A flexible precontact CNT-Al2O3 fiber sensor resistant to extreme temperatures.

As a new soft electronic product, a flexible precontact sensor provides spatial position sensing ability. However, the properties of traditional polymer materials change in industrial environments with extreme temperatures, which can cause the sensor function to decline or even fail. In this study, we propose a flexible fiber sensor based on the capacitor principle, which achieves a stable spatial positioning function and is not affected by a wide range of temperature changes. The fiber element of the sensor is obtained through the deposition of a flexible Al2O3 ceramic coating onto the surface of a carbon nanotube fiber (CNTF) via atomic layer deposition (ALD) technology. Coatings of different thicknesses (100 nm, 200 nm, and 300 nm) show different colors. The temperature resistance and flame retardancy of Al2O3 keep the morphology of the composite fiber unaffected by flame or high temperatures. Even at extreme temperatures (-78 °C to 500 °C), the sensor's sensing ability exhibits excellent stability. In addition, the spatial perception of the fibers remained viable after repeated bending (10 000 times). We demonstrate the potential of the sensor to acquire position information during high-temperature industrial pipe docking.

Self-Perceptional Soft Robotics by a Dielectric Elastomer.

Soft robotics has been a rapidly growing field in recent decades due to its advantages of softness, deformability, and adaptability to various environments. However, the separation of perception and actuation in soft robot research hinders its progress toward compactness and flexibility. To address this limitation, we propose the use of a dielectric elastomer actuator (DEA), which exhibits both an actuation capability and perception stability. Specifically, we developed a DEA array to localize the 3D spatial position of objects. Subsequently, we integrate the actuation and sensing properties of DEA into soft robots to achieve self-perception. We have developed a system that integrates actuation and sensing and have proposed two modes to achieve this integration. Furthermore, we demonstrated the feasibility of this system for soft robots. When the robots detect an obstacle or an approaching object, they can swiftly respond by avoiding or escaping the obstacle. By eliminating the need for separate perception and motion considerations, self-perceptional soft robots can achieve an enhanced response performance and enable applications in a more compact and flexible field.

Low hysteresis zwitterionic supramolecular polymer ion-conductive elastomers with anti-freezing properties, high stretchability, and self-adhesion for flexible electronic devices.

The fabrication of stretchable ionic conductors with low hysteresis and anti-freezing properties to enhance the durability and reliability of flexible electronics even at low temperatures remains an unmet challenge. Here, we report a facile strategy to fabricate low hysteresis, high stretchability, self-adhesion and anti-freezing zwitterionic supramolecular polymer ion-conductive elastomers (ICEs) by photoinitiated polymerization of aqueous precursor solutions containing a newly designed zwitterionic monomer carboxybetaine ureido acrylate (CBUIA) followed by solvent evaporation. The resultant poly(carboxybetaine ureido acrylate) (PCBUIA) ICEs are highly stretchable and self-adhesive owing to the presence of strong hydrogen bonds between ureido groups and dipole-dipole interactions of zwitterions. The zwitterion groups on the polymer side chains and loaded-lithium chloride endow PCBUIA ICEs with excellent anti-freezing properties, demonstrating mechanical flexibility and ionic transport properties even at a low temperature (-20 °C). Remarkably, the PCBUIA ICEs demonstrate a low hysteresis (≈10%) during cyclic mechanical loading-unloading (≤500%), and are successfully applied as wearable strain sensors and triboelectric nanogenerators (TENGs) for energy harvesting and human motion monitoring. In addition, the PCBUIA ICE-based TENG was used as a wireless sensing terminal for Internet of Things smart devices to enable wireless sensing of finger motion state detection.

Bioinspired Flexible Capacitive Sensor for Robot Positioning in Unstructured Environments.

