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In this paper, drained consolidation tests of saturated remolded cohesive soil were carried out at different loading rates, and the samples at different loading stages were measured by nuclear magnetic resonance (NMR) testing. Through qualitative analysis of the relationship between the transverse relaxation time T2, peak area and porosity, the deformation micro-response of the saturated remolded cohesive soil was studied. The results showed that the transverse relaxation time T2 of the saturated remolded cohesive soil samples during the initial stage consisted of two spectral peaks, representing pores with large and small pore diameters, respectively. As loading progressed, the pore diameters in each sample group gradually shifted to smaller sizes, and the final form of the T2 spectrum was unimodal, in which the pores became denser and more uniform. In the initial stage of loading, the T2 spectrum of the soil sample with faster loading showed no obvious change trend due to the influence of drainage lag. After a sufficiently long dead load time, the final shape of the T2 spectrum was very similar regardless of loading rate, indicating that the loading rate only affected the intermediate state of the soil sample, and the final state was determined by the initial state of the sample. At the same time, after a certain dead load time, the changes in pore diameter were no longer obvious, indicating that the flowing fluid in the pore was drained and that drainage consolidation was complete. According to the positive correlation between the loading rate and maximum pore pressure, a boundary rate was obtained. We could reasonably infer that if the controlled loading rate was less than this value, drainage consolidation was complete in the main consolidation stage.

The interaction between protein and DNA plays an essential function in various critical natural processes, like DNA replication, transcription, splicing, and repair. Studying the binding affinity of proteins to DNA helps to understand the recognition mechanism of protein-DNA complexes. Since there are still many limitations on the protein-DNA binding affinity data measured by experiments, accurate and reliable calculation methods are necessarily required. So we put forward a computational approach in this paper, called PreDBA, that can forecast protein-DNA binding affinity effectively by using heterogeneous ensemble models. One hundred protein-DNA complexes are manually collected from the related literature as a data set for protein-DNA binding affinity. Then, 52 sequence and structural features are obtained. Based on this, the correlation between these 52 characteristics and protein-DNA binding affinity is calculated. Furthermore, we found that the protein-DNA binding affinity is affected by the DNA molecule structure of the compound. We classify all protein-DNA compounds into five classifications based on the DNA structure related to the proteins that make up the protein-DNA complexes. In each group, a stacked heterogeneous ensemble model is constructed based on the obtained features. In the end, based on the binding affinity data set, we used the leave-one-out cross-validation to evaluate the proposed method comprehensively. In the five categories, the Pearson correlation coefficient values of our recommended method range from 0.735 to 0.926. We have demonstrated the advantages of the proposed method compared to other machine learning methods and currently existing protein-DNA binding affinity prediction approach.

In this paper, the moisture content on the wetting front is obtained by using the Van Genuchten (VG) model of unsaturated soil, and then the distribution of moisture content in the upper part of the wetting front is simplified as a trapezoid. The Green-Ampt (G-A) infiltration model of infinite slope with unsaturated characteristics is derived. The analytical expression of safety coefficient (FOS) of infinite slope with rainfall is solved by combining the limit equilibrium method with the unsaturated soil shear strength theory phase. The results show that: 1) compared with the traditional G-A model and the combined rectangular and 1/4 oval model, the upper part of the wetting front is simplified to a trapezoidal model, which has great advantages in infiltration rate and cumulative infiltration, especially when the slope is large or the rain intensity is heavy; 2) since the distribution of soil moisture content above the wetting front is considered, the matrix suction at the wetting front is not neglected, and the safety coefficient calculated by the method proposed in this paper is closer to the actual situation than the traditional G-A model.

