Higher Root Mean Square Research Articles

Using a blind EC mechanism for modelling the interaction between binaural and temporal speech processing

We reanalyzed a study that investigated binaural and temporal integration of speech reflections with different amplitudes, delays, and interaural phase differences. We used a blind binaural speech intelligibility model (bBSIM), applying an equalization-cancellation process for modeling binaural release from masking. bBSIM is blind, as it requires only the mixed binaural speech and noise signals and no auxiliary information about the listening conditions. bBSIM was combined with two non-blind back-ends: The speech intelligibility index (SII) and the speech transmission index (STI) resulting in hybrid-models. Furthermore, bBSIM was combined with the non-intrusive short-time objective intelligibility (NI-STOI) resulting in a fully blind model. The fully non-blind reference model used in the previous study achieved the best prediction accuracy (R2 = 0.91 and RMSE = 1 dB). The fully blind model yielded a coefficient of determination (R2 = 0.87) similar to that of the reference model but also the highest root mean square error of the models tested in this study (RMSE = 4.4 dB). By adjusting the binaural processing errors of bBSIM as done in the reference model, the RMSE could be decreased to 1.9 dB. Furthermore, in this study, the dynamic range of the SII had to be adjusted to predict the low SRTs of the speech material used.

Evaluation of tree-based ensemble algorithms for predicting the big five personality traits based on social media photos: Evidence from an Iranian sample

Machine learning has been widely used in psychological issues. In this study, Iranian Instagram users' photos were used to predict the big five personality traits with extra trees, gradient boosted trees and random forest. The big five personality traits of 142 users were measured using the short form of Goldberg's 50-item personality scale, and 30 color features including classified color histogram, color level mean, and color level median in RGB (Red, Green, and Blue) and HSV (Hue, Saturation, and Value) spaces were extracted from the 1348 photos. Predictive models were cross-validated, and the highest root mean square error and mean absolute error values for all models did not reach 0.16 and 0.13, respectively, which were related to the prediction of Extraversion. Overall, all models had good performance and generalizability in prediction. Results showed that each personality trait has a special type of color preference and photo posting. The features' importance was evaluated by the Gini method. Color histogram and HSV color space were the most important features in modeling. As a result, it can be said that the proposed methodology is effective in practice, but care must be taken with multicollinearity, accuracy, and integrity of social media data.

Preliminary Characterization of Predictive Factors of the Visual Change after Epi-On and Epi-Off Corneal Collagen Crosslinking Techniques.

Purpose To investigate the potential predictive factors of the visual change achieved with accelerated epi-on and epi-off corneal collagen crosslinking (CXL) in keratoconus. Methods This retrospective comparative study analyzed 67 eyes treated with an accelerated epithelium-on (epi-on group) and epithelium-off (epi-off group) CXL. The clinical outcomes were evaluated and compared during a 1-year follow-up. Likewise, the relationship of the change achieved with both CXL techniques in the corrected distance visual acuity (CDVA) with different preoperative data was investigated. Results The mean CDVA change at 3 months postoperatively was −0.04 ± 0.19 and −0.07 ± 0.25 in the epi-on and epi-off groups, respectively (p = 0.809). In the epi-on group, this change was significantly correlated with the preoperative apical (r = −0.375, p = 0.045) and central corneal thickness (r = −0.402, p = 0.031). In the epi-off group, the CDVA change was significantly correlated with not only the preoperative apical (r = 0.402, p = 0.028) and central corneal thickness (r = 0.367, p = 0.046) but also with some topometric and aberrometric indices (r ≤ −0.374, p ≤ 0.042). Furthermore, the change in CDVA in the epi-on group could be predicted from age, preoperative refractive astigmatism J45 component, anterior corneal asphericity, and posterior corneal high order aberration root mean square (p = 0.002, R2 = 0.503). In the epi-off group, the CDVA change could be predicted from the preoperative minimum corneal thickness and magnitude of the vertical anterior corneal primary coma component (p = 0.001, R2 = 0.446). Conclusions Clearly, different predictive factors of the visual change induced with the accelerated epi-on and epi-off CXL techniques are present, suggesting a different mechanism of action for stiffening the cornea and inducing changes in this structure.

