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Traditional rigid robots face significant challenges in congested and tight environments, including bulky size, maneuverability, and safety limitations. Thus, soft continuum robots, inspired by the incredible capabilities of biological appendages such as octopus arms, starfish, and worms, have shown promising performance in complex environments due to their compliance, adaptability, and safety. Different actuation techniques are implemented in soft continuum robots to achieve a smoothly bending backbone, including cable-driven actuators, pneumatic actuators, and hydraulic actuation systems. However, designing and developing efficient actuation mechanisms, motion planning approaches, and control algorithms are challenging due to the high degree of redundancy and non-linearity of soft continuum robots. This article profoundly reviews the merits and drawbacks of soft robots’ actuation systems concerning their applications to provide the readers with a brief review reference to explore the recent development of soft robots’ actuation mechanisms technology. Moreover, the authors have surveyed the recent review studies in controller design of continuum robots as a guidance for future applications.

Recent deep learning-based remote sensing analysis models often struggle with performance degradation due to domain shifts caused by illumination variations (clear to overcast), changing atmospheric conditions (clear to foggy, dusty), and physical scene changes (clear to snowy). Addressing domain shift in aerial image segmentation is challenging due to limited training data availability, including costly data collection and annotation. We propose Multi-Weather DomainShifter, a comprehensive multi-weather domain transfer system that augments single-domain images into various weather conditions without additional laborious annotation, coordinated by a large language model (LLM) agent. Specifically, we utilize Unreal Engine to construct a synthetic dataset featuring images captured under diverse conditions such as overcast, foggy, and dusty settings. We then propose a latent space style transfer model that generates alternate domain versions based on real aerial datasets. Additionally, we present a multi-modal snowy scene diffusion model with LLM-assisted scene descriptors to add snowy elements into scenes. Multi-weather DomainShifter integrates these two approaches into a tool library and leverages the agent for tool selection and execution. Extensive experiments on the ISPRS Vaihingen and Potsdam dataset demonstrate that domain shift caused by weather change in aerial image-leads to significant performance drops, then verify our proposal’s capacity to adapt models to perform well in shifted domains while maintaining their effectiveness in the original domain.

To obtain the knowledge that contributes to the safer operation of nuclear power plants and their prompt recovery and termination in the event of an accident, soft epoxy resins with rubber-based additives—used as insulators and airtight sealants in electrical penetrations in nuclear power plants—were aged under several simulated severe accident environments with different conditions of heat, gamma rays, and exposure to superheated steam containing no oxygen. Then, changes in structural, dynamic mechanical, mechanical, and dielectric properties were examined. It has been found that this resin becomes hard as a result of cross-linking if aged by irradiation with gamma rays. Since the cross-linking slows down the molecular motions, the glass transition temperature increases, whereas the dielectric permittivity and the dielectric loss factor decrease unless the steam penetrates the sample. Although the sample melts and disappears if directly exposed to superheated steam at 171 °C or 200 °C, the irradiation with gamma rays conducted prior to the steam exposure can mitigate the hydrolysis induced by the steam. Although the soft epoxy resin shows drastic changes in various properties, its properties after the aging approach or exceed the corresponding ones of the non-degraded ordinary hard epoxy resin. Therefore, it seems that using soft epoxy resin according to its purposes would not be a problem.

Strong electron correlation is the essential mediator that creates various exotic phases in two-dimensional electronic systems which has been continuously intriguing in modern condensed-matter physics. Such electronic states as Mott insulators, charge orders, and high-temperature superconductivity would be simply Fermi-degenerated metals unless the strong correlation plays essential roles. However, how it emerges, particularly to overcome screening effects upon doping band insulators, has not been experimentally studied. In this study, we report evolution of a strongly correlated electron system from a band-insulating organic semiconductor. Carriers are continuously doped via electric double layers up to a density of 10 14  cm −2 . Notably, significant deviations from a simple metallic system are observed even at far from half-filled band, possibly due to charge-order instability. The findings reveal that off-site Coulomb energy can compete with Thomas–Fermi screening. This competition enables the emergence of strongly correlated exotic phases, even in systems distant from Mott insulators. The emergence of a strong electron correlation has not been experimentally studied. Here, the authors report its evolution from a band-insulating organic semiconductor and observe significant deviations from a simple metallic system even at far from half-filled band due to charge-order instability.