Dust pollution is one of the most severe environmental issues in open pit mines, hindering green mining development. Open pit mine dust has characteristics of multiple dust-generating points, is irregular, influenced by climatic conditions, and has a high degree of distribution with a wide dispersion range in three dimensions. Consequently, evaluating the quantity of dust dispersion and controlling environmental pollution are crucial for supporting green mining. In this paper, dust monitoring above the open pit mine was carried out with an unmanned aerial vehicle (UAV) on board. The dust distribution patterns above the open pit mine were studied in different vertical and horizontal directions at different heights. The results show that the temperature changes less in the morning and more at noon in winter. At the same time, the isothermal layer becomes thinner and thinner as the temperature rises, which makes it easy for dust to spread. The horizontal dust is mainly concentrated at 1300 and 1550 elevations. The dust concentration is polarized at 1350–1450 elevation. The most serious exceedance is at 1400 elevation, with TSP (the concentration of total suspended particulate), PM10 (particulates with aerodynamic diameter

Earth fissures caused by tectonic forces, human activities, or both seriously threaten the safety of people’s lives and properties. The Taiyuan Basin, a Cenozoic downfaulted basin located in the centre of the Fen-Wei Basin tectonic belt, in northwestern China, presents the ideal study area for a hazard assessment of earth fissures. A total of 104 earth fissures have been observed in the Taiyuan Basin, with a total length of approximately 128 km. In this paper, we proposed a probabilistic method for mapping earth fissure hazards by integrating the analytic hierarchy process (AHP), the area under the curve (AUC), and the certainty factor model (CFM). Geomorphic units, geologic formations, active faults and land subsidence zones of the Taiyuan Basin were mapped in detail. Correlations between these factors and earth fissures were evaluated through spatial modelling in ArcGIS. The AUC was introduced into the AHP to weight each factor and thus, to derive an earth fissure susceptibility map. Finally, the modelled earth fissure susceptibility was compared with a digital inventory of earth fissures to develop a probability function and map the spatial variability in failure probability through the CFM. The study indicates that active faults have the greatest contribution to the generation of earth fissures. Earth fissures are prone to develop in the piedmont alluvial-diluvial clinoplain and the transitional zone near the geomorphic boundary. This mapping procedure can assist in making rational decisions regarding urban planning and infrastructure development in areas susceptible to earth fissures.

Plasmonic metamaterial absorbers are particularly important in different applications such as photodetectors, microbolometers and solar cells. In this paper, we propose a tungsten boride (WB, a refractory ceramic) based broadband metamaterial absorber whose optical properties is numerically analyzed and experimentally characterized. We have also analyzed the damage characteristics of this absorber using a femtosecond laser and compared with an ordinary Au metamaterial absorber. We observe that WB has almost the double absorption bandwidth with absorption more than 90% over the spectral range of 950 to 1400 nm when compared with the Au counterpart. Furthermore, we show that Au metamaterial is damaged at the power of around 36.4 mW whereas WB metamaterial is not damaged at that power (WB has high Tammann temperature than Au)-however the atom of WB material was knocked off by the bombardment of a femtosecond laser.

Celiac disease (CeD) is a multifactorial disease influenced by both genetic and environmental risk factors. CeD genetic components are mainly due to HLA class II genes, which account for approximately 40% of the disease heritability. The environmental factor is linked to gliadin ingestion. Despite genetic and epigenetic studies, the pathological molecular mechanism remains unclarified. The strong genetic component does not explain more than half of the hereditability; we identified several epigenetic features that contribute to the understanding of the missing hereditability. The lipid profile of infants has been proposed as a potential biomarker of CeD metabolism that can be measured before they exhibit developmental disorders and clinical symptoms. We suggest that the state of the host is a main factor for the abnormal immune response to gluten. Long before any exposure to the offending agent or any production of specific antibodies, several molecular mechanisms are differentially expressed in infants who will develop CeD compared to their peers matched for the same genetic profile. The present study explored the serum phospholipid profile of a group of infants at risk for celiac disease, followed up to 8 years to monitor the onset of CeD. We compared 30 patients who developed the disease with 20 age- and sex-matched peers with similar genetic profiles who did not develop the disease within 8 years. Serum phospholipids were analysed at 4 months, before exposure to gluten, and at 12 months of age, when none showed any marker of disease. In the 30 CeD patients, we also analysed the serum at the time of diagnosis (>24 months). The serum phospholipid profile was fairly constant across 4 and 12 months of age and, in CeD, up to 24–36 months. The phospholipid signature was dramatically different in infants who developed CeD when compared to that of control NY-CeD (Not Yet developing Celiac Disease) peers. We identified a specific serum phospholipid signature that predicts the onset of celiac disease in HLA at-risk infants years before the appearance of antibodies specific for CeD in the serum and before any clinical symptoms, even before gluten introduction into the diet at 4 months. Specifically, lysophosphatidylcholine, phosphatidylcholine, alkylacyl-phosphatidylcholine, phosphoethanolamines, phosphatidylserines, phosphatidylglycerol and phosphatidylinositol were found to be differentially represented in CeD versus NY-CeD. A set constituted by a limited number of alkylacyl-phosphatidylcholine and lyso-phosphatidylcholine, together with the duration of breast-feeding, allows the discrimination of infants who develop celiac disease before 8 years of age from those at a similar genetic risk who do not develop the disease. In addition to recent discovery, our paper unveiled a specifc phopholipid profile, able to discriminate infants who eventually develop celiac disease years before antibodies or clinical symptoms ensue.

