sotorasib generated 0.44 QAYL with a total cost of $84372.59. Compwered with docetaxel, the ICER value of sotorasib was $102701.84/QALY, which was higher than WTP, so sotorasib had no economic advantage. In the US, sotorasib obtained 0.35 QALY more than docetaxel, ICER was $15,976.50 /QALY, which was low than WTP, indicating that the increased cost of sotorasib was acceptable. Conclusions: Sotorasib had cost effect from the perspective of the US. Howere, sotorasib had no cost effect from the perspective of China, and only when the willingness to pay(WTP) exceeds $102,500, the probability of sotorasib having cost effect increases from 0 to 49%.

Disease relapse in relation to lymph node sampling in lung carcinoid patients.

The predictive value of the extent of peri-operative lymph node (LN) sampling in relation to disease relapse in patients with pulmonary carcinoid (PC) is unknown. Furthermore, post-surgery follow-up recommendations rely on institutional retrospective studies with short follow-ups. We aimed to address these shortcomings by examining the relation between LN sampling and relapse in a population-based cohort with long-term follow-up. By combining the Dutch nationwide pathology and cancer registries, all patients with surgically resected PC (2003-2012) were included in this analysis (last update 2020). The extent of surgical LN dissection was scored for the number of LN samples, location (hilar/mediastinal), and completeness of resection according to European Society of Thoracic Surgeons (ESTS) guidelines. Relapse-free interval (RFI) was evaluated using Kaplan Meier and multivariate regression analysis. 662 patients were included. The median follow-up was 87.5 months. Relapse occurred in 10% of patients, mostly liver (51.8%) and locoregional sites (45%). The median RFI was 48.1 months (95% CI 36.8-59.4). Poor prognostic factors were atypical carcinoid, pN1/2, and R1/R2 resection. In 546 patients LN dissection data could be retrieved; at least one N2 LN was examined in 44% and completeness according to ESTS in merely 7%. In 477 cN0 patients, 5.9% had pN1 and 2.5% had pN2 disease. In conclusion, relapse occurred in 10% of PC patients with a median RFI of 48.1 months thereby underscoring the necessity of long-term follow-up. Extended mediastinal LN sampling was rarely performed but systematic nodal evaluation is recommended as it provides prognostic information on distant relapse.

Revealing heterogeneity in chronic lymphocytic leukemia: AI-driven insights into aggressive and indolent disease subtypes.

e19029 Background: Chronic lymphocytic leukemia (CLL) exhibits a broad spectrum of clinical behaviors, from indolent courses requiring minimal intervention to aggressive forms demanding immediate treatment. The underlying heterogeneity of CLL poses significant challenges in predicting disease progression and tailoring patient-specific therapeutic strategies. Leveraging advanced and novel artificial intelligence (AI) and machine learning (ML) methodologies, our study aims to dissect the complexity of CLL, focusing on the identification and characterization of biomarkers that differentiate aggressive and indolent disease forms. Methods: The NetraAI is a priorietaryAI/ML system that is also being utilized in a research collaboration at a leading American oncology institute (not disclosed) that integrates machine intelligence, augmented intelligence, and an agent that interacts with AI-derived patient representations. This system was designed to learn from datasets with a small number of samples and many independent variables. This system operates by generating hypotheses in the form of interactive representations of patient populations that reveal heterogeneity along with statistically significant driving factors. Finally, an automated agent called AutoPlay interacts with these representations and generates an accounting of all variables, subpopulations of samples, and it also generates a human readable perspective on what was discovered by the data set. Results: We applied the NetraAI to a CLL dataset and used it to refine the analysis and interpretation of the corresponding transcriptomic data (GSE39411). Significantly lower levels of FADS3, GSDME, LPL, IMMT, NMB, and AEBP1 and higher expressions of COBLL1, P2RY1R, PDE8A, SYNE2, and FCRL3 were identified as hallmarks of indolent CLL, suggesting these markers could inform less aggressive disease management strategies. Furthermore, within a total of 104 CLL samples, we uncovered two distinct subpopulations within aggressive CLL, delineated by unique genetic markers. Group A, encompassing 31 aggressive CLL samples is predominantly characterized by the expression of lipoprotein lipase (LPL), while Group B, comprising 22/23 aggressive samples, is characterized by ZBTB20 and SYNE2. Our poster will also provide glimpses into a more refined heterogeneity that emerged from the analysis. Our results not only underscore the heterogeneity within aggressive CLL, but also highlights the potential for these markers to guide prognostic assessments and therapeutic decisions. Conclusions: This study not only advances our understanding of CLL heterogeneity, but also sets the stage for the development of more personalized and effective treatment modalities. By identifying drivers of disease aggression and indolence, we pave the way for targeted therapies that could significantly improve patient outcomes in CLL.

