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In heterogeneous wireless networks, random packet loss and high latency lead to conventional TCP variants performing unsatisfactorily in the case of competing communications. Especially on high-latency wireless links, conventional TCP variants are unable to estimate congestion degrees accurately for fine-grained congestion control because of the effects of random packet loss and delay oscillations. This paper proposes a TCP variant at the sender side to identify congestion degrees, namely TCP-WBQ, which quickly responses to the real congestion and effectively shields against random packet loss and oscillations of latency time. The proposed algorithm of congestion control firstly constructs a backlog-queue model based on the dynamics of the congestion window, and deduces the two bounds of the model which delimit oscillations of the backlog queue for non-congestion and random packet loss respectively. TCP-WBQ detects congestion degrees more accurately and thus implements the corresponding schemes of adjusting the congestion window, maintaining a tradeoff between high throughputs and congestion avoidance. The comprehensive simulations show that TCP-WBQ works efficiently in bandwidth utilization with single and multiple bottleneck scenarios, and achieves high performance and competitive fairness in heterogeneous wireless networks.

Allosteric partial inhibition of soluble, monomeric proteases can offer major regulatory advantages, but remains a concept on paper to date; although it has been routinely documented for receptors and oligomeric proteins. Thrombin, a key protease of the coagulation cascade, displays significant conformational plasticity, which presents an attractive opportunity to discover small molecule probes that induce sub-maximal allosteric inhibition. We synthesized a focused library of some 36 sulfated coumarins to discover two agents that display sub-maximal efficacy (~50%), high potency (150-fold). Michaelis-Menten, competitive inhibition, and site-directed mutagenesis studies identified exosite 2 as the site of binding for the most potent sulfated coumarin. Stern-Volmer quenching of active site-labeled fluorophore suggested that the allosteric regulators induce intermediate structural changes in the active site as compared to those that display ~80–100% efficacy. Antithrombin inactivation of thrombin was impaired in the presence of the sulfated coumarins suggesting that allosteric partial inhibition arises from catalytic dysfunction of the active site. Overall, sulfated coumarins represent first-in-class, sub-maximal inhibitors of thrombin. The probes establish the concept of allosteric partial inhibition of soluble, monomeric proteins. This concept may lead to a new class of anticoagulants that are completely devoid of bleeding.

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.

Currently, surgical staples are composed of non–biodegradable titanium (Ti) that can cause allergic reactions and interfere with imaging. This paper proposes a novel biodegradable magnesium (Mg) alloy staple and discusses analyses conducted to evaluate its safety and feasibility. Specifically, finite element analysis revealed that the proposed staple has a suitable stress distribution while stapling and maintaining closure. Further, an immersion test using artificial intestinal juice produced satisfactory biodegradable behavior, mechanical durability, and biocompatibility in vitro. Hydrogen resulting from rapid corrosion of Mg was observed in small quantities only in the first week of immersion, and most staples maintained their shapes until at least the fourth week. Further, the tensile force was maintained for more than a week and was reduced to approximately one-half by the fourth week. In addition, the Mg concentration of the intestinal artificial juice was at a low cytotoxic level. In porcine intestinal anastomoses, the Mg alloy staples caused neither technical failure nor such complications as anastomotic leakage, hematoma, or adhesion. No necrosis or serious inflammation reaction was histopathologically recognized. Thus, the proposed Mg alloy staple offers a promising alternative to Ti alloy staples.

Biomarkers are measurable changes associated with the disease. Urine can reflect the changes of the body while blood is under control of the homeostatic mechanisms; thus, urine is considered an important source for early and sensitive disease biomarker discovery. A comprehensive profile of the urinary proteome will provide a basic understanding of urinary proteins. In this paper, we present an in-depth analysis of the urinary proteome based on different separation strategies, including direct one dimensional liquid chromatography–tandem mass spectrometry (LC/MS/MS), two dimensional LC/MS/MS, and gel-eluted liquid fraction entrapment electrophoresis/liquid-phase isoelectric focusing followed by two dimensional LC/MS/MS. A total of 6085 proteins were identified in healthy urine, of which 2001 were not reported in previous studies and the concentrations of 2571 proteins were estimated (spanning a magnitude of 106) with an intensity-based absolute quantification algorithm. The urinary proteins were annotated by their tissue distribution. Detailed information can be accessed at the “Human Urine Proteome Database” (www.urimarker.com/urine).

