scholarly journals Verification of Linear Programming Solutions, with Emphasis on Supply Implications

1977 ◽  
Vol 9 (2) ◽  
pp. 1-8
Author(s):  
C. Richard Shumway ◽  
Hovav Talpaz
Keyword(s):  
Linear Programming ◽  
Real World ◽  
Agricultural Production ◽  
Programming Models ◽  
Production Economics ◽  
Widespread Application ◽  
Wide Range ◽  
Recursive Programming ◽  
Programming Systems

Linear programming (LP) models have been developed for a wide range of normative purposes in agricultural production economics. Despite their widespread application, a pervading concern among users is reliability — how well does a particular model actually describe and/or predict real world phenomena when it is so designed.Much attention has been devoted in recent years to methods for making programming models produce results more in line with those actually observed. These efforts have included development of more detail in production activities and restrictions, incorporation of flexibility constraints into recursive programming systems, specification of more realistic behavioral properties, and development of guidelines for reducing aggregation error.

Online Learning for Linear Programming with Time-Dependent Cost

2011 ◽  
Vol 230-232 ◽  
pp. 793-797
Author(s):  
Wei Li ◽  
Chong Yang Deng
Keyword(s):  
Machine Learning ◽  
Linear Programming ◽  
Online Learning ◽  
Real World ◽  
Time Dependent ◽  
Programming Models ◽  
Intelligent Optimization ◽  
Learning Parameter ◽  
Real World Problems

Machine learning and linear programming with time dependent cost are two popular intelligent optimization tools to handle uncertainty in real world problems. Thus, combining these two technologies is quite attractive. This paper proposed an effective framework to deal with uncertainty in practice, based on combing introducing learning parameter into linear programming models.

scholarly journals Validation of Linear Programming Models

1986 ◽  
Vol 18 (2) ◽  
pp. 155-164 ◽  
Author(s):  
Bruce A. McCarl ◽  
Jeffrey Apland
Keyword(s):  
Linear Programming ◽  
Mathematical Programming ◽  
Real World ◽  
Evaluation Criteria ◽  
Model Performance ◽  
Programming Models ◽  
Programming Formulation ◽  
Alternative Evaluation ◽  
Economic Problems ◽  
Linear Programming Formulation

AbstractSystematic approaches to validation of linear programming models are discussed for prescriptive and predictive applications to economic problems. Specific references are made to a general linear programming formulation, however, the approaches are applicable to mathematical programming applications in general. Detailed procedures are outlined for validating various aspects of model performance given complete or partial sets of observed, real world values of variables. Alternative evaluation criteria are presented along with procedures for correcting validation problems.

The way of value of Correlation of genomic DNA microsatellite loci and live weight of Chukchi reindeer

2020 ◽  
pp. 27-32
Author(s):  
V. Dodokhov ◽  
N. Pavlova ◽  
T. Rumyantseva ◽  
L. Kalashnikova
Keyword(s):  
Microsatellite Markers ◽  
Agricultural Production ◽  
Live Weight ◽  
Biodiversity Loss ◽  
Tribal Community ◽  
High Germination ◽  
Wide Range ◽  
The Mean ◽  
High Level ◽  
Dna Microsatellite

The article presents the genetic characteristic of the Chukchi reindeer breed. The object of the study was of the Chukchi reindeer. In recent years, the number of reindeer of the Chukchi breed has declined sharply. Reduced reindeer numbers could lead to biodiversity loss. The Chukchi breed of deer has good meat qualities, has high germination viability and is adapted in adverse tundra conditions of Yakutia. Herding of the Chukchi breed of deer in Yakutia are engaged only in the Nizhnekolymsky district. There are four generic communities and the largest of which is the agricultural production cooperative of nomadic tribal community «Turvaurgin», which was chosen to assess the genetic processes of breed using microsatellite markers: Rt6, BMS1788, Rt 30, Rt1, Rt9, FCB193, Rt7, BMS745, C 143, Rt24, OheQ, C217, C32, NVHRT16, T40, C276. It was found that microsatellite markers have a wide range of alleles and generally have a high informative value for identifying of genetic differences between animals and groups of animal. The number of identified alleles is one of the indicators of the genetic diversity of the population. The total number of detected alleles was 127. The Chukchi breed of deer is characterized by a high level of heterozygosity, and the random crossing system prevails over inbreeding in the population. On average, there were 7.9 alleles (Na) per locus, and the mean number of effective alleles (Ne) was 4.1. The index of fixation averaged 0.001. The polymorphism index (PIC) ranged from 0.217 to 0.946, with an average of 0.695.

