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There is a general interest in the study of schemes for the measurement of the efficiency of universities, which generates demand but at the same time is controversial because of the complexity of the problem. This problem is associated with the highly combinatorial characteristics that occur when facing the selection of the proper combination of the attributes, namely inputs and outputs. This investigation proposes an approach to measure the institutional efficiency in higher educational institutions combining Analytic Hierarchy Process (AHP) with Data Envelopment Analysis (DEA). Both methods are frequently used independently, on a global level in areas such as government, business, industry, health care and education. The use of the two methodologies as an evaluation tool is novel and very useful in institutional efficiency studies where results already exist, in order to obtain and confirm important equivalences. The use of the proposed approach is demonstrated using the Queretaro State University -Universidad Autónoma de Querétaro (UAQ) -as a case study.

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& One of the most important research topics in the area of Intelligent Manufacture Systems (IMS) is the automatic detection of tool breakage, wear of chipping during the cutting process. Sensor-based techniques are available for cutting force measurements, but there are drawbacks in this approach in cost and idle times. This work proposes a sensorless monitoring system for tool monitoring in order to detect breakage and chipping by exploiting the wavelet transform and a neural network. Previous works have made use of these tools for monitoring several machining parameters, but we propose an integrated low-cost approach to detect quickly the changes in the tool integrity for monitoring. The system output produces an accurate detection of the tool integrity that enables the system to prevent damage due to tool breakage. This approach allows for an industrial solution to be developed.