Идентификация кинетических параметров процесса инактивации бактерий в присутствии паров йода

Penelitian ini bertujuan untuk mengetahui berpengaruh Kompensasi (Upah dan gaji, Tunjangan dan Fasilitas terhadap Kinerja Pegawai pada PT Pelindo (Persero) IV. jenis data dalam penelitian ini adalah data kuantitatif menggunakan analisis korelasi pearsonproductmoment, koefisien determinasi dan uji t. sedangkan sumber data adalah data primer dan data sekunder. Metode pengumpulan data wawancara dan kuesioner. Berdasarkan hasil penelitian yang dilakukankesimpulanya adalah sebagai berikut;1) Kompensasi upah dan gaji (X1), Nilai koefisien korelasi (r) diperoleh = 0,217 nilai koefisien determinasi (r²) = 47%, nilai t hitung = 1,954> t tabel = 1,301. Berarti pengaruh pemberian Kompensasi berupa Upah dan Gaji berpengaruh signifikan terhadap kinerja Pegawai pada PT Pelindo (Persero) IV, 2) Kompensasi Tunjangan (X2), Nilai koefisien korelasi (r) diperoleh = 0,227 nilai koefisien determinasi (r²) = 51%, nilai t hitung = 2,137> t tabel = 1,301. Berarti pengaruh pemberian Kompensasi berupa Tunjangan berpengaruh signifikan terhadap kinerja Pegawai pada PT Pelindo (Persero) IV, 3) Kompensasi Fasilitas (X3), Nilai koefisien korelasi (r) diperoleh = 0,226 nilai koefisien determinasi (r²) = 51%, nilai t hitung = 2,116> t tabel = 1,301. Berarti pengaruh pemberian Kompensasi berupa Fasilitas berpengaruh signifikan terhadap kinerja Pegawai pada PT Pelindo (Persero) IV, 4) Kompensasi upah dan gaji, Tunjangan dan fasiilitas (X), Nilai koefisien korelasi (r) diperoleh = 0,295 nilai koefisien determinasi (r²) = 87%, nilai t hitung = 5,365> t tabel = 1,301. Berarti pengaruh pemberian Kompensasi berupa Upah dan Gaji, Tunjangan dan Fasilitas berpengaruh signifikan terhadap kinerja Pegawai pada PT Pelindo (Persero) IV. This research aims to determine the effect of Compensation (Wages and salaries, Allowances and Facilities on Employee Performance at PT Pelindo (Persero) IV. The type of data in this research is quantitative data using Pearson product moment correlation analysis, coefficient of determination and t test. While the data source are primary data and secondary data. Data collection methods are interviews and questionnaires. Based on the results of the research conducted, the conclusions are as follows: 1) Wage and salary compensation (X1), the correlation coefficient value (r) is obtained = 0.217, the value of the coefficient of determination (r²) = 47%, calculated t value = 1.954> t table = 1.301. This means that the effect of providing compensation in the form of wages and salaries has a significant effect on employee performance at PT Pelindo (Persero) IV, 2) Compensation allowances (X2), the correlation coefficient value (r) is obtained = 0.227, the coefficient of determination value (r²) = 51%, the t value count = 2.137> t table = 1.301. This means that the effect of providing compensation in the form of allowances has a significant effect on employee performance at PT Pelindo (Persero) IV, 3) Facility Compensation (X3), the correlation coefficient value (r) is obtained = 0.226, the coefficient of determination value (r²) = 51%, the calculated t value = 2.116> t table = 1.301. This means that the effect of providing compensation in the form of facilities has a significant effect on employee performance at PT Pelindo (Persero) IV, 4) Wage and salary compensation, allowances and facilities (X), the correlation coefficient value (r) obtained = 0.295, the coefficient of determination value (r²) = 87 %, calculated t value = 5.365> t table = 1.301. This means that the influence of providing compensation in the form of wages and salaries, allowances and facilities has a significant effect on employee performance at PT Pelindo (Persero) IV.

