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Cyanobacterial harmful algal blooms (CyanoHABs), mainly composed of the genus Microcystis, occur frequently throughout the Laurentian Great Lakes. We used artificial neural networks (ANNs) involving 31 hydrological and meteorological predictors to model total phytoplankton (as chlorophyll a) and Microcystis biomass from 2009 to 2011 in western Lake Erie. Continuous ANNs provided modeled-measured correspondences (and modeling efficiencies) ranging from 0.87 to 0.97 (0.75 to 0.94) and 0.71 to 0.90 (0.45 to 0.88) for training-cross-validation and test data subsets of chlorophyll a concentrations and Microcystis biovolumes, respectively. Classification ANNs correctly assigned up to 94% of instances for Microcystis presenceabsence. The influences of select predictors on phytoplankton and CyanoHAB niches were visualized using biplots and threedimensional response surfaces. These then were used to generate mathematical expressions for the relationships between modeled CyanoHAB outcomes and the direct and interactive influences of environmental factors. Based on identified conditions (~40 to 50 µg total phosphorus (TP) x [L.sup.-1], 22 to 26°C, and prolonged wind speeds less than ~19 km x [h.sup.-1]) underlying the likelihood of occurrence and accumulation of phytoplankton and Microcystis, a 'target' concentration of 30 µg TP x [L.sup.- 1] appears appropriate for alleviating blooms. ANNs generated robust ecological niche models for Microcystis, providing a predictive framework for quantitative visualization of nonlinear CyanoHAB-environmental interactions. Les fleurs d'eau de cyanobacteries nuisibles (CyanoHAB), constituees principalement du genre Microcystis, sont frequentes dans tous les Grands Lacs laurentiens. Nous avons utilise des reseaux neuronaux artificiels (<< ANNs >>) incluant 31 predicteurs hydrologiques et meteorologiques pour modeliser la biomasse de phytoplancton totale (chlorophylle a) et de Microcystis, de 2009 a 2011, dans la partie occidentale du lac Erie. Des ANNs continus ont fourni des correspondances modele-mesures (et des valeurs d'efficacite de la modelisation) allant de 0,87 a 0,97 (0,75 a 0,94) et de 0,71 a 0,90 (0,45 a 0,88) pour des sous-ensembles de donnees d'entrainement-de contrevalidation et d'essai des concentrations de la chlorophylle a et des biovolumes de Microcystis, respectivement. Les ANNs de classification ont affecte correctement jusqu'il 94 % des cas d'absence ou de presence de Microcystis. Les influences de predicteurs selectionnes sur les niches de phytoplancton et de CyanoHAB ont ete visualisees a l'aide de diagrammes de double projection et de surfaces de reponse tridimensionnelles, qui ont ensuite ete utilises pour produire des expressions mathematiques pour les relations entre les CyanoHAB modelisees et les influences directes et interactives de facteurs environnementaux. A la lumiere des conditions cernees (~40 a 50 µg phosphore total (PT)x [L.sup.-1], de 22 a 26°C et des vitesses du vent soutenues inferieures a ~19 km x [h.sup.-1]) soustendant la probabilite de presence et d'accumulation de phytoplancton et de Microcystis, une concentration << cible >> de 30 µg PT x [L.sup.-1] semble adequate pour reduire les fleurs d'eau. Les ANNs ont produit des modeles de niche ecologique robustes pour Microcystis, fournissant ainsi un cadre previsionnel pour la visualisation quantitative d'interactions non lineaires entre le milieu ambiant et les CyanoHAB. [Traduit par la Redaction], Introduction The characterization of biotic responses to environmental controls, along with the dynamics of those responses, fuels ecological modeling on local scales and forms the foundation for regional forecasting efforts. [...]

The profitability of every manufacturing plant is dependent on its pricing strategy and a production plan to support the customers’ demand. In this paper, a new robust multi-product and multi-period model for planning and pricing is proposed. The demand is considered to be uncertain and price-dependent. Thus, for each price, a range of demands is possible. The unsatisfied demand is considered to be lost and hence, no backlogging is allowed. The objective is to maximise the profit over the planning horizon, which consists of a finite number of periods. To solve the proposed model, a modified unconscious search (US) algorithm is introduced. Several artificial test problems along with a real case implementation of the model in a textile manufacturing plant are used to show the applicability of the model and effectiveness of the US for tackling this problem. The results show that the proposed model can improve the profitability of the plant and the US is able to find high quality solutions in a very short time compared to exact methods. [ABSTRACT FROM AUTHOR]

Within the retail industry, after customers place orders, their orders are sent to fulfillment centers and their orders are often cartonized on a first-come-first-serve basis. This strategy has the benefit of reducing shipping costs, but it is far from an optimal solution. This paper explores an integrated mathematical model for fulfillment center assignment and simultaneous cartonization. Considering the complexity of the model for larger-scale problems that arise in real-world scenarios, a decoupled model is proposed. Moreover, a fast and effective heuristic, a genetic algorithm with modified two-stage crossover and mutation operators and a simulated annealing with two cooling schedules are also introduced. The results of applying the integrated and decoupled models using the proposed heuristic and metaheuristics are presented for various testing scenarios. The proposed heuristic and metaheuristics are shown to be more efficient when applied to testing instances that are tightly constrained. [ABSTRACT FROM AUTHOR]

