Partial Structure Research Articles

A real-time temperature responsive material based on partial inverse opal photonic crystals and cholesteric liquid crystals

In this paper, we fabricated a temperature-sensitive material with a partial inverse opal structure, which can reversibly and quickly identify small temperature changes at room temperature and show significant color differences. First, we synthesized silica microsphere and prepared silica photonic crystals (PCs), then we composited PCs with polymer. After covering the mask, we prepared partial inverse opal silica PCs template by using selective etching. The temperature-sensitive liquid crystals (LCs) composed of cholesterol oleyl carbonate (COC), cholesterol nonanoate (CPE) and cholesterol benzoate (CBZ) were filled in partial inverse opal silica PCs template. In the end, we sealed the film by methyl methacrylate (MMA) and polyethylene glycol diacrylate (PEGDA). In this way, we prepared the fast temperature-sensitive dual-opal PCs/LCs composite film, and studied the mechanism of temperature regulation on material display mode. As the temperature gone from 26 °C to 30 °C, the material changed colors rapidly from red to blue. The result showed that this PCs/LCs composite film had well temperature response performance, which can monitor slight environmental temperature change. By selective etching the material, we designed PCs film patterns. The different patterning states displayed and changed fast at different temperatures, showing that the material had a good application prospect in the fields of temperature sensor, anti-counterfeiting, and display device.

Soybean Yield Preharvest Prediction Based on Bean Pods and Leaves Image Recognition Using Deep Learning Neural Network Combined With GRNN.

Soybean yield is a highly complex trait determined by multiple factors such as genotype, environment, and their interactions. The earlier the prediction during the growing season the better. Accurate soybean yield prediction is important for germplasm innovation and planting environment factor improvement. But until now, soybean yield has been determined by weight measurement manually after soybean plant harvest which is time-consuming, has high cost and low precision. This paper proposed a soybean yield in-field prediction method based on bean pods and leaves image recognition using a deep learning algorithm combined with a generalized regression neural network (GRNN). A faster region-convolutional neural network (Faster R-CNN), feature pyramid network (FPN), single shot multibox detector (SSD), and You Only Look Once (YOLOv3) were employed for bean pods recognition in which recognition precision and speed were 86.2, 89.8, 80.1, 87.4%, and 13 frames per second (FPS), 7 FPS, 24 FPS, and 39 FPS, respectively. Therefore, YOLOv3 was selected considering both recognition precision and speed. For enhancing detection performance, YOLOv3 was improved by changing IoU loss function, using the anchor frame clustering algorithm, and utilizing the partial neural network structure with which recognition precision increased to 90.3%. In order to improve soybean yield prediction precision, leaves were identified and counted, moreover, pods were further classified as single, double, treble, four, and five seeds types by improved YOLOv3 because each type seed weight varies. In addition, soybean seed number prediction models of each soybean planter were built using PLSR, BP, and GRNN with the input of different type pod numbers and leaf numbers with which prediction results were 96.24, 96.97, and 97.5%, respectively. Finally, the soybean yield of each planter was obtained by accumulating the weight of all soybean pod types and the average accuracy was up to 97.43%. The results show that it is feasible to predict the soybean yield of plants in situ with high precision by fusing the number of leaves and different type soybean pods recognized by a deep neural network combined with GRNN which can speed up germplasm innovation and planting environmental factor optimization.

