ALBERT

Description: This paper presents the design of NaviPBx, an ultrasound-navigated prostate cancer biopsy system. NaviPBx is designed to support an affordable and sustainable national healthcare program in Senegal. It uses spatiotemporal navigation and multiparametric transrectal ultrasound to guide biopsies. NaviPBx integrates concepts and methods that have been independently validated previously in clinical feasibility studies and deploys them together in a practical prostate cancer biopsy system. NaviPBx is based entirely on free open-source software and will be shared as a free open-source program with no restriction on its use. NaviPBx is set to be deployed and sustained nationwide through the Senegalese Military Health Service. This paper reports on the results of the design process of NaviPBx. Our approach concentrates on “frugal technology”, intended to be affordable for low–middle income (LMIC) countries. Our project promises the wide-scale application of prostate biopsy and will foster time-efficient development and programmatic implementation of ultrasound-guided diagnostic and therapeutic interventions in Senegal and beyond.

Description: Traditional research methods in software defect prediction use part of the data in the same project to train the defect prediction model and predict the defect label of the remaining part of the data. However, in the practical realm of software development, the software project that needs to be predicted is generally a brand new software project, and there is not enough labeled data to build a defect prediction model; therefore, traditional methods are no longer applicable. Cross-project defect prediction uses the labeled data of the same type of project similar to the target project to build the defect prediction model, so as to solve the problem of data loss in traditional methods. However, the difference in data distribution between the same type of project and the target project reduces the performance of defect prediction. To solve this problem, this paper proposes a cross-project defect prediction method based on manifold feature transformation. This method transforms the original feature space of the project into a manifold space, then reduces the difference in data distribution of the transformed source project and the transformed target project in the manifold space, and finally uses the transformed source project to train a naive Bayes prediction model with better performance. A comparative experiment was carried out using the Relink dataset and the AEEEM dataset. The experimental results show that compared with the benchmark method and several cross-project defect prediction methods, the proposed method effectively reduces the difference in data distribution between the source project and the target project, and obtains a higher F1 value, which is an indicator commonly used to measure the performance of the two-class model.

Description: This paper discusses the performance of the short pitch-based carbon fiber reinforced mortar (CFRM) composite considering its key properties and cost-effectiveness. Five different types of mortar composite were produced using 0–4% volume contents of short pitch-based carbon fibers. The mortar composites were tested for inverted slump cone flow (flow time and volume flow), unit weight, air content, compressive strength, flexural strength, impact resistance, and water absorption. The cost-effectiveness of CFRM was assessed based on the performance to cost ratio (PCR), which was calculated for each mortar composite, considering its workability, mechanical properties, and durability. The inverted slump cone volume flow was counted as a measure of workability, whereas the compressive strength, flexural strength, and impact resistance were considered as the major attributes of the mechanical behavior. In addition, the water absorption was used as a measure of durability. The test results revealed that the mortar composite made with 3% carbon fibers provided adequate workability, a relatively high unit weight and low air content, the highest compressive strength, excellent flexural strength, good impact resistance, and the lowest water absorption. It was also found that the PCR increased up to 3% carbon fibers. Beyond a 3% fiber content, the PCR significantly decreased. The overall research findings revealed that the mortar with 3% carbon fibers was the optimum and most cost-effective mortar composite.

Description: In modern society, it is impossible to imagine life without polymeric materials. However, managing the waste composed of these materials is one of the most significant environmental issues confronting us in the present day. Recycling polymeric waste is the most important action currently available to reduce environmental impacts worldwide and is one of the most dynamic areas in industry today. Utilizing this waste could not only benefit the environment but also promote sustainable development and circular economy management. In its program statement, the European Union has committed to support the use of sorted polymeric waste. This study reviews recent attempts to recycle this waste and convert it by alternative technologies into fine, nano-, and microscale fibers using electrospinning, blowing, melt, or centrifugal spinning. This review provides information regarding applying reprocessed fine fibers in various areas and a concrete approach to mitigate the threat of pollution caused by polymeric materials.

Description: The present study aims to compare the levels of micro-RNA-146a and micro-RNA-126 in oral subgingival plaque and coronary plaque from artery walls in patients with coronary artery disease who suffer from generalized periodontitis. A total of 75 participants were selected and grouped into three categories of 25 patients each: GP+CAD, GP, and HP groups. GP+CAD consisted of patients diagnosed with generalized periodontitis (GP) and coronary artery disease (CAD). The GP+CAD group was further divided into two groups—GP+CADa: where subgingival plaque samples were collected; GP+CADb group: where coronary plaque samples were collected while the patient underwent a coronary artery bypass grafting surgery. The GP group consisted of 25 patients diagnosed with only generalized periodontitis. The HP group consisted of 25 systemically and periodontally healthy controls. miRNA-146a and miRNA126 levels were assessed in subgingival plaque (SP) samples from all groups. Results revealed that miRNA-146a was expressed at higher levels and miRNA-126 was downregulated in the GP+CAD group. microRNAs in subgingival plaque samples showed a significant correlation with the coronary plaque samples in the GP+CAD group. miRNA-146a and miRNA-126 were present in coronary artery disease patients with periodontitis. These micro-RNAs may serve as risk biomarkers for coronary artery disease and generalized periodontitis.

Description: Electronic e-waste (e-waste) is a growing problem worldwide. In 2019, total global production reached 53.6 million tons, and is estimated to increase to 74.7 million tons by 2030. This rapid increase is largely fuelled by higher consumption rates of electrical and electronic goods, shorter life cycles and fewer repair options. E-waste is classed as a hazardous substance, and if not collected and recycled properly, can have adverse environmental impacts. The recoverable material in e-waste represents significant economic value, with the total value of e-waste generated in 2019 estimated to be US $57 billion. Despite the inherent value of this waste, only 17.4% of e-waste was recycled globally in 2019, which highlights the need to establish proper recycling processes at a regional level. This review provides an overview of global e-waste production and current technologies for recycling e-waste and recovery of valuable material such as glass, plastic and metals. The paper also discusses the barriers and enablers influencing e-waste recycling with a specific focus on Oceania.

Description: Heavy metal is released from many industries into water. Before the industrial wastewater is discharged, the contamination level should be reduced to meet the recommended level as prescribed by the local laws of a country. They may be poisonous or cancerous in origin. Their presence does not only damage people, but also animals and vegetation because of their mobility, toxicity, and non-biodegradability into aquatic ecosystems. The review comprehensively discusses the progress made by various adsorbents such as natural materials, synthetic, agricultural, biopolymers, and commercial for extraction of the metal ions such as Ni2+, Cu2+, Pb2+, Cd2+, As2+ and Zn2+ along with their adsorption mechanisms. The adsorption isotherm indicates the relation between the amount adsorbed by the adsorbent and the concentration. The Freundlich isotherm explains the effective physical adsorption of the solute particle from the solution on the adsorbent and Langmuir isotherm gives an idea about the effect of various factors on the adsorption process. The adsorption kinetics data provide valuable insights into the reaction pathways, the mechanism of the sorption reaction, and solute uptake. The pseudo-first-order and pseudo-second-order models were applied to describe the sorption kinetics. The presented information can be used for the development of bio-based water treatment strategies.

Description: This paper combines the interval analysis tools with the nonlinear model predictive control (NMPC). The NMPC strategy is formulated based on an uncertain dynamic model expressed as nonlinear ordinary differential equations (ODEs). All the dynamic parameters are identified in a guaranteed way considering the various uncertainties on the embedded sensors and the system’s design. The NMPC problem is solved at each time step using validated simulation and interval analysis methods to compute the optimal and safe control inputs over a finite prediction horizon. This approach considers several constraints which are crucial for the system’s safety and stability, namely the state and the control limits. The proposed controller consists of two steps: filtering and branching procedures enabling to find the input intervals that fulfill the state constraints and ensure the convergence to the reference set. Then, the optimization procedure allows for computing the optimal and punctual control input that must be sent to the system’s actuators for the pendulum stabilization. The validated NMPC capabilities are illustrated through several simulations under the DynIbex library and experiments using an inverted pendulum.

Description: In order to enhance the corrosion resistance of concrete to chloride salt, 5% NaCl solution was used to corrode ordinary concrete (OC) and rubber concrete (RC) with 5%, 10%, and 15% rubber content, respectively. By testing the compressive strength, mass, chloride ion concentration at different depths and relative dynamic elastic modulus, the erosion mechanism was analyzed by means of SEM scanning and EDS patterns, and the mechanical properties and deterioration degree of ordinary concrete (OC) and rubber concrete (RC) under the corrosion environment of chloride salt were studied. The results show that: the quality of rubber mixed into concrete increases first and then decreases, and rubber can increase the compressive strength of concrete, improve its internal structure. At the same time, the mechanical properties of concrete in the corrosion environment of chloride salt are improved to a certain extent, and the deterioration degree is reduced. Considering the comprehensive performance of OC and RC in the dry–wet alternation mechanism under chloride salt corrosion, the best content of rubber is 10%.

