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Abstract The current parallel ankle rehabilitation robot (ARR) suffers from the problem of difficult real-time alignment of the human-robot joint center of rotation, which may lead to secondary injuries to the patient. This study investigates type synthesis of a parallel self-alignment ankle rehabilitation robot (PSAARR) based on the kinematic characteristics of ankle joint rotation center drift from the perspective of introducing "suitable passive degrees of freedom (DOF)" with a suitable number and form. First, the self-alignment principle of parallel ARR was proposed by deriving conditions for transforming a human-robot closed chain (HRCC) formed by an ARR and human body into a kinematic suitable constrained system and introducing conditions of "decoupled" and "less limb". Second, the relationship between the self-alignment principle and actuation wrenches (twists) of PSAARR was analyzed with the velocity Jacobian matrix as a "bridge". Subsequently, the type synthesis conditions of PSAARR were proposed. Third, a PSAARR synthesis method was proposed based on the screw theory and type of PSAARR synthesis conducted. Finally, an HRCC kinematic model was established to verify the self-alignment capability of the PSAARR. In this study, 93 types of PSAARR limb structures were synthesized and the self-alignment capability of a human-robot joint axis was verified through kinematic analysis, which provides a theoretical basis for the design of such an ARR.

When applying continuous motion estimation (CME) model based on sEMG to human-robot system, it is inevitable to encounter scenarios in which the motions performed by the user are different from the motions in the training stage of the model. It has been demonstrated that the prediction accuracy of the currently effective approaches on untrained motions will be significantly reduced. Therefore, we proposed a novel CME method by introducing muscle synergy as feature to achieve better prediction accuracy on untrained motion tasks. Specifically, deep non-smooth NMF (Deep-nsNMF) was firstly introduced on synergy extraction to improve the efficiency of synergy decomposition. After obtaining the activation primitives from various training motions, we proposed a redundancy classification algorithm (RC) to identify shared and task-specific synergies, optimizing the original redundancy segmentation algorithm (RS). NARX neural network was set as the regression model for training. Finally, the model was tested on prediction tasks of eight untrained motions. The prediction accuracy of the proposed method was found to perform better than using time-domain feature as input of the network. Through Deep-nsNMF with RS, the highest accuracy reached 99.7%. Deep-nsNMF with RC performed similarly well and its stability was relatively higher among different motions and subjects. Limitation of the approach is that the problem of positive correlation between the prediction error and the absolute value of real angle remains to be further addressed. Generally, this research demonstrates the potential for CME models to perform well in complex scenarios, providing the feasibility of dedicating CME to real-world applications.

As the proper noun for geographical entities, place names provide an intuitive way to identify and access specific geographic locations, playing a key role in semantic expression and spatial retrieval. However, existing research has insufficiently explored the spatio-temporal derivation relationships of place names, failing to fully utilize these relationships to enhance the connectivity between place names and improve spatial retrieval capabilities. Therefore, this paper conducts research on the spatio-temporal derivation relationships of place names, defines them in a standardized manner, clarifies the boundary conditions and identification methods, and then constructs a spatio-temporal derivation network of place names for expression and uses this network to carry out reasoning research on spatial adjacency relationships. Experiments and results showed that using the theory and methods of this paper to identify the spatio-temporal derivation relationships of Canadian place names achieves an accuracy rate of 98.5% and a recall rate of 93.4%, and the reasoning results can effectively improve the accuracy of query results. The research enriches the theoretical framework of spatio-temporal derivation relationships of place names, solves the current problems of unclear definition and inability to automatically identify spatio-temporal derivation relationships, and provides new perspectives and tools for the application practice in the field of geographical information science.