Flexible robots have exhibited impressive features in working in congested environments due to their compliance behavior and morphological structure. However, designing motion planning techniques and robust control strategies that actively control their deformations are challenging in many applications. Thus, this article presents the learning by Demonstration (LbD) approach for planning the spatial point-to-point motions of a multi-section continuum robot. Via teleoperation, the human demonstrations are captured by moving the flexible interface with similar kinematics of the active robot in front of the Motion Capture System (MCS). Meanwhile, a Nonlinear Model Predictive Control (NMPC) scheme is proposed based on the robot’s kinematic model to follow the reference trajectories while respecting the constraints imposed by the cable lengths and control actions. The simulation results prove the efficiency of the LbD approach in reproducing and generalizing the spatial motions of the robot’s tip and avoiding obstacles and external disturbances. On the other hand, the numerical simulations show the performance of NMPC scheme in terms of trajectory tracking and avoiding static and dynamic obstacles. Additionally, its robustness is analyzed by comparing it to the Pseudo-Inverse Jacobian Kinematic Control (PIJKC) while considering the constraints of cable lengths. Finally, the stability of NMPC is evaluated against input perturbations using the Monte Carlo simulations.

Prediction of material properties of newly designed molecules is a long-term goal in organic electronics. In general, it is a difficult problem, because the material properties are dominated by the unknown packing structure. We present a practical method to obtain charge transport properties of organic single crystals, without use of experimental single-crystal data. As a demonstration, we employ the promising molecule C 10 –DNBDT. We succeeded in quantitative evaluation of charge mobility of the single crystal using our quantum wave-packet dynamical simulation method. Here, the single-crystal data is computationally obtained by searching possible packing structures from structural formula of the molecule. We increase accuracy in identifying the actual crystal structure from suggested ones by using not only crystal energy but also similarity between calculated and experimental powder X-ray diffraction patterns. The proposed methodology can be a theoretical design technique for efficiently developing new high-performance organic semiconductors, since it can estimate the charge transport properties at early stage in the process of material development.

Lately, robot swarm has widely employed in many applications like search and rescue missions, fire forest detection and navigation in hazard environments. Each robot in a swarm is supposed to move without collision and avoid obstacles while performing the assigned job. Therefore, a formation control is required to achieve the robot swarm three tasks. In this article, we introduce a decentralized formation control algorithm based on the potential field method for robot swarm. Our formation control algorithm is proposed to achieve the three tasks: avoid obstacles in the environment, keep a fixed distance among robots to maintain a formation and perform an assigned task. An artificial neural network is engaged in the online optimization of the parameters of the potential force. Then, real-time experiments are conducted to confirm the reliability and applicability of our proposed decentralized formation control algorithm. The real-time experiment results prove that the proposed decentralized formation control algorithm enables the swarm to avoid obstacles and maintain formation while performing a certain task. The swarm manages to reach a certain goal and tracks a given trajectory. Moreover, the proposed decentralized formation control algorithm enables the swarm to escape from local minima, to pass through two narrow placed obstacles without oscillation near them. From a comparison between the proposed decentralized formation control algorithm and the traditional PFM, we obtained that NN-swarm successes to reach its goal with average accuracy 0.14 m compared to 0.22 m for the T-swarm. The NN-swarm also keeps a fixed distance between robots with a higher swarming error reaches 34.83%, while the T-swarm reaches 23.59%. Also, the NN-swarm is more accurate in tracking a trajectory with a higher tracking error reaches 0.0086 m compared to min. error of T-swarm equals to 0.01 m. Besides, the NN-swarm maintains formation much longer than T-swarm while tracking trajectory reaches 94.31% while the T-swarm reaches 81.07% from the execution time, in environments with different numbers of obstacles.