Myoelectric hand prostheses offer a way for upper-limb amputees to recover gesture and prehensile abilities to ease rehabilitation and daily life activities. However, studies with prosthesis users found that a lack of intuitiveness and ease-of-use in the human-machine control interface are among the main driving factors in the low user acceptance of these devices. This paper proposes a highly intuitive, responsive and reliable real-time myoelectric hand prosthesis control strategy with an emphasis on the demonstration and report of real-time evaluation metrics. The presented solution leverages surface high-density electromyography (HD-EMG) and a convolutional neural network (CNN) to adapt itself to each unique user and his/her specific voluntary muscle contraction patterns. Furthermore, a transfer learning approach is presented to drastically reduce the training time and allow for easy installation and calibration processes. The CNN-based gesture recognition system was evaluated in real-time with a group of 12 able-bodied users. A real-time test for 6 classes/grip modes resulted in mean and median positive predictive values (PPV) of 93.43% and 100%, respectively. Each gesture state is instantly accessible from any other state, with no mode switching required for increased responsiveness and natural seamless control. The system is able to output a correct prediction within less than 116 ms latency. 100% PPV has been attained in many trials and is realistically achievable consistently with user practice and/or employing a thresholded majority vote inference. Using transfer learning, these results are achievable after a sensor installation, data recording and network training/fine-tuning routine taking less than 10 min to complete, a reduction of 89.4% in the setup time of the traditional, non-transfer learning approach.

An amendment to this paper has been published and can be accessed via a link at the top of the paper.

Single-cell sequencing provides a new level of granularity in studying the heterogeneous nature of cancer cells. For some cancers, this heterogeneity is the result of copy number changes of genes within the cellular genomes. The ability to accurately determine such copy number changes is critical in tracing and understanding tumorigenesis. Current single-cell genome sequencing methodologies infer copy numbers based on statistical approaches followed by rounding decimal numbers to integer values. Such methodologies are sample dependent, have varying calling sensitivities which heavily depend on the sample’s ploidy and are sensitive to noise in sequencing data. In this paper we have demonstrated the concept of integer-counting by using a novel bioinformatic algorithm built on our library construction chemistry in order to detect the discrete nature of the genome. Supplementary material: [https://cherry.chem.bg.ac.rs/handle/123456789/4686]

In this paper, we show that anisotropic photosensitive nematic liquid crystals (PNLC) made by incorporating anisotropic absorbing dyes are promising candidates for constructing all-optical elements by virtue of the extraordinarily large optical nonlinearity of the nematic host. In particular, we have demonstrated several room-temperature ‘prototype’ PNLC-based all-optical devices such as optical diode, optical transistor and all primary logic gate operations (OR, AND, NOT) based on such optical transistor. Owing to the anisotropic absorption property and the optical activity of the twist alignment nematic cell, spatially non-reciprocal transmission response can be obtained within a sizeable optical isolation region of ~210 mW. Exploiting the same mechanisms, a tri-terminal configuration as an all-optical analogue of a bipolar junction transistor is fabricated. Its ability to be switched by an optical field enables us to realize an all-optical transistor and demonstrate cascadability, signal fan-out, logic restoration and various logical gate operations such as OR, AND and NOT. Due to the possibility of synthesizing anisotropic dyes and wide ranging choice of liquid crystals nonlinear optical mechanisms, these all-optical operations can be optimized to have much lower thresholds and faster response speeds. The demonstrated capabilities of these devices have shown great potential in all-optical control system and photonic integrated circuits.

