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Seed priming with nanomaterials is an emerging approach for improving plant stress tolerance. Here, we demonstrated a mechanism for enhancing salt tolerance in rice under salt stress via priming with nonstimulatory nanoparticles such as selenium nanoparticles (SeNPs), distinct from stimulatory nanomaterials. Due to the dynamic transformation ability of SeNPs, SeNP priming could enhance rice salt tolerance by mediating the glutathione cycle to eliminate excess reactive oxygen species (ROS). During priming, SeNPs penetrated rice seeds and transitioned into a soluble form (99.9%) within the embryo endosperm. Subsequently, the soluble selenium (Se) was transported to rice roots and metabolized into various Se-related derivatives, including selenomethionine (SeMet), Na2SeO3(Se IV), selenocysteine (SeCys2), and methylselenocysteine (MeSeCys). These derivatives significantly enhanced the root activities of key enzymes such as glutathione peroxidase (GSH-PX), glutathione reductase (GR), catalase (CAT), peroxidase (POD), and superoxide dismutase (SOD) by 24.97%, 47.98%, 16.23%, 16.81%, and 14.82%, respectively, thus reinforcing the glutathione cycle and ROS scavenging pathways. Moreover, these alterations induced transcriptional changes in rice seedlings, with genes involved in signal transduction, transcription factors (TFs), ROS scavenging, and protein folding being upregulated, activating signal perception and self-repair mechanisms. These findings offer valuable insights for the agricultural application of nanomaterials.

Redundant manipulators, as mechanical equipments imitating human arms, have been applied to various areas in recent years from the perspective of control. Different from pure control technologies, the motion capability of a human arm is achieved by a complex and efficient neural system, with the cerebellum playing a pivotal role. Motivated by this fact, we design a cerebellum model based on an echo state network (ESN) for the learning and control of redundant manipulators. In addition, to simulate the skillful control ability of the cerebellum over movements of human arms, the proposed model is constructed at the joint velocity level. Furthermore, to improve the accuracy and applicability, we propose an ESN-based Kalman-filter-incorporated and cerebellum-inspired (KFICI) scheme for the learning and control of redundant manipulators with Kalman filter incorporated. The proposed scheme enables a redundant manipulator to track the desired trajectory at the velocity level and tolerate noises. Finally, simulations and experiments based on a physical redundant manipulator are performed to verify the effectiveness of the proposed control scheme.

Redundant manipulators are widely used in various fields due to their multiple degrees of freedom characteristics, and their tracking control is an important problem in the field of robotics. In order to control manipulators with unknown models in practical applications, this article proposes a discrete data-driven Jacobian matrix adaptive control (DDJMAC) scheme. The scheme is composed of a discrete Jacobian matrix estimator, a discrete neural dynamics controller, and a Kalman filter. Subsequently, the convergence and robustness of the DDJMAC scheme are demonstrated by theoretical analyses. Finally, simulations, comparisons, and physical experiments are performed on redundant manipulators, and the results confirm the effectiveness, superiority, and practicality of the proposed scheme.

Merely relying on precise motion control alone, a manipulator faces significant challenges when attempting to perform tasks involving interactions with objects, such as polishing. Furthermore, the presence of noises further impedes the successful completion of such tasks. Given these problems, this article proposes a neural-dynamics-based planning (NDP) scheme, which contains a neural-dynamics-based motion/force periodic motion control (MFPMC) strategy and a neural-dynamics-based fuzzy-parameter and dynamic neural network (FP-DNN) solver. Specifically, the MFPMC strategy enables the tracking of the desired task trajectory while exerting a necessary force on the object's surface. Subsequently, an improved FP-DNN solver, incorporating automatic adjustment on the convergent parameter, assists the MFPMC strategy in mitigating the effects of noises. Furthermore, theoretical analyses are conducted to prove the convergence and robustness of the proposed NDP scheme. Finally, comparative simulations under noise-free and noisy cases are conducted to showcase the effectiveness, robustness, and superiority of the proposed NDP scheme, while experiments on a robotic platform are performed to verify its feasibility.

