Background and objectives: The reproductive performance of dairy cows is directly linked to uterine involution and resumption of ovarian cyclicity in the postpartum period. Many factors, such as negative energy balance, metabolic diseases, and hormonal agents can affect reproductive performance in the postpartum period. Hypocalcemia through reduced feed intake reduced nutrient supply to the ovary and delayed return to normal uterus function has a potential role to reduce the reproductive performance of cows after parturition. The aim of this study was to examine the association between the peripartum concentrations of CALCIUM with reproductive performance and also determine the critical threshold of CALCIUM for predicting the possibility of pregnancy in the first insemination after calving. Materials and methods: the study was conducted on 97 Holstein dairy cows. Serum concentrations of CALCIUM were measured in all cows on day 10 prepartum, parturition, 1-2 days after parturition, and on the week 1, 2, 4, and 6 postpartum. Based on Pregnancy success after the first insemination, the interval from calving to first insemination, the interval from calving to first pregnancy and number of services per conception, cows were divided to two, two, three and two groups, respectively and differences of CALCIUM concentration were evaluated among these groups. Receiver Operator Characteristics (ROC) analysis was applied to determine the critical CALCIUM threshold at different times for predicting pregnancy success after the first insemination. Results: Results showed that CALCIUM concentration in pregnant cows with the first insemination was significantly higher than non-pregnant cow in the fourth week. Also, the cows that were became pregnant before 80 days after parturition had the significantly higher CALCIUM concentration than the other cows at 1-2 days and the fourth week after parturition. Based on results of ROC analysis, the CALCIUM concentration at the fourth week after parturition (P = 0. 04) was appropriate to predict the pregnancy success after the first insemination. The optimum critical threshold that had the highest combined sensitivity (57. 1%) and specificity (62. 3%), for CALCIUM in the fourth week was 9. 05 mg/dl. In the cows with success pregnancy before 80 days after parturition the CALCIUM concentration was significantly higher than other cows (P = 0. 03). Conclusion: the results of this study showed that there was a significant relationship between change in CALCIUM concentration and reproductive performance indexes in the postpartum period. It seems that the monitoring of blood CALCIUM concentration in the fourth week after parturition is a complementary index along with other reproductive performance indexes to evaluate the possibility of successful pregnancy after parturition.

Introduction: Reducing unnecessary LABORATORY tests is an essential issue in the Intensive Care Unit. One solution for this issue is to predict the VALUE of a LABORATORY test to specify the necessity of ordering the tests. The aim of this paper was to propose a clinical decision support system for predicting LABORATORY tests VALUEs. CALCIUM LABORATORY tests of three categories of patients, including upper and lower gastrointestinal bleeding, and unspecified hemorrhage of gastrointestinal tract, have been selected as the case studies for this research.Method: In this research, the data have been collected from MIMIC-II database. For predicting CALCIUM LABORATORY VALUEs, a Fuzzy Takagi-Sugeno model is used and the input variables of the model are heart rate and previous VALUE of CALCIUM LABORATORY test.Results: The results showed that the VALUEs of CALCIUM LABORATORY test for the understudy patients were predictable with an acceptable accuracy. In average, the mean absolute errors of the system for the three categories of the patients are 0.27, 0.29, and 0.28, respectively.Conclusion: In this research, using fuzzy modeling and two variables of heart rate and previous CALCIUM LABORATORY VALUEs, a clinical decision support system was proposed for predicting LABORATORY VALUEs of three categories of patients with gastrointestinal bleeding. Using these two clinical VALUEs as input variables, the obtained results were acceptable and showed the capability of the proposed system in predicting CALCIUM LABORATORY VALUEs. For achieving better results, the impact of more input variables should be studied. Since, the proposed system predicts the LABORATORY VALUEs instead of just predicting the necessity of the LABORATORY tests; it was more generalized than previous studies. So, the proposed method let the specialists make the decision depending on the condition of each patient.

The gross calorific VALUE (GCV) or heating VALUE of a sample of fuel is one of the important properties which defines the energy of the fuel. Many researchers have proposed empirical formulas for estimating GCV VALUE of coal. There are some known methods like Bomb Calorimeter for determining the GCV in the LABORATORY. But these methods are cumbersome, costly and time consuming. In this paper, multivariate regression analysis and Co-active neuro-fuzzy inference system (CANFIS) backed by genetic algorithm technique is used for the PREDICTION of GCV, taking all the major constituents of the proximate and ultimate analyses properties as input parameters and the suitability of one technique over the other has been proposed based on the results.Correlations have been developed using multivariate regression analysis that are simple to use based on the proximate and ultimate analysis of data sets from 25 different states of USA because a very through study has been done and the data available is less variable. Also, CANFIS backed by genetic algorithm model is designed to predict the GCV of 4540 US coal samples from the abovementioned datasets. Optimization of the network architecture is done using a systematic approach (genetic algorithm). The network was trained with 4371, cross validation with 100, predicted with rest 69 datasets and the predicted results were compared with the observed VALUEs.The mean average percentage error in PREDICTION is found to be negligible (0.2913%) and the generalization capability of the model was established to be excellent. A useful concept of sensitivity analysis is adopted to set the hierarchy of influence of input factors. The results of the present investigation provide functional and vital information for PREDICTION of GCV of any type of coal in USA.

