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Aiming at the problems of severe tool wear and difficulty in ensuring machining surface quality during the cutting process of Ni-base superalloy, a micro-texture design method resembling a shark surface is proposed, which simplifies the skin teeth on the shark surface into a diamond shaped structure with ribs. Different micro-textured samples were prepared using femtosecond laser, and surface wettability characterization and friction and wear experiments were conducted to study the wear reduction and anti wear effects of micro-texture. The parameter combinations with the maximum contact angle and the minimum coefficient of friction were selected. The wear mechanism of YG8 carbide alloy and GH4742, as well as the wear reduction mechanism of microstructure were revealed. Biomimetic cutting tools were prepared and their effectiveness in controlling tool wear, reducing cutting forces, and improving machining quality was verified through milling experiments.

ABSTRACTAlthough miR-199a-5p is linked to the development of numerous cancers, its regulatory role in skin cancer is unclear. In this work, the impact of miR-199a-5p produced by adipose-derived stem cells on malignant melanoma skin cancer was investigated.30 pair tumor tissues and adjacent tissues were obtained from skin cancer patients. Adipose-derived stem cell (ADSCs) were isolated from adipose tissues harvested from healthy subjects. The mRNA relative expression was evaluated via qRT-PCR. Cell proliferation ability was measured via CCK-8 assay. Apoptosis was evaluated via flow cytometry. The connection between miR-199a-5p and SOX4 was confirmed via luciferase reporter assay. Western blot was conducted to evaluate protein expression. MiR-199a-5p was higher expressed in ADSCs exosomes and was lower expressed in skin cancer tissues and cells. ADSCs-derived exosomes inhibited cell invasion of skin cancer. MiR-199a-5p inhibitor enhanced cell viability and invasion. In addition, miR-199a-5p inhibitor suppressed cell apoptosis. MiR-199a-5p NC transfected ADSCs inhibited cell viability and invasion while miR-199a-5p mimic transfected ADSCs further inhibited cell viability and invasion. In addition, miR-199a-5p NC transfected ADSCs enhanced cell apoptosis while miR-199a-5p mimic transfected ADSCs further enhanced cell apoptosis. Luciferase supported the targetscan prediction that miR-199a-5p might control SOX4 expression. SOX4 expression was noticeably lower in the miR-199a-5p mimic group.Exosomes from adipose-derived stem cells inhibited skin cancer progression via miR-199a-5p/SOX4.

Camouflaged object detection has been considered a challenging task due to its inherent similarity and interference from background noise. It requires accurate identification of targets that blend seamlessly with the environment at the pixel level. Although existing methods have achieved considerable success, they still face two key problems. The first one is the difficulty in removing texture noise interference and thus obtaining accurate edge and frequency domain information, leading to poor performance when dealing with complex camouflage strategies. The latter is that the fusion of multiple information obtained from auxiliary subtasks is often insufficient, leading to the introduction of new noise. In order to solve the first problem, we propose a frequency domain reconstruction module based on contrast learning, through which we can obtain high-confidence frequency domain components, thus enhancing the model’s ability to discriminate target objects. In addition, we design a frequency domain representation decoupling module for solving the second problem to align and fuse features from the RGB domain and the reconstructed frequency domain. This allows us to obtain accurate edge information while resisting noise interference. Experimental results show that our method outperforms 12 state-of-the-art methods in three benchmark camouflaged object detection datasets. In addition, our method shows excellent performance in other downstream tasks such as polyp segmentation, surface defect detection, and transparent object detection.

Fully integrated theranostic devices are highly esteemed in clinical applications, offering immense potential in real-time disease monitoring and personalized care. Microneedles (MNs), as innovative and wearable devices, boast important advantages in biosensing and therapy, thus holding significant promise in the advancement of diagnostic and therapeutic platforms. Encouragingly, advancements in electrochemical sensing technology, micronano fabrication, and biocompatible materials are propelling momentum for MNs-based closed-loop systems, enhancing detection capabilities, biocompatibility, and cost-effectiveness. Moreover, the notable progress in integrating MN chips with other biochips signifies a frontier for growth. Successful clinical trials in target molecule monitoring and drug delivery domains herald excellent clinical translational prospects for the aforementioned theranostic platform. Finally, we delineate both challenges and opportunities in the development of integrated diagnostic and therapeutic MN systems, including continuous monitoring, intelligent control algorithms, safety, and regulatory considerations.