The evolution of bionic machines into intelligent robots to adapt to real scenarios is inseparable from positioning sensors. However, traditional positioning methods such as camera arrays, ultrasound, or GPS are limited in narrow concealed spaces, harsh temperatures, or dynamic light fields, which hinder the practical application of special robots. Here, we report a flexible sensor inspired by Gnathonemus petersii that enables robots to achieve contactless and high-precision spatial localization independent of the unstructured features of the environment. Sensors are obtained from low-cost materials (carbon nanotubes and polyimides) and simple structures (fibers) and preparation processes (spin-coating). Experiments and simulations confirmed the high resolution (<1 mm) of the sensor over a large distance detection range (>150 mm) and high bandwidth (0-520 MPa) of contact force. Moreover, the sensing capability is still feasible when the sensor is bent to various curvatures and not affected under harsh conditions such as ultralow temperatures (below -78 °C), ultrahigh temperatures (over 250 °C), darkness, or brightness. We demonstrate the practical potential of the proposed sensors for a biomimetic hyper-redundant continuum robot to locate and avoid collisions in unstructured environments.

Archimedean Heronian mean operators based on complex intuitionistic fuzzy sets and their applications in decision-making problems.

In this article, we derive the Archimedean aggregation operators for complex intuitionistic fuzzy sets, for this, first, we evaluate some Archimedean operational laws based on complex intuitionistic fuzzy values and then we discuss their special cases because the Archimedean norms are the general form of all existing norms, for instance, algebraic, Einstein, Hamacher, and Frank operational laws. Furthermore, we present the complex intuitionistic fuzzy Archimedean Heronian aggregation operator and complex intuitionistic fuzzy weighted Archimedean Heronian aggregation operator. Several special cases and the basic properties of the above-proposed operators are also diagnosed, because proposing the Heronian mean operators based on Archimedean norms are very challenging and complicated tasks, because of their features and structure. Additionally, a decision-making process is developed under the identified operators by using complex intuitionistic fuzzy information. Finally, we illustrate several examples to show the multi-attribute decision-making technique is more flexible than the prevailing works with the help of sensitive analysis between explored and certain prevailing works.

Partitioned dual Maclaurin symmetric mean operators based on picture fuzzy sets and their applications in multi-attribute decision-making problems.

The partitioned Dual Maclaurin symmetric mean (PDMSM) operator has the supremacy that can justify the interrelationship of distinct characteristics and there are a lot of exploration consequences for it. However, it has not been employed to manage "multi-attribute decision-making" (MADM) problems represented by picture fuzzy numbers. The basic inspiration of this identification is to develop the novel theory of picture fuzzy PDMSM operator, and weighted picture fuzzy PDMSM operator and to identify their important results (Idempotency, Monotonicity, and Boundedness). Further, to identify the best decision, every expert realized that they needed the best way to find the beneficial optimal using the proper decision-making procedure, for this, we diagnosed the MADM tool in the consideration of deliberated approaches based on PF information. Finally, to drive the characteristics of the invented work, several examples are utilized to test the manifest of the comparative analysis with various more existing theories, which is a fascinating and meaningful technique to deeply explain the features and exhibited of the proposed approaches.

New Soft Actuating Way by Rapidly Temperature Change of Electrocaloric Effect.

Compared to traditional temperature control methods, the electrocaloric (EC) effect offers several advantages such as small size, rapid response, and environmental friendliness. However, current EC effects are generally used for the cooling area rather than heating. Here, poly(vinylidenefluorideter-trifluoroethylene-ter-chlorofluoroethylene) [P(VDF-TrFE-CFE)] film is combined with an electrothermal actuator (ETA) composed of polyethylene (PE) film and carbon nanotube (CNT) film. The heating and cooling process of the EC effect is used to help drive the ETA. The P(VDF-TrFE-CFE) film can produce a temperature change (ΔT) of 3.7 °C at 90 MV/m, and this process occurs within 0.1 s. With this ΔT, the composite film actuator can produce a deflection of 10°. In addition, due to the electrostrictive effect of P(VDF-TrFE-CFE), the composite film can also be used as an actuator. At 90 MV/m, the composite film actuator can produce a deflection over 240° within 0.05 s. Apart from other current driving modes for thermally responsive actuators, in this paper, a new type of soft actuating composite film by the temperature change of the EC effect is proposed. Except from ETAs, the EC effect can also have a wide application prospect in other thermally responsive actuators, including shape memory polymer actuators, shape memory alloy actuators, and so on.

m6A-related lncRNA-based immune infiltration characteristic analysis and prognostic model for colonic adenocarcinoma.