In this study, a novel AgNbO3/Bi2MoO6/PANI double Z-scheme heterojunction photocatalyst was created via a solvothermal method, and the method investigates its photocatalytic degradation performance toward norfloxacin (NOR) and other antibiotics. When the content of AgNbO3is 5 wt % and the content of PANI is 1 wt %, the rate of degradation of AgNbO3/Bi2MoO6/PANI on NOR under visible light is 95.56%, the rate of removal of total organic carbon is ∼57.45%, and its pseudo-first-order reaction rate constant is 0.01878 min–1, which surpasses those of AgNbO3, Bi2MoO6, and AgNbO3/Bi2MoO6by factors of 14.22, 2.46, and 1.35, respectively. At the same time, the AgNbO3/Bi2MoO6/PANI photocatalyst still showed good stability after three cycles. The results demonstrated that the augmented photocatalytic performance of AgNbO3/Bi2MoO6/PANI can be attributed to the formation of a double Z-scheme heterojunction and the incorporation of PANI with excellent conductivity, resulting in the higher efficiency of migration of charge carriers while retaining strong redox ability. This work affords a high-efficiency and environmentally friendly reference for the development of a Bi2MoO6-based heterojunction photocatalyst and its application in the purification of antibiotics in water.

Conductive nanofiltration (CNF) membranes hold great promise for removing small organic pollutants from water through enhanced Donnan exclusion and electrocatalytic degradation. However, current CNF membranes face limitations in conductivity, structural stability, and nanochannel control strategies. This work addresses these challenges by introducing aniline-functionalized carbon nanotubes (NH2-CNTs) as an interlayer. NH2-CNTs enhance the dispersibility and adhesion of pristine carbon nanotubes, leading to a more conductive and stable composite nanofiltration membrane. The redesigned NH2-CNTs interlayered conductive nanofiltration (NICNF) membrane exhibits a 10-fold increase in conductivity and a high response degree (80%) with excellent cyclic stability, surpassing existing CNF membranes. The synergistic effects of enhanced Donnan exclusion, voltage switching, and electrocatalysis enable the NICNF membrane to achieve selective recovery of mixed dyes, 98.97% removal of residual wastewater toxicity, and a 5.2-fold increase in permeance compared to the commercial NF270 membrane. This research paves the way for next-generation multifunctional membranes capable of the efficient recovery and degradation of toxic organic pollutants in wastewater.

Purpose To estimate the effect of atropine eyedrops at different concentrations for myopia control in children.Methods We conducted a Bayesian random-effects network meta-analysis based on randomized controlled trials (RCT). Primary outcomes include changes in spherical equivalent error (SER) and changes in axial length (AL), mean difference (MD) together with 95% credible interval (CrI) were used to evaluate the efficacy.Results 28 RCTs (6608 children) were included in this review. Comparing ten atropine eyedrops (0.0025%, 0.005%, 0.01%, 0.02%, 0.025%, 0.05%, 0.1%, 0.25%, 0.5% and 1% concentrations) with the placebo, the MDs and 95%CrIs of changes in SER are −0.006 (−0.269, 0.256) D, 0.216 (−0.078, 0.508) D, 0.146 (0.094, 0.199) D, 0.167 (0.039, 0.297) D, 0.201 (0.064, 0.341) D, 0.344 (0.251, 0.440) D, 0.255 (0.114, 0.396) D, 0.296 (0.140, 0.452) D, 0.331 (0.215, 0.447) D, and 0.286 (0.195, 0.337) D, respectively. The MDs and 95%CrIs of changes in AL are −0.048 (−0.182, 0.085) mm, −0.078 (−0.222, 0.066) mm, −0.095 (−0.130, −0.060) mm, −0.096 (−0.183, −0.009) mm, −0.083 (−0.164, −0.004) mm, −0.114 (−0.176, −0.056) mm, −0.134 (−0.198, −0.032) mm, −0.174 (−0.315, −0.061) mm, −0.184 (−0.291, −0.073) mm, and −0.171 (−0.203, −0.097) mm, respectively.Whether evaluated by SER or AL, 1% concentration ranks first in efficacy, but the risk of photophobia is 17 times higher than 0.01% concentration.Conclusions 0.01% or higher concentration atropine eyedrops are effective for myopia control, while 0.0025% and 0.005% concentrations may not. As the concentration increases, the effect tends to increase, 1% concentration may have the strongest effect.

Enlightened by competitive and collaborative coordination behaviors widely observed in natural swarm systems, this work emphasizes these coordinating modes in multirobot systems and optimizes system stability along with resource utilization. Then, schemes are constructed to describe and model these two modes, where a $k$k-winner-take-all concept is introduced as the driving principle of multirobot competition. In addition, a distributed coordination approach is established to effectively handle the above schemes aided with optimality theory, which is developed by a fusion of a recurrent neural dynamics solver and a distributed solver. The former is a single-layer neural dynamics model with a simple structure, and the latter transforms the involved global information to a distributed type via consensus. Both of them are carried out in the discrete-time domain to fit the actual application. Finally, the convergence and stability of the proposed coordination approach are proved via theoretical analysis and further demonstrated through simulations and experiments.