A novel single source multilevel inverter with hybrid switching technique

SummaryA novel multilevel inverter (MLI) configuration with the hybrid switching technique is presented in this paper. The proposed MLI consists of the H‐bridge combination with unidirectional switches, half‐bridges, and transformers. The suggested MLI with the additional cascaded connection increases to higher voltage levels. The number of employed components in this topology is drastically minimized. Therefore, the complexity, cost, and volume of the proposed topology are also reduced. The operation of the suggested topology is tested through the improved novel switching technique. This modulation method reduces the total harmonic distortion (THD) and produces high root mean square (RMS) voltage. Further, a comprehensive comparison with the recent MLI topologies is performed to validate the merits of the suggested inverter. Simulation and experimental results verify the suggested topology performance using the new modulation technique at different loading conditions and modulation indices.

Online cleaning method of power grid energy anomaly data based on improved random forest

Aiming at the problem of high root mean square error of traditional power grid energy anomaly data online cleaning, a power grid energy anomaly data online cleaning method based on improved random forest is designed. Firstly, an outlier data recognition model of isolated forest is designed to identify outliers in the data. Secondly, an improved random forest regression model is established to improve the adaptability of random forest to mixed abnormal data, and the data trend is fitted and predicted. Finally, the improved random forest data cleaning method is used to compensate the missing data after removing the mixed abnormal data, so as to clean the abnormal energy data of the power grid. The experimental results show that when the amount of power grid energy anomaly data increases, the cleaning root mean square error of the experimental group is significantly lower than that of the control group. The method in this paper solves the problem of high root-mean-square error in the online cleaning of abnormal data of traditional grid energy.

Predictive Microbial Modeling of Enterococcus faecium NRRL B-2354 Inactivation during Baking of a Multicomponent Low-Moisture Food

The use of baking ovens as a microbial kill step should be validated based on results of thermal inactivation models. Although traditional isothermal models may not be appropriate for these dynamic processes, they are being used by the food industry. Previous research indicates that the impact of additional process conditions, such as humidity, should be considered when validating thermal processes for the control of microbial hazards in low-moisture foods. In this study, the predictive performance of traditional and modified thermal inactivation kinetic models accounting for process humidity were assessed for predicting inactivation of Enterococcus faecium NRRL B-2354 in a multi-ingredient composite food during baking. Ingredients (milk powder, protein powder, peanut butter, and whole wheat flour) were individually inoculated to achieve ∼6 log CFU/g, equilibrated to a water activity of 0.25, and then mixed to form a cookie dough. An isothermal inactivation study was conducted for the dough to obtain traditional D- and z-values (n = 63). In a separate experiment, cookies were baked under four dynamic heating conditions: 135°C, high humidity; 135°C, low humidity; 150°C, high humidity; and 150°C, low humidity. Process humidity measurements; time-temperature profiles for the product core, surface, and bulk air; and microbial survivor ratios were collected for the four conditions at six residence times (n = 144). The traditional isothermal model had a high root mean square error (RMSE) of 856.51 log CFU/g, significantly overpredicting bacterial inactivation during the process. The modified model accounting for the dynamic time-temperature profile and process humidity data was a better predictor with an RMSE of 0.55 log CFU/g. These results indicate the importance of accounting for additional process parameters in baking inactivation models and that model performance can be improved by utilizing model parameters obtained directly from industrial-scale experimental data.