This study evaluated 7 inbred lines of maize (Zea mays L.), 3 testers, and 21 hybrids produced using the line × tester mating design. The study was conducted in Konya, in The Central Anatolia Region of Turkey. CD (coefficient of determination) values were calculated to detect the effect of inbred lines on grain yield and other physiological properties of the test crosses. The mean values of all the parameters of the genotypes were evaluated using the Duncan?s multiple range test for grouping progenies and parent groups. The CD values between parent groups and progenies were statistically significant for photosynthetic efficiency (r² = 0.11**; P

Rapid analysis of glucose, fructose and sucrose contents of commercial soft drinks using Raman spectroscopy

The aim of this research was to: (i) determine the inactivation pattern of a pressure-resistant strain of Pediococcus damnosus by high hydrostatic pressure in phosphate buffer (pH 6.7) and gilt-head seabream using the linear, biphasic and Weibull models; and (ii) validate the applicability of the Weibull model to predict survival curves at other experimental pressure levels. A pressure-resistant strain of P. damnosus was exposed to a range of pressures (500, 550, 600 and 650 MPa) in phosphate buffer (pH 6.7) and gilt-head seabream for up to 8 min at ambient temperature (23 degrees C). Inactivation kinetics were described by the linear, biphasic and Weibull models. Increasing the magnitude of the pressure applied resulted in increasing levels of inactivation. Pronounced tailing effect was observed at pressures over 600 MPa. The Weibull and biphasic models consistently produced better fit than the linear model as inferred by the values of the root mean squared error, coefficient of determination (R2) and accuracy factor (A(f)). The scale factor (b) of the Weibull model was linearly correlated with pressure (P) treatment in the whole pressure range. Substituting the b parameter in the initial Weibull function and calculating the shape factor (n) by linear interpolation, high pressure (P) was directly incorporated into the model providing reasonable predictions of the survival curves at 570 and 630 MPa. Comparison between the survival curves in phosphate buffer and gilt-head seabream showed a clear protective effect of the food matrix on the resistance of the micro-organism, especially at 500 and 550 MPa. The Weibull and biphasic models were more flexible to describe the survival curves of P. damnosus in the experimental pressure range, taking also into account the tailing effect that could not be included in the linear model. The Weibull model could also give reasonable predictions of the survival curves at other experimental pressures in both pressure menstrua. As the food matrix has a protective effect in microbial inactivation, the development of accurate mathematical models should be done directly on real food to avoid under- or over-processing times. The development of accurate models to describe the survival curves of micro-organisms under high hydrostatic pressure treatment would be very important to the food industry for process optimisation, food safety and extension of the applicability of high pressure processing.

Malaysia is a country which is rich in natural resources and one of it is a gold. Gold has already become an important national commodity. This study is conducted to determine a model that can be well fitted with the gold production in Malaysia from the year 1995-2010. Five nonlinear models are presented in this study which are Logistic model, Gompertz, Richard, Weibull and Chapman-Richard model. These model are used to fit the cumulative gold production in Malaysia. The best model is then selected based on the model performance. The performance of the fitted model is measured by sum squares error, root mean squares error, coefficient of determination, mean relative error, mean absolute error and mean absolute percentage error. This study has found that a Weibull model is shown to have significantly outperform compare to the other models. To confirm that Weibull is the best model, the latest data are fitted to the model. Once again, Weibull model gives the lowest readings at all types of measurement error. We can concluded that the future gold production in Malaysia can be predicted according to the Weibull model and this could be important findings for Malaysia to plan their economic activities.

The efficacy of saving energy standards depends on the ability to anticipate the heat loss of buildings. Environmentally friendly materials, also known as eco-friendly or sustainable materials, have a minimal negative impact on the environment throughout their life cycle. These materials are designed to conserve resources, reduce pollution, and promote sustainability. The characteristics of non-stationary and non-linear heat loss through environmentally friendly materials make it challenging to anticipate accurately. At the same time, many of the industry's presently accessible computational models have been created with this in mind; the majority call for powerful computers and time-consuming computations. The artificial neural network (ANN) has been utilized for prediction, and ground-breaking research has shown the viability of this strategy. This research proposes an artificial neural network (ANN) prototype to estimate construction cooling load usage. ANN is integrated with the vortex search algorithm (VS), stochastic fractal search (SFS), and multi-verse optimizer (MVO) models to compare the three models' outcomes and suggest a more accurate strategy. These techniques make a linear mapping among the output and input parameters, often utilized for modeling and regression. The value of the multiple determination coefficient is also determined. The values of the training R2 (coefficient of multiple determination) are 0.9464, 0.99827, and 0.99522 for VS-MLP, SFS-MLP, and MVO-MLP, respectively, with an unknown dataset which is acceptable. The training RMSE amounts for VS-MLP, SFS-MLP, and MVO-MLP are 0.06433, 0.00619, and 0.01028 for the unknown dataset, which is acceptable. According to the MAE values of 0.0082902, 0.0047834, and 0.0076534 in the training phase for VS-MLP, SFS-MLP, and MVO-MLP approaches and the values of testing MAE error of 0.029107, 0.018167, and 0.029212 for VS-MLP, SFS-MLP, and MVO-MLP approaches, respectively, it is obtained that the SFS-MLP has a lower MAE value. The lowest RMSE value and the higher R2 value indicate the favorable accuracy of the SFS-MLP technique.