Intrahepatic cholangiocarcinoma (ICC) ranks as the second most primary liver cancer that often goes unnoticed with a high mortality rate. Hepatectomy is the main treatment for ICC, but only 15% of patients are suitable for surgery. Despite advancements in therapeutic approaches, ICC has an unfavorable prognosis, largely due to lymph node metastasis (LNM) that is closely linked to the elevated recurrence rates. Consequently, the identification of precise and suitable techniques for the detection and staging of LNM assumes paramount importance for ICC therapy. While preoperative imaging plays a crucial role in ICC diagnosis, its efficacy in accurately diagnosing LNM remains unsatisfactory. The inclusion of lymph node dissection as part of the hepatectomy procedures is significant for the accurate pathological diagnosis of LNM, although it continues to be a topic of debate. The concept of sentinel lymph node in ICC has presented a novel and potentially valuable approach for diagnosing LNM. This review aims to explore the current state and prospects of LNM in ICC, offering a promising avenue for enhancing the clinical diagnosis and treatment of ICC to improve patient prognosis.

Two precast shear walls connected by sleeve and high-strength threaded rod connectors and a precast shear wall connected by grouting sleeves were tested under cyclic loading. The results show that, compared with the precast shear wall connected by the grouting sleeve, the ductility of the new type of shear wall increased by 16.7%, the energy dissipation capacity decreased by 3%, and the bearing capacity and stiffness degradation ability were similar. A finite element parameter analysis was conducted, studying the effects of the axial compression ratio and shear span ratio on the performance of the new type of precast shear wall. The results show that as the axial compression ratio increased from 0.1 to 0.3, the bearing capacity increased by 21.8%, and the peak load displacement decreased by 36.1%. As the shear span ratio increased from 1.09 to 1.82, the failure mode of the specimen showed a trend of changing from shear-compression failure to bending failure. In addition, based on the experimental results and simulation results, a calculation formula for the lateral bearing capacity of shear walls has been proposed, providing a basis for the design of new types of shear walls.

Cu2O-ZnO blended films with the structure of ZnO nanoparticles randomly distributed in the Cu2O matrix were synthesized using an in situ thermal oxidation method. The microstructure and growth mechanism of the prepared Cu2O-ZnO films were revealed depending on elemental mapping and thermogravimetric analysis. Benefiting from the unique structure, an effective built-in electric field formed at the numerous p–n interfaces, enabling efficient spatial separation of carriers. Consequently, photoelectrochemical evaluation of the Cu2O-ZnO film performed under chopped simulated AM 1.5G illumination exhibited a high photocurrent of −6.8 mA/cm2at 0 V vs RHE, which was 4.5 times higher than that of bare Cu2O film. The onset potential was also positively shifted from 0.62 V vs RHE to 0.74 V vs RHE. The introduction of ZnO also resulted in a high carrier density of 1.96 × 1018cm–3and reduced carrier transfer resistance in an electrolyte. To evaluate the potential of long-term application, a 20 nm TiO2protective layer was deposited by a low energy ion beam technique on the Cu2O-ZnO film; the attenuation of photocurrent was effectively suppressed due to the isolation of the electrolyte. The obtained Cu2O-ZnO blended films with rich p–n interfaces may serve as a good light absorber and carrier provider for use in photoelectric conversion and photoelectrochemical applications.

Nipah virus is a zoonotic paramyxovirus responsible for disease outbreaks with high fatality rates in south and southeast Asia. However, knowledge of the potential geographical extent and risk patterns of the virus is poor. We aimed to establish an integrated spatiotemporal and phylogenetic database of Nipah virus infections in humans and animals across south and southeast Asia.

