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Unique rotatable anisotropy is designed and achieved in Fe4N thin film with quasi-single magnetic domain structure by the exchange coupling between magnetic crystalline grains. It is found that the rotatable anisotropy can be characterized with isotropic rectangular in-plane hysteresis loops with remanence ratio close to 0.9 and angle-independent permeability spectra with quasi-isotropic resonance frequency, line-width and amplitude. Strength of the rotatable anisotropy is estimated by the random anisotropy model theoretically and it is comparable with the experimental results. Its rotatable feature can be understood from the equivalent remanence state established along different directions and the nucleation magnetization reversal. Moreover, a static method is established to determine rotatable anisotropy of the Fe4N thin film. A 50 Oe rotatable anisotropy was determined by M-H curve measured by B-H loop analyzer, which is close to 58 Oe derived from the permeability spectra. Our work provides the new way to search and characterize the rotatable anisotropy in magnetic systems.

Epitaxial PbTiO3 films with a smooth and dense surface were fabricated by a promising hydrothermal synthesis on an Nb:SrTiO3(100) substrate. The resulting coated substrate was used to fabricate a Pt/PbTiO3/Nb:SrTiO3 heterostructure device. The device exhibited a multilevel storage capacity with an appropriate R OFF / R ON ratio and excellent endurance and retention. An electric conduction analysis indicated that the resistive switching behavior of the device was attributed to the trap controlled space-charge-limited current conduction that was caused by the oxygen vacancies in the PbTiO3 hydrothermal films. The modulation of the Pt/PbTiO3 Schottky-like junction depletion under an applied electric field is thought to be responsible for the resistive switching behavior of the device in the carrier injection-trapped/detrapped process.

In order to find out the effect of lattice configuration on the structural stability of Rare earth magnesium nickel compounds, two La0.65Nd0.15Mg0.20Ni3.50 isomerides with single Ce2Ni7-type (2H) and single Gd2Co7-type (3R) structure, respectively, have been prepared for the first time. It is found that, after hydrogenation/dehydrogenization cycles, the volume of [A2B4] subunit in 3R-type structure shrinks while that of [A2B4] in 2H-type structure gets expanded. Meanwhile, the [AB5] subunits in both structures expand, which have smaller initial volumes than [A2B4] subunits. The standard deviation of subunit volumes σ(V[A2B4], V[AB5]) in 3R-type structure is always smaller than that of 2H-type structure, and the σ(V[A2B4], V[AB5]) in 3R-type structure is only 0.45 A3, far less than that of 2H-type compound (2.11 A3) after 50 cycles. The lattice microstrain in 3R-type structure is quite lower than that in 2H-type structure and the 3R-type structure possesses a superior structural stability and higher anti-pulverization ability. After 50 cycles, the capacity retention of 3R-type compounds is 100% while that of 2H-type compounds is only 87.44%. Transmission electron microscope and X-ray diffraction analyses show that the crystal structure of 3R-type compound is almost unchanged after 50 cycles, while, severe lattice deformation is observed in 2H-type structure.

As the source of water for the South-to-North Water Diversion Project of China, the water quality of the Danjiangkou Reservoir (DJKR) is related to the safety of drinking water for billions of residents. Consequently, microplastics in surface water and sediment samples of the DJKR were investigated in this study. Microplastics were observed in all water and sediment samples with abundances varying from 467 to 15,017 n/m3 and 15 to 40 n/kg wet weight, respectively. Microplastics were rich in colour and dominated by fibrous items. Small-sized particles (< 2 mm) were more frequently observed than other sizes. Analysis by micro-Raman spectroscopy showed that polypropylene was the major polymer type. These systematic results demonstrated that the DJKR is suffering from the pollution of microplastics, which should be paid more attention based on its potential threat to the aquatic organisms and residents impacted by the drinking water source pollution.

Understanding the hierarchical architectural elements of fluvial sand bodies is important for planning their development strategy and to enhance oil recovery. Red-Green-Blue (RGB) blending of multiple seismic attributes and forwarding seismic modelling are commonly used in the analysis of compound sand bodies. However, RGB blending of multiple seismic attributes can only qualitatively describe the boundaries and thickness of sand bodies. The forward seismic modelling techniques previously documented in the literature are not effective when depicting the geometry of, and stacking relationships between, sand bodies (i.e., reservoir compartmentalisation). Hence, we propose in this work a new workflow that combines fused spectral-decomposition seismic attributes (SDSAs) and forwarding seismic modelling to quantitatively predict sand thickness, and to characterise stacking relationships between sand bodies. First, we employ a Support Vector Machine (SVM) algorithm to fuse high, middle, and low frequency components (attributes) of seismic data so as to quantitatively predict the thickness of sand bodies. Second, we define the seismic waveform response patterns corresponding to the typical conceptual stacked sand bodies. With the constraints of waveform patterns and predicted sand thickness (fused SDSAs), the geometry and stacking relationships of the sand bodies are characterised by forward seismic modelling. To illustrate the effectiveness of our proposed workflow, we apply it to the Neogene Minghuazhen Formation (Nm) of the QHD 32–6 oil field, Bohai Bay Basin, China. We define five architectural elements of a meandering fluvial reservoir by analysing the hierarchy of sand bodies using our workflow. The predicted sand bodies in this workflow were further proven by horizontal drilling and production data.