The chloroplast is one of two organelles containing a separate genome that codes for essential and distinct cellular functions such as photosynthesis. Given the importance of chloroplasts in plant metabolism, the genomic architecture and gene content have been strongly conserved through long periods of time and as such are useful molecular tools for evolutionary inferences. At present, complete chloroplast genomes from over 4000 species have been deposited into publicly accessible databases. Despite the large number of complete chloroplast genomes, comprehensive analyses regarding genome architecture and gene content have not been conducted for many lineages with complete species sampling. In this study, we employed the genus Populus to assess how more comprehensively sampled chloroplast genome analyses can be used in understanding chloroplast evolution in a broadly studied lineage of angiosperms. We conducted comparative analyses across Populus in order to elucidate variation in key genome features such as genome size, gene number, gene content, repeat type and number, SSR (Simple Sequence Repeat) abundance, and boundary positioning between the four main units of the genome. We found that some genome annotations were variable across the genus owing in part from errors in assembly or data checking and from this provided corrected annotations. We also employed complete chloroplast genomes for phylogenetic analyses including the dating of divergence times throughout the genus. Lastly, we utilized re-sequencing data to describe the variations of pan-chloroplast genomes at the population level for P. euphratica. The analyses used in this paper provide a blueprint for the types of analyses that can be conducted with publicly available chloroplast genomes as well as methods for building upon existing datasets to improve evolutionary inference.

The quantum secret sharing is an essential and fundamental technique for sharing a secret with the all participants in quantum cryptography. It can be used to design many complex protocols such as secure multiparty summation, multiplication, sorting, voting, etc. Recently, Song et al. have discussed a quantum protocol for secret sharing, which has (t, n) threshold approach and modulo d, where t and n denote the threshold number of participants and total number of participants, respectively. Kao et al. point out that the secret in the Song et al.’s protocol cannot be reconstructed without other participants’ information. In this paper, we discuss a protocol that overcomes this problem.

Understanding the externalities of transportation networks in the process of the agglomeration and diffusion of production factors has theoretical and practical significance for the coordinated development of China's economic growth in urban agglomerations. Therefore, the social network analysis method is introduced in this paper with the case of the Pan Pearl River Delta Urban Agglomeration to analyze the characteristics of the traffic connection network of the production factor flow within this urban agglomeration, and subsequently, an econometric panel model is adopted to quantitatively analyze the effect of the connection network on the economic growth of the urban agglomeration. The results show that (1) the traffic connection of the Pan Pearl River Delta Urban Agglomeration has network characteristics typical of a “small world”. Although the connections between cities are gradually strengthening, the regional differences are obvious, showing a core–edge pattern of eastern agglomeration and western sparseness. (2) Among the network nodes, Guangzhou, Shenzhen and other cities have obvious agglomeration and diffusion effects, stabilizing economic growth while driving the development of surrounding cities. The "polarization effect" in Chongqing and Chengdu has significantly increased, and the accumulation of factors mainly meets their own economic development but has not yet spread. (3) The Pan Pearl River Delta Urban Agglomeration's transportation network influences the region’s economic growth through the structural effect, as it strengthens the economic ties between cities, and through the action of resource factors, as the network represents the aggregation and diffusion path of factor flow. (4) Due to the different traffic connections and industrial structures across the Pan Pearl River Delta Urban Agglomeration, the factor flow of each suburban agglomeration has a differentiated impact on the regional economic growth under the traffic connection network. Therefore, to realize the coordinated economic development of the Pan Pearl River Delta Urban Agglomeration, it is necessary to "adjust measures to local conditions" and formulate accurate and precise policies.

We study quasi-spatially periodic signals (QSPSs) as a class of input signals of interest, which maintain their shapes quasi-periodically (with a phase change and a time shift) during propagation in an optical fibre. Instead of the computationally expensive nonlinear Fourier transform (NFT), the property of quasi-periodic shape invariant could be used as an alternative for decoding at the receiver. In this paper, properties of QSPSs and the signal design problem are studied, including the trade-off between various system parameters.