BACKGROUND: Colonic adenocarcinoma (COAD) is a common gastrointestinal tract tumor, and its occurrence and progression are typically associated with genomic instability, tumor-suppressor gene and oncogene mutations, and tumor mutational load. N6-methyladenosine (m6A) modification of RNAs and long non-coding RNA (lncRNA) expression are important in tumorigenesis and progression. However, the regulatory roles of m6A-associated lncRNAs in the tumor microenvironment, stratification of prognosis, and immunotherapy are unclear. METHODS: We screened 43 prognostic lncRNAs linked to m6A and performed consistent molecular typing of COAD using consensus clustering. The single-sample Gene Set Enrichment Analysis and ESTIMATE algorithms were used to assess the immune characteristics of different subgroups. Covariation between methylation-related prognostic lncRNAs was eliminated by least absolute shrinkage and selection operator Cox regression. A nomogram was created and evaluated by combining the methylation-related prognostic lncRNA model with other clinical factors. The relationship between the prognostic model grouping and microsatellite instability, immunophenotype score, and tumor mutation burden was validated using R scripts. Finally, we used a linkage map to filter sensitive medicines to suppress the expression of high-risk genes. Three m6A-associated lncRNA modes were identified in 446 COAD specimens with different clinical endpoints and biological statuses. Risk scores were constructed based on the m6A-associated lncRNA signature genes. Patients with lower risk scores showed superior immunotherapy responses and clinical benefits compared to those with higher risk scores. Lower risk scores were also correlated with higher immunophenotype scores, tumor mutation burden, and mutation rates in significantly mutated genes (e.g., FAT4 and MUC16). Piperidolate, quinostatin, and mecamylamin were screened for their abilities to suppress the expression of high-risk genes in the model. CONCLUSIONS: Quantitative assessment of m6A-associated lncRNAs in single tumors can enhance the understanding of tumor microenvironment profiles. The prognostic model constructed using m6A-associated lncRNAs may facilitate prognosis and immunotherapy stratification of patients with COAD; finally, three drugs with potential therapeutic value were screened based on the model.

Al/CuO@CNFs Conductive Flexible Energetic Films with High Electrical Safety.

The development of energetic materials with both high energy and high safety has always been the focus of the field of energetic materials. In this paper, a low-current-sensitivity flexible energetic film composed of carbon nanofibers (CNFs)-coated Al/CuO metastable intermolecular composites (MICs) was prepared by a blow-spinning combined with controlled heat treatment technique. Hotspots can hardly generate in this kind of energetic film due to the increased electrical and thermal conductivity, leading to low sensitivity of MICs. It evenly stays at a high voltage (60 V) for 24 h without raising the temperature significantly. The energetic films keep the high energy release of MICs due to the additional violent reactions between CuO and CNFs as well as the light weight of CNFs, showing the heat release of 2864 J/g. In addition, the obtained films exhibit good mechanical properties and can maintain the structural integrity after 1000 cycles of repeated bending to a 6 mm curvature radius. The above characteristics reveal that energetic films presented in this paper have certain safety, high energy, and flexibility and have potential applications in transient electronics with flexible requirements such as micro-electromechanical system.

Cost-effectiveness analysis of pembrolizumab plus chemotherapy versus chemotherapy as the first-line treatment for advanced esophageal cancer.

