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Sensor-based rehabilitation physical training assessment methods have attracted significant attention in refined evaluation scenarios. A refined rehabilitation evaluation method combines the expertise of clinicians with advanced sensor-based technology to capture and analyze subtle movement variations often unobserved by traditional subjective methods. Current approaches center on either body postures or muscle strength, which lack more sophisticated analysis features of muscle activation and coordination, thereby hindering analysis efficacy in deep rehabilitation feature exploration. To address this issue, we present a multimodal network algorithm that integrates surface electromyography (sEMG) and stress distribution signals. The algorithm considers the physical knowledge a priori to interpret the current rehabilitation stage and efficiently handles temporal dynamics arising from diverse user profiles in an online setting. Besides, we verified the performance of this model using a learned-nonuse phenomenon assessment task in 24 subjects, achieving an accuracy of 94.7%. Our results surpass those of conventional feature-based, distance-based, and ensemble baseline models, highlighting the advantages of incorporating multimodal information rather than relying solely on unimodal data. Moreover, the proposed model presents a network design solution for rehabilitation physical training that requires deep bioinformatic features and can potentially assist real-time and home-based physical training work.

A practical base-promoted tandem condensation N-alkylation reaction for the formation of trisubstituted hydrazones has been developed employing aldehydes and hydrazines with alkyl halides. Crucially, this reaction successfully overcomes chemoselectivity problems, allowing for the reaction of multiple components in a one-pot manner. Halo- and heterofunctional groups, as well as free hydroxyl and amino groups, are tolerated in this transformation to produce a wide range of trisubstituted hydrazones in good to excellent yields.

Micromotors capable of autonomous movement, sensitive detection and rapid removal of pollutants have attracted wide attention in environmental monitoring and water pollution remediation. In this paper, a ramie-derived hierarchical MgAl-LDH/MFZ@HRP magnetic microtube micromotor with peroxidase activity was proposed for simultaneous detection and removal of catechol from water. The Mn2O3decomposition of H2O2produces O2bubbles to realize the chemical drive of the micromotor with a speed of 128.33 ± 5.012 μm s−1(5% H2O2). Due to the high peroxidase activity and voluntary movement of MgAl-LDH/MFZ@HRP, catechol could be detected sensitively with a detection limit of 6.92 × 10−7M. Moreover, catechol could be rapidly degraded by hydroxyl radical (OH) and superoxide anion (O2−) produced by Fenton reaction, and the degradation rate could reach 96%. This study provides a promising multifunctional micromotor for simultaneous determination and removal of organic pollutants from wastewater.

Self-driven micro–nanomotors hold great promise for environmental monitoring and remediation. However, developing a nanomotor with dual functions of colorimetric detection and degradation is still a considerable challenge. In this paper, a novel nanomotor with both selective and catalytic activities was synthesized using a combination of natural enzymes and nanozymes. The nanomotor was modeled on natural mineral halloysite nanotubes (HNTs) and had a unique layered structure. MnO2in the inner layer was the fuel catalytic unit, which achieved actuation by catalyzing hydrogen peroxide (H2O2) to produce oxygen bubbles, and the diffusion coefficient of the nanomotor could reach 2.65 μm2s−1in 5% H2O2solution. Lac/Fe-BTC@NiFe-LDH in the outer layer was the catalytic functional unit, where Lac/Fe-BTC@NiFe-LDH not only exhibited high peroxide-like active enzyme activity, but also provided abundant active sites for the loading of natural laccase. The integration of natural enzymes and nanozymes provided the material with stronger catalytic activity, which could detect the target pollutant minocycline (MC) in water more sensitively and degrade it efficiently, and provided a new idea for the removal of antibiotics from water.

Evidence reasoning (ER) rule can effectively integrate qualitative knowledge and quantitative data and simultaneously flexibly deal with various types of uncertain information. Although ER rule has been applied in many engineering fields, many challenges still need to be addressed. For example, due to the lack of labeled data samples and the inherent fuzziness in information transformation of semantic reference values in health assessment of complex electromechanical systems, the accuracy of the assessment results is difficult to be measured quantitatively, which reduces the credibility of the ER model. To address this issue, a new ER model considering the credibility of results is proposed in this article, denoted as the ER-c model. First, a method for calculating the assessment results credibility is designed by establishing the relationship between the internal and external measurement information of a complex electromechanical system. Second, the objective function for the ER-c model optimization process is established according to the credibility and optimized using the covariance matrix adaptation evolution strategy (CMA-ES) algorithm. Finally, in the fields of instrument measurement in practical engineering, the problem that the hidden information such as health status of complex electromechanical system cannot be accurately measured is improved in this article. A case study of a body-in-white welding robot is conducted, and the results demonstrate the efficacy of the proposed method and provide a reliable foundation for measuring the credibility of assessment results.