Description: This work presents a two-dimensional numerical analysis of a wave channel and a oscillating water column (OWC) device. The main goal is to validate a methodology which uses transient velocity data as a means to impose velocity boundary condition for the generation of numerical waves. To achieve this, a numerical wave channel was simulated using regular waves with the same parameters as those used in a laboratory experiment. First, these waves were imposed as prescribed velocity boundary condition and compared with the analytical solution; then, the OWC device was inserted into the computational domain, aiming to validate this methodology. For the numerical analysis, computational fluid dynamics ANSYS Fluent software was employed, and to tackle with water–air interaction, the nonlinear multiphase model volume of fluid (VOF) was applied. Although the results obtained through the use of discrete data as velocity boundary condition presented a little disparity; in general, they showed a good agreement with laboratory experiment results. Since many studies use regular waves, there is a lack of analysis with ocean waves realistic data; thus, the proposed methodology stands out for its capacity of using realistic sea state data in numerical simulations regarding wave energy converters (WECs).

Description: Belt grinding of flat surfaces of typical parts made of steel and alloys, such as grooves, shoulders, ends, and long workpieces, is a good alternative to milling. Several factors can influence the belt grinding process of flat surfaces of metals, such as cutting speed and pressure. In this work, the importance of pressure in the belt grinding was investigated in terms of technological and experimental aspects. The grinding experiments were performed on structural alloy steel 30KhGSN2/30KhGSNA, structural carbon steel AISI 1045, corrosion-resistant and heat-resistant stainless steel AISI 321, and heat-resistant nickel alloy KHN77TYuR. The performance of the grinding belt was investigated in terms of surface roughness, material removal rate (MRR), grinding belt wear, performance index. Estimated indicators of the belt grinding process were developed: cutting ability; reduced cutting ability for belt grinding of steels and heat-resistant alloy. It was found that with an increase in pressure p, the surface roughness of the processed surface Ra decreased while the tool wear VB and MRR increased. With a decrease in plasticity and difficulty of machinability, the roughness, material removal rate, reduced cutting capacity (Performance index) qper, material removal Q decreased, and the tool wear VB increased. The obtained research results can be used by technologists when creating belt grinding operations for steels and alloys to ensure the required performance is met.

Description: Increasingly advanced applications of polymer fibers are driving the demand for new, high-performance fiber types. One way to produce polymer fibers is by electrospinning from polymer solutions and melts. Polymer melt electrospinning produces fibers with small diameters through solvent-free processing and has applications within different fields, ranging from textile and construction, to the biotech and pharmaceutical industries. Modeling of the electrospinning process has been mainly limited to simulations of geometry-dependent electric field distributions. The associated large change in viscosity upon fiber formation and elongation is a key issue governing the electrospinning process, apart from other environmental factors. This paper investigates the melt electrospinning of aerogel-containing fibers and proposes a logistic viscosity model approach with parametric ramping in a finite element method (FEM) simulation. The formation of melt electrospun fibers is studied with regard to the spinning temperature and the distance to the collector. The formation of PET-Aerogel composite fibers by pneumatic transport is demonstrated, and the critical parameter is found to be the temperature of the gas phase. The experimental results form the basis for the electrospinning model, which is shown to reproduce the trend for the fiber diameter, both for polymer as well as polymer-aerogel composites.

Description: In this paper, we present a novel technique to design fixed structure controllers, for both continuous-time and discrete-time systems, through an H∞ mixed sensitivity approach. We first define the feasible controller parameter set, which is the set of the controller parameters that guarantee robust stability of the closed-loop system and the achievement of the nominal performance requirements. Then, thanks to Putinar positivstellensatz, we compute a convex relaxation of the original feasible controller parameter set and we formulate the original H∞ controller design problem as the non-emptiness test of a set defined by sum-of-squares polynomials. Two numerical simulations and one experimental example show the effectiveness of the proposed approach.

Description: This paper investigated the influence of recycled ceramics and grazed hollow beads on the mechanical, thermal conductivity and material properties of concrete. The results showed that the concentration of recycled ceramics and grazed hollow beads has significant optimization on the workability and thermal properties of the concrete. However, the superabundant concentration can reduce the hydration degree of the concrete, which results in the suppressed production of C-S-H gel and the increase of material defects. In summary, considering the coordinated development of key factors such as thermal insulation properties, mechanical properties and microstructure, 10% RCE and 60% GHB are the optimal material system design methods.

Description: An increase in wave energy converter (WEC) efficiency requires not only consideration of the nonlinear effects in the WEC dynamics and the power take-off (PTO) mechanisms, but also more integrated treatment of the whole system, i.e., the buoy dynamics, the PTO system, and the control strategy. It results in an optimization formulation that has a nonquadratic and nonstandard cost functional. This article presents the application of real-time nonlinear model predictive controller (NMPC) to two degrees of freedom point absorber type WEC with highly nonlinear PTO characteristics. The nonlinear effects, such as the fluid viscous drag, are also included in the plant dynamics. The controller is implemented on a real-time target machine, and the WEC device is emulated in real-time using the WECSIM toolbox. The results for the successful performance of the design are presented for irregular waves under linear and nonlinear hydrodynamic conditions.

Description: For refractory application, amongst others, inorganic chemical binders are used to shape and process loose, unpacked materials. The binder influences the chemical composition within the ceramic body during setting, aging and firing and thus the finally reached properties of the refractory material. For an effective design of tailored materials with required properties, the mode of action of the binder systems should carefully be investigated. A combination of both structure analysis techniques and macroscopic property investigations proved to be a powerful tool for a detailed description of structure–property correlations. This is shown on the basis of X-ray powder diffraction and nuclear magnetic resonance spectroscopy analyses combined with observation of (thermo)mechanical and chemical investigations.

Description: High-pressure hydrogen tanks which are composed of an aluminum alloy liner and a carbon fiber wound layer are currently the most popular means to store hydrogen on vehicles. Nevertheless, the aluminum alloy is easily affected by high-pressure hydrogen, which leads to the appearance of hydrogen embrittlement (HE). Serious HE of hydrogen tank represents a huge dangers to the safety of vehicles and passengers. It is critical and timely to outline the mainstream approach and point out potential avenues for further investigation of HE. An analysis, including the mechanism (including hydrogen-enhanced local plasticity model, hydrogen-enhanced decohesion mechanism and hydrogen pressure theory), the detection (including slow strain rate test, linearly increasing stress test and so on) and methods for the prevention of HE on aluminum alloys of hydrogen vehicles (such as coating) are systematically presented in this work. Moreover, the entire experimental detection procedures for HE are expounded. Ultimately, the prevention measures are discussed in detail. It is believed that further prevention measures will rely on the integration of multiple prevention methods. Successfully solving this problem is of great significance to reduce the risk of failure of hydrogen storage tanks and improve the reliability of aluminum alloys for engineering applications in various industries including automotive and aerospace.

Description: With the rapid development of electronics, thermal management has become one of the most crucial issues. Intense research has focused on surface modifications used to enhance heat transfer. In this study, multilayer copper micromeshes (MCMs) are developed for commercial compact electronic cooling. Boiling heat transfer performance, including critical heat flux (CHF), heat transfer coefficients (HTCs), and the onset of nucleate boiling (ONB), are investigated. The effect of micromesh layers on the boiling performance is studied, and the bubbling characteristics are analyzed. In the study, MCM-5 shows the highest critical heat flux (CHF) of 207.5 W/cm2 and an HTC of 16.5 W(cm2·K) because of its abundant micropores serving as nucleate sites, and outstanding capillary wicking capability. In addition, MCMs are compared with other surface structures in the literature and perform with high competitiveness and potential in commercial applications for high-power cooling.

Description: Creating a widely excepted model on the measure of intelligence became inevitable due to the existence of an abundance of different intelligent systems. Measuring intelligence would provide feedback for the developers and ultimately lead us to create better artificial systems. In the present paper, we show a solution where learning as a process is examined, aiming to detect pre-written solutions and separate them from the knowledge acquired by the system. In our approach, we examine image recognition software by executing different transformations on objects and detect if the software was resilient to it. A system with the required intelligence is supposed to become resilient to the transformation after experiencing it several times. The method is successfully tested on a simple neural network, which is not able to learn most of the transformations examined. The method can be applied to any image recognition software to test its abstraction capabilities.