Interaction of hyperaccumulating plants with Zn and Cd nanoparticles

Metal hyperaccumulating plant species are an interesting example of natural selection and environmental adaptation but they may also be useful to developing new technologies of environmental monitoring and remediation. Noccaea caerulescens and Arabidopsis halleri are both Brassicaceae and are known metal hyperaccumulators. This study evaluated tolerance, uptake and translocation of zinc sulfide quantum dots by N. cearulescens and cadmium sulfide quantum dots by A. halleri in direct comparison with the non-hyperaccumulator, genetically similar T. perfoliatum and A. thaliana. Growth media were supplied with two different concentrations of metal in either salt (ZnSO4 and CdSO4) or nanoscale form (ZnS QDs and CdS QDs). After 30 days of exposure, the concentration of metals in the soil, roots and leaves was determined. Uptake and localization of the metal in both nanoscale and non-nanoscale form inside plant tissues was investigated by Environmental Scanning Electron Microscopy (ESEM) equipped with an X-ray probe. Specifically, the hyperaccumulators in comparison with the non-hyperaccumulators accumulate ionic and nanoscale Zn and Cd in the aerial parts with a BCF ratio of 45.9 for Zn ion, 49.6 for nanoscale Zn, 2.64 for Cd ion and 2.54 for nanoscale Cd. Results obtained with a differential extraction analytical procedure also showed that a significant fraction of nanoscale metals remained inside the plants in a form compatible with the retention of at least a partial initial structure. The molecular consequences of the hyperaccumulation of nanoscale materials are discussed considering data obtained with hyperaccumulation of ionic metal. This is the first report of conventional hyperaccumulating plants demonstrating an ability to hyperaccumulate also engineered nanomaterials (ENMs) and suggests a potential novel strategy for not only understanding plant-nanomaterial interactions but also for potential biomonitoring in the environment to avoid their entering into the food chains.

Hybrid ISTA: Unfolding ISTA With Convergence Guarantees Using Free-Form Deep Neural Networks.

It is promising to solve linear inverse problems by unfolding iterative algorithms (e.g., iterative shrinkage thresholding algorithm (ISTA)) as deep neural networks (DNNs) with learnable parameters. However, existing ISTA-based unfolded algorithms restrict the network architectures for iterative updates with the partial weight coupling structure to guarantee convergence. In this paper, we propose hybrid ISTA to unfold ISTA with both pre-computed and learned parameters by incorporating free-form DNNs (i.e., DNNs with arbitrary feasible and reasonable network architectures), while ensuring theoretical convergence. We first develop HCISTA to improve the efficiency and flexibility of classical ISTA (with pre-computed parameters) without compromising the convergence rate in theory. Furthermore, the DNN-based hybrid algorithm is generalized to popular variants of learned ISTA, dubbed HLISTA, to enable a free architecture of learned parameters with a guarantee of linear convergence. To our best knowledge, this paper is the first to provide a convergence-provable framework that enables free-form DNNs in ISTA-based unfolded algorithms. This framework is general to endow arbitrary DNNs for solving linear inverse problems with convergence guarantees. Extensive experiments demonstrate that hybrid ISTA can reduce the reconstruction error with an improved convergence rate in the tasks of sparse recovery and compressive sensing.

Federated Online Deep Learning for CSIT and CSIR Estimation of FDD Multi-User Massive MIMO Systems

In this paper, we propose a federated online training framework for deep neural network (DNN)-based channel estimation (CE) in frequency-division duplexing (FDD) multi-user massive multiple-input multiple-output (MU-MIMO) systems. The proposed DNN consists of two stages, where Stage-I explores the partial common sparsity structure among the users, while Stage-II estimates the channel state information (CSI) with reduced pilot overhead leveraging the common support information provided by Stage-I. To realize online training, we first propose three axioms for a legitimate online loss function, based on which we develop the federated online training algorithm with convergence analysis. The proposed two-tier DNN is trained online at the base station (BS) based on real-time pilot measurements distributively fed back from the users without the need of true channel labels, and the estimates for the CSI at the transmitter (CSIT) can be simultaneously generated in real-time. Meanwhile, the weights of Stage-II can be broadcasted to the users for real-time estimation of the CSI at the receiver (CSIR) at each user. Simulation shows that the proposed solution achieves higher CE performance than traditional compressive sensing (CS)-based algorithms while enjoying much faster computation. The solution is also robust to the channel model mismatches induced by the change of propagation environment, and is able to track the time-varying channel model.