Amyotrophic lateral sclerosis (ALS) patients suffer a great inconvenience in their daily lives due to the gradual loss of their motion abilities. In order to help ALS patients regain their self-care abilities at a certain level, an eye-tracking and brain–computer interface (BCI)-based human–environment interactive (HEI) system is proposed. Through a single-channel EEG recorder and a pair of eye-tracking glasses, the user’s attention levels and gaze points are detected. Based on the two types of intention information, the target objects are recognized by a You Only Look Once v5 (YOLOv5) model. Then, according to voice instructions, users can control the household electric appliances. The system performed a high success rate of 89.3% experimentally, providing a reliable and capable assistive solution for ALS patients.

Cyber security is of significance in today’s e-commerce applications. In this article, we present a piezoelectric touch sensing supported keystroke dynamics based identity authentication technique, for providing a secure access manner to smartphones. Here, the polyvinylidene fluoride (PVDF) based piezoelectric touch panel can learn detailed force touch habits of users. With a support vector machine (SVM) algorithm, our proposed frequency domain features experimentally demonstrate a better authentication accuracy of 98.3%, compared to the traditional time domain features. The work showcases a feasible method of combining functional materials and artificial intelligence (AI) for satisfying highly secure requirements.

Sleep apnea syndrome (SAS) is a high incidence respiratory disorder disease with potential mortality risk. Thus, researchers continuously present diverse techniques to monitor sleeping posture and respiratory rate (RR) which are closely correlated to the occurrence of SAS. Current techniques can implement high detection accuracy in both sleeping posture and RR, nevertheless, some undesired attributes still remain, such as high device cost, personal privacy leakage, and heavy burden on hardware. To satisfy the expectations, in this paper, we develop an integrated sleep monitoring system, based on flexible piezoresistive architectures and machine learning algorithms, in a low-cost and privacy protection manner. The experimental results successfully demonstrate the feasibility of the proposed technique, by showing a high sleeping posture classification accuracy of 98.1% and RR estimation accuracy of 97.5%, indicating a strong potential in practical utilization for SAS patients.

Teleoperation has been intensively used in diverse application scenarios in recent years. However, the frequently reported fatigue issue limits its successful use in tasks requiring long term and precise operations. One of key factors giving rise to the fatigue issue is the posture misalignment between the user’s arm and the robotic arm. Although conventional computer vision (CV) based solutions can implement postures alignment, they introduce potential privacy leakage problems at the same time. To address this issue, in this article we present an inertial measurement unit based technique for learning the posture status of the user’s arm and the robotic arm. Through a smart mapping algorithm, the proposed technique is proved to be a feasible manner to synchronize the two postures, potentially reducing the mental fatigue and preventing the privacy leakage possibilities.

The pathogenesis of nonalcoholic fatty liver disease (NAFLD) is closely linked to the imbalance of lipid and glucose metabolism, in which peroxisome proliferator-activated receptors (PPARs) play essential roles. The clinical trials have shown the beneficial effects of the PPARs’ ligands on NAFLD. In this study, we screen the extracts from the marine fungus Acremonium citrinumand identify the natural compounds dihydrotrichodimerol (L1A) and trichodimerol (L1B) as the ligands of PPARs, of which L1A is a dual PPARα/γ agonist, whereas L1B is a selective PPARγ agonist. L1A but not L1B significantly prevents hepatic lipid accumulation in an oleic acid-induced NAFLD cell model as well as in a high-fat-diet-induced NAFLD mouse model. Moreover, L1A potently inhibits hepatic steatosis in a PPARα-dependent manner in another NAFLD mouse model constructed by using a choline-deficient and amino acid-defined diet. Mechanistically, L1A transcriptionally up-regulates the expression of SIRT1 in a PPARα-dependent manner, followed by the activation of AMPK and inactivation of ACC, resulting in the inhibition of lipid anabolism and the increase of lipid catabolism. Taken together, our study reveals a dual ligand of PPARα/γ with a distinct structure and therapeutic effect on NAFLD, providing a potential drug candidate bridging the currently urgent need for the management of NAFLD.