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.

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.

As optical metasurfaces become progressively ubiquitous, the expectations from them are becoming increasingly complex. The limited number of structural parameters in the conventional metasurface building blocks, and existing phase engineering rules do not completely support the growth rate of metasurface applications. In this paper, we present digitized-binary elements, as alternative high-dimensional building blocks, to accommodate the needs of complex-tailorable-multifunctional applications. To design these complicated platforms, we demonstrate adaptive genetic algorithm (AGA), as a powerful evolutionary optimizer, capable of handling such demanding design expectations. We solve four complex problems of high current interest to the optics community, namely, a binary-pattern plasmonic reflectarray with high tolerance to fabrication imperfections and high reflection efficiency for beam-steering purposes, a dual-beam aperiodic leaky-wave antenna, which diffracts TE and TM excitation waveguides modes to arbitrarily chosen directions, a compact birefringent all-dielectric metasurface with finer pixel resolution compared to canonical nano-antennas, and a visible-transparent infrared emitting/absorbing metasurface that shows high promise for solar-cell cooling applications, to showcase the advantages of the combination of binary-pattern metasurfaces and the AGA technique. Each of these novel applications encounters computational and fabrication challenges under conventional design methods, and is chosen carefully to highlight one of the unique advantages of the AGA technique. Finally, we show that large surplus datasets produced as by-products of the evolutionary optimizers can be employed as ingredients of the new-age computational algorithms, such as, machine learning and deep leaning. In doing so, we open a new gateway of predicting the solution to a problem in the fastest possible way based on statistical analysis of the datasets rather than researching the whole solution space.

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.

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.

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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.

The South China Sea (SCS) is a highly semi-enclosed marginal sea located in the East Asian monsoon region. This paper proposes interesting aspects of the unique feature of the SCS waves in response to tropical cyclone's passage when large-scale winter monsoon winds prevail. We use the wave model WaveWatch III to study the wave characteristics of typhoon Durian (2006) passing over the middle of SCS in early December 2006, and state the new understanding acquired in the aspects of the tropical cyclone generated waves in the SCS during winter monsoon surge. In light of this, the role of the large-scale NE monsoon winds on winter typhoon wave field characteristics in the SCS are highlighted by conducting sensitivity experiments with and without the NE monsoon winds. The NE monsoon winds weakly affect the SWH field near the typhoon track and strongly away from the track, especially in the deep water area of the northern SCS where the NE monsoon winds produce high waves. Comparisons between the two experiments show the effect of the NE monsoon winds on the directional wave spectra in the SCS, suggesting that the monsoon-generated swells do not decay and remain throughout the typhoon period.

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.

It is well-known that cell adhesion is important in many biological processes such as cell migration and proliferation. A better understanding of the cell adhesion process will shed insight into these cellular biological responses as well as cell adhesion-related diseases treatment. However, there is little research which has attempted to investigate the process of cell adhesion and its mechanism. Thus, this paper aims to study the time-dependent adhesion properties of single living chondrocytes using an advanced coupled experimental-numerical approach. Atomic Force Microscopy (AFM) tips will be used to apply lateral forces to detach chondrocytes that are seeded for three different periods. An advanced Finite Element Analysis (FEA) model combining porohyperelastic (PHE) constitutive model and cohesive zone formulation is developed to explore the mechanism of adhesion. The results revealed that the cells can resist normal traction better than tangential traction in the beginning of adhesion. This is when the cell adhesion molecules establish early attachment to the substrates. After that when the cells are spreading, stress fiber bundles generate tangential traction on the substrate to form strong adhesion. Both simulation and experimental results agree well with each other, providing a powerful tool to study the cellular adhesion process.