Ebola virus (EBOV) infection causes a high death toll, killing a high proportion of EBOV infected patients within 7 days. Comprehensive data on EBOV infection are very fragmented, hampering efforts in developing therapeutics and vaccines against EBOV. Under this circumstance, mathematical models become valuable resources to explore potential controlling strategies. In this paper, we employed experimental data of EBOV-infected nonhuman primates (NHPs) to construct a mathematical framework for determining windows of opportunity for treatment and vaccination. Considering a prophylactic vaccine based on recombinant vesicular stomatitis virus expressing the EBOV glycoprotein (VSV-EBOV), we found that the time window can be subject-specific, but vaccination could be protective if a subject is vaccinated during a period from one week to four months before infection. For the case of a therapeutic vaccine based on monoclonal antibodies (mAbs), a single dose might resolve the invasive EBOV replication even it was administrated as late as four days after infection. Our mathematical models can be used as building blocks for developing therapeutic and vaccine modalities as well as for evaluating public health intervention strategies in outbreaks. Future laboratory experiments will help to validate and refine the estimates of the windows of opportunity proposed here.

An amendment to this paper has been published and can be accessed via a link at the top of the paper.

Zinc–air batteries proffer high energy density and cyclic stability at low costs but lack disadvantages like sluggish reactions at the cathode and the formation of by-products at the cathode. To resolve these issues, a new perovskite material, CaCu3Ti4O12 (CCTO), is proposed as an efficacious electrocatalyst for oxygen evolution/reduction reactions to develop zinc–air batteries (ZAB). Synthesis of this material adopted an effective oxalate route, which led to the purity in the electrocatalyst composition. The CCTO material is a proven potential candidate for energy applications because of its high dielectric permittivity (ε) and occupies an improved ORR-OER activity with better onset potential, current density, and stability. The Tafel value for CCTO was obtained out to be 80 mV dec−1. The CCTO perovskite was also evaluated for the zinc–air battery as an air electrode, corresponding to the high specific capacitance of 801 mAh g−1 with the greater cyclic efficiency and minimum variations in both charge/discharge processes. The highest power density (Pmax) measured was 127 mW cm−2. Also, the CCTO based paper battery shows an excellent performance achieving a specific capacity of 614 mAh g−1. The obtained results promise CCTO as a potential and cheap electrocatalyst for energy applications.

Control chart methods have been received much attentions in biosurvillance studies. The correlation between charting statistics or regions could be considerably important in monitoring the states of multiple outcomes or regions. In addition, the process variable distribution is unknown in most situations. In this paper, we propose a new nonparametric strategy for multivariate process monitoring when the distribution of a process variable is unknown. We discuss the EWMA control chart based on rank methods for a multivariate process, and the approach is completely nonparametric. A simulation study demonstrates that the proposed method is efficient in detecting shifts for multivariate processes. A real Japanese influenza data example is given to illustrate the performance of the proposed method.

Nearly ~ 108 types of High entropy alloys (HEAs) can be developed from about 64 elements in the periodic table. A major challenge for materials scientists and metallurgists at this stage is to predict their crystal structure and, therefore, their mechanical properties to reduce experimental efforts, which are energy and time intensive. Through this paper, we show that it is possible to use machine learning (ML) in this arena for phase prediction to develop novel HEAs. We tested five robust algorithms namely, K-nearest neighbours (KNN), support vector machine (SVM), decision tree classifier (DTC), random forest classifier (RFC) and XGBoost (XGB) in their vanilla form (base models) on a large dataset screened specifically from experimental data concerning HEA fabrication using melting and casting manufacturing methods. This was necessary to avoid the discrepancy inherent with comparing HEAs obtained from different synthesis routes as it causes spurious effects while treating an imbalanced data—an erroneous practice we observed in the reported literature. We found that (i) RFC model predictions were more reliable in contrast to other models and (ii) the synthetic data augmentation is not a neat practice in materials science specially to develop HEAs, where it cannot assure phase information reliably. To substantiate our claim, we compared the vanilla RFC (V-RFC) model for original data (1200 datasets) with SMOTE-Tomek links augmented RFC (ST-RFC) model for the new datasets (1200 original + 192 generated = 1392 datasets). We found that although the ST-RFC model showed a higher average test accuracy of 92%, no significant breakthroughs were observed, when testing the number of correct and incorrect predictions using confusion matrix and ROC-AUC scores for individual phases. Based on our RFC model, we report the development of a new HEA (Ni25Cu18.75Fe25Co25Al6.25) exhibiting an FCC phase proving the robustness of our predictions.