Drivers of the Consumers Adoption of Fintech Services

Aim/Purpose: This study aimed to explore the impact of environmental drivers and trust on consumers’ adoption of Fintech services in the Jordanian context. It had also evaluated the mediating role of trust on the relation between environmental drivers and consumers adoption of Fintech services. Background: The reviewed studies on Fintech adoption demonstrated a lack of focus on the role of external or environmental drivers on consumers’ intentions to use and continue to use of Fintech services. Amongst the analyzed studies, the majority had examined the role of consumers perception of services usefulness and ease of use while few had included some environmental variables within the investigated variables such as social influence and government support. Furthermore, shortage of Fintech adoption related research in the developing countries, especially the Jordanian context was noted. Methodology: The study conceptual model was derived from Social Cognitive Theory (SCT) and Technological Personal Environmental (TPE) framework. This study was a quantitative one that employed survey method to empirically address its research questions and test the proposed hypotheses. Jordanian residents over the age of 18 who are familiar with Fintech were targeted, and convenience sampling was applied to get representative sample. Data was assembled from 323 respondents using an online questionnaire. Partial Least Squares Structure Equation Modeling (PLS-SEM) was applied to analyze the gathered data through SMART-PLS software. Contribution: This article adds to the existing literature on multiple stands, as it adds to literature related to Fintech adoption, as well as the interaction between consumer environment and their level of adoption. It also enriches the limited literature on the influence of COVID-19 to drive consumer usage of innovative services. Moreover, it supplements the scarce literature on Fintech adoption in the Jordanian settings. Findings: The main findings revealed the positive influence of both environmental drivers and trust as predictors of consumer intention to use Fintech services. It had also asserted the positive mediating effect of trust on the relationship amongst environmental drivers and consumer usage intent. Recommendations for Practitioners: By understanding the importance of consumer environment and trust on encouraging consumer to adopt Fintech services, governments, policy makers and practitioners can utilize this knowledge to adopt their offered services. They need to work on enhancing the technological infrastructure, as well as establishing general technological knowledge. They also need to highlight the role of Fintech service in fighting Covid-19, by adhering to the social distancing rules. Moreover, they need to guarantee the security and reliability of the developed services to increase their level of trust in the offered services. Recommendation for Researchers: This research has confirmed the positive influence of consumer environment represented by social influence, government support, technological readiness, and COVID-19 on their adoption of Fintech services. It has also established the mediating influence of consumer trust on the relation between environmental drivers and consumer intent to use Fintech services. This area is unexplored and needs more validation. Impact on Society: By understanding the factors affecting the Jordanian society in adopting Fintech services, this research provides set of recommendation to the Jordanian government and policy makers that can lead for more adoption of the developed Fintech services, which in turn would lead to better services provided to the society as well as increasing the financial inclusion level in the Jordanian society. Future Research: Future research can explore other environmental variables that were not included in the current research. Future research can also investigate the moderating effect of personal attributes such as consumer’s demographics, or more personal attributes such as self-efficacy, inherit innovativeness or risk aversion. It can also examine the moderating effect of financial literacy and/ or technological background.

Rule acquisition based on attribute partial order structure diagram

Rule acquisition based on attribute partial order structure diagram

Rule acquisition based on attribute partial order structure diagram

Rule acquisition based on attribute partial order structure diagram

Antimicrobial activity and partial chemical structure of acylpolyamines isolated from the venom of the spider Acanthoscurria natalensis

Background: Acylpolyamines are one of the main non-peptide compounds present in spider venom and represent a promising alternative in the search for new molecules with antimicrobial action.Methods: The venom of Acanthoscurria natalensis spider was fractionated by reverse-phase liquid chromatography (RP-HPLC) and the antimicrobial activity of the fractions was tested using a liquid growth inhibition assay. The main antimicrobial fraction containing acylpolyamines (ApAn) was submitted to two additional chromatographic steps and analyzed by MALDI-TOF. Fractions of interest were accumulated for ultraviolet (UV) spectroscopy and ESI-MS/MS analysis and for minimum inhibitory concentration (MIC) and hemolytic activity determination.Results: Five acylpolyamines were isolated from the venom with molecular masses between 614 Da and 756 Da, being named ApAn728, ApAn614a, ApAn614b, ApAn742 and ApAn756. The analysis of UV absorption profile of each ApAn and the fragmentation pattern obtained by ESI-MS/MS suggested the presence of a tyrosyl unit as chromophore and a terminal polyamine chain consistent with structural units PA43 or PA53. ApAn presented MIC between 128 µM and 256 µM against Escherichia coli and Staphylococcus aureus, without causing hemolysis against mouse erythrocytes.Conclusion: The antimicrobial and non-hemolytic properties of the analyzed ApAn may be relevant for their application as possible therapeutic agents and the identification of an unconventional chromophore for spider acylpolyamines suggests an even greater chemical diversity.