Description: The SE coast of Iran is of great economic and environmental importance. Global climate change affects this coastline through sea level rise (SLR), compounded by a decrease in sediment budgets in coastal areas. This study developed a Coastal Vulnerability Index (CVI) for the SE coast of Iran using satellite, instrumental and field data. Eight risk variables were defined: coastal slope, regional coastal elevation, mean tidal range, mean significant wave height, rate of relative sea-level change, rate of shoreline change, environmental sensitivity and socio-economic sensitivity. The coast was divided into 27 segments based on geomorphic, environmental and socioeconomic traits. Coastal segments were categorized based on their vulnerability to each risk factor using a CVI. The resulting maps highlighted the vulnerability of each coastal segment to SLR. Approximately 50% of the coast is comprised of mostly rocky shores, which are less vulnerable to SLR. Approximately 33% of the coastal length, including sandy beaches, tidal flats and mangrove forests, were determined to be highly vulnerable to SLR. Approximately 12% of the coastline was determined to be moderately vulnerable. Population centers and infrastructure were ranked as highly-to-moderately vulnerable to SLR. This study highlighted the high vulnerability of low-lying areas, such as lagoons and mangroves, in the western part of the Iranian coast of Makran. Proper coastal management and mitigation plans are essential in the future to protect coastal societies and environments.

Description: 3D printing or additive manufacturing (AM) (in the industrial context) is an innovative, as opposed to subtractive, technology, bringing new opportunities and benefits to the spare part supply chain (SPSC). The aim of this work is to capture the views of the stakeholders at the end of the chain, extruding factors that will benefit the end-user and the factors that are likely to be an obstacle, by employing the questionnaire method. Company objectives regarding spares (cost reductions, improvement of services, space reduction) have been prioritized differently by the stakeholders. The most important barriers according to the participants are the quality assurance of the spare parts made by the new technology followed by the know-how and skills of staff. Other views such as suitable parts are suggested. The practical value of this work, in addition to assessing the readiness of the industry, is that it provides guidance for the successful implementation of AM in the maritime industry.

Description: Structure and strength are responsible for soil physical properties. This paper determines in a uniaxial compression test the strength of artificial soils containing different proportions of various clay-size minerals (cementing agents) and silt-size feldspar/quartz (skeletal particles). A novel empirical model relating the maximum stress and the Young’s modulus to the mineral content basing on the Langmuir-type curve was proposed. By using mercury intrusion porosimetry (MIP), bulk density (BD), and scanning electron microscopy (SEM), structural parameters influencing the strength of the soils were estimated and related to mechanical parameters. Size and shape of particles are considered as primary factors responsible for soil strength. In our experiments, the soil strength depended primarily on the location of fine particles in respect to silt grains and then, on a mineral particle size. The surface fractal dimension of mineral particles played a role of a shape parameter governing soil strength. Soils containing minerals of higher surface fractal dimensions (rougher surfaces) were more mechanically resistant. The two latter findings appear to be recognized herein for the first time.

Description: This paper presents new methodology for determining the actual stress–strain diagram based on analytical equations, in combination with numerical and experimental data. The first step was to use the 3D digital image correlation (DIC) to estimate true stress–strain diagram by replacing common analytical expression for contraction with measured values. Next step was to estimate the stress concentration by using a new methodology, based on recently introduced analytical expressions and numerical verification by the finite element method (FEM), to obtain actual stress–strain diagrams, as named in this paper. The essence of new methodology is to introduce stress concentration factor into the procedure of actual stress evaluation. New methodology is then applied to determine actual stress–strain diagrams for two undermatched welded joints with different rectangular cross-section and groove shapes, made of martensitic steels X10 CrMoVNb 9-1 and Armox 500T. Results indicated that new methodology is a general one, since it is not dependent on welded joint material and geometry.

Description: The characteristics of bridge pile-group foundation have a significant influence on the dynamic performance of the superstructure. Most of the existing analysis methods for the pile-group foundation impedance take the trait of strong specialty, which cannot be generalized in practical projects. Therefore, a project-oriented numerical solution algorithm is proposed to compute the dynamic impedance of bridge pile-group foundation. Based on the theory of viscous-spring artificial boundary, the derivation and solution of the impedance function are transferred to numerical modeling and harmonic analysis, which can be carried out through the finite element method. By taking a typical pile-group foundation as a case study, the results based on the algorithm are compared with those from existing literature. Moreover, an impact experiment of a real pile-group foundation was implemented, the results of which are also compared with those resulting from the proposed numerical algorithm. Both comparisons show that the proposed numerical algorithm satisfies engineering precision, thus showing good effectiveness in application.

Description: Medical image analysis research requires medical image datasets. Nevertheless, due to various impediments, researchers have been described as “data starved”. We hypothesize that implicit evolving community standards require researchers to use ever-growing datasets. In Phase I of this research, we scanned the MICCAI (Medical Image Computing and Computer-Assisted Intervention) conference proceedings from 2011 to 2018. We identified 907 papers involving human MRI, CT or fMRI datasets and extracted their sizes. The median dataset size had grown by 3–10 times from 2011 to 2018, depending on imaging modality. Statistical analysis revealed exponential growth of the geometric mean dataset size with an annual growth of 21% for MRI, 24% for CT and 31% for fMRI. Thereupon, we had issued a forecast for dataset sizes in MICCAI 2019 well before the conference. In Phase II of this research, we examined the MICCAI 2019 proceedings and analyzed 308 relevant papers. The MICCAI 2019 statistics compare well with the forecast. The revised annual growth rates of the geometric mean dataset size are 27% for MRI, 30% for CT and 32% for fMRI. We predict the respective dataset sizes in the MICCAI 2020 conference (that we have not yet analyzed) and the future MICCAI 2021 conference.

Description: The gamma-ray shielding ability of various Bentonite–Cement mixed materials from northeast Egypt have been examined by determining their theoretical and experimental mass attenuation coefficients, μm (cm2g−1), at photon energies of 59.6, 121.78, 344.28, 661.66, 964.13, 1173.23, 1332.5 and 1408.01 keV emitted from 241Am, 137Cs, 152Eu and 60Co point sources. The μm was theoretically calculated using the chemical compositions obtained by Energy Dispersive X-ray Analysis (EDX), while a NaI (Tl) scintillation detector was used to experimentally determine the μm (cm2g−1) of the mixed samples. The theoretical values are in acceptable agreement with the experimental calculations of the XCom software. The linear attenuation coefficient (μ), mean free path (MFP), half-value layer (HVL) and the exposure buildup factor (EBF) were also calculated by knowing the μm values of the examined samples. The gamma-radiation shielding ability of the selected Bentonite–Cement mixed samples have been studied against other puplished shielding materials. Knowledge of various factors such as thermo-chemical stability, availability and water holding capacity of the bentonite–cement mixed samples can be analyzed to determine the effectiveness of the materials to shield gamma rays.

Description: Topologically nontrivial spin textures host great promise for future spintronic applications. Skyrmions in particular are of burgeoning interest owing to their nanometric size, topological protection, and high mobility via ultra-low current densities. It has been previously reported through magnetic susceptibility, microscopy, and scattering techniques that Co8Zn8Mn4 forms an above room temperature triangular skyrmion lattice. Here, we report the synthesis procedure and characterization of a polycrystalline Co8Zn8Mn4 disordered bulk sample. We employ powder X-ray diffraction and backscatter Laue diffraction as characterization tools of the crystallinity of the samples, while magnetic susceptibility and Small Angle Neutron Scattering (SANS) measurements are performed to study the skyrmion phase. Magnetic susceptibility measurements show a dip anomaly in the magnetization curves, which persists over a range of approximately 305 K–315 K. SANS measurements reveal a rotationally disordered polydomain skyrmion lattice. Applying a symmetry-breaking magnetic field sequence, we were able to orient and order the previously jammed state to yield the prototypical hexagonal diffraction patterns with secondary diffraction rings. This emergence of the skyrmion order serves as a unique demonstration of the fundamental interplay of structural disorder and anisotropy in stabilizing the thermal equilibrium phase.