The inferior temperature stability of piezoelectric response is the main drawback of KNN-based ceramics. Here, the Ba-doped 0.97(K0.48Na0.52)(Nb0.96Sb0.04)O3-0.03Bax(Bi0.5Ag0.5)1-xZrO3(abbreviated as KNNS-BxBAZ) textured ceramics were prepared by the template grain growth (TGG) method. Excellent comprehensive properties (d33=(406 ± 15) pC/N, TC = 274 °C, strain is 0.17%) were achieved in KNNS-BxBAZ textured ceramics with x = 0.2. Meanwhile, its piezoelectric and strain properties also show superior temperature stability (d33maintained within ±20% change in a wide temperature range from 25 °C to 200 °C and strain variation was less than 5% in the temperature range from room temperature to 165 °C). The high O-T phase transition temperature (TO-Tis 110 °C) induced by incorporating Ba ions accounts for the enhanced temperature stability of piezoelectric properties. In addition, the crystal texture always maintains the contribution of piezoelectric anisotropy to the piezoelectric properties during elevated temperature, which significantly improved the temperature stability of piezoelectric properties. This work provides an effective strategy for simultaneously achieving high piezoelectric response and excellent temperature stability in KNN-based ceramics.

In terms of developing supercritical CO2(sCO2) coal-fired power plants, enhancing cooling wall performance is one of significant factors to improve system performance. In this paper, a new cooling wall tube structure is proposed to match the non-uniform heat flux (NUH) with the thermal resistance by changing the cooling wall tube eccentricity. A three-dimensional multi-physical coupling model of cooling wall is constructed to compare the novel structure to the conventional structures. The properties of fluid dynamics, thermal stress, coupled heat transfer and cooling wall deformation are analyzed. In contrast to the traditional structure, the maximum temperature and circumferential temperature difference (CTD) of the proposed structure can be reduced by 2% and 27.4%, respectively. The essential working parameters related to the performances of the cooling wall tube are discussed. The maximum temperature of the new structure is reduced by 8–13 K and the maximum thermal stress is reduced by about 10%–15% under all the simulated working conditions when the eccentricity changes from 0 to 0.2. The proposed structure can effectively reduce the maximum temperature and circumferential temperature gradient under NUH. Consequently, a novel insight is put out for the design and optimization of the cooling wall tube in coal-fired power plants.

In recent years, human activity recognition (HAR) technologies in e-health have triggered broad interest. In literature, mainstream works focus on the body's spatial information (i.e. postures) which lacks the interpretation of key bioinformatics associated with movements, limiting the use in applications requiring comprehensively evaluating motion tasks' correctness. To address the issue, in this article, a Wearables-based Multi-column Neural Network (WMNN) for HAR based on multi-sensor fusion and deep learning is presented. Here, the Tai Chi Eight Methods were utilized as an example as in which both postures and muscle activity strengths are significant. The research work was validated by recruiting 14 subjects in total, and we experimentally show 96.9% and 92.5% accuracy for training and testing, for a total of 144 postures and corresponding muscle activities. The method is then provided with a human-machine interface (HMI), which returns users with motion suggestions (i.e. postures and muscle strength). The report demonstrates that the proposed HAR technique can enhance users' self-training efficiency, potentially promoting the development of the HAR area.

Scedosporium boydiiis considered as emerging opportunists affecting both immunocompromised and immunocompetent individuals. Diagnosis and treatment of Scedosporiuminfections can be challenging, and the outcome is frequently fatal, especially in patients with disseminated infections. Metagenomic next-generation sequencing (mNGS) is a promising adjunctive diagnostic approach for uncommon pathogens. Here, we report a rare case of brain abscess caused by S. boydiiin a systemic lupus erythematosus (SLE) patient. This case highlights the importance of mNGS in the early diagnosis of S. boydii. Additionally, the combined application of culture and molecular tools may be promising in the clinical diagnosis of fungal disease.

Nowadays, electronic devices are taking over the market and touch panel is a necessary part, among which capacitive touch panel has greater development potential and development space because of its high transparency, multitouch support and low cost. Electrical impedance tomography (EIT) is a technology with the goal of reconstructing tissue image with impedance distribution, which can realize the goal of accurate measurement and maintaining the original characteristics of the medium to the greatest extent at the same time. In this paper, we use EIT‐based approach to achieve a large area touch perception imaging system with high light transmittance, which is to obtain the distribution of the permittivity of the touch penal by just detecting the impedance of the electrode scattered around the panel. We use Sensitivity theory to solve the mapping problem between the permittivity distribution and the signal collected by the electrode in the EIT problem. Besides, we innovatively combine LBP with Landweber algorithm to achieve high precision and fast imaging of touch area, which greatly reduces the discomfort caused by inverse problem in EIT method.