Detection of early neoplastic lesions is crucial for improving the survival rates of patients with gastric cancer. Optical enhancement mode 2 is a new image‐enhanced endoscopic technique that offers bright images and can improve the visibility of neoplastic lesions. This study aimed to compare the detection of neoplastic lesions with optical enhancement mode 2 and white‐light imaging (WLI) in a high‐risk population. In this prospective multicenter randomized controlled trial, patients were randomly assigned to optical enhancement mode 2 or WLI groups. Detection of suspicious neoplastic lesions during the examinations was recorded, and pathological diagnoses served as the gold standard. A total of 1211 and 1219 individuals were included in the optical enhancement mode 2 and WLI groups, respectively. The detection rate of neoplastic lesions was significantly higher in the optical enhancement mode 2 group (5.1% vs. 1.9%; risk ratio, 2.656 [95% confidence interval, 1.630–4.330]; p< 0.001). The detection rate of neoplastic lesions with an atrophic gastritis background was significantly higher in the optical enhancement mode 2 group (8.6% vs. 2.6%, p< 0.001). The optical enhancement mode 2 group also had a higher detection rate among endoscopists with different experiences. Optical enhancement mode 2 was more effective than WLI for detecting neoplastic lesions in the stomach, and can serve as a new method for screening early gastric cancer in clinical practice. United States National Library of Medicine (https://www.clinicaltrials.gov), ID: NCT040720521.

Amyloid-like aggregation widely occurs during the processing and production of natural proteins, with evidence indicating its presence following the thermal processing of wheat gluten. However, significant gaps remain in understanding the underlying fibrillation mechanisms and structural polymorphisms. In this study, the amyloid-like aggregation behavior of wheat gluten and its components (glutenin and gliadin) during cooking was systematically analyzed through physicochemical assessment and structural characterization. The presence of amyloid-like fibrils (AFs) was confirmed using X-ray diffraction and Congo red staining, while Thioflavin T fluorescence revealed different patterns and rates of AFs growth among wheat gluten, glutenin, and gliadin. AFs in gliadin exhibited linear growth curves, while those in gluten and glutenin showed S-shaped curves, with the shortest lag phase and fastest growth rate (t1/2= 2.11 min) observed in glutenin. Molecular weight analyses revealed AFs primarily in the 10–15 kDa range, shifting to higher weights over time. Glutenin-derived AFs had the smallest ζ-potential value (−19.5 mV) and the most significant size increase post cooking (approximately 400 nm). AFs in gluten involve interchain reorganization, hydrophobic interactions, and conformational transitions, leading to additional cross β-sheets. Atomic force microscopy depicted varying fibril structures during cooking, notably longer, taller, and stiffer AFs from glutenin.

As essential primary producers, cyanobacteria play a major role in global carbon and nitrogen cycles. Though the influence of nanoplastics on the carbon metabolism of cyanobacteria is well-studied, little is known about how nanoplastics affect their nitrogen metabolism, especially under environmentally relevant nitrogen concentrations. Here, we show that nitrogen forms regulated growth inhibition, nitrogen consumption, and the synthesis and release of microcystin (MC) in Microcystis aeruginosaexposed to 10 µg/mL amino-modified polystyrene nanoplastics (PS-NH2) with a particle size of 50 nm under environmentally relevant nitrogen concentrations of nitrate, ammonium, and urea. We demonstrate that PS-NH2inhibit M. aeruginosadifferently in nitrate, urea, and ammonium, with inhibition rates of 51.87, 39.70, and 36.69%, respectively. It is caused through the differences in impairing cell membrane integrity, disrupting redox homeostasis, and varying nitrogen transport pathways under different nitrogen forms. M. aeruginosarespond to exposure of PS-NH2by utilizing additional nitrogen to boost the production of amino acids, thereby enhancing the synthesis of MC, extracellular polymeric substances, and membrane phospholipids. Our results found that the threat of nanoplastics on primary producers can be regulated by the nitrogen forms in freshwater ecosystems, contributing to a better understanding of nanoplastic risks under environmentally relevant conditions.

• Surface roughness of TBC-substrate significantly increases by dust accumulation. • A heat transfer model considering dust accumulation on TBC-substrate system is developed. • Surface roughening disturbs film cooling and significantly increases substrate temperature. • Localized overheating by dust accumulation induced surface roughening is responsible for substrate ablation. Thermal barrier coatings (TBC) on superalloy blade/vane enable an increase in turbine inlet temperature (TIT) and aero-engine efficiency. However, the deposition of dust on TBC significantly ablates superalloys and becomes a bottleneck for advanced engine development. This paper presents a comprehensive modeling approach, based on an experiment involving dust deposition on a TBC-coated substrate, aimed at understanding the fundamental cause of substrate ablation. The results show that a significant increase in surface roughness is obtained experimentally with dust accumulation. The temperature field within the TBC-substrate system is disturbed by surface roughening. Consequently, the film cooling is seriously destroyed. While the dust accumulated on the TBC surface also contributes to heat insulation by increasing the effective TBC thickness, localized overheating of the substrate occurs and then leads to ablation and premature failure of the substrate. This elucidation of the overheating mechanism could inform the development of strategies to resist dust accumulation in advanced aero engines. [Display omitted] [ABSTRACT FROM AUTHOR]

