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Conductive sponge sensors based on crack structure have attracted widespread attention in the field of wearable electronics for their excellent compressibility. However, the conventional preparation methods such as primary-coating and single-layer crack structure tend to result in poor stability and sensitivity of conductive sponge sensors. Therefore, a high-performance sensor of conductive nickel@polyurethane sponge prepared by secondary coating based on the double-layer crack structure is proposed in this study. A uniform conductive Ni-layer was coated on polyurethane sponge by secondary-coating metal nickel via physical vapor deposition (PVD) and electroless deposition (ELD), and a double-layer crack structure was designed by using the cyclic compression method to construct cracks in the primary and secondary-coated Ni layers, respectively. The sensor exhibited excellent stability due to the secondary-coating method, and the double-layer crack structure improved the sensitivity of the sensor to detect small deformations (< 10%) (GF = 99.33), which was nearly 3.8 times higher than that of the secondary-coated conductive sponge sensor with single-layer crack structure. Meanwhile, the sensor possessed an ultra-wide sensing range, low resistance relaxation time, and fast response time of 122 ms, as well as excellent reproducibility, and showed excellent sensitivity and application potential of application detection in areas such as speech recognition, human motion monitoring, and physiological signal monitoring. [ABSTRACT FROM AUTHOR]

To improve the precision of the dynamic modeling of bolted joints, a novel method is presented to model the dynamics by using a radially segmented annular virtual material equivalent bolted joints considering the thickness in this work. Firstly, on the basis of obtaining the contact pressure distribution at bolted joints, the division criterion of the virtual material layer along the radial segments is proposed, and the finite element (FE) model of the equivalent bolted joints of the radially segmented annular virtual material considering the thickness is established. Secondly, the characteristic parameters (elastic modulus, poisson's ratio, density, and thickness) of the radially segmented annular virtual material are used as inputs to calculate the natural frequencies of the bolted joints system, and the deep neural network (DNN) model is constructed and trained on this data sample. Finally, the cuckoo search (CS) algorithm is used to optimally identify the characteristic parameters of radially segmented annular virtual material. The bolted joints of the self-designed solid model of the bolted joints system are studied and analyzed as an example, and the maximum relative error of the first six orders natural frequencies lowers than 1.5 %. [ABSTRACT FROM AUTHOR]

To improve dynamic performance and steady-state accuracy of position leap control of the direct current (DC) servo motor, a fuzzy inference system (FIS) enabled artificial neural network (ANN) feedforward compensation control method is proposed in this study. In the method, a proportional-integral-derivative (PID) controller is used to generate the baseline control law. Then, an ANN identifier is constructed to online learn the reverse model of the DC servo motor system. Meanwhile, the learned parameters are passed in real-time to an ANN compensator to provide feedforward compensation control law accurately. Next, according to system tracking error and network modeling error, an FIS decider consisting of an FI basic module and an FI finetuning module is developed to adjust the compensation quantity and prevent uncertain disturbance from undertrained ANN adaptively. Finally, the feasibility and efficiency of the proposed method are verified by the tracking experiments of step and square signals on the DC servo motor testbed. Experimental results show that the proposed FIS-enabled ANN feedforward compensation control method achieves lower overshoot, faster adjustment, and higher precision than other comparative control methods. [ABSTRACT FROM AUTHOR]

The thermal conductivity of soil is an essential parameter for designing geothermal energy foundations and borehole heat exchange systems. To effectively improve the development efficiency of geothermal resources in the loess region of China, the plane heat source method was used to measure the thermal conductivity of loess samples with different water contents and temperatures, and scanning electron microscopy tests were conducted. Analyze the effects of water content, temperature, and soil microstructure on thermal conductivity. A weighted geometric mean model considering the soil temperature effect is proposed and compared with the traditional model. The experimental results reveal that soil thermal conductivity increases in stages as the temperature rises. After the temperature exceeds 30℃, the contribution of steam latent heat transfer to soil thermal conductivity gradually becomes significant. Within the temperature range of 1–60℃, the thermal conductivity increases with increasing saturation and tends to stabilize after saturation exceeds 60%; When the temperature is greater than 60℃, the thermal conductivity peaks near a saturation of 60%. As the water content increases, there is a significant difference in the thermal conductivity of undisturbed and remolded loess, which is related to the microstructure changes of the soil. The model calculation shows that the weighted geometric mean model proposed in this paper has the best prediction performance. The Leong model has good predictive performance for the thermal conductivity of loess, while the Gori and Lusen models have the lowest predictive accuracy. [ABSTRACT FROM AUTHOR]