Pengaruh Kualitas Pelayanan Fiskus, Pemahaman Perpajakan dan Insentif Pajak terhadap Kepatuhan Wajib Pajak

This research aims to determine the influence of tax service quality, understanding of taxation and tax incentives on taxpayer compliance. This type of research is quantitative research using a random method (Random Sampling) in collecting data samples and the type of data used is primary data. The population of this research is Taxpayers registered at KPP Pratama Serpong. The number of samples distributed was 55 respondents. The data analysis techniques used are descriptive statistics, data quality testing, classical assumption testing and hypothesis testing. The research results show that the results of the F statistical test show that simultaneously the value of Fcount > Ftable (11.531 > 2.79), so it can be concluded that the Quality of Fiscus Services, Understanding of Taxation and Tax Incentives simultaneously have a significant effect on Taxpayer Compliance. The quality of Fiscus Services in particular has no effect on Taxpayer Compliance. Understanding Taxation partially influences Taxpayer Compliance. Partial tax incentives have no effect on Taxpayer Compliance.

A transfer learning-based graph neural network model using mIF images to predict neoadjuvant immunochemotherapy response in patients with gastrointestinal cancer.

e13592 Background: Neoadjuvant immunochemotherapy has great potential to revolutionize the treatment of gastrointestinal cancer. However, effective biomarkers to classify responders from non-responders are still lacking. We aimed to build a response prediction model using multiplex immunofluorescence (mIF) images from patients with gastrointestinal cancer. Methods: We proposed a graph neural network-based (GNN) approach based on the pre-treatment mIF samples collected from 77 patients, including 30 gastric cancer (GC) patients, 34 colorectal cancer (CRC) patients, and 13 esophageal cancer (ESCA) patients. All the tissue samples were stained by two panels of mIF agents, namely Panel-A (CD3, CD8, PD1, PD-L1) and Panel-B (CD56, CD68, CD163). Cell coordinates, agent densities, and cell morphology features were used to generate cell graphs for model training. Results: Initially, we trained a pan-cancer GNN model for each panel and assessed its performance on the patch- and patient-level using five-fold cross-validation. The Panel-A model achieved higher areas under the receiver operating characteristic curve (AUROC) (patch-level: 0.71 ± 0.036, patient-level: 0.65 ± 0.010) than the Panel-B model (patch-level: 0.59 ±0.017, patient-level: 0.62 ± 0.021) and conventional mIF-based markers (i.e., positive cell ratios/counts of fluorescence agents, AUROC: 0.34 ~ 0.54). Specifically, the performance of CRC was better in the Panel-A model (patch-level: 0.75± 0.053, patient-level: 0.76 ± 0.053) than that in Panel-B (patch-level: 0.52 ± 0.027, patient-level: 0.60 ± 0.028), while the performance of GC was better in Panel-B (patch-level: 0.64 ± 0.030, patient-level: 0.71 ± 0.034) than that in Panel-A (patch-level: 0.53 ± 0.019, patient-level: 0.51 ± 0.018). The AUROCs of both panels of ESCA were less than 0.5, reflecting feature differences associated with treatment response between ESCA and GC/CRC. Furthermore, discrepancies of GC and CRC between Panel-A and -B motivated us to further optimize the performance for individual cancer types using transfer learning (TL). The GC TL-model performance (patch-level: 0.691±0.025, patient-level: 0.751 ± 0.046) was improved compared with the pan-cancer model, but not for the CRC TL-model (patch-level: 0.636 ± 0.066, patient-level: 0.697 ± 0.039). Moreover, the best patient-level specificity and sensitivity under the optimal Youden-Index were 70% and 88% for CRC, and 93% and 62% for GC. Conclusions: The GNN-based approach using mIF images demonstrated promising potential in predicting the immunochemotherapy response of gastrointestinal cancer patients, despite the limited number of samples were curated. The performance variation among cancer types indicated the cancer heterogeneity and the necessity of building single-cancer models when more data becomes available.

