Novabase (Portugal)

ABSTRACT‘Happy cow messages’ are instances of commercial speech by the animal industry which, by action or by omission, mislead consumers about the harmful effects that the industry has for non‐human animals, the environment, or human health. Despite their ubiquity, happy cow messages have received little philosophical scrutiny. This article aims to call attention to this form of speech, and to make the case for its restriction. To do so we first conceptualize happy cow messages. Second, we argue that they encroach upon individual autonomy in two particularly objectionable ways: by impairing our ability to behave according to our moral convictions, and by thwarting our capacity to identify and reason about our moral duties. This, we hold, grounds a pro tanto case for stricter and more comprehensive regulation of commercial speech by the animal industry. Third, and against the objection that restricting happy cow messages would violate freedom of speech, we contend that none of the most influential arguments for free speech grants them protection. Indeed, we suggest, those rationales seem to call for their restriction. Fourth, and finally, we review some possible ways to defuse the misleading potential of happy cow messages through commercial speech regulations.

Living resources directly support the livelihood of coastal communities in Sao Tome e Principe, while oil and gas reserves are needed to improve economic growth. Yet these resources are widely coveted, resulting in illegal, unreported and unregulated fishing, as well as illegal oil bunkering and theft of crude oil. STP has settled its maritime boundaries with Equatorial Guinea and Gabon, but not with Nigeria. Instead, the two countries agreed on a joint development regime in the disputed maritime area, albeit unsuccessfully. This article examines two key challenges currently facing STP: unblocking the STP-Nigeria joint development agreement and addressing IUUF.

Load profiles are a crucial tool for power system planning and operation, and also in several operations of electricity markets. This article proposes a new methodology for the determination of load profiles based on a two-step approach. The first phase employs a neural network autoencoder to reduce the dimensionality of the input vectors. The second phase is a clustering process based on the Kohonen Self-Organizing Maps, to identify cohesive consumers' classes. The implemented approach produces classes based on load diagrams and, simultaneously, a class identification based on consumers' billing data.