Herein, a facile and universal strategy for preparing metal phosphides using metal organic frameworks (MOFs) was designed and a highly efficient hydrogen evolution reaction (HER) catalyst was developed. By employing MIL-101(Fe) as the precursor, through carbonization, in situ reduction and a direct phosphating process in the presence of NaH2PO2, carbon-coated FeP nanoparticles (FeP-NPs/C) with a large specific surface area (114.3 m2 g−1) were successfully synthesized. When applied for HER testing in 0.5 M H2SO4 solution, the catalyst exhibited superior HER activity with an onset overpotential of 23 mV, a Tafel slope of 72 mV dec−1, an exchange current density of 0.925 mA cm−2 and an overpotential of 72 mV at a current density of 10 mA cm−2, which were comparable to or even better than those of most of the catalysts reported so far. Moreover, it exhibited no obvious decay after 1000 cycles in a long-term durability test.

In this work, we characterized the optical transmittance, optical band gap, nonlinear refraction and nonlinear absorption of a series of GeS2-Sb2S3-CsCl chalcogenide glasses, and monitored their compositional dependencies. We found that the number of lone-pair electrons and the bandgap energy are two dominate factors that determined these linear and third-order nonlinear optical properties. Besides, evaluation of figure of merit verified that the GeS2-Sb2S3-CsCl glasses are suitable for optical limiting devices.

Volatile flavor compounds created in the mixture of enzymatic hydrolysates of soy sauce residue and defatted soybean by reaction flavor technology (RFT) were analyzed and compared using solid phase micro-extraction/gas chromatography/mass selective detector to develop a seasoning sauce. Using response surface methodology analysis, RFT was performed by adding 0.50% fructose, 0.33% glutamic acid, 0.68% arginine, 0.37% methionine, and 0.86% glycine in the base and reaction conditions at 93°C for 120 min. A total of 57 compounds, 43 in RFT and 45 in control, were detected, including 8 aldehydes and ketones, 6 aromatic hydrocarbons, 3 acids, 12 alcohols, 6 esters, 4 furans, 9 nitrogen-containing compounds, 4 sulfur-containing compounds, and 5 miscellaneous. In RFT samples, aldehydes and ketones, aromatic hydrocarbons, alcohols, esters, and S-containing compounds were significantly increased. Sulfur-containing compounds were increased by 687 fold compared to control samples (P

Objective Few studies have systematically assessed symptom-based diagnoses of functional gastrointestinal disorders (FGID) in China. This study aimed to evaluate the positive endoscopic and ultrasonographic findings in patients with symptom-based FGID. Methods Adult patients with gastrointestinal symptoms but not red flag symptoms who were empirically diagnosed with FGID underwent an upper or lower gastrointestinal endoscopy. An abdominal ultrasonography was also performed to screen for potential organic diseases. Patients' data were collected and the Rome III questionnaire was used for symptom-based assessment. Results Positive endoscopic and ultrasonographic findings were detected in 31.8%, 26.2% and 9.9% of patients with esophageal, gastroduodenal and intestinal disorders, respectively. Among patients diagnosed with FGID using the questionnaire, the rates of positive endoscopic and ultrasonographic findings were higher in patients with upper gastrointestinal symptoms than in those with lower gastrointestinal symptoms. Moreover, patients elder than 34.5 years with upper gastrointestinal symptoms and those elder than 47.5 years with lower gastrointestinal symptoms were more likely to have positive endoscopic and ultrasonographic findings. Conclusions The Rome III questionnaire is a valuable diagnostic tool for screening FGID in China. However, the rates of positive endoscopic and ultrasonographic findings vary depending on the location of symptoms. Endoscopy and ultrasonography have irreplaceable value, especially for investigating upper gastrointestinal symptoms.

Microplastics pollution in aquatic ecosystems has aroused increasing global concern, leading to an explosive growth of studies regarding microplastics published in the past few years. To date, there is still a lack of standardized methodologies used for the detection of microplastics within environmental samples, thus hampering comparison of the reported data. This review summarizes the currently used methodologies for sampling, extracting and identifying microplastics in three kinds of aquatic environmental matrices (water, sediment and aquatic biota) and includes a critical discussion of the advantages and limitations of these methodologies. The quality control and quality assurance measures taken to reduce background contamination and validate analytical methods are also discussed. Finally, this review highlights the current challenges and gives suggestions for the future research.