Climate change is causing permafrost in the Qinghai–Tibet Plateau to degrade, triggering thermokarst hazards and impacting the environment. Despite their ecological importance, the distribution and risks of thermokarst lakes are not well understood due to complex influencing factors. In this study, we introduced a new interpretable ensemble learning method designed to improve the global and local interpretation of susceptibility assessments for thermokarst lakes. Our primary aim was to offer scientific support for precisely evaluating areas prone to thermokarst lake formation. In the thermokarst lake susceptibility assessment, we identified ten conditioning factors related to the formation and distribution of thermokarst lakes. In this highly accurate stacking model, the primary learning units were the random forest (RF), extremely randomized trees (EXTs), extreme gradient boosting (XGBoost), and categorical boosting (CatBoost) algorithms. Meanwhile, gradient boosted decision trees (GBDTs) were employed as the secondary learning unit. Based on the stacking model, we assessed thermokarst lake susceptibility and validated accuracy through six evaluation indices. We examined the interpretability of the stacking model using three interpretation methods: accumulated local effects (ALE), local interpretable model-agnostic explanations (LIME), and Shapley additive explanations (SHAP). The results showed that the ensemble learning stacking model demonstrated superior performance and the highest prediction accuracy. Approximately 91.20% of the total thermokarst hazard points fell within the high and very high susceptible areas, encompassing 20.08% of the permafrost expanse in the QTP. The conclusive findings revealed that slope, elevation, the topographic wetness index (TWI), and precipitation were the primary factors influencing the assessment of thermokarst lake susceptibility. This comprehensive analysis extends to the broader impacts of thermokarst hazards, with the identified high and very high susceptibility zones affecting significant stretches of railway and highway infrastructure, substantial soil organic carbon reserves, and vast alpine grasslands. This interpretable ensemble learning model, which exhibits high accuracy, offers substantial practical significance for project route selection, construction, and operation in the QTP.

With the development of computer technology, landslide recognition based on machine learning methods has been widely applied in geological disaster management and research. However, in landslide identification, the problems of an insufficient number of samples and an imbalance of samples are often ignored; that is, landslide samples are much smaller than non-landslide samples. In order to solve this problem, taking the main urban area of Lanzhou City as an example, this paper proposes to construct a semi-supervised generated countermeasure network (SSGAN) model, which aims to achieve high performance with a limited number of labeled samples for precise landslide identification, and to help prevent and reduce the harm caused by disasters. In order to express the environmental characteristics of landslide development and the optical texture features of landslide occurrence, the study constructs three sets of samples to represent landslide features, including a landslide influencing factor sample set, a Sentinel-2A optical remote sensing sample set, a joint influencing factor and Sentinel-2A sample set. The three kinds of sample sets are transferred to SSGAN for training to form a comparative study. The results show that the joint sample set has excellent feature results in discriminator and generator. Through the experimental comparison, the model proposed in this paper is compared with the model without semi-supervised generated confrontation training. The experimental results show that the proposed method is better than the unsupervised adversarial learning model in terms of accuracy, F1 score, Kappa coefficient, and MIoU. A total of 160 landslides have been identified in the study area, with a total area of 10.328 km2, with an accuracy rate of 83%. Therefore, the generated results are accurate and reliable, and show that SSGAN can better distinguish landslides from non-landslides in an image, under the condition of obtaining a large number of unmarked environmental features; enhance the effect of landslide classification in complex geographical environment; and then put forward effective suggestions for the prevention and control of landslides and geological disasters in the main urban area of Lanzhou.

Abstract The purpose of this paper was to assess the value of ultrasonography in the prognosis of diffuse large b-cell lymphoma (DLBCL) by developing a new prognostic model. One hundred and eleven DLBCL patients with complete clinical information and ultrasound findings were enrolled in our study. Univariate and multivariate regression analyses were used to identify independent risk factors for progression-free survival (PFS) and overall survival (OS). Receiver operator characteristic (ROC) curves were plotted and the corresponding area under the curve (AUC) was calculated to assess the accuracy of the international prognostic index (IPI) and new model in DLBCL risk stratification. The results suggested that hilum loss and ineffective treatment were independent risk variables for both PFS and OS in DLBCL patients. Additionally, the new model that added hilum loss and ineffective treatment to IPI had a better AUC for PFS and OS than IPI alone (AUC: 0.90, 0.88, and 0.82 vs. 0.71, 0.74, and 0.68 for 1-, 3-, and 5-year PFS, respectively; AUC: 0.92, 0.85 and 0.86 vs. 0.71, 0.75 and 0.76, for 1-, 3-, and 5-year OS, respectively). The model based on ultrasound images could better suggest PFS and OS of DLBCL, allowing for better risk stratification.

