Large Coefficient Values Research Articles

Faktor-Faktor yang Mempengaruhi Psychological Well Being Ibu Hamil dan Ibu Menyusui Saat Masa Pandemi COVID-19 di Surabaya

COVID-19 has become a global health emergency and has a complex impact on every aspect of people's lives. One of the vulnerable groups is pregnant women and breastfeeding mothers. Psychological well-being needs to be achieved by pregnant women and breastfeeding mothers to be able to get through conditions of uncertainty and threats during the pandemic. The purpose of this study was to analyze the factors that affect psychological well being in pregnant women and breastfeeding mothers during the COVID-19 in Surabaya. This research was an analytic correlational study with a cross sectional approach. The population of this study were all pregnant women and mothers who were breastfeeding with babies aged less than 6 months at the time of data collection, domiciled or resided in Surabaya. The sampling technique in this study was simple random sampling with a total sample of 198 respondents consisting of 130 pregnant women and 68 breastfeeding mothers. The results of the spearman rho test showed that income, history of pregnancy/birth history, history of COVID-19, and family support had an effect on the psychological well being of pregnant and lactating women. The COVID-19 history variable has the largest coefficient value which indicates that this variable has a strong influence on the psychological well being of pregnant women and breastfeeding mothers during the COVID-19 in Surabaya.

The relationship of patient satisfaction level with word of mouth on health services: systematic review

Health services, especially in hospitals, are now increasingly required to provide excellent service to patients. One of easy way for patients to get information about services is to ask someone who has received the same service before. This is called word of mouth (WOM). This research was conducted using the literature review method. The data was synthesized by comparing 5 literatures that met the inclusion and exclusion criteria. The database sources were Google scholar and PubMed. Data is processed and presented in tabulated form. From 5 literatures that have been reviewed, there were 4 articles stating that there was a significant relationship between patient satisfaction and WOM on health services. The smallest significance number is =0.000, and the largest coefficient value is 0.533, which means that the increasing of patient satisfaction unit will cause a change in WOM to health services by 53.3%. There is only 1 article that finds that there is no relationship between patient satisfaction and WOM with a value of 0.117. There is a significant relationship between patient satisfaction and WOM on health services. Health services such as hospitals should pay attention to patient satisfaction, because it greatly affects the image of the hospital in customer’s point of view.

Green Competitive Advantage Moderate:Environmental Performance, Corporate Image And Corporate Social Performance On Economic Performance

This research aims to determine the effect of environmental performance, corporate image, and corporate social performance on economic performance with moderated by green competitive advantage partially on manufacturing companies in Indonesia. The population in this research is all manufacturing companies listed on Indonesia Stock Exchange during the period 2013-2017. The total samples tested were 13 companies selected by purposive sampling technique. Data type in this research use secondary data obtained from Indonesia Stock Exchange and site respectively of company being sampled. Data analysis technique use panel data regression with Eviews 9.0 program. The result indicates that environmental performance have a positive effect on economic performance and after moderated by green competitive advantage of environmental performance has a positive effect on economic performance with a larger coefficient value. Corporate image have no effect on economic performance but after moderated by green competitive advantage of Corporate image has a positive effect on economic performance. Corporate social performance have no effect on economic performance and after moderated by green competitive advantage of Corporate image also has not effect on economic performance.

PLS-SEM Analysis to Know Factors Affecting The Interest of Buying Halal Food in Muslim Students

The increase of halal food products has led to increase in culinary tourism in recent years. One of the districts that has experienced a rapid increase in culinary tourism is Sidoarjo Regency. The development of culinary tourism in the last few years in Sidoarjo Regency generally targets are the students. This study will aim to determine the factors that influence the interest in buying halal food and what factors have the greatest influence on the interest in buying halal food. The analysis technique uses the Partial Least Squares Structural Equation Modeling (PLS-SEM. Based on the results of the analysis, it is known that of the 5 predictor variables used in the analysis process, there are 4 variables that have a significant effect on Purchase Interest (Y). It can be concluded that increasing of Halal Awareness (X1), Halal Certification (X2), Health (X3) and Value Perception (X5) will further increase Purchase Interest (Y). Meanwhile, based on value of coefficient on each variable, it is known that Health (X3) has the largest coefficient value (0.260), so it can be concluded that Health (X3) has the greatest influence on Purchase Interest (Y).