Colletotrichum gloeosporioides is the causal agent of rubber tree anthracnose and leads to serious losses of natural rubber production. The pathogenesis of C. gloeosporioides on rubber tree remains unknown. Cerato-platanin proteins are small, secreted cysteine-rich proteins that contribute to virulence and function in plant–fungal interactions. A gene encoding cerato-platanin protein, CgCP1, was identified in C. gloeosporioides. In silico analysis indicated that CgCP1 belongs to a new branch of the cerato-platanin protein family. The CgCP1 knockout mutants (ΔCgCP1) and complementary strain (Res-ΔCgCP1) were generated to investigate its biological function. The results showed that the speed of growth of aerial hyphae was not significantly different among the wild-type (WT), ΔCgCP1, and Res-ΔCgCP1 strains, but conidiation of ΔCgCP1 significantly decreased in comparison with the WT. The pathogenicity test proved that the severity of symptoms caused by ΔCgCP1 was reduced significantly compared with those caused by the Res-ΔCgCP1 and WT strains. Additionally, CgCP1 induced necrosis-like cell death on tobacco leaf and accumulation of reactive oxygen species in rubber tree mesophyll protoplasts. Altogether, these data indicate the involvement of C. gloeosporioides CgCP1 in conidiation and the interaction with rubber tree.

Metal-hydride based hydrogen storage materials have recently been designed for rechargeable batteries where protons serve as charge carriers. However, the metal-hydride materials generally suffer from poor cycling stability during electrochemical hydrogen storage process. Herein, we report, for the first time, a new AB4-type single-phase La0.78Mg0.22Ni3.67Al0.10 metal-hydride superlattice compound with excellent electrochemical hydrogen storage properties. This metal-hydride compound is able to deliver a high discharge capacity of 393 mAh g−1, according to its hydrogen gas absorbability (1.50 wt%), and exhibit a superior rate capability of 180 mAh g−1 even at 1.8 A g−1. In addition, the compound retains its crystalline structure when subjects to electrochemical cycling and shows strong anti-oxidation ability in alkaline electrolyte, indicative of excellent cyclability. Our results demonstrate that the AB4-type superlattice structure metal-hydride can be a promising electrode material for hydrogen storage, and provide a new avenue for the applications of metal-hydride batteries.

Objective. To develop and validate a prediction model for high ovarian response in in vitro fertilization-embryo transfer (IVF-ET) cycles. Methods. Totally, 480 eligible outpatients with infertility who underwent IVF-ET were selected and randomly divided into the training set for developing the prediction model and the testing set for validating the model. Univariate and multivariate logistic regressions were carried out to explore the predictive factors of high ovarian response, and then, the prediction model was constructed. Nomogram was plotted for visualizing the model. Area under the receiver-operating characteristic (ROC) curve, Hosmer-Lemeshow test and calibration curve were used to evaluate the performance of the prediction model. Results. Antral follicle count (AFC), anti-Müllerian hormone (AMH) at menstrual cycle day 3 (MC3), and progesterone (P) level on human chorionic gonadotropin (HCG) day were identified as the independent predictors of high ovarian response. The value of area under the curve (AUC) for our multivariate model reached 0.958 (95% CI: 0.936-0.981) with the sensitivity of 0.916 (95% CI: 0.863-0.953) and the specificity of 0.911 (95% CI: 0.858-0.949), suggesting the good discrimination of the prediction model. The Hosmer-Lemeshow test and the calibration curve both suggested model’s good calibration. Conclusion. The developed prediction model had good discrimination and accuracy via internal validation, which could help clinicians efficiently identify patients with high ovarian response, thereby improving the pregnancy rates and clinical outcomes in IVF-ET cycles. However, the conclusion needs to be confirmed by more related studies.

Background. Bortezomib is used for treating multiple myeloma (MM); however, it has considerable adverse effects. Emodin has been reported to exhibit inhibitory effects on MM cell lines. We investigated the efficacy of emodin 35 (E35), an emodin derivative, using U266 and MM1s cell lines in treating MM and the efficacy of combining bortezomib and E35. Methods. MTT assays were used to observe the effects of E35 on MM cell growth. The effects on cellular apoptosis were then observed using Annexin V/propidium iodide (PI) staining assay. The expression of apoptosis-related genes, including the caspase family, was examined. The efficacy of combining bortezomib and E35 was investigated by examining the expression of the Akt/mTOR/4EBP1 signaling pathway-related proteins. Results. We report that E35 inhibited the growth of U266 and MM1s cells by inducing cellular apoptosis. Moreover, E35 downregulated the expression of apoptosis-related genes and suppressed the phosphorylation of Akt/mTOR/4EBP1 signaling pathway-related genes, thus exhibiting synergistic effects with bortezomib. All observed effects were dose-dependent. Conclusion. The results showed that E35 exhibited cytotoxic effects in MM cell lines in protein levels. Thus, E35, particularly in combination with bortezomib, may be considered as a promising treatment for MM; however, this requires further investigation in vivo.