Rheumatoid arthritis is a chronic autoimmune disease, with the features of recurrent chronic inflammation of synovial tissue, destruction of cartilage, and bone erosion, which further affects joints tissue, organs, and systems, and eventually leads to irreversible joint deformities and body dysfunction. Therapeutic drugs for rheumatoid arthritis mainly reduce inflammation through regulating inflammatory factors. Transdermal administration is gradually being applied to the treatment of rheumatoid arthritis, which can allow the drug to overcome the skin stratum corneum barrier, reduce gastrointestinal side effects, and avoid the first-pass effect, thus improving bioavailability and relieving inflammation. This paper reviewed the latest research progress of transdermal drug delivery in the treatment of rheumatoid arthritis, and discussed in detail the dosage forms such as gel (microemulsion gel, nanoemulsion gel, nanomicelle gel, sanaplastic nano-vesiclegel, ethosomal gel, transfersomal gel, nanoparticles gel), patch, drug microneedles, nanostructured lipid carrier, transfersomes, lyotropic liquid crystal, and drug loaded electrospinning nanofibers, which provide inspiration for the rich dosage forms of transdermal drug delivery systems for rheumatoid arthritis.

The Rubik’s Cube mechanism (RCM) is a kind of reconfigurable mechanism with multiple characteristics such as multiple configurations, variable topology, strong coupling, and reconfigurability. Crossover research on the RCM with mathematics, chemistry, cryptography, and other disciplines has led to important breakthroughs and progress. It is obvious that the invention and creation of a new RCM can provide important ideological inspiration and theoretical guidance for the accelerated iterative updating of Rubik’s Cube products and the expansion of their applications. This paper investigates the type synthesis method for a regular hexagonal prism (RHP) RCM (RHPRCM). Through analysis of the reconfigurable movement process of the RCM, two mechanism factors are abstracted, a type synthesis process for the RHPRCM is proposed, a symmetry layout method for the RCM’s revolute axis based on the RHP space polyhedron is proposed, and an analysis method for the intersection of the revolute pair contact surfaces (RPCSs) based on the adjacency matrix is proposed. Taking a revolute axis passing through the center of an RHP and having only one RPCS for each revolute axis as an example, an RHPRCM with different topological structures is synthesized. The relevant research in this paper can provide methodological guidance for the synthesis of other spatial RCMs.

Abstract Fine needle aspiration biopsy is a crucial method for preoperative diagnosis of thyroid nodules. However, thyroid nodules classified as Bethesda categories III–V cannot obtain definite cytological results. Our aim was to study the diagnostic value of thyroid imaging reporting and data system combined with BRAFV600E mutation analysis in Bethesda categories III–V thyroid nodules, so as to provide more precise direction for the follow-up treatments. A total of 174 Bethesda categories III–V thyroid nodules performed TIRADS and BRAFV600E mutation analysis were included in the study. We retrospectively analyzed the ultrasound features as well as the results of BRAFV600E mutation of the 174 thyroid nodules. In the multiple regression analysis models, ultrasound features including lobulated or irregular margin, punctate echogenic foci, and shape with taller-than-wide were statistically significant in malignant nodules (p

Traditional mobile robots have limitations in obstacle-crossing ability, motion stability and load-bearing capacity in complex environments, which make them difficult to be applied on a large scale. Based on the Rubik’s Cube mechanism (RCM) with strong coupling and variable topology, a polymorphous wheel-legged mobile robot (WLMR) is proposed. Aiming at the problems of the classical three-order RCM, such as small internal space, difficult processing and demanding precision, a new type of chute third-order RCM is designed, and its mechanical characteristics analysis and feasibility analysis are carried out. What’s more, a driving configuration analysis method is established according to different driving configuration relationships, and the configuration of WLMR is determined by the configuration stability analysis. Then, a WLMR with polymorphism is designed, and gait planning and gait stability analysis are conducted. Eventually, the co-simulation and prototype experiments are performed to verify the efficiency of the WLMR’s straight motion, in-situ rotation, obstacle-crossing and morphology transformation in complex environments. This research not only provides a reference for the design of polymorphous mobile robots, but also opens up ideas for the application of the RCM in daily production and life.

