Effect of time and temperature before chilling on the hygiene of carcasses in wild boar hunted in central Italy

The interest in certified game meat chains highlights the need for the evaluation and the management of factors affecting carcass hygiene along the peculiar steps of the production. The effects of time and temperature before chilling were specifically evaluated on aerobic colony count and Enterobacteriaceae count in hunted wild boar carcasses. Thirty wild boars were considered in two process steps where the hunted animal are still not chilled: after evisceration and just before chilling. Environmental temperature, carcass temperature and the elapse time between the two-step considered were registered. Furthermore, surface microbial loads were analyzed on the inner part of the carcasses. The mean time between the two sampling steps was 6 hours with an average environmental temperature of 20.49°C. A carcass temperature 9.6°C drop was observed during this period. In this lap of time aerobic colony count and Enterobacteriaceae count increased of 0.68 Log CFU/cm2 and 1.01 Log CFU/cm2 respectively, with a moderate correlation with the time but not with the temperature delta. The results reveal that the temperature conditions in central Italy hunting areas were not able to quickly reduce the carcass temperature and therefore the time between carcass evisceration and chilling should not exceed 6 hours.

Role of mentors in developing the social competencies (SC) of their protégée-entrepreneurs (PE) in high-tech incubators (HTI)

High-tech incubators offer their entrepreneurs mentoring services to help them achieve goals faster. In a successful mentoring relationship protégées learn from the statements, actions, questions, and communication styles of their mentors. Mentors can play an important role in developing their protégées’ social competencies, which allow them to increase their social capital. This research tests a predictive model for the contribution of mentors to the development of their protégées’ social competencies in a high-tech incubation environment. The predictor variables of the model are the active communication-time between mentors and their protégée entrepreneurs, and the age of a mentoring relationship, referred to as elapse-time. The outcome variable is the development of social competencies of protégée-entrepreneurs. Moreover, the levels of trust from protégée-entrepreneurs towards their mentors might moderate this time social competency relationship. The social competencies of individuals involve six elements: emotional expressivity, emotional sensitivity, emotional control, social expressivity, social sensitivity, and social control. The Social Skills Inventory (SSI), an established psychometric scale that captures all six dimensions of social competencies, is used to test this model. After the participation of 99 protégées entrepreneurs from 10 incubators at Ryerson University, a new seven-item trust scale has been validated; however, the roles of elapse-time and communication-time in developing the social competencies of protégée-entrepreneurs are not supported. Surprisingly, after the verification of the SSI, it turned out that it is not valid to the participating sample set. In conclusion, despite the claimed generalizability of the SSI, it is now questionable, and the creation of a social competency scale for incubated entrepreneurs is an opportunity for future research.

Role of mentors in developing the social competencies (SC) of their protégée-entrepreneurs (PE) in high-tech incubators (HTI)

High-tech incubators offer their entrepreneurs mentoring services to help them achieve goals faster. In a successful mentoring relationship protégées learn from the statements, actions, questions, and communication styles of their mentors. Mentors can play an important role in developing their protégées’ social competencies, which allow them to increase their social capital. This research tests a predictive model for the contribution of mentors to the development of their protégées’ social competencies in a high-tech incubation environment. The predictor variables of the model are the active communication-time between mentors and their protégée entrepreneurs, and the age of a mentoring relationship, referred to as elapse-time. The outcome variable is the development of social competencies of protégée-entrepreneurs. Moreover, the levels of trust from protégée-entrepreneurs towards their mentors might moderate this time social competency relationship. The social competencies of individuals involve six elements: emotional expressivity, emotional sensitivity, emotional control, social expressivity, social sensitivity, and social control. The Social Skills Inventory (SSI), an established psychometric scale that captures all six dimensions of social competencies, is used to test this model. After the participation of 99 protégées entrepreneurs from 10 incubators at Ryerson University, a new seven-item trust scale has been validated; however, the roles of elapse-time and communication-time in developing the social competencies of protégée-entrepreneurs are not supported. Surprisingly, after the verification of the SSI, it turned out that it is not valid to the participating sample set. In conclusion, despite the claimed generalizability of the SSI, it is now questionable, and the creation of a social competency scale for incubated entrepreneurs is an opportunity for future research.