In the cutting process, reasonable evaluation of tool remaining useful life (RUL) is essential to ensure machining stability and safety. However, traditional data-driven methods rely on datasets with complete degradation data and prior knowledge, which are usually difficult to obtain in real scenarios. For these problems, this paper proposed a model-data hybrid driven approach for RUL prediction of cutting tool based on improved inverse Gaussian process. The tool wear degradation is considered as a random process, the cutting physical model and the data-driven stochastic process model are fused, so that the prior knowledge from the physical model and the statistical law of measured data are fully utilized to improve the accuracy of tool RUL prediction. The cutting physical model is established, and the prior knowledge of tool wear degradation is obtained based on physical simulation method. A data-driven random effects inverse Gaussian process model is constructed for tool RUL modeling and prediction. According to Bayesian method, the unknown parameters are estimated and updated integrating physical simulated and measured wear data, so as to obtain the probability density function for specific tool RUL, and quantify the prediction uncertainty. Finally, comparative experiments are conducted to validate the performance of the proposed method. The results suggest that the hybrid approach has the best performance in different tool RUL prediction.

As a leak identification method in chemical processes, deep learning is limited by inadequate sample labels and highly coupled, nonlinear process variables, therefore difficult to model with a high identification accuracy. To address these problems, a fusion correlation-distance graph (CDG) coding aided deep learning method is proposed. First, the mechanism model is built based on dynamic process data with different leakage labels. Then, the weighted degree and spatial distribution of the dynamic dataset are computed through innovatively designed correlation and distance coding, aided by sliding window fusion into a correlation-distance matrix. Finally, the well-trained long short-term memory (LSTM) is constructed to learn the deep features of the leakage process and identify them. The applications in two industrial cases, CO2capture and ammonia synthesis, show that CDG-LSTM presents state-of-the-art identification performance with R2scores of 0.994 and 0.986. 6.6% and 18.4% improvement over LSTM. In addition, the proposed method effectively categorizes the unknown leak samples into known labels with nearby locations, providing a valuable reference for operators to determine the unknown leak locations.

Introduction:The menstrual cycle is an important indicator of health in female athletes. Female elite adolescent dancers are expected to control their weight while also training intensely, which can lead to menstrual disorders. This study aimed to investigate the relationship between body composition and menstrual status in female elite adolescent dancers. Methods: In total, 131 female elite adolescent dancers (age: 15.9 ± 1.5 years) were enrolled in this study. We measured the height, weight, and body fat percentage (BFP) for each participant and calculated their body mass index (BMI). We gathered information on individual menstruation patterns and the participants’ menstrual cycles over the previous year using recall methods. We then compared the differences between dancers with menstrual cycle disorders and those without. Primary amenorrhea was defined as menarche occurring after the age of 15, while secondary amenorrhe was defined as experiencing fewer than 5 or no menstrual periods for at least 3 of the previous 12 months. We conducted a reliability test using the same questionnaire 2 weeks later. Statistical significance was defined as P< .05, and we calculated the effect sizes (d) and 95% confidence intervals (95% CI). Results: The average BMI and BFP were 22.6 ± 3.0% and 19.4 ± 2.2 kg/m2, respectively. Low BFP and low BMI were observed in 51 (38.6%) and 47 (35.6%) participants, respectively. Primary amenorrhea in 3 participants (2.3%) and 29 (22.1%) reported experiencing secondary amenorrhea; they had lower BFP than the dancers who did not experience amenorrhea (P= .041, 95% CI, −2.51 to −0.05). Conclusion: Female elite adolescent dancers in China may have lower BFP and menstrual problems. Given that lower BFP may contribute to the occurrence of menstruation disorders, it is essential to pay an attention to both BFP and the menstruation status in female elite adolescent dancers.

