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The short-circuit withstand time and on-resistance trade-off is investigated in 4H-SiC Power MOSFETs. We compare the static and short circuit withstand time results along with JFET width dependency and PWELL doping variation of MOSFET. We present that smaller Rds,on by wider JFET width and lighter doped PWELL results in worse short circuit withstand time.

We present a revised channel mobility model for 4H-SiC MOSFETs. Mobility measurements are performed on 4H-SiC lateral MOSFET test structures in the temperature range of 25-175 °C. We observe that the temperature and P-well concentration dependence of channel mobility cannot be predicted by popular mobility models available within commercial TCAD tools. A careful investigation revels that channel mobility components need to be revised and replaced using a comprehensive model that accurately describes the predominant scattering mechanisms. We present a well calibrated channel mobility model for 4H-SiC using a revised treatment of bulk, surface roughness and surface phonon components. An excellent agreement with measured data is obtained using this model, making it more suitable for predictive device simulation using TCAD tools.

Since its introduction, generative artificial intelligence (AI) has seamlessly integrated into everyday life and dramatically changed our workflows. With its growing presence, it has become increasingly important to explore the full extent of its capabilities to ensure responsible use. While previous research has investigated generative AI’s abilities in creating synthetic data, its potential for manipulating existing datasets has remained largely unexplored. In our study, GPT-4 demonstrated remarkable proficiency in manipulating numerical datasets to achieve desired statistical outcomes, transforming statistically non-significant data into significant results. Additionally, it adeptly manipulated interdependent datasets using logical and mathematical reasoning, adjusting multiple economic indicators based on changes to U.S. job growth data. GPT-4 also demonstrated proficiency in manipulating textual data, modifying the sentiment of interview transcripts, and appropriately imputing inaudible portions of the interviews. Lastly, Adobe Firefly effectively manipulated visual data, integrating a large body of water into a Mars Rover photograph. The profound versatility and capability of widely available generative AI assistants in manipulating different data types introduce significant ethical concerns. Our discussion explores the potential and limitations of generative AI in data manipulation, the ethical implications of its misuse, and strategies to prevent its occurrence. This study underscores the urgent need for stakeholders to develop and implement robust mitigation guidelines, paving the way for responsible innovation in generative AI.

The advancement of generative AI has introduced transformative changes in the scientific domain. This technology, recognized for its ability to fabricate research data and manuscripts, now extends its potential to crafting scientific images, a realm yet to be fully explored. The research employed OpenAI's DALL-E 3 to generate images for various scientific contexts, such as laboratory techniques, medical imaging diagnostics, and geological representations. DALL-E 3 has shown a remarkable capability to produce highly accurate representations of complex scientific visualizations. However, the study also uncovers the AI model's inherent limitations, particularly its struggle to achieve high precision and detail in specific contexts. This underscores the necessity for human oversight and emphasizes the need for caution. Additionally, the study delves into the ethical dimensions of utilizing generative AI for scientific imagery. It extends beyond the risks associated with data fabrication, examining issues such as biases in AI algorithms, copyright challenges, the provenance of data, and the consequences of inaccurately portraying scientific information. The research advocates for a comprehensive strategy to mitigate these risks, suggesting the development of digital watermarking, AI detection tools, enhanced training and education, and the formulation of ethical guidelines for AI-generated images. This study emphasizes the critical need for human oversight in the use of AI for scientific visualizations, urging caution and a balanced approach to employing AI-generated images. The findings provide valuable insights into the strengths and limitations of generative AI in scientific visualization, setting a foundation for future exploration and advancement in this rapidly evolving field.

Relapsing polychondritis (RP) is a systemic inflammatory disorder of cartilage that lacks validated disease activity measures. Our objective was to test physician global assessment (PhGA), a measure of disease activity commonly used in rheumatic diseases, in a cohort of patients with RP, which has not been done before. Adult patients in an observational cohort of RP underwent standardized, comprehensive evaluation at approximately 6‐month intervals. PhGA was scored by 3 physicians from the evaluating institution on a scale of 0–10 for each visit. A random subset of 20 visits was scored by 3 independent physicians not affiliated with the evaluating institution. Treatment change between consecutive visits was categorized as increased, decreased, or unchanged. In total, 78 patients were evaluated over 164 visits. The intraclass correlation coefficient (ICC2,1) for the 3 raters from the evaluating institution was excellent (0.79 [95% confidence interval (95% CI) 0.73, 0.84]) but was poor in the subset of cases scored by the additional raters (ICC2,10.27 [95% CI −0.01, 0.53]). Median PhGA was 3 (range 0–7). PhGA weakly correlated with C‐reactive protein level (rs= 0.30, P< 0.01). In response to increased treatment, median PhGA decreased from 3 (interquartile range [IQR] 2, 4) to 2 (IQR 2, 3) (P< 0.01) but rarely went to 0. Within a single center, PhGA can be used to quantify disease activity and monitor disease response in RP. Persistent disease activity despite treatment, rather than a relapsing‐remitting pattern, is observed for most patients with RP. Reliability of PhGA may not generalize across different institutions. A validated disease‐specific activity index is needed in RP.

Pyoderma gangrenosum is a great masquerader in wound diagnosis and management. Frequently misdiagnosed as a necrotizing infection, the elusive nature of its etiology and pathogenesis has thwarted the establishment of a standardized management algorithm, leaving immunosuppressant therapies as the mainstay of treatment. The present report describes a 61-year-old woman presenting with temporally discrete bilateral dorsal hand lesions successfully managed with distinctive multimodality therapies. The initial lesion was managed under the auspices of a necrotizing process using a combination of hyperbaric oxygen therapy and skin grafting with a negative-pressure dressing, both individually demonstrated to be effective for prompt wound stabilization and coverage. A subsequent contralateral hand lesion was similarly managed as a necrotizing infection before a diagnosis of pyoderma gangrenosum was considered. Stabilization and eventual resolution was achieved using intravenous and topical steroids followed by hyperbaric oxygen therapy, again highlighting the benefits of multimodality therapy in the setting of pyoderma gangrenosum.

The article discusses issues regarding shore power being made available at trucks stops all over the U.S. Shore power, or the system by which a truck can derive electricity from outlets at fixed stations and does not rely on its own batteries or engine-driven power generation, enables truckers to use heaters, and other appliances. The Shorepower Truck Electrification Project (STEP) aims to make such facility available in more stops, while more trucks are made compatible with this system.