In this article, we describe the flipped classroom, which reverses the traditional instructional approach. In the flipped classroom, students watch instructional videos or review teacher-created instructional materials at home. These instructional videos replace the traditional in-class lecture. Afterwards, students participate in learning exercises in class with the instructor's help. This replaces the homework assignments traditionally reviewed outside of class time. The flipped classroom is not a method, but rather a philosophy to maximize the value of the time students spend in class. The philosophies behind flipping the classroom are that teachers can spend time working with students who need their help during class time, and that students can work together to solve problems instead of sitting at home, alone with work they might not understand and with nobody to ask for help. By minimizing lecture time, the flipped model provides more interaction between the student and the teacher. It enhances this interaction, freeing time for the instructor to lead learning activities and provide individualized instruction as needed. The flipped classroom provides a higher level of learning. When students view lectures outside of class, they are better able to understand and remember course concepts. When students review lectures before class, they can approach class time with a baseline level of knowledge. During class time, through exercises and discussions, students can learn more effectively by applying these course concepts and analyzing situations with some base knowledge as a point of reference. Throughout the article, we will describe experiences "flipping" an undergraduate management class, technology options for preparing online lecture content, and strategies for creating in-class learning exercises and group discussion tactics. Finally, we will review lessons learned from "flipping" the classroom. [ABSTRACT FROM AUTHOR]

In an age of technological advancement, video games have been found to be effective teaching aids in middle school science classrooms. A National Science Foundation (NSF) project at Ohio University, known as STEAM, (Science and Technology Enrichment for Appalachian Middle Schoolers), has examined Second Life as a curriculum aid through the design, creation, and deployment of educational games cre-ated for the middle school science classrooms. Three Second Life games are discussed in this paper and examined in their implementation in middle school classrooms. Advantages, disadvantages, and design issues are highlighted. [ABSTRACT FROM AUTHOR]

Radio frequency identification (RFID) technology is used for asset tracking due to its accuracy and speed. RFID tracking systems are being used to locate tagged objects in indoor environments, however; reliability is low due to interferences. To overcome this limitation, artificial neural networks (ANNs) can be used to determining a device's location in the proximity of interference. This research presents a proof of concept to an industrial application of using ANNs as an RFID localization algorithm when objects are subjected to metallic and human interference. To prove this concept, random samples are collected using the received signal strength indication (RSSI) values from passive RFID readers and antennas. The test results show that ANNs can determine the location of a passive RFID tag accurately in the presence of noise and shows that data preprocessing techniques can improve the predictive capabilities of the ANN-RFID localization algorithm. Significance: Research shows that passive tags are better suited for tracking items that are high volume, low cost, or have a short shelf life. A passive RFID tag system is explored in this research due to the inexpensive nature of the tags. Thus, there is a great potential for many industrial applications if advancements are made to increase the reliability of determining a tag's location. Constructing an ANN-RFID localization algorithm is significant because ANNs are capable of predicting non-linear, noisy, or incomplete readings that are obtain from RFID antennas. These models can ultimately decrease the setup time needed to implement and increase the accuracy of a location system in the presence of noise. [ABSTRACT FROM AUTHOR]

This study proposes a two-echelon location routing framework for cash-in-transit. In order to mitigate the risk of robbery in cash transportation, a dynamic risk index is considered. The utilized risk function encompasses both the amount of cash carried by a vehicle and the travel time of a route, and it relaxes a pre-defined parameter of risk threshold in the literature. Multiple exact and metaheuristic methodologies are utilized and evaluated on several small to medium-sized instances and a case study. The effectiveness of the proposed methods is evaluated and shown by examining various multi-objective performance measures. The case study is researched in more depth to obtain managerial insights, and the results show that depending on the risk or cost efficiency of the solutions on a Pareto frontier, the risk of traversing longer routes or transporting larger amounts of cash can be determining in locating new bank vaults. • A bi-objective two-echelon LRP with a dynamic risk index is proposed. • Several metaheuristic approaches are utilized to solve the proposed model. • A real case study is examined in detail using the proposed metaheuristics. • The managerial implications of the findings are discussed in depth. [ABSTRACT FROM AUTHOR]

As the level of greenhouse gas emissions increases, so does the importance of the energy performance of buildings (EPB). One of the main factors to measure EPB is a structure's heating load (HL) and cooling load (CL). HLs and CLs depend on several variables, such as relative compactness, surface area, wall area, roof area, overall height, orientation, glazing area, and glazing area distribution. This research uses deep neural networks (DNNs) to forecast HLs and CLs for a variety of structures. The DNNs explored in this research include multi-layer perceptron (MLP) networks, and each of the models in this research was developed through extensive testing with a myriad number of layers, process elements, and other data preprocessing techniques. As a result, a DNN is shown to be an improvement for modeling HLs and CLs compared to traditional artificial neural network (ANN) models. In order to extract knowledge from a trained model, a post-processing technique, called sensitivity analysis (SA), was applied to the model that performed the best with respect to the selected goodness-of-fit metric on an independent set of testing data. There are two forms of SA—local and global methods—but both have the same purpose in terms of determining the significance of independent variables within a model. Local SA assumes inputs are independent of each other, while global SA does not. To further the contribution of the research presented within this article, the results of a global SA, called state-based sensitivity analysis (SBSA), are compared to the results obtained from a traditional local technique, called sensitivity analysis about the mean (SAAM). The results of the research demonstrate an improvement over existing conclusions found in literature, which is of particular interest to decision-makers and designers of building structures. [ABSTRACT FROM AUTHOR]