This letter proposes a novel approach for optimizing power allocation in multi-cell multi-user (MCMU) networks using a hypergraph neural network (HGNN). In MCMU networks, each base station (BS) serves multiple user equipments (UEs). This multivariate and implicit connection introduces computational overheads and is unsuitable for pairwise relationship modeling. To address this challenge, we first propose a hypergraph structure that represents BSs as hyperedges, capturing the complex interactions and multiple dependencies within the network. Second, a synchronous training loss is developed, which includes negative weighted sum rate and parameter regularity terms. The first term can learn the distribution without relying on labeled data. The second term avoids overfitting and improves scalability. Third, power constraints are embedded into the network architecture to ensure the feasibility of the power allocation. Extensive simulations demonstrate that our proposed HGNN achieves higher sum rate than the baselines and exhibits its excellent scalability with the increase of complexity in future networks.

Due to the prevalence of electronic devices, intelligent sensors have attracted much interest for the detecting and providing alarms with respect to indoor electrical safety. Nonetheless, how to effectively identify various indoor electrical safety hazards remains a challenge. In this study, we fabricated single-walled carbon nanotube/poly(3-hexylthiophene-2,5-diyl) (SWCNT/P3HT) composites with exceptional bifunctional thermoelectric and photoelectric responses. Through synergy of the thermo-/photoelectric effects, the composites yielded greatly enhanced output voltages compared with the use of thermoelectric effects alone. Interestingly, modes of heat transfer can be effectively distinguished using the nominal Seebeck coefficients. Based on the remarkable output voltages and deviations in the nominal Seebeck coefficients, we developed indoor intelligent sensors capable of effectively identifying and monitoring diverse indoor electrical conditions, including electrical overheating, fire, and air conditioning flow. This pioneering investigation proposes a novel avenue for designing intelligent sensors that can recognize heat transfer modes and hence effectively monitor indoor electrical safety hazards.

The garden pea (Pisum sativumL.) is a significant cool-season legume, serving as crucial food sources, animal feed, and industrial raw materials. The advancement of functional genomics over the past two decades has provided substantial theoretical foundations and progress to pea breeding. Notably, the release of the pea reference genome has enhanced our understanding of plant architecture, symbiotic nitrogen fixation (SNF), flowering time, floral organ development, seed development, and stress resistance. However, a considerable gap remains between pea functional genomics and molecular breeding. This review summarizes the current advancements in pea functional genomics and breeding while highlighting the future challenges in pea molecular breeding.

Wideband Terahertz (THz) beamforming is a promising technology in the sixth generation (6G) wireless communications. The combination of ultra-wide bandwidth and ultra-massive multiple-input multiple-output (UM-MIMO) results in severe beam squint issues, leading to sharp decrease in the spectral efficiency. Replacing the phase shifters by a large amount of true-time-delays (TTDs) could eliminate the beam squint effect, but leads to unbearable power consumption. It becomes an enormous challenge to mitigate the beam squint effect while maintaining high energy efficiency simultaneously. This paper proposes a novel dynamic group-connected hybrid beamforming architecture with TTD (DGC-TTD) to tackle the above issue. Furthermore, a switch-aided delay-phase beamforming (SDPB) algorithm is proposed for the DGC-TTD architecture, which consists of a delay-phase design (DPD) procedure and a random switch selection (RSS) procedure to achieve the optimal tradeoffs between the spectral efficiency and the power consumption. The simulation results demonstrate that the DGC-TTD architecture with the proposed algorithm provides the near-optimal spectral efficiency and the maximal energy efficiency comparing with existing benchmark results.

ABSTRACT The effects of low fatigue load level with lower limit of 0.1 Pu and upper limit of 0.2 Pu or 0.3 Pu on chloride diffusivity of RC beams were [...]

The prenylated flavonoid icaritin (ICT, 1), a new drug for treating advanced hepatocellular carcinoma (HCC), was selected as a template to develop more potent inhibitors. An initial semisynthetic modification of ICT was performed to obtain a structure–activity relationship (SAR), which indicated that the cytotoxicity is enhanced by OH-3 rhamnosylation and that OH-7 is an important modification site. Based on the results of the SAR study, 46 N-containing ICT derivatives were synthesized and evaluated as the anti-HCC inhibitors. The results showed that most of the derivatives produced inhibited three HCC cell lines used (Hep3B, HepG2 and SMMC-7721). The modification strategy was validated by 3D-QSAR, which provided information for the further design and optimization of ICT. The most potent compound, 11c, exhibited IC50values of 7.6 and 3.1 μM against HepG2 and SMMC-7721 cells, respectively, which were more potent than those of ICT and sorafenib, respectively. Further mechanistic studies indicated that 11ccaused arrest at the G0/G1phase in the cell cycle and induced cell apoptosis in HepG2 and SMMC-7721 cells.