Safety Helmet Wearing Detection in Aerial Images Using Improved YOLOv4

In construction, it is important to check whether workers wear safety helmets in real time. We proposed using an unmanned aerial vehicle (UAV) to monitor construction workers in real time. As the small target of aerial photography poses challenges to safety-helmet-wearing detection, we proposed an improved YOLOv4 model to detect the helmet-wearing condition in aerial photography: (1) By increasing the dimension of the effective feature layer of the backbone network, the model's receptive field is reduced, and the utilization rate of fine-grained features is improved. (2) By introducing the cross stage partial (CSP) structure into path aggregation network (PANet), the calculation amount of the model is reduced, and the aggregation efficiency of effective features at different scales is improved. (3) The complexity of the YOLOv4 model is reduced by introducing group convolution and the pruning PANet multi-scale detection mode for de-redundancy. Experimental results show that the improved YOLOv4 model achieved the highest performance in the UAV helmet detection task, that the mean average precision (mAP) increased from 83.67% of the original YOLOv4 model to 91.03%, and that the parameter amount of the model is reduced by 24.7%. The results prove that the improved YOLOv4 model can effectively respond to the requirements of real-time detection of helmet wearing by UAV aerial photography.

Development of anthocyanin markers: gene mapping, genomic analysis and genetic diversity studies in Ipomoea species.

The anthocyanins are pigments responsible for a wide range of colours in plants, from blue, red and purple, play essential biological roles as well as their genes are evolutionarily conserved. Purple sweet potatoes have anthocyanins as the predominant colour, even though they are present in orange roots masked by carotenoids. Several studies have focused on molecular aspects of anthocyanin genes, mainly in wild Ipomoea species, although the structure and segregation analysis of those genes in sweet potato hexaploid species are still unknown. Based on an "exon-primed intron-crossing" (EPIC) approach, fourteen pairs of primers were designed, on five structural anthocyanin genes as candidates. The strategy exploits the Intron Length Polymorphism (ILP) from Candidate Genes (CG), resulting in 93% of successful markers giving scorable and reproducible alleles. The results allowed to define partial structure and sequence of the introns and exons from the selected CG, and to determine patterns of sequence variation. The evaluation of marker dosage and allelic segregations in an Ipomoea batatas (L.) Lam mapping population identified several alleles for linkage analysis. The study validated the utility of ILP-CG markers for genetic diversity and conservation applicability and a successful amplification gradient across wild Ipomoea species validated their transferability.

Finite-size scaling and thermodynamics of model supercooled liquids: long-range concentration fluctuations and the role of attractive interactions.

We compute partial structure factors, Kirkwood-Buff integrals (KBIs) and chemical potentials of model supercooled liquids with and without attractive interactions. We aim at investigating whether relatively small differences in the tail of the radial distribution functions result in contrasting thermodynamic properties. Our results suggest that the attractive potential favours the nucleation of long-range structures. Indeed, upon decreasing temperature, Bathia-Thornton structure factors display anomalous behaviour in the k→0 limit. KBIs extrapolated to the thermodynamic limit confirm this picture, and excess coordination numbers identify the anomaly with long-range concentration fluctuations. By contrast, the purely repulsive system remains perfectly miscible for the same temperature interval and only reveals qualitatively similar concentration fluctuations in the crystalline state. Furthermore, differences in both isothermal compressibilities and chemical potentials show that thermodynamics is not entirely governed by the short-range repulsive part of the interaction potential, emphasising the nonperturbative role of attractive interactions. Finally, at higher density, where both systems display nearly identical dynamical properties and repulsive interactions become dominant, the anomaly disappears, and both systems also exhibit similar thermodynamic properties.

