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Studies and research relevant to Dependent Personality Disorder (DPD) recently have shown controversy regarding whether genetic or external factors contributed more to the development of DPD. This review aims to investigate the relationship between parental overprotection and DPD from the perspectives of the specific effect of overprotective parenting styles on DPD, factors regarding overprotection in DPD, and relevant interventions for DPD. It was found that there was an association between overprotective parenting styles and the development of DPD symptoms. Factors related to overprotection, such as distress intolerance and sensitivity to falsehoods detected from facial expressions, have a relationship with DPD traits. Different psychotherapies have demonstrated their effectiveness. Cognitive behavioral therapy (CBT), clarification-oriented psychotherapy (COP), existential group therapy, and cognitive analytic therapy (CAT) are effective to treat clients' DPD, targeting different symptoms. It is concluded that CBT can aid decision-making, self-confidence, and proper self-cognition. Furthermore, COP and existential group therapy can facilitate emotional and social adjustment. Lastly, CAT can improve identity recognition and reassurance-seeking. Regardless of these effectual treatments, the small sample sizes of previous studies may lower the generalizability. More efforts should be devoted to this field of personality disorder and expand the number of empirical research. The present paper may offer some guidelines for the later development of family intervention studies and programs.

HighlightsBitter gourd slices were dried using vacuum infrared coupling drying method.Increased drying rate and effective water diffusivity and greatly reduced energy consumption.Provided a theoretical reference for the drying of high quality agricultural products.Increased the added value of agricultural products.Abstract.To improve the processing rate and product quality of bitter gourd, a new type of vacuum infrared coupled drying method was proposed. The impact of drying temperature, vacuum, and thickness were investigated on the physicochemical features. The results showed that the moisture content was greatly affected by temperature change but less influenced by the change of vacuum degree. The thickness had little influence on the moisture content change when the slice thickness increased to a certain value about 6mm. The experiments revealed that the drying rate reached to the maximum when the slice thickness was 2 mm. The drying quality of bitter gourd slices could reach to the best smell, tissue state and color when the drying test was carried out at thickness 4 mm, vacuum 10 kPa, and temperature 55°C. Taking drying rate as evaluation index and temperature, thickness, and vacuum degree as influencing factors, a regression model of drying rate was established. Design-expert software was used to solve the optimal drying process conditions. The results showed that the drying rate was 26.47 g/(g.h) when temperature, thickness and vacuum degree were 56°C, 3 mm, and 11 kPa. Therefore, bitter gourd slices would be dried quickly and effectively by using the simultaneous technique of vacuum infrared drying method. Keywords: Bitter gourd slice, Drying quality, Moisture content, Vacuum infrared coupled drying.

The defrosting performance of automotive air conditioners plays an important role in driving safety. This paper uses CFD to simulate the internal flow field of the automobile numerically. Simulation results show that the flow distribution is unreasonable. The horizontal grilles are added at the outlets to improve the defrosting performance of the automobile. Air-flow jet angle and the length of the air conditioning outlets (L1, L2) are selected as design variables based on the radial basis neural network to find the optimal combination scheme. The area of the defrosting dead corner has been reduced from 20-5% after optimization, and the frost layer of the front windshield has been completely melted in 25 minutes. The experiment test is conducted to verify the improvement of the defrosting performance of automotive air conditioners. The design methodology can be applied to the development of the air conditioner.

Construction has been regarded as one of the most stressful industries, and the COVID-19 pandemic has deteriorated this situation. This research developed and tested a model of the impact of COVID-19 pandemic perception on job stress of construction workers. Both problem-focused and emotion-focused coping were considered as mediators. Empirical data were collected using a detailed questionnaire from the Chinese construction industry. The results showed that pandemic perception was significantly related to psychological and physical stress. Emotion-focused coping was mainly triggered by pandemic fear and job insecurity, while problem-focused coping was mainly triggered by organizational pandemic response. Furthermore, the effects of pandemic fear and organizational pandemic response on job stress were mediated by problem-focused coping. Finally, the theoretical and practical significance, research limitations, and future research directions of this study are discussed.