The rapid and efficient elimination of CO by-products in mining and tunneling blasting operations poses a significant challenge. In this study, an in-situ synchronous elimination method (ISSE) for CO was proposed based on catalytic oxidation principles by deploying nano-scale catalysts in boreholes. Initially, Co3O4catalyst was prepared and characterized. Subsequently, the catalyst was processed into a packing bag for in-situ CO elimination within boreholes. Field experiments were conducted at the Lujiatuo coal mine to verify the ISSE technology, and the effects of packaging materials, charging configuration, catalyst concentration, and length-to-diameter ratio of the packing bag on CO elimination efficiency were investigated. The experimental results revealed that when utilizing a nylon water cannon mud bag and charging 35 g of catalyst at the orifice, the peak CO concentration decreased from 735 ppm to 243 ppm, achieving an elimination efficiency of 66.9 % and reducing CO exceedance time by 28.5 %. These findings indicate that the ISSE technology surpasses existing techniques in terms of CO eliminating during mining and tunneling operations rapidly and effectively, that are expected to find widespread application in blasting production.

•16S rRNA sequence of AV208 was 100% with Aerococcus urinaeequiand 99.9% with A. viridans•There was 96.24% ANI between AV208 and A. urinaeequi, and it was higher than that with Aerococcus viridans(94.9%)•AV208 was resistant to many drugs including vancomycin with a MIC >256 μg mL−1•A Tn1546transposon-like structure was located in the genome of strain AV208•Whole-genome sequencing combined with ANI could give an accurate identification

The general silhouette‐based gait recognition methods usually rely on binary human silhouette, which is easily affected by external factors, making it unsuitable for situations while wearing heavy clothes or carrying objects, etc. In this study, a new skeleton‐based gait recognition model is proposed. The model first extracts the spatial and temporal features of gait using the space and time relationship between body joints, and second, it eliminates redundant features by decomposing the feature map, to achieve a better recognition accuracy in the presence of external factors. Through abundant experiments on two common datasets, CASIA‐B and OUMVLP‐Pose, the proposed model has been proved to have higher recognition accuracy and remarkable robustness.

The total content of Al and Ti in advanced Ni-based wrought superalloys is up to 7.5wt.%, which makes it easier to form harmful nonequilibrium eutectic (γ+γ′) and η phase. It has been reported that the addition of certain amount of Zr can modify precipitation of the nonequilibrium phases obviously, but the mechanism is still controversial. The effect of Zr ranging from <0.0006wt.% to 0.150wt.% on solidification behavior, segregation and microstructure of a Ni-based superalloy with high Al and Ti contents was investigated, eliminating the interferences of C and B. Results show that increase in Zr content significantly promotes the formation of eutectic (γ+γ′), η and Zr-rich phase in the interdendritic region. Besides the Zr-rich phase, Zr dissolves slightly in the eutectic γ′ and obviously in the η phase. An interesting phenomenon is discovered that the Zr addition significantly increases the area fraction of liquid pools and enlarges the forming range of γ dendrites, which suggests that Zr markedly retards the solidification. Zr affects the eutectic (γ+γ′) and η formation mainly due to the retard of solidification and dissolution of Zr in them. The retard of solidification obviously increases the residual liquid fraction and undercooling. Zr can serve as a forming element for the eutectic (γ+γ′) and η phase, and the obvious dissolution of Zr in η phase significantly decreases the critical concentration of Ti for its precipitation.

Using supercritical CO2(S-CO2) instead of water steam as heat transfer fluid for coal-fired power plants is another way to further improve the power generation efficiency. In this paper, a multi-field coupling model that integrates fluid flow, heat transfer and thermal stress is proposed to solve the complex problem. The effects of the key operating parameters on the performances of the cooling wall are numerically investigated. More details of the characteristics of fluid dynamics, thermal stress, pressure drop, coupled heat transfer and the whole flow and temperature fields in the S-CO2cooling wall are revealed and discussed. A comparison study is made between the cooling wall tube and waterwall tube. Both temperature and thermal deformations of cooling wall tube are much higher than that of water. Numerical simulation results indicate that inclination angle has little effects on the temperature distribution, while increase of pressure drop is obvious as the inclination angle increases. Increasing tube diameter can effectively reduce both the pressure drop and the temperature of the cooling wall tube. As a result, a new insight is introduced for the design of S-CO2power cycle.