Cutting burrs, which are common in the manufacturing process of aluminum alloy wheel hubs, can severely affect the quality of the wheel hub surface and increase the scrap rate. An accurate prediction of the cutting burr size is the basis for solving the burr problem using optimization means. However, wheel hub cutting burrs can be measured only by offline microscopy, which makes acquiring burr size samples challenging, and traditional data fitting and prediction methods perform poorly for limited number of samples. To solve this problem, this paper proposes an improved method for constructing a burr length prediction model. A constitutive model of the wheel hub material A356.2 aluminum alloy is constructed using mechanical tests. This constitutive model is applied to simulate the wheel cutting burr, and the simulation results are verified using cutting experiments. Then, a large amount of simulation is performed, and a one-dimensional residual network (1D-ResNet) is constructed and trained with the simulation data; the results show that the 1D-ResNet model has stronger stability and robustness and improved prediction accuracy compared to the traditional data processing methods. Based on the transfer learning method, the trained 1D-ResNet model is fine-tuned by using the cutting experimental data, and a burr size prediction model fusing the simulation data and the experimental data is constructed. The verification results show that the proposed method can achieve high prediction accuracy with limited number of samples, thus effectively solving the engineering problem of wheel cutting burr size prediction.

In order to analyze the propagation path of vibration noise, and carry out finite element numerical calculation research based on electro-magnetic–solid-acoustic multiphysics coupling, a single-phase two-column dry-type transformer is used as the noise source in this paper. According to the distribution characteristics of the spatial sound field and the vibration noise amplitude–frequency characteristics of the transformer, a topology design method of the space coiled acoustic metamaterial (SCAMM) unit is proposed and the analytical calculation methods of acoustic parameters such as sound pressure reflection/transmission coefficient of various SCAMM units are deduced. Besides, the effects of parameters such as the longitudinal width a, the waveguide width b, the ratio of waveguide width to longitudinal width b/aand transverse width ton the SCAMM unit sound transmission loss (STL) are studied, and the anti-resonance frequencies are adjusted by parameter optimization method to achieve the optimal sound insulation effect. In the end, the sound field control barrier based on the SCAMM for transformer vibration and noise suppression is designed, and relevant experiments are carried out. The experiments show that the sound field control barrier based on the SCAMM can reduce the transformer noise sound pressure level (SPL) of the main noise frequencies by up to 25 dB with a thickness of only 4 cm and ventilation.

Due to the high thermal resistance, limited ventilation efficiency, and substantial weight of mascot costumes, staffs wearing mascot costumes in summer are subjected to significant thermal stress, resulting in physiological strain and posing a grave threat to their health. To improve the thermal comfort of staff wearing mascot costumes, this study employed a phase change material (PCM) vest with a phase-change temperature of 26 °C as a cooling measure. The 32 participants were recruited and randomly divided into two groups to analyze the effect of the PCM vest. The results showed that, participants wearing mascot costumes along with an additional PCM vest underneath (MC-PCM) experienced a decrease in oral temperature by 0.06 °C and mean skin temperature by 0.63 °C, compared to those wearing common mascot costumes (CMC). However, there was an increase in body-mass loss relative to initial weight by 0.045 % and heart rate by 5.08 bpm. Wearing the additional PCM vest can improve humidity sensation and thermal sensation, In addition, the average thermal comfort voting score of participants wearing MC-PCM was 0.24 lower than that of participants wearing CMC. It was showed that employing a PCM vest reduced heat stress evidently in staff wearing mascot costumes in high-temperature environments and improved their thermal comfort.

