Number of wells drilled by private and public stakeholders, as well as nongovernmental organizations in the Menoua Division are unproductive. This is due to the lack of preliminary surveys assessing ...groundwater potential (GWP). A combined remote sensing (RS) and analytical hierarchy process (AHP) approach handled on a geographic information system (GIS) environment is efficient for such an investigation. For this article, seven environmental parameters, with significant contribution to groundwater occurrence, are integrated. Those parameters are drainage density, elevation, lineament density, land use/land cover (LULC), rainfall, slope, and topographic wetness index (TWI). RS and GIS techniques said to be quick and simple for exploring GWP whatever the geological settings, have the advantage of investigating large areas with little financial resources. Although these techniques are widely used in the world, this is the first time they are applied in the Menoua Division. The outcome, which is a sound GWP map, has been sorted into five zones: very low potential for 13 %, low potential for 27 %, medium potential also for 27 %, high potential for 23 %, and very high potential for 11 % of the Menoua Division. This may help to reduce the rate of noncompliant hydrogeophysical surveys and the number of unproductive boreholes by converging hydrogeophysical surveys on high GWP sites.
The Meat Standards Australia (MSA) grading scheme has the ability to predict beef eating quality for each 'cut×cooking method combination' from animal and carcass traits such as sex, age, breed, ...marbling, hot carcass weight and fatness, ageing time, etc. Following MSA testing protocols, a total of 22 different muscles, cooked by four different cooking methods and to three different degrees of doneness, were tasted by over 19 000 consumers from Northern Ireland, Poland, Ireland, France and Australia. Consumers scored the sensory characteristics (tenderness, flavor liking, juiciness and overall liking) and then allocated samples to one of four quality grades: unsatisfactory, good-every-day, better-than-every-day and premium. We observed that 26% of the beef was unsatisfactory. As previously reported, 68% of samples were allocated to the correct quality grades using the MSA grading scheme. Furthermore, only 7% of the beef unsatisfactory to consumers was misclassified as acceptable. Overall, we concluded that an MSA-like grading scheme could be used to predict beef eating quality and hence underpin commercial brands or labels in a number of European countries, and possibly the whole of Europe. In addition, such an eating quality guarantee system may allow the implementation of an MSA genetic index to improve eating quality through genetics as well as through management. Finally, such an eating quality guarantee system is likely to generate economic benefits to be shared along the beef supply chain from farmers to retailors, as consumers are willing to pay more for a better quality product.
European conformation and fat grades are a major factor determining carcass value throughout Europe. The relationships between these scores and sensory scores were investigated. A total of 3786 ...French, Polish and Irish consumers evaluated steaks, grilled to a medium doneness, according to protocols of the Meat Standards Australia system, from 18 muscles representing 455 local, commercial cattle from commercial abattoirs. A mixed linear effects model was used for the analysis. There was a negative relationship between juiciness and European conformation score. For the other sensory scores, a maximum of three muscles out of a possible 18 demonstrated negative effects of conformation score on sensory scores. There was a positive effect of European fat score on three individual muscles. However, this was accounted for by marbling score. Thus, while the European carcass classification system may indicate yield, it has no consistent relationship with sensory scores at a carcass level that is suitable for use in a commercial system. The industry should consider using an additional system related to eating quality to aid in the determination of the monetary value of carcasses, rewarding eating quality in addition to yield.