Bu araştırmada Romanov kuzularının vücut özelliklerinin gelişiminin tanımlanması amacı ile 50 Romanov kuzusunun doğumdan itibaren 180. yaş gününe kadar olan büyüme döneminde her 15 günde bir canlı ağırlık, vücut uzunluğu, cidago yüksekliği özelliklerine ilişkin kayıtlar alınmış ve elde edilen veriler ile doğrusal modellerden Linear, Kuadratik ve Kübik model, doğrusal olmayan modellerden Gompertz ve Lojistik modelleri kullanılarak büyüme eğrilerine ait parametreler hesaplanmıştır. Büyüme eğrileri içinde en iyi uyumu gösteren modelin belirlenmesinde belirtme katsayısı (R2), hata kareler ortalaması (HKO) ve ortalama mutlak sapma (OMS) değerlerinden yararlanılmıştır. Elde edilen sonuçlara göre en yüksek R2 değeri ile en düşük HKO değerleri sırası ile canlı ağırlıkta 0,992-0,591, cidago yüksekliğinde 0,993-0,441, vücut uzunluğunda 0,986-1,164 olarak tespit edilmiştir. Ele alınan bütün vücut özelliklerinde en yüksek R2 değeri kübik modelden elde edilmiştir. Büyüme eğrisi modellerine ait parametrelerin belirlenebilmesi için SPSS istatistik programı kullanılmıştır. Bu sonuçlara göre Romanov kuzularının vücut özelliklerinin gelişimini en iyi açıklayan ayrıca büyüme eğrilerine en uyumlu modelin Kübik model olduğu tespit edilmiştir.

The survival of bacterial contaminants at moderate processing temperatures is of interest to many food producers, especially in terms of the safety and quality of the final products. That is why the heat resistance of Staphylococcus aureus 2064, an isolate from artisanal Slovakian cheese, was studied in the moderate temperature range (57–61 °C) by the capillary method. The fourth decimal reduction time t<sub>4D</sub>- and z-values were estimated in two steps by traditional log-linear Bigelow and non-linear Weibull models. In addition, a one-step fitting procedure using the Weibull model was also applied. All the approaches provided comparable t<sub>4D</sub>-values resulting in the following z-values of 11.8 °C, 12.3 °C and 11.3 °C, respectively. Moreover, the one-step approach takes all the primary data into z-value calculation at once, thus providing a more representative output at the reasonable high coefficient of determination R<sub>2</sub> = 0.961

Sweeping frequency ultrasound washing is a recent technology of applying varying ultrasound frequencies in the decontamination of fresh produce. Ultrasound inactivation of Escherichia coli and Listeria innocua inoculated on fresh‐cut Chinese cabbage was investigated at sweeping frequencies of 28 ± 2 to 68 ± 2 kHz for washing time of 5–40 min. The survival ratio of E. coli and L. innocua decreased with the time of exposure to ultrasound washing and varying frequencies. E. coli were more sensitive to ultrasound washing than L. innocua, achieving >3.0 log reductions after 10 min of washing with 40 ± 2 kHz. A nonlinear Weibull model was used to describe the experimental data, and the fitness of the model was evaluated by the coefficient of determination (R2) and the root mean square error (RMSE). The model used provided a good fit with R2 ≥ .98 for both bacteria and RMSE values in the range of 0.03–0.21 and 0.02–0.32 for E. coli and L. innocua, respectively.Practical ApplicationUltrasound decontamination is an alternative nonthermal technology that has been applied in recent years to improve the microbial safety of fresh produce. The bacterial inactivation is predominantly attributed to cavitation, an occurrence that interrupts cellular arrangement and function. With the known significance of sanitization in the processing of vegetables, it is imperative to develop and appraise novel approaches that contribute to the microbial safety of these fresh produce. Largely, the results of our study could guide the design of new ultrasonic fresh produce wash systems in addition to current industrial practice related to frequency selection and use of ultrasound during fresh produce sanitation.