This paper presents a comparative analysis of thermal and mechanical behaviors of the thermally stratified heat storage system using three different heat transfer materials, which are the binary nitrate salt, ternary nitrate salt and liquid lead–bismuth eutectic (LBE) alloy. The discharging behaviors of the tank using three different heat transfer materials are investigated by using both the algebraic and numerical models. For all the three materials, the algebraic results are consistent with the simulation ones. During the discharging process, in comparison, the thermal stratification thickness of the tank using the binary nitrate salt is relatively smaller, which can lead to a better discharging performance. The comparison results of expanding behaviors of the standby tank reveal that the thermal stratification thicknesses of tanks using the three different heat transfer materials all increase with the standby time increased. Compared with the other two materials, the thermal stratification thickness of the tank using the binary nitrate salt is the smallest at the same standby time. The comparison results of mechanical behaviors of the tank demonstrate that the maximum mechanical stress of the tank using the liquid LBE is the greatest, while the tank using the molten salts can have better mechanical properties.

The backpropagation neural network (BPNN) algorithm of artificial intelligence (AI) is utilized to predict A+H shares price for helping investors reduce the risk of stock investment. First, the genetic algorithm (GA) is used to optimize BPNN, and a model that can predict multi-day stock prices is established. Then, the Principal Component Analysis (PCA) algorithm is introduced to improve the GA-BP model, aiming to provide a practical approach for analyzing the market risks of the A+H shares. The experimental results show that for A shares, the model has the best prediction effect on the price of Bank of China (BC), and the average prediction errors of opening price, maximum price, minimum price, as well as closing price are 0.0236, 0.0262, 0.0294 and 0.0339, respectively. For H shares, the model constructed has the best effect on the price prediction of China Merchants Bank (CMB). The average prediction errors of opening price, maximum price, minimum price and closing price are 0.0276, 0.0422, 0.0194 and 0.0619, respectively.

The limited lifespan of batteries is a challenge in the application of implantable electronic devices. Existing wireless power technologies such as ultrasound, near-infrared light and magnetic fields cannot charge devices implanted in deep tissues, resulting in energy attenuation through tissues and thermal generation. Herein, an ultra-low frequency magnetic energy focusing (ULFMEF) methodology was developed for the highly effective wireless powering of deep-tissue implantable devices. A portable transmitter was used to output the low-frequency magnetic field (<50 Hz), which remotely drives the synchronous rotation of a magnetic core integrated within the pellet-like implantable device, generating an internal rotating magnetic field to induce wireless electricity on the coupled coils of the device. The ULFMEF can achieve energy transfer across thick tissues (up to 20 cm) with excellent transferred power (4–15 mW) and non-heat effects in tissues, which is remarkably superior to existing wireless powering technologies. The ULFMEF is demonstrated to wirelessly power implantable micro-LED devices for optogenetic neuromodulation, and wirelessly charged an implantable battery for programmable electrical stimulation on the sciatic nerve. It also bypassed thick and tough protective shells to power the implanted devices. The ULFMEF thus offers a highly advanced methodology for the generation of wireless powered biodevices.A methodology of ultra-low frequency magnetic energy focusing is developed for wireless powering, which transmits energy through low-frequency electromagnetic waves with long-distance transmission and low energy attenuation through thick biological tissues.

Identification of abnormal conditions is essential in the chemical process. With the rapid development of artificial intelligence technology, deep learning has attracted a lot of attention as a promising fault identification method in chemical process recently. In the high-dimensional data identification using deep neural networks, problems such as insufficient data and missing data, measurement noise, redundant variables, and high coupling of data are often encountered. To tackle these problems, a feature based deep belief networks (DBN) method is proposed in this paper. First, a generative adversarial network (GAN) is used to reconstruct the random and non-random missing data of chemical process. Second, the feature variables are selected by Spearman's rank correlation coefficient (SRCC) from high-dimensional data to eliminate the noise and redundant variables and, as a consequence, compress data dimension of chemical process. Finally, the feature filtered data is deeply abstracted, learned and tuned by DBN for multi-case fault identification. The application in the Tennessee Eastman (TE) process demonstrates the fast convergence and high accuracy of this proposal in identifying abnormal conditions for chemical process, compared with the traditional fault identification algorithms.