A previous study on agricultural drains in Khuzestan province of Iran has indicated that carbonate CALCIUM is the main component of the deposited salts in drainage envelops. To investigate the process of precipitation of carbonate CALCIUM under different conditions including chemical, bio-chemical, aerobic and an-aerobic, a through LABORATORY investigation on geotextile drain envelop was conducted. The results of the studies showed that activity of the micro-organisms in aerobic conditions causes an increase in the bicarbonate content and consequently, increase in the pH of the medium. Any increase in pH would intensify the precipitation of carbonate CALCIUM. The results also indicated that due to the activity of micro-organisms, more than 90% of existing CALCIUM would be precipitated. While in a chemical environment, only 30% of existing CALCIUM would precipitate. Thus, in a biochemical environment, micro-organisms would cause more CALCIUM to be deposited.The results also indicated that the activity of the bacteria would cause formation of bio-films which in turn would provide a more suitable environment for precipitation of salts. By intensifying deposition of salts, there is higher potential for clogging of the drainage envelops including geotextile filters.

Background: The aim of the current study was to assess the VALUE of simultaneous EKG with EEG to diagnose cardiac disorders in patients with seizure- like attacks and to investigate the extent of misdiagnosis of epilepsy.Materials and Methods: 210 patients presenting with transient loss of consciousness (TLoC) and paroxysmal events suspicious to seizure were studied. All patients had undergone EEG simultaneous with an EKG. Besides reporting the EEG, a cardiologist analyzed the EKG in an attempt to recognize abnormal EKG as a potential cause of TLoC. Moreover, in all patients it was ascertained if any cardiac disorders in patients and their first degree relatives had been diagnosed.Results: Sixteen out of two hundred ten (7.6%) patients had abnormal EKG which was significantly potential cause of symptom of whom 6 patients were found to have bradycardia and 10 patients had dysrhythmias. Moreover, in patients with previous cardiovascular disorder, 30.4% had abnormal EKG where as in patients without cardiac disease only 4.8% had EKG abnormalities. Besides in patients with positive family history for cardiac disease 20.3% had abnormal EKG while in patients without family history of cardiac disease, abnormal EKG was revealed in only 2.05% patients.Conclusion: Simultaneous EKG with EEG is valuable in declining rate of misdiagnosis of epilepsy. Besides, EKG abnormalities in these patients have a significant association with any previous cardiovascular disorders in the patients and in their first degree relatives.

Wet chemical methods are the most widely used routes for biphasic CALCIUM phosphate bioceramics synthesis in which control of synthesis condition and reaction factors are very important. To predict and control the synthesis condition, mathematical models can be used. Artificial neural networks are computational tools inspired by the nervous systems of living organisms that help us to get better understanding about the complicated problems.Powders were synthesized using aqueous solution containing different CALCIUM/phosphorus ratio. HNO3 and NH4OH were used to adjust the pH of the solution mixture during the process. The precipitation was calcined at 1100oC for one hour. The chemical composition and Ca/P ratio were determined by inductively coupled plasma atomic emission spectroscopy. Phase identification of powders and evaluation of the functional groups of specimens were carried out by X-ray diffraction and Fourier transform infrared spectroscopy respectively.Four three-layered feed forward networks with ten neurons in the hidden layer, linear sigmoid stimulation and Levenberg-Marquardt learning algorithm were trained using data obtained from the experiments designed in four different patterns. The best result was obtained with the network consists of 80% training process, 15% validation process and 5% testing process samples by changing number of samples in each step. To ensure optimal performance of four networks, each network was studied using four new data. The predicted results show a good comparison with those obtained experimentally.

PREDICTION and clear understanding of the behavior of a phenomenon plays a major role in adopting strategies and decisions. All-round development and deepening of the capital market as the driving engine of economic development requires the public trust of participants in its efficiency and correctness in determining the fair price of securities. On the other hand, predicting company VALUE, price fluctuations, or stock returns is very important in portfolio selection, asset management, and even stock pricing of newly listed companies. In this research, using the data of 159 companies during a 10-year period including 2011-2020 and the factors affecting the company's VALUE, including financial ratios, corporate governance mechanisms, macroeconomic factors, and the stock market, the company's VALUE has been predicted. In this research, two structures of deep learning methods including GRU and BLSTM are used for better evaluation. The results of examining the data collected using deep learning techniques indicated that the combined model with a lower RMSE error than the GRU model predicted the VALUE of the company.