Colchicine reduces atherothrombotic cardiovascular events in coronary artery disease because of its anti-inflammatory effect. However, the effects of the other anti-inflammatory drugs in coronary artery disease remain unclear. This study included 132 patients aged 18–80 years who completed the planned percutaneous coronary interventions and were treated with aggressive secondary prevention strategies for 4 weeks. The subjects were randomly assigned to 1 of the following treatment groups for 4 weeks: (1) control: no additional intervention; (2) colchicine: 0.5 mg once a day; (3) tranilast: 0.1 g thrice a day; or (4) oridonin: 0.5 g thrice a day. The primary outcome was the percentage change in high-sensitivity C-reactive protein (hsCRP) levels at the end of 4 weeks. In total, 109 patients completed the study. The mean age was 58.33 years, 81 (74.31%) were male, and 28 (25.69%) were female. The percentage changes in hsCRP after 4 weeks of treatment were −11.62%, −48.28%, −21.60%, and −7.81%, in the control, colchicine, tranilast, and the oridonin groups, respectively. Compared with the control group, only the colchicine group showed significantly greater reduction in hsCRP levels (P= 0.022). In targeted proteomic analysis, proteins associated with neutrophil activation (azurocidin, myeloperoxidase, and myeloblastin), platelet aggregation (glycoprotein VI), and endothelial damage (galectin-3) were reduced with colchicine therapy. These results show that of 3 anti-inflammatory drugs only colchicine could reduce hsCRP in patients after percutaneous coronary interventions.

Neural networks have evolved into one of the most critical tools in the field of artificial intelligence. As a kind of shallow feedforward neural network, the broad learning system (BLS) uses a training process based on random and pseudoinverse methods, and it does not need to go through a complete training cycle to obtain new parameters when adding nodes. Instead, it performs rapid update iterations on the basis of existing parameters through a series of dynamic update algorithms, which enables BLS to combine high efficiency and accuracy flexibly. The training strategy of BLS is completely different from the existing mainstream neural network training strategy based on the gradient descent algorithm, and the superiority of the former has been proven in many experiments. This article applies an ingenious method of pseudoinversion to the weight updating process in BLS and employs it as an alternative strategy for the dynamic update algorithms in the original BLS. Theoretical analyses and numerical experiments demonstrate the efficiency and effectiveness of BLS aided with this method. The research presented in this article can be regarded as an extended study of the BLS theory, providing an innovative idea and direction for future research on BLS.

Automotive lightweighting has been a goal pursued by the automotive industry in recent years, and hot formed steel is the material support for achieving automotive safety lightweighting. In the current research process of hot formed steel in high-strength steel for automobiles, whether it is the QP steel series that obtains residual austenite through carbon partitioning, or the medium Mn steel that stabilizes austenite by adding Mn content, its strengthening and toughening mechanism is enhanced and plasticized through residual austenite. This study, represented by 20 Mn2Cr steel, systematically analyzed the microstructure of the material at different aging temperatures after continuous annealing at 830 °C using characterization methods such as XRD, EBSD, SEM, and TEM. The results showed that there was no obvious austenite peak in the XRD spectrum when the aging temperature was 270 °C; when the aging temperature is 300 °C and 360 °C, only at 2θ, a weak austenite peak appeared at an angle of about 74°, indicating that the residual austenite content in the experimental steel after continuous annealing treatment was very low, only 2.38 % and 4.09 %. This new generation of hot formed steel uses the strengthening and toughening mechanism of a small amount of fine high dislocation density ferrite, Cr carbide particles and refined ferrite flat noodles to achieve ultra-high strength and good plasticity, but uses the strengthening and toughening mechanism that combines high dislocation density ferrite and Cr carbide second-phase particles with refined ferrite flat noodles to improve the high performance of the new generation of automotive steel.

Abstract: Using a pulsed laser deposition (PLD) process on a ZnO target in an oxygen atmosphere, thin films of this material have been deposited on Si(111) substrates. An Nd: YAG pulsed laser with a wavelength of 1064 nm was used as the laser source. The influences of the deposition temperature, laser energy, annealing temperature and focus lens position on the crystallinity of ZnO films were analyzed by X-ray diffraction. The results show that the ZnO thin films obtained at the deposition temperature of 400°C and the laser energy of 250 mJ have the best crystalline quality in our experimental conditions. The ZnO thin films fabricated at substrate temperature 400°C were annealed at the temperatures from 400°C to 800°C in an atmosphere of N2. The results show that crystalline quality has been improved by annealing, the optimum temperature being 600°C. The position of the focusing lens has a strong influence on pulsed laser deposition of the ZnO thin films and the optimum position is 59.5 cm from the target surface for optics with a focal length of 70 cm.