Rapid development of high-throughput technologies has permitted the identification of an increasing number of disease-associated genes (DAGs), which are important for understanding disease initiation and developing precision therapeutics. However, DAGs often contain large amounts of redundant or false positive information, leading to difficulties in quantifying and prioritizing potential relationships between these DAGs and human diseases. In this study, a network-oriented gene entropyapproach (NOGEA) is proposed for accurately inferring master genes that contribute to specific diseases by quantitatively calculating their perturbation abilities on directed disease-specific gene networks. In addition, we confirmed that the master genes identified by NOGEA have a high reliability for predicting disease-specific initiation events and progression risk. Master genes may also be used to extract the underlying information of different diseases, thus revealing mechanisms of disease comorbidity. More importantly, approved therapeutic targets are topologically localized in a small neighborhood of master genes in the interactome network, which provides a new way for predicting drug-disease associations. Through this method, 11 old drugs were newly identified and predicted to be effective for treating pancreatic cancer and then validated by in vitroexperiments. Collectively, the NOGEA was useful for identifying master genes that control disease initiation and co-occurrence, thus providing a valuable strategy for drug efficacy screening and repositioning. NOGEA codes are publicly available at https://github.com/guozihuaa/NOGEA.

The simultaneous strengthening of mechanical and magnetic properties is an ideal fabrication strategy for soft-magnetic materials. A non-equiatomic Al19Co20Fe20Ni41eutectic high-entropy alloy was prepared to investigate the alloying effect of B on the microstructure evolution, phase formation, mechanical and soft-magnetic properties. With the increase in B content, the microstructures of (Al19Co20Fe20Ni41)100−xBxalloys transformed from the initial lamellar eutectic structure (x=0) to the divorced eutectic structure (x>0.6). Fine borides precipitated in the intergranular phase (x≥0.6). The hardness of alloys increased from HV 328.66 to HV 436.34 and the compression mechanical performance displayed a transition from plastic material to brittle material. The Al19Co20Fe20Ni41alloy possesses good soft-magnetic properties, and the minor B addition has little effect on it. Increasing the resistivity can effectively reduce the eddy current loss when used as a soft-magnetic material.

The current methods for synthesizing acylhydrazones are limited by their multistep processes, narrow substrate scope, low selectivities, and poor yields. Herein, a fundamentally novel approach to bioactive acylhydrazones was developed based on the palladium-catalyzed multicomponent tandem condensation carbonylation of halides with aldehydes and hydrazines. This method provides a useful and efficient strategy for generating grams of various acylhydrazones in a one-pot manner. Mechanistic studies provided evidence of facile carbonylation of halides with the weak nucleophile hydrazones facilitated by a base.

Force touch function in interactive displays gains increasing interests due to its ability in detecting 3D information from customers, giving rise to plenty of novel and advanced services for users and therefore increasing the efficiency of human‐machine interactivity. In this article, piezoelectric based force touch architecture and machine learning algorithms are smoothly combined for implementation of user‐centered force level recognition and user identity authentication.

Insole gait analysis has become a research focus in recent years, owing to its potential in sports training improvement, and real-time and long-term health status monitoring. Among diverse insole gait analysis technologies, piezoelectric-based techniques receive growing attention, due to its nature in converting force to electric signals, potentially ensuring long-battery lifetime compared to its counterparts. However, current literature mainly reports on the detection of 6–8 main plantar positions for motion interpretation, lacking in providing enough information for illness diagnosis, e.g., hip disease. In this article, smart integration of piezoelectric films with different polarization orientations and 32 electrodes is presented, for supporting both normal and shear stress detection with high three-dimensional resolution. High sensitivities at 56 and 173 mN are demonstrated experimentally for normal and shear stress sensing. With our comprehensive algorithms, accurate and real-time gait monitoring for the majority parts of the plantar area is obtained. This paper can support diverse utilization purposes, advancing the area of insole gait analysis.

AbstractIn this article, the pivotal inference is proposed to estimate the two unknown parameters of the inverse exponentiated Rayleigh distribution based on progressive censored data. We derive the point estimator and construct an interval estimator using the pivotal quantity method. To compare the performance of this proposed method and the traditional maximum likelihood estimation method, a simulation study is conducted. The simulation results show that the proposed method performs better in terms of bias and mean squared error. Finally, a real dataset is used to illustrate the proposed approaches.

