While controllable Text-to-Speech (TTS) has achieved notable progress, most existing methods remain limited to inter-utterance-level control, making fine-grained intra-utterance expression challenging due to their reliance on non-public datasets or complex multi-stage training. In this paper, we propose TED-TTS, a training-free controllable framework for pretrained zero-shot TTS to enable intra-utterance emotion and duration expression. Specifically, we propose a segment-aware emotion conditioning strategy that combines causal masking with monotonic stream alignment filtering to isolate emotion conditioning and schedule mask transitions, enabling smooth intra-utterance emotion shifts while preserving global semantic coherence. Based on this, we further propose a segment-aware duration steering strategy to combine local duration embedding steering with global EOS logit modulation, allowing local duration adjustment while ensuring globally consistent termination. To eliminate the need for segment-level manual prompt engineering, we construct a 30,000-sample multi-emotion and duration-annotated text dataset to enable LLM-based automatic prompt construction. Extensive experiments demonstrate that our training-free method not only achieves state-of-the-art intra-utterance consistency in multi-emotion and duration control, but also maintains baseline-level speech quality of the underlying TTS model. Audio samples are available at https://aclanonymous111.github.io/TED-TTS-DemoPage/.

This paper is a preliminary exploration of how to solve the dual-manipulator path-planning problem from an energy perspective. A virtual spring is set up between the two manipulator bodies and becomes compressed as they move into the area of danger, thus producing elastic potential energy. The initial paths of the manipulators are modelled as two ends-fixed elastic ropes. The elastic potential energy stored in the virtual spring is distributed between the two elastic ropes in a certain proportion so as to deform them. In this way, the original paths of the two manipulators will deviate toward the direction of their respective bases thereby avoiding any collision crisis that may potentially occur. When the dual-manipulator moves away from the danger area, the elastic potential energy caused by the deviation of the elastic ropes will be converted back into energy stored by the virtual spring, such that the elastic ropes revert to their original state and drive the manipulators back to their initial paths. Simple but representative simulations are established and results of the simulations show the reliability of our proposed method.