Reproducible and high sample throughput isomiR next-generation sequencing for cancer diagnosis.

e15013 Background: Next-generation sequencing (NGS) can produce up to 6 Tb of data per run with single-nucleotide accuracy, making it ideal for quantifying isomiRs, which encompass both canonical miRNAs and their variants, for clinical applications. However, NGS has poor reproducibility and low sample throughput in quantifying circulating isomiRs due to significant technical variations and the limitations of the multiplex strategy, as evidenced by the fact that no isomiR NGS technique has been successfully used to diagnose cancer. Methods: To address these challenges, a library construction method including a dual unique-dual-index (DUDI) technology was developed. DUDI uses a pair of Inner UDI (IUDI) and outer UDI (OUDI) to label a sample. Twelve independent batches of isomiR NGS were carried out, including three repeated batches. Each batch included 100 gastric cancer and 100 control plasma samples. Batch effect, correlation coefficient (R), and principal component (PCA) analyses were used to evaluate technical reproducibility. Machine learning binary classification was used to assess biological reproducibility, with each pair of batch data serving interchangeably as both training and testing data. Results: In this multicenter study, over 700G of isomiR data were generated from 402 gastric cancer and 498 control samples, with a maximum error rate of 1 in 7 million isomiRs being assigned to wrong samples. The PCA plot indicates high technical reproducibility across the three repeated batches, shown by the extensive intermingling of data points from each batch and the lack of distinct batch-wise clustering. This observation is reinforced by that the R value for each of 239 isomiRs between the repeated batches are close to 1. While the mutual machine learning validations between the repeated batches yielded ~95% accuracy, indicating high biological reproducibility. The accuracies of the validations between the different batches of different samples range from 70% to 82%. The lower accuracy is as expected, given the high genetic heterogeneity of cancer and the small sample size. Furthermore, the IsomiR differentiated expression profiles from the current NGS study closely match those from prior qPCR studies. Conclusions: The DUDI library construction method can produce reproducible high sample throughput NGS data, yet it is cost-effective and straightforward. The maximum number of samples that can be multiplexed in an NGS project is almost one million, i.e., 976 * 976, as IUDI and OUDI can be any of the 976 designed DUDIs. This number far exceeds the high sample throughput requirements of any NGS application. While the capability to distinguish true biological variations of IsomiRs from technical noise, demonstrated by the high technical and biological reproducibility and concordance with the qPCR data, enables the development of robust machine learning algorithms for cancer diagnostics.

Fast Incomplete Multi-View Clustering With View-Independent Anchors.

Multi-view clustering (MVC) methods aim to exploit consistent and complementary information among each view and achieve encouraging performance improvement than single-view counterparts. In practical applications, it is common to obtain instances with partially available information, raising researches of incomplete multi-view clustering (IMC) issues. Recently, several fast IMC methods have been proposed to process the large-scale partial data. Though with considerable acceleration, these methods seek view-shared anchors and ignore specific information among single views. To tackle the above issue, we propose a fast IMC with view-independent anchors (FIMVC-VIA) method in this article. Specifically, we learn individual anchors based on the diversity of distribution among each incomplete view and construct a unified anchor graph following the principle of consistent clustering structure. By constructing an anchor graph instead of pairwise full graph, the time and space complexities of our proposed FIMVC-VIA are proven to be linearly related to the number of samples, which can efficiently solve the large-scale task. The experiment performed on benchmarks with different missing rate illustrates the improvement in complexity and effectiveness of our method compared with other IMC methods. Our code is publicly available at https://github.com/Tracesource/ FIMVC-VIA.

Proximate Composition and Antioxidant Activity of Honey Bee Collected Pollen in the Main Flowering Season, in West Shewa Zone, Central Ethiopia

Abstract This study aimed to assess the effect of botanical and geographical differences on the protein, minerals, total phenolic content and the antioxidant activity of bee pollen collected from five districts in West Shewa Zone, Ethiopia. Pollens of Guizotia sp., Brassica sp., Trifolium sp., Eucalyptus sp., Plantago lanceolata and Vicia faba were color-sorted from multifloral pollens and subjected to chemical analysis. The results revealed the mean content of moisture 10.6% of fresh weight (FW), protein 19.4% of dry weight (DW) and ash 2.4% (DW); the most dominant minerals, K (9765.6), Ca (963.4), Mg (960.5), Fe (142.8) and P (126.5) in mg/kg DW. The average of the total phenolic content (TPC) and the antioxidant activity (DPPH radical) were 40.3 GAE mg/g FW and 13.7 AAE mg/g FW (gallic and ascorbic acid equivalent, respectively). V. faba pollen had the highest protein, ash, P and Cu content. Four pollen taxa had the highest Ca values, while multifloral pollens contained the highest Fe content. The samples from Ejere had the highest ash, Ca, Na and Cu content. The results showed a high content of the tested chemicals and a strong antioxidant capacity of bee pollen, varying with botanical and geographical origins. These findings would also serve as inputs for further assessment of the nutritional values of pollen for honey bee colonies and potential human consumption. However, comprehensive chemical investigations of larger number of samples representing diversified flora and locations are crucial to developing a broad regional information base on the nutritional profile of bee pollen.