Quantifying consumer responses to beef across a broad range of demographics, nationalities and cooking methods is vitally important for any system evaluating beef eating quality. On the basis of ...previous work, it was expected that consumer scores would be highly accurate in determining quality grades for beef, thereby providing evidence that such a technique could be used to form the basis of and eating quality grading system for beef. Following the Australian MSA (Meat Standards Australia) testing protocols, over 19 000 consumers from Northern Ireland, Poland, Ireland, France and Australia tasted cooked beef samples, then allocated them to a quality grade; unsatisfactory, good-every-day, better-than-every-day and premium. The consumers also scored beef samples for tenderness, juiciness, flavour-liking and overall-liking. The beef was sourced from all countries involved in the study and cooked by four different cooking methods and to three different degrees of doneness, with each experimental group in the study consisting of a single cooking doneness within a cooking method for each country. For each experimental group, and for the data set as a whole, a linear discriminant function was calculated, using the four sensory scores which were used to predict the quality grade. This process was repeated using two conglomerate scores which are derived from weighting and combining the consumer sensory scores for tenderness, juiciness, flavour-liking and overall-liking, the original meat quality 4 score (oMQ4) (0.4, 0.1, 0.2, 0.3) and current meat quality 4 score (cMQ4) (0.3, 0.1, 0.3, 0.3). From the results of these analyses, the optimal weightings of the sensory scores to generate an 'ideal meat quality 4 score (MQ4)' for each country were calculated, and the MQ4 values that reflected the boundaries between the four quality grades were determined. The oMQ4 weightings were far more accurate in categorising European meat samples than the cMQ4 weightings, highlighting that tenderness is more important than flavour to the consumer when determining quality. The accuracy of the discriminant analysis to predict the consumer scored quality grades was similar across all consumer groups, 68%, and similar to previously reported values. These results demonstrate that this technique, as used in the MSA system, could be used to predict consumer assessment of beef eating quality and therefore to underpin a commercial eating quality guarantee for all European consumers.
An experiment was set up for (i) comparing Australian and French consumer preferences to beef and to (ii) quantify how well the Meat Standards Australia (MSA) grading model could predict the eating ...quality of beef in France. Six muscles from 18 Australian and 18 French cattle were tested as paired samples. In France, steaks were grilled ‘medium’ or ‘rare’, whereas in Australia ‘medium’ cooking was used. In total, 360 French consumers took part in the ‘medium’ cooking test, with each eating half Australian beef and half French beef and 180 French consumers tested the ‘rare’ beef. Consumers scored steaks for tenderness (tn), juiciness (ju), flavour liking (fl) and overall liking (ov). They also assigned a quality rating to each sample: ‘unsatisfactory’, ‘satisfactory everyday quality’ (3*), ‘better than everyday quality’ (4*) or ‘premium quality’ (5*). The prediction of the final ratings (3*, 4*, 5*) by the French consumers using the MSA-weighted eating quality score (0.3 tn + 0.1 ju + 0.3 fl + 0.3 ov) was over 70%, which is at least similar to the Australian experience. The boundaries between ‘unsatisfactory’, 3*, 4* and 5* were found to be ca. 38, 61 and 80, respectively. The differences between extreme classes are therefore slightly more important in France than in Australia. On average, even though it does not have predictive equations for bull meat, the mean predicted scores calculated by the MSA model deviated from observed values by a maximum of 5 points on a 0 to 100 scale except for the Australian oyster blade and the French topside, rump and outside (deviating by <15). Overall, the data indicate that it would be possible to manage a grading system in France as there is high agreement and consistency across consumers. The ‘rare’ and ‘medium’ results are also very similar, indicating that a common set of weightings and cut-offs can be employed.