When the structure information of a redundant manipulator is unknown, motion control methods that do not rely on its model are attractive. Due to the numerous advantages of model predictive control (MPC), such as the direct handling of constraints, this article proposes a model-free MPC algorithm for redundant manipulators with unknown structure information. In this article, a cerebellum-inspired model based on the echo state network (ESN) is employed to replace the kinematic model of the redundant manipulator, and an MPC algorithm based on the cerebellum model and neural dynamics (ND) approach is developed. Unlike existing studies, this work considers both performance optimization and system constraints of the redundant manipulator, and can achieve high-precision prediction and tracking by designing an online training algorithm for the cerebellum model. Furthermore, this article proposes an ND-based correction algorithm to modify the prediction model and an ND solver to solve the MPC scheme. Theoretical analyses confirm the convergence of both the ND-based correction algorithm and ND solver. Simulation and experimental results consistently demonstrate that the proposed cerebellum-inspired MPC (CIMPC) algorithm is effective and outperforms comparison algorithms in terms of tracking performance.

Nowadays, many dynamic systems show the characteristics of multiple-input-multiple-output and nonlinearity, which usually involve the solution of time-dependent nonlinear equations. This article focuses on the accurate solution of time-dependent nonlinear equations with periodic noises considered. In the hardware or numerical implementations of an actual system, inevitable internal disturbances or external factors caused by the changeable scene may greatly affect the accuracy of the solution. Most of the existing time-dependent anti-noise neural dynamic (ND) models can effectively suppress some constant noise, but they are not satisfactory when facing some periodic noise with high frequency. To solve this problem, a characteristics-capturing ND (CCND) model is designed, which considers the periodic noise from the perspective of harmonic expansion so as to effectively capture harmonic characteristics and eliminate them. Theoretical analysis proves the robustness of the CCND model with periodic noise disturbance considered. Moreover, numerical simulations and robotic experiments further confirm the effectiveness of the CCND model.

In response to the current issues of intelligent elevator face recognition models, such as poor real-time performance, low recognition accuracy, weak multi-target detection capabilities, and high missed detection rates, a binocular vision-based improved YOLO-v7 intelligent elevator face recognition model is proposed. Initially, according to the face recognition framework, distortion correction and MATLAB-based camera calibration methods are employed to model the binocular imaging system, ensuring the accuracy of image input. Then, the elevator face recognition model makes use of the YOLO-v7 architecture and uses the Scylla-IoU (SIoU) loss function to measure the actual and forecast bounding box distance precisely. This makes the model more accurate at identifying targets and can handle smaller targets with more ease. The Soft NMS (Non-Maximum Suppression) algorithm is applied to retain detection boxes with lower scores but containing valid targets, reducing the impact of occlusions on target detection and minimizing false positives and missed detections in multi-target recognition tasks. Moreover, the Bottle transformer network is substituted for the original Efficient layer aggregation networks in the model to enhance global attention and feature extraction capabilities, while maintaining computational efficiency and improving recognition accuracy. Finally, extensive experiments using an elevator face dataset are carried out to confirm the rationality and efficiency of the improved model. With Precision, Recall, mAP@0.5, and mAP@0.5–0.9 metrics of 94.18%, 96.23%, 95.18%, and 92.73%, respectively, the experimental findings show that the revised YOLO-v7 model achieves noteworthy performance. The face recognition model exhibits strong feature recognition capabilities, particularly excelling in multi-target detection with a low missed detection rate and high recognition accuracy. The detection time for a single image is 4.1 ms, demonstrating good real-time performance and meeting the requirements for efficient detection, making it suitable for practical application scenarios in intelligent elevators.

Remote center of motion (RCM) has become a rising research direction in the field of robotics. It means that a robot with a rod-shaped end-effector operates through a tiny hole on the surface. Thereinto, a key issue is the deviation of the RCM point's position, apart from the operating accuracy. In addition, considering that an RCM robotic system generally consists of a commercial robot and a specialized rod-shaped end-effector, there exist some errors in the structural information related to the attached end-effector. In this article, a remote center of cyclic motion scheme with a data-driven technology is proposed to control robots, of which end-effectors' structural parameters are inaccurate. Meanwhile, a recurrent neural network is proposed to figure out the scheme's solution, with the relevant theoretical analysis given. Furthermore, simulative and physical experiments on a FRANKA Panda robot with a rod-shaped end-effector are conducted to validate the control scheme's effectiveness distinctly.