The performance of latent thermal energy storage units (LTESU) is limited by the low thermal conductivity of phase change materials (PCM). Copper foam can enhance PCM's heat conduction, but its structure inhibits the internal natural convection. Based on this, the perforated copper foam was proposed to enhance the natural convection of PCM. The contrast experiment built three LTESUs (Namely, the LTESU only with the copper tube (LTESU-CT), the LTESU with full copper foam (LTESU-FCF) and the LTESU with perforated copper foam (LTESU-PCF). The melting and solidifying state, temperature distribution, and thermal efficiency were compared in three LTESUs during thermal charging and discharging processes. Results showed using copper foam could enhance the thermal performance of LTESU obviously and the perforated copper foam could generate the convective heat exchange, which had a time-average Nusselt number 34.1 % higher in LTESU-PCF than that in LTESU-FCF. Due to the reduced heat transfer area, LTESU-PCF had the lower thermal efficiency than LTESU-FCF, but its thermal storage capacity and release capacity were 6.4 % and 1.4 % higher than that in LTESU-FCF, respectively. It was confirmed that employing perforated copper foam could s enhance the natural convection and heat transfer of PCM significantly, but the perforation parameter still needs to be further explored to achieve higher thermal performance.

Concept design is vital important in development of auto-body and it has great effects on later design work. In this paper, a two-level cross-sectional optimization approach is presented to shorten concept design cycles. First, an exact structural analysis approach for spatial semi-rigid framed structures, i.e., the transfer stiffness matrix method proposed in our previous study, is adopted for both static and dynamic analyses of body-in-white (BIW) structure. A two-level cross-sectional optimization approach is then proposed for an automotive BIW lightweight design, and genetic algorithm is used to solve the optimization models. Afterward, an object-oriented MATLAB toolbox, using distributed parallel computing techniques, is developed to promote the concept design of the BIW structure. Finally, relevant numerical examples demonstrate the validity and accuracy of the proposed method.

The single Phase-Change Material (PCM) only plays the thermal regulation capacity in the certain season, due to the fact that the narrow range of phase-change temperature cannot meet the thermal demand between summer and winter. According to this condition, the double-PCM layer is proposed to improve the seasonal adaption. In order to evaluate the efficacy of the double-PCM layer in improving seasonal adaptation, two walls with the double-PCM layer were constructed using different locations of Paraffin 13 and 27, while three walls without PCM or with single-PCM layers were taken as references. A dynamic heat transfer model with phase-change was developed and validated. The numerical results showed that the double-PCM layer is able to meet the thermal demands of summer and winter, although it may not achieve optimal performance in most months. Its overall performance was found to be the best in the entire year due to its high seasonal adaptability. Additionally, the delay time and the attenuation coefficient have the strong correlation with the variation range of liquid fraction and their linear fitting equations have been gained. The double-PCM layer increases the average delay time by 4.65 h and reduces the average attenuation coefficient by about 80 % in summer and winter, while the single-PCM layer could only demonstrate its effectiveness in one season.

Large-scale screening of molecules in organisms requires high-throughput and cost-effective evaluating tools during preclinical development. Here, a novel in vivoscreening strategy combining hierarchically structured biohybrid triboelectric nanogenerators (HB-TENGs) arrays with computational bioinformatics analysis for high-throughput pharmacological evaluation using Caenorhabditis elegansis described. Unlike the traditional methods for behavioral monitoring of the animals, which are laborious and costly, HB-TENGs with micropillars are designed to efficiently convert animals’ behaviors into friction deformation and result in a contact–separation motion between two triboelectric layers to generate electrical outputs. The triboelectric signals are recorded and extracted to various bioinformation for each screened compound. Moreover, the information-rich electrical readouts are successfully demonstrated to be sufficient to predict a drug’s identity by multiple-Gaussian-kernels-based machine learning methods. This proposed strategy can be readily applied to various fields and is especially useful in in vivoexplorations to accelerate the identification of novel therapeutics.