Description: The La2O3-doped basaltic glass simulated high-level waste form (HLW) was prepared by the solid-state melt method. The simulated waste La2O3 maximum loading and the doping effect on structure, thermal stability, leaching behavior, density, and hardness of basaltic glasses were studied. XRD and SEM results show that the simulated waste loading of La2O3 in basaltic glass can be up to ~46 wt.%, and apatite (CaLa4(SiO4)3O) precipitates when the content of La2O3 reaches 56 wt.%. Raman results indicate that the addition of La2O3 breaks the Si–O–Si bond of large-membered and four-membered, but the number of A13+ involved in the formation of the network increase. Low content of La2O3 can help to repair the glass network, but it destroys the network as above 26 wt.%. DSC results show the thermal stability of simulated waste forms first increases and then decreases with the increase of La2O3 content. With the increase of La2O3 content, the density of the simulated waste form increases, and the hardness decreases. The leaching chemical stability of samples was evaluated by the ASTM Product Consistency Test (PCT) Method, which show that all the samples have good chemical stability. The leaching rates of La and Fe are three orders of magnitude lower than those of the other elements. Among them, L36 has the best comprehensive leaching performance.

Description: By optimizing the distribution of steel fibers in fiber-reinforced cementitious mortar (FRCM) through the layered structure, the role of fibers can be fully utilized, thus improving the flexural behavior. In this study, the flexural behavior of layered FRCM at different thicknesses (25 mm, 50 mm, 75 mm, 100 mm) of the steel fiber layer was investigated. The evolution of the crack propagation behavior was analyzed using the digital image correlation (DIC) technique. The results showed that the steel fiber layer thickness of 75 mm has the best flexural behavior. Moreover, the crack propagation path is more tortuous. The maximum value of crack opening displacement (COM) increases with the increase in fiber thickness. In addition, increasing the bottom layer thickness can increase the height of the tensile zone, but the interface inhibits the increase of the tensile zone.

Description: The organic residues on titanium(IV) oxide may be a significant factor that decreases the efficiency of dye-sensitized solar cells (DSSC). Here, we suggest the UV-ozone cleaning process to remove impurities from the surface of TiO2 nanoparticles before dye-sensitizing. Data obtained from scanning electron microscopy, Kelvin probe, Fourier-transform infrared spectroscopy, and Raman spectroscopy showed that the amounts of organic contamination were successfully reduced. Additionally, the UV-VIS spectrophotometry, spectrofluorometry, and secondary ion mass spectrometry proved that after ozonization, the dyeing process was relevantly enhanced. Due to the removal of organics, the power conversion efficiency (PCE) of the prepared DSSC devices was boosted from 4.59% to 5.89%, which was mostly caused by the increment of short circuit current (Jsc) and slight improvement of the open circuit voltage (Voc).

Description: The demand for LNG-powered ships and related equipment is rapidly increasing among major domestic and foreign carriers due to the strengthened IMO regulations on the sulfur content of ship fuel oil. LNG operation in a cryogenic environment requires a storage tank and fuel supply system that uses steel with excellent brittleness and fatigue strength. A ship using LNG is very sensitive to explosion and fire. For this reason, 9% Ni is often used, because ships require high quality products with special materials and structural technologies that ensure operability at cryogenic temperatures. However, research to derive uniform welding quality is urgent because the deterioration of weld quality in the 9% Ni steel welding process is caused by high process difficulty and differences in welding quality depending on a welder’s skill set. This study proposes a method to guarantee a uniform quality of 9% Ni steel in a fiber laser welding process by categorizing weld joint hardness according to the dilution ratio of a base material and establishing a standard for quantitative evaluation.

Description: Low pressure fluid transport (1) applications often require low and precise volumetric flow rates (2) including low leakage to reduce additional costly and complex sensors. A peristaltic pump design (3) was realized, with the fluid’s flexible transport channel formed by a solid cavity and a wobbling plate comprising a rigid and a soft layer (4). In operation, the wobbling plate is driven externally by an electric motor, hence, the soft layer is contracted and unloaded (5) during pump-cycles transporting fluid from low to high pressure sides. A thorough characterization of the pump system is required to design and dimension the components of the peristaltic pump. To capture all these parameters and their dependencies on various operation-states, often complex and long-lasting dynamic 3D FE-simulations are required. We present, here, a holistic design methodology (6) including analytical as well as numerical calculations, and experimental validations for a peristaltic pump with certain specifications of flow-rate range, maximum pressures, and temperatures. An experimental material selection process is established and material data of candidate materials (7) (liquid silicone rubber, acrylonitrile rubber, thermoplastic-elastomer) are directly applied to predict the required drive torque. For the prediction, a semi-physical, analytical model was derived and validated by characterizing the pump prototype.

Description: Many smart city and society applications such as smart health (elderly care, medical applications), smart surveillance, sports, and robotics require the recognition of user activities, an important class of problems known as human activity recognition (HAR). Several issues have hindered progress in HAR research, particularly due to the emergence of fog and edge computing, which brings many new opportunities (a low latency, dynamic and real-time decision making, etc.) but comes with its challenges. This paper focuses on addressing two important research gaps in HAR research: (i) improving the HAR prediction accuracy and (ii) managing the frequent changes in the environment and data related to user activities. To address this, we propose an HAR method based on Soft-Voting and Self-Learning (SVSL). SVSL uses two strategies. First, to enhance accuracy, it combines the capabilities of Deep Learning (DL), Generalized Linear Model (GLM), Random Forest (RF), and AdaBoost classifiers using soft-voting. Second, to classify the most challenging data instances, the SVSL method is equipped with a self-training mechanism that generates training data and retrains itself. We investigate the performance of our proposed SVSL method using two publicly available datasets on six human activities related to lying, sitting, and walking positions. The first dataset consists of 562 features and the second dataset consists of five features. The data are collected using the accelerometer and gyroscope smartphone sensors. The results show that the proposed method provides 6.26%, 1.75%, 1.51%, and 4.40% better prediction accuracy (average over the two datasets) compared to GLM, DL, RF, and AdaBoost, respectively. We also analyze and compare the class-wise performance of the SVSL methods with that of DL, GLM, RF, and AdaBoost.

Description: Motivation Accurate automatic annotation of protein function relies on both innovative models and robust data sets. Due to their importance in biological processes, the identification of DNA-binding proteins directly from protein sequence has been the focus of many studies. However, the data sets used to train and evaluate these methods have suffered from substantial flaws. We describe some of the weaknesses of the data sets used in previous DNA-binding protein literature and provide several new data sets addressing these problems. We suggest new evaluative benchmark tasks that more realistically assess real-world performance for protein annotation models. We propose a simple new model for the prediction of DNA-binding proteins and compare its performance on the improved data sets to two previously published models. Additionally, we provide extensive tests showing how the best models predict across taxonomies. Results Our new gradient boosting model, which uses features derived from a published protein language model, outperforms the earlier models. Perhaps surprisingly, so does a baseline nearest neighbor model using BLAST percent identity. We evaluate the sensitivity of these models to perturbations of DNA-binding regions and control regions of protein sequences. The successful data-driven models learn to focus on DNA-binding regions. When predicting across taxonomies, the best models are highly accurate across species in the same kingdom and can provide some information when predicting across kingdoms. Code and Data Availability The data and results for this paper can be found at https://doi.org/10.5281/zenodo.5153906. The code for this paper can be found at https://doi.org/10.5281/zenodo.5153683. The code, data and results can also be found at https://github.com/AZaitzeff/tools_for_dna_binding_proteins.

Description: With easy-to-process 3D printing materials and fast production, the quality of dental services can be improved. In the conventional procedure, the dentist makes temporary crowns directly in the patient’s mouth, e.g., from the most commonly used bis-acrylic composites. Temporary crowns made directly in the office without the use of CAD/CAM are often of inferior quality, which directly results in impaired hygiene, poorer masticatory mechanics, greater deposition of plaque, calculus and sediment, and may adversely affect periodontal and gum health. The mechanical strength, resistance to aging and abrasion of 3D printing materials are higher than those of the soft materials used in conventional methods. This translates into durability. The patient leaves the surgery with a restoration of higher utility quality compared to the conventional method. The objective of the paper was to determine the influence of aging in artificial saliva of AM (additive manufacturing) orthodontic composites on their functional properties. For the purpose of the study, fillings well-known worldwide were selected. These were traditional UV-curable resins (M I, M II, M III, M V) and a hybrid material based on a UV-curable resin (M VI). Samples were stored in artificial saliva at 37 ± 1 °C in a thermal chamber for 6 months. Indentation hardness, frictional tests and sliding wear measurements were conducted. A comparison between various materials was made. Descriptive statistics, degradation coefficients, H2E, Archard wear and specific wear rate were calculated. The Weibull statistical test for indentation hardness was performed and Hertzian contact stresses for the frictional association were calculated for unaged (M I, M II, M III, M V, M VI) and aged (M I AS, M II AS, M III AS, M V AS, M VI AS) samples. M I exhibited the lowest average hardness among the unaged materials, while M III AS had the lowest average hardness among the aged materials. Comparably low hardness was demonstrated by the M I AS material. The coefficient of friction values for the aged samples were found to be higher. The lowest wear value was demonstrated by the M I material. The wear resistance of most of the tested materials deteriorated after aging. The M VI AS material had the highest increase in wear. According to the results provided, not only the chemical composition and structure, but also aging have a great impact on the indentation hardness and wear resistance of the tested orthodontic materials.