Although plant-specific NAC transcription factors play crucial roles in response to abiotic stress, few reports describe the regulation of NAC genes in maize (Zea mays) by the cis-natural antisense transcripts (cis-NATs). In this study, 521 NAC genes from Gramineae were classified, of which 51 NAC genes contained cis-NATs. ZmNAC48 and cis-NATZmNAC48 co-localized to the same cell nucleus, and both transcripts responded to drought stress. Arabidopsis plants overexpressing ZmNAC48 had improved drought tolerance, lower rate of water loss, enhanced stomatal closure, and higher rates of survival. Transient expression in both maize protoplasts and tobacco leaves indicated that cis-NATZmNAC48 reduced ZmNAC48 expression. Western blotting and ribosome profiling analyses confirmed that cis-NATZmNAC48 lacked protein coding potential. Furthermore, the cis-NAT-derived small-interfering RNAs (nat-siRNAs) generated from the overlapping regions of ZmNAC48 and cis-NATZmNAC48 were detected in maize and transgenic Arabidopsis. Cis-NATZmNAC48 overexpressing maize showed higher water loss rate, increased stomatal opening, and had more dead leaves. Expression of ZmNAC48 and nat-siRNA was decreased in these plants. Taken together, our study indicates that both ZmNAC48 and cis-NATZmNAC48 are involved in plant drought stress responses, and that the double-stranded RNA-dependent mechanism is involved in the interaction between cis-NATZmNAC48 and ZmNAC48. Additionally, cis-NATZmNAC48 may negatively regulate ZmNAC48 to affect stomatal closure of maize.

This work was to investigate the impact of drying on the physical, chemical stability and character properties of P. aibuhitensis hydrolysate. Properties including amino acid composition, color stability, molecular weight distribution, powder morphology, etc. were compared between the freeze drying powder (FD) and spray drying powder (SD). They were fractionated with ultra filtration in antioxidant activities test. FD and SD contained amounts of amino acids and umami amino acids. SD exhibited the higher lightness and whiteness. SD had more compounds between 451 Da and 6511 Da. The surface morphology of FD was porous and flaky while SD was microsphere. SD had more volatile flavor substances and higher antioxidant activities on DPPH, hydroxyl, and superoxide radical-scavenging. In summary, results demonstrated that drying methods indeed affected the characteristics of hydrolysate, and the one prepared by spray drying method had the potential to be utilized for antioxidant food development and seafood seasoning.

DNA methylation is important for plant growth, development, and stress response. To understand DNA methylation dynamics in maize roots under water stress (WS), we reanalyzed DNA methylation sequencing data to profile DNA methylation and the gene expression landscape of two inbred lines with different drought sensitivities, as well as two of their derived recombination inbred lines (RILs). Combined with genotyping-by-sequencing, we found that the inheritance pattern of DNA methylation between RILs and parental lines was sequence-dependent. Increased DNA methylation levels were observed under WS and the methylome of drought-tolerant inbred lines were much more stable than that of the drought-sensitive inbred lines. Distinctive differentially methylated genes were found among diverse genetic backgrounds, suggesting that inbred lines with different drought sensitivities may have responded to stress in varying ways. Gene body DNA methylation showed a negative correlation with gene expression but a positive correlation with exon splicing events. Furthermore, a positive correlation of a varying extent was observed between small interfering RNA (siRNA) and DNA methylation, which at different genic regions. The response of siRNAs under WS was consistent with the differential DNA methylation. Taken together, our data can be useful in deciphering the roles of DNA methylation in plant drought-tolerance variations and in emphasizing its function in alternative splicing.

Wheat (Triticum aestivum L.) dough strength and extensibility are mainly determined by the polymerization of glutenin. The number of high-molecular-weight glutenin subunits (HMW-GS) differs in various wheat varieties due to the silencing of some genes. The effects of Ax1 or Dx2 subunit absence on glutenin polymerization, dough mixing properties and gluten micro structure were investigated with two groups of near-isogenic lines. The results showed that Ax1 or Dx2 absence decreased the accumulation rate of glutenin polymers and thus delayed the rapid increase period for glutenin polymerization by at least ten days, which led to lower percentage of polymeric protein in mature grain. Ax1 or Dx2 absence significantly decreased the dough development time and dough stability, but increased the uniformity of micro structure. Lacunarity, derived from quantitative analysis of gluten network, is suggested as a new indicator for wheat quality.