Enzyme-induced carbonate precipitation (EICP) is an emanating, eco-friendly and potentially sound technique that has presented promise in various geotechnical applications. However, the durability and microscopic characteristics of EICP-treated specimens against the impact of drying-wetting (D-W) cycles is under-explored yet. This study investigates the evolution of mechanical behavior and pore characteristics of EICP-treated sea sand subjected to D-W cycles. The uniaxial compressive strength (UCS) tests, synchrotron radiation micro-computed tomography (micro-CT), and three-dimensional (3D) reconstruction of CT images were performed to study the multiscale evolution characteristics of EICP-reinforced sea sand under the effect of D-W cycles. The potential correlations between microstructure characteristics and macro-mechanical property deterioration were investigated using gray relational analysis (GRA). Results showed that the UCS of EICP-treated specimens decreases by 63.7% after 15 D-W cycles. The proportion of mesopores gradually decreases whereas the proportion of macropores increases due to the exfoliated calcium carbonate with increasing number of D-W cycles. The microstructure in EICP-reinforced sea sand was gradually disintegrated, resulting in increasing pore size and development of pore shape from ellipsoidal to columnar and branched. The gray relational degree suggested that the weight loss rate and UCS deterioration were attributed to the development of branched pores with a size of 100–1000 μm under the action of D-W cycles. Overall, the results in this study provide a useful guidancee for the long-term stability and evolution characteristics of EICP-reinforced sea sand under D-W weathering conditions.

A periodic mathematical model of cancer treatment by radiotherapy followed by chemotherapy is presented and studied in this paper. Sufficient conditions on the coexistence of the healthy and cancer cells are obtained. Conditions on the existence and globally asymptotic stability of the positive periodic solution, the cancer eradication periodic solution and the cancer win periodic solution are derived. Numerical examples are shown to verify the validity of the results. A discussion is presented for further study. [ABSTRACT FROM AUTHOR]

A series of 6,7-dimethoxyquinazoline derivatives connected by diaryl urea scaffolds was designed, synthesized and their in vitroantitumor activities were evaluated. Most of them showed an excellent potency against the four tested cancer cell lines as compared with sorafenib. Particularly, a promising compound 20was identified, which showed the most potent antitumor activities with IC50values of 0.08, 0.09, 0.16 and 0.19 µmol/L against H460, HT-29, MKN-45 and MDA-MB-231 cell lines, respectively. The structure-activity relationship(SAR) analysis indicated that compounds with dimethylamino or diethylamino group at the C4 position of 6,7-dimethoxyquinazoline moiety exhibited superior activities than compounds bearing morpholino groups.

This paper tests the inflation hedging ability of four categories of important financial assets in China: Commodity futures, real estate, gold and industry stock and select the assets that have significant inflation hedging effect. Then the authors construct the mean-variance model under the inflation factor, using the selected assets to construct the inflation hedging portfolio, solving the model and obtain the optimal investment strategy with inflation protection function. The result shows that the portfolio constructed by the model have more stable real returns and its inflation hedging ability can be even better if the short selling restriction of stocks is eliminated.

A series of novel indolin-2-one derivatives containing 4-thiazolidinone moiety(7a–7r) were synthesized and evaluated for their in vitroantitumour activities against MDA-MB-231(human breast cancer), H460(human lung cancer) and HT-29(human colon cancer) cell lines by standard 3-(4,5-dimethylthiazae-2-yl)-2,5-diphenyltetrazolium bromide(MTT) assay. Representative compounds(7d, 7k, 7m, 7p) with higher cytotoxicity were further examined against one normal cell line(WI-38, human fetal lung fibroblasts). The preliminary investigation shows that most of the compounds display moderate to excellent potency and high selectivity against different human cancer cell lines. In particular, the most potent compound 7m shows promising cytotoxicity against MDA-MB-231, H460 and HT-29 cell lines with IC50values of 0.78, 0.056 and 0.018 μmol/L, respectively. The potency is much higher than that of Sunitinib(IC50=3.46 μmol/L against MDA-MB-231, IC50=2.59 μmol/L against H460, IC50=1.50 μmol/L against HT-29) by 4.4, 46.3 and 83.3 fold.