MP81-15 TWO-MINUTE PROSTATE MAGNETIC RESONANCE IMAGING PREDICTS GLEASON SCORE: AN ADVANCED MACHINE LEANING OF RAPID T2-WEIGHTED IMAGING

You have accessJournal of UrologyProstate Cancer: Detection & Screening VIII (MP81)1 Apr 2020MP81-15 TWO-MINUTE PROSTATE MAGNETIC RESONANCE IMAGING PREDICTS GLEASON SCORE: AN ADVANCED MACHINE LEANING OF RAPID T2-WEIGHTED IMAGING Ileana Montoya Perez*, Jussi Toivonen, Harri Merisaari, Pekka Taimen, Otto Ettala, Tapio Pahikkala, Peter Boström, Hannu Aronen, and Ivan Jambor Ileana Montoya Perez*Ileana Montoya Perez* More articles by this author , Jussi ToivonenJussi Toivonen More articles by this author , Harri MerisaariHarri Merisaari More articles by this author , Pekka TaimenPekka Taimen More articles by this author , Otto EttalaOtto Ettala More articles by this author , Tapio PahikkalaTapio Pahikkala More articles by this author , Peter BoströmPeter Boström More articles by this author , Hannu AronenHannu Aronen More articles by this author , and Ivan JamborIvan Jambor More articles by this author View All Author Informationhttps://doi.org/10.1097/JU.0000000000000973.015AboutPDF ToolsAdd to favoritesDownload CitationsTrack CitationsPermissionsReprints ShareFacebookLinked InTwitterEmail Abstract INTRODUCTION AND OBJECTIVE: To develop and validate Gleason score classifier using texture features and advance machine learning algorithms of rapid prostate T2-weighted magnetic resonance imaging (T2w) with acquisition time less than 2 minutes. METHODS: Eighty-two prospectively enrolled patients with histologically confirmed prostate cancer (PCa) underwent 3 Tesla magnetic resonance imaging before prostatectomy. T2-weighted magnetic resonance imaging (T2w) was performed using single shot turbo spin echo sequence with repetition time/echo time 4668/130 ms, field of view 250x250 mm2, acquisition matrix size 250x320, and acquisition time 1 minute 10 seconds. A histogram alignment method was used to correct a non-standardness of T2w (“intensity drift”). Prostate cancer lesions were delineated using whole mount prostatectomy sections as the ground true. In total, 1631 unique texture features were extracted including Gabor function, Haar transform, image moments, Sobel operator, local binary patterns (LBP), gray-level co-occurrence matrix (GLCM). Classifier was built using logistic regression with either L1 or L2 regularization to compensate the high dimensionality of the data by penalizing large coefficient values of the inferred linear models. The classification performance (Gleason score 3+3 vs >3+3), was evaluated by area under a receiver operating characteristic curve (AUC) values. The classification performance of the model built by the regularized logistic regression algorithms was estimated by a nested cross validation strategy with an outer leave-pair-out cross-validation and an inner 10-fold cross validation for hyperparameter selection (Figure 1). RESULTS: The final data set was composed of 126 PCa lesions, 36 and 90 lesions had Gleason score 3+3 and >3+3, respectively. The best performing texture features belonged to GLCM and Gabor function groups. The classifier achieved AUC (95% confidence interval) of 0.85 (0.74 - 0.92). CONCLUSIONS: Machine learning classifier using radiomic and texture features of 2-minute prostate MRI demonstrated a good performance in the classification of prostate cancer Gleason score. Rapid T2-weighted imaging with acquisition time less than 2 minutes and advanced machine learning are promising tools for non-invasive Gleason score prediction. Source of Funding: This study was financially supported by grants from Sigrid Jusélius Foundation, Finnish Cultural Foundation, Orion Pharma Research Foundation, TYKS-SAPA Research funds, Finnish Cancer Society, and Instrumentarium Research Foundation. © 2020 by American Urological Association Education and Research, Inc.FiguresReferencesRelatedDetails Volume 203Issue Supplement 4April 2020Page: e1242-e1242 Advertisement Copyright & Permissions© 2020 by American Urological Association Education and Research, Inc.MetricsAuthor Information Ileana Montoya Perez* More articles by this author Jussi Toivonen More articles by this author Harri Merisaari More articles by this author Pekka Taimen More articles by this author Otto Ettala More articles by this author Tapio Pahikkala More articles by this author Peter Boström More articles by this author Hannu Aronen More articles by this author Ivan Jambor More articles by this author Expand All Advertisement PDF downloadLoading ...

