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We design a practical variant of an encoding for range Top-2 query (RT2Q) and evaluate its performance. Given an array |$A[1,n]$| of |$n$| elements from a total order, the range Top-2 encoding problem is to construct a data structure that answers |${\textsf{RT2Q}}{}$|⁠ , which returns the positions of the first and second largest elements within a given range of |$A$|⁠ , without accessing the array |$A$| at query time. We design the following two implementations: (i) an implementation based on an alternative representation of Davoodi et a l.'s [Phil. Trans. Royal Soc. A, 2016] data structure, which supports queries efficiently. Experimental results show that our implementation is efficient in practice and gives improved time-space trade-offs compared with the indexing data structures (which keep the original array |$A$| as part of the data structure) for range maximum queries. (ii) Another implementation based on Jo et al.'s |${\textsf{RT2Q}}{}$| encoding on |$2 \times n$| array [CPM, 2016], which can be constructed in |$O(n)$| time. We compare our encoding with Gawrychowski and Nicholson's optimal encoding [ICALP, 2015] and show that in most cases, our encoding shows faster construction time while using a competitive space in practice. [ABSTRACT FROM AUTHOR]

Flexible and bio‐integrated electronics have attracted great attention due to their enormous contributions to personalized medical devices. Power sources, serving as one of the most important components, have been suffering from many problems, including deficient biocompatibility, poor stretchability, and unstable electrical outputs under deformed conditions, which limits the practical applications in flexible and bio‐integrated electronics. Here, we reported a fully stretchable magnesium (Mg)–air battery based on dual‐ions‐conducting hydrogels (SDICH). The high‐performance battery enables long‐term operation with lighting 120 lighting emitting diodes (LEDs) for over 5 h. Benefiting from the advanced materials and mechanical designs, the battery exhibits stability electrical outputs under stretching, which allows to operate ordinarily under various mechanical deformations without performance decay. Furthermore, the great biocompatibility of the battery offers great opportunity for biomedical applications, which is demonstrated by a self‐adaption wound dressing system. The in vitro and in vivo results prove that the self‐adaption wound dressing can effectively prevent wound inflammation and promote wound healing. By exploiting thermal feedback mechanics, the system can adjust antibiotic release rate and dosage spontaneously according to the real‐time wound conditions. The proposed fully stretchable Mg–air battery and self‐adaption wound dressing display great potential in skin‐integrated electronics and personalized medicine. [ABSTRACT FROM AUTHOR]

Multimodal force identification by electronic skin (E‐skin) provides comprehensive and precise tactile information that can enhance elaborated functions of fine tactile discrimination, robotic manipulation, and gesture recognition. However, it remains a great challenge to realize such complicated functions from a highly simplified E‐skin sensing system. Here, mimicking the multi‐mechanoreceptor synergistic effect in human skin, a simple, thin, soft, intelligent tactile sensor composed of an asymmetrically arrangement of strain sensing units array for multimodal tactile information decoding is reported. The E‐skin type tactile sensor exhibits good performance with excellent sensitivity for shear force (2.5 N–1), vertical force (0.11 kPa–1), that enables 3D force refactoring, as well as accurate recognition of sliding force for direction tracking. Another unique feature of the E‐skin type tactile sensor is the side force screen effect, that allows the tactile sensor to yield great accuracy and a gauge factor of 1492 in strain measuring. Furthermore, integration of the sensors into E‐skin array offers the capability of capturing dynamic and real‐time stress distribution on the hand in various conditions. This work provides a new route for complex force‐sensing from the simple structure and shows great potential in robotics, human‐machine interface, and virtual reality/augmented reality (VR/AR). [ABSTRACT FROM AUTHOR]

With the high flexibility, good conformability and lightweight, the next‐generation energy harvesters could exist in the format of epidermal electronics. They are able to convert the mechanical energy from daily body motions into electrical signals for energy harvesting and human‐machine interfaces. Triboelectric nanogenerators (TENGs) have proven to be excellent candidates for wearable energy harvesters, however, the reported TENGs commonly face the hurdles of poor adhesion to skin and relative thick in geometry up to several cm. Herein, a series of ultrathin, soft, tattoo‐like triboelectric nanogenerators (TL‐TENGs) with well‐designed aesthetic patterns are introduced. With the ultrathin materials applied and state of art processing techniques in epidermal electronics, the TL‐TENGs present an outstanding mechanical property of high robustness and thickness of tens of μm. Besides, TL‐TENGs own remarkable electrical characteristics, the open‐circuit voltage and short circuit current can reach up to ≈180 V and ≈2.2 μA under constant tapping (≈16 kPa), respectively. With the well structural mechanics designs, the TL‐TENGs can be customized into various tattoo patterns, such as twelve Chinese zodiac signs. Demonstrations of TL‐TENGs in energy harvesting and the human‐machine interface indicate great potential in next generation wearable nanogenerators and internet of things. [ABSTRACT FROM AUTHOR]

AbstractBackgroundMethodsResultsConclusionsImmune checkpoint inhibitors (ICIs) show promise in cancer treatment but can lead to immune-related adverse events (irAEs), notably affecting the skin. Understanding the factors behind these skin reactions is crucial for effective management during treatment. Hence, the aim of this study was to uncover associations between patient characteristics and cutaneous adverse reactions among cancer patients undergoing ICI treatment.The study involved 209 cancer patients receiving ICIs. Statistical methods, including the chi-square test, Fisher’s exact test, and multivariable logistic regression, were employed to analyze variables such as hypertension, antihistamine use, cancer metastasis, diabetes, and opioid usage. Additionally, machine learning techniques, including logistic regression, elastic net, random forest, and support vector machines (SVM), were utilized to develop predictive models anticipating skin-related adverse events.Results highlighted significant associations between specific patient attributes and the incidence of skin reactions post-ICI treatment. Notably, patients using antihistamines or with cancer metastasis exhibited higher rates of skin adverse reactions, while those with diabetes or using opioids displayed lower incidence rates. Robust performance in forecasting these adverse events was observed, particularly in the predictive models employing logistic regression and elastic net.This pioneering study contributes crucial insights into predictive modeling for ICI-induced skin reactions, emphasizing the importance of personalized treatment strategies. By identifying risk factors and utilizing tailored predictive models, healthcare providers can proactively manage adverse events, optimizing the benefits of ICIs while mitigating potential side effects. [ABSTRACT FROM AUTHOR]