ObjectivesTo develop and validate a nomogram to predict the overall survival (OS) of patients with primary nodal diffuse large B-cell lymphoma(N-DLBCL) based on radiomic features and clinical features.Materials and methodsA retrospective analysis was performed on 145 patients confirmed with N-DLBCL and they were randomly assigned to training set(n=78), internal validation set(n=33), external validation set(n=34). First, a clinical model (model 1) was established according to clinical features and ultrasound (US) results. Then, based on the radiomics features extracted from conventional ultrasound images, a radiomic signature was constructed (model 2), and the radiomics score (Rad-Score) was calculated. Finally, a comprehensive model was established (model 3) combined with Rad-score and clinical features. Receiver operating characteristic (ROC) curves were employed to evaluate the performance of model 1, model 2 and model 3. Based on model 3, we plotted a nomogram. Calibration curves were used to test the effectiveness of the nomogram, and decision curve analysis (DCA) was used to asset the nomogram in clinical use.ResultsAccording to multivariate analysis, 3 clinical features and Rad-score were finally selected to construct the model 3, which showed better predictive value for OS in patients with N-DLBCL than mode 1 and model 2 in training (AUC,0. 891 vs. 0.779 vs.0.756), internal validation (AUC, 0.868 vs. 0.713, vs.0.756) and external validation (AUC, 914 vs. 0.866, vs.0.789) sets. Decision curve analysis demonstrated that the nomogram based on model 3 was more clinically useful than the other two models.ConclusionThe developed nomogram is a useful tool for precisely analyzing the prognosis of N-DLBCL patients, which could help clinicians in making personalized survival predictions and assessing individualized clinical options.

The core/shell structure nanofiber membrane loaded with curcumin and silver nanoparticles was prepared by coaxial electrospinning technology, which is a high-efficiency combined antibacterial material composed of photodynamic antibacterial agent and metal nanoparticle. As a photosensitizer, curcumin could generate singlet oxygen under laser irradiation. Silver nanoparticles have antibacterial properties, and could also enhance the singlet oxygen production of curcumin due to the metal-enhanced singlet oxygen effect, thereby producing a synergistic antibacterial effect. Compared with the antibacterial rate of uniaxial curcumin fiber membrane (45.65%) and uniaxial silver nanoparticle-loaded fiber membrane (66.96%), the antibacterial rate of curcumin@Ag core/shell structure fiber membrane against Staphylococcus aureus is as high as 93.04%. In addition, the antibacterial experiments show that the core/shell fiber membrane also has excellent antibacterial effects on Escherichia coli.

Ankle injury and dysfunction are always accompanied by abnormal peripheral tissues. In order to achieve a more comprehensive rehabilitation training, this article firstly studies the movement characteristics of the surrounding muscles of the ankle-foot. The human musculoskeletal model in OpenSim is used for human exercise experiment. And the relationship between different muscles around the ankle-foot and the direction of the ankle-foot movement are measured respectively, so as to formulate ankle-foot static/dynamic rehabilitation strategies related to the auxiliary training of the ankle-foot muscle. In order to implement the established rehabilitation exercise strategy and to reduce the control difficulty of rehabilitation robot, a new type of decoupling series-parallel mechanism with three rotations and one movement is proposed. After analyzing the degree of freedom and the positive kinematics solution, it is proved that it is completely decoupled in kinematics, can realize the independence of motion control, and there is no singular space. Then, under the premise of meeting the space required for the implementation of the rehabilitation strategy, the proposed new ankle-foot rehabilitation robot is optimized in terms of structure and component size, which improves its space utilization and optimizes the overall mechanical performance. Finally, the dynamic simulation experiment is carried out with the dynamic rehabilitation strategy as the goal, which proves that the robot can realize the normal gait simulation movement in sitting posture, and the motion control of the robot is independent of each other and shows good dynamic performance. Through investigation, the ankle-foot rehabilitation robot designed in this article is cost-effective.