OBJECTIVE: The KEYNOTE-590 trial showed that individuals with advanced esophageal cancer who received Pembrolizumab in combination with chemotherapy as a first-line regimen achieved a significant extension of survival. However, this treatment option increases the financial burden on patients and the economic benefits remain to be further evaluated. METHODS: A Markov model was used to simulate 10-year survival of patients with esophageal cancer from the perspective of United States (US) Medicare payers. We evaluated the economics of Pembrolizumab plus chemotherapy in the PD-L1 positive score (CPS ≥10) and any PD-L1 expression groups, respectively. We estimated total costs, quality-adjusted life years (QALYs), and calculated incremental cost effectiveness ratios (ICERs). Sensitivity analyses were conducted to explore the impact of uncertainties on the results. Subgroup analysis was also performed. RESULTS: The analysis results showed that the ICER for pembrolizumab plus chemotherapy versus chemotherapy alone was $293,513.17/QALYs in the any PD-L1 expression group. This exceeded the threshold of willingness to pay ($150,000/QALYs). ICERs were most sensitive to the cost of pembrolizumab and the ICERs exceeded $150,000/QALYs in all subgroups. CONCLUSIONS: Evidence suggests that first-line pembrolizumab in combination with chemotherapy is not a cost-effective option for advanced esophageal cancer in the US, regardless of PD-L1 expression status.

Applications of the Multiattribute Decision-Making for the Development of the Tourism Industry Using Complex Intuitionistic Fuzzy Hamy Mean Operators.

In the aggregation of uncertain information, it is very important to consider the interrelationship of the input information. Hamy mean (HM) is one of the fine tools to deal with such scenarios. This paper aims to extend the idea of the HM operator and dual HM (DHM) operator in the framework of complex intuitionistic fuzzy sets (CIFSs). The main benefit of using the frame of complex intuitionistic fuzzy CIF information is that it handles two possibilities of the truth degree (TD) and falsity degree (FD) of the uncertain information. We proposed four types of HM operators: CIF Hamy mean (CIFHM), CIF weighted Hamy mean (CIFWHM), CIF dual Hamy mean (CIFDHM), and CIF weighted dual Hamy mean (CIFWDHM) operators. The validity of the proposed HM operators is numerically established. The proposed HM operators are utilized to assess a multiattribute decision-making (MADM) problem where the case study of tourism destination places is discussed. For this purpose, a MADM algorithm involving the proposed HM operators is proposed and applied to the numerical example. The effectiveness and flexibility of the proposed method are also discussed, and the sensitivity of the involved parameters is studied. The conclusive remarks, after a comparative study, show that the results obtained in the frame of CIFSs improve the accuracy of the results by using the proposed HM operators.

Random Forest enhancement using improved Artificial Fish Swarm for the medial knee contact force prediction.

Knee contact force (KCF) is an important factor to evaluate the knee joint function for the patients with knee joint impairment. However, the KCF measurement based on the instrumented prosthetic implants or inverse dynamics analysis is limited due to the invasive, expensive price and time consumption. In this work, we propose a KCF prediction method by integrating the Artificial Fish Swarm and the Random Forest algorithm. First, we train a Random Forest to learn the nonlinear relation between gait parameters (input) and contact pressures (output) based on a dataset of three patients instrumented with knee replacement. Then, we use the improved artificial fish group algorithm to optimize the main parameters of the Random Forest based KCF prediction model. The extensive experiments verify that our method can predict the medial knee contact force both before and after the intervention of gait patterns, and the performance outperforms the classical multi-body dynamics analysis and artificial neural network model.

Random forest-based classsification and analysis of hemiplegia gait using low-cost depth cameras.