Because the abstracts contain complex information and the labels of abstracts do not contain information about categories, it is difficult for cognitive models to extract comprehensive features to match the corresponding labels. In this paper, a cognitively inspired multi-granularity model incorporating label information (LIMG) is proposed to solve these problems. Firstly, we use information of abstracts to give labels the actual semantics. It can improve the semantic representation of word embeddings. Secondly, the model uses the dual channel pooling convolutional neural network (DCP-CNN) and the timescale shrink gated recurrent units (TSGRU) to extract multi-granularity information of abstracts. One of the channels in DCP-CNN highlights the key content and the other is used for TSGRU to extract context-related features of abstracts. Finally, TSGRU adds a timescale to retain the long-term dependence by recuring the past information and a soft thresholding algorithm to realize the noise reduction. Experiments were carried out on four benchmark datasets: Arxiv Academic Paper Dataset (AAPD), Web of Science (WOS), Amazon Review and Yahoo! Answers. As compared to the baseline models, the accuracy is improved by up to 3.36%. On AAPD (54,840 abstracts) and WOS (46,985 abstracts) datasets, the micro-F1 score reached 75.62% and 81.68%, respectively. The results show that acquiring label semantics from abstracts can enhance text representations and multi-granularity feature extraction can inspire the cognitive system's understanding of the complex information in abstracts. [ABSTRACT FROM AUTHOR]

Deep learning-based data-driven methods have been successfully developed in tool wear condition monitoring (TWCM), relying on the massive available labeled samples and the same probability distribution between training and testing data. However, these two prerequisites are often difficult to satisfy in actual industries, which results in significant performance deterioration of those methods. This paper proposes an intelligent cross-domain data-driven TWCM method based on feature transfer by a deep adversarial domain confusion network (DADCN) model. In this model, source and target feature extractors sharing the same network architecture are employed to obtain high-level representation from time–frequency spectrums of vibration signals in the different domains respectively. An independent adversarial learning mechanism is designed in domain obfuscator to learn domain-invariant feature knowledge, while the maximum mean discrepancy is applied to measure the distribution difference between different domains. A cross-domain classifier is utilized for tool wear condition monitoring across machining processes. The performances of the proposed DADCN model under two distribution measure criteria are experimentally demonstrated using six transfer tasks between laboratory and factory platforms. The results indicate that the DADCN model can improve the monitoring accuracy and exhibit distinct clustering of tool wear conditions, promoting a successful application of data-driven methods in actual industrial fields. [ABSTRACT FROM AUTHOR]

The toxic metals cadmium, lead, and mercury are endocrine-disrupting agents that could produce estrogenic effects involving breast carcinogenesis. In this study, we further explored the relationship between exposure to these metals and prevalent breast cancer among female participants, aged 20 years or older, in the 2007–2016 National Health and Nutrition Examination Survey (NHANES). Exposure was determined by measuring urinary concentrations of metals using inductively coupled plasma mass spectrometry. Urine creatinine-corrected concentrations of metals were calculated for each study participant. Multivariate logistic regression models were constructed to examine the association between urinary metals and prevalent breast cancer, adjusting for potential confounders. Of the 3352 study participants, 106 had been diagnosed with breast cancer (weighted prevalence, 3.13%). The results show that women with breast cancer had significantly higher urinary concentrations of lead and cadmium (both p < 0.0001) than those without breast cancer. After adjusting for all the covariates included in the study, however, only urinary lead was shown to be significantly associated with increased prevalence of breast cancer, with an odds ratio of 2.95 (95% CI: 1.13, 7.70) in the highest quartile of urinary lead concentrations (≥ 0.71 µg/g creatinine) as compared with the lowest quartile. No statistically significant associations were observed between urine cadmium or mercury levels and breast cancer. This study demonstrates a potential association between lead exposure and prevalent breast cancer among US women. Prospective and mechanistic studies are warranted to further investigate this interaction and explore the role of lead in breast carcinogenesis. [ABSTRACT FROM AUTHOR]