Kinerja Keuangan Perusahaan Perbankan Yang Terdaftar Di Bursa Efek Indonesia

This study aimed to evaluate the financial performance of banks in terms of Return on Assets. Besides how big a factor the Capital Adequacy Ratio (CAR) , Loan to Deposit Ratio (LDR), Non Performing Loan (NPL), Operational Cost Ratio (BOPO), Net Interest Margin (NIM) affect the rise and fall of Return on Assets (ROA). Population of this study are bank listed companies in Indonesia Stock Exchange (IDX). Analysis technique of data used multi linear regression of ordinary least square and hypotheses test used t statistic for testing partial regression coefficient and F statistic for testing simultaneous influence at level of significance 5%. This research demonstrated that BOPO) and NPL partially have significant influence negative towards ROA. Then for NIM partially have significant influence positive towards ROA. On the contrary, the CAR and LDR partially have no significant influence towards the ROA. The research also shows that NIM coefficient become largest coefficient values and have the biggest significant influaences. Simultaneously, the CAR, LDR, NPL, BOPO and the NIM has significant influence towards the ROA of Bank companies in IDX.

CUSTOMER RELATIONSHIP MANAGEMENT DAN PENGARUHNYA TERHADAP LOYALITAS NASABAH BANK: STUDI PADA BANK MUAMALAT INDONESIA

Customer Relationship Management and Its Influence on customer loyalty Bank: A Case Study of Bank Muamalat Indonesia. The aim of this research is to analyze the influence of customer relationship management (CRM) to customer’s loyalty. Data collection is using primary and secondary data. The sampling method that used on this research is accidental sampling with 100 respondents. The analysis method on this research is multiple regressions. The CRM variables that used on this research are identify, acquire, retain, develop, and sustainable marketing. From the result, show that the CRM variable had an impact simultaneously on customer’s loyalty. Otherwise, only three variables had an impact on customer’s loyalty, such as acquire activity, retain, and sustainable marketing. There are also show that only acquire variable had an dominant influence on customer’s loyalty in Bank Muamalat Indonesia branch Slipi-Jakarta, because it has the largest coefficient value. DOI: 10.15408/ess.v5i1.2332

A systematic study of multiple minerals precipitation modelling in wastewater treatment

Mineral solids precipitation is important in wastewater treatment. However approaches to minerals precipitation modelling are varied, often empirical, and mostly focused on single precipitate classes. A common approach, applicable to multi-species precipitates, is needed to integrate into existing wastewater treatment models. The present study systematically tested a semi-mechanistic modelling approach, using various experimental platforms with multiple minerals precipitation. Experiments included dynamic titration with addition of sodium hydroxide to synthetic wastewater, and aeration to progressively increase pH and induce precipitation in real piggery digestate and sewage sludge digestate. The model approach consisted of an equilibrium part for aqueous phase reactions and a kinetic part for minerals precipitation. The model was fitted to dissolved calcium, magnesium, total inorganic carbon and phosphate. Results indicated that precipitation was dominated by the mineral struvite, forming together with varied and minor amounts of calcium phosphate and calcium carbonate. The model approach was noted to have the advantage of requiring a minimal number of fitted parameters, so the model was readily identifiable. Kinetic rate coefficients, which were statistically fitted, were generally in the range 0.35–11.6 h−1 with confidence intervals of 10–80% relative. Confidence regions for the kinetic rate coefficients were often asymmetric with model-data residuals increasing more gradually with larger coefficient values. This suggests that a large kinetic coefficient could be used when actual measured data is lacking for a particular precipitate-matrix combination. Correlation between the kinetic rate coefficients of different minerals was low, indicating that parameter values for individual minerals could be independently fitted (keeping all other model parameters constant). Implementation was therefore relatively flexible, and would be readily expandable to include other minerals.

