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The Orlando Magic Stats App is a mobile application that provides all the latest statistics and information about the Orlando Magic basketball team. This app is a must-have for any fan of the team, as it allows users to stay updated on game results, player stats, and team news. The **main idea** behind this app is to provide fans with an easy and convenient way to access all the information they need about the Orlando Magic. With just a few taps on their smartphone, users can quickly find out the score of the latest game, check the stats of their favorite players, and read articles about the team. The app also features a schedule of upcoming games, so fans can plan ahead and make sure they don't miss any action. Additionally, the app provides real-time updates during games, such as scoring plays and player substitutions.

4.7% ABV - Dunkelweizen

In cherries, the spectral information obtained when the fruit is healthy differs noticeably from that obtained during an infestation by larvae, which is caused by the fruit flesh damage, resulting in noticeable changes in Vis NIR spectral information 17. The concept was simple choose a red and white wine, taste them alone, and then taste them after shocking your tongue with pure salt and then lemon juice.

Additionally, the app provides real-time updates during games, such as scoring plays and player substitutions. This feature allows fans to stay engaged and feel like they are right there at the game, even if they are unable to attend in person. Overall, the Orlando Magic Stats App is an essential tool for any fan who wants to stay connected and informed about their favorite basketball team.

Detection and Classification of Citrus Fruit Infestation by Bactrocera dorsalis (Hendel) Using a Multi-Path Vis/NIR Spectroscopy System

In this study, a multi-path Vis/NIR spectroscopy system was developed to detect the presence of Bactrocera dorsalis (Hendel) infestations of citrus fruit. Spectra were acquired for 252 citrus fruit, 126 of which were infested. Two hundred and fifty-two spectra were acquired for modeling in their un-infested stage, slightly infested stage, and seriously infested stage. The location of the infestation is unclear, and considering the impact of the light path on the location of the infestation, each citrus fruit was tested in three orientations (i.e., fruit stalks facing upward (A), fruit stalks facing horizontally (B), and fruit stalks facing downward (C)). Classification models based on joint X-Y distance, multiple transmittance calibration, competitive adaptive reweighted sampling, and partial least squares discriminant analysis (SPXY-MSC-CARS-PLS-DA) were developed on the spectra of each light path, and the average spectra of the four light paths was calculated, to compare their performance in infestation classification. The results show the classification result changed with the light path and fruit orientation. The average spectra for each fruit orientation consistently gave better classification results, with overall accuracies of 92.9%, 89.3%, and 90.5% for orientations A, B, and C, respectively. Moreover, the best model had a Kappa value of 0.89, and gave 95.2%, 80.1%, and 100.0% accuracy for un-infested, slightly infested, and seriously infested citrus fruit. Furthermore, the classification results for infested citrus fruits were better when using the average spectra than using the spectrum of each single light path. Therefore, the multi-path Vis/NIR spectroscopy system is conducive to the detection of B. dorsalis infestation in citrus fruits.

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