P-dichlorobenzene (p-DCB) is a pest repellent and air deodorant that is commonly found in the household and public buildings. Exposure to p-DCB has been suggested to have potential metabolic and endocrine effects. Little is known about its association with endocrine-related female cancers. In this cross-sectional study, a nationally representative subsample of 4459 women, aged 20 years or older, in the 2003–2016 National Health and Nutrition Examination Survey was analyzed for the association between p-DCB exposure, measured as urinary concentrations of 2,5-dichlorophenol (2,5-DCP), the primary metabolite of p-DCB, and prevalent endocrine-related female cancers (defined as breast, ovarian, and uterine cancers) using multivariate logistic regression models, adjusting for potential confounders. Of the study participants, 202 women (weighted prevalence, 4.20%) reported being diagnosed with any of these endocrine-related reproductive cancers. Women with reproductive cancers showed a statistically significant increase in urinary 2,5-DCP concentrations (weighted geometric mean, 7.97 vs. 5.84 µg/g creatinine; p < 0.0001), compared to women without these cancers. After adjusting for potential confounders, we found that women in the moderate (1.94– < 28.10 µg/g creatinine) and high level (≥ 28.10 µg/g creatinine) of 2,5-DCP had significantly increased odds of endocrine-related reproductive cancers (odds ratio of 1.66 (95% CI: 1.02, 2.71) and 1.89 (1.08, 3.29), respectively), as compared with those in the low exposure group (< 1.94 µg/g creatinine). This study demonstrates a potential relation between p-DCB exposure and prevalent endocrine-related reproductive cancers in US women. Prospective and mechanistic studies would further explore these interactions and elucidate the pathogenesis of endocrine-related female cancers potentially associated with p-DCB exposure. [ABSTRACT FROM AUTHOR]

For improving the dynamic quality and steady-state performance, the hybrid controller based on recurrent neural network (RNN) is designed to implement the position control of the magnetic levitation ball system in this study. This hybrid controller consists of a baseline controller, an RNN identifier, and an RNN controller. In the hybrid controller, the baseline controller based on the control law of proportional-integral-derivative is firstly employed to provide the online learning sample and maintain the system stability at the early control phase. Then, the RNN identifier is trained online to learn the accurate inverse model of the controlled object. Next, the RNN controller shared the same structures and parameters with the RNN identifier is applied to add the precise compensation control quantity in real-time. Finally, the effectiveness and advancement of the proposed hybrid control strategy are comprehensively validated by the simulation and experimental tests of tracking step, square, sinusoidal, and trapezoidal signals. The results indicate that the RNN-based hybrid controller can obtain higher precision and faster adjustment than the comparison controllers and has strong anti-interference ability and robustness. [ABSTRACT FROM AUTHOR]

Aiming at the problem of poor transient performance of the control system caused by the control uncertainty of the undertrained neural network, a neural network compensation control method based on fuzzy inference is proposed in this paper. The method includes three control substructures: fuzzy inference block, neural network control block and basic control block. The fuzzy inference block adaptively adjusts the neural network compensation control quantity according to the control error and the error rate of change, and adds a dynamic adjustment factor to ensure the control quality at the initial stage of network learning or at the moment of signal transition. The neural network control block is composed of an identifier and a controller with the same network structure. After the identifier learns the dynamic inverse model of the controlled object online, its training parameters are dynamically copied to the controller for real-time compensation control. The basic control block uses a traditional PID controller to provide online learning samples for the neural network control block. The simulation and experimental results of the position control of the magnetic levitation ball show that the proposed method significantly reduces the overshoot and settling time of the control system without sacrificing the steady-state accuracy of neural network compensation control, and has good transient and steady-state performance and strong robustness simultaneously. [ABSTRACT FROM AUTHOR]