This study attempts to examine environmental controls of the underground CO2 concentration, taking the CO2 concentration 4 m beneath the soil as an example. An SVD-PCA-ANN (singular value decomposition-principal component analysis-artificial neural network) preview model is proposed with the data of underground CO2 concentration and 12 environmental variables (the soil and meteorological data). The R2, RMSE, and RPD values of the proposed model are, respectively, 0.8874, 0.3351, and 2.7929, performing better than the popular preview models like SAE (stacked autoencoders), SVM (support vector machine), and LSTM (long short-term memory). It is proved that the underground CO2 concentration can be approximated by a nonlinear function of the considered variables. Soil temperature, salinity, and wind speed are the leading environmental controls, which explain 32.04%, 13.68%, and 11.21% in the variability of the underground CO2 concentration, respectively. Possible mechanisms associated with the environmental controls are also preliminarily discussed.

AI face-changing is a new technology that is developing in recent years, and its appearance may bring unnecessary trouble to certain fields of human society. On special occasions, there is an urgent need for a model that can autonomously determine whether an image has undergone AI face-exchanging processing.Proposed two convolutional structures based on the braod learning system (BLS), and give the specific algorithm flow. Using the convolution method for the basic BLS structure, try to directly convolve the feature mapping layer and the input data. The experimental results show that the BLS with convolutional structure performs well on small face data sets, and a very simple structure can achieve satisfactory accuracy and training time on the data set. It reflects the simplicity and reliability of the BLS-Convolution structure.

This study was to preview the risk of 30-day mortality in sepsis patients using sentiment analysis. The clinical data of patients and nursing notes were collected from the Medical Information Mart for Intensive Care (MIMIC-III) database. The factors influencing 30-day mortality were analyzed using the Cox regression model. And, the prognostic index (PI) was estimated. The receiver operating characteristic (ROC) curve was used to determine the PI cut-off point and assess the prediction ability of the model. In total, 1844 of 3560 patients were eligible for the study, with a 30-day mortality of 37.58%. Multivariate Cox analysis showed that sentiment polarity scores, sentiment subjectivity scores, simplified acute physiology score (SAPS)-II, age, and intensive care unit (ICU) types were all associated with the risk of 30-day mortality ( P < 0.05 ). In the preview of 30-day mortality, the area under the curve (AUC) of ROC was 0.78 (95%CI: 0.74–0.81, P < 0.001 ) when the cut-off point of PI was 0.467. The documented notes from nurses were described for the first time. Sentiment scores measured in nursing notes are associated with the risk of 30-day mortality in sepsis patients and may improve the preview of 30-day mortality.

Image fusion is widely used in many fields, such as medical research and remote sensing nowadays. To better address thermal human face image recognition problem in surveillance field, extensive research is done using advanced fusion techniques such as Gabor Filtering and Genetic Algorithm. While traditional fusion techniques are often neglected in most cases. For efficient industrial usage, we compared the recognition rates of six conventional image fusion techniques using the benchmark Tufts Face Databases as data source and using recent iteratively reweighted regularized robust coding (IR3C) algorithm as evaluation method. Final results showed a great improvement in thermal image recognition rates even compared with advanced methods. Especially the Weighted Average technique and Principal Component Analysis (PCA) shows 99.488% and 98.721% recognition accuracy with a stable performance in relatively small-scale dataset. Discrete Wavelet Transform (DWT), Pyramid Fusion and Select Maximum technique also maintained over 93% recognition rates. Discussions of those high-performance fusion techniques for particular conditions in surveillance field and certain limitations of our work are proposed.