scholarly journals Goal-driven active learning

2021 ◽  
Vol 35 (2) ◽  
Author(s):  
Nicolas Bougie ◽  
Ryutaro Ichise
Keyword(s):  
Decision Making ◽  
Reinforcement Learning ◽  
Learning Process ◽  
Real World ◽  
Imitation Learning ◽  
Learning Approaches ◽  
Wide Range ◽  
Fixed Set ◽  
Complex Decision Making ◽  
Complex Decision

AbstractDeep reinforcement learning methods have achieved significant successes in complex decision-making problems. In fact, they traditionally rely on well-designed extrinsic rewards, which limits their applicability to many real-world tasks where rewards are naturally sparse. While cloning behaviors provided by an expert is a promising approach to the exploration problem, learning from a fixed set of demonstrations may be impracticable due to lack of state coverage or distribution mismatch—when the learner’s goal deviates from the demonstrated behaviors. Besides, we are interested in learning how to reach a wide range of goals from the same set of demonstrations. In this work we propose a novel goal-conditioned method that leverages very small sets of goal-driven demonstrations to massively accelerate the learning process. Crucially, we introduce the concept of active goal-driven demonstrations to query the demonstrator only in hard-to-learn and uncertain regions of the state space. We further present a strategy for prioritizing sampling of goals where the disagreement between the expert and the policy is maximized. We evaluate our method on a variety of benchmark environments from the Mujoco domain. Experimental results show that our method outperforms prior imitation learning approaches in most of the tasks in terms of exploration efficiency and average scores.

scholarly journals A surrogate virus neutralization test to quantify antibody-mediated inhibition of SARS-CoV-2 in finger stick dried blood spot samples

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Amelia E. Sancilio ◽  
Richard T. D’Aquila ◽  
Elizabeth M. McNally ◽  
Matthew P. Velez ◽  
Michael G. Ison ◽  
...  
Keyword(s):  
Neutralizing Antibodies ◽  
Venous Blood ◽  
Host Cells ◽  
Dried Blood Spot ◽  
Blood Collection ◽  
Widespread Application ◽  
Live Virus ◽  
Dilution Series ◽  
Wide Range ◽  
Blood Spot

AbstractThe spike protein of SARS-CoV-2 engages the human angiotensin-converting enzyme 2 (ACE2) receptor to enter host cells, and neutralizing antibodies are effective at blocking this interaction to prevent infection. Widespread application of this important marker of protective immunity is limited by logistical and technical challenges associated with live virus methods and venous blood collection. To address this gap, we validated an immunoassay-based method for quantifying neutralization of the spike-ACE2 interaction in a single drop of capillary whole blood, collected on filter paper as a dried blood spot (DBS) sample. Samples are eluted overnight and incubated in the presence of spike antigen and ACE2 in a 96-well solid phase plate. Competitive immunoassay with electrochemiluminescent label is used to quantify neutralizing activity. The following measures of assay performance were evaluated: dilution series of confirmed positive and negative samples, agreement with results from matched DBS-serum samples, analysis of results from DBS samples with known COVID-19 status, and precision (intra-assay percent coefficient of variation; %CV) and reliability (inter-assay; %CV). Dilution series produced the expected pattern of dose–response. Agreement between results from serum and DBS samples was high, with concordance correlation = 0.991. Analysis of three control samples across the measurement range indicated acceptable levels of precision and reliability. Median % surrogate neutralization was 46.9 for PCR confirmed convalescent COVID-19 samples and 0.1 for negative samples. Large-scale testing is important for quantifying neutralizing antibodies that can provide protection against COVID-19 in order to estimate the level of immunity in the general population. DBS provides a minimally-invasive, low cost alternative to venous blood collection, and this scalable immunoassay-based method for quantifying inhibition of the spike-ACE2 interaction can be used as a surrogate for virus-based assays to expand testing across a wide range of settings and populations.

