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Lateral corrugated tubes (LCTs) with a sinusoidal cross-section have a wider design space than the ordinary circular tube, which is suitable for design of high performance energy absorbers. To investigate the influence of its structural parameters on crashworthiness, systematic parametric study of the LCTs has been carried out via finite element methods. Results show that LCTs have more deformation modes and more complicated deformations than circular tubes. The amplitude A and the number of corrugations N play a decisive role in the deformation mode and energy absorption. The analysis of different LCTs shows that the specific energy absorption (SEA) of LCTs with (N = 5, A = 3.5 mm) and (N = 7, A = 2.5 mm) increased by 33.68% and 34.97% compared with that of the circular tube, respectively. By changing the basic nominal radius (R0), the SEAs of the LCTs with (N = 6, R0 = 20 mm) and (N = 8, R0 = 18 mm) are 38.33% and 42.66% higher than those of the circular tubes, respectively. The effect of wall thickness on the energy absorption of LCTs was developed. Moreover, the deformation modes of LCTs will be improved by adding ribs inside the structures, significantly increasing its energy absorption. The sinusoidal function is introduced into the structural energy-absorbing design, and structural parameters such as A, N, and R0 can be configured to realize an adjustable and controllable design mechanism. The structural configuration and the design method of LCTs provide a practical reference for the design of energy absorbers. [ABSTRACT FROM AUTHOR]

Background: Distinguishing between viral encephalitis (VE) and autoimmune limbic encephalitis (ALE) presents a clinical challenge due to the overlap in symptoms. We aimed to develop and validate a diagnostic prediction model to differentiate VE and ALE. Methods: A prospective observational multicentre cohort study, which continuously enrolled patients diagnosed with either ALE or VE from October 2011 to April 2023. The demographic data, clinical features, and laboratory test results were collected and subjected to logistic regression analyses. The model was displayed as a web-based nomogram and then modified into a scored prediction tool. Model performance was assessed in both derivation and external validation cohorts. Results: A total of 2423 individuals were recruited, and 1001 (496 VE, 505 ALE) patients were included. Based on the derivation cohort (389 VE, 388 ALE), the model was developed with eight variables including age at onset, acuity, fever, headache, nausea/vomiting, psychiatric or memory complaints, status epilepticus, and CSF white blood cell count. The model showed good discrimination and calibration in both derivation (AUC 0.890; 0.868–0.913) and external validation (107 VE, 117 ALE, AUC 0.872; 0.827–0.917) cohorts. The scored prediction tool had a total point that ranged from − 4 to 10 also showing good discrimination and calibration in both derivation (AUC 0.885, 0.863–0.908) and external validation (AUC 0.868, 0.823–0.913) cohorts. Conclusions: The prediction model provides a reliable and user-friendly tool for differentiating between the VE and ALE, which would benefit early diagnosis and appropriate treatment and alleviate economic burdens on both patients and society. [ABSTRACT FROM AUTHOR]

Purpose: To investigate fluid absorption and its influencing factors during flexible ureteroscopy with intelligent control of renal pelvic pressure (RPP). Methods: A total of 80 patients with upper urinary tract calculi underwent flexible ureteroscopy with intelligent control of RPP by pressure-measuring ureteral access sheath and were randomly divided into four groups. The RPP of Groups A, B, and C were set at – 5, 0 and 5 mmHg, respectively. Conventional flexible ureteroscopy with uncontrolled pressure served as control Group D. The perfusion flow rate was set at 100 ml/min in the four groups, with 20 patients in each group. The fluid absorption was measured by 1% ethanol every 10 min. Operation time, stone-free rate, and complications were recorded. Result: Seventy-three patients were finally included in the RCT. The general and preoperative data of the patients were comparable between the groups. The fluid absorption of Groups A, B, and C was significantly less than that of Group D (P < 0.01). Fluid absorption and operation time were positively correlated, and the correlation coefficients R were 0.864, 0.896, 0.918, and 0.947, respectively (P < 0.01). The fluid absorption of patients with vomiting, fever and ureteral injury was greater than that of patients without complications in the four groups (P < 0.01). In different groups, fluid absorption was greater in patients with ureteral injury Post-Ureteroscopic Lesion Scale (PULS) 1–3 than in noninjured patients (P < 0.01). Conclusion: Flexible ureteroscopy with intelligent control of RPP effectively reduces the absorption of perfusion fluid. Operation time and ureteral injury are also key factors affecting perfusion fluid absorption. Registration number and date: NCT05201599; August 11, 2021. [ABSTRACT FROM AUTHOR]