Research and analysis on fault diagnosis of multistage centrifugal pump for mining

Abstract The vibration data collected by the fault experiment are analyzed in the time domain and frequency domain respectively, and the conclusion shows that the frequency characteristics of some vibration data cannot be fully characterized due to noise interference. In order to solve the problem of noise interference, this paper uses the VMD algorithm based on the kurtosis peak to denoise the collected data, constructs wavelet time-frequency map samples through the wavelet transform method to provide input sources for the subsequent convolutional neural network, and uses the image samples for fault diagnosis and identification. [1] Through the convolutional neural network built to diagnose and identify the different states of the multistage centrifugal pump, it can be seen that the recognition accuracy, training time, and recognition time of a single sample of various image samples in the three cases of no noise reduction, noise reduction and a small number of samples can be found through the method of comparative analysis, and compared with the traditional SVM method, the superiority of the convolutional neural network using image recognition can be found.

Determinants associated with carpal tunnel syndrome complaints

Introduction: Carpal Tunnel Syndrome (CTS) is one of the most common problems in the upper extremities caused by the narrowing of the carpal tunnel, which results in pressure on the median nerve. Increased pressure in the carpal tunnel causes compression and damage to the median nerve. Objective: determinants associated with carpal tunnel syndrome . Methods: quantitative research with a cross-sectional research design. The sampling technique in this study used a total sampling technique in which the entire population is equal to the number of samples, namely 36 people. Data obtained by using a questionnaire. The analytical method used was univariate and bivariate analysis using the chi-square test. Result: showed that there was a relationship between length of work and carpal tunnel syndrome (p-value = 0.0190. There is a relationship between age and complaints of carpal tunnel syndrome (p-value = 0.032. There is no relationship between personal protective equipment and complaints of carpal tunnel syndrome (p-value = 0.759). There is no relationship between repetitive movements and carpal tunnel syndrome (p-value = 0.787. Conclusion: It was concluded that there is a relationship between length of service and carpal tunnel syndrome complaints, there is a relationship between age and carpal tunnel syndrome complaints and there is no relationship between body mass index and carpal tunnel syndrome complaints . In this study, researchers suggest to companies to pay more attention to employees who have a long working period so as not to do excessive work and for employees to take advantage of adequate rest time to minimize the occurrence of complaints of Carpal Tunnel Syndrome (CTS).

Performance Analysis of Deep Learning and Machine Learning Methods for Music Genre Classification System

Performance Analysis of Deep Learning and Machine Learning Methods for Music Genre Classification System

The endogenous association among MMP2/miR-1248/Circ_0087558/miR-643/ MAP2K6 axis can contribute to brain metastasis in basal-like subtype of breast cancer

Brain metastasis in basal-like breast cancer poses a significant challenge in cancer management due to its aggressive nature and limited treatment options. This study conducted a comprehensive analysis to explore the potential role of circular RNAs (circRNAs) as members of endogenous networks in developing breast cancer brain metastasis.Here, we utilized RNA sequencing data from primary breast cancer and brain metastasis tissue with basal-like subtype (n = 11). After quality controlling and preprocessing of fastq files, gene expression of mRNA and circRNAs were extracted from matched samples and normalized. Then, we employed the weighted gene co-expression network analysis approach to identify brain metastasis-associated circRNA modules (SpearmanCorrelation>0.5, P−value<0.05). Moreover, we found five protein-coding genes of PHLDA1, SLC12A2, MMP2, RGP1, and MAP2K6, significantly upregulated in brain metastatic tissues compared to primary breast cancer (FDR<0.05). These genes were enriched in the "GnRH signaling pathway" and "Fluid shear stress and atherosclerosis" pathways (FDR<0.05).Next, to explore the potential interactions between circRNAs and protein-coding genes, we reconstructed a competing endogenous RNA (ceRNA) network using mutual miRNAs between the circRNA module and upregulated mRNAs. Notably, we could detect two axes of circ_0087558/miR-604/MMP2 and MMP2/miR-1248/Circ_0087558/miR-643/MAP2K6 in ceRNA network.In conclusion, the identified circRNA-miRNA-mRNA axes might be therapeutic targets or diagnostic biomarkers for this challenging subtype of breast cancer. However, due to the small number of samples, further experimental validations are essential.