Highlights: [•] Holling type III functional response reflect exactly interactions between vertebrates. [•] Adopt a modified analysis skill of papers [2,17]. [•] Figures illustrate the permanence of the system from three different point of views. [ABSTRACT FROM AUTHOR]

Abstract: In this paper, we study a periodic predator–prey system with prey impulsive diffusion in two patches. On the basis of comparison theorem of impulsive differential equation and other analysis methods, sufficient and necessary conditions on the predator–prey system where predator have not other food source are established. Two examples and numerical simulations are presented to illustrate the feasibility of our results. A conclusion is given in the end. [ABSTRACT FROM AUTHOR]

Abstract: An impulsive reaction-diffusion periodic food-chain system with Holling type III functional response is presented and studied in this paper. Sufficient conditions for the ultimate boundedness and permanence of the food-chain system are established based on the upper and lower solution method and comparison theory of differential equation. By constructing appropriate auxiliary function, the conditions for the existence of a unique globally stable positive periodic solution are also obtained. Some numerical examples are shown to illustrate our results. A discussion is given in the end of the paper. [Copyright &y& Elsevier]

In this paper, we study a periodic single-species logistic system with impulsive diffusion in two patches. By using the iterative method, sufficient and necessary conditions on the existence, uniqueness and global attractivity of positive periodic solution and the extinction of species for this system are established. Two examples and numerical simulations are presented to illustrate the feasibility of our results.

Salmonella entericaserovar Pullorum is the pathogen of pullorum disease, which leads to severe economic losses in many developing countries. In contrast to the strong inflammatory response induced by Salmonella entericaserovar Typhimurium and Salmonella entericaserovar Enteritidis, S. Pullorum causes systemic infection with little inflammation. The effector proteins secreted by Salmonellaoften play a crucial role in modulating host signal transduction and cellular processes to the pathogen’s advantage.

In this paper, an age-structured infectious disease dynamical model that considers two diseases simultaneously but with limited medical resources is proposed and analyzed. The asymptotic smoothness and persistence of the solution semi-flow are investigated. Then conditions for the existence of a global attractor are derived, which means that disease persists when ℜ 0 > 1. By using a Lyapunov function, it is shown that the infection-free equilibrium is globally asymptotically stable if ℜ 0 < 1 and the infection equilibrium is globally asymptotically stable if ℜ 0 > 1. In the presence of limited medical resources, the results suggest that equitable distribution for the limited medical resources is significant when treating low-risk and high-risk diseases and that keeping a resource sharing coefficient at a moderate level helps to eliminate the disease. • We proposed an age-structured infectious disease model with limited medical resources. • some important parameters were included in ℜ 0. • ℜ 0 determines the global stability of the equilibria. • Discussions of the effects of the parameters and biological significance were provided. [ABSTRACT FROM AUTHOR]

In this paper, a tumor-immune system with impulse comprehensive therapy and stochastic perturbation is investigated. The combination of pulsed chemotherapy and pulsed immunotherapy and the effect of environmental random disturbance are reflected in this model. The existence and uniqueness of global positive solution of the system are proved. It is determined that the expectation of the solution is always less than a constant by utilizing the comparison theorems of impulsive differential equations and that the tumor cells will become weakly persistent in the mean or extinct under some sufficient conditions. Our results and numerical simulations show that random disturbance can inhibit the growth of tumor cells, and the combination of chemotherapy and immunotherapy can reduce the damage of therapy to the healthy cells. [ABSTRACT FROM AUTHOR]

• Impulsive differential equations were introduced to model intermittent androgen deprivation therapy. • The effects of white noises and tumour antigenicity on the dynamics of prostate cancer cells were discussed. • Sufficient conditions for the stationary distribution and ergodicity of the system were provided. • Immunotherapy can promote the therapeutic effect of intermittent androgen deprivation therapy. Intermittent androgen deprivation therapy is often used to treat prostate cancer, but there are few mathematical modelling studies of it. To explore the mechanisms of such therapy, we describe intermittent therapy with impulsive differential equations, then we propose a novel mathematical model of intermittent androgen deprivation therapy with white noise. We first studied the model's basic properties including the existence and uniqueness of the solution. By using the theory of stochastic differential equations, we investigated the thresholds for the extinction and persistence of prostate cancer cells, which are markedly affected by the antigenicity of tumours and noise parameters. Moreover, sufficient conditions for the stationary distribution and ergodicity of the system are provided. The results show that reducing the period of pulsed interventions or increasing the dosages (or frequencies) of the therapy will be helpful for curing prostate cancer. [ABSTRACT FROM AUTHOR]