Check out their product page … link Get the Chemometrics and Spectroscopy News in real time on Twitter @ CalibModel and follow us. Near-Infrared Spectroscopy (NIRS) "Integrated soluble solid and nitrate content assessment of spinach plants using portable NIRS sensors along the supply chain" LINK "Evaluation of Near Infrared Spectroscopy (NIRS) and Remote Sensing (RS) for Estimating Pasture Quality in Mediterranean Montado Ecosystem" LINK "Evaluation of Homogeneity in Drug Seizures Using Near-Infrared (NIR) Hyperspectral Imaging and Principal Component Analysis (PCA)"LINK "FT-NIRS Coupled with PLS Regression as a Complement to HPLC Routine Analysis of Caffeine in Tea Samples" Foods LINK Infrared Spectroscopy (IR) and Near-Infrared Spectroscopy (NIR) "Model based optimization of transflection near infrared spectroscopy as a process analytical tool in a continuous flash pasteurizer" LINK "EXPRESS: Monitoring Polyurethane Foaming Reactions Using Near-Infrared Hyperspectral Imaging" LINK ...

Not surprisingly, researchers are working on new methods with a view to reducing the carbon footprint of these machines. In June, American company OpenAI unveiled the world's largest text generator. Called GPT-3, the new artificial intelligence (AI) model can, among other things, write creative fiction and translate legal jargon into plain English, two functions that have been achieved using deep learning. However, above and beyond these technological breakthroughs, it is important to bear in mind that the creation of this new tool generated an enormous amount of pollution. The extent to which deep learning and computing are polluting is often overlooked. A recent study by the University of Massachusetts has shown that the training of a deep learning machine, which can take several hours or even days, can produce up to 283,000 kilograms of greenhouse gas.

In poultry farming, the systematic choice, update, and implementation of periodic (t) action plans define the feed conversion rate (FCR[t]), which is an acceptable measure for successful production. Appropriate action plans provide tailored resources for broilers, allowing them to grow within the so-called thermal comfort zone, without wast or lack of resources. Although the implementation of an action plan is automatic, its configuration depends on the knowledge of the specialist, tending to be inefficient and error-prone, besides to result in different FCR[t] for each poultry house. In this article, we claim that the specialist's perception can be reproduced, to some extent, by computational intelligence. By combining deep learning and genetic algorithm techniques, we show how action plans can adapt their performance over the time, based on previous well succeeded plans. We also implement a distributed network infrastructure that allows to replicate our method over distributed poultry houses, for their smart, interconnected, and adaptive control. A supervision system is provided as interface to users. Experiments conducted over real data show that our method improves 5% on the performance of the most productive specialist, staying very close to the optimal FCR[t].

In this work, we attempt to solve the integer-weight knapsack problem using the D-Wave 2000Q adiabatic quantum computer. The knapsack problem is a well-known NP-complete problem in computer science, with applications in economics, business, finance, etc. We attempt to solve a number of small knapsack problems whose optimal solutions are known; we find that adiabatic quantum optimization fails to produce solutions corresponding to optimal filling of the knapsack in all problem instances. We compare results obtained on the quantum hardware to the classical simulated annealing algorithm and two solvers employing a hybrid branch-and-bound algorithm. The simulated annealing algorithm also fails to produce the optimal filling of the knapsack, though solutions obtained by simulated and quantum annealing are no more similar to each other than to the correct solution. We discuss potential causes for this observed failure of adiabatic quantum optimization.

In the power and energy systems area, a progressive increase of literature contributions containing applications of metaheuristic algorithms is occurring. In many cases, these applications are merely aimed at proposing the testing of an existing metaheuristic algorithm on a specific problem, claiming that the proposed method is better than other methods based on weak comparisons. This 'rush to heuristics' does not happen in the evolutionary computation domain, where the rules for setting up rigorous comparisons are stricter, but are typical of the domains of application of the metaheuristics. This paper considers the applications to power and energy systems, and aims at providing a comprehensive view of the main issues concerning the use of metaheuristics for global optimization problems. A set of underlying principles that characterize the metaheuristic algorithms is presented. The customization of metaheuristic algorithms to fit the constraints of specific problems is discussed. Some weaknesses and pitfalls found in literature contributions are identified, and specific guidelines are provided on how to prepare sound contributions on the application of metaheuristic algorithms to specific problems.