543 Late-Breaking: Development of a Model to Predict Dietary Metabolizable Energy from Digestible Energy in Beef Cattle

Abstract We aimed to assess whether predicting the metabolizable energy (ME) to digestible energy (DE) ratio (MDR), rather than a prediction of ME with DE, is feasible and to develop a model equation to predict MDR in beef cattle. For this, we constructed a literature database based on published data. A meta-analysis was conducted with 306 means from 69 studies containing both dietary DE and ME concentrations measured by calorimetry to test whether the exclusion of the y-intercept is adequate in the linear relationship between DE and ME. A random coefficient model with study as the random variable was used to develop equations to predict MDR in growing and finishing beef cattle. The developed equations were evaluated with other published equations. The no-intercept linear equation represented the relationship between DE and ME more appropriately than the equation with a y-intercept. Within our growing and finishing cattle data, the animal’s physiological stage was not a significant variable affecting MDR after accounting for the study effect (P = 0.213). The mean (± SE) of MDR was 0.849 (± 0.0063). Two linear equations with the dry matter intake and content of several dietary nutrients were developed to predict MDR. When using these equations, the observed ME was predicted with high precision (R2 = 0.92). The model accuracy was also high, as shown by the high concordance correlation coefficient (> 0.95) and small root mean square error of prediction (RMSEP), less than 5% of the observed mean. Moreover, a significant portion of the RMSEP was due to random bias (> 93%), without mean or slope bias (P > 0.05). We concluded that dietary ME in beef cattle could be accurately estimated from dietary DE and its conversion factor, MDR, using the two equations developed in this study.

Ripening assessment of ‘Ortanique’ (Citrus reticulata Blanco x Citrus sinensis (L) Osbeck) on tree by SW-NIR reflectance spectroscopy-based calibration models

The aim of this study was the non-destructive assessment of ‘Ortanique’ (Citrus reticulata Blanco x Citrus sinensis (L) Osbeck) ripening, based on the prediction of internal quality attributes (IQA) by short-wave near-infrared reflectance spectroscopy (SW-NIRS) calibration models. Spectra from fruit of 50 trees located in two different orchards, were acquired on tree using a customized portable visible near-infrared (vis-NIR) system. Partial least squares (PLS) was used to build the various IQA calibration models. The models were tested through internal validation (IV) and external validation (EV). Generally, the IV results were always superior to those of EV: regarding IV, a high regression coefficient (R2) and low root mean square error of prediction (RMSEP) were achieved, revealing a good predictive performance for juice pH (R2 = 0.80; RMSEP = 0.10; SDR = 2.23), soluble solids content (SSC) (R2 = 0.79; RMSEP = 0.75 %; SDR = 2.27), titratable acidity (TA) (R² = 0.73; RMSEP = 0.24 % citric acid; SDR = 1.94) and the maturation index (MI) (R2 = 0.80; RMSEP = 1.38; SDR = 2.2). The best EV predictions were obtained for TA (R2 = 0.69; RMSEP = 0.38 % citric acid; SDR = 1.24), and MI (R2 = 0.69; RMSEP = 2.07; SDR = 1.49). Calibration models for glucose, fructose and sucrose showed medium-coarse predictions for both validation strategies. A detailed investigation of MI models was performed, to understand the causes of their poor EV results. In the context of EV, model updating strategies were explored by using some validation samples to improve the calibration model. The methods of bias correction and spiking were tested, showing a clear improvement in the predictions.

Elucidation of steady shear flow properties of β‐glucan solutions under different thermal and environmental conditions by different rheological models

AbstractThe present study was carried out to investigate flow behavior of β‐glucan of hull‐less barley cultivar Lut, as influences of concentrations (0.25, 0.5, 0.75, and 1%); temperatures (5, 25, 45, and 60°C); NaCl and CaCl2 salts; pHs; and sugar. Shear stress data were fitted versus shear rates using the models with yield stress such as Casson, Heinz–Casson, Mizrahi–Berk, Generalized Herschel–Bulkley, Vocaldo, and Schulmann–Haroske–Reher together with general model of power law. Power law model provided the most appropriate fit for flow behavior properties of β‐glucan at various concentrations and temperatures with high R2 and low root mean square error. By increase in temperature, consistency coefficient was decreased and flow behavior index was enhanced. Results showed that consistency coefficient increased nonlinearly along with increase in concentration. The highest dependency of consistency coefficient on temperature was observed at 1% concentration (Ea = 31.4 kJ/mol). Apparent viscosity was decreased at 1% concentration of salts. The reduction of apparent viscosity was sharper for NaCl than CaCl2. The highest value of consistency coefficients and the lowest flow behavior indices were found in pH range of 5–7. Increase in sucrose concentration up to 30% elevated consistency coefficient but had no significant effect on flow behavior index. In general, the results showed that the flow behavior of β‐glucan in different conditions can be well interpreted by most rheological models. β‐glucan can also be introduced to the food industry as a suitable thickener in the variety of food solutions with different physicochemical properties (temperature, pH, and the presence of salts and sugar).Practical ApplicationsResearch in the field of rheological behaviors of food fluids plays critical role in designing the processes, transportation systems (pumps and pipes), heat transfer equipment (heat exchangers and evaporators), and determination of the correlation between sensory and textural properties of foods. Temperature, concentration, and environmental conditions (pH, salt, and sugar) are predicted to have a significant effect on the flow behavior of β‐glucan solution. Evaluation of flow behavior of β‐glucan solutions using rheological models provides a reliable prediction about flow behavior of them under various conditions of processing, production, and storage. Food liquids experience different concentrations, temperatures and pH during production, storage, transportation, distribution, marketing, and so forth. Therefore, modeling and simulating the flow behavior of β‐glucan solutions helps foods containing this dietary fiber (as a hydrocolloid) to easily withstand various environmental and thermal conditions in industrial processes without any problems.

