As AI technologies become more prevalent, it is crucial for students to develop responsible, ethical, and proactive AI engagement skills. Recent educational initiatives have focused on enhancing CS and engineering students' AI ethics education but have largely overlooked integrating these concepts across other disciplines. This paper presents and assesses a pioneering initiative that integrates AI ethics into university curricula through a collaborative framework between CS and domain educators. We introduced 1-3 week AI ethics modules in seven diverse courses from Art to Chemistry, incorporating case studies and hands-on activities using chat- or image-based Large Language Models (LLMs). Student surveys indicated significant gains in confidence regarding AI ethics discussions, application of principles, and reasoning skills. Our approach advocates for utilizing structured frameworks and faculty collaboration in embedding AI ethics into university curricula, enhancing students' practical skills and ethical understanding across diverse professional settings.

SUMMARY: To meet the increased need of making biomedical resources more accessible and reusable, Web Application Programming Interfaces (APIs) or web services have become a common way to disseminate knowledge sources. The BioThings APIs are a collection of high-performance, scalable, annotation as a service APIs that automate the integration of biological annotations from disparate data sources. This collection of APIs currently includes MyGene.info, MyVariant.info and MyChem.info for integrating annotations on genes, variants and chemical compounds, respectively. These APIs are used by both individual researchers and application developers to simplify the process of annotation retrieval and identifier mapping. Here, we describe the BioThings Software Development Kit (SDK), a generalizable and reusable toolkit for integrating data from multiple disparate data sources and creating high-performance APIs. This toolkit allows users to easily create their own BioThings APIs for any data type of interest to them, as well as keep APIs up-to-date with their underlying data sources. AVAILABILITY AND IMPLEMENTATION: The BioThings SDK is built in Python and released via PyPI (https://pypi.org/project/biothings/). Its source code is hosted at its github repository (https://github.com/biothings/biothings.api). SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Background: Medicines are increasingly purchased online, yet little is known regarding the ocular information-seeking behavior with medicine queries in search engines. A share of pharmacies found via search engines operate unlicensed and sell prescription-only medicines without a prescription. This study aimed to investigate how search engine users distinguish between genuine and falsified sources of information in terms of unlicensed and licensed online pharmacies in the case of medicine queries.Methods: Eye-tracking of search tasks (transactional, navigational, informational and two limited results) in a Google search engine environment with retrospective gaze-cued think aloud protocol. Purposive sample of N = 50 across three hospitals and one general practitioner. Results: Discovery of a trichotomy of ocular search strategies based on the inclusion or exclusion of URLs in the information-seeking process. Finding of dissonance to existing studies related to fixation duration of search engine result page (SERP) elements. Discovery of an addition to information foraging theory (IFT): proximal cues are, in environments with non-credible information, used in both positive and negative ways.

Traditional inaccuracies during manufacturing test of the thermal sensor circuit require excessive guard-bands. These guard-bands increase the chance of unnecessary microprocessor throttling and could introduce a less-than optimum power and thermal design envelope. Circuit techniques that minimize these errors are discussed, including an improved temperature-independent voltage pump, a remote thermal sensing scheme for hot-spot to sensor offset reduction, and a self-heating error calibration method. Experimental data obtained on a high performance 65nm Intel® Pentium® 4 microprocessor demonstrates the feasibility and effectiveness of these techniques, providing a combined potential accuracy improvement of up to 17°C.

This work presents the implementation of the self-leakage test, a new approach for unconnected I/O leakage testing. It provides a path for leakage current through the on-chip leakers and uses the voltage drop at the pad to detect a pass/fail condition. A detailed methodology for defining the self-leakage test specifications has been developed. Preliminary silicon data shows that self-leakage test methodology provide a viable method for high-volume monitoring of I/O leakage at minimal on-die DFT (design-for-test) overhead.