Analysis of Simple K-Mean and Parallel K-Mean Clustering for Software Products and Organizational Performance Using Education Sector Dataset

Context. Educational Data Mining (EDM) is a new and emerging research area. Data mining techniques are used in the educational field in order to extract useful information on employee or student progress behaviors. Recent increase in the availability of learning data has given importance and momentum to educational data mining to better understand and optimize the learning process and the environments in which it takes place. Objective. Data are the most valuable commodity for any organization. It is very difficult to extract useful information from such a large and massive collection of data. Data mining techniques are used to forecast and evaluate academic performance of students based on their academic record and participation in the forum. Although several studies have been carried out to evaluate the academic performance of students worldwide, there is a lack of appropriate studies to assess factors that can boost the academic performance of students. Methodology. The current study sought to weigh up factors that contribute to improving student academic performance in Pakistan. In this paper, both the simple and parallel clustering techniques are implemented and analyzed to point out their best features. The Parallel K-Mean algorithms overcome the problems of simple algorithm and the outcomes of the parallel algorithms are always the same, which improves the cluster quality, number of iterations, and elapsed time. Results. Both the algorithms are tested and compared with each other for a dataset of 10,000 and 5000 integer data items. The datasets are evaluated 10 times for minimum elapse time-varying K value from 1 to 10. The proposed study is more useful for scientific research data sorting. Scientific research data statistics are more accurate.

Electrochemical detection of cholesterol in a buffer solution with a glassy carbon electrode doctored by ZnO/SnO2/RuO2 nanomaterials

This electrochemical study performed to develop a cholesterol sensor using a glassy carbon electrode (GCE) coating with ternary low-dimensional ZnO/SnO2/RuO2 nanomaterials (NMs). The ZnO/SnO2/RuO2 NMs characterized using FESEM, XPS, EDS and XRD analysis. The desired cholesterol sensor fabricated by coating a GCE with ZnO/SnO2/RuO2 NMs as a film of the thin-layer using suspension of ethanol with 5% Nafion binder, which performed to the analysis of cholesterol electrochemically in the phosphate buffer phase. The resulted electrochemical responses have exhibited the linearity from 0.1nM ~ 0.01mM of cholesterol in current versus concentration plot, which defined as a calibration curve of this sensor development. The linear concentration (0.1nM ~ 0.01mM) of cholesterol corresponding with the current response is known as the dynamic range (LDR) for detection of target analyte. The sensitivity is calculated from the slope of calibration-curve found as 11.3513 µAµM− 1cm− 2. The lower limit of detection (91.42 ± 4.57 pM) is obtained from signal/noise (S/N = 3) at 3. In the real-samples detection process, the fabricated cholesterol sensor is exhibited good reproducibility, fast response time and ability to perform in long-duration of sensor elapse time. In the end, this method is shown the reliable detection of cholesterol in the buffer phase and would be very perspective in the recent future in-term of a simple as well as reliable technique in the field of pathological diagnosis.

Latent Class Trajectories of Biochemical Parameters and Their Relationship With Risk of Mortality Among Acute Organophosphorus Poisoning Patients

Objectives: To determine varying biochemical parameters and their relationship with mortality among organophosphorus poisoning patients through a latent class trajectory analysis.Methods: It was a retrospective cohort study. Patients with organophosphorus poisoning (OP) were admitted to the Intensive Care Unit (ICU) of Ruth Pfau Civil Hospital Karachi from August 2010 to September 2016. A total of 299 OP poisoning patients’ data along with demographic and biochemical parameters were retrieved from medical records. The key outcome measure was in-hospital mortality among acute poisoning patients accounting for gender, age, elapse time since poison ingestion, ICU stay, and biochemical parameters including random blood sugar, creatinine, urea, and electrolytes (sodium, chloride, potassium). The trajectories of parameters were formed using longitudinal latent profile analysis. These trajectories and repeated measures were used as independent variables to determine and compare the risk of mortality by Cox-Proportional-Hazards models. A p-value of <0.05 was considered statistically significant.Results: A total of 299 patients’ data were included with a mean age of 25.4±9.7 years and in-hospital mortality was 13.7%(n=41). In trajectory analysis, patients with high-declining and normal-increasing creatinine, high-remitting, and normal-increasing urea, high-remitting sodium, trajectories observed the highest mortality i.e. 67%(2/3), 75%(6/8), 67%(2/3), 75%(6/8), and 80% (4/5) respectively compared with other trajectories. On multivariable analysis, patients in high-declining creatinine class were sixteen times [HR:15.7,95%CI:3.4-71.6], normal-increasing was fifteen times [HR:15.2,95%CI:4.2-54.6] more likely to die compared with those who had normal consistent creatinine levels. Patients in extremely high-remitting urea trajectory were fifteen times [HR:15.4,95%CI:3.4-69.7], normal-increasing urea trajectory was four times [HR:3.9,95%CI:1.4-11.5] and in high-remitting sodium, the trajectory was six times [HR:5.6,95%C.I:2.0-15.8] more likely to die compared with those who were in normal-consistent trajectories of urea and sodium respectively.Conclusion: Using the latent profile approach, biochemical parameters (creatinine, urea, and sodium levels) were significantly associated with increased risk of mortality among OP poisoning patients.