Groundwater pollution risk assessment is a useful tool for groundwater pollution prevention and control. However, it is difficult to accurately quantify groundwater flow and contaminant fluxes in karst areas and different types of karst areas have different hydrogeological characteristics. Therefore, the assessment of groundwater pollution risk in karst areas must use different assessment indicator systems. This study developed a new methodology that modified the vulnerability assessment model PLEIK, determined pollutant fluxes considering hydrogeological conditions, and revised parameter weights using the random forest method. The resulting PLEIKD-RF model was used to assess the risk of groundwater contamination in the southwestern karst region and its validity was verified. The results showed that the groundwater pollution risk in the region was low, with 65.64% of the low and relatively low risk areas located in the middle and high mountainous regions. 11.81% of the high and relatively high risk areas were sporadically located in the western and central regions, which were mainly controlled by the distribution of the pollution sources and the karst development. The accuracy of the results of groundwater pollution risk assessment in the study area was 71.87% as verified by the horizontal difference method. The results of the sensitivity analysis indicated that accurate, detailed, and representative data on the protective layer, surface water-groundwater interactions, and pollution source loads would improve the accuracy of groundwater pollution risk zoning. This assessment method provided a reference for similar assessments and the results provide a basis for the protection and management of groundwater resources in the region.

Mutations accumulate in the genome of every cell of the body throughout life, causing cancer and other diseases1,2. Most mutations begin as nucleotide mismatches or damage in one of the two strands of the DNA before becoming double-strand mutations if unrepaired or misrepaired3,4. However, current DNA-sequencing technologies cannot accurately resolve these initial single-strand events. Here we develop a single-molecule, long-read sequencing method (Hairpin Duplex Enhanced Fidelity sequencing (HiDEF-seq)) that achieves single-molecule fidelity for base substitutions when present in either one or both DNA strands. HiDEF-seq also detects cytosine deamination—a common type of DNA damage—with single-molecule fidelity. We profiled 134 samples from diverse tissues, including from individuals with cancer predisposition syndromes, and derive from them single-strand mismatch and damage signatures. We find correspondences between these single-strand signatures and known double-strand mutational signatures, which resolves the identity of the initiating lesions. Tumours deficient in both mismatch repair and replicative polymerase proofreading show distinct single-strand mismatch patterns compared to samples that are deficient in only polymerase proofreading. We also define a single-strand damage signature for APOBEC3A. In the mitochondrial genome, our findings support a mutagenic mechanism occurring primarily during replication. As double-strand DNA mutations are only the end point of the mutation process, our approach to detect the initiating single-strand events at single-molecule resolution will enable studies of how mutations arise in a variety of contexts, especially in cancer and ageing.

In consideration of the limitation of the communication and the possibility that redundant robots might deliver information at different power levels, cases under weight-unbalanced directed graphs from the network topology perspective are in larger accordance with those in multiple redundant robot systems. By moving forward along this direction, a distributed controller is proposed in this article to handle circumstances of collaborative control of multiple redundant robots under weight-unbalanced directed graphs. This kind of control problem is modeled into generalized quadratic programming (QP) problems with equality and inequality constraints. Then, the above QP problems are solved by a proposed neural-dynamics-based method, whose stability and convergence are theoretically proved subsequently. Besides, several experimental examples are conducted, and related comparisons are provided to demonstrate the feasibility of the proposed controller.

Existing neural-network-based solutions for controlling a redundant robot are trapped by the relatively high computational complexity and the lack of the incorporation of orientation tracking. In order to remedy these two weaknesses, this article proposes a new multicriteria control scheme aided with a training-free dynamic neural network (DNN), which simultaneously considers the orientation-tracking constraint and physical constraints. Meanwhile, compared with existing methods for handling the same task, the proposed DNN solver is of low computational complexity. Theoretical analyses confirm that the proposed scheme based on the DNN solver globally and exponentially converges to the theoretical solution of the robotic motion generation. Besides, illustrative simulations and physical experiments based on a Franka Emika Panda manipulator demonstrate the validity and feasibility of the proposed scheme with the DNN solver.