Degradation kinetic modeling of bioactive compounds and enzyme activity in wheat germ during stabilization

The stability of α-tocopherol (α-toco), total phenolic content (TPC), antioxidant capacity (DPPH and ABTS•+ radical scavenging capacity assays), activity of lipase (LA) and lipoxygenase (LOX) were investigated during stabilization of raw wheat germ with an industrial convection oven at different temperatures (120, 130, 140, 150 and 160 °C) and times (0, 5, 10, 15, and 20 min). Degradation kinetics were best fitted by first-order reaction models for all measured parameters with high R2 and low root mean square (RMSE) values, except ABTSassay which was modeled accurately with the second-order model. The temperature dependence of rate constants for all degradation kinetic parameters were calculated according to the Arrhenius, Eyring-Polanyi, and Ball models. Following the Arrhenius and Eyring models, activation energy (Ea) ranged from 20.69 to 55.04 kJ mol−1, activation enthalpy (ΔH*) ranged from 20.44 to 51.61 kJ mol−1 whereas the activation entropy (ΔS*) varied between −179.313 and −213.447 J mol−1K−1 for the stabilized germ, respectively. It can be concluded that heat treatment at 120 °C for 20 min was the best processing conditions to stabilize the raw germ to preserve its valuable bioactive compounds, antioxidant activity and the models can be useful to design different heat treatment conditions.

Approximate Message Passing Algorithms for Low Complexity OFDM-IM Detection

Low complexity approximate message passing (AMP) orthogonal frequency division multiplexing combined with index modulation (OFDM-IM) detection algorithms are proposed, which exploit the sparse structure of the frequency domain (FD) OFDM-IM symbols. To circumvent the high root mean square error (RMSE) in the conventional AMP algorithm, a minimum mean square error (MMSE) denoiser is proposed based on the classic Bayesian approach and on the state evolution of AMP. Our simulation results demonstrate that it is capable of improving both the RMSE as well as the convergence rate. However, in practice, the channel's diagonal FD matrix may be a non-Gaussian sensing matrix, hence a damping strategy is conceived. In conclusion, the proposed MMSE denoiser based damping-assisted AMP-aided detector strikes a compelling bit error ratio vs. complexity trade-off.

Self-Healing Dilemmas in Distributed Systems: Fault Correction vs. Fault Tolerance