The beef industry must become more responsive to the changing market place and consumer demands. An essential part of this is quantifying a consumer's perception of the eating quality of beef and ...their willingness to pay for that quality, across a broad range of demographics. Over 19 000 consumers from Northern Ireland, Poland, Ireland and France each tasted seven beef samples and scored them for tenderness, juiciness, flavour liking and overall liking. These scores were weighted and combined to create a fifth score, termed the Meat Quality 4 score (MQ4) (0.3×tenderness, 0.1×juiciness, 0.3×flavour liking and 0.3×overall liking). They also allocated the beef samples into one of four quality grades that best described the sample; unsatisfactory, good-every-day, better-than-every-day or premium. After the completion of the tasting panel, consumers were then asked to detail, in their own currency, their willingness to pay for these four categories which was subsequently converted to a proportion relative to the good-every-day category (P-WTP). Consumers also answered a short demographic questionnaire. The four sensory scores, the MQ4 score and the P-WTP were analysed separately, as dependant variables in linear mixed effects models. The answers from the demographic questionnaire were included in the model as fixed effects. Overall, there were only small differences in consumer scores and P-WTP between demographic groups. Consumers who preferred their beef cooked medium or well-done scored beef higher, except in Poland, where the opposite trend was found. This may be because Polish consumers were more likely to prefer their beef cooked well-done, but samples were cooked medium for this group. There was a small positive relationship with the importance of beef in the diet, increasing sensory scores by about 4% in Poland and Northern Ireland. Men also scored beef about 2% higher than women for most sensory scores in most countries. In most countries, consumers were willing to pay between 150 and 200% more for premium beef, and there was a 50% penalty in value for unsatisfactory beef. After quality grade, by far the greatest influence on P-WTP was country of origin. Consumer age also had a small negative relationship with P-WTP. The results indicate that a single quality score could reliably describe the eating quality experienced by all consumers. In addition, if reliable quality information is delivered to consumers they will pay more for better quality beef, which would add value to the beef industry and encourage improvements in quality.
The matching complex $M(G)$ of a graph G is the set of all matchings in G. A Buchsbaum simplicial complex is a generalization of both a homology manifold and a Cohen–Macaulay complex. We give a ...complete characterization of the graphs G for which $M(G)$ is a two-dimensional Buchsbaum complex. As an intermediate step, we determine which graphs have matching complexes that are themselves connected graphs.
Delivering beef of consistent quality to the consumer is vital for consumer satisfaction and will help to ensure demand and therefore profitability within the beef industry. In Australia, this is ...being tackled with Meat Standards Australia (MSA), which uses carcass traits and processing factors to deliver an individual eating quality guarantee to the consumer for 135 different 'cut by cooking methods' from each carcass. The carcass traits used in the MSA model, such as ossification score, carcass weight and marbling explain the majority of the differences between breeds and sexes. Therefore, it was expected that the model would predict with eating quality of bulls and dairy breeds with good accuracy. In total, 8128 muscle samples from 482 carcasses from France, Poland, Ireland and Northern Ireland were MSA graded at slaughter then evaluated for tenderness, juiciness, flavour liking and overall liking by untrained consumers, according to MSA protocols. The scores were weighted (0.3, 0.1, 0.3, 0.3) and combined to form a global eating quality (meat quality (MQ4)) score. The carcasses were grouped into one of the three breed categories: beef breeds, dairy breeds and crosses. The difference between the actual and the MSA-predicted MQ4 scores were analysed using a linear mixed effects model including fixed effects for carcass hang method, cook type, muscle type, sex, country, breed category and postmortem ageing period, and random terms for animal identification, consumer country and kill group. Bulls had lower MQ4 scores than steers and females and were predicted less accurately by the MSA model. Beef breeds had lower eating quality scores than dairy breeds and crosses for five out of the 16 muscles tested. Beef breeds were also over predicted in comparison with the cross and dairy breeds for six out of the 16 muscles tested. Therefore, even after accounting for differences in carcass traits, bulls still differ in eating quality when compared with females and steers. Breed also influenced eating quality beyond differences in carcass traits. However, in this case, it was only for certain muscles. This should be taken into account when estimating the eating quality of meat. In addition, the coefficients used by the Australian MSA model for some muscles, marbling score and ultimate pH do not exactly reflect the influence of these factors on eating quality in this data set, and if this system was to be applied to Europe then the coefficients for these muscles and covariates would need further investigation.
The MonALISA (Monitoring Agents in a Large Integrated Services Architecture) framework provides a set of distributed services for monitoring, control, management and global optimization for large ...scale distributed systems. It is based on an ensemble of autonomous, multi-threaded, agent-based subsystems which are registered as dynamic services. They can be automatically discovered and used by other services or clients. The distributed agents can collaborate and cooperate in performing a wide range of management, control and global optimization tasks using real time monitoring information.