This study explores the design and performance of axisymmetric hexachiral honeycombs, utilizing the hexachiral honeycomb framework and axisymmetric design method. Four axisymmetric hexachiral honeycombs with distinct arrangements were developed: left–right symmetry hexachiral honeycomb (LSHH), up–down symmetry hexachiral honeycomb (USHH), central symmetry hexachiral honeycomb (CSHH), and subunits symmetry hexachiral honeycomb (SSHH). The deformation patterns and compression behaviors of these honeycombs were comprehensively examined through experimental and numerical simulations, and comparisons were made with a non-symmetric hexachiral honeycomb (NHH). The results indicate that symmetrically designed honeycombs exhibit a larger mean plateau stress than the asymmetrically designed the NHH during low-velocity impacts. The study further discusses deformation patterns, specific energy absorption, and the negative Poisson's ratio effect across the five honeycombs under different parameters. Notably, symmetrically designed honeycombs demonstrate superior specific energy absorption, and the negative Poisson's ratio effect becomes evident at an impact velocity of 10 m/s. However, the advantages of axisymmetric honeycombs diminish at higher impact velocities of 50 m/s and 100 m/s. The Poisson's ratio effects of symmetric honeycombs weaken with an increase in the circular ligament r of the honeycomb. Additionally, the study identifies that platform stress and SEA increase for honeycombs with horizontal cell numbers greater than 6. [ABSTRACT FROM AUTHOR]

To improve the impact resistance of the honeycomb, a self-similar spider-web hierarchical structure is introduced by adding smaller hexagons to the centers of the primitive cells in the underlying hexagonal network and connecting the vertices of the smaller hexagons to the center points of the cell walls via straight beams. The hierarchical structure is introduced into the regular hexagonal honeycomb (RH) cells, and some novel hierarchical bio-honeycombs are proposed for the development of innovative hierarchical bio-honeycombs. Compression simulation was performed to validate the finite element model. Subsequently, a finite element model of the bio-honeycomb was developed to investigate its out-of-plane crashworthiness. The results show that the introduction of bionic cells and the RH cell walls produces significant interaction effects, with more severe plastic deformation at their intersections and the formation of more folded flaps in the cell walls. The effect of geometric parameters on the crashworthiness of the novel biomimetic bio-honeycomb structures is further discussed. It is shown that smaller bionic honeycomb pore sizes are beneficial for improving the efficiency of material utilization. [ABSTRACT FROM AUTHOR]

Vehicle tracking is a core problem hindering multisensor fusion in intelligent driving. The interference caused by measurement outliers and motion estimation model mismatch seriously affects the estimation accuracy of target states. In view of these problems, an adaptive vehicle target tracking enhancement algorithm based on fuzzy interacting multiple model robust cubature Kalman filtering (FLIMM-IARCKF) is developed. In this algorithm, we constructed a derivative-free adaptive robust cubature Kalman filter (IARCKF) to suppress measurement outliers and errors in motion estimation modeling. Furthermore, a combined fuzzy reasoning method is developed to work with the interacting multiple model algorithm, which further enhances the target tracking performance by increasing the efficiency of model probability updating and by adaptively regulating the process noise covariance matrix. Simulated experiments verify the effectiveness of the IARCKF algorithm and reflect the advantages of the FLIMM-IARCKF algorithm in estimation accuracy, robustness, and model transformation efficiency when compared to the STCKF (Yun et al in Measurement 191:110063, 2022), Huber-CKF (Tseng et al in J Navig 70(3):527–546, 2017), MRCKF (Wu et al Acta Phys Sin 64(21):218401, 2015. https://doi.org/10.7498/aps.64.218401) and IMM-CKF (Song et al in ISA Trans 12(6):387–395, 2020) algorithms. The results show that the mean absolute percentage error (MAPE) of FLIMM-IARCKF in average position and velocity improved over the aforementioned approaches by 33.48%, and the average root-mean-square error (RMSE) improved by 32.64%. Real vehicle experiments showed that the average MAPE of FLIMM-IARCKF when used to determine position and velocity improved by 25.70%; the average RMSE improved by 28.96% when compared to the aforementioned algorithms, with an average operational time of 56.13 ms. Experimental results further revealed that FLIMM-IARCKF shows superior performance in vehicle target tracking without influencing execution efficiency. [ABSTRACT FROM AUTHOR]