Large-scale decentralized systems of autonomous agents interacting via asynchronous communication often experience the following self-healing dilemma: fault detection inherits network uncertainties making a remote faulty process indistinguishable from a slow process. In the case of a slow process without fault, fault correction is undesirable as it can trigger new faults that could be prevented with fault tolerance that is a more proactive system maintenance. But in the case of an actual faulty process, fault tolerance alone without eventually correcting persistent faults can make systems underperforming. Measuring, understanding and resolving such self-healing dilemmas is a timely challenge and critical requirement given the rise of distributed ledgers, edge computing, the Internet of Things in several energy, transport and health applications. This paper contributes a novel and general-purpose modeling of fault scenarios during system runtime. They are used to accurately measure and predict inconsistencies generated by the undesirable outcomes of fault correction and fault tolerance as the means to improve self-healing of large-scale decentralized systems at the design phase. A rigorous experimental methodology is designed that evaluates 696 experimental settings of different fault scales, fault profiles and fault detection thresholds in a prototyped decentralized network of 3000 nodes. Almost 9 million measurements of inconsistencies were collected in a network, where each node monitors the health status of another node, while both can defect. The prediction performance of the modeled fault scenarios is validated in a challenging application scenario of decentralized and dynamic in-network data aggregation using real-world data from a Smart Grid pilot project. Findings confirm the origin of inconsistencies at design phase and provide new insights how to tune self-healing at an early stage. Strikingly, the aggregation accuracy is well predicted as shown by high correlations and low root mean square errors.

Effect of intracorneal ring segment implantation on high order aberrations comparing patients with eccentric versus central keratoconus.

To assess potential differences between central and eccentric cones in the aberrometric corneal profile and in visual and keratometric outcomes 6 months after intracorneal ring segment (ICRS) implantation for keratoconus. This study compared two groups consisting of 12 patients each, with central or eccentric keratoconus who were treated with femtosecond laser-assisted Keraring implantation. Uncorrected (UDVA) and corrected (CDVA) distance visual acuity, keratometric readings and higher order aberrations (HOAs) including high order aberrations root mean square (HOARMS), coma, spherical aberration and trefoil were measured preoperatively and 6 months after ICRS implantation. Trefoil and spherical aberration were significantly reduced after ICRS implantation compared to preoperative values in eccentric keratoconus (Trefoil, p = 0.0049; Spherical aberration, p < 0.0001). In central keratoconus spherical aberration was reduced not significantly after ICRS implantation compared to preoperative values (p = 0.087). Coma showed a significant reduction in central (p = 0.0001) and in eccentric keratoconus (p = 0.0001). The reduction of spherical aberration in central keratoconus was significantly positively correlated to improvement in UDVA (Pearson's correlation coefficient, r = -0.66; p = 0.02). In eccentric keratoconus there was a significant positive correlation between reduction of trefoil and improvement in UDVA (Spearmans R, r = -0.69; p = 0.01). Patients both with central and eccentric keratoconus benefit from ICRS implantation. Specifically, our data provide a slightly higher gain in visual performance for eccentric cones 6 month after ICRS implantation, which is accentuated by a greater reduction in spherical aberration and trefoil. Improvements in UDVA are positively correlated with reductions in HOAs.

A cheap handheld NIR spectrometric system for automatic determination of methane, ethane, and propane in natural gas and biogas

In this study, a new NIR detection system using a cheap handheld NIR spectrometer, and a tungsten lamp coupled to a lab-made NIR flow cell was developed for the successful automatic quantification of methane, ethane, and propane in natural gas and biogas samples. To prepare standard gaseous mixtures for full-spectrum PLS multivariate calibration modeling and to record the NIR spectra of these mixtures and samples, this NIR detection system was connected to a flow automatic system and a gas chromatograph. The raw and second-derivative NIR spectra of the calibration set were then employed to construct PLS models using full cross-validation leave-one-out. All models presented high correlation coefficients (>0.9) and root mean square error (<5% mol mol−1) values. However, the RMSEP values using raw and preprocessed datasets were statistically similar according to the F-test at the 95% confidence level. The advantage of using raw data is the elimination of the preprocessing step for future prediction of samples to be analyzed. Once the NIR detection system has been previously calibrated via an automatic or batch process, it can be used to monitor light hydrocarbons or other gases inline (as in pipelines).