Program title: MonALISA
Catalogue identifier: AEEZ_v1_0
Program summary URL:
http://cpc.cs.qub.ac.uk/summaries/AEEZ_v1_0.html
Program obtainable from: CPC Program Library, Queen's University, Belfast, N. Ireland
Licensing provisions: Caltech License – free for all non-commercial activities
No. of lines in distributed program, including test data, etc.: 147 802
No. of bytes in distributed program, including test data, etc.: 2 5913 689
Distribution format: tar.gz
Programming language: Java, additional APIs available in Java, C, C++, Perl and python
Computer: Computing Clusters, Network Devices, Storage Systems, Large scale data intensive applications
Operating system: The MonALISA service is mainly used in Linux, the MonALISA client runs on all major platforms (Windows, Linux, Solaris, MacOS).
Has the code been vectorized or parallelized?: It is a multithreaded application. It will efficiently use all the available processors.
RAM: for the MonALISA service the minimum required memory is 64 MB; if the JVM is started allocating more memory this will be used for internal caching. The MonALISA client requires typically 256–512 MB of memory.
Classification: 6.5
External routines: Requires Java: JRE or JDK to run. These external packages are used (they are included in the distribution): JINI, JFreeChart, PostgreSQL (optional).
Nature of problem: To monitor and control distributed computing clusters and grids, the network infrastructure, the storage systems, and the applications used on such facilities. The monitoring information gathered is used for developing the required higher level services, the components that provide decision support and some degree of automated decisions and for maintaining and optimizing workflow in large scale distributed systems.
Solution method: The MonALISA framework is designed as an ensemble of autonomous self-describing agent-based subsystems which are registered as dynamic services. These services are able to collaborate and cooperate in performing a wide range of distributed information-gathering and processing tasks.
Running time: MonALISA services are designed to run continuously to collect monitoring data and to trigger alarms or to take automatic actions in case it is necessary.
References:
1
http://monalisa.caltech.edu.
The ability of the biochemical measurements, haem iron, intramuscular fat (IMF%), moisture content, and total, soluble and insoluble collagen contents, to predict untrained consumer sensory scores ...both across different muscles and within the same muscle from different carcasses were investigated. Sensory scores from 540 untrained French consumers (tenderness, flavour liking, juiciness and overall liking) were obtained for six muscles; outside (m. biceps femoris), topside (m. semimembranosus), striploin (m. longissimus thoracis), rump (m. gluteus medius), oyster blade (m. infraspinatus) and tenderloin (m. psoas major) from each of 18 French and 18 Australian cattle. The four sensory scores were weighted and combined into a single score termed MQ4, which was also analysed. All sensory scores were highly correlated with each other and with MQ4. This in part reflects the fact that MQ4 is derived from the consumer scores for tenderness, juiciness, flavour and overall liking and also reflects an interrelationship between the sensory scores themselves and in turn validates the use of the MQ4 term to reflect the scope of the consumer eating experience. When evaluated across the six different muscles, all biochemical measurements, except soluble collagen, had a significant effect on all of the sensory scores and MQ4. The average magnitude of impact of IMF%, haem iron, moisture content, total and insoluble collagen contents across the four different sensory scores are 34.9, 5.1, 7.2, 36.3 and 41.3, respectively. When evaluated within the same muscle, only IMF% and moisture content had a significant effect on overall liking (5.9 and 6.2, respectively) and flavour liking (6.1 and 6.4, respectively). These results indicate that in a commercial eating quality prediction model including muscle type, only IMF% or moisture content has the capacity to add any precision. However, all tested biochemical measurements, particularly IMF% and insoluble collagen contents, are strong predictors of eating quality when muscle type is not known. This demonstrates their potential usefulness in extrapolating the sensory data derived from these six muscles to other muscles with no sensory data, but with similar biochemical parameters, and therefore reducing the amount of future sensory testing required.
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