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

The most distinctive characteristic of synthetic aperture radar (SAR) is that it can acquire data under all weather conditions and at all times. However, its coherent imaging mechanism introduces a great deal of speckle noise into SAR images, which makes the segmentation of target and shadow regions in SAR images very difficult. This paper proposes a new SAR image segmentation method based on wavelet decomposition and a constant false alarm rate (WD-CFAR). The WD-CFAR algorithm not only is insensitive to the speckle noise in SAR images but also can segment target and shadow regions simultaneously, and it is also able to effectively segment SAR images with a low signal-to-clutter ratio (SCR). Experiments were performed to assess the performance of the new algorithm on various SAR images. The experimental results show that the proposed method is effective and feasible and possesses good characteristics for general application.

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

This paper proposes a metasurface design approach with perforated labyrinthine path coil structure to manipulate the acoustic transmission with inexpensive materials. The medium in the labyrinthine path coils in this design is air, but not limited to air. A systematic approach has been proposed for the unit cell design of acoustic metamaterials with adjustable resonance peak frequencies and bandgap width. The theory demonstrates that the length of pipe segments determines resonance peak frequencies and the cross-sectional area ratio adjusts the bandgap width. The proposed design approach uses an equivalent pipe circuit based analytical model to design the high transmission (high pass) and high reflection (low pass) unit cell. The simulation and experiment has been performed to evaluate the validity of the theory. Although there exists some assumptions in the theory, the theory still has enough accuracy to guide the metasurface design illustrated by the simulation and experiment results.

The Indium Tin Oxide (ITO) platform is one of the promising solutions for state-of-the-art integrated optical modulators towards low-loss silicon photonics applications. One of the key challenges on this way is to optimize ITO-based thin films stacks for electro-optic modulators with both high extinction ratio and low insertion loss. In this paper we demonstrate the e-beam evaporation technology of 20 nm-thick ITO films with low extinction coefficient of 0.14 (Nc = 3.7·1020 cm−3) at 1550 nm wavelength and wide range of carrier concentrations (from 1 to 10 × 1020 cm−3). We investigate ITO films with amorphous, heterogeneously crystalline, homogeneously crystalline with hidden coarse grains and pronounced coarsely crystalline structure to achieve the desired optical and electrical parameters. Here we report the mechanism of oxygen migration in ITO film crystallization based on observed morphological features under low-energy growth conditions. Finally, we experimentally compare the current–voltage and optical characteristics of three electro-optic active elements based on ITO film stacks and reach strong ITO dielectric permittivity variation induced by charge accumulation/depletion (Δn = 0.199, Δk = 0.240 at λ = 1550 nm under ± 16 V). Our simulations and experimental results demonstrate the unique potential to create integrated GHz-range electro-optical modulators with sub-dB losses.

Microbial fuel cell (MFC) technology is a simple way to accelerate the treatment of the oily sludge which is a major problem affecting the quality of oil fields and surrounding environment while generating electricity. To investigate the oil removal and the characteristics of changes in the composition of bacteria, sediment microbial fuel cells (SMFCs) supplemented with oily sludge was constructed. The results showed that the degradation efficiency of total petroleum hydrocarbon (TPH) of SMFC treatment was 10.1 times higher than the common anaerobic degradation. In addition, the degradation rate of n-alkanes followed the order of high carbon number > low carbon number > medium carbon number. The odd–even alkane predominance (OEP) increased, indicating that a high contribution of even alkanes whose degradation predominates. The OUT number, Shannon index, AEC index, and Chao1 index of the sludge treated with SMFC (YN2) are greater than those of the original sludge (YN1), showing that the microbial diversity of sludge increased after SMFC treatment. After SMFC treatment the relative abundance of Chloroflexi, Bacteroidia and Pseudomonadales which are essential for the degradation of the organic matter and electricity production increased significantly in YN2. These results will play a crucial role in improving the performance of oily sludge MFC.

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.