Growth Value Two-Factor Model

This study developed and tested a stock return prediction model called the Growth Value Two-Factor Model (GVM). The proposed GVM considers beginning and ending book value-to-price ratios (BPRs) and future return on equity (ROE) during a given period. We also used a mean-reverting process to estimate future BPR and ROE, and derived a Growth Value Indicator (GVI) able to help select stocks that deliver better performance. The GVI includes one model coefficient, determined by fitting actual market data. In the GVI, a large coefficient value implies a strong mean-reverting effect on BPR and ROE (that is, the market is overreacting and stock returns are dominated by the value factor). Conversely, a small coefficient implies a weak mean-reverting effect on BPR and ROE (that is, the market is underreacting and stock returns are dominated by the growth factor). The GVM provides a reasonable theory to explain the long-debated issue of whether value stocks or growth stocks provide the best returns. We used Standard & Poor 500 component stocks to examine the effectiveness of the GVM during the period 1998–2008. Conclusions include: (1) value and growth factors are both critical to explaining and understanding stock return performance. As value stocks and growth stocks are not opposites of one another, they should be treated two-dimensionally. (2) BPR and (1+ROE) are clearly affected by mean-reverting phenomena. The exponential-decay mean-reverting model fits well with actual stock data. (3) GVI performed better than traditional stock selection factors for selecting stocks with better performance, which indirectly demonstrated the effectiveness of the GVM.

The study of magnon-drag effects in the thermopower of CuCr2X4 (X = S, Se and Te)

The electrical resistivity ρ and thermopower S of metallic ferromagnets CuCr2X4 (X=S,Se or Te) are measured at low temperatures and in an external magnetic field of 1.5 Tesla. These properties are dominated by strong scattering effects in their ferromagnetic phase, resulting in (a) ρ(T) ~A T2dependence with very large value of coefficient A ≈ 1 − 3×10−8 Ω-cm/K2 in resistivity and (b) a peak in the thermopower around TC/3 . Thermopower is further increased by crystalline order and by the alignment of magnetic domains in an external magnetic field as has been found for Cu1+x Cr2Te4. We have interpreted these effects as due to strongly coherent momentum conserving electron-magnon scattering giving a large T2 – contribution in resistivity and a large drag effect in thermopower.

Hierarchically compressed wavelet synopses

The wavelet decomposition is a proven tool for constructing concise synopses of large data sets that can be used to obtain fast approximate answers. Existing research studies focus on selecting an optimal set of wavelet coefficients to store so as to minimize some error metric, without however seeking to reduce the size of the wavelet coefficients themselves. In many real data sets the existence of large spikes in the data values results in many large coefficient values lying on paths of a conceptual tree structure known as the error tree. To exploit this fact, we introduce in this paper a novel compression scheme for wavelet synopses, termed hierarchically compressed wavelet synopses, that fully exploits hierarchical relationships among coefficients in order to reduce their storage. Our proposed compression scheme allows for a larger number of coefficients to be stored for a given space constraint thus resulting in increased accuracy of the produced synopsis. We propose optimal, approximate and greedy algorithms for constructing hierarchically compressed wavelet synopses that minimize the sum squared error while not exceeding a given space budget. Extensive experimental results on both synthetic and real-world data sets validate our novel compression scheme and demonstrate the effectiveness of our algorithms against existing synopsis construction algorithms.

