Suchergebnisse

State antitrust laws are broadly constructed. With sweeping, general terms, often mirroring the language of the federal anti- trust laws, most state antitrust statutes manifest a legislative design to prevent-and to punish a variety of commercial activities that are anticompetitive in purpose or effect. These statutes, in conjunction with consumer protection statutes, constitute the primary vehicles through which state authorities protect consumers from harmful, anticompetitive behavior. Of course, despite the importance of state antitrust laws in preserving a competitive marketplace, the Constitution confines their reach. Through the Commerce Clause, the Constitution vests in Congress the exclusive power to regulate interstate commerce. Accordingly, since passage of the Sherman Act in 1890, Congress has promulgated an extensive body of antitrust legislation regulating interstate commercial conduct. Another federal constraint on state antitrust laws arises through the Supremacy Clause. To the extent that state antitrust laws conflict with federal legislation in the same field, courts will find them constitutionally invalid. State antitrust statutes, however, are not completely preempted by federal antitrust laws. Instead, the legislative history accompanying most state and federal antitrust statutes indicates that the two sets of statutes were designed to function as equally potent ingredients in a comprehensive protective scheme. In fact, early federal antitrust legislation directly reflected the policies behind the state antitrust laws of the late nineteenth century; they also reflected contemporary principles of common law. As coexisting and complementary instruments, state and federal antitrust statutes form an excellent example of the potential for effective multi-layered legislation. In one court's analysis, the relationship between the federal and state antitrust laws is a quintessential ex- ample of "cooperative federalism."'

Unoccupied aerial vehicles (UAVs) have become increasingly commonplace in aiding planning and management decisions in agricultural and horticultural crop production. The ability of UAV-based sensing technologies to provide high spatial (<1 m) and temporal (on-demand) resolution data facilitates monitoring of individual plants over time and can provide essential information about health, yield, and growth in a timely and quantifiable manner. Such applications would be beneficial for cropped banana plants due to their distinctive growth characteristics. Limited studies have employed UAV data for mapping banana crops and to our knowledge only one other investigation features multi-temporal detection of banana crowns. The purpose of this study was to determine the suitability of multiple-date UAV-captured multi-spectral data for the automated detection of individual plants using convolutional neural network (CNN), template matching (TM), and local maximum filter (LMF) methods in a geographic object-based image analysis (GEOBIA) software framework coupled with basic classification refinement. The results indicate that CNN returns the highest plant detection accuracies, with the developed rule set and model providing greater transferability between dates (F-score ranging between 0.93 and 0.85) than TM (0.86–0.74) and LMF (0.86–0.73) approaches. The findings provide a foundation for UAV-based individual banana plant counting and crop monitoring, which may be used for precision agricultural applications to monitor health, estimate yield, and to inform on fertilizer, pesticide, and other input requirements for optimized farm management. ; The authors would like to acknowledge the support from Earle Lawrence (farm holder) and Barry Sullivan (Australian Banana Growers Council), and fieldwork assistance from Yu-Hsuan Tu and Dan Wu. D.W.L. acknowledges the support of Food Agility CRC Ltd., funded under the Commonwealth Government CRC Program. The CRC Program supports industry-led collaborations between industry, researchers, and the community.