Cell cutting is a significant task in biology study, but the highly productive non-embedded cell cutting is still a big challenge for current techniques. This paper proposes a vision-based nano robotic system and then realizes automatic non-embedded cell cutting with this system. First, the nano robotic system is developed and integrated with a nanoknife inside an environmental scanning electron microscopy (ESEM). Then, the positions of the nanoknife and the single cell are recognized, and the distance between them is calculated dynamically based on image processing. To guarantee the positioning accuracy and the working efficiency, we propose a distance-regulated speed adapting strategy, in which the moving speed is adjusted intelligently based on the distance between the nanoknife and the target cell. The results indicate that the automatic non-embedded cutting is able to be achieved within 1–2 mins with low invasion benefiting from the high precise nanorobot system and the sharp edge of nanoknife. This research paves a way for the high-throughput cell cutting at cell’s natural condition, which is expected to make significant impact on the biology studies, especially for the in-situ analysis at cellular and subcellular scale, such as cell interaction investigation, neural signal transduction and low invasive cell surgery.

In this paper, we demonstrate the relation between cloaking effect and its nonradiating state by considering the destructive multipolar interaction between near-field scattering by bare object and surrounding coating located in its proximity. This cloaking effect is underpinned by anapole mode excitation and it occurs as destructive interference between the electric dipole moment, generated by a bare object (here a central metallic scatterer) and the toroidal moment, formed inside the cloak (a surrounding cluster of dielectric cylinders). Numerical results show how a cloaking effect based on the formation of the anapole mode can lead to an overall nonradiating metamolecule with all-dielectric materials in the coating region.

Recently, public displays such as liquid crystal displays (LCDs) are often used in urban green spaces, however, the display devices can spoil green landscape of urban green spaces because they look like artificial materials. We previously proposed a green landscape-friendly grass animation display method by controlling a pixel-by-pixel grass color dynamically. The grass color can be changed by moving a green grass length in yellow grass, and the grass animation display can play simple animations using grayscale images. In the previous research, the color scale was mapped to the green grass length subjectively, however, this method has not achieved displaying the grass colors corresponding to the color scale based on objective evaluations. Here, we introduce a dynamic grass color scale display technique based on a grass length. In this paper, we developed a grass color scale setting procedure to map the grass length to the color scale with five levels through image processing. Through the outdoor experiment of the grass color scale setting procedure, the color scale can correspond to the green grass length based on a viewpoint. After the experiments, we demonstrated a grass animation display to show the animations with the color scale using the experiment results., 17 pages

We present a simple, facile method to micropattern planar metal electrodes defined by the geometry of a microfluidic channel network template. By introducing aqueous solutions of metal into reversibly adhered PDMS devices by desiccation instead of flow, we are able to produce difficult to pattern “dead end” or discontinuous features with ease. We characterize electrodes fabricated using this method and perform electrical lysis of mammalian cancer cells and demonstrate their use as part of an antibody capture assay for GFP. Cell lysis in microwell arrays is achieved using the electrodes and the protein released is detected using an antibody microarray. We show how the template channels used as part of the workflow for patterning the electrodes may be produced using photolithography-free methods, such as laser micromachining and PDMS master moulding, and demonstrate how the use of an immiscible phase may be employed to create electrode spacings on the order of 25–50 μm, that overcome the current resolution limits of such methods. This work demonstrates how the rapid prototyping of electrodes for use in total analysis systems can be achieved on the bench with little or no need for centralized facilities.

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

Many flexible electronic devices contain metal films on polymer substrates to satisfy requirements for both electrical conductivity and mechanical durability. Despite numerous trials to date, the stretchability of metal interconnects remains an issue. In this paper, we have demonstrated a stretchable metal interconnect through control of the texture of a copper film with columnar grown grains on a polyimide (PI) substrate. The columnar grown copper films (CGC films) were deposited by regulating radio frequency (RF) sputtering powers. CGC films were able to sustain their electrical conductivity at strains above 100%. Instead of ultimate electrical discontinuity by channel crack propagation, CGC films maintained their conductivity by forming ligament structures, or a ‘conductive net,’ through trapped micro-cracks. XRD, AFM and in situ SEM analysis were used to investigate these stretchable conductors.

