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In this paper, a CMOS optoelectronic analog front-end (AFE) preamplifier with cross-coupled active loads for short range LiDAR applications is presented, which consists of a spatially modulated P+/N-well on-chip avalanche photodiode (APD), the differential input stage with cross-coupled active loads, and an output buffer. Particularly, another on-chip dummy APD is inserted at the differential input node to improve the common-mode noise rejection ratio significantly better than conventional single-ended TIAs. Moreover, the cross-coupled active loads are exploited at the output nodes of the preamplifier not only to help generate symmetric output waveforms, but also to enable the limiting operations even without the following post-amplifiers. In addition, the inductive behavior of the cross-coupled active loads extends the bandwidth further. The proposed AFE preamplifier implemented in a 180-nm CMOS process demonstrate the measured results of 63.5 dB dynamic range (i.e., 1 µApp~1.5 mApp input current recovery), 67.8 dBΩ transimpedance gain, 1.6 GHz bandwidth for the APD capacitance of 490 fF, 6.83 pA⁄√Hz noise current spectral density, 85 dB power supply rejection ratio, and 32.4 mW power dissipation from a single 1.8 V supply. The chip core occupies the area of 206 × 150 µm2. [ABSTRACT FROM AUTHOR]

This paper presents a low-noise CMOS transimpedance-limiting amplifier (CTLA) for application in LiDAR sensor systems. The proposed CTLA employs a dual-feedback architecture that combines the passive and active feedback mechanisms simultaneously, thereby enabling automatic limiting operations for input photocurrents exceeding 100 µApp (up to 1.06 mApp) without introducing signal distortions. This design methodology can eliminate the need for a power-hungry multi-stage limiting amplifier, hence significantly improving the power efficiency of LiDAR sensors. The practical implementation for this purpose is to insert a simple NMOS switch between the on-chip avalanche photodiode (APD) and the active feedback amplifier, which then can provide automatic on/off switching in response to variations of the input currents. In particular, the feedback resistor in the active feedback path should be carefully optimized to guarantee the circuit's robustness and stability. To validate its practicality, the proposed CTLA chips were fabricated in a 180 nm CMOS process, demonstrating a transimpedance gain of 88.8 dBΩ, a −3 dB bandwidth of 629 MHz, a noise current spectral density of 2.31 pA/√Hz, an input dynamic range of 56.6 dB, and a power dissipation of 23.6 mW from a single 1.8 V supply. The chip core was realized within a compact area of 180 × 50 µm2. The proposed CTLA shows a potential solution that is well-suited for power-efficient LiDAR sensor systems in real-world scenarios. [ABSTRACT FROM AUTHOR]

This paper presents an optoelectronic transceiver (OTRx) realized in a 180 nm CMOS technology for applications of short-range LiDAR sensors, in which a modified current-mode single-ended VCSEL driver (m-CMVD) is exploited as a transmitter (Tx) and a voltage-mode fully differential transimpedance amplifier (FD-TIA) is employed as a receiver (Rx). Especially for Tx, a concurrent automatic power control (APC) circuit is incorporated to compensate for the inevitable increase in the threshold current in a VCSEL diode. For Rx, two on-chip spatially modulated P+/N- well avalanche photodiodes (APDs) are integrated with the FD-TIA to achieve circuit symmetry. Also, an extra APD is added to facilitate the APC operations in Tx, i.e., concurrently adjusting the bias current of the VCSEL diode by the action of the newly proposed APC path in Rx. Measured results of test chips demonstrate that the proposed OTRx causes the DC bias current to increase from 0.93 mA to 1.42 mA as the input current decreases from 250 µApp to 3 µApp, highlighting its suitability for short-range sensor applications utilizing a cost-effective CMOS process. [ABSTRACT FROM AUTHOR]

As the use of artificial intelligence (AI) technologies, particularly generative AI (Gen AI), becomes increasingly prevalent in nursing education, it is paramount to address the ethical implications of their implementation. This article explores the realm of cyberethics (a field of applied ethics that focuses on the ethical, legal, and social implications of cybertechnology), highlighting the ethical principles of autonomy, nonmaleficence, beneficence, justice, and explicability as a roadmap for facilitating AI integration into nursing education. Research findings suggest that ethical dilemmas that challenge these five principles can emerge within the context of nursing education; however, adherence to these very principles, which is essential to improving patient care, can offer solutions to these dilemmas. To ensure the ethical and responsible use of Gen AI in nursing education, these principles must be woven into the fabric of curricula, and appropriate guidelines must be developed. Nurse educators have a pivotal role in strategizing comprehensive approaches for ethical AI integration, establishing clear guidelines, and instilling critical thinking among students. Fostering lifelong learning and adaptability is key to ensuring that future nurses can successfully navigate the constantly evolving landscape of health care technology. Future research should investigate the long-term impacts of AI utilization on learning outcomes and ethical decision-making. [ABSTRACT FROM AUTHOR]

