THE IMPACT OF BEAN RESEARCH IN HONDURAS

Since the mid-1980s, bean research in Honduras has focused on the development of improved varieties resistant to key diseases, principally Bean Golden Yellow Mosaic Virus (BGYMV), one of the main constraints to bean production in the country. This paper presents evidence of recent adoption rates of improved bean varieties, the farm-level economic impact of adoption, and the ex post rate of return to bean research in Honduras from 1982-2010. Results from a 2001 farm-level survey in the two principal bean-producing regions in Honduras show that 46 to 51% of bean farmers (depending upon the season) have adopted an improved variety, and that adoption is scale-neutral with respect to farm-size and market orientation. Due to the potential problem of sample selection bias in the adoption of disease-resistant varieties, the farm-level impact of the new varieties was estimated using experimental data to approximate the yield differential between resistant and non-resistant varieties under disease pressure, and survey data was used to approximate the frequency of disease incidence in farmers' fields. An expected utility framework assuming risk neutrality demonstrates that adopters gain an average of 20% in bean income from increased yield stability under disease pressure, although these gains are reduced by 7 to 16% due to market price discounts for the resistant varieties. Under base-level assumptions, the economic rate of return bean research in Honduras during the period 1982-2010 is 40 %.Crop Production/Industries, Research and Development/Tech Change/Emerging Technologies,

Energy-based metrics for arthroscopic skills assessment

Minimally invasive skills assessment methods are essential in developing efficient surgical simulators and implementing consistent skills evaluation. Although numerous methods have been investigated in the literature, there is still a need to further improve the accuracy of surgical skills assessment. Energy expenditure can be an indication of motor skills proficiency. The goals of this study are to develop objective metrics based on energy expenditure, normalize these metrics, and investigate classifying trainees using these metrics. To this end, different forms of energy consisting of mechanical energy and work were considered and their values were divided by the related value of an ideal performance to develop normalized metrics. These metrics were used as inputs for various machine learning algorithms including support vector machines (SVM) and neural networks (NNs) for classification. The accuracy of the combination of the normalized energy-based metrics with these classifiers was evaluated through a leave-one-subject-out cross-validation. The proposed method was validated using 26 subjects at two experience levels (novices and experts) in three arthroscopic tasks. The results showed that there are statistically significant differences between novices and experts for almost all of the normalized energy-based metrics. The accuracy of classification using SVM and NN methods was between 70% and 95% for the various tasks. The results show that the normalized energy-based metrics and their combination with SVM and NN classifiers are capable of providing accurate classification of trainees. The assessment method proposed in this study can enhance surgical training by providing appropriate feedback to trainees about their level of expertise and can be used in the evaluation of proficiency

Force/position-based modular system for minimally invasive surgery,”

Abstract-The limitations of minimally invasive surgery include the inability to sense forces exerted by the instruments on tissue and the limited visual cues available through the endoscope. A modular laparoscopic instrument capable of measuring force and position has been designed to address these limitations. Novel image-based position tracking software has been developed and integrated within a graphical user interface. This modular system is low cost, versatile, and could be used for training, localization of critical features or for guidance during surgical procedures

A sensorized glove for therapist skill performance assessment during neck manipulation

© 2017 IEEE. Skills performance assessment in physical therapy is mainly evaluated by observational means from an expert. Recent advancements in technology enable the creation of smart instrumentation that can aid trainees and evaluators during the learning and evaluation process. Towards this goal, this paper presents the design and development of a sensorized glove for measuring movement and interaction forces of the hand during neck manipulation. The glove can measure forces with an error of less than 1 Newton and is capable of detecting motion and forces during manual rehabilitation maneuvers such as high-velocity, low amplitude neck manipulation

A computational model for estimating tumor margins in complementary tactile and 3D ultrasound images