In this work, a thermal and wide pH range (from 1 to 13) stable biological agent was extracted from Perinereis aibuhitensis (P. aibuhitensis), and the antithrombotic activity of this P. aibuhitensis extract (PaE) was investigated. In vitro antithrombotic activity test indicated that PaE could significantly extend the clotting time of recalcified rabbit blood: the clotting time of the 10 mg mL-1, 20 mg mL-1 and 40 mg mL-1 PaE groups increased by 24.24%, 68.64% and 130.76%, respectively. Further platelet poor plasma (PPP) coagulation study proved that PaE could obviously inhibit the intrinsic coagulation pathway and slightly inhibit the extrinsic coagulation pathway. Moreover, PaE could effectively inhibit fibrinogen turning into fibrin induced by thrombin. More importantly, PaE exhibited thermal stability, since P. aibuhitensis was boiled in the PaE preparation process, as well as tolerance to strong acid and alkali, since its fibrinogen inhibition activity was retained after treatment with a wide range of pH value conditions. Finally, the antithrombotic activity ingredients in PaE were preliminarily identified, the results of which indicated that the ingredients were ethanol insoluble, insensitive to papain and pepsin, and could be degraded by H2O2. Additionally, we demonstrated that PaE at a concentration of not more than 20 mg mL-1, the concentration at which PaE showed high anticoagulant activity, exhibited favorable biocompatibility via in vitro hemolysis rate test and MTT assay. In summary, the results evidenced that this conveniently prepared and stable PaE was a safe and effective antithrombotic agent, and had the potential to be utilized for antithrombotic food development.

The viscoelastic properties of wheat dough are due mainly to the structure of gluten and interactions within the protein complex. We determined the effects of high-molecular-weight glutenin subunit (HMW-GS) on the polymerization of glutenin during grain development and on the secondary and micro-structures of gluten, using Xinong1718 and its four near-isogenic lines (NILs). The polymerization of glutenin was monitored based on the percentage of unextractable polymeric protein (%UPP). Xi1718-3 with the superior allele Bx17 + By18 exhibited a rapid increase in %UPP five days earlier than the other lines with variations at Glu-B1, thereby obtained the highest %UPP at maturity. The secondary structures, such as the proportion of β-sheets and the α-helix/β-sheet ratio differed significantly among the NILs. The micro-structure of gluten in NILs varied in terms of the pore size distribution and cross-linkage patterns. Significant correlations were found between %UPP with the β-sheet content and α-helix/β-sheet ratio, so these secondary structures could also be used as indicators of wheat flour quality.

Due to the non-linear and non-stationary characteristics of the wind speed time series, it is generally difficult to model and predict such series by single forecasting models. In this paper, two novel hybrid models, which combine empirical mode decomposition (EMD), feature selection with artificial neural network (ANN) and support vector machine (SVM), are proposed for short-term wind speed prediction. First, the original wind speed time series is decomposed into a set of sub-series by EMD. Second, the initial features (input variables) and targets are constructed from all the sub-series and the original series. Then, a feature selection process is introduced to constitute the relevant and informative features. Finally, a predictive model (ANN or SVM) is established using these selected features. The effectiveness of the proposed models has been assessed on the real datasets recorded from three wind farms in China. Compared with the single ANN, SVM, traditional EMD-based ANN, and traditional EMD-based SVM, the experimental results show that the proposed models have satisfactory performance, which are suitable for the wind speed prediction.

This paper proposes a hybrid model based on autoregressive (AR) model and Gaussian process regression (GPR) for probabilistic wind speed forecasting. In the proposed approach, the AR model is employed to capture the overall structure from wind speed series, and the GPR is adopted to extract the local structure. Additionally, automatic relevance determination (ARD) is used to take into account the relative importance of different inputs, and different types of covariance functions are combined to capture the characteristics of the data. The proposed hybrid model is compared with the persistence model, artificial neural network (ANN), and support vector machine (SVM) for one-step ahead forecasting, using wind speed data collected from three wind farms in China. The forecasting results indicate that the proposed method can not only improve point forecasts compared with other methods, but also generate satisfactory prediction intervals.