The effects of sintering temperature on the dielectric, ferroelectric, and magnetic performance of Co0.8Cu0.2Fe2O4@(Pb0.95La0.05)(Zr0.86Ti0.14)O3 (CCFO@PLZT) composite ceramics with core–shell structure were investigated. The X-ray diffraction results confirmed the bi-phase structure of the composites, with insufficient sintering temperature and inadequate reaction leading to the presence of Pb2O3 and ZrO2. Transmission electron microscope images showed that the core–shell structure was prepared, and the scanning electron microscope images showed that the average grain size of the ceramic samples increased with the increase of the sintering temperature. The dielectric constant does not change linearly as the sintering temperature rises, but tends to increase and then decrease, which could be owing to the fact the crystallinity of impurities increases, affecting their dielectric properties. The dispersion indices were 1.58, 1.64, 1.60, 1.61, and 1.70 with increasing temperature, indicating that the relaxation of CCFO@PLZT ceramics was enhanced. The residual polarization intensity (Pr) of CCFO@PLZT ceramics increased first and then decreased with increasing sintering temperature, reaching a maximum value of Pr ~ 1.849 μC/cm2 at 1050 °C, but this may be caused by the excessive leakage current. Grain growth, density reduction and partial reduction of Fe3+ ions to Fe2+ ions lead to a decrease in saturation magnetization intensity (Ms), residual magnetization intensity (Mr) and coercivity (Hc) with the increase of sintering temperature. [ABSTRACT FROM AUTHOR]

The bioinspired sinusoidal honeycomb (BSH) structure has been proved to have excellent energy absorption performances. To further increase its performance and optimize its structural parameters, the surrogate model of specific energy absorption (SEA) and initial peak crushing force (PCF) is constructed. Non-dominated sorting genetic algorithm II is used to carry out the multi-objective optimization for BSH. Parametric studies show that the relationship between the SEA and amplitude is not obvious, while the PCF increases with the increase of amplitude, and both the SEA and PCF increase as the wall thickness increases. Moreover, the Pareto frontiers of the four BSHs with different wavenumbers have a certain similarity. Compared with the original solution, the optimal solution of the BSH is 2.5 T under the constraints, where the PCF decreases by 15.52%, and the SEA increases by 6.77%. Finally, the accuracy of the optimal solution is verified numerically. [ABSTRACT FROM AUTHOR]

The strain SCPG-7 was isolated from saline soil in a cotton field. It is confirmed that the strain SCPG-7 is Pseudomonas sp. by means of the analysis of its phenotypic features and 16S rRNA sequence. SCPG-7 was capable of dissolving mineral tri-calcium phosphate (Ca3(PO4)2) and tri-magnesium phosphate (Mg3(PO4)2). In contrast, no showing iron phosphate (FePO4) or aluminum phosphate (AlPO4) solubilizing activities were detected by this experimental approach. The ratio of the dissolved P diameter to the colony diameter was 1.86. To study the phosphate dissolving mechanisms of the strain, we analyzed the changes of the pH value, the soluble phosphate content, the concentration of alkaline phosphatase, and the production of organic acid in the insoluble phosphate liquid medium. 2-keto-D-gluconicacid, α-ketoglutaric acid, succinic acid, etc. were characterized by LC-MS/MS in NBRIP medium. The concentration of 2-keto-D-gluconicacid increased to 88.6 mg/L after being cultured for 216 h. The strain decreased the pH value of the medium from 7.4 to 4.7 and the released soluble phosphate up to 516 mg/L, which proved the production of organic acids and alkaline phosphatase to be mechanism for solubilizing P. Under low phosphorus stress, Pseudomonas global regulatory protein PhoB regulates the transcription of the alkaline phosphatase gene. IAA and siderophore were secreted by SCPG-7. After treatment with SCPG-7, the individual plant height and dry weight of pepper increased by 23.3 and 31.2%, respectively, compared to the control group. The results show that the strain SCPG-7 has the potential to convert insoluble inorganic phosphorus to plant-available phosphorus. It can enhance soil phosphorus release through biological pathways, thereby increasing crop yield, and providing germplasm resources for the development of biological fertilizers. [ABSTRACT FROM AUTHOR]