Lean-diluted combustion can enhance thermal efficiency and reduce exhaust gas emissions from spark-ignited (SI) gasoline engines. However, excessive lean mixture with external dilution leads to combustion instability due to high cycle-to-cycle variations (CCV). The CCV should be controlled as low as possible to achieve stable combustion, high engine performance, and low emissions. Therefore, a stable combustion control function is required to predict the combustion phase with a low calculation load. A machine learning-based function is developed in this work to predict the 50 % mass fraction burn location (MFB50). Input parameters to the machine learning model consist of 1-, 2-, 3-, and 4-cycle from a three-cylinder production-based gasoline engine operated under stoichiometric to the lean-burn mixture. The results show that the MFB50 prediction model achieves high accuracy when 2-cycle data are used relative to 1-cycle data, which implies that the previous cycle data affects the predicted MFB50 of the next cycle. As a result, the neural network model can predict the cyclic MFB50 error within ± 3 °CA CCV and ± 5 °CA CCV with 70 % and 90 % accuracy, respectively. However, an increasing number of cycle data worsens the prediction accuracy due to model over-learning.

Benzo[ de ]isoquinolino[1,8- gh ]quinolinetetracarboxylic diimide (BQQDI) is an n-type organic semiconductor that has shown unique multi-fold intermolecular hydrogen-bonding interactions, leading to aggregated structures with excellent charge transports and electron mobility properties. However, the strong intermolecular anchoring of BQQDI presents challenges for fine-tuning the molecular assembly and improving the semiconducting properties. Herein, we report the design and synthesis of two BQQDI derivatives with phenyl- and cyclohexyl substituents (Ph–BQQDI and Cy 6 –BQQDI), where the two organic semiconductors show distinct molecular assemblies and degrees of intermolecular orbital overlaps. In addition, the difference in their packing motifs leads to strikingly different band structures that give rise to contrasting charge-transport capabilities. More specifically, Cy 6 –BQQDI bearing bulky substituents exhibits isotropic intermolecular orbital overlaps resulting in equal averaged transfer integrals in both π-π stacking directions, even when dynamic disorders are taken into account; whereas Ph–BQQDI exhibits anisotropic averaged transfer integrals in these directions. As a result, Cy 6 –BQQDI shows excellent device performances in both single-crystalline and polycrystalline thin-film organic field-effect transistors up to 2.3 and 1.0 cm 2 V −1 s −1 , respectively. n-type organic semiconductors exhibiting two-dimensional isotropic charge transport are rarely reported. Here the authors show that using bulky substituents, BQQDI demonstrates near-isotropic charge transport, resilience to dynamic disorder, as well as high electron mobility both in single- and polycrystalline thin-film transistors.

Prokineticins, multifunctional secreted proteins, activate two endogenous G protein-coupled receptors PKR1 and PKR2. From in situ analysis of the mouse brain, we discovered that PKR2 is predominantly expressed in the olfactory bulb (OB). To examine the role of PKR2 in the OB, we created PKR1-and PKR2-gene-disrupted mice ($Pkr1^{-/-}$and$Pkr2^{-/-}$, respectively). Phenotypic analysis indicated that not$Pkr1^{-/-}$but$Pkr2^{-/-}$mice exhibited hypoplasia of the OB. This abnormality was observed in the early developmental stages of fetal OB in the$Pkr2^{-/-}$mice. In addition, the$Pkr2^{-/-}$mice showed severe atrophy of the reproductive system, including the testis, ovary, uterus, vagina, and mammary gland. In the$Pkr2^{-/-}$mice, the plasma levels of testosterone and follicle-stimulating hormone were decreased, and the mRNA transcription levels of gonadotropin-releasing hormone in the hypothalamus and luteinizing hormone and follicle-stimulating hormone in the pituitary were also significantly reduced. Immunohistochemical analysis revealed that gonadotropin-releasing hormone neurons were absent in the hypothalamus in the$Pkr2^{-/-}$mice. The phenotype of the$Pkr2^{-/-}$mice showed similarity to the clinical features of Kallmann syndrome, a human disease characterized by association of hypogonadotropic hypogonadism and anosmia. Our current findings demonstrated that physiological activation of PKR2 is essential for normal development of the OB and sexual maturation.