Objective. This research attempted to explore the neuroprotective effect of choline and establish evidence for future dietary recommendations and nutritional interventions to maintain a proper cognitive function among elders aged >60 years in the US. Method. This cross-sectional study retrieved data of 2,393 eligible elderly participants from the 2011-2014 National Health and Nutrition Examination Survey. Combining dietary and supplement choline intake, total choline intake was evaluated using the 24-hour dietary recall method and the dietary supplement questionnaire. Total choline intake was categorized into tertiles, which ranged at 399.50 mg/day (T3). The Consortium to Establish a Registry for Alzheimer’s Disease (CERAD) Word Learning subtest, Animal Fluency (AF) test, and Digit Symbol Substitution test (DSST) was used to measure cognitive function. Participants who scored the lowest 25th percentile in each cognitive test were classified in the low cognitive function (LC) group. Logistic regression models were implemented to examine the association between total choline intake and the incidence of LC. Results. In the CERAD test, the risk of LC was significantly lower in T2 than T1 (OR: 0.668, 95% CI: 0.493-0.904, and P = 0.006 ) when adjusted for age, gender, BMI, alcohol consumption, and hypertension. Similarly, T2 was associated with a significantly lower risk of LC when assessed by the AF test (OR: 0.606, 95% CI: 0.580-0.724, and P < 0.001 ) and DSST (0.584, 95% CI: 0.515-0.661, and P < 0.001 ). In all three cognitive measures, the T3 of the total choline intake was not associated with cognitive function compared to T1. Conclusion. Total choline intake at 187.06-399.50 mg/day reduces the risk of LC by approximately 50% compared to intake at

In this paper, we formulate and investigate new switched HIV/AIDS models with drug treatment involving Caputo-fractional derivatives. Initially, due to the fractional derivative order related to the memory and hereditary effects and supposing that the model coefficients are time-varying parameters, we develop a Caputo-fractional order HIV/AIDS models with switching parameters and study their dynamics utilizing Lyapunov–Razumikhin technique. Furthermore, the results show that the fractional derivative α ( 0 < α < 1 ) and the switching parameters are related to the critical threshold value ( R ^ or R ¯ ) which ensures disease eradication under the condition of R ^ < 1 or R ¯ < 1 . Then, a treatment compartment is introduced into the above model from the asymptomatic infected individuals until the full blown AIDS individuals. Novel sufficient conditions on the threshold value are derived to verify that the disease is eventually cleared as the critical threshold parameter is below unity. Finally, some simulations are employed to support the main results and one future research direction is presented.

This chapter aims to explain the processes of machine cognition for a better understanding of environmental changes at the current level of machine intelligence and conjecture how evolution of the machine brain would change the future way of knowledge discovery (data mining) in environments sensing. In order to strengthen the continuity of Chap. 2, we present a parallel framework based on MPI for large dataset to extract power spectrum features of EEG signals so as to improve the speed of brain signal processing. At present, the Welch method has been wildly used to estimate the power spectrum. However, the traditional Welch method takes a lot of time especially for large dataset. In view of this, we added the MPI into the traditional Welch method and developed it into a reusable Master-Slave parallel framework. As long as the EEG data of any format is converted into the text file of a specified format, the power spectrum features can be extracted quickly by this parallel framework. In the proposed parallel framework, the EEG signals recorded by a channel are divided into N overlapping data segments. Then the PSD of N segments are computed by some nodes in parallel. The results are collected and summarized by the master node. The final PSD results of each channel are saved in the text file, which can be read and analyzed by the Microsoft Excel. This framework can be implemented not only on the clusters but also on the desktop computer. In the experiment, we deploy this framework on a desktop computer with a 4-core Intel CPU. It took only a few minutes to extract the power spectrum features from 2.85 GB EEG dataset, seven times faster than using the Python. This framework makes it easy for users, who do not have any parallel programming experience in constructing the parallel algorithms to extract EEG power spectrum. At the end of this chapter, reinforcement learning for solving a blind separation problem is carried out to highlight the necessity to change the way of data mining in environments sensing.

This chapter aims to explain how to construct a machine learning system and summarize the current level of machine intelligence as cognitive computation. One-dimensional deep neural network is utilized for illustration and detection of recorded epileptic seizure activity in Electroencephalogram (EEG) segments is given as a practical application. Manual recognition is a time-consuming and laborious process so that it places a heavy burden on neurologists, and hence, the automatic identification of epilepsy has become an important issue. Such intelligent detection is crucial for the classification of seizures. Traditional EEG recognition models depend on artificial experience and are of weak generalization ability. To break these limitations, we propose a novel one-dimensional deep neural network for robust detection of seizures, which consists of three convolutional blocks and three fully connected layers. Thereinto, each convolutional block consists of five layers: convolutional layer, Batch normalization layer, non-linear activation layer, dropout layer, and max-pooling layer. Machine cognition performance is evaluated on the University of Bonn dataset, which achieves an accuracy of 97.6–99.5% in the two-class classification problem, 93.7–98.1% in the three-class EEG classification problem, and 93.6% in classifying the complicated five-class problem.