Description: The aim of this research project is to analyze support panels that are based on aramid fabrics which are reinforced with polybenzoxazine/urethane (poly(BA-a/PU)) composites and contain multiwalled carbon nanotubes (MWCNTs). Through the measurement of mechanical properties and a series of ballistic-impact tests that used 7.62 × 51 mm2 projectiles (National Institute of Justice (NIJ), level III), the incorporated MWCNTs were found to enhance the energy-absorption (EAbs) property of the composites, improve ballistic performance, and reduce damage. The perforation process and the ballistic limit (V50) of the composite were also studied via numerical simulation, and the calculated damage patterns were correlated with the experimental results. The result indicated hard armor based on polybenzoxazine nanocomposites could completely protect the perforation of a 7.62 × 51 mm2 projectile at impact velocity range of 847 ± 9.1 m/s. The results revealed the potential for using the poly(BA-a/PU) nanocomposites as energy-absorption panels for hard armor.

Description: Background: Circulating tumor DNA (ctDNA) and exosome have been widely researched in the field of medical technology and diagnosis platforms. The purpose of our study was to improve the capturing properties of ctDNA and exosome, which involved combining two beads using approaches that may provide a new method for cancer diagnoses. Methods: We present a dual isolation system including a polydopamine (PDA)–silica-coated alginate bead for circulating tumor DNA (ctDNA) capture and an anti-CD63 immobilized bead for exosome capture. We examined the ctDNA mutation in pre-operative plasma samples obtained from 91 colorectal cancer (CRC) patients using a droplet digital PCR (ddPCR). Results: The area under the curve (AUROC) of ctKRAS G12D mutation in the buffy coat was 0.718 (95% CI: 0.598−0.838; p = 0.001). Patients with CRC that had unmethylation of MLH1 and MSH2 showed significantly higher buffy coat ctKRAS G12D mutations, ascites ctKRAS G12D mutations, miR-31-5, and mixed scores than the patients with a methylation of MLH1 and MSH2. Conclusion: Our proposed alginate bead using the specific gravity-free method suggests that the screening of mutated ctKRAS DNA and miR-31-5 by liquid biopsy aids in identifying the patients, predicting a primary tumor, and monitoring in the early detection of a tumor.

Description: The reinforcement of plywood is demonstrated by laminating pretensioned basalt fibers between veneer sheets, to fabricate so-called prestressed plywood. Belt type basalt fibers bearing a specific adhesion promoting silane sizing were aligned between veneer sheets with 20 mm spacing and were pretensioned at 150 N. Three-layer plywood samples were prepared and tested for tensile strength at room temperature and at 150 °C. The room temperature tensile tests revealed a 35% increase in tensile strength for prestressed plywood compared to that of the conventional specimen. The reinforcement effect deteriorated at 150 °C but was restored upon cooling to room temperature. The deterioration is attributed to the weakening of bonding between the basalt fibers and phenolic resin matrix at elevated temperatures due to the softening of the resin.

Description: Visual Question Generation (VQG) from images is a rising research topic in both fields of natural language processing and computer vision. Although there are some recent efforts towards generating questions from images in the open domain, the VQG task in the medical domain has not been well-studied so far due to the lack of labeled data. In this paper, we introduce a goal-driven VQG approach for radiology images called VQGRaD that generates questions targeting specific image aspects such as modality and abnormality. In particular, we study generating natural language questions based on the visual content of the image and on additional information such as the image caption and the question category. VQGRaD encodes the dense vectors of different inputs into two latent spaces, which allows generating, for a specific question category, relevant questions about the images, with or without their captions. We also explore the impact of domain knowledge incorporation (e.g., medical entities and semantic types) and data augmentation techniques on visual question generation in the medical domain. Experiments performed on the VQA-RAD dataset of clinical visual questions showed that VQGRaD achieves 61.86% BLEU score and outperforms strong baselines. We also performed a blinded human evaluation of the grammaticality, fluency, and relevance of the generated questions. The human evaluation demonstrated the better quality of VQGRaD outputs and showed that incorporating medical entities improves the quality of the generated questions. Using the test data and evaluation process of the ImageCLEF 2020 VQA-Med challenge, we found that relying on the proposed data augmentation technique to generate new training samples by applying different kinds of transformations, can mitigate the lack of data, avoid overfitting, and bring a substantial improvement in medical VQG.

Description: A passive micromixer was designed by combining two mixing units: the cross-channel split and recombined (CC-SAR) and a mixing cell with baffles (MC-B). The passive micromixer was comprised of eight mixing slots that corresponded to four combination units; two mixing slots were grouped as one combination unit. The combination of the two mixing units was based on four combination schemes: (A) first mixing unit, (B) first combination unit, (C) first combination module, and (D) second combination module. The statistical significance of the four combination schemes was analyzed using analysis of variance (ANOVA) in terms of the degree of mixing (DOM) and mixing energy cost (MEC). The DOM and MEC were simulated numerically for three Reynolds numbers (Re = 0.5, 2, and 50), representing three mixing regimes. The combination scheme (B), using different mixing units in the first two mixing slots, was significant for Re = 2 and 50. The four combination schemes had little effect on the mixing performance of a passive micromixer operating in the mixing regime of molecular dominance. The combination scheme (B) was generalized to arbitrary mixing slots, and its significance was analyzed for Re = 2 and 50. The general combination scheme meant two different mixing units in two consecutive mixing slots. The numerical simulation results showed that the general combination scheme was statistically significant in the first three combination units for Re = 2, and significant in the first two combination units for Re = 50. The combined micromixer based on the general combination scheme throughout the entire micromixer showed the best mixing performance over a wide range of Reynolds numbers, compared to other micromixers that did not adopt completely the general combination scheme. The most significant enhancement due to the general combination scheme was observed in the transition mixing scheme and was negligible in the molecular dominance scheme. The combination order was less significant after three combination units.

Description: In this paper, we construct novel numerical algorithms to solve the heat or diffusion equation. We start with 105 different leapfrog-hopscotch algorithm combinations and narrow this selection down to five during subsequent tests. We demonstrate the performance of these top five methods in the case of large systems with random parameters and discontinuous initial conditions, by comparing them with other methods. We verify the methods by reproducing an analytical solution using a non-equidistant mesh. Then, we construct a new nontrivial analytical solution containing the Kummer functions for the heat equation with time-dependent coefficients, and also reproduce this solution. The new methods are then applied to the nonlinear Fisher equation. Finally, we analytically prove that the order of accuracy of the methods is two, and present evidence that they are unconditionally stable.

Description: An artificial neural network (ANN) model was designed to predict the tensile properties in high-strength, low-carbon bainitic steels with a focus on the fraction of constituents such as PF (polygonal ferrite), AF (acicular ferrite), GB (granular bainite), and BF (bainitic ferrite). The input parameters of the model were the fraction of constituents, while the output parameters of the model were composed of the yield strength, yield-to-tensile ratio, and uniform elongation. The ANN model to predict the tensile properties exhibited a higher accuracy than the multi linear regression (MLR) model. According to the average index of the relative importance for the input parameters, the yield strength, yield-to-tensile ratio, and uniform elongation could be effectively improved by increasing the fraction of AF, bainitic microstructures (AF, GB, and BF), and PF, respectively, in terms of the work hardening and dislocation slip behavior depending on their microstructural characteristics such as grain size and dislocation density. The ANN model is expected to provide a clearer understanding of the complex relationships between constituent fraction and tensile properties in high-strength, low-carbon bainitic steels.

Description: Hydraulic power take-off (HPTO) is considered to be one of the most effective power take-off schemes for wave energy conversion systems (WECs). The HPTO unit can be constructed using standard hydraulic components that are readily available from the hydraulic industry market. However, the construction and operation of the HPTO unit are more complex rather than other types of power take-off, as many components parameters need to be considered during the optimization. Generator damping, hydraulic motor displacement, hydraulic cylinder and accumulator size are among the important parameters that influence the HPTO performance in generating usable electricity. Therefore, the influence of these parameters on the amount of generated electrical power from the HPTO unit was investigated in the present study. A simulation study was conducted using MATLAB/Simulink software, in which a complete model of WECs was developed using the Simscape fluids toolbox. During the simulation, each parameters study of the HPTO unit were separately manipulated to investigate its effects on the WECs performance in five different sea states. Finally, the simulated result of the effect of HPTO parameters on the amount of generated electrical power from the HPTO unit in different sea states is given and discussed.