Herein, facile room-temperature self-assembly and high-temperature pyrolysis strategy was successively conducted for in situ synthesizing novel TiO2/TiN@N-C heterostructure by using typical sandwich-like precursors (MXene/ZIF-8). Zero-dimensional (0D) TiO2, TiN and N-doped carbon nanoparticles were in situ formed and randomly anchored on the two-dimensional (2D) N-doped carbon substrate surface, making TiO2/TiN@N-C exhibit unique 0D/2D heterostructure. Relative to the extensively studied ZIF-8-derived N-doped carbon nanoparticles, TiO2/TiN@N-C heterostructure displayed greatly boosted electrochemical active specific surface. Benefiting from the enhanced electrochemical property of TiO2/TiN@N-C heterostructure, remarkable signal enhancement effect was achieved in terms of the oxidation of multiple hazardous substances, including clozapine, sunset yellow and benomyl. As a result, a novel electrochemical platform was constructed, the linear detection range were 10–1000 nM, 2.5–1250 nM, 10–1000 nM while the detection limits were evaluated to be 3.5 nM, 1.2 nM, 4.5 nM for clozapine, sunset yellow and benomyl, respectively. Besides, the practicability of the newly developed electrochemical method was verified by assessing the content of clozapine, sunset yellow and benomyl in real food samples.

A noise-tolerant $k$-winners-take-all ($k$-WTA) model injecting a summation item of error is constructed and applied to execute a $k$-WTA operation in this article. Then, theoretical analyses and proofs on the convergence of the noise-tolerant $k$-WTA model with different noise disturbances are carried out. In addition, numerical experiments synthesized by the noise-tolerant $k$-WTA model and other existing $k$-WTA models are provided to conduct comparisons to demonstrate the superior robustness and stability of the proposed model. Finally, tracking task experiments of a multirobot system with noise considered are operated to further illustrate the advantages of the proposed $k$-WTA model over the existing ones.

Bacterial infections and antibiotic abuse are a global threat to human health. In recent years, there has been a boom in research on antimicrobial agents with low toxicity and efficient nanomaterials. Boric acid-functionalized carbon dots (B-CDs) with negative surface charge were synthesized by the hydrothermal method. Covalent bonds were formed between the boric acid groups and the cis-diol groups of the polysaccharide in the bacterial cell wall, and numerous B-CDs were trapped on the bacterial surface. In the experiments of antibacterial activity, B-CDs presented strong bactericidal activity against Escherichia coli(E. coli) with a minimum bactericidal concentration of 12.5 μg/mL. The antibacterial mechanism suggested that B-CDs entered the cell interior by diffusion and posed significant damage to the double helix structure of E. coliDNA. Furthermore, B-CDs exhibited low toxicity. The results demonstrated that the novel antimicrobial B-CDs not only fought against E. coliinfection and antibiotic misuse but also provided new ideas for safe and effective antimicrobial agents of carbon nanomaterials.

A systematic study has been made on a Cu-40%Zn alloy treated by an electric current pulse (ECP) and by the examination of the microstructure and the crystallographic features of both the parent and the product phases. The β precipitates under ECP show a Kurdjumov Sachs Orientation Relation (K-S OR) in the vicinity of the grain boundaries (GBs), but a Nishiyama Wasserman (N-W) OR within the grains. Along the GBs the {111}α /&lt;110>α dislocation arrays were spotted, whereas the {111}α /&lt;112>α stacking faults were observed in the grain interiors. A closer examination of the lattice strain required for the phase transformation revealed that the maximum lattice deformation under the K-S OR is a shear on the {111}α plane in the &lt;110>α direction. The dislocations arrays existing along the GBs offer the pre-strain that favors the precipitation of β particles obeying the K-S OR. Oppositely, the stacking faults within the grains provide pre-stains for the formation of the β precipitates respecting the N-W OR. This study sheds some light on the mechanisms by which crystal defects initiate phase transformation in a Cu-40%Zn alloy.

In recent years, dynamic overdetermined systems have sprung up and been broadly employed for handling different problems in real time. This article makes improvements in this direction by proposing, investigating, and analyzing an integral neural solution (INS) to solve the dynamic overdetermined system. Notably, an error function is constructed in the first place. Then, aided with a generated neural dynamic framework, an INS model is devised, which exploits not only saturated or even noncontinuous projection functions but also possesses noise-suppression ability with integral enhancement information. Theoretical analyses and computer simulations manifest that the proposed INS model is able to acquire the least-squares (LS) solution with superior convergence property, contrasted with the existing methods, e.g., zeroing neural network (ZNN). On top of that, applications to data fitting as well as parameters estimation ulteriorly validate the feasibility and effectiveness of the proposed INS model for handling the dynamic overdetermined system.