The effect of pretreatment, osmotic–ultrasonic dehydration, on the rehydration kinetic of quince at three temperatures (25, 45 and 70C) was examined. The values of the effective moisture diffusivity of quinces were in the range 8.114 × 10−11 to 2.020 × 10−10 for untreated samples and 6.085 × 10−11 to 1.308 × 10−10 for pretreated samples, respectively. The temperature dependence of the diffusivity coefficient was also described by the Arrhenius-type relationship. The activation energy was found to be 14.48 and 17.27 kJ/mol, for pretreated and untreated samples quince, respectively. Five different models, Fick's second law of diffusion, Peleg, Weibull, first-order and exponential association, were evaluated based on coefficient of determination (R2), reduced chi-square (χ2) and root means error (RMSE). The Weibull model gives the highest values of R2 and the lowest values of χ2, SSE and RMSE for untreated and pretreated quince samples. PRACTICAL APPLICATIONS Rehydration is a complex process aimed at the restoration of raw-material properties when dried material comes in contact with water and can be considered as a measure of the injury to the material caused by drying. The capacity of the dry material to be rehydrated depends on certain intrinsic properties of the vegetal tissue and also on how the rehydration process is carried out (process conditions) and on the previous pretreatments applied to the product. These pretreatments are usually used to obtain a better quality of the final product (i.e., better preserved from undesirable changes) and in this sense applying an osmotic–ultrasonic dehydration step prior to the rehydration process could be interesting. The objective of rehydration study is to attain as many products in their original characteristics as fast as possible. An improved knowledge of rehydration kinetics would significantly enhance the feasibility of this process.

This study was to build and evaluate a non-linear model to describe the macro-mineral requirement of the laying hen. Using micro-mineral (Calcium, Total Phosphor, and Available Phosphor) requirements from 8 to 35 weeks, non-linear (Logistic, Gompertz, and Weibull) models were used to estimate patterns of 644 data from 23 laying hen breeds. Data collected from Hendrix Genetics and Lohmann Tierzucht. Data were converted to metabolism body weight (BW0.75) and transformed using min-max transformation. The non-linear model was run in R-base 3.6.0 using the build-in function nls and SS. The RMSE and MAE used to validate the model. The lowest value states that the model has high uniformity and consistency. The lowest RMSE value (0.0629) of the non-linear model of calcium requirement was Logistic, but the lowest MAE value (0.0401) was Weibull. The lowest RMSE and MAE values of the non-linear model of the total phosphor requirement were Weibull models with values of 0.0554 and 0.0424, respectively. The lowest RMSE and MAE values of the non-linear model of the available phosphor requirement were Weibull models with values of 0.0689 and 0.0568, respectively. Therefore, based on the RMSE and MAE, the macro-mineral can accurately be described by the Weibull model.

The objectives of this study were: (i) to compare five models (Wood, Cobby & Le Du, Wilmink, Cappio Borlino, Djikstra) for describing the lactation curve of Chios sheep, (ii) to identify variation in lactation parameters related to environmental factors (season) and animal factors (parity). A data base on 61,705 recordings of daily milk production obtained from an automatic milking system was used. The lactation models were individually adjusted for each lactation. Analysis of variance was performed for the comparison of the parameter estimates. The goodness of fit measures used for comparisons of the models was the coefficient of determination (R), mean of mean square error (MMSE), Akaike information criterion (AIC), corrected Akaike information criterion (AICc) and Bayesian information criterion (BIC). Wood model had the lowest values for information criteria (MMSE = 347.4681, AIC = 1,056.436, AICc = 1,056.733, BIC = 1,063.856) and the highest value for the coefficient of determination (R=0.79). The highest values for information criteria were found for Djikstra’s model (MMSE = 636.6438, AIC = 1,076.621, AICc = 1,077.117, BIC = 1,086.582) having the same time the lowest value for the coefficient of determination (R=0.59). Overall, Wood (1967) model showed the best adjustment. Despite of being more recent, the model by Djikstra (1997) mechanist based and with a higher number of parameters showed a low convergence for the data used. Wood model (1967) has a greater advantage of producing a good fit measurement with only three parameters.