Urban sewer systems consist of wastewater and stormwater sewers, of which only wastewater is processed before being discharged. Occasionally, misconnections or damages in the network occur, resulting in untreated wastewater entering natural water bodies via the stormwater system. Cultivation of faecal indicator bacteria (e.g. Escherichia coli; E. coli) is the current standard for tracing wastewater contamination. This method is cheap but has limited specificity and mobility. Here, we compared the E. coli culturing approach with two sequencing-based methodologies (Illumina MiSeq 16S rRNA gene amplicon sequencing and Oxford Nanopore MinION shotgun metagenomic sequencing), analysing 73 stormwater samples collected in Stockholm. High correlations were obtained between E. coli culturing counts and frequencies of human gut microbiome amplicon sequences, indicating E. coli is indeed a good indicator of faecal contamination. However, the amplicon data further holds information on contamination source or alternatively how much time has elapsed since the faecal matter has entered the system. Shotgun metagenomic sequencing on a subset of the samples using a portable real-time sequencer, MinION, correlated well with the amplicon sequencing data. This study demonstrates the use of DNA sequencing to detect human faecal contamination in stormwater systems and the potential of tracing faecal contamination directly in the field.

In this paper, the memristive switching behavior of Cu/ HfO2/p++ Si devices fabricated by an organic-polymer-assisted sol-gel spin-coating method, coupled with post-annealing and shadow-mask metal sputtering steps, is examined. HfO2 layers of about 190 nm and 80 nm, are established using cost-effective spin-coating method, at deposition speeds of 2000 and 4000 rotations per minute (RPM), respectively. For two types of devices, the memristive characteristics (Von, Ion, and Vreset) and device-to-device electrical repeatability are primarily discussed in correlation with the oxide layer uniformity and thickness. The devices presented in this work exhibit an electroforming free and bipolar memory-resistive switching behavior that is typical of an Electrochemical Metallization (ECM) I-V fingerprint. The sample devices deposited at 4000 RPM generally show less variation in electrical performance parameters compared to those prepared at halved spin-coating speed. Typically, the samples prepared at 4000 RPM (n = 8) display a mean switching voltage Von of 3.0 V (±0.3) and mean reset voltage Vreset of −1.1 V (±0.5) over 50 consecutive sweep cycles. These devices exhibit a large Roff/Ron window (up to 104), and sufficient electrical endurance and retention properties to be further examined for radiation sensing. As they exhibit less statistical uncertainty compared to the samples fabricated at 2000 RPM, the devices prepared at 4000 RPM are tested for the detection of soft gamma rays (emitted from low-activity Cs-137 and Am-241 radioactive sources), by assessing the variation in the on-state resistance value upon exposure. The analysis of the probability distributions of the logarithmic Ron values measured over repeated ON-OFF cycles, before, during and after exposing the devices to radiation, demonstrate a statistical difference. These results pave the way for the fabrication and development of cost-effective soft-gamma ray detectors.

With the presence of novel coronavirus disease at the end of 2019, several approaches were proposed to help physicians detect the disease, such as using deep learning to recognize lung involvement based on the pattern of pneumonia. These approaches rely on analyzing the CT images and exploring the COVID-19 pathologies in the lung. Most of the successful methods are based on the deep learning technique, which is state-of-the-art. Nevertheless, the big drawback of the deep approaches is their need for many samples, which is not always possible. This work proposes a combined deep architecture that benefits both employed architectures of DenseNet and CapsNet. To more generalize the deep model, we propose a regularization term with much fewer parameters. The network convergence significantly improved, especially when the number of training data is small. We also propose a novel Cost-sensitive loss function for imbalanced data that makes our model feasible for the condition with a limited number of positive data. Our novelties make our approach more intelligent and potent in real-world situations with imbalanced data, popular in hospitals. We analyzed our approach on two publicly available datasets, HUST and COVID-CT, with different protocols. In the first protocol of HUST, we followed the original paper setup and outperformed it. With the second protocol of HUST, we show our approach superiority concerning imbalanced data. Finally, with three different validations of the COVID-CT, we provide evaluations in the presence of a low number of data along with a comparison with state-of-the-art.

