Spatial arrangement using deep reinforcement learning to minimise rearrangement in ship block stockyards

As the shipbuilding industry is an engineering-to-order industry, different types of products are manufactured according to customer requests, and each product goes through different processes and workshops. During the shipbuilding process, if the product is not able to go directly to the subsequent process due to physical constraints of workshop, it temporarily waits in a stockyard. Since the waiting process involves unpredictable circumstances, plans regarding time and space cannot be established in advance. Therefore, unnecessary movement often occurs when ship blocks enter or depart from the stockyard. In this study, a reinforcement learning approach was proposed to minimise rearrangement in such circumstances. For this purpose, an environment in which blocks are arranged and rearranged was defined. Rewards based on the simplified rules were logically defined, and simulation was performed for quantitative evaluation using the proposed reinforcement learning algorithm. This algorithm was verified using an example model derived from actual data from a shipyard. The method proposed in this study can be used not only to the arrangement problem of ship block stockyards but also to the various arrangement and allocation problems or logistics problems in the manufacturing industry.     

Sensitive Wearable Temperature Sensor with Seamless Monolithic Integration

Accurate temperature field measurement provides critical information in many scientific problems. Herein, a new paradigm for highly sensitive, flexible, negative temperature coefficient (NTC) thermistor-based artificial skin is reported, with the highest temperature sensing ability reported to date among previously reported NTC thermistors. This artificial skin is achieved through the development of a novel monolithic laser-induced reductive sintering scheme and unique monolithic structures. The unique seamless monolithic structure simultaneously integrates two different components (a metal electrode and metal oxide sensing channel) from the same material at ambient pressure, which cannot be achieved by conventional heterogeneous integration through multiple, complex steps of photolithography or vacuum deposition. In addition to superior performance, electronic skin with high temperature sensitivity can be fabricated on heat-sensitive polymer substrates due to the low-temperature requirements of the process. As a proof of concept, temperature-sensitive artificial skin is tested with conformally attachable physiological temperature sensor arrays in the measurement of the temperatures of exhaled breath for the early detection of pathogenic progression in the respiratory system. The proposed highly sensitive flexible temperature sensor and monolithic selective laser reductive sintering are expected to greatly contribute to the development of essential components in various emerging research fields, including soft robotics and healthcare systems.     

Advanced Materials and Systems for Biodegradable,Transient Electronics

Transient electronics refers to an emerging class of advanced technology, defined by an ability to chemically or physically dissolve, disintegrate, and degrade in actively or passively controlled fashions to leave environmentally and physiologically harmless by-products in environments, particularly in bio-fluids or aqueous solutions. The unusual properties that are opposite to operational modes in conventional electronics for a nearly infinite time frame offer unprecedented opportunities in research areas of eco-friendly electronics, temporary biomedical implants, data-secure hardware systems, and others. This review highlights the developments of transient electronics, including materials, manufacturing strategies, electronic components, and transient kinetics, along with various potential applications.     

Defect-Detection Model for Underground Parking Lots Using Image Object-Detection Method

The demand for defect diagnoses is gradually gaining ground owing to the growing necessity to implement safe inspection methods to ensure the durability and quality of structures. However, conventional manpower-based inspection methods not only incur considerable cost and time, but also cause frequent disputes regarding defects owing to poor inspections. Therefore, the demand for an effective and efficient defect-diagnosis model for concrete structures is imminent, as the reduction in maintenance costs is significant from a long-term perspective. Thus, this paper proposes a deep learning-based image object-identification method to detect the defects of paint peeling, leakage peeling, and leakage traces that mostly occur in underground parking lots made of concrete structures. The deep learning-based object-detection method can replace conventional visual inspection methods. A faster region-based convolutional neural network (R-CNN) model was used with a training dataset of 6,281 images that utilized a region proposal network to objectively localize the regions of interest and detect the surface defects. The defects were classified according to their type, and the learning of each exclusive model was ensured through test sets obtained from real underground parking lots. As a result, average precision scores of 37.76%, 36.42%, and 61.29% were obtained for paint peeling, leakage peeling, and leakage trace defects, respectively. Thus, this study verified the performance of the faster RCNN-based defect-detection algorithm along with its applicability to underground parking lots.     

Nanoengineered Immune Niches for Reprogramming the Immunosuppressive Tumor Microenvironment and Enhancing Cancer Immunotherapy

Cancer immunotherapies that harness the body's immune system to combat tumors have received extensive attention and become mainstream strategies for treating cancer. Despite promising results, some problems remain, such as the limited patient response rate and the emergence of severe immune‐related adverse effects. For most patients, the therapeutic efficacy of cancer immunotherapy is mainly limited by the immunosuppressive tumor microenvironment (TME). To overcome such obstacles in the TME, the immunomodulation of immunosuppressive factors and therapeutic immune cells (e.g., T cells and antigen‐presenting cells) should be carefully designed and evaluated. Nanoengineered synthetic immune niches have emerged as highly customizable platforms with a potent capability for reprogramming the immunosuppressive TME. Here, recent developments in nano‐biomaterials that are rationally designed to modulate the immunosuppressive TME in a spatiotemporal manner for enhanced cancer immunotherapy which are rationally designed to modulate the immunosuppressive TME in a spatiotemporal manner for enhanced cancer immunotherapy are highlighted.     

Interface‐Driven Partial Dislocation Formation in 2D Heterostructures

Van der Waals (vdW) epitaxy allows the fabrication of various heterostructures with dramatically released lattice matching conditions. This study demonstrates interface‐driven stacking boundaries in WS₂ using epitaxially grown tungsten disulfide (WS₂) on wrinkled graphene. Graphene wrinkles function as highly reactive nucleation sites on WS₂ epilayers; however, they impede lateral growth and induce additional stress in the epilayer due to anisotropic friction. Moreover, partial dislocation‐driven in‐plane strain facilitates out‐of‐plane buckling with a height of 1 nm to release in‐plane strain. Remarkably, in‐plane strain relaxation at partial dislocations restores the bandgap to that of monolayer WS₂ due to reduced interlayer interaction. These findings clarify significant substrate morphology effects even in vdW epitaxy and are potentially useful for various applications involving modifying optical and electronic properties by manipulating extended 1D defects via substrate morphology control.     

Highly Stable Sb/C Anode for K+ and Na+ Energy Storage Enabled by Pulsed Laser Ablation and Polydopamine Coating

Potassium- and sodium-ion batteries (PIBs and SIBs) have great potential as the next-generation energy application owing to the natural abundance of K and Na. Antimony (Sb) is a suitable alloying-type anode for PIBs and SIBs due to its high theoretical capacity and proper operation voltage; yet, the severe volume variation remains a challenge. Herein, a preparation of N-doped carbon-wrapped Sb nanoparticles (L-Sb/NC) using pulsed laser ablation and polydopamine coating techniques, is reported. As the anode for PIB and SIB, the L-Sb/NC delivers superior rate capabilities and excellent cycle stabilities (442.2 and 390.5 mA h g⁻¹ after 250 cycles with the capacity decay of 0.037% and 0.038% per cycle) at the current densities of 0.5 and 1.0 A g⁻¹, respectively. Operando X-ray diffraction reveals the facilitated and stable potassiation and sodiation mechanisms of L-Sb/NC enabled by its optimal core–shell structure. Furthermore, the SIB full cell fabricated with L-Sb/NC and Na₃V₂(PO₄)₂F₃ shows outstanding electrochemical performances, demonstrating its practical energy storage application.