A Study on Structural Optimization of proper arrangement of damping material for a Partial Structure using SEA by Finite Element Model

A Study on Structural Optimization of proper arrangement of damping material for a Partial Structure using SEA by Finite Element Model

A model on an absolute scale for the small-angle X-ray scattering from bovine casein micelles.

Casein micelles extracted from milk are 100-400 nm-sized particles, made up of proteins and calcium phosphates, with the latter as colloidal calcium phosphate particles (CCPs) in a size range of 2-4 nm embedded in a protein network. The hierarchical structures give rise to a variation of scattering intensity over many orders of magnitude, which can be measured by small-angle X-ray scattering and static light scattering. Expressions for the scattering intensity of a general simple model for composite particles with polydispersities of overall size and subparticles are derived, and some approximations are checked by generating scattering data for systems generated by Monte Carlo simulations. Based on the simpler models, a new model has been developed for casein micelles, where the scattering is expressed on an absolute scale and where the concentrations of, respectively, protein and CCPs are used as constraints, providing a consistent model. The CCPs are modelled as oblate ellipsoids and the protein as star structures. Correlations between the substructures of CCPs and protein structures are taken into account in terms of partial structure factors. The overall structure as well as some heterogeneities at intermediate length scale are modelled as polydisperse spheres. The model fits the data very well on all length scales and demonstrates that both the scattering from CCPs and protein is important. Thus, the model provides a detailed description of the casein structure, which is consistent with the information available in the literature.

Feel like quitting the job? A causal attribution approach to social and work overload consequences

PurposeIn an effort to study the effects of stress factors i.e. work overload and social overload, the authors integrate causal attribution research to develop a stress outcome model. Drawing on Affective Events theory, this study aims to investigate how work and social overload lead to turnover intentions with the mediating role of emotional exhaustion.Design/methodology/approachA quantitative approach to examine the direct linkage of stress factors to turnover intention was tested for 409 respondents working at middle and senior manager level posts in the banking sector of Lahore, Pakistan. Partial least square structure equation modeling technique was applied through Smart_PLS3.0 for hypothesis testing.FindingsResults revealed a strong relationship between stress factors (i.e. social overload and work overload) and turnover intentions in the presence of emotional exhaustion as a significant mediator.Originality/valueThe study adds value to the theory and practice by examining the understudied stress factors (social overload) along with work overload and their consequences on the employees.

Molecular dynamics simulation of molten strontium chloride based on deep potential

In this work, a scheme based on ab initio calculations and machine learning is applied to train deep learning interatomic potentials (DP) for molten SrCl2. With the trained DP, we investigate the local structures and properties of molten SrCl2 in the temperature range of 1200–1500 K. The results show that deep potential molecular dynamics (DPMD) simulations can accurately reproduce the radial distribution functions and angle distribution functions of ab initio molecular dynamics simulations. Meanwhile, the simulated partial structure factors of DPMD simulations are in good agreement with those obtained by the thermal neutron scattering technique. Additionally, the predicted density, self-diffusion coefficient, viscosity, and ionic conductivity are satisfactory compared with the experimental data. This study indicates that DP models can serve as an effective tool for the theoretical study of molten salts.

Influence of surface Ce/Zr ratio on formation of κ-Ce2Zr2O8 superstructure and its application in three-way catalysis