Time series forecasting is one of the most important issues in numerous applications in real life. The objective of this study was to propose a hybrid neural network model based on wavelet transform (WT) and feature extraction for time series forecasting. The motivation of the proposed model, which is called PCA-WCCNN, is to establish a single simplified model with shorter training time and satisfactory forecasting performance. This model combines the principal component analysis (PCA) and WT with artificial neural networks (ANNs). Given a forecasting sequence, order of the original forecasting model is determined firstly. Secondly, the original time series is decomposed into approximation and detail components by employing WT technique. Then, instead of using all the components as inputs, feature inputs are extracted from all the sub-series obtained from the above step. Finally, based on the extracted features and all the sub-series, a famous neural network construction method called cascade-correlation algorithm is applied to train neural network model to learn the dynamics. As an illustration, the proposed model is compared with two classical models and two hybrid models, respectively. They are the traditional cascade-correlation neural network, back-propagation neural network, wavelet-based cascade-correlation network using all the wavelet components as inputs to establish one model (WCCNN) and wavelet-based cascade-correlation network with combination of each sub-series model (WCCNN multi-models). Results obtained from this study indicate that the proposed method improves the accuracy of ANN and can yield better efficiency than other four neural network models.

Glutenin is one of the critical gluten proteins that affect the processing quality of wheat dough. High-molecular-weight glutenin subunits (HMW-GS) affect rheological behavior of wheat dough. This research demonstrated the effects of four variations of HMW-GS composition at the Glu-B1 locus on secondary and micro structures of gluten and rheological properties of wheat dough, using the bread wheat Xinong 1330 and its three near-isogenic lines (NILs). Results indicated that the Amide I bands of the four wheat lines shifted slightly, but the secondary structure, such as content of α-helices, β-sheets, disulfide bands, tryptophan bands and tyrosine bands, differed significantly among the four NILs. The micro structure of gluten in NIL 2 (Bx14+By15) and NIL 3 (Bx17+By18) showed more cross linkage, with two contrasting patterns. Correlation analysis demonstrated that the content of β-sheets and disulfide bonds has a significant relationship with dough stability, which suggests that the secondary structures could be used as predictors of wheat quality.

The multilayer perceptrons (MLPs) have been widely used in many communication applications, however, the learning process of the multilayer perceptrons often becomes very slow, which is due to the existence of the singularities in the parameter space. As the singularities significantly affect the learning dynamics of MLPs, the standard gradient descent method is not Fisher efficient. In order to overcome this problem, natural gradient method and adaptive natural gradient method were proposed to accelerate the learning process. As is well known, step size in each iteration plays a key role in the performance of algorithms. In this paper, the modified adaptive natural gradient method is proposed where the step size in each iteration is adaptive modified. The aim of the proposed algorithm is to accelerate the convergence speed and increase the performance of MLPs. The simulation results verify the validity of the analytical results.

Core Ideas Low P stress is a global issue for grain production. Significant phenotypic differences were detected among 13 traits in 356 maize lines under P‐sufficient and P‐deficient conditions. Significant single nucleotide polymorphisms (SNPs) and low‐P stress‐responsive genes were identified for 13 maize root traits based on a genome‐wide association study. Hap5, harboring 12 favorable SNPs, could enhance strong root systems and P absorption under low‐P stress. Phosphorus is an essential macronutrient required for normal plant growth and development. Determining the genetic basis of root traits will enhance our understanding of maize's (Zea mays L.) tolerance to low‐P stress. Here, we identified significant phenotypic differences for 13 traits in maize seedlings subjected to P‐sufficient and P‐deficient conditions. Six extremely sensitive and seven low‐P stress tolerant inbreds were selected from 356 inbred lines of maize. No significant differences were observed between temperate and tropical–subtropical groups with respect to trait ratios associated with the adaptation to low‐P stress. The broad‐sense heritability of these traits ranged from relatively moderate (0.59) to high (0.90). Through genome‐wide association mapping with 541,575 informative single nucleotide polymorphisms (SNPs), 551, 1140 and 1157 significant SNPs were detected for the 13 traits in 2012, 2016 and both years combined, respectively, along with 23 shared candidate genes, seven of which overlapped with reported quantitative trait loci and genes for low‐P stress. Five haplotypes located in candidate gene GRMZM2G009544 were identified; among these, Hap5, harboring 12 favorable SNP alleles, showed significantly greater values for the root traits studied than the other four haplotypes under both experimental conditions. The candidate genes and favorable haplotypes and alleles identified here provide promising resources for genetic studies and molecular breeding for improving tolerance to abiotic stress in maize.