Patients with pediatric cancers such as neuroblastoma (NB) are often unresponsive to checkpoint blockade immunotherapy. One major factor in pediatric tumor resistance to immunotherapy is considered to be the low mutation rate of pediatric tumors. Another factor may be the overexpression of additional inhibitory pathways. While analyzing the RNA-sequencing database TARGET, we found that human NB tumors overexpress immune checkpoint molecule CD200. To determine its significance and impact on tumor immune microenvironment, we analyzed 49 cases of previously untreated, surgically removed NB tumors using immunohistochemistry and multi-color flow cytometry (FACS). We found that CD200 is overexpressed in more than 90% of NB tumors. In the tumor microenvironment of NB, CD200 is mainly overexpressed in CD45− NB tumor cells, while its cognate receptor (CD200R) is mainly expressed in HLA-DR+CD14+ myeloid cells and CD11c+ dendritic cells. Low-level expression of CD200R is also observed in tumor-infiltrating CD4+ and CD8+ T cells. In NB tumors with higher CD200 expression (CD200high), we observed lower numbers of HLA-DR+CD14+ myeloid cells and less tumor-infiltrating CD4+ and CD8+ T cells. Moreover, we found that CD4+ and CD8+ T cells produced less IFN-γ and/or TNF-α in CD200high NB tumors. Thus, CD200–CD200R pathway appears to downregulate anti-tumor immunity in the tumor microenvironment of NB tumors, and blockade of this pathway may be beneficial for NB patients. [ABSTRACT FROM AUTHOR]

During the prediction of the tool point frequency response function (FRF) based on receptance coupling substructure analysis (RCSA), in allusion to the problems that the existing tool model cannot simulate the contour of the tool precisely and the large amount of calculation for joint parameter identification during the calculation of the rotational FRF of the spindle, the joint model and the optimization algorithm affect the identification accuracy when acquiring the rotational FRF of the spindle. This study proposes a rapid prediction method for the tool point FRF on the basis of RCSA. The method divides the spindle-holder-tool system into two parts: the spindle-holder-partial shank and the remaining tool. On this basis, this study proposes a method of accurately establishing the model of the remaining tool with a 3D scanner and improves the calculation method of the rotational FRF of the spindle-holder-partial shank assembly through the reverse RCSA and finite difference method directly through the modal impact testing on the cutting tool. This study provides the experiments on a vertical machining center and compares the predicted FRF and the measured FRF on the tool point. The result indicates that the predicted FRF curve is consistent with the measured FRF curve with the relative error of natural frequency within 4.0%. This study confirms that the method is feasible, effective, and convenient. [ABSTRACT FROM AUTHOR]

Tool wear monitoring has been increasingly important in intelligent manufacturing to increase machining efficiency. Multi-domain features can effectively characterize tool wear condition, but manual feature fusion lowers monitoring efficiency and hinders the further improvement of predicting accuracy. In order to overcome these deficiencies, a new tool wear predicting method based on multi-domain feature fusion by deep convolutional neural network (DCNN) is proposed in this paper. In this method, multi-domain (including time-domain, frequency domain and time–frequency domain) features are respectively extracted from multisensory signals (e.g. three-dimensional cutting force and vibration) as health indictors of tool wear condition, then the relationship between these features and real-time tool wear is directly established based on the designed DCNN model to combine adaptive feature fusion with automatic continuous prediction. The performance of the proposed tool wear predicting method is experimentally validated by using three tool run-to-failure datasets measured from three-flute ball nose tungsten carbide cutter of high-speed CNC machine under dry milling operations. The experimental results show that the predicting accuracy of the proposed method is significantly higher than other advanced methods. [ABSTRACT FROM AUTHOR]

A growing body of evidence has pointed to a role of environmental chemical exposures in breast cancer etiology. This study was to examine the association between exposure to the endocrine-disrupting metals, including cadmium (Cd), lead (Pb), and mercury (Hg), and breast cancer in US women. A nationally representative subsample of 9260 women aged ≥ 20 years in the 2003-2012 National Health and Nutrition Examination Survey was analyzed for the association of blood levels of these metals with prevalent breast cancer using multivariate logistic regression models. Of the study participants, 284 women (weighted prevalence, 2.8%) were self-reported being diagnosed with breast cancer during 2003-2012. Breast cancer women showed significantly elevated blood levels of Cd and Pb, but not Hg. After adjusting for potential confounders, we found that women in all of the higher quartiles of blood lead levels (BLLs) had significantly increased odds ratio of prevalent breast cancer compared with those in the lowest quartile. However, a significant association with prevalent breast cancer was not seen with blood levels of either Cd or Hg. Our study demonstrates a potential relationship between lead exposure, measured as BLLs, and female breast cancer. Additional epidemiologic and mechanistic studies would further explore these interactions and elucidate the potential role of lead exposure in breast cancer etiology. [ABSTRACT FROM AUTHOR]