Hemiplegia is a form of paralysis that typically has the symptom of dysbasia. In current clinical rehabilitations, to measure the level of hemiplegia gaits, clinicians often conduct subject evaluations through observations, which is unreliable and inaccurate. The Microsoft Kinect sensor (MS Kinect) is a widely used, low-cost depth sensor that can be used to detect human behaviors in real time. The purpose of this study is to investigate the usage of the Kinect data for the classification and analysis of hemiplegia gait. We first acquire the gait data by using a MS Kinect and extract a set of gait features including the stride length, gait speed, left/right moving distances, and up/down moving distances. With the gait data of 60 subjects including 20 hemiplegia patients and 40 healthy subjects, we employ a random forest-based classification approach to analyze the importances of different gait features for hemiplegia classification. Thanks to the over-fitting avoidance nature of the random forest approach, we do not need to have a careful control over the percentage of patients in the training data. In our experiments, our approach obtained the averaged classification accuracy of 90.65% among all the combinations of the gait features, which substantially outperformed state-of-the-art methods. The best classification accuracy of our approach is 95.45%, which is superior than all existing methods. Additionally, our approach also correctly reveals the importance of different gait features for hemiplegia classification. Our random forest-based approach outperforms support vector machine-based method and the Bayesian-based method, and can effectively extract gait features of subjects with hemiplegia for the classification and analysis of hemiplegia. Graphical Abstract Random Forest based Classsification and Analysis of Hemiplegia Gait using Low-cost Depth Cameras. Left: Motion capture with MS Kinect; Top-right: Random Forest Classsification based on the extracted gait features; Bottom-right: Sensitivity and specificity evaluation of the proposed classification approach.

[Automatic recognition and analysis of hemiplegia gait].

In this paper, the research has been conducted by the Microsoft kinect for windows v2 for obtaining the walking trajectory data from hemiplegic patients, based on which we achieved automatic identification of the hemiplegic gait and sorted the significance of identified features. First of all, the experimental group and two control groups were set up in the study. The three groups of subjects respectively completed the prescribed standard movements according to the requirements. The walking track data of the subjects were obtained straightaway by Kinect, from which the gait identification features were extracted: the moving range of pace, stride and center of mass (up and down/left and right). Then, the bayesian classification algorithm was utilized to classify the sample set of these features so as to automatically recognize the hemiplegia gait. Finally, the random forest algorithm was used to identify the significance of each feature, providing references for the diagnose of disease by ranking the importance of each feature. This thesis states that the accuracy of classification approach based on bayesian algorithm reaches 96%; the sequence of significance based on the random forest algorithm is step speed, stride, left-right moving distance of the center of mass, and up-down moving distance of the center of mass. The combination of step speed and stride, and the combination of step speed and center of mass moving distance are important reference for analyzing and diagnosing of the hemiplegia gait. The results may provide creative mind and new references for the intelligent diagnosis of hemiplegia gait.

High-Temperature Particulate Matter Filtration with Resilient Yttria-Stabilized ZrO2 Nanofiber Sponge.

Particulate matter (PM) is a major air pollutant in many regions, jeopardizing ecosystems and public health. Filtration at pollutant source is one of the most important ways to protect the environment, however, considering the high-temperature exhaust gas emissions, effective removal of PM and related pollutants from their sources remains a major challenge. In this study, a resilient, heat-resisting, and high-efficiency PM filter based on yttria-stabilized ZrO2 (YSZ) nanofiber sponge produced with a scalable solution blow spinning process is reported. The porous 3D sponge composed of YSZ nanofibers is lightweight (density of 20 mg cm-3 ) and resilient at both room temperature and high temperatures. At room-temperature conditions, the YSZ nanofiber sponge exhibits 99.4% filtration efficiency for aerosol particles with size in the range of 20-600 nm, associated with a low pressure drop of only 57 Pa under an airflow velocity of 4.8 cm s-1 . At a high temperature of 750 °C, the ceramic sponge maintains a high filtration efficiency of 99.97% for PM0.3-2.5 under a high airflow velocity of 10 cm s-1 . A practical vehicle exhaust filter to capture particles with filtration efficiency of >98.3% is also assembled. Hence, the YSZ nanofiber sponge has enormous potential to be applied in industry.

Roll-to-Roll Production of Transparent Silver-Nanofiber-Network Electrodes for Flexible Electrochromic Smart Windows.

Electrochromic smart windows (ECSWs) are considered as the most promising alternative to traditional dimming devices. However, the electrode technology in ECSWs remains stagnant, wherein inflexible indium tin oxide and fluorine-doped tin oxide are the main materials being used. Although various complicated production methods, such as high-temperature calcination and sputtering, have been reported, the mass production of flexible and transparent electrodes remains challenging. Here, a nonheated roll-to-roll process is developed for the continuous production of flexible, extralarge, and transparent silver nanofiber (AgNF) network electrodes. The optical and mechanical properties, as well as the electrical conductivity of these products (i.e., 12 Ω sq-1 at 95% transmittance) are comparable with those AgNF networks produced via high-temperature sintering. Moreover, the as-prepared AgNF network is successfully assembled into an A4-sized ECSW with short switching time, good coloration efficiency, and flexibility.