Sub-micro sized Gd 2 (WO 4 ) 3 :Yb 3+ /Ho 3+ phosphor was synthesized via a co-precipitation reaction. The crystal structure and morphology of the phosphor were characterized by XRD and SEM. The time scanning of green and red upconversion emissions displayed that the upconversion luminescent intensities were dependent on the irradiation time and the excitation powder, which was resulted from the thermal effect induced by LD (laser diode) irradiation. The upconversion luminescence of Gd 2 (WO 4 ) 3 :Yb 3+ /Ho 3+ phosphor at different sample temperatures was studied. It was found that at room temperature the red and green upconversion emissions were 1.5- and 2-photon processes, respectively. With increasing sample temperature the 2-photon process for the red upconversion emission and 3-photon process for green upconversion emission occurred.

Feature selection is a significant preprocessing procedure for many high-dimensional forecasting problems. In multivariate time series forecasting, the purpose of feature selection is to select a relevant feature subset from the original time series. One of the most common strategies for feature selection is mutual information (MI) criterion. K-nearest neighbors (k-NN) is a powerful means which has been widely used to estimate the MI between two high-dimensional variables directly from data set. In order to improve the effect of feature selection, a novel procedure is proposed in this paper to use MI and k-NN to perform feature selection in multivariate time series forecasting. This feature selection procedure not only reduces the irrelevance between the inputs and outputs variables, but also removes the redundant input variables. The procedure of this method is elaborated on a synthetic data set as well as on a real-world example.

As one of critical gluten proteins, high-molecular-weight glutenin subunits (HMW-GS) mainly affect the rheological behaviour of wheat dough. The influence of HMW-GS variations at the Glu-A1 and Glu-D1 loci on both secondary and micro structures of gluten and rheological properties of wheat dough was investigated in this study. Results showed that the Amide I bands of the three near-isogenic lines (NILs) shifted slightly, but the secondary structures differed significantly. The micro structure of gluten in NIL 4 (Ax null) showed bigger apertures and less connection, compared to that in Xinong 1330 (Ax1). The micro structure of gluten in NIL 5 (Dx5+Dy10) showed more compact than that in Xinong 1330 (Dx2+Dy12). Correlation analysis demonstrated that the content of β-sheets and disulfide bonds in gluten has a significant relationship with dough properties. The secondary structures of native gluten are suggested to be used as predictors of wheat quality.

For modeling of multivariate time series, input variable selection is a key problem. Feature selection is to select a relevant subset to reduce the dimensionality of the problem without significant loss of information. This paper presents the estimation of mutual information and its application in feature selection problem. Mutual information is one of the most common strategies borrowed from information theory for feature selection. However, the calculation of probability density function (PDF) according to the definition of mutual information is difficult, especially for high dimensional variables. A k-nearest neighbor (k-NN) method based estimator is widely used to estimate the mutual information between two variables directly from the data set. Nevertheless, this estimator depends on smoothing parameter. There is no theoretically method to choose the parameter. This paper purposes to solve two problems: one is to employ resampling methods to help the mutual information estimator to improve feature selection and the other is to apply these methods to a wind power prediction problem.

Reliable and accurate short-term wind speed forecasting is of great importance for secure power system operations. In this study, a novel two-step method to construct a multi-model ensemble, which consists of linear regression, multi-layer perceptrons and support vector machines, is proposed. The ensemble members first compete with each other in a number of training rounds, and the one with the best forecasting accuracy in each round is recorded. Then, after all the training rounds, the occurrence frequency of each member is calculated and used as the weight to form the final multi-model ensemble. The effectiveness of the proposed multi-model ensemble has been assessed on the real datasets collected from three wind farms in China. The experimental results indicate that the proposed ensemble is capable of providing better performance than the single predictive models composing it.

An efficient and compact all-solid-state continuous wave 67-nm red laser is generated by intracavity frequency doubling of a diode pumped Nd∶YVO4 laser at 1342 nm while suppressing the higher gain transition near 1064 nm. With the incident pump power of 40 W and a frequency doubling crystal lithium triborate, as high as 10.5-W output power at 671 nm is achieved. The optical-to-optical conversion efficiency is 26.3% and the output power stability during 8 h of operation is better than 2.3%. To the best of our knowledge, this is the highest conversion efficiency of watt-level laser at 671 nm generated by intracavity frequency doubling of a diode pumped Nd∶YVO4 laser at 1342 nm. © 2012 Society of Photo-Optical