A number of studies has pointed to air pollution as an additional factor that could be associated with preterm birth. We assessed in this study the association between exposure to PM2.5 in ambient air during pregnancy and preterm birth in metropolitan areas of the state of Georgia, where the rate of preterm birth has been among the highest in the nation over the years. Birth data were obtained from the National Center for Health Statistics natality dataset. The study population consisted of 53,094 singleton live births between January 1 and December 31, 2004 in nine metropolitan counties of Georgia. Preterm birth was defined as birth, which occurs before 37 weeks of gestation. County-level daily air quality index (AQI) data obtained from the US Environmental Protection Agency (EPA) was used to estimate individual exposure levels of PM2.5 for each study participant based on the county of residence for the duration of the pregnancy. A multivariate logistic regression analysis was conducted to assess the association, adjusting for potential confounders. Of the infants whose mothers resided in the nine metropolitan counties of Georgia, 4543 (8.6%) were born preterm. A higher rate of preterm birth (9.8%) was observed in infants whose mothers were exposed to ambient PM2.5 with AQI values > 50 than the ones with AQI ≤ 50 (EPA standard for good air quality conditions). Mothers with exposure to PM2.5 at average AQI values greater than 50 during the entire pregnancy were at increased risk of preterm birth (odds ratio 1.15; 95% CI 1.07, 1.25), after adjusting for sex of infant, mother's age, race/ethnicity, education, marital status, prenatal care, cigarette smoking, alcohol consumption, and season of conception. The study provides more evidence on the role of PM2.5 in preterm birth. Reducing exposure to ambient particulate matter, especially in urban areas, for pregnant women would be necessary to improve the health of infants. [ABSTRACT FROM AUTHOR]

Nutritional status could affect arsenic metabolism and toxicity in the general population chronically exposed to low levels of inorganic arsenic. In this study, we examined the association of serum folate and cobalamin with urinary concentrations of dimethylarsinic acid (DMA), the most abundant metabolite of inorganic arsenic measured in urine, in children and adults who participated in the 2003-2006 US National Health and Nutrition Examination Surveys. A total of 1161 children (aged 6-19 years) and 1938 adults (aged 20-85 years) were analyzed for the association using multivariate general linear models, adjusting for potential confounders. We observed a positive association between serum levels of folate and cobalamin and creatinine-corrected urinary concentrations of DMA in both children and adults. Furthermore, serum levels of folate and cobalamin were inversely associated with homocysteine (Hcy). These results suggest that dietary intake of folate and cobalamin may exhibit protective functions against arsenic toxicity by increasing arsenic metabolism to the less toxic metabolite DMA and decreasing serum levels of Hcy. [ABSTRACT FROM AUTHOR]

Taurine has been reported to influence osteogenic differentiation, but the role of taurine on cartilaginous differentiation using human umbilical cord-derived mesenchymal stem cells (hUC-MSCs) remains unclear. In this study, we investigated the effect of taurine (0, 1, 5 and 10 mM) on the proliferation and chondrogenesis of hUC-MSCs by analyzing cell proliferation, accumulation of glycosaminoglycans and expression of cartilage specific mRNA. The results show though taurine did not affected the proliferation of hUC-MSCs, 5 mM of taurine is sufficient to enhanced the accumulation of glycosaminoglycans and up-regulate cartilage specific mRNA expression, namely collagen type II, aggrecan and SOX9. Taurine also inhibits chondrocyte dedifferentiation by reducing expression of collagen type I mRNA. Taken together, our study reveals that taurine promotes and maintains the chondrogenesis of hUC-MSCs. [ABSTRACT FROM AUTHOR]

We investigated in this study the relationship between exposure to para-dichlorobenzene ( p-DCB), measured as urinary concentrations of 2,5-dichlorophenol (2,5-DCP), and metabolic syndrome in non-diabetic adult participants. A nationally representative subsample of 1706 non-diabetic adult participants aged 20-79 years randomly selected for measurement of urinary concentrations of 2,5-DCP in the 2007-2010 US National Health and Nutrition Examination Survey was analyzed. A dose-dependent increase in the prevalence of metabolic syndrome was observed in the study participants across quartiles of urinary 2,5-DCP ( p-trend = 0.0025). After adjusting for potential confounders, individuals in the third and fourth quartile of urinary 2,5-DCP had 1.47 (95 % CI 1.02, 2.14) and 1.56 (95 % CI 1.10, 2.23) increased odds of metabolic syndrome, respectively, compared with individuals with the lowest quartile. Of the five components of metabolic syndrome, waist circumference and HDL-cholesterol showed a significant and monotonic association with urinary 2,5-DCP. Participants with the highest quartile of 2,5-DCP had 3.18 cm (95 % CI 1.34, 5.02) higher mean waist circumference and 2.83 mg/dL (95 % CI −4.68, −0.98) lower mean HDL-cholesterol than the participants in the lowest quartile. This study suggests a potential relationship between p-DCB exposure and metabolic syndrome in non-diabetic adults. Prospective epidemiological and mechanistic studies are needed to further explore these interactions. [ABSTRACT FROM AUTHOR]