Cochlear hair cells are critical for the conversion of acoustic into electrical signals and their dysfunction is a primary cause of acquired hearing impairments, which worsen with aging. Piezoelectric materials can reproduce the acoustic-electrical transduction properties of the cochlea and represent promising candidates for future cochlear prostheses. The majority of piezoelectric hearing devices so far developed are based on thin films, which have not managed to simultaneously provide the desired flexibility, high sensitivity, wide frequency selectivity, and biocompatibility. To overcome these issues, we hypothesized that fibrous membranes made up of polymeric piezoelectric biocompatible nanofibers could be employed to mimic the function of the basilar membrane, by selectively vibrating in response to different frequencies of sound and transmitting the resulting electrical impulses to the vestibulocochlear nerve. In this study, poly(vinylidene fluoride-trifluoroethylene) piezoelectric nanofiber-based acoustic circular sensors were designed and fabricated using the electrospinning technique. The performance of the sensors was investigated with particular focus on the identification of the resonance frequencies and acoustic-electrical conversion in fibrous membrane with different size and fiber orientation. The voltage output (1–17 mV) varied in the range of low resonance frequency (100–400 Hz) depending on the diameter of the macroscale sensors and alignment of the fibers. The devices developed can be regarded as a proof-of-concept demonstrating the possibility of using piezoelectric fibers to convert acoustic waves into electrical signals, through possible synergistic effects of piezoelectricity and triboelectricity. The study has paved the way for the development of self-powered nanofibrous implantable auditory sensors.

In this work, a special synthetic process of charring-foaming agent nanosheet (2d-CFA) creates soft and smooth tactility, no granule with a soft quality of extension. This well-designed 2d-CFA matches well with ammonium polyphosphate (APP) to form intumescent flame retardant (IFR), obtaining well-pleasing carbonization ability, fire safety, and mechanical properties for polylactic acid (PLA) composite. 2d-CFA can reduce the flammability of the composites by forming a physical barrier against heat and mass transport under early pyrolysis of PLA. Furthermore, heat, CO, and CO2production in PLA composites are significantly decreased by loading low IFR. The mechanisms of enhanced carbonization ability and fire safety for PLA by loading 2d-CFA/APP are proposed based on gaseous and condensed phase products. This novel and facile modification method provides a new strategy for improving the flame retardant efficiency of IFR.

Fueled by the recent proliferation of energy-efficient and energy-autonomous or self-powered nanotechnology-based wearable smart systems, human motion intention prediction (MIP) plays a critical role in a wide range of applications, such as rehabilitation and assistive robotics, to enable more natural, biologically inspired, and seamless integrated motion assistance task execution, including for elders and physically impaired patients. With the increasing complexity of human-machine interactions and the need for personalized assistance, there is a growing demand for real-time and accurate MIP systems. This review aims to provide a comprehensive understanding of the interdisciplinary field of MIP, under the logic of its physiological foundations, by discussing state-of-the-art sensing technologies, including brain-computer interfaces (BCI), electromyography (EMG), and motion sensors, alongside the relevant data processing techniques and decoding algorithms. We emphasize the importance of fostering collaboration among scholars from different domains to capture the intricate dependencies between the set of stimuli and responses of the central nervous system and the activation of the complex set of muscles and joints that produce human motion. By offering insights into the recent advancements and future prospects of the field, this review seeks to stimulate further research and innovation in the rapidly evolving area of human motion intention prediction, for a future where technologies understand and respond to complex human intentions patterns, anticipating their needs.

ABSTRACTIn this study, corrosion behaviour of double-phase Al1.3CrFeNi chemically complex alloy was investigated, including hot corrosion and electrochemical corrosion. Hot corrosion behaviour of Al1.3CrFeNi alloy was explored in molten 75 wt-% Na2SO4 + 25 wt-% NaCl salt. The result revealed that the corrosion kinetic curve of Al1.3CrFeNi alloy followed the exponential rate rule through mass loss measurement. In addition, it prevented that the formed corrosion layer had obvious stratification including external granular Al2O3and inner porous Cr2O3when corrosion time was up to 100 h. Besides, it should be noted that the Al1.3CrFeNi alloy was sensitive to the molten salt containing chlorine, which makes the alloy surface leave voids and bring about acceleration of corrosion. Meanwhile, electrochemical corrosion resistance of Al1.3CrFeNi alloy in NaCl solution with different concentrations (0.6, 1.0 and 2.0 mol L−1) was investigated at room temperature. The results revealed that Al1.3CrFeNi alloy showed superior corrosion resistance in NaCl solution due to the existence of Al and Cr which aid the formation of protective oxide layer.

•27 linezolid-non-susceptible-Enterococcuswere identified at a tertiary care hospital of China.•OptrAwas the main gene mediating LNSE in 23 isolates, with ST16 E. faecalisclonal dissemination.•PCR-based sequencing and WGS revealed 7 optrAvariants in 16 isolates.•OptrAwas found downstream of fexAby searching pE349 sequence on WGS data.•This is the first report on the prevalence of LNSE and the optrAgene in Nanjing, China.

