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Leveraging large language models to assist philosophical counseling: prospective techniques, value, and challenges

Large language models (LLMs) have emerged as transformative tools with the potential to revolutionize philosophical counseling. By harnessing their advanced natural language processing and reasoning capabilities, LLMs offer innovative solutions to overcome limitations inherent in traditional counseling approaches—such as counselor scarcity, difficulties in identifying mental health issues, subjective outcome assessment, and cultural adaptation challenges. In this study, we explore cutting‐edge technical strategies—including prompt engineering, fine‐tuning, and retrieval‐augmented generation—to integrate LLMs into the counseling process. Our analysis demonstrates that LLM-assisted systems can provide counselor recommendations, streamline session evaluations, broaden service accessibility, and improve cultural adaptation. We also critically examine challenges related to user trust, data privacy, and the inherent inability of current AI systems to genuinely understand or empathize. Overall, this work presents both theoretical insights and practical guidelines for the responsible development and deployment of AI-assisted philosophical counseling practices.

Maternal effects in the model system Daphnia: the ecological past meets the epigenetic future

Maternal effects have been shown to play influential roles in many evolutionary and ecological processes. However, understanding how environmental stimuli induce within-generation responses that transverse across generations remains elusive, particularly when attempting to segregate confounding effects from offspring genotypes. This review synthesizes literature regarding resource- and predation-driven maternal effects in the model system Daphnia, detailing how the maternal generation responds to the environmental stimuli and the maternal effects seen in the offspring generation(s). Our goal is to demonstrate the value of Daphnia as a model system by showing how general principles of maternal effects emerge from studies on this system. By integrating the results across different types of biotic drivers of maternal effects, we identified broadly applicable shared characteristics: 1. Many, but not all, maternal effects involve offspring size, influencing resistance to starvation, infection, predation, and toxins. 2. Maternal effects manifest more strongly when the offspring’s environment is poor. 3. Strong within-generation responses are typically associated with strong across-generation responses. 4. The timing of the maternal stress matters and can raise or lower the magnitude of the effect on the offspring’s phenotype. 5. Embryonic exposure effects could be mistaken for maternal effects. We outline questions to prioritize for future research and discuss the possibilities for integration of ecologically relevant studies of maternal effects in natural populations with the molecular mechanisms that make them possible, specifically by addressing genetic variation and incorporating information on epigenetics. These small crustaceans can unravel how and why non-genetic information gets passed to future generations.

Efficient data-driven joint-level calibration of cable-driven surgical robots

Accurate joint position estimation is crucial for the control of cable-driven laparoscopic surgical robots like the RAVEN-II. However, any slack and stretch in the cable can lead to errors in kinematic estimation, complicating precise control. This work proposes an efficient data-driven calibration method, requiring no additional sensors post-training. The calibration takes 8–21 min and maintains high accuracy during a 6-hour heavily loaded operating. The Deep Neural Network (DNN) model reduces errors by 76%, achieving accuracy of 0.104, 0.120, and 0.118 mm for joints 1, 2, and 3, respectively. Compared to end-to-end models, the DNN achieves better accuracy and faster convergence by correcting original inaccurate joint positions. Additionally, a linear regression model offers 160 times faster inference speed than the DNN, suitable for RAVEN’s 1000 Hz control loop, with slight compromises in accuracy. This approach should significantly enhance the accuracy of similar cable-driven robots.

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