Here, we report the atomic-scale microstructural characterization and dielectric properties of crystalline cubic pyrochlore BiMgNbO (BMN) nanoparticles with mean size of 70 nm, which were synthesized by sol-gel method. The crystallinity, phase formation, morphology, and surface microstructure of the BMN nanoparticles were characterized by X-ray diffraction (XRD), Raman spectra, transmission electron microscopy (TEM), and high-resolution transmission electron microscopy (HRTEM), respectively. The phase evolution of the BMN nanoparticles investigated by XRD patterns showed that uniform cubic pyrochlore BMN nanoparticles were obtained after calcination at temperature of 800 °C, and their structural information was revealed by Raman spectrum. TEM images demonstrated that the BMN nanoparticles had a spherical morphology with an average particle size of 70 nm, and their crystalline nature was revealed by HRTEM images. In addition, HRTEM images also demonstrate a terrace-ledge-kink (TLK) surface structure at the edges of rough BMN nanoparticles, where the terrace was on the (100) plane, and the ledge on the (001) plane. The formation of such a TLK surface structure can be well explained by a theory of periodic bond chains. Due to the surface structural reconstruction in the BMN nanoparticles, the formation of a tetragonal structure in a rough BMN nanoparticle was also revealed by HRTEM image. The BMN nanoparticles exhibited dielectric constants of 50 at 100 kHz and 30 at 1 MHz, and the dielectric loss of 0.19 at 1 MHz. [ABSTRACT FROM AUTHOR]

Many scholars carried out large quantity of researches on oil and gas preservative conditions of marine carbonate rocks from the aspects of cap rocks, faults, formation water, hydrodynamic, and tectonism. This article gives dynamic evaluation on oil and gas preservative conditions of marine stratum in Jianghan (江汉) plain of multiphase tectonic disturbance from the view of paleofluid geochemistry. The conclusion shows that there mainly existed fluid filling of two periods in the reservoir of Lower-Middle Triassic to Permian. The fluid filled in the earlier period came from Lower Palaeozoic. The interchange of fluid in Lower-Middle Triassic to Permian suggested the oil and gas in Lower Palaeozoic had been broken up. The fluid filled in the later period (Lower-Middle Triassic to Permian) came from the same or adjacent strata and lacked anatectic fluidogenous features coming from Palaeozoic. With good preservative conditions of bulk fluid at the time, the fluid of Lower-Middle Triassic to Permian and that of Lower Palaeozoic did not connect with each other. However, the hydrocarbon generation peak of marine source rocks had passed or the paleo-oil and gas reservoirs had been destroyed at that time and the marine stratum of Palaeozoic to Triassic in the research area did not put out commercial oil and gas flow. [ABSTRACT FROM AUTHOR]

The (Ba1−xSrx)TiO3 (BST) ferroelectric thin films exhibit outstanding dielectric properties, even at high frequencies (>1 GHz), and large, electric-field dielectric tunability. This feature makes them suitable for developing a new class of tunable microwave devices. The dielectric properties and dielectric tuning property of BST thin films are closely related to the film compositions, substrate types, and post-deposition process. The successful implementation of BST films as high-frequency dielectrics in electrically tunable microwave devices requires a detailed understanding of both their processing and material properties. This paper will review the recent progress of BST thin films as active dielectrics for tunable microwave devices. The technical aspects of BST thin films, such as processing methods, post-annealing process, film compositions, film stress, oxygen defects, and interfacial structures between film and substrate, are briefly reviewed and discussed with specific samples from the recent literature. The major issues requiring additional investigations to improve the dielectric properties of BST thin films for tunable microwave applications are also discussed. [ABSTRACT FROM AUTHOR]