Abstract

Piezoelectric-based force sensing in interactive displays has attracted worldwide attention due to its intrinsic ability to efficiently convert mechanical energy into electric energy. However, because of faulty detection accuracy, commercialized products using piezoelectric materials have not succeeded in fully entering the market. To explain this situation, we investigate the matter through a finite element analysis (FEA) method. The results of our investigation reveal that unstable forcevoltage responsivity and propagated stress resulting from adjacent force-touch locations are two key issues that substantially degrade detection accuracy.

Applying sensors in biomedical institutions and home‐based stroke rehabilitation is now a global research focus. In this review paper, the relationship between stroke diseases’ physiology mechanism and diverse sensors’ functionalities is detailed explained. The review starts by interpreting how stroke influences motion abilities and then introduces broadly adopted physical training methods. After, the working principles of sensors and their use to objectively provide patients’ body information for stroke rehabilitation status are discussed. The content of the paper aims to not only review the state‐of‐the‐art works of developing sensors for assisting in evaluating stroke patients’ status but also bridging the gap between medical staff and engineers. In this review paper, the relationship between stroke diseases’ physiology mechanism and diverse sensors’ functionalities is explained in detail. The process of stroke affecting motor ability, the working principles of sensors, and their use to objectively provide patients’ body information for stroke rehabilitation status are discussed, bridging the gap between medical staff and engineers.

Over panel stress non‐uniformity strongly limits the detection accuracy of piezo based force sensing in interactive displays. In this work, nested artificial neural networks based technique is presented to address the issue of stress non‐uniformity. High detection accuracy in terms of touch position and force amplitude is demonstrated by the proposed technique.

This paper presents a flexible graphene/polyvinylidene difluoride (PVDF)/graphene sandwich for three-dimensional touch interactivity. Here, x–yplane touch is sensed using graphene capacitive elements, while force sensing in the z-direction is by a piezoelectric PVDF/graphene sandwich. By employing different frequency bands for the capacitive- and force-induced electrical signals, the two stimuli are detected simultaneously, achieving three-dimensional touch sensing. Static force sensing and elimination of propagated stress are achieved by augmenting the transient piezo output with the capacitive touch, thus overcoming the intrinsic inability of the piezoelectric material in detecting nontransient force signals and avoiding force touch mis-registration by propagated stress.

In this work, force touch sensing is achieved in conventional capacitive touch panels, by employing piezoelectric materials. To achieve high sensitivity and stability in force detection, deep analysis of employing piezoelectric material in interactive displays is provided, together with possible solutions for piezoelectric principle related force sensing issues.

Studies exploring the role and value of energy storage in deep decarbonization often overlook the balance between the energy capacity and the power rating of storage systems—a key performance parameter that can affect every part of storage operation. Here, we quantitatively evaluate the system-wide impacts of battery storage systems with various energy-to-power ratios (EPRs) and at different levels of renewable penetration. We take Jiangsu province in China as our case study, due to its high electricity consumption and aggressive renewable energy targets. Our results show the evolving role of storage: as renewable penetration increases, higher EPRs are favored, as they lead to system-wide cost reductions, lower GHG emissions, and higher power system reliability. Whereas existing studies make exogenous assumptions about the lifetime of storage, we show that lifetimes across EPRs and renewable scenarios span 10 to 20 years. Existing research can thus send false signals to investors and grid planners, delaying the deployment of storage and retarding the energy transition. By showing how different EPRs yield different benefits at different stages of the energy transition, our results help investors, policy makers, and system planners design forward-thinking and dynamic policies that encourage prudent storage uptake.

•S. capitiswas the main linezolid-resistant strain in CoNS•Linezolid resistance was mediated mainly by 23S rRNA V mutations and acquisition of cfr•Linezolid-resistant S. capitishas stronger biofilm-forming ability•The detection rate of the bapgene in LRSC strains was higher than that in LSSC•Mass spectrometric biomarkers can identify linezolid-resistant S. capitis

Alzheimer’s disease (AD) and frontotemporal dementia (FTD) are two common forms of primary neurodegenerative dementia that show overlapping clinical symptoms. The aim of this study was to perform genetic analyses on GRN, VCP, CHMP2B, FUS, TARDBP, C9orf72and MAPTgenes in Chinese AD and FTD patients. We performed gene sequencing of the GRN, VCP, CHMP2B, FUS, TARDBP, MAPTand C9orf72genes in 61 clinical AD and 38 FTD Chinese patients. We identified a known mutation of MAPT(p.Pro301Leu, c.902C>T) in four patients from an autosomal dominant FTD family with behavioral variant FTD (bvFTD) and progressive nonfluent aphasia (PNFA) phenotypes, and a novel mutation in MAPT(p.Leu48Val, c.142 G>C) in a sporadic progressive supranuclear palsy patient. Two novel variations in VCP(p.Thr127Ala, c. 379A>G; p.Asn401Ser, c.1202A>G) were present in both a sporadic FTD and an AD case, and a novel deletion in GRN(560del p.Leufs) was found in a sporadic primary progressive aphasia patient. Mutations of VCP, GRNand MAPTgenes are present in Chinese FTD cases. In the case of the MAPTmutation, the family presented with both bvFTD and PNFA phenotypes, while the VCPmutation was also related to an early-onset AD phenotype.