Conventional surgical methods are effective for treating lung tumors; however, they impose high trauma and pain to patients. Minimally invasive surgery is a safer alternative as smaller incisions are required to reach the lung; however, it is challenging due to inadequate intraoperative tumor localization. To address this issue, a mechatronic palpation device was developed that incorporates tactile and ultrasound sensors capable of acquiring surface and cross-sectional images of palpated tissue. Initial work focused on tactile image segmentation and fusion of position-tracked tactile images, resulting in a reconstruction of the palpated surface to compute the spatial locations of underlying tumors. This paper presents a computational model capable of analyzing orthogonally-paired tactile and ultrasound images to compute the surface circumference and depth margins of a tumor. The framework also integrates an error compensation technique and an algebraic model to align all of the image pairs and to estimate the tumor depths within the tracked thickness of a palpated tissue. For validation, an ex vivo experimental study was conducted involving the complete palpation of 11 porcine liver tissues injected with iodine-agar tumors of varying sizes and shapes. The resulting tactile and ultrasound images were then processed using the proposed model to compute the tumor margins and compare them to fluoroscopy based physical measurements. The results show a good negative correlation (r =-0.783, p = 0.004) between the tumor surface margins and a good positive correlation (r = 0.743, p = 0.009) between the tumor depth margins

An autoclavable wireless palpation instrument for minimally invasive surgery

Minimally invasive surgery prevents surgeons from manually palpating organs to locate subsurface tumors and other structures. One solution is to use ultrasound; however, it is not always reliable. Various minimally invasive surgery instruments that provide tactile feedback have been proposed to augment ultrasound sensing for tumor localization; however, current designs have limitations such as cumbersome wiring, difficulty in manipulation, lack of sterilizability and high cost. This paper presents a novel, autoclavable, wireless, hand-held palpation instrument that uses a custom, low-cost, disposable tactile sensor to provide tactile and kinesthetic force feedback. The use of a replaceable, disposable tactile sensor avoids deterioration in sensor performance due to repeated autoclaving. The instrument features a passive joint in the end effector that allows the sensor to self-align to the palpation surface in a wide range of orientations. All of the electronics are packaged in a removable module that allows the rest of the instrument to be easily cleaned and autoclaved. Two versions of the tactile sensor, using piezoresistive sensing and capacitive sensing respectively, have been designed for use with this instrument. The instrument is shown to be able to detect 6 mm diameter spherical tumors at a depth of 9-10 mm in ex vivo tissue samples

A Sterilizable Force-Sensing Instrument for Laparoscopic Surgery

Abstract-Although some technologies have been developed to measure tool-tissue interaction forces during minimally invasive surgery (MIS), none of these technologies have been approved for use in humans. The primary factor preventing the use of sensorized instruments in humans is their inability to withstand the stringent conditions present during cleaning and sterilization. This paper presents a series of experiments that were performed to develop a sterilizable instrument capable of measuring tool-tissue interaction forces in three degrees of freedom using strain gauges. The experiments provided an appropriate choice of cables and connectors, as well as an optimal combination of strain gauge adhesives and coatings that allow the sensors to withstand autoclave sterilization. A prototype of the sensorized instruments was developed and tested. The final prototype was able to withstand a sterilization cycle with excellent results (0.10-0.21 N accuracy, 0.05-0.20 N repeatability and 0.06-0.21 N hysteresis depending on the measurement direction). This work shows that autoclave sterilization is possible for a strain-gauge instrumented device

THE IMPACT OF BEAN RESEARCH IN HONDURAS

Since the mid-1980s, bean research in Honduras has focused on the development of improved varieties resistant to key diseases, principally Bean Golden Yellow Mosaic Virus (BGYMV), one of the main constraints to bean production in the country. This paper presents evidence of recent adoption rates of improved bean varieties, the farm-level economic impact of adoption, and the ex post rate of return to bean research in Honduras from 1982-2010. Results from a 2001 farm-level survey in the two principal bean-producing regions in Honduras show that 46 to 51% of bean farmers (depending upon the season) have adopted an improved variety, and that adoption is scale-neutral with respect to farm-size and market orientation. Due to the potential problem of sample selection bias in the adoption of disease-resistant varieties, the farm-level impact of the new varieties was estimated using experimental data to approximate the yield differential between resistant and non-resistant varieties under disease pressure, and survey data was used to approximate the frequency of disease incidence in farmers' fields. An expected utility framework assuming risk neutrality demonstrates that adopters gain an average of 20% in bean income from increased yield stability under disease pressure, although these gains are reduced by 7 to 16% due to market price discounts for the resistant varieties. Under base-level assumptions, the economic rate of return bean research in Honduras during the period 1982-2010 is 40 %