Projected fuzzy [formula omitted]-means clustering algorithm with instance penalty

At present, high-dimensional data clustering has become a vital research field in machine learning. Traditional clustering algorithms cannot perform well on high-dimensional data, where the clustering task is usually divided into two stages: dimensionality reduction first and clustering later. In general, the existing high-dimensional clustering methods usually have the following shortcomings: (1) the two-stage strategy splits the connection between clustering and dimensionality reduction; (2) these algorithms do not consider the impact of anomalous instances in high-dimensional data on clustering performance. Therefore, to address these problems, a projected fuzzy c-means clustering algorithm with instance penalty (PCIP) is proposed. Firstly, we construct an instance penalty matrix and assign an instance penalty coefficient to each sample. Secondly, a model for clustering high-dimensional data is constructed by integrating fuzzy c-means clustering (FCM) and principal component analysis (PCA). The proposed model can perform dimensionality reduction and clustering simultaneously. In addition, the time complexity of the proposed algorithm is linearly related to the number of samples n, which can efficiently deal with large data sets. The proposed PCIP algorithm is verified by experiments using clustering accuracy and normalized mutual information (NMI) as evaluation metrics. The experimental results on 10 image datasets show that the average accuracy and average NMI of the PCIP algorithm are improved by 0.0375 and 0.0275, respectively, compared to the second-ranked algorithm.

Active learning for efficient data-driven aerodynamic modeling in spaceplane design

Data-driven surrogate model is extensively applied in the aerodynamics prediction for flight vehicle design. However, for three-dimensional problems, modeling costs hugely in acquiring adequate data. Adaptive and efficient sampling represents a promising approach, so we employ Active Learning (AL) to assess the sampling space. An aerodynamics prediction framework based on the Loss for Active Learning (LLAL) is proposed for capturing the sample insufficient space. The LLAL-based method refines the model by estimating the biases between the ground truths and the predictions as a measure of the high-value samples, then selecting top-K samples for infilling. We first validate our proposed method using an analytical benchmark two-dimensional function, followed by its application to aerodynamics prediction of spaceplane with the number of 60 and the deformation dimensions of 18. Through using both the Latin Hypercube Sampling and LLAL-based methods to infill samples, we observed the R2 of lift-to-drag ratio improves from 0.82 to 0.85. The AL method can enhance the accuracy of models with a limited number of samples, thereby reducing sampling costs and improving the efficiency of aerodynamic design.

Runoff Prediction in Different Forecast Periods via a Hybrid Machine Learning Model for Ganjiang River Basin, China

Accurate forecasting of monthly runoff is essential for efficient management, allocation, and utilization of water resources. To improve the prediction accuracy of monthly runoff, the long and short memory neural networks (LSTM) coupled with variational mode decomposition (VMD) and principal component analysis (PCA), namely VMD-PCA-LSTM, was developed and applied at the Waizhou station in the Ganjiang River Basin. The process begins with identifying the main forecasting factors from 130 atmospheric circulation indexes using the PCA method and extracting the stationary components from the original monthly runoff series using the VMD method. Then, the correlation coefficient method is used to determine the lag of the above factors. Lastly, the monthly runoff is simulated by combining the stationary components and key forecasting factors via the LSTM model. Results show that the VMD-PCA-LSTM model effectively addresses the issue of low prediction accuracy at high flows caused by a limited number of samples. Compared to the single LSTM and VMD-LSTM models, this comprehensive approach significantly enhances the model’s predictive accuracy, particularly during the flood season.

HSS: enhancing IoT malicious traffic classification leveraging hybrid sampling strategy

Using deep learning models to deal with the classification tasks in network traffic offers a new approach to address the imbalanced Internet of Things malicious traffic classification problems. However, the employment difficulty of these models may be immense due to their high resource consumption and inadequate interpretability. Fortunately, the effectiveness of sampling methods based on the statistical principles in imbalance data distribution indicates the path. In this paper, we address these challenges by proposing a hybrid sampling method, termed HSS, which integrates undersampling and oversampling techniques. Our approach not only mitigates the imbalance in malicious traffic but also fine-tunes the sampling threshold to optimize performance, as substantiated through validation tests. Employed across three distinct classification tasks, this method furnishes simplified yet representative samples, enhancing the baseline models’ classification capabilities by a minimum of 6.02% and a maximum of 182.66%. Moreover, it notably reduces resource consumption, with sample numbers diminishing to a ratio of at least 83.53%. This investigation serves as a foundation, demonstrating the efficacy of HSS in bolstering security measures in IoT networks, potentially guiding the development of more adept and resource-efficient solutions.