Comprehensive error analysis of satellite precipitation estimates based on Fengyun-2 and GPM over Chinese mainland

The spatial-temporal error characteristics of four mainstream satellite precipitation products developed by China and United States, respectively, including the Fengyun-based (FY-2F and FY-2G) and the GPM-based (IMERG-Late and IMERG-Final) over Chinese mainland were comprehensively analyzed from January 2018 to December 2019. In general, both IMERG-Final and FY-2G perform better at the hourly and daily scales. While the FY-2F product has the relatively worst performance with lowest correlation coefficient (CC) and highest root mean square error (RMSE) values. In particular, FY-2F has considerable total bias and missed precipitation errors in summer when compared to other three precipitation products. As for winter, the IMERG product suites exhibit significantly overestimation in north-central region of China, while an opposite underestimation occurred in the Fengyun products. Among the four precipitation estimates, IMERG-Final and FY-2G have the lowest normalized-RMSE (NRMSE) at the elevation range of 100–300 m and 300–500 m at daily scale, respectively. The performance at the hourly scale was found to be similar for IMERG-Final and IMERG-Late, but both of which are slightly superior to FY-2G across the elevation ranges. In terms of the detectability for different rain intensities, the IMERG products performed best at higher rain rates, while the Fengyun-based precipitation estimates are superior to IMERG at relatively lower rain ones. The assessment results reported here will provide some valuable feedbacks for algorithm developers of Fengyun products, and enable data users to further understand the error characteristics and potential deficiencies of Fengyun precipitation estimates.

AstroSat and MAXI view of Cygnus X-1: Signature of an ‘extreme’ soft nature

ABSTRACT We present a detailed spectral and timing analysis of Cygnus X-1 with multi-epoch observations, during 2016–2019, by SXT and LAXPC onboard AstroSat. We model the spectra in broad energy range of $0.5\!-\!70.0\, \rm {keV}$ to study the evolution of spectral properties while Cygnus X-1 transited from hard state to an extreme soft state via intermediate states in 2017. Simultaneous timing features are also examined by modelling the power density spectra in $3.0\!-\!50.0\, \rm {keV}$. We find that during high-soft state (HSS) observations, made by AstroSat on 2017 October 24 (MJD 58050), the energy spectrum of the source exhibits an inner disc temperature ($kT\rm _{in}$) of $0.46 \pm 0.01\, \rm {keV}$, a very steep photon index (Γ) of 3.15 ± 0.03 along with a fractional disc flux contribution of ∼45 per cent. The power density spectrum in the range of $0.006\!-\!50.0\, \rm {Hz}$ is also very steep with a power-law index of 1.12 ± 0.04 along with a high root mean square value of ∼25 per cent. Comparing the spectral softness of HSS with those of previously reported, we confirm that AstroSat observed Cygnus X-1 in the ‘softest’ state. The lowest MAXI spectral hardness ratio of ∼0.229 corroborates the softest nature of the source. Moreover, we estimate the spin of the black hole by continuum-fitting method, which indicates that Cygnus X-1 is a maximally rotating ‘hole’. Further, Monte Carlo simulations are performed to estimate the uncertainty in spin parameter, which is constrained as a* &amp;gt; 0.9981 with 3σ confidence interval. Finally, we discuss the implications of our findings.

Effect of span length on the trueness of an intraoral scanner. (An in-vitro study)

Abstract Statement of the problem: Digital scanning has been widely used nowadays, however its accuracy in relation to the bridge span length needs further investigations. Objective: The aim of this study was to determine the effect of span length on the trueness of an intra-oral scanner. Materials and methods: On a typodont (Nissin Dental Model, Japan) preparations for two bridge span lengths were made. One resembling missing upper second premolar representing short straight span length (SS) while the second resembling missing upper first and second premolars representing long curved span (LS). Then typodont was scanned by a laboratory scanner (InEos X5 scanner) (Sirona Dental System, Bensheim, Germany) to act as a reference model scan to compare measurements for trueness. Then for each group five scans were taken for the typodont by Medit intraoral scanner (iScan version 1.2.0.1; Medit, Seoul, Korea) to be compared with the reference scan. Then data set from the scans were transferred to STL format and then exported to exocad (exocad version 2.3 Matera exocad GmbH,Darmstadt, Germany) software to get the measurements to determine the trueness. Results: Results of this study showed that in the comparison between short and long span bridges, the long span bridge scans showed statistically significant higher root mean square (RMS) mean value (0.23±0.10 mm), lower trueness than short span bridge scans (0.06±0.04 mm). Conclusions: Long span fixed bridge had an adverse effect on the scanning accuracy. Trueness of intraoral scanner may be affected by the complexity and length of the scanning area.