A method to predict time-series using multiple deep learners and a Bayesian network is proposed. In this study, the input explanatory variables are Bayesian network nodes that are associated with learners. Training data are divided using K-means clustering, and multiple deep learners are trained depending on the cluster. A Bayesian network is used to determine which deep learner is in charge of predicting a time-series. We determine a threshold value and select learners with a posterior probability equal to or greater than the threshold value, which could facilitate more robust prediction. The proposed method is applied to financial time-series data, and the predicted results for the Nikkei 225 index are demonstrated.

Short-term forecasting is an important tool in understanding environmental processes. In this paper, we incorporate machine learning algorithms into a conditional distribution estimator for the purposes of forecasting tropical cyclone intensity. Many machine learning techniques give a single-point prediction of the conditional distribution of the target variable, which does not give a full accounting of the prediction variability. Conditional distribution estimation can provide extra insight on predicted response behavior, which could influence decision-making and policy. We propose a technique that simultaneously estimates the entire conditional distribution and flexibly allows for machine learning techniques to be incorporated. A smooth model is fit over both the target variable and covariates, and a logistic transformation is applied on the model output layer to produce an expression of the conditional density function. We provide two examples of machine learning models that can be used, polynomial regression and deep learning models. To achieve computational efficiency we propose a case-control sampling approximation to the conditional distribution. A simulation study for four different data distributions highlights the effectiveness of our method compared to other machine learning-based conditional distribution estimation techniques. We then demonstrate the utility of our approach for forecasting purposes using tropical cyclone data from the Atlantic Seaboard. This paper gives a proof of concept for the promise of our method, further computational developments can fully unlock its insights in more complex forecasting and other applications.

Scientists are attempting to replicate the brain's frugality to make deep learning less polluting. Not surprisingly, researchers are working on new methods with a view to reducing the carbon footprint of these machines. In June, American company OpenAI unveiled the world's largest text generator. Called GPT-3, the new artificial intelligence (AI) model can, among other things, write creative fiction and translate legal jargon into plain English, two functions that have been achieved using deep learning. However, above and beyond these technological breakthroughs, it is important to bear in mind that the creation of this new tool generated an enormous amount of pollution.

If AI (Artificial Intelligence) is the future, then AI plus Space equals future times two. According to Kenneth's Research, AI in Space Exploration market was valued at approximately USD $2 billion in 2018 and is anticipated to grow at a rate of more than 7.25% by 2026. This includes machine-learning solutions for detecting new planets, space weather using magnetosphere and atmosphere measurement, integration of AI in space vehicles and satellites, as well as AI-based robots that can perform highly complex tasks. In addition to Space Exploration, there is a more prominent market for AI in the Earth Observation (EO) sector, where according to the 10th edition of Euroconsult's report, the EO data and services market should reach USD 8.5 billion by 2026 based on current growth trajectories. This same report predicts a market value of USD 6.5 billion by 2026, considering only new applications and solutions that will be developed to open new markets.

This study introduces SECODA, a novel general-purpose unsupervised non-parametric anomaly detection algorithm for datasets containing continuous and categorical attributes. The method is guaranteed to identify cases with unique or sparse combinations of attribute values. Continuous attributes are discretized repeatedly in order to correctly determine the frequency of such value combinations. The concept of constellations, exponentially increasing weights and discretization cut points, as well as a pruning heuristic are used to detect anomalies with an optimal number of iterations. Moreover, the algorithm has a low memory imprint and its runtime performance scales linearly with the size of the dataset. An evaluation with simulated and real-life datasets shows that this algorithm is able to identify many different types of anomalies, including complex multidimensional instances. An evaluation in terms of a data quality use case with a real dataset demonstrates that SECODA can bring relevant and practical value to real-world settings.