Ultralight, scalable, and high-temperature-resilient ceramic nanofiber sponges.

Ultralight and resilient porous nanostructures have been fabricated in various material forms, including carbon, polymers, and metals. However, the development of ultralight and high-temperature resilient structures still remains extremely challenging. Ceramics exhibit good mechanical and chemical stability at high temperatures, but their brittleness and sensitivity to flaws significantly complicate the fabrication of resilient porous ceramic nanostructures. We report the manufacturing of large-scale, lightweight, high-temperature resilient, three-dimensional sponges based on a variety of oxide ceramic (for example, TiO2, ZrO2, yttria-stabilized ZrO2, and BaTiO3) nanofibers through an efficient solution blow-spinning process. The ceramic sponges consist of numerous tangled ceramic nanofibers, with densities varying from 8 to 40 mg/cm3. In situ uniaxial compression in a scanning electron microscope showed that the TiO2 nanofiber sponge exhibits high energy absorption (for example, dissipation of up to 29.6 mJ/cm3 in energy density at 50% strain) and recovers rapidly after compression in excess of 20% strain at both room temperature and 400°C. The sponge exhibits excellent resilience with residual strains of only ~1% at 800°C after 10 cycles of 10% compression strain and maintains good recoverability after compression at ~1300°C. We show that ceramic nanofiber sponges can serve multiple functions, such as elasticity-dependent electrical resistance, photocatalytic activity, and thermal insulation.

Highly Flexible Indium Tin Oxide Nanofiber Transparent Electrodes by Blow Spinning.

Transparent conductive film (TCF) has found wide applications. Indium tin oxide (ITO) is currently the most widely used transparent electrode. However, major problem of ITO is the lacking of flexibility, which totally limits its applications. Here, we report a highly flexible transparent electrode consisting of freestanding ITO nanofiber network fabricated by blow spinning, the advantage of which is its high-efficiency, low cost and safety. When the bending radius decreased to 0.5 mm, the resistance of the transparent electrodes only increased by 18.4%. Furthermore, the resistance was almost unchanged after thousands of bending cycles at 3.5 mm bending radius.

Chicken surimi fortified by omega-3 fatty acid addition: manufacturing and quality properties.

BACKGROUND: The meat of spent hens is hard to use owing to its small amount and poor quality. A washing process to remove sarcoplasmic proteins and other impurities can prolong the shelf life of surimi-like products. Owing to the benefits of omega-3 polyunsaturated fatty acids (ω-3 PUFAs), functional foods fortified with ω-3 PUFAs are increasingly being marketed. Hence, in this study, ω-3 FA-fortified chicken surimi was manufactured, and how to ameliorate its lipid peroxidation during frozen storage was investigated. RESULTS: A 0.10% (w/v) solution of sodium chloride (NaCl) instead of distilled water in the third washing step decreased (P < 0.05) myofibrillar protein loss and moisture content of spent hen breast protein recoveries. Oil droplets in fish, flaxseed or soybean oil-added chicken surimi were well distributed. Moreover, flaxseed oil addition increased (P < 0.05) total ω-3 FAs and ω-3/ω-6 FA ratio, while only fish oil provided long-chain PUFAs. Oil addition decreased (P < 0.05) hardness and gumminess of chicken surimi, while flaxseed oil resulted in more (P < 0.05) yellow surimi than fish and soybean oil. Fish oil-added samples showed higher (P < 0.05) lipid oxidation than flaxseed or soybean oil-added samples under -15 to -10 °C storage, but α-tocopherol addition ameliorated it. CONCLUSION: A novel semi-manufactured chicken surimi product with nutritional benefits could be developed by fortification with fish or flaxseed oil.