Background. To analyze the association between serum 25-hydroxyvitamin D level (25(OH)D) and stroke risk based on the National Health and Nutrition Examination Survey (NHANES). Methods. Between 2007 and 2018, the baseline information of participants from NHNES was collected. Univariate analysis was used to identify the covariates. Multivariate logistic regression model was used to analyze the association between serum 25(OH)D level and the stroke risk. Results. Of the 8,523 participants, there were 310 participants with stroke and 8,213 participants without stroke. The multivariate logistic analysis showed that serum 25(OH)D deficiency (odds ratio (OR): 1.993, 95% confidence intervals (CI): 1.141-3.481, and P = 0.012 ) was the significant risk factors for stroke. Subgroup analysis showed that non-Hispanic whites with serum 25(OH)D deficiency (OR: 2.501, 95% CI: 1.094-5.720, and P = 0.001 ) and insufficiency (OR: 1.853, 95% CI: 1.170-2.934, and P = 0.006 ) were associated with a higher risk of stroke than those with normal 25(OH)D levels. Conclusions. Serum 25(OH)D deficiency may be associated with an increased risk of stroke.

To understand the basic evolution law of the machine brain, we need first understand machine cognition, which majorly depends on machine vision, machine touch and etc. Artificial intelligence (AI) has been rapidly developed in the latest decade and its importance to machine cognition has been widely recognized. But machine minds is still a dream about the unknowable future. This chapter presents a perspective characterization of machine cognition in the future, taking the future of medical rescues as a practical example. A conjecture on key points of theoretical framework of machine cognition processes in the considered scenes—the concept of “ambulanceye” is advanced. Ambulanceye incorporates vision, touch and minds of the future intelligent ambulances with geospatial artificial intelligence (geoAI). The role of geoAI is hypothesized as a significant contributor to driving security through real-time danger caution. Such danger caution is based on the advanced driver assistant systems (ADAS) for detecting and tracking “eigenobjects”, which are defined as potential dangerous objects hidden in spatial data. Simulated performances demonstrate the significance of ADAS for ambulances to overcome ocular restriction resulted from weathering extremes and other accidents that make sights blurred. Moreover, there are still considerable uncertainties in real-time recognition and the trace characterization of eigenobjects. GeoADAS (Improved ADAS with geoAI) is hence required for more effective objects recognition and tracking. Perspective applications of geoADAS are preliminarily discussed and at the end of this chapter, the outline of this book is also presented.

This chapter finally presents a characterization of interdisciplinary evolution of the machine brain. Perspective schemes for rebuilding a real vision brain in the future are analyzed, along with the major principles to construct the machine brain, are presented, which include memory, thinking, imagination, feeling, speaking and other aspects associated with machine vision, machine touch and machine minds. This explicitly developed the theoretical framework of brain-inspired intelligence from the vision brain hypothesis, the vision-minds hypothesis and the skin brain hypothesis. Based on Chaps. 2– 5, development of machine intelligence during the past decades have experienced three stages—machine computation, learning and understanding. Machine leaning includes data mining. Environmental sensing helps to acquire the data. Pattern analysis and scene understanding are significant parts of machine understanding. Scientists have taught machine how to collect and treat data and discover knowledge from the data. Evolution of machine brain will experience another two stages—machine meta-learning (learning to learn) and self-directed development (improving the capability of machine brain utilizing the learned knowledge). There are still great challenges in realization of the dream.

This chapter aims to explain the pattern of machine understanding, utilizing medical test as a practical example. The explanation is based on the semantic information theory. After long arguments between positivism and falsificationism, the verification of universal hypotheses was replaced with the confirmation of uncertain major premises. Unfortunately, Hemple proposed the Raven Paradox. Then, Carnap used the increment of logical probability as the confirmation measure. So far, many confirmation measures have been proposed. Measure F proposed by Kemeny and Oppenheim among them possesses symmetries and asymmetries proposed by Elles and Fitelson, monotonicity proposed by Greco et al., and normalizing property suggested by many researchers. Based on the semantic information theory, a measure b* similar to F is derived from the medical test. Like the likelihood ratio, measures b* and F can only indicate the quality of channels or the testing means instead of the quality of probability predictions. Furthermore, it is still not easy to use b*, F, or another measure to clarify the Raven Paradox. For this reason, measure c* similar to the correct rate is derived. Measure c* supports the Nicod Criterion and undermines the Equivalence Condition, and hence, can be used to eliminate the Raven Paradox. An example indicates that measures F and b* are helpful for diagnosing the infection of Novel Coronavirus, whereas most popular confirmation measures are not. Another example reveals that all popular confirmation measures cannot be used to explain that a black raven can confirm “Ravens are black” more strongly than a piece of chalk. Measures F, b*, and c* indicate that the existence of fewer counterexamples is more important than more positive examples’ existence, and hence, are compatible with Popper’s falsification thought.