Ceria-zirconia based (CZ) materials are still a crucial component of three-way catalysts (TWCs) now. Compared with traditional cubic or tetragonal phase, CZ material with pyrochlore-type κ-Ce2Zr2O8 phase shows an excellent oxygen storage capacity, and thus is examined as a promising material for developing TWCs. Considering the remarkable surface Ce/Zr ratio change during the κ-Ce2Zr2O8 formation, we investigated the effects of surface Ce/Zr ratio on the formation of κ-Ce2Zr2O8 and on the supported Pd-only three-way catalysts. Raman and transmission electron microscopy (TEM) results demonstrate that partial κ-Ce2Zr2O8 structure is formed in the three oxides, and the content of κ-Ce2Zr2O8 structure increases with increasing surface Ce/Zr ratio, but its stability declines. After thermal aging, the proportions of Ce3+ and oxidized palladium (Pdδ++Pd2+) in the catalysts, which are shown to be positively correlated with catalytic activity, follow the same sequence as the stability of κ-Ce2Zr2O8, namely Pd/M0.33ROa > Pd/M0.4ROa > Pd/CeM0.33ROa. In particular, Pd/M0.33RO, which possesses 100% ((Pdδ++Pd2+)/Pd) even after thermal aging at 900 °C for 4 h, owing to the strengthened metal-support interaction by the presence of stable κ-Ce2Zr2O8, shows the best catalytic performance. This work is instructive to the application of κ-Ce2Zr2O8 in three-way catalysis.

Concentration dependence of the bulk and surface properties of liquid AlZn alloys at 1000 K

The properties of mixing of the segregating liquid AlZn alloys at 1000 K have been investigated using the joint effect of size ratio and enthalpic contribution as well as entropic effect. The theoretical values of the bulk properties i.e. thermodynamic properties such as excess free energy of mixing, free energy of mixing, entropy of mixing, heat of mixing as well as the structural properties like concentration-concentration partial structure factor (or concentration fluctuations in the long wavelength limit) and short-range order parameter have been compared with the corresponding experimental data available in the literature. Again Butler model has been applied to study the surface properties such as the surface concentration, surface tension and surface concentration-concentration partial structure factor (or surface concentration fluctuations) of liquid AlZn alloys at 1000 K. The theoretical data of the surface tension have been compared with the experimental data available in the literature which show a qualitative agreement.

The Impact of Artificial Marble Wastes on Heat Deflection Temperature, Crystallization, and Impact Properties of Polybutylene Terephthalate.

As artificial marble is abundant and widely used in residential and commercial fields, the resource utilization of artificial marble wastes (AMWs) has become extremely important in order to protect the environment. In this paper, polybutylene terephthalate/artificial marble wastes (PBT/AMWs) composites were prepared by melt blending to maximize resource utilization and increase PBT performance. The research results showed that the filling of AMWs was beneficial to the improvement of PBT-related performance. X-ray diffraction analysis results indicated that after filling AMWs into the PBT matrix, the crystal structure of PBT was not changed. Heat deflection temperature (HDT) analysis results indicated that the HDT of PBT composites with 20 wt% AMWs reached 66.68 °C, which was 9.12 °C higher than that of neat PBT. Differential scanning calorimetry analysis results showed that heterogeneous nucleation could be well achieved when the filling content was 15 wt%; impact and scanning electron microscope analysis results showed that due to the partial core-shell structure of the AMWs, the impact strength of PBT was significantly improved after filling. When the filling amount was 20 wt%, the impact strength of the PBT composites reached 23.20 kJ/m2, which was 17.94 kJ/m2 higher than that of neat PBT. This research will not only provide new insights into the efficient and high-value utilization of AMWs, but also provide a good reference for improved applications of other polymers.

Partial structure factors derived from coarse-grained phase-separation dynamics on a disordered lattice with density fluctuations

The simplest and most time-efficient phase-separation dynamics simulations are carried out on a disordered lattice to calculate the partial structure factors of coarse-grained A-B binary mixtures. The typical coarse-grained phase-separation models use regular lattices and can describe the local concentrations but cannot describe both local density and concentration fluctuations. To introduce fluctuation for local density in the model, the particle positions from a hard sphere fluid model are determined as disordered lattice points for the model. Then we place the local order parameter as the difference of the concentrations of A and B components on each lattice point. The concentration at each lattice point is time-evolved by discrete equations derived from the Cahn-Hilliard equation. From both fluctuations, Bhatia and Thornton’s structure factor can be accurately calculated. The structure factor for concentration fluctuations at the large wavenumber region gives us the correct mean concentrations of the components. Using the mean concentrations, partial structure factors can be converted from three of Bhatia and Thornton’s structure factors. The present model and procedures can provide a means of analysing the structural properties of many materials that exhibit complex morphological changes.