Description: The use of gradient descent training to optimize the performance of a rule-fact network expert system via updating the network’s rule weightings was previously demonstrated. Along with this, four training techniques were proposed: two used a single path for optimization and two use multiple paths. The performance of the single path techniques was previously evaluated under a variety of experimental conditions. The multiple path techniques, when compared, outperformed the single path ones; however, these techniques were not evaluated with different network types, training velocities or training levels. This paper considers the multi-path techniques under a similar variety of experimental conditions to the prior assessment of the single-path techniques and demonstrates their effectiveness under multiple operating conditions.

Description: In on-orbit services, the relative position and attitude estimation of non-cooperative spacecraft has become the key issues to be solved in many space missions. Because of the lack of prior knowledge about manual marks and the inability to communicate between non-cooperative space targets, the relative position and attitude estimation system poses great challenges in terms of accuracy, intelligence, and power consumptions. To address these issues, this study uses a stereo camera to extract the feature points of a non-cooperative spacecraft. Then, the 3D position of the feature points is calculated according to the camera model to estimate the relationship. The optical flow method is also used to obtain the geometric constraint information between frames. In addition, an extended Kalman filter is used to update the measurement results and obtain more accurate pose optimization results. Moreover, we present a closed-loop simulation system, in which the Unity simulation engine is employed to simulate the relative motion of the spacecraft and binocular vision images, and a JetsonTX2 supercomputer is involved to conduct the proposed autonomous relative navigation algorithm. The simulation results show that our approach can estimate the non-cooperative target’s relative pose accurately.

Description: This paper considers the metallurgical processes of dissociation, ionization, oxidation, deoxidation, and dissolution of oxides during underwater wet cutting. A multiphase mechanism of underwater wet cutting consisting of working and idle cycles of the electrical process in a pulsating vapor gas bubble is proposed. A model of arc penetration into metal due to metal oxidation and stabilization of the arc by the inner walls of a narrow kerf is proposed. For underwater cutting of 10 KhSND, 304L steel, CuAl5, and AlMg4.5Mn0.7 alloy, we provide a principle of modeling the phase composition of the gas mixture based on high oxygen concentration, improving ionization, enthalpy, heat capacity, and thermal conductivity of plasma through the use of a mixture of KNO3, FeCO3 and aluminum. The method of improving the thermophysical properties and ionization of plasma due to the exothermic effect when introducing Fe3O4, MoO2, WO2 oxides and Al, Mg, Ti deoxidizers is proposed. Although a negative effect of refractory slag was revealed, it could be removed by using the method of reducing surface tension through the ionic dissolution of refractory oxides in Na3AlF6 cryolite. In underwater cutting of 10 KhSND and 304L, the steel welding current was 344–402 A with a voltage of 36–39 V; in cutting of CuAl5 and AlMg4.5Mn0.7 alloy, the welding current was 360–406; 240 A, with a voltage of 35–37; 38 V, respectively, with the optimal composition of flux-cored wire: 50–60% FeCO3 and KNO3, 20–30% aluminum, 20% Na3AlF6. Application of flux-cored wires of the KNO3-FeCO3-Na3AlF6-Al system allowed stable cutting of 10KhSND, AISI 304L steels, and CuAl5 bronze with kerf width up to 2.5–4.7 mm.

Description: Organic–inorganic nanoparticles, which can improve and modify the mechanical and chemical properties of polymers, have been used as fillers to prepare high-performance hybrid nanocomposite membranes. In this study, we explored whether the incorporation of organic nanofillers (graphene (G), graphene oxide (GO), carbon nanotubes (CNTs), or oxidized carbon nanotubes (CNTOxi)) into polysulfone (PSF) and montmorillonite (MMt)-modified PSF membranes could enhance membrane performance for the removal of heavy metal ions from contaminated solutions. These hybrid membranes were prepared by a phase inversion method using chloroform as the solvent. The surface morphologies of the membranes revealed good dispersibility of the organoclay and carbon nanomaterials in the PSF matrix. The hybrid nanocomposite membranes showed significantly improved thermal stability and mechanical properties as compared to the pristine PSF and PSF/MMt membranes. The adsorption efficiencies of these hybrid adsorptive membranes for Hg(II), Pb(II), Sr(II), Fe(III), Zn(II), Ni(II), Al(III), Co(II), Y(III), and Cr(III) were investigated. The PSF/MMt/CNTOxi and PSF/MMt/GO membranes exhibited the highest adsorption efficiencies. In particular, these adsorptive membranes showed selectivity toward Hg(II), and the Hg(II) extraction percentage was maximized at pH 2. The maximum Hg(II) adsorption capacities of PSF/MMt/CNTOxi and PSF/MMt/GO were 151.36 and 144.89 mg/g, respectively, and the adsorption isotherm was in approval with the Langmuir model. These hybrid nanocomposites can be used in water purification application.

Description: The article deals with research related to the issues of nanomodification of elastomers as a basis of electric heaters with self-regulating temperature. The effect of multistage mechanical activation of multilayer carbon nanotubes (MCNTs) with graphite on the uniformity of the temperature field distribution on the surface of nanomodified organosilicon elastomer has been studied. The influence of the stages of mechanical action on the parameters of MCNTs is revealed. It has been ascertained that for the MCNTs/graphite bulk material, which has passed the stage of mechanical activation in the vortex layer apparatus, a more uniform distribution of the temperature field and an increase in temperature to 57.1 °C at the supply voltage of 100 V are typical. The distribution of the temperature field in the centrifugal paddle mixer “WF-20B” for mixing MCNTs with graphite has been investigated. It has been found that there is also a thermal effect in addition to the mechanical action on the MCNTs in the paddle mixer “WF-20B”. The thermal effect is associated with the transfer of the mechanical energy of friction of the binary mixture MCNTs/graphite on the paddle and the walls of the vessel. The multiplicity of the starting current Ip to the nominal In (Ip/In) is 5 for the first sample, 7.5 for the second sample, and 10 for the third sample at the supply voltage of 100 V. The effect of reducing the starting current and stabilizing the temperature indicates the presence of self-regulation, which is expressed in maintaining a certain level of temperature.

Description: The intricate nature of congenital heart disease requires understanding of the complex, patient-specific three-dimensional dynamic anatomy of the heart, from imaging data such as three-dimensional echocardiography for successful outcomes from surgical and interventional procedures. Conventional clinical systems use flat screens, and therefore, display remains two-dimensional, which undermines the full understanding of the three-dimensional dynamic data. Additionally, the control of three-dimensional visualisation with two-dimensional tools is often difficult, so used only by imaging specialists. In this paper, we describe a virtual reality system for immersive surgery planning using dynamic three-dimensional echocardiography, which enables fast prototyping for visualisation such as volume rendering, multiplanar reformatting, flow visualisation and advanced interaction such as three-dimensional cropping, windowing, measurement, haptic feedback, automatic image orientation and multiuser interactions. The available features were evaluated by imaging and nonimaging clinicians, showing that the virtual reality system can help improve the understanding and communication of three-dimensional echocardiography imaging and potentially benefit congenital heart disease treatment.

Description: Graphite is used as a state-of-the-art anode in commercial lithium-ion batteries (LIBs) due to its highly reversible lithium-ion storage capability and low electrode potential. However, graphite anodes exhibit sluggish diffusion kinetics for lithium-ion intercalation/deintercalation, thus limiting the rate capability of commercial LIBs. In order to determine the lithium-ion diffusion coefficient of commercial graphite anodes, we employed a galvanostatic intermittent titration technique (GITT) to quantify the quasi-equilibrium open circuit potential and diffusion coefficient as a function of lithium-ion concentration and potential for a commercial graphite electrode. Three plateaus are observed in the quasi-equilibrium open circuit potential curves, which are indicative of a mixed phase upon lithium-ion intercalation/deintercalation. The obtained diffusion coefficients tend to increase with increasing lithium concentration and exhibit an insignificant difference between charge and discharge conditions. This study reveals that the diffusion coefficient of graphite obtained with the GITT (1 × 10−11 cm2/s to 4 × 10−10 cm2/s) is in reasonable agreement with literature values obtained from electrochemical impedance spectroscopy. The GITT is comparatively simple and direct and therefore enables systematic measurements of ion intercalation/deintercalation diffusion coefficients for secondary ion battery materials.