BackgroundHealth professions students use social media to communicate with other students and health professionals, discuss career plans or coursework, and share the results of research projects or new information. These platforms allow students to share thoughts and perceptions that are not disclosed in formal education settings. Twitter provides an excellent window through which health professions educators can observe students’ sociocultural and learning needs. However, despite its merits, cyberincivility on Twitter among health professions students has been reported. Cyber means using electronic technologies, and incivility is a general term for bad manners. As such, cyberincivility refers to any act of disrespectful, insensitive, or disruptive behavior in an electronic environment. ObjectiveThis study aims to describe the characteristics and instances of cyberincivility posted on Twitter by self-identified health professions students. A further objective of the study is to analyze the prevalence of tweets perceived as inappropriate or potentially objectionable while describing patterns and differences in the instances of cyberincivility posted by those users. MethodsWe used a cross-sectional descriptive Twitter data mining method to collect quantitative and qualitative data from August 2019 to February 2020. The sample was taken from users who self-identified as health professions students (eg, medicine, nursing, dental, pharmacy, physician assistant, and physical therapy) in their user description. Data management and analysis were performed with a combination of SAS 9.4 for descriptive and inferential statistics, including logistic regression, and NVivo 12 for descriptive patterns of textual data. ResultsWe analyzed 20 of the most recent tweets for each account (N=12,820). A total of 639 user accounts were analyzed for quantitative analysis, including 280 (43.8%) medicine students and 329 (51.5%) nursing students in 22 countries: the United States (287/639, 44.9%), the United Kingdom (197/639, 30.8%), unknown countries (104/639, 16.3%), and 19 other countries (51/639, 8.0%). Of the 639 accounts, 193 (30.2%) were coded as having instances of cyberincivility. Of these, 61.7% (119/193), 32.6% (63/193), and 5.7% (11/193) belonged to students in nursing, medicine, and other disciplines, respectively. Among 502 instances of cyberincivility identified from 641 qualitative analysis samples, the largest categories were profanity and product promotion. Several aggressive or biased comments toward other users, politicians, or certain groups of people were also found. ConclusionsCyberincivility is a multifaceted phenomenon that must be considered in its complexity if health professions students are to embrace a culture of mutual respect and collaboration. Students’ perceptions and reports of their Twitter experiences offer insights into behavior on the web and the evolving role of cyberspace, and potentially problematic posts provide opportunities for teaching digital professionalism. Our study indicates that there is a continued need to provide students with guidance and training regarding the importance of maintaining a professional persona on the web.

Background: Given the wide range of metaverse technologies, there is a need to synthesize evidence of metaverse pedagogy used effectively for nursing education. Purpose: This umbrella review synthesized systematic reviews on the use of metaverse in nursing education. Methods: A search was performed in MEDLINE, EMBASE, CINAHL, Web of Science, and Education Full Text. This umbrella review was conducted with reference to the Joanna Briggs Institute (JBI) Reviewer's Manual and reported using Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. The JBI Critical Appraisal Checklist for Systematic Review was used to assess the quality of studies. Results: The final review comprised 15 articles published between 2013 and 2021, most of which indicate that metaverse interventions support increased knowledge, self-confidence, engagement, satisfaction, and performance in nursing students. Several articles in this review presented mixed findings related to certain learning outcomes. Conclusion: This umbrella review supports the viability and effectiveness of metaverse in nursing education. [ABSTRACT FROM AUTHOR]

This study examines the effectiveness of interventions for decreasing the fear of falling by synthesizing the results of individual studies of fear of falling prevention. The mean weighted effect sizes (MWES) of six studies are computed. The overall MWES is .21 (N = 957: 95% CI .068-.362). Results indicate that interventions are effective in reducing the fear of falling or increasing fall efficacy. The MWES is .249, .024. and .418 for the combined intervention, exercise intervention only, and hip protector groups, respectively. The MWES of the 3 months or less group was .000, and the 4-months group was .240. The MWES is .022, .228, and .418 for the facility-based, community-based, and home-based intervention groups, respectively. Overall, interventions were effective in decreasing fear of falling and increasing tall efficacy. Results suggest that the best outcome is obtained after 4 months, indicating that interventions need some time to obtain their effects. Keywords: fear of falling; falls; elderly; intervention; meta-analysis