Purpose: The purpose of the study was to evaluate the efficacy and safety of a patented perfusion and suctioning platform and ureteral access sheath in the treatment of large ureteral stones (≥ 1.5 cm) below L4 level.Methods: We recruited 122 patients with large ureteral stones below L4 level at our hospital from December 2014 to June 2017. The patients were randomly divided into the study and control groups. Multiple operative and perioperative parameters were compared between the two groups.Results: The study group had shorter operation time, less cases of postoperative fever, lower serum levels of PCT, IL-6 and BET within 24 h after surgery, as well as less number of cases receiving secondary surgery than the control group. Moreover, the former had a significantly higher stone clearance rate than the latter (P < 0.05; t-test or χ2 test).Conclusions: The patented perfusion and suctioning platform and ureteral access sheath are safe and effective in treating large ureteral stones (≥ 1.5 cm) below L4 level. [ABSTRACT FROM AUTHOR]

The abuse of the illicit drug methamphetamine (METH) is a major concern because it can cause terminal degeneration and neuronal cell death in the brain. METH-induced cell death occurs via processes that resemble apoptosis. In the present review, we discuss the role of various apoptotic events in the causation of METH-induced neuronal apoptosis in vitro and in vivo. Studies using comprehensive approaches to gene expression profiling have allowed for the identification of several genes that are up-regulated or down-regulated after an apoptosis-inducing dose of the drug. Further experiments have also documented the fact that the drug can cause demise of striatal enkephalinergic neurons by cross-talks between mitochondria-, endo-plasmic reticulum- and receptor-mediated apoptotic events. These neuropathological observations have also been reported in models of drug-induced neuroplastic alterations used to mimic drug addiction (Nestler, 2001). [ABSTRACT FROM AUTHOR]

(1 − x)BaTiO3–xBiAlO3 relaxor ferroelectric ceramics were prepared by the conventional sintering (CS) and the microwave sintering (MWS) methods, respectively. The microstructure, the dielectric properties, the relaxation characteristic, the ferroelectric and the energy storage properties were investigated in detail. The results show that a single perovskite phase exists in ceramic samples sintered by CS and MWS. The addition of BiAlO3 into BaTiO3 and the MWS method result in the reduction of the grain size and the porosity of BaTiO3 ceramic. This leads to the enhanced diffuse transition and frequency dispersion. The dielectric constant and loss decrease with increasing the BiAlO3 content, and the temperature and the frequency stabilities are enhanced by introducing BiAlO3 into BaTiO3. The remnant polarization (Pr) increases initially and then decreases with the increase of BiAlO3 content, the maximum values of Pr (9.5 μC/cm2 for CS and 7.27 μC/cm2 for MWS) are obtained at x = 0.02. The addition of BiAlO3 can elevate the variation of polarization ΔP (Pmax − Pr), its maximum values (12.56 μC/cm2 for CS ceramic and 12.2 μC/cm2 for MWS ceramic) are acquired at x = 0.04, 0.08, respectively. The energy storage properties indicate that the energy storage density (Wrec) increases, while the energy storage efficiency (η) firstly decreases and then increases with the increase of x. [ABSTRACT FROM AUTHOR]