Description: Chenopodium formosanum (CF), rich in nutrients and antioxidants, is a native plant in Taiwan. During the harvest, the seeds are collected, while the roots, stems, and leaves remain on the field as agricultural waste. In this study, di(phenyl)-(2,4,6-trinitrophenyl)iminoazanium (DPPH) radical scavenging ability and 2,2′-azino-bis(3-ethylbenzthiazoline-6-sulfonic acid) (ABTS) radical scavenging ability experiments of seeds, leaves, stems, and roots were designed using the Taguchi method (TM) under three conditions: Ethanol concentration (0–100%), temperature (25–65 °C), and extraction time (30–150 min). The result demonstrates that seeds and leaves have higher radical scavenging ability than stems and roots. Many studies focused on CF seeds. Therefore, this study selected CF leaves and optimized DPPH, ABTS, total phenol content (TPC), total flavonoid content (TFC), and reducing power (RP) through TM, showing that the predicted value of the leaf is close to the actual value. The optimized results of CF leaves were DPPH 85.22%, ABTS 46.51%, TPC 116.54 µg GAE/mL, TFC 143.46 µg QE/mL, and RP 23.29 µg VCE (vitamin C equivalent)/mL. The DPPH and ABTS of CF leaves were second only to the results of CF seeds. It can be seen that CF leaves have the potential as a source of antioxidants and help in waste reduction.

Description: This study set out to extract the charging characteristics of an electrical vehicle (EV) from massive real operating data. Firstly, an unsupervised learning method based on self-organizing map (SOM) is developed to deal with the power supply side data of various charging operators. Secondly, a multi-dimensional evaluation index system is constructed for charging operation and vehicle-to-grid (V2G). Finally, according to more than five million pieces of charging operating data collected over a period of two years, the charging load composition and characteristics under different charging station types, daily types and weather conditions are analyzed. The results show that bus, high-way, and urban public charging loads are different in concentration and regulation flexibility, however, they all have the potential to synergy with power grid and cooperate with renewable energy. Especially in an urban area, more than 37 GWh of photovoltaic (PV) power can be consumed by smart charging at the current penetration rate of EVs.

Description: Volatile fatty acids obtained from the fermentation of the organic fraction of municipal solid waste can be used as raw materials for non-toxic ethyl ester (EE) synthesis as well as feedstock for the production of polyhydroxyalkanoates (PHAs). Taking advantage of the concept of an integrated process of a bio-refinery, in the present paper, a systematic investigation on the extraction of intracellular poly(3-hydroxybutyrate-co-3-hydroxyvalerate), produced by mixed microbial culture by using EEs was reported. Among the tested EEs, ethyl acetate (EA) was the best solvent, dissolving the copolymer at the lowest temperature. Then, extraction experiments were carried out by EA at different temperatures on two biomass samples containing PHAs with different average molecular weights. The parallel characterization of the extracted and non-extracted PHAs evidenced that at the lower temperature (100 °C) EA solubilizes preferentially the polymer fractions richer in 3HV comonomers and with the lower molecular weight. By increasing the extraction temperature from 100 °C to 125 °C, an increase of recovery from about 50 to 80 wt% and a molecular weight reduction from 48% to 65% was observed. The results highlighted that the extracted polymer purity is always above 90 wt% and that it is possible to choose the proper extraction condition to maximize the recovery yield at the expense of polymer fractionation and degradation at high temperatures or use milder conditions to maintain the original properties of a polymer.

Description: During transmission of digital images, secret messages can be embedded using data hiding techniques. Such techniques can transfer private secrets without drawing the attention of eavesdroppers. To reduce the amount of transmitted data, image compression methods are widely applied. Hiding secret data in compressed images is a hot issue recently. In this paper, we apply the de-clustering concept and the indicator-free search-order coding (IFSOC) technique to hide information into vector quantization (VQ) compressed images. Experimental results show that the proposed two-layer reversible data hiding scheme for IFSOC-encoded VQ index table can hide a large amount of secret data among state-of-the-art methods with a relatively lower bit rate and high security.

Description: This research uses EBSD data of two thermo-mechanically processed medium carbon (C45EC) steel samples to simulate micromechanical deformation and damage behavior. Two samples with 83% and 97% spheroidization degrees are subjected to virtual monotonic quasi-static tensile loading. The ferrite phase is assigned already reported elastic and plastic parameters, while the cementite particles are assigned elastic properties. A phenomenological constitutive material model with critical plastic strain-based ductile damage criterion is implemented in the DAMASK framework for the ferrite matrix. At the global level, the calibrated material model response matches well with experimental results, with up to ~97% accuracy. The simulation results provide essential insight into damage initiation and propagation based on the stress and strain localization due to cementite particle size, distribution, and ferrite grain orientations. In general, it is observed that the ferrite–cementite interface is prone to damage initiation at earlier stages triggered by the cementite particle clustering. Furthermore, it is observed that the crystallographic orientation strongly affects the stress and stress localization and consequently nucleating initial damage.

Description: In this work, we propose a mechanism for knowledge transfer between Convolutional Neural Networks via the geometric regularization of local features produced by the activations of convolutional layers. We formulate appropriate loss functions, driving a “student” model to adapt such that its local features exhibit similar geometrical characteristics to those of an “instructor” model, at corresponding layers. The investigated functions, inspired by manifold-to-manifold distance measures, are designed to compare the neighboring information inside the feature space of the involved activations without any restrictions in the features’ dimensionality, thus enabling knowledge transfer between different architectures. Experimental evidence demonstrates that the proposed technique is effective in different settings, including knowledge-transfer to smaller models, transfer between different deep architectures and harnessing knowledge from external data, producing models with increased accuracy compared to a typical training. Furthermore, results indicate that the presented method can work synergistically with methods such as knowledge distillation, further increasing the accuracy of the trained models. Finally, experiments on training with limited data show that a combined regularization scheme can achieve the same generalization as a non-regularized training with 50% of the data in the CIFAR-10 classification task.

Description: Chalcones are naturally occurring compounds exhibiting multiple biological functions related to their structure. The investigation of complexes formed by chalcones, namely 2′,4′-dihydroxy-2-methoxychalcone (DH-2-MC) and 2′,4′-dihydroxy-3-methoxychalcone (DH-3-MC), with organo-functionalized Fe3O4 magnetic nanoparticles using mass spectrometric techniques is reported. The magnetic nanoparticles were obtained by the silanization of Fe3O4 particles with 3-aminopropyltrimethosysilane, which were subsequently reacted with 3-hydroxybenzaldehyde (3-HBA) or 2-pyridinecarboxaldehyde (2-PCA), resulting in the formation of Schiff base derivatives. The formation of their complexes with chalcones was studied using electrospray (ESI) and flowing atmosphere-pressure afterglow (FAPA) mass spectrometric (MS) ionization techniques. The functional nanoparticles which were synthesized using 3-hydroxybenzaldehyde displayed higher affinity towards examined chalcones than their counterparts obtained using 2-pyridinecarboxaldehyde, which has been proved by both ESI and FAPA techniques. For the examined chalcones, two calibration curves were obtained using the ESI-MS method, which allowed for the quantitative analysis of the performed adsorption processes. The presence of Cu(II) ions in the system significantly hindered the formation of material–chalcone complexes, which was proved by the ESI and FAPA techniques. These results indicate that both mass spectrometric techniques used in our study possess a large potential for the investigation of the binding properties of various functional nanoparticles.

Description: The aim of this work was to determine the influence of the tungsten addition to TiB2 coatings on their microstructure and brittle cracking resistance. Four coatings of different compositions (0, 7, 15, and 20 at.% of W) were deposited by magnetron sputtering from TiB2 and W targets. The coatings were investigated by the following methods: X-ray diffraction (XRD), transmission electron microscopy (TEM), atomic force microscopy (AFM) and X-ray photoelectron spectroscopy (XPS). All coatings had a homogeneous columnar structure with decreasing column width as the tungsten content increased. XRD and XPS analysis showed the presence of TiB2 and nonstoichiometric TiBx phases with an excess or deficiency of boron depending on composition. The crystalline size decreased from 27 nm to 10 nm with increasing W content. The brittle cracking resistance improved with increasing content of TiBx phase with deficiency of B and decreasing crystalline size.

Description: This work focuses on the temperature evolution of the martensitic phase ε (hexagonal close packed) induced by the severe plastic deformation via High Speed High Pressure Torsion method in Fe57Mn27Si11Cr5 (at %) alloy. The iron rich alloy crystalline structure, magnetic and transport properties were investigated on samples subjected to room temperature High Speed High Pressure Torsion incorporating 1.86 degree of deformation and also hot-compression. Thermo-resistivity as well as thermomagnetic measurements indicate an antiferromagnetic behavior with the Néel temperature (TN) around 244 K, directly related to the austenitic γ-phase. The sudden increase of the resistivity on cooling below the Néel temperature can be explained by an increased phonon-electron interaction. In-situ magnetic and electric transport measurements up to 900 K are equivalent to thermal treatments and lead to the appearance of the bcc-ferrite-like type phase, to the detriment of the ε(hcp) martensite and the γ (fcc) austenite phases.