Study on The Evaluation of The Sustainability Physical Facilities (Infrastructures) of Ruminant-Slaughterhouses in Indonesia

Physical infrastructure at urban in Indonesia ruminant slaughterhouse is typically old and in need of urgent rehabilitation. Infrastructure and equipment at ruminant slaughterhouses, in particular, are very poor and in many cases new facilities may be the only option for medium to long-term solutions. This study was carried out to evaluate the sustainability on physical conditions, and functional status of infrastructure of ruminant slaughterhouses. The survey data were collected the December 2017 until at April 2019 for this study. Twelve ruminant slaughterhouses situated at Bogor City, IPB, Depok, Jakarta, Bekasi, Yogyakarta, Denpasar, Yogyakarta, Semarang, Kudus, Surabaya, and Malang were selected for the study. Data were collected through interview as well as through observations. The data were coded, classified, tabulated and analysed using the software Statistical Package for the Social Science (SPSS) and Multidimensional Scaling (MDS). Furthermore, influential variables are processed through MDS analysis to find out the Ruminant Slaughterhouse infrastructure sustainability index. The research finding that the sustainability status value is 77.30 % (good sustainable), and leverage analysis showed that the attribute that has the highest Root Mean Square (RMS) value is Infrastructure of Water supply source (4.0119).

Fast RFGG-FG-FFT/IKA for Scattering From Multiple Targets Above a Rough Surface

To solve scattering from multiple complex targets above the rough surface with a high root mean square, a hybrid method combining real coefficients fitting both Green's function and its gradient with the fast Fourier transform (RFGG-FG-FFT) and the iterative Kirchhoff approximation (IKA) is developed. Due to its great performance for solving complex fine structures, RFGG-FG-FFT is applied to compute scattering from complex targets, while IKA is used to solve scattering from rough surface. Interactions between the targets with rough surface are solved iteratively and these are the most time-consuming part of the entire computational process. To overcome this computational bottleneck, the combined multilevel fast multipole algorithm (MLFMA), ray-propagation fast multipole algorithm (RPFMA), and fast far-field approximation (FAFFA) are incorporated into interactions between the targets and rough surface. To effectively implement the MLFMA-RPFMA-FAFFA algorithm, the rough surface region is divided into near area, adjacent area, and far area according to distance, where the interactions between these areas with targets are solved by MLFMA, RPFMA, and FAFFA, respectively. The high efficiency and accuracy of the proposed method are verified by a comparison with the numerical method.

Bidirectional Modeling of Surface Winds and Significant Wave Heights in the Caribbean Sea

Though the ocean is sparsely populated by buoys that feature co-located instruments to measure surface winds and waves, their data is of vital importance. However, due to either minor instrumentation failure or maintenance, intermittency can be a problem for either variable. This paper attempts to mitigate the loss of valuable data from two opposite but equivalent perspectives: the conventional reconstruction of significant wave height (SWH) from Caribbean Sea buoy-observed surface wind speeds (WSP) and the inverse modeling of WSP from SWH using the long short-term memory (LSTM) network. In either direction, LSTM is strongly able to recreate either variable from its counterpart with the lowest correlation coefficient (r2) measured at 0.95, the highest root mean square error (RMSE) is 0.26 m/s for WSP, and 0.16 m for SWH. The highest mean absolute percentage errors (MAPE) for WSP and SWH are 1.22% and 5%, respectively. Additionally, in the event of complete instrument failure or the absence of a buoy in a specific area, the Simulating WAves Nearshore (SWAN) wave model is first validated and used to simulate mean and extreme SWH before, during, and after the passage of Hurricane Matthew (2016). Synthetic SWH is then fed to LSTM in a joint SWAN—LSTM model, and the corresponding WSP is reconstructed and compared with observations. Although the reconstruction is highly accurate (r2 &gt; 0.9, RMSE &lt; 1.3 m/s, MAPE &lt; 0.8%), there remains great room for improvement in minimizing error and capturing high-frequency events.