Murayama and Jach offer valuable suggestions for how to integrate computational processes into motivation theory, but these processes cannot do away with motivation altogether. Rewards are only rewarding because people want and like them – that is, because of motivation. Sexual desire is not primarily a quest for rewarding information. Elucidating the interface between motivation and cognition seems a promising way forward.

Although higher-level constructs often fail to explain the mechanisms underlying motivation, we argue that purely mechanistic approaches have limitations. Lower-level neural data help us identify “biologically plausible” mechanisms, while higher-level constructs are critical to formulate measurable behavioral outcomes when constructing computational models. Therefore, we propose that a multi-level, multi-measure approach is required to fully unpack the black box of motivated behavior.

Melina Constantine Bell (2021) argues that J. S. Mill's harm principle permits society to coercively interfere with the use of bigoted insults, since these insults are harmful on “a more expansive, modern, conception of harm.” According to Bell, these insults are harmful in virtue of their contributing to detrimental objective states like health problems. I argue that people with illiberal dispositions might have intense and sustained negative subjective reactions to behavior that the harm principle ought to protect, reactions intense enough to affect their health or other objective interests. Bell's way of thinking about harm therefore has illiberal implications. Yet I agree with her that bigoted insults should be regarded as harmful. I therefore propose an alternative way of understanding harm according to which subjective pain is a harm when it is intentionally caused.

Artificial intelligence (AI) is increasingly being integrated into sentencing within the criminal justice system. This research examines the impact of AI on sentencing, addressing the challenges and opportunities for fairness and justice. The main problem explored is AI’s potential to perpetuate biases, undermining fair-trial principles. This study intends to assess AI’s influence on sentencing, identify legal and ethical challenges, and propose a framework for equitable AI use in judicial decisions. Key research questions include: (1) How does AI influence sentencing decisions? (2) What concerns arise from AI in sentencing? (3) What safeguards can mitigate those concerns and prejudices? Utilizing qualitative methodology, including doctrinal analysis and comparative studies, the research reveals AI’s potential to enhance sentencing efficiency but also to risk reinforcing biases. The study recommends robust regulatory frameworks, transparency in AI algorithms, and judicial oversight to ensure AI supports justice rather than impedes it, advocating for a balanced integration that prioritizes human rights and fairness.

A limited number of herbicides and sites of action are registered for use in sugarcane in Louisiana. Repeated use of the same sites of action can lead to the evolution of herbicide resistance in weeds. Therefore, it is critical to evaluate additional sites of action to provide growers with options for rotating herbicides to reduce the risk of resistance. Topramezone, indaziflam, and a formulation including mesotrione, bicyclopyrone, atrazine, and S-metolachlor, along with more common herbicide applications (pendimethalin, and metribuzin, clomazone, and diuron), were evaluated in the spring for injury to sugarcane, weed control, sugarcane yield, and sugar yield. Of these treatments, clomazone applied with diuron was the only herbicide combination to consistently injure the crop, with injury estimates ranging from 11 to 36%, which frequently resulted in reduced sugar yield with losses between 2.3% to 24.1% of the non-treated control. In most treatments, an increase in itchgrass counts was observed between harvests, indicating that additional control strategies will be needed in fields infested with this weed. However, topramezone alone and with triclopyr was well tolerated by sugarcane, with injuries ranging from 0 to 11% two weeks after treatment. Indaziflam and combined application of mesotrione, bicyclopyrone, atrazine, and S-metolachlor injury was at or under 10% two weeks after treatment. The tolerance of sugarcane for these herbicides suggests that they can be incorporated into weed management strategies in sugarcane. These herbicides would increase the sites of action available to be applied in sugarcane and help mitigate the risk of herbicide-resistant weeds.

Large Language Models (LLMs) could facilitate both more efficient administrative decision-making on the one hand, and better access to legal explanations and remedies to individuals concerned by administrative decisions on the other hand. However, it is an open research question of how performant such domain-specific models could be. Furthermore, they pose legal challenges, touching especially upon administrative law, fundamental rights, data protection law, AI regulation, and copyright law. The article provides an introduction into LLMs, outlines potential use cases for such models in the context of administrative decisions, and presents a non-exhaustive introduction to practical and legal challenges that require in-depth interdisciplinary research. A focus lies on open practical and legal challenges with respect to legal reasoning through LLMs. The article points out under which circumstances administrations can fulfil their duty to provide reasons with LLM-generated reasons. It highlights the importance of human oversight and the need to design LLM-based systems in a way that enables users such as administrative decision-makers to effectively oversee them. Furthermore, the article addresses the protection of training data and trade-offs with model performance, bias prevention and explainability to highlight the need for interdisciplinary research projects.

I argue that Murayama and Jach's claim that higher-order motivational constructs face the “black-box” problem is misconceived because it doesn't clearly distinguish between personal and subpersonal explanations. To solve it they propose interpreting motivations as causal effects of mental computational processes. I suggest that their solution might be more compellingly presented as providing a fictionalist perspective on some personal-level constructs.

Percutaneous interventions have become significant in the management of congenital heart diseases, with transcatheter procedures being increasingly used for valve dysfunction, particularly for cases requiring repetitive surgeries. This abstract presents a successful transcatheter valve-in-valve implantation in a 16-year-old patient with severe tricuspid regurgitation following a bioprosthetic tricuspid valve replacement. The procedure involved transcatheter tricuspid valve implantation using the Mammoth 25x40 mm balloon catheter and the 26 mm Myval transcatheter heart valve system (Meril Life Sciences Pvt. Ltd, Vapi, Gujarat, India), resulting in immediate improvement in right atrial pressure and regurgitation. The patient underwent an electrophysiological assessment as part of the follow-up and was discharged with a normal sinus rhythm. Tricuspid valve interventions, although less common, are essential in congenital heart diseases, which necessitate prosthetic heart valve implantation due to long-term complications. The valve-in-valve procedure offers a safe alternative, especially in paediatric patients, for reducing risks caused by repetitive surgeries, providing a valuable treatment option in experienced centres.