Deep learning system (DLS) always require a time-consuming process because of too many hyperparameters and complex structure. When we update some data in DLS, a complete retraining process was encountered which spending too much time. To get over these shortcomings, the Broad Learning System (BLS) is established by C.L.Philip Chen, which aims to make a different way of artificial intelligence. By the BLS, the origin inputs are transformed and the structure of the system expanded in the feature nodes (The following is expressed as fn) and enhancement nodes (The following is expressed as en) in the wide sense. In this paper, we apply the BLS to the recognition of fake faces. Differing from previous studies, we further validate the anti-interference ability of BLS by involving a perturbation matrix P, which does not change incremental learning processes of fn and en. According to the performance on the considered dataset, BLS is more effective than DLS in recognition of fake faces. Further validation on other datasets are still necessary.

In the process of a finger vein image acquisition, finger vein images are susceptible to external factors like finger posture and light source conditions, which will result in poor recognition accuracy. Therefore, a finger vein recognition method based on improved convolution neural net work is proposed to improve the accuracy and robustness of the image recognition. Firstly, the collected finger vein image is preprocessed by image segmentation, finger root key point location and image extraction in the region of interest (ROI). Secondly, according to the application context of finger vein recognition, the convolution neural network structure is adjusted appropriately, and the output of convolution layer is standardized in batches. The optimized neural network is used to automatically extract, classify and identify the features of the preprocessed images. A large number of experiments were performed on public finger print data sets of Shandong University. The optimal recognition rates are 90% respectively. The experiments verify the effectiveness of this method.

The need for occlusion face recognition in daily life, professional fields like criminal investigation and the recent epidemic is increasing, so it is still a hot issue in the current research and many researchers are pursuing the technical excellence for the face recognition of random occlusion. In the previous research, RRC model is proposed for the excellent performance in the occlusion face recognition and the IR3C algorithm is adopted to solve this model. Among them, the weight function in this algorithm plays a big role, which will directly affect the recognition rate and running time of the experiment. In this paper, three weight functions: Inverse trigonometric function, Hyperbolic tangent function and Log function are proposed for the purpose of improving the robustness and time efficiency of IR3C algorithm. Applying these functions on the comparable types and levels of random occlusion and then comparing them with the previous research, the experimental results indicate that the inverse trigonometric function is superior to other functions with better recognition rates and shorter running time.

Magnesium hydride (MgH2) with high gravimetric hydrogen storage capacity is considered as one of the most potential hydrogen storage materials; however, its development has been plagued by the high operating temperature and slow kinetics. In this study, we have design and synthesize a core-shell Ni/Fe3O4@MIL additive to aid the (de)hydrogenation of MgH2/Mg system via the co-catalytic effect of in-situ formed Mg2NiH4/Mg2Ni and Fe. The initial dehydrogenation temperature significantly reduces from 613 K to 517 K, and the MgH2-Ni/Fe3O4@MIL composite can reabsorb 4.17 wt% H2 within 3600 s under 3.0 MPa H2 at 373 K. Remarkably, the dehydrogenation activation energy of the composite decreases by 61.77 kJ/mol compared to the pure MgH2 (159.71 kJ/mol). Moreover, the composite also shows good cycling stability without distinct capacity decay after cycling twenty times. Studies show that during dehydrogenation and hydrogenation processes, the Mg2NiH4/Mg2Ni act as catalysts to induce hydrogen desorption/absorption of MgH2/Mg. Meanwhile, the unique core-shell structure of the Ni/Fe3O4@MIL not only provides reaction sites, but also prevents the agglomeration of nanoparticles and maintains stable catalytic activity. This study provides a new idea for designing stable transition metal heterogeneous catalytic system to improve hydrogen storage performance of MgH2.

Germanium (Ge)-sulfur (S) (GeS) binary system is a simple and green chalcogenide glass system. In this work, we prepared a Ge-rich non-stoichiometric GeS film and investigated its optical property and network structure. Findings show that improving or modifying the optical properties (optical transmittance, refractive index, etc.) of the GeS film with a one-step annealing process, and the structural evolution of GeS film network that is attribute to the annealing-induced property changing is identified.