Farm specific estimates of forage variability result in larger optimal control limits for forage quality monitoring

Variation in forage composition decreases the accuracy of diets delivered to dairy cows. However, variability of forages can be managed using a renewal reward model (RRM) and genetic algorithm (GA) to optimize sampling and monitoring practices for farm conditions. Specifically, use of quality-control-charts to monitor forage composition can identify changes in composition for which adjustment in the formulated diet will result in a better match of the nutrients delivered to cows. The objectives of this study were 1) assess the use of a clustering algorithm to estimate the mean time the process is stable or in-control (d) (TStable) and the magnitude of the change in forage composition between stable periods (ΔForage) for corn silage and alfalfa-grass silage which are input parameters for the RRM; 2) compare optimized farm-specific sampling practices (number of samples (n), sampling interval (TSample) and control limits (ΔLimit) using previously proposed defaults and our estimates for the TStable and ΔForage input parameters; and 3) conduct a simulation study to compare the number of recommended diet changes costs of quality control under the proposed sampling and monitoring protocols. We estimated the TStable and ΔForage parameters for corn silage NDF and starch and alfalfa-grass silage NDF and CP using a k-means clustering approach applied to forage samples collected from 8 farms, 3x/week during a 16-week period. We compared 4 sampling and monitoring protocols that resulted from the 2 methods for estimating TStable and ΔForage (default values and our proposed method) and either optimizing only the control limit (Optim1) or optimizing the control limits, the number of samples, and the number of days between sampling (Optim2). We simulated the outcomes of implementing the optimized monitoring protocols using a quality control chart for corn silage and alfalfa-grass silage of each farm. Estimates of T^Stable and Δ^Forage from the k-means clustering analysis were, respectively, shorter and larger than previously proposed default values. In the simulated quality control monitoring, larger Δ^Forage estimates increased the optimized ΔLimit resulting in fewer detected shifts in composition of forages and a lower frequency of false alarms and a lower quality control cost ($/d). Recommended diet reformulation intervals from the simulated quality control analysis were specific for the type of forage and farm management practices. The median of the diet reformulation intervals for all farms using our optimal protocols was 14 d (Q1 = 8, Q3 = 26) for corn silage and 16 d (Q1 = 8, Q3 = 26) for alfalfa-grass silage.

PERAN INOVASI PRODUK DALAM MEMEDIASI PENGARUH ORIENTASI PASAR TERHADAP KINERJA PEMASARAN

One of the SME industrial units that is currently developing and is in great demand by the public today is the coffee industry, along with the large consumption of coffee in Indonesia. North Kuta District is one area that has felt the impact of the development of the coffee shop business. The increasing number of coffee shop developments makes competition between similar businesses higher so that good and optimal marketing performance is needed in maintaining business. This study aims to explain the role of product innovation in mediating the effect of market orientation on marketing performance at coffee shops in North Kuta District. The number of samples in this study were 100 respondents with purposive sampling method. Data collection through the distribution of questionnaires (google form). Data analysis using path analysis techniques. The results showed that market orientation had a positive and significant effect on marketing performance, market orientation had a positive and significant effect on product innovation, product innovation had a positive and significant effect on marketing performance and product innovation could mediate the effect of market orientation on marketing performance

PENGARUH ELECTRONIC WORD OF MOUTH, KESESUAIAN HARGA DAN KUALITAS PELAYANAN TERHADAP KEPUTUSAN PEMBELIAN

Purchasing decision is a final decision or choice that consumers have to buy products or use services with various considerations. This study aims to explain the effect of electronic word of mouth, price compitability and service quality on purchasing decisions for consumers of Kytha Coffee in Denpasar City. The number of samples used was 140 respondents and they had made purchases at Kytha Coffee in Denpasar City. The sampling method used in this research is non-probability sampling with accidental sampling technique. Data collection was obtained from the results of distributing questionnaires using the Google form as a research instrument. The results of multiple linear regression analysis show that the variables electronic word of mouth, price compitability and service quality have a positive and significant effect on purchasing decisions. The results of this study indicate that the higher the electronic word of mouth, the purchasing decision will increase, as well as the compitability of price and service quality, the better it will increase purchasing decisions.