In order to detect Android malware more effectively, an Android malware detection model was proposed based on improved naive Bayes classification. Firstly, considering the unknown permission that may be malicious in detection samples, and in order to improve the Android detection rate, the algorithm of malware detection is proposed based on improved naive Bayes. Considering the limited training samples, limited permissions, and the new malicious permissions in the test samples, we used the impact of the new malware permissions and training permissions as the weight. The weighted naive Bayesian algorithm improves the Android malware detection efficiency. Secondly, taking into account the detection model, we proposed a detection model of permissions and information theory based on the improved naive Bayes algorithm. We analyzed the correlation of the permission. By calculating the Pearson correlation coefficient, we determined the value of Pearson correlation coefficient r, and delete the permissions whose value r is less than the threshold $$\rho $$ρ and get the new permission set. So, we got the improved detection model by clustering based on information theory. Finally, we detected the 1725 Android malware and 945 non malicious application of multiple data sets in the same simulation environment. The detection rate of the improved the naive Bayes algorithm is 86.54%, and the detection rate of the non-malicious application is increased to 97.59%. Based on the improved naive Bayes algorithm, the false detection rate of the improved detection model is reduced by 8.25%.In order to detect Android malware more effectively, an Android malware detection model was proposed based on improved naive Bayes classification. Firstly, considering the unknown permission that may be malicious in detection samples, and in order to improve the Android detection rate, the algorithm of malware detection is proposed based on improved naive Bayes. Considering the limited training samples, limited permissions, and the new malicious permissions in the test samples, we used the impact of the new malware permissions and training permissions as the weight. The weighted naive Bayesian algorithm improves the Android malware detection efficiency. Secondly, taking into account the detection model, we proposed a detection model of permissions and information theory based on the improved naive Bayes algorithm. We analyzed the correlation of the permission. By calculating the Pearson correlation coefficient, we determined the value of Pearson correlation coefficient r, and delete the permissions whose value r is less than the threshold $$\rho $$ρ and get the new permission set. So, we got the improved detection model by clustering based on information theory. Finally, we detected the 1725 Android malware and 945 non malicious application of multiple data sets in the same simulation environment. The detection rate of the improved the naive Bayes algorithm is 86.54%, and the detection rate of the non-malicious application is increased to 97.59%. Based on the improved naive Bayes algorithm, the false detection rate of the improved detection model is reduced by 8.25%.In order to detect Android malware more effectively, an Android malware detection model was proposed based on improved naive Bayes classification. Firstly, considering the unknown permission that may be malicious in detection samples, and in order to improve the Android detection rate, the algorithm of malware detection is proposed based on improved naive Bayes. Considering the limited training samples, limited permissions, and the new malicious permissions in the test samples, we used the impact of the new malware permissions and training permissions as the weight. The weighted naive Bayesian algorithm improves the Android malware detection efficiency. Secondly, taking into account the detection model, we proposed a detection model of permissions and information theory based on the improved naive Bayes algorithm. We analyzed the correlation of the permission. By calculating the Pearson correlation coefficient, we determined the value of Pearson correlation coefficient r, and delete the permissions whose value r is less than the threshold $$\rho $$ρ and get the new permission set. So, we got the improved detection model by clustering based on information theory. Finally, we detected the 1725 Android malware and 945 non malicious application of multiple data sets in the same simulation environment. The detection rate of the improved the naive Bayes algorithm is 86.54%, and the detection rate of the non-malicious application is increased to 97.59%. Based on the improved naive Bayes algorithm, the false detection rate of the improved detection model is reduced by 8.25%.In order to detect Android malware more effectively, an Android malware detection model was proposed based on improved naive Bayes classification. Firstly, considering the unknown permission that may be malicious in detection samples, and in order to improve the Android detection rate, the algorithm of malware detection is proposed based on improved naive Bayes. Considering the limited training samples, limited permissions, and the new malicious permissions in the test samples, we used the impact of the new malware permissions and training permissions as the weight. The weighted naive Bayesian algorithm improves the Android malware detection efficiency. Secondly, taking into account the detection model, we proposed a detection model of permissions and information theory based on the improved naive Bayes algorithm. We analyzed the correlation of the permission. By calculating the Pearson correlation coefficient, we determined the value of Pearson correlation coefficient r, and delete the permissions whose value r is less than the threshold $$\rho $$ρ and get the new permission set. So, we got the improved detection model by clustering based on information theory. Finally, we detected the 1725 Android malware and 945 non malicious application of multiple data sets in the same simulation environment. The detection rate of the improved the naive Bayes algorithm is 86.54%, and the detection rate of the non-malicious application is increased to 97.59%. Based on the improved naive Bayes algorithm, the false detection rate of the improved detection model is reduced by 8.25%.In order to