Considering the advantages of high Curie temperature and environment-friendly nature of KNN piezoelectric ceramics, the limitation of weak piezoelectric response and their temperature sensitivity to applications is worth exploring. Herein, the <001> textured (1-x)(K0.5Na0.5)(Nb0.96Sb0.04)O3-x(Bi0.5Na0.5)HfO3(x= 0.01−0.045) lead-free ceramics were synthesized by templated grain-growth method. The high piezoelectric performance (d33of 474 pC/N and strain of 0.21%) and excellent temperature stability (unipolar strain maintained within 4.3% change between 30∘C and 165∘C) were simultaneously achieved in the textured KNNS-0.03BNH ceramics. The high piezoelectric performance can be attributed to the summation of the crystallographic anisotropy and phase structure contributions in <001> textured ceramics. The superior temperature stability of piezoelectric properties can be interpreted by the contribution of crystal anisotropy to piezoelectric properties reduces the effect of phase transition on piezoelectric properties deterioration. This study provides an effective strategy for simultaneously achieving high piezoelectric properties and superior temperature stability in KNN-based textured ceramics.

Shale oil has received increasing attention as an essential replacement for conventional oil resources. Shale oil recovery is a complex process controlled by interactions of many factors whose impact could be significantly different from conventional reservoirs. This study hence aims to fill the gaps in the literature by studying various aspects of the phase behavior of shale oil and their significance in different aspects of the recovery from oil shale. In the first part of this study, the standard practices, including experimental and theoretical methods for calculating the pressure, volume, temperature (PVT), and phase behavior of shale oil, is discussed in detail. Next, the effects of factors such as the composition of fluids, pore structure, and capillary forces on the phase behavior of hydrocarbon fluids are explained. The third part focuses on applying phase behavior for oil shale development. Moreover, the geological and geochemical processes that lead to the maturity of kerogen, the formation of shale oil, and the experimental methods by which those processes are currently studied are scrutinized. By studying the thermal and burial history of the hydrocarbon-generating strata and hydrocarbon-generating kinetics, the shale formation’s oil and gas phase distribution can be predicted. Consequently, the sweet spots for the recovery of light condensate oil can be more accurately determined. The application of enhanced oil recovery methods is an inevitable part of recovery from conventional and unconventional formations. Therefore, the last part of this study analyses the changes in the phase behavior of shale oil when an external component, i.e., CO2or CH4, is injected into the reservoir. Reviewing the literature revealed that a more accurate prediction of hydrocarbon phase behavior can be made by combining different disciplines of science to achieve optimized plans for efficient shale oil development, making shale oil a more economically viable energy resource.

Images from the superresolution refers to a series of low resolution observation image reconstruction of high resolution images. Compressed and shaping of diffractive spot with the throne of circular mask is studied in this article, in order to improve the resolution of the image. Theory and simulation results of superresolution is given based on the diffraction spot compression and shaping in this paper，Which is very important for the study in laser communications and other field.

Rapid development of high-throughput technologies has permitted the identification of an increasing number of disease-associated genes (DAGs), which are important for understanding disease initiation and developing precision therapeutics. However, DAGs often contain large amounts of redundant or false positive information, leading to difficulties in quantifying and prioritizing potential relationships between these DAGs and human diseases. In this study, a network-oriented gene entropy approach (NOGEA) is proposed for accurately inferring master genes that contribute to specific diseases by quantitatively calculating their perturbation abilities on directed disease-specific gene networks. In addition, we confirmed that the master genes identified by NOGEA have a high reliability for predicting disease-specific initiation events and progression risk. Master genes may also be used to extract the underlying information of different diseases, thus revealing mechanisms of disease comorbidity. More importantly, approved therapeutic targets are topologically localized in a small neighborhood of master genes on the interactome network, which provides a new way for predicting drug-disease associations. Through this method, 11 old drugs were newly identified and predicted to be effective for treating pancreatic cancer and then validated by in vitroexperiments. Collectively, the NOGEA was useful for identifying master genes that control disease initiation and co-occurrence, thus providing a valuable strategy for drug efficacy screening and repositioning. NOGEA codes are publicly available at https://github.com/guozihuaa/NOGEA.