Dielectric and Piezoelectric Properties of Sn2P2S6 Single Crystals

Dependence investigations of the hydrostatic piezoelectric coefficient (d h ) and the dielectric permittivity (ϵ) on pressure (p) are presented. Calculations of the voltage piezoelectric coefficient g h (p,T) are made. It was determined, that Sn2P2S6 single crystal has large values of coefficient (g h ). That allows us to use Sn2P2S6 single crystals as piezoelectric sensors in the wide temperature and pressure range.

The Tap-Leakage Algorithm: An Algorithm for the Stable Operation of a Digitally Implemented, Fractionally Spaced Adaptive Equalizer

A fractionally spaced equalizer is a nonrecursive adaptive filter whose tap weights are spaced a fraction of a symbol interval apart. Such an equalizer can significantly enhance modem performance in the presence of severe linear distortion, when compared with a conventional synchronous equalizer whose taps are spaced a symbol interval apart. However, a digitally implemented, fractionally spaced equalizer generally will exhibit long-term instability when the conventional tap-adjustment algorithm is used. This occurs because, in contrast to the synchronous equalizer, a fractionally spaced equalizer generally will have many sets of tap values, which result in nearly equal values of mean-squared error (mse). Some of these tap settings — which invariably will be attained because of biases in the digital tap-updating circuitry — are large enough to cause register overflows and consequent performance deterioration. In this paper we report how a simple modification in the tap-adjustment algorithm provides a solution to the above problem. The modified tap-adjustment algorithm prevents the buildup of large coefficient values by systematically “leaking” or decreasing the magnitudes of all the equalizer tap weights. For an experimental modem operating at 9.6 kb/s, it has been demonstrated that the tap-leakage adjustment algorithm prevents the accumulation of large equalizer tap values, while permitting the full performance gain of a fractionally spaced equalizer to be realized.

Entrainment of motor-unit discharges as a neuronal mechanism of synchronization.

The neuronal mechanism which gives rise to the synchronization of motor-unit discharges has been inferred from an analysis of the interspike intervals of individual motor-unit discharges recorded from the soleus muscle. The motor units were divided into two groups on the basis of characteristic changes in their spike trains. The first group maintained a stationary discharge pattern throughout the process of synchronization with a firing rate of approximately 10 spikes/s. Small unidentified units simultaneously recorded gradually grouped around the individual spikes of the first group motor unit and with this process, high-frequency force oscillation appeared phase-locked with each of grouped discharges. The mean period of force oscillation was almost identical to the mean discharge interval. Therefore, the first group motor unit was considered as a pacemaker of this force-oscillation. The second group motor unit underwent from its initially stationary process to a transitional process characterized by spike dropouts from an otherwise regular spike train. When both groups of motor units were recorded by the same electrode, it was found that the firing rate of the second group motor unit discharges gradually approached that of the first group, and the spikes of the first and the second group motor units occurred near or at the same time. The number of double intervals decreased in a highly predictable fashion with an increase in a firing rate. It was furthermore observed that the spikes of a given motor unit whose discharges interval-to-period ratio is smaller at the beginning of transitional process was entrained to the first group motor-unit discharges with a faster time course than the unit whose discharge interval-to-period ratio is larger. The synchronizing process was described from the relations between shorter discharge interval-to-period ratios and the longer-to-shorter interval ratios obtained at several stages from the beginning of transitional process to the final synchronization. Their relations were best drawn by the second-order regression lines. The faster time course of synchronization was reflected in the larger value of coefficient a in the equation. The results of this and previous study (23) further provided evidence to justify that interaction of motor-unit discharges is responsible for the synchronization. Although the neuronal limiting device of the firing-rate control to approximately 10 spikes/s still remains unsolved, the possibility was considered that a disinhibitory neuronal network first acts to synchronize independently firing motoneurons and leads to the oscillation of the stretch reflex loop. This closed-loop system was considered as a site for the stored motor program and the use of disinhibitory neuronal network was discussed in relation to the Harmon's model of neuromimes.