The content, distribution, and origin of aluminum, lithium, gallium, and rare earth elements (REEs) in the Permo-Carboniferous coal of the Datong coalfield, China, were investigated using optical microscopy and field-emission scanning electron microscopy in conjunction with energy-dispersive X-ray spectrometry, X-ray powder diffraction, and inductively coupled plasma mass spectrometry. The Permo-Carboniferous coal of the Datong coalfield had a high volatile bituminous rank (Ro,max = 0.72% on average), and low sulfur and medium ash contents (0.58% and 25.74% on average, respectively). The main minerals in the Datong coal included kaolinite, boehmite, muscovite, calcite, dolomite, siderite, aluminum phosphate, and quartz. Of these, kaolinite, boehmite, calcite, quartz, and phosphates mainly occurred as cell fillings, indicating an authigenic origin. Compared to hard coals worldwide, the Datong coal is enriched in SiO2 (49.12%; up to 89.93%), Al2O3 (38.88%; up to 71.37%), Li (67 μg/g; up to 354.1 μg/g), Ga (18.1 μg/g; up to 70.2 μg/g), Y (20.8 μg/g; up to 46.1 μg/g), La (34 μg/g; up to 158.5 μg/g), Ce (61.3 μg/g; up to 353.8 μg/g), Tb (0.7 μg/g; up to 3.0 μg/g), Er (2.3 μg/g; up to 5.7 μg/g), and Yb (2.2 μg/g; up to 5.4 μg/g). High-alumina coal (Al2O3 content in ash >40%) occurred in the northern and southern parts of the coalfield. In the No. 3–5 coal seam, the areas of Li enrichment were located in the north and middle of the coalfield. In the Tashan and Chaigou mines, the Ga content exceeded 30 μg/g. An area of REEs enrichment (REEs content >300 μg/g) was identified in the Tashan and Xiaoyu mines. In the No. 3–5 coal seam, the REEs distribution pattern showed a wide and gentle “V” curve with a high left and a low right limb. These seams were characterized by a marked depletion in Eu (δEu = 0.51) and a weak negative Ce anomaly (δCe = 0.95). In the No. 3–5 coal seam, Al, Li, Ga, and REEs mainly occurred in kaolinite. Boehmite and phosphate minerals were the main carriers of Ga and REEs, respectively. The enrichment in Al-Li-Ga-REEs in the No. 3–5 coal seam was mainly influenced by the sedimentary provenance and the environment of coal accumulation. The sediment sources were oxidized bauxite in the weathering crust of the Benxi Formation and the Middle Proterozoic moyite of the Yinshan Oldland to the north and west of the coalfield. Al, Li, Ga, and REEs were transported from the source region as solutes and colloids, and they became enriched in the swamp facies of the delta plain. The Li, Ga, and REEs contents gradually increased from the bottom to the top of the Permo-Carboniferous coal seam. This increase is mainly attributed to the weak influence of seawater. The marked enrichment in heavy REEs at the bottom of the No. 5 coal seam may be related to marine transgression.

Using blockchain technology (BCT) in securing data has received a lot of attention these days. This technology is used not only as a new way with the aim of enhancing the cyber and physical security of power systems, but also has been applied to enhance the exchanged information between units. When Fuel Cell (FC), photovoltaic cells (PV) and wind turbines (WT) are used as sources of distributed generation (DG), there will be a situation called Islanding Micro-Grid (MG), so that this situation has the ability to control voltage, frequency and current of the system with no dependence on the utility. Hence, for overcoming load variations and disturbances, designing and implementing a robust controller is vital. Accordingly, a robust General Type-2 Fuzzy sliding mode controller (GT2FSMC) on the basis of gray wolf optimization algorithm is presented in the following study with the purpose of enhancing the power sharing amongst the distributed generations operating in the off-grid MG. The offered controller, in addition to warrant the stability and robust versus uncertainties of the lumped generated via extrinsic disturbances and un-modelled dynamics, has the ability to significantly decrease the control chattering inherent in primary sliding mode control. In the following paper, it is assumed that there are two distributed generations, each with specific duties. One has the duty of regulating the frequency and voltage through a reference signal, and another is responsible for working in load current control mode with the aim of sharing load correctly among each of them. For stopping malicious attacks, an assured policy framework according to the blockchain (BC) has been created to enhance the security of interchanged data among production units. Plus, two low-voltage parallel DG units exist in the MG. With the aim of evaluating the mentioned way, the simulation results have been done via MATLAB environment. According to the outcomes, it can be concluded that this mentioned controller has the ability of providing lower steady state error and Total Harmonic Distortion (THD) with faster response in comparison to the further controllers that generally are used. The simulation results show that the DG unit operations within the islanding MG have the ability of coordinating successfully under that mentioned control system with the aim of confirming stable operation of the throughout MG.

Micro(nano)plastics are emerging environmental contaminants of concern. The prevalence of micro(nano)plastics in soils has aroused increasing interest regarding their potential effects on soil biota including terrestrial plants. With the rapid increase in published studies on plant uptake and impacts of micro(nano)plastics, a review summarizing the current research progress and highlighting future needs is warranted. A growing body of evidence indicates that many terrestrial plants can potentially take up micro(nano)plastics via roots and translocate them to aboveground portions via the vascular system, primarily driven by the transpiration stream. Exposure to micro(nano)plastics can cause a variety of effects on the biometrical, biochemical, and physiological parameters of terrestrial plants, but the specific effects vary considerably as a function of plastic properties, plant species, and experimental conditions. The presence of micro(nano)plastics can also affect the bioavailability of other associated toxicants to terrestrial plants. Based on analysis of the available literature, this review identifies current knowledge gaps and suggests prospective lines for further research.