A CFD Study of Focused Extreme Wave Impact on Decks of Offshore Structures

2014 ◽  
Author(s):  
Xin Lu ◽  
Pankaj Kumar ◽  
Anand Bahuguni ◽  
Yanling Wu
Keyword(s):  
Real World ◽  
Offshore Structures ◽  
Air Gap ◽  
Irregular Waves ◽  
Linear Wave ◽  
Extreme Wave ◽  
Wave Impact ◽  
Loading Test ◽  
Wide Range ◽  
Wave Maker

The design of offshore structures for extreme/abnormal waves assumes that there is sufficient air gap such that waves will not hit the platform deck. Due to inaccuracies in the predictions of extreme wave crests in addition to settlement or sea-level increases, the required air gap between the crest of the extreme wave and the deck is often inadequate in existing platforms and therefore wave-in-deck loads need to be considered when assessing the integrity of such platforms. The problem of wave-in-deck loading involves very complex physics and demands intensive study. In the Computational Fluid Mechanics (CFD) approach, two critical issues must be addressed, namely the efficient, realistic numerical wave maker and the accurate free surface capturing methodology. Most reported CFD research on wave-in-deck loads consider regular waves only, for instance the Stokes fifth-order waves. They are, however, recognized by designers as approximate approaches since “real world” sea states consist of random irregular waves. In our work, we report a recently developed focused extreme wave maker based on the NewWave theory. This model can better approximate the “real world” conditions, and is more efficient than conventional random wave makers. It is able to efficiently generate targeted waves at a prescribed time and location. The work is implemented and integrated with OpenFOAM, an open source platform that receives more and more attention in a wide range of industrial applications. We will describe the developed numerical method of predicting highly non-linear wave-in-deck loads in the time domain. The model’s capability is firstly demonstrated against 3D model testing experiments on a fixed block with various deck orientations under random waves. A detailed loading analysis is conducted and compared with available numerical and measurement data. It is then applied to an extreme wave loading test on a selected bridge with multiple under-deck girders. The waves are focused extreme irregular waves derived from NewWave theory and JONSWAP spectra.

scholarly journals Classification of unlabeled online media

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Sakthi Kumar Arul Prakash ◽  
Conrad Tucker
Keyword(s):  
Social Media ◽  
Real World ◽  
Graphical Model ◽  
Ground Truth ◽  
Classification Problem ◽  
Machine Learning Algorithms ◽  
Social Media Networks ◽  
Online Social Media ◽  
Wide Range

AbstractThis work investigates the ability to classify misinformation in online social media networks in a manner that avoids the need for ground truth labels. Rather than approach the classification problem as a task for humans or machine learning algorithms, this work leverages user–user and user–media (i.e.,media likes) interactions to infer the type of information (fake vs. authentic) being spread, without needing to know the actual details of the information itself. To study the inception and evolution of user–user and user–media interactions over time, we create an experimental platform that mimics the functionality of real-world social media networks. We develop a graphical model that considers the evolution of this network topology to model the uncertainty (entropy) propagation when fake and authentic media disseminates across the network. The creation of a real-world social media network enables a wide range of hypotheses to be tested pertaining to users, their interactions with other users, and with media content. The discovery that the entropy of user–user and user–media interactions approximate fake and authentic media likes, enables us to classify fake media in an unsupervised learning manner.

scholarly journals An information theoretic approach to link prediction in multiplex networks

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Seyed Hossein Jafari ◽  
Amir Mahdi Abdolhosseini-Qomi ◽  
Masoud Asadpour ◽  
Maseud Rahgozar ◽  
Naser Yazdani
Keyword(s):  
Real World ◽  
Link Prediction ◽  
Large Scale ◽  
Similarity Measures ◽  
Prediction Method ◽  
General Purpose ◽  
Fast Method ◽  
Theoretic Approach ◽  
Multiplex Networks ◽  
Wide Range

AbstractThe entities of real-world networks are connected via different types of connections (i.e., layers). The task of link prediction in multiplex networks is about finding missing connections based on both intra-layer and inter-layer correlations. Our observations confirm that in a wide range of real-world multiplex networks, from social to biological and technological, a positive correlation exists between connection probability in one layer and similarity in other layers. Accordingly, a similarity-based automatic general-purpose multiplex link prediction method—SimBins—is devised that quantifies the amount of connection uncertainty based on observed inter-layer correlations in a multiplex network. Moreover, SimBins enhances the prediction quality in the target layer by incorporating the effect of link overlap across layers. Applying SimBins to various datasets from diverse domains, our findings indicate that SimBins outperforms the compared methods (both baseline and state-of-the-art methods) in most instances when predicting links. Furthermore, it is discussed that SimBins imposes minor computational overhead to the base similarity measures making it a potentially fast method, suitable for large-scale multiplex networks.

Sign in / Sign up

Export Citation Format

Share Document