Data on 24 weaned guinea pigs were used to evaluate the relationship between live body weights and linear measurements. The traits measured were body weight (BW), body length (BL), heart girth (HG), trunk length (TL), fore leg length (FLL), hind leg length (HLL) and ear length (EL). Measurements of body components were regressed against live weight at 4, 6, 8, 10 and 12 weeks using linear, quadratic and exponential regression analyses. The regression equations, estimates of parameters and coefficient of determination (R2 ) for the fitted functions were determined. Strong interrelationships (p<0.05) were observed among the parameters. The coefficient of determination (R2 ) values varied from 0.00 to 0.42%, 0.16 to 0.78%, 0.10 to 0.81%, 0.15 to 0.80% and 0.03 to 0.68% at 4, 6, 8, 10 and 12 weeks respectively. Body length (BL) had higher R2 value at 4 weeks (0.42%), 6 weeks (0.78%) and 8 weeks (0.81%) respectively with the exponential function while; heart girth had higher R2 value at 10 weeks with the exponential function (0.80%) and at 12 weeks (0.68%) with quadratic function. Based on R2 value, exponential function was generally superior in terms of goodness of fit to the data and its ability to predict body weight. (Keywords: Body length, heart girth, trunk length, fore leg length, hind leg length, ear length)

Morphometric traits also called linear body measurements or conformation traits are important parameters in predicting body weight especially in commercial breeders and producers. Thus, the study was carried out to predict body weight of broiler using linear body measurement. In this study, a total of 270 day old broiler chicks comprising of 90 chicks each of Abor Acre, Ross and Marshal Strains were used. Data were collected on body weight using body measurements to include breast length (BRL), thigh width (TW), shank length (SL), keel length (KL), wing length and drumstick length (DL). The regression analysis was simple linear regression. The values of the coefficient of determination (R2) in Abor Acre, Ross and Marshall strains ranged from 89.8 – 99.8; 88.4 – 98.9; and 80.8 – 99.5 respectively with thigh width showing the highest % R2 value of 99.8% in week 2; 66.5 – 97.9; 60.3 – 80.4 and 28.6 – 72.3 respectively with breast length (97.9%) having the highest % R2 value. This showed that breast length was the best predictor of the body weight of the broiler in week 4; 38.5 – 100; 88.0 – 98.6; 17.0 – 94.8 with shank length (100%) showing a 100% R2 value. This showed that breast length was the best predictor of the body weight of the broiler in week 4; 38.5 – 100; 88.0 – 98.6; 17.0 – 94.8 with shank length (100%) showing a 100% R2 value in week 6; 76.9 – 96.3, 72.2 – 88.8 and 58.1 – 97.6 respectively with wing length recording the highest % R2 value in week 6; 76.9 – 96.3, 72.2 – 88.8 and 58.1 – 97.6 respectively with wing length recording the highest value (97.9%) week 8. The different strains had different coefficient of determination (R2) values above 50% with different linear body parameter at different ages of the birds, indicating that any of the linear body parameter could be used to predict body weight of broiler chicken although, accuracy of prediction increased with increasing R2 value. Amongst all the linear body parameters evaluated, the shank length of Abor Acres strain had highest R2 value (100%) in week 6. Thus shank length was the best linear body parameter with 100% accuracy of prediction, and may be useful criterion in estimation of growth and prediction of body weight.

Isomeric structure of diphenyl urethane during synthesis from phenylisocyanate and phenol has been investigated by modeling the reaction extent as an inverse kinetic problem, using FT-IR difference spectroscopy, to obtain structural information on the formation of the isomeric structure. The aim of this study was to determine the primary algebraic structures (an inverse problem), which describe the chemical reaction system in terms of spectroscopic observables. Moreover, a conventional description of the evolution of chemical species and of the change of moles of the observable species, as a function of time, was explored, defined in terms of the extent of reaction ξ and the reaction stoichiometries ν, based on the Jouguet-de Donder equation, for an invariant system in batch experiments. Two processes for diphenyl urethane with hydrogen bonding and their free form were identified. Experimental input for the identification is a matrix of in situ spectroscopic data A (FT-IR/ATR spectra measured during the reaction process) and a matrix of initial moles (N(0)). Subsequently, (1) the number of observable reactions present, (2) the change of moles and their extent of reactions ξ, (3) the reaction stoichiometries v, (4) the concentration of all observable species (C), and finally (5) the kinetic rate constants were determined. Meaningful extraction of such algebraic system information (an inverse algebraic problem) is a mandatory prerequisite for the subsequent detailed kinetic modeling (an inverse kinetic problem). This research opens up the possibility of modeling the extent of the reaction and performing a kinetic analysis of the hydrogen bonding in an organic system. Important information could be extracted, for understanding of different functions and interactions of hydrogen bonding in a supramolecular system.