Provisional Access of Workflow Scheduling with Mobile Agents in Agricultural Application

In mobile computing, if agent experiences are assessed and available among end communication for environmental modelling, it helps in improving exploration load for unknown or unvisited circumstances. Therefore, it may speed learning procedure. As, building an accurate and effectual model with constraint time is also an essential factor, specifically for difficult conditions, this work initiates reinforcement model base learning approach based on work flow scheduling to acquire lesser memory consumption and effectual modelling. Here, two methods have been compared for attaining a real time experience and to produce virtual experiences as elapse time in learning process is reduced. However, this two modelling is appropriate for knowledge sharing. This analysis is inspired with knowledge sharing concept in multi agent based systems where agents has the competency to generate global modelling from scattering these models provided by individual agents. Subsequently, it may increase accuracy modelling; therefore it may offer valid experience for learning at earlier learning stage. To reduce make span process, anticipated model uses cost, reward and action techniques to grafting workflow scheduling need and resourceful experience from experienced system indeed of merging entire model. Simulation outcomes depict that anticipated scheduling model can acquire sample learning and efficiency model based acceleration in Multi-agent application objectives. Here, MATLAB environment is used for simulation. Metrics like cost, Make span is evaluated for agricultural dataset. Comparison is done with anticipated Dense mobile Network and Deep Q Network. Here, DMN shows better trade off than DQN model and more appropriate for agricultural dataset.

P5264There is no significant effect of ischemic time and elapse time since cardiac allograft transplant on myocardial T1 relaxation time

Background Graft failure caused by allograft rejection and vasculopathy is the most common cause of mortality after heart transplantation. To detect an early allograft rejection, endomyocardial biopsy is still needed. Tissue characterization by T1-mapping and Late gadolinium enhancement is well established in acute and chronic myocardial tissue alterations. Therefore several studies investigated T1-mapping as a potential noninvasive parameter to monitor cardiac allograft vasculopathy and allograft rejection. However it is unclear if T1 is also influenced by pretransplant ischemic time and elapsed time since transplantation. Purpose It was the aim of our study to examine the influence of ischemic and elapsed time since transplantation to the cardiac allograft tissue characteristics measured by CMR T1 relaxation times. Methods Allograft transplant patients underwent stress CMR on a yearly routine. T1-maps were acquired using a modified look locker sequence (MOLLI 3(2)3(2)5) in the midventricular septum. Uni- and multi linear regression analysis was used to predict T1 by ischemic time, time since transplantation, troponin and NT-Pro-BNP. Results 49 cardiac allograft transplanted patients underwent stress CMR (mean age 58.6±11.7 years, left ventricular ejection fraction 62.1±6.8%; indexed enddiastolic volume 68.4±14.7 ml/m2; native T1 1120±51 ms, extracellular volume 0.27±0.04). A significant correlation was found between T1 and NT-Pro-BNP (1519±3639 pg/ml, p=0.003) and a trend for troponin (17.0±12.8 ng/dl, p=0.051). We saw no correlation between T1 and the ischemic time (198.4±44.9 minutes, p=0.1172) and elapse time since transplantation (47±7 month, p=0.9868). In the multivariate regression analysis none of the four parameters were independently associated with the T1 time (p=0.1017). Table 1 Characteristics Mean ± SD p Ischemic time (minutes) 198.4±44.9 0.1172 Time since transplant (month) 47±7 0.9868 NT-Pro-BNP (pg/ml) 1519±3639 0.003 Troponine (ng/dl) 17.0±12.8 0.051 Conclusion There was no significant effect of pretransplant ischemic time and elapse time since transplantation on native T1 times, whereas native T1 was significantly correlated with troponine and NT-Pro-BNP-Levels. T1 is excellently suited to detect acute changes in allograft transplant patients without being influenced by aging of the transplanted heart and the heart's pretransplant condition.

Mobi-X Architecture Modelling for Mobile Agent using Association Pattern Mining

In a mobile agent system, if agents’ functionality can be assessed and evaluated between peers of environmental modelling, it can reduce the exploration burden of unvisited states and unseen situations, thus an effectual learning process has to be accelerated. So as to construct an accurate and effectual model in certain time period is a significant problem, specifically in complex environment. To overcome this crisis, the investigation anticipates a model based data mining approach based on tree structure to achieve co-ordination amongst the mobile agent, effectual modelling and less memory utilization. The anticipated model suggests Mobi-X architecture to mobile agent system with a tree structure for effectual modelling. The construction of tree for real time mobile agent system is utilized to generate virtual experiences like elapse time during mining of tree structure. In addition, this model is appropriate for knowledge mining. This work is inspired by knowledge mining concept in mobile agent systems where an agent can built a global model from scattered local model held by individual agents. Subsequently, it increases modelling accuracy to offer valid simulation outcome for indirect learning at initial stage of mining. In order to simplify mining procedure, this anticipated model relies on re-sampling approach with associative rule mining to grafting branches of constructed tree. The tree structure provides the functions of mobile agents with useful experience from peer to peer connectivity, indeed of combining all the available agents. The simulation outcomes shows that proposed re-sampling can attain efficiency and accelerate the functionality of mobile agents based cooperation applications.