Periodic Replenish and Recount Policy to Address Record Inaccuracy from Stock Loss

Inventory record inaccuracy (IRI) often arises in retail environments due to unaccounted stock loss. Theft, misplacement, spoilage, and transaction errors will reduce the true inventory values without changing the inventory record. As previous inventory replenishment policies assume perfect record accuracy, increasing IRI can cause unexpected stockout events, mistimed reorders and replenishment freezes. Solutions to rectifying IRI vary from the use of improved tracking technologies to prevent it initially occurring at all to recounting programs which estimate true inventory value. Unfortunately, in retail environments, high‑tracking technology is unsuitable and continuous counting programs are too costly. To address the limitations of current solutions, we offer a Periodic Replenish and Recount Policy (PRRP) which accounts for stochastic stock loss and minimizes total costs including recounting. The theoretical foundation of PRRP allows for the discovery of both an optimal order quantity as well as optimal count frequency for a given inventory system. We find that in instances of stochastic stock loss, PRRP balances the trade-offs between shortage, surplus and counting costs

Islet Assessment for Transplantation

Author Manuscript: 2010 December 1.Purpose of review: There is a critical need for meaningful viability and potency assays that characterize islet preparations for release prior to clinical islet cell transplantation (ICT). Development, testing, and validation of such assays have been the subject of intense investigation for the past decade. These efforts are reviewed, highlighting the most recent results while focusing on the most promising assays. Recent Findings: Assays based on membrane integrity do not reflect true viability when applied to either intact islets or dispersed islet cells. Assays requiring disaggregation of intact islets into individual cells for assessment introduce additional problems of cell damage and loss. Assays evaluating mitochondrial function, specifically mitochondrial membrane potential, bioenergetic status, and cellular oxygen consumption rate (OCR), especially when conducted with intact islets, appear most promising in evaluating their quality prior to ICT. Prospective, quantitative assays based on measurements of OCR with intact islets have been developed, validated and their results correlated with transplant outcomes in the diabetic nude mouse bioassay. Conclusion: More sensitive and reliable islet viability and potency tests have been recently developed and tested. Those evaluating mitochondrial function are most promising, correlate with transplant outcomes in mice, and are currently being evaluated in the clinical setting.National Center for Research Resources (U.S.) (Grant)National Institutes of Health (U.S.) (Grant U42 RR 016598–01)National Institutes of Health (U.S.) (Grant RO1-DK063108–01A1)Iacocca FoundationSchott FoundationCarol Olson Memorial Diabetes Research Fun

Lift Force Analysis for an Electrodynamic Wheel Maglev Vehicle

This paper used an analytic based 3-D second order vector potential model to study the vertical dynamic force ripple and dynamic airgap height change when using a one pole-pair electrodynamic wheel (EDW) maglev vehicle. A one-pole pair EDW creates the lowest lift specific power; however transient finite element analysis (FEA) also shows that the one pole-pair EDW will create a large oscillating vertical force when maintaining a static airgap height. A dynamically coupled eddy current model was used to confirm that when the airgap length is allowed to change with time then an increase in vertical airgap creates a large decrease in lift force thereby mitigating any large oscillatory airgap height changes from being created by the one pole-pair EDW. The small airgap height variation was experimentally confirmed by using a four-wheeled proof-of-principle radial EDW maglev vehicle

Proof-Pattern Recognition and Lemma Discovery in ACL2

We present a novel technique for combining statistical machine learning for proof-pattern recognition with symbolic methods for lemma discovery. The resulting tool, ACL2(ml), gathers proof statistics and uses statistical pattern-recognition to pre-processes data from libraries, and then suggests auxiliary lemmas in new proofs by analogy with already seen examples. This paper presents the implementation of ACL2(ml) alongside theoretical descriptions of the proof-pattern recognition and lemma discovery methods involved in it

Predicting Nutrient Content, Plant Health, and Site Suitability: A Case Study of Eragrostis tef

Advancements in agricultural and geographic principals have led to worldwide food and agricultural globalization. Because agricultural production continues to further in global interconnectedness, confirmed precision agriculture (PA) methods are required to monitor crops in-field. PA utilizes a remote sensing method referred to as imaging spectroscopy (IS). IS is often performed using a field spectroradiometer that identifies reflectance values. The reflectance values obtained have been utilized in agricultural studies to correlate spectral reflectance to biochemical and biophysical properties. However, while there is a large body of research focusing on IS predicting these agricultural characteristics, many studies have only employed the research in a single region/location resulting in findings that may lacking reproducibility and replication (R&R) for more than a single environment. The lack of regionally comparative IS methods for nutrient and plant health analysis is important as varying geographies may prove to have an effect on IS findings. Therefore, the proposed research utilizes IS methods to predict nutrient and plant health values utilizing tef (Eragrostis tef) as a case study as its cultivated in Ethiopia and the United States. Currently, in the United States, the cultivation of tef is limited thus the United States could benefit from an exploration of site suitability analysis to aid expansion of tef cultivation in the U.S. It is through this interdisciplinary study that potential improvement to geography and remote sensing theory/methods can be obtained to achieve goals within food/agriculture geography

Evaluation of chickpea (Cicer arietinum L.) genotypes for tolerance to Frost in controlled environment

The study aimed to evaluate the frost tolerance variability of Ethiopian chickpea (Cicer arietinum L.) germplasm under controlled environment using growth chamber. A total of 72 genotypes were screened for frost tolerance using complete randomized design with two replications. The analysis of variance result indicated that there was a significant (P<0.01) difference amongst genotypes for plant height, number of foliage, number of primary branch, growth rate, and fresh biomass weight. Based on plant survival rate (SR), 31 (43.1%) genotypes scored above 0.8 values. Based on Freezing tolerance rate (FTR), 37(51.4%) and 31(43.1%) genotypes were rated at a score of 1 to 3 in freezing test 1 (T1) and freezing test 2 (T2), respectively. There was a strong negative correlation between fresh biomass yields with SR (-0.75** for T1 and -0.71** for T2 at p<0.01), while a strong positive correlation with FTR value (0.74** at p<0.01). Based on the combined result of FTR and SR scores, 26 genotypes were found to be frost-tolerant genotypes at a temperature level as low as -5oC at seedling stage. Based on our findings, Ethiopian chickpea germplasm has a genetic potential for frost-tolerance traits for use in breeding programs

Computing in Additive Networks with Bounded-Information Codes

This paper studies the theory of the additive wireless network model, in which the received signal is abstracted as an addition of the transmitted signals. Our central observation is that the crucial challenge for computing in this model is not high contention, as assumed previously, but rather guaranteeing a bounded amount of \emph{information} in each neighborhood per round, a property that we show is achievable using a new random coding technique. Technically, we provide efficient algorithms for fundamental distributed tasks in additive networks, such as solving various symmetry breaking problems, approximating network parameters, and solving an \emph{asymmetry revealing} problem such as computing a maximal input. The key method used is a novel random coding technique that allows a node to successfully decode the received information, as long as it does not contain too many distinct values. We then design our algorithms to produce a limited amount of information in each neighborhood in order to leverage our enriched toolbox for computing in additive networks

Some Empirical Criteria for Attributing Creativity to a Computer Program

Peer reviewedPostprin