Ionic liquids (ILs) have received considerable attention as a promising green solvent for extracting metal ions from aqueous solutions. However, the recycling of ILs remains difficult and challenging because of the leaching of ILs, which is caused by the ion exchange extraction mechanism and hydrolysis of ILs in acidic aqueous conditions. In this study, a series of imidazolium-based ILs were confined in a metal–organic framework (MOF) material (UiO-66) to overcome the limitations when used in solvent extraction. The effect of the various anions and cations of the ILs on the adsorption ability of AuCl4− was studied, and 1-hexyl-3-methylimidazole tetrafluoroborate ([HMIm]+[BF4]−@UiO-66) was used for the construction of a stable composite. The adsorption properties and mechanism of [HMIm]+[BF4]−@UiO-66 for Au(III) adsorption were also studied. The concentrations of tetrafluoroborate ([BF4]−) in the aqueous phase after Au(III) adsorption by [HMIm]+[BF4]−@UiO-66 and liquid–liquid extraction by [HMIm]+[BF4]− IL were 0.122 mg/L and 18040 mg/L, respectively. The results reveal that Au(III) coordinated with the N-containing functional groups, while [BF4]− was effectively confined in UiO-66, instead of undergoing anion exchange in liquid–liquid extraction. Electrostatic interactions and the reduction of Au(III) to Au(0) were also important factors determining the adsorption ability of Au(III). [HMIm]+[BF4]−@UiO-66 could be easily regenerated and reused for three cycles without any significant drop in the adsorption capacity.

Ligands plays an important role in the extraction procedures for the determination of cadmium in rice samples by using flame atomic absorption spectrometry (FAAS). In the present study, comparative evaluation of 10 commercially available ligands for formation of Cd(II)-ligand complex and determination of cadmium in rice samples by ultrasound-assisted dispersive liquid–liquid microextraction (UADLLME) combined with FAAS was developed. Sodium diethyldithiocarbamate (DDTC) provided a high distribution coefficient as well as a good absorbance signal, therefore DDTC was used as a ligand in UADLLME. A low density and less toxic solvent, 1-heptanol, was used as the extraction solvent and ethanol was used as the disperser solvent. In addition, the experimental conditions of UADLLME were optimized in standard solution first and then applied in rice, such as the type and volume of extractant and dispersant, pH, extraction time, and temperature. Under the optimal experimental conditions, the detection limit (3σ) was 0.69 μg/L for Cd(II). The proposed method was applied for the determination of Cd(II) in three different rice samples (polished rice, brown rice, and glutinous rice), the recovery test was carried out, and the results ranged between 96.7 to 113.6%. The proposed method has the advantages of simplicity, low cost, and accurate and was successfully applied to analyze Cd(II) in rice.

Background/Aims: Blepharophimosis-ptosis-epicanthus inversus syndrome (BPES) is a rare autosomal dominant disease caused by FOXL2 gene mutations, and it is clinically characterized by an eyelid malformation associated (type I) or not (type II) with premature ovarian failure (POF). Functional study of novel mutations is especially critical for female patients, as it may allow the prediction of infertility and early planning of an appropriate therapy. Methods: A clinical and molecular genetic investigation was performed in all members of a Chinese family with BPES. Genomic DNA was extracted, and the FOXL2 coding region was sequenced. Subcellular localization was performed by confocal microscopy. Transactivation studies were performed by real-time PCR, dual luciferase reporter assays and electrophoretic mobility shift assays. Results: A novel deletion mutation (C.634_641 del, CCCATGC) between the forkhead domain and the polyalanine domain was found, resulting in a frameshift mutation and a truncated protein. Functional studies showed a strong cytoplasmic mislocalization and abnormal transactivation activity, implying a type I kind mutation with a large chance of infertility. Conclusion: This study identifies that this mutation indicates the probability of developing into POF and shows the importance and necessity of early recognition of BPES type through mutation testing for female patients. Prompt personalized therapy and follow-up is of great clinical significance for female patients carrying this kind of mutation.