detect Android malware more effectively, an Android malware detection model was proposed based on improved naive Bayes classification. Firstly, considering the unknown permission that may be malicious in detection samples, and in order to improve the Android detection rate, the algorithm of malware detection is proposed based on improved naive Bayes. Considering the limited training samples, limited permissions, and the new malicious permissions in the test samples, we used the impact of the new malware permissions and training permissions as the weight. The weighted naive Bayesian algorithm improves the Android malware detection efficiency. Secondly, taking into account the detection model, we proposed a detection model of permissions and information theory based on the improved naive Bayes algorithm. We analyzed the correlation of the permission. By calculating the Pearson correlation coefficient, we determined the value of Pearson correlation coefficient r, and delete the permissions whose value r is less than the threshold $$\rho $$ρ and get the new permission set. So, we got the improved detection model by clustering based on information theory. Finally, we detected the 1725 Android malware and 945 non malicious application of multiple data sets in the same simulation environment. The detection rate of the improved the naive Bayes algorithm is 86.54%, and the detection rate of the non-malicious application is increased to 97.59%. Based on the improved naive Bayes algorithm, the false detection rate of the improved detection model is reduced by 8.25%.In order to detect Android malware more effectively, an Android malware detection model was proposed based on improved naive Bayes classification. Firstly, considering the unknown permission that may be malicious in detection samples, and in order to improve the Android detection rate, the algorithm of malware detection is proposed based on improved naive Bayes. Considering the limited training samples, limited permissions, and the new malicious permissions in the test samples, we used the impact of the new malware permissions and training permissions as the weight. The weighted naive Bayesian algorithm improves the Android malware detection efficiency. Secondly, taking into account the detection model, we proposed a detection model of permissions and information theory based on the improved naive Bayes algorithm. We analyzed the correlation of the permission. By calculating the Pearson correlation coefficient, we determined the value of Pearson correlation coefficient r, and delete the permissions whose value r is less than the threshold $$\rho $$ρ and get the new permission set. So, we got the improved detection model by clustering based on information theory. Finally, we detected the 1725 Android malware and 945 non malicious application of multiple data sets in the same simulation environment. The detection rate of the improved the naive Bayes algorithm is 86.54%, and the detection rate of the non-malicious application is increased to 97.59%. Based on the improved naive Bayes algorithm, the false detection rate of the improved detection model is reduced by 8.25%.In order to detect Android malware more effectively, an Android malware detection model was proposed based on improved naive Bayes classification. Firstly, considering the unknown permission that may be malicious in detection samples, and in order to improve the Android detection rate, the algorithm of malware detection is proposed based on improved naive Bayes. Considering the limited training samples, limited permissions, and the new malicious permissions in the test samples, we used the impact of the new malware permissions and training permissions as the weight. The weighted naive Bayesian algorithm improves the Android malware detection efficiency. Secondly, taking into account the detection model, we proposed a detection model of permissions and information theory based on the improved naive Bayes algorithm. We analyzed the correlation of the permission. By calculating the Pearson correlation coefficient, we determined the value of Pearson correlation coefficient r, and delete the permissions whose value r is less than the threshold $$\rho $$ρ and get the new permission set. So, we got the improved detection model by clustering based on information theory. Finally, we detected the 1725 Android malware and 945 non malicious application of multiple data sets in the same simulation environment. The detection rate of the improved the naive Bayes algorithm is 86.54%, and the detection rate of the non-malicious application is increased to 97.59%. Based on the improved naive Bayes algorithm, the false detection rate of the improved detection model is reduced by 8.25%.In order to detect Android malware more effectively, an Android malware detection model was proposed based on improved naive Bayes classification. Firstly, considering the unknown permission that may be malicious in detection samples, and in order to improve the Android detection rate, the algorithm of malware detection is proposed based on improved naive Bayes. Considering the limited training samples, limited permissions, and the new malicious permissions in the test samples, we used the impact of the new malware permissions and training permissions as the weight. The weighted naive Bayesian algorithm improves the Android malware detection efficiency. Secondly, taking into account the detection model, we proposed a detection model of permissions and information theory based on the improved naive Bayes algorithm. We analyzed the correlation of the permission. By calculating the Pearson correlation coefficient, we determined the value of Pearson correlation coefficient r, and delete the permissions whose value r is less than the threshold $$\rho $$ρ and get the new permission set. So, we got the improved detection model by clustering based on information theory. Finally, we detected the 1725 Android malware and 945 non malicious application of multiple data sets in the same simulation environment. The detection rate of the improved the naive Bayes algorithm is 86.54%, and the detection rate of the non-malicious application is increased to 97.59%. Based on the improved naive Bayes algorithm, the false detection rate of the improved detection model is reduced by 8.25%. [ABSTRACT FROM AUTHOR]