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.

Realistic neurons may hold complex anatomical structure, for example, autapse connection to some internuncial neurons, which this specific synapse can connect to its body via a close loop. Continuous exchanges of charged ions across the membrane can induce complex distribution fluctuation of intracellular and extracellular charged ions of cell, and a time-varying electromagnetic field is set to modulate the membrane potential of neuron. In this paper, an autapse-modulated neuron model is presented and the effect of electromagnetic induction is considered by using magnetic flux. Bifurcation analysis and sampled time series for membrane potentials are calculated to investigate the mode transition in electrical activities and the biological function of autapse connection is discussed. Furthermore, the Gaussian white noise and electromagnetic radiation are considered on the improved neuron model, it is found appropriate setting and selection for feedback gain and time delay in autapse can suppress the bursting in neuronal behaviors. It indicates the formation of autapse can enhance the self-adaption of neuron so that appropriate response to external forcing can be selected, this biological function is helpful for encoding and signal propagation of neurons. It can be useful for investigation about collective behaviors in neuronal networks exposed to electromagnetic radiation.

To understand the formation, evolution, and function of complex systems, it is crucial to understand the internal organization of their interaction networks. Partly due to the impossibility of visualizing large complex networks, resolving network structure remains a challenging problem. Here we overcome this difficulty by combining the visual pattern recognition ability of humans with the high processing speed of computers to develop an exploratory method for discovering groups of nodes characterized by common network properties, including but not limited to communities of densely connected nodes. Without any prior information about the nature of the groups, the method simultaneously identifies the number of groups, the group assignment, and the properties that define these groups. The results of applying our method to real networks suggest the possibility that most group structures lurk undiscovered in the fast-growing inventory of social, biological, and technological networks of scientific interest., Comment: Software implementing the method described in the paper is available at http://purl.oclc.org/net/find_structural_groups and is accompanied by a demo video available at http://www.nature.com/srep/2011/111109/srep00151/extref/srep00151-s2.mov

This paper analyzes the stability of a coexistence equilibrium point of a model for competition between two stage-structured populations. In this model, for each population, competition for resources may affect any one of the following population parameters: reproduction, juvenile survival, maturation rate, or adult survival. The results show that the competitive strength of a population is affected by (1) the ratio of the population parameter influenced by competition under no resource limitation (maximum compensatory capacity) over the same parameter under a resource limitation due to competition (equilibrium rate) and (2) the ratio of interspecific competition over intraspecific competition; this ratio was previously shown to depend on resource-use overlap. The former ratio, which we define as fitness, can be equalized by adjusting organisms' life history strategies, thereby promoting coexistence. We conclude that in addition to niche differentiation among populations, the life history strategies of organisms play an important role in coexistence.

We report the impact of anti-urothelial autoantibody (AUAb) on urinary bladder phenotype in BALB/c mice deficient of the FcγRIIb and PD-1. AUAb was present in serum samples from approximately half of the double-knockout (DKO) mice, as detected by immunofluorescence and immunoblots for urothelial proteins including uroplakin IIIa. The AUAb-positive DKO mice showed degeneration of urothelial plaque and umbrella cells, along with infiltration of inflammatory cells in the suburothelial layer. TNFα and IL-1β were upregulated in the bladder and the urine of AUAb-positive DKO mice. Voiding behavior of mice was analyzed by the Voided Stain on Paper method. 10-week-old and older AUAb-positive DKO mice voided significantly less urine per void than did wild type (WT) mice. Furthermore, administration of the AUAb-containing serum to WT mice significantly reduced their urine volume per void. In summary, this report presents a novel comprehensive mouse model of autoimmune cystitis.