With the growing application of high-k dielectrics, the interface between HfO2 and Al2O3 play a crucial role in CTM devices. To clearly understand the interaction of the HfO-AlO interface at the atomic and electronic scale, the bonding feature, electronic properties and charge localized character of c- HfO2 (0 0 1)/α-Al2O3 (1 -1 0 2) interface has been investigated by first principle calculations. The c- HfO2 (0 0 1)/α-Al2O3 (1 -1 0 2) interface has adhesive energy about -1.754 J/m2, suggesting that this interface can exist stably. Through analysis of Bader charge and charge density difference, the intrinsic interfacial gap states are mainly originated from the OII and OIII types oxygen atoms at the interface, and only OIII type oxygen atoms can localized electrons effectively and are provided with good reliability during P/E cycles, which theoretically validate the experimental results that HfO2/Al2O3 multi-layered charge trapping layer can generate more effective traps in memory device. Furthermore, the influence of interfacial gap states during P/E cycles in the defective interface system have also been studied, and the results imply that defective system displays the degradation on the reliability during P/E cycles, while, the charge localized ability of interfacial states is stronger than intrinsic oxygen vacancy in the trapping layer. Besides, these charge localized characters are further explained by the analysis of the density of states correspondingly. In sum, our results compare well with similar experimental observations in other literatures, and the study of the interfacial gap states in this work would facilitate further development of interface passivation.

Six different interfaces namely, armchair Graphene (aGNR), zigzag Graphene (zGNR), and surface defect zigzag Graphene (zGNR1) nanoribbons with uni- and bi-laminar -oriented NiO were studied. First, the Mulliken mean and difference populations, the interface energy, and the interface adhesion energy were calculated by the Cambridge sequential total energy package (CASTEP). The aGNR/NiO interface showed higher interface adhesion energy and Mulliken population mean as compared to the other interface structures (i.e., aGNR/NiO was more compact than the rest of interfaces). Moreover, the lowest interface energy and Mulliken difference population values along with the negligible aberration state clearly revealed aGNR/NiO to be the best interface among those studied herein. Subsequently, the current–voltage (I–V) curves indicate the aGNR/NiO/aGNR device presents memory effect while tracing the path back in the current data, but not switching between positive and negative voltages due to the device unipolar behavior. The mechanism of resistive switching is demonstrated by performing density functional tight binding and much more (DFTB+) dynamics.

This paper presents a solution to the issue of high frictional resistance between the chain plate and guide rail in cigarette conveying systems, which can result in chain plate failure. The proposed solution is an intelligent drag reduction system that incorporates lubrication and automatic dust removal. The system includes a conveyor chain plate, supporting roller assembly, air knife dust removal assembly, chain plate lubrication assembly, and controller. The controller manages the dust removal assembly's air source output and the lubrication of the chain plate assembly through the opening and closing of an electromagnetic valve, which controls the oil output to decrease resistance. Experimental testing of the system shows that it effectively reduces the chain plate's frictional resistance in the cigarette conveying line, resulting in improved system efficiency. [ABSTRACT FROM AUTHOR]

Currently, the efficacy of albumin-bound paclitaxel (PTX@Alb) is still limited due to the impaired PTX@Alb accumulation in tumors partly mediated by the dense collagen distribution. Meanwhile, acquired immune resistance always occurs due to the enhanced programmed cell death-ligand 1 (PD-L1) expression after PTX@Alb treatment, which then leads to immune tolerance. To fill these gaps, we newly revealed that tamoxifen (TAM), a clinically widely used adjuvant therapy for breast cancer with mitochondrial metabolism blockade capacity, could also be used as a novel effective PD-L1 and TGF-β dual-inhibitor via inducing the phosphorylation of adenosine 5ʹ-monophosphate-activated protein kinase (AMPK) protein. Following this, to obtain a more significant effect, TPP-TAM was prepared by conjugating mitochondria-targeted triphenylphosphine (TPP) with TAM, which then further self-assembled with albumin (Alb) to form TPP-TAM@Alb nanoparticles. By doing this, TPP-TAM@Alb nanoparticles effectively decreased the expression of collagen in vitro, which then led to the enhanced accumulation of PTX@Alb in 4T1 tumors. Besides, TPP-TAM@Alb also effectively decreased the expression of PD-L1 and TGF-β in tumors to better sensitize PTX@Alb-mediated chemo-immunotherapy by enhancing T cell infiltration. All in all, we newly put forward a novel mitochondrial metabolism blockade strategy to inhibit PTX@Alb-resistant tumors, further supporting its better clinical application.