Description: In powder bed fusion–electron beam melting, the alloy powder can scatter under electron beam irradiation. When this phenomenon—known as smoking—occurs, it makes the PBF-EBM process almost impossible. Therefore, avoiding smoking in EBM is an important research issue. In this study, we aimed to clarify the effects of powder bed preheating and mechanical stimulation on the suppression of smoking in the powder bed fusion–electron beam melting process. Direct current electrical resistivity and alternating current impedance spectroscopy measurements were conducted on Inconel 718 alloy powder at room temperature and elevated temperatures before and after mechanical stimulation (ball milling for 10–60 min) to investigate changes in the electrical properties of the surface oxide film, alongside X-ray photoelectron spectroscopy to identify the surface chemical composition. Smoking tests confirmed that preheating and ball milling both suppressed smoking. Furthermore, smoking did not occur after ball milling, even when the powder bed was not preheated. This is because the oxide film undergoes a dielectric–metallic transition due to the lattice strain introduced by ball milling. Our results are expected to benefit the development of the powder bed fusion–electron beam melting processes from the perspective of materials technology and optimization of the process conditions and powder properties to suppress smoking.

Description: The chemically cross-linking 1-ethyl-3-(3-dimethylaminopropylcarbodiimide hydrochloride/N-hydroxy-succinimide (EDC/NHS) collagen membrane endows such natural polymers with promising mechanical properties. Nevertheless, it is inadequate to advance the modulation of foreign body response (FBR) after implantation or guidance of tissue regeneration. In previous research, macrophages have a strong regulatory effect on regeneration, and such enhanced membranes underwent the modification with Epigallocatechin-3-gallate (EGCG) could adjust the recruitment and phenotypes of macrophages. Accordingly, we develop EGCG-EDC/NHS membranes, prepared with physical immersion, while focusing on the surface morphology through SEM, the biological activity of collagen was determined by FTIR, the activity and adhesion of cell culture in vitro, angiogenesis and monocyte/macrophage recruitment after subcutaneous implantation in vivo, are characterized. It could be concluded that it is hopeful EGCG-EDC/NHS collagen membrane can be used in implant dentistry for it not only retains the advantages of the collagen membrane itself, but also improves cell viability, adhesion, vascularization, and immunoregulation tendency.

Description: TiMoNbZrx refractory high-entropy alloys were prepared by vacuum arc melting, and the influence of the Zr alloying element and its content on the phases, microstructure, mechanical properties, and wear resistance of TiMoNbZrx alloys was explored. It was found that the alloys after Zr addition were composed of a single BCC phase. Upon increasing the Zr content, the grain size of the as-cast alloy decreased first and then increased, and TiMoNbZr0.5 exhibited the smallest grain size. Adding an appropriate amount of Zr increased the strength and hardness of the alloys. TiMoNbZr0.5 exhibited the best wear resistance, with a friction coefficient of about 0.33. It also displayed the widest wear scar, the shallowest depth, and the greatest degree of wear on the grinding ball because of the formation of an oxide film during wear.

Description: Concentrated solar power (CSP) is an important option as a competitive, secure, and sustainable energy system. At the moment, cost-effective solutions are required for a wider-scale deployment of the CSP technology: in particular, the industrial exploitation of CSP has been so far hindered by limitations in the materials used for the central receiver—a key component in the system. In this context, the H2020 NEXTOWER project is focused on next-generation CSP technologies, particularly on advanced materials for high temperatures (e.g., 〉900 °C) and extreme applications environments (e.g., corrosive). The research activity described in this paper is focused on two industrial solutions for new SiC ceramic receivers for high thermal gradient continued operations: porous SiC and silicon-infiltrated silicon carbide ceramics (SiSiC). The new receivers should be mechanically tough and highly thermally conductive. This paper presents the activity related to the manufacturing of these components, their joining, and characterization.

Description: The term “osseointegrated implants” mainly relates to structural systems that contain open spaces, which enable osteoblasts and connecting tissue to migrate during natural bone growth. Consequently, the coherency and bonding strength between the implant and natural bone can be significantly increased, for example in operations related to dental and orthopedic applications. The present study aims to evaluate the prospects of a Ti–6Al–4V lattice, produced by selective laser melting (SLM) and infiltrated with biodegradable Zn2%Fe alloy, as an OI–TiZn system implant in in vitro conditions. This combined material structure is designated by this study as an osseointegrated implant (OI–TiZn) system. The microstructure of the tested alloys was examined both optically and using scanning electron microscopy (SEM) and X-ray diffraction (XRD) analysis. The mechanical properties were assessed in terms of compression strength, as is commonly acceptable in cases of lattice-based structures. The corrosion performance was evaluated by immersion tests and electrochemical analysis in terms of potentiodynamic polarization and electrochemical impedance spectroscopy (EIS), all in simulated physiological environments in the form of phosphate buffered saline (PBS) solution. The cytotoxicity was evaluated in terms of indirect cell viability. The results obtained demonstrate the adequate performance of the OI–TiZn system as a non-cytotoxic structural material that can maintain its mechanical integrity under compression, while presenting acceptable corrosion rate degradation.

Description: The present study aims to reduce the pitch motion of an assault amphibious vehicle system in seaways by waterjet impeller revolution rate control. A series of seakeeping tests were performed in a towing tank with a 1/4.5-scale model. This vehicle is manufactured as a box-shaped hull, and since an appendage that generates lift force is attached, the amount of change in pitch motion is large according to the forward speed. For pitch motion reduction, the impeller revolution rate and resultant pitch moment were controlled through a proportional-integral-derivative controller. Improvements in seakeeping performance were examined in both regular and irregular conditions by the model tests in terms of root mean square of pitch motion. The tuned controller decreased pitch motion by more than 60%.

Description: The development of switching converters to perform with the power processing of photovoltaic (PV) applications has been a topic receiving growing interest in recent years. This work presents a nonisolated buck-boost converter with a quadratic voltage conversion gain based on the I–IIA noncascading structure. The converter has a reduced component count and it is formed by a pair of L–C networks and two active switches, which are operated synchronously to achieve a wide conversion ratio and a quadratic dependence with the duty ratio. Additionally, the analysis using different sources and loads demonstrates the differences in the behavior of the converter, as well as the pertinence of including PV devices (current sources) into the analysis of new switching converter topologies for PV applications. In this work, the voltage conversion ratio, steady-state operating conditions and semiconductor stresses of the proposed converter are discussed in the context of PV applications. The operation of the converter in a PV scenario is verified by experimental results.

Description: Application of machine and deep learning methods in drug discovery and cancer research has gained a considerable amount of attention in the past years. As the field grows, it becomes crucial to systematically evaluate the performance of novel computational solutions in relation to established techniques. To this end, we compare rule-based and data-driven molecular representations in prediction of drug combination sensitivity and drug synergy scores using standardized results of 14 high-throughput screening studies, comprising 64 200 unique combinations of 4153 molecules tested in 112 cancer cell lines. We evaluate the clustering performance of molecular representations and quantify their similarity by adapting the Centered Kernel Alignment metric. Our work demonstrates that to identify an optimal molecular representation type, it is necessary to supplement quantitative benchmark results with qualitative considerations, such as model interpretability and robustness, which may vary between and throughout preclinical drug development projects.

Description: Motivation The investigation of quantitative trait loci (QTL) is an essential component in our understanding of how organisms vary phenotypically. However, many important crop species are polyploid (carrying more than two copies of each chromosome), requiring specialized tools for such analyses. Moreover, deciphering meiotic processes at higher ploidy levels is not straightforward, but is necessary to understand the reproductive dynamics of these species, or uncover potential barriers to their genetic improvement. Results Here, we present polyqtlR, a novel software tool to facilitate such analyses in (auto)polyploid crops. It performs QTL interval mapping in F1 populations of outcrossing polyploids of any ploidy level using identity-by-descent probabilities. The allelic composition of discovered QTL can be explored, enabling favourable alleles to be identified and tracked in the population. Visualization tools within the package facilitate this process, and options to include genetic co-factors and experimental factors are included. Detailed information on polyploid meiosis including prediction of multivalent pairing structures, detection of preferential chromosomal pairing and location of double reduction events can be performed. Availabilityand implementation polyqtlR is freely available from the Comprehensive R Archive Network (CRAN) at http://cran.r-project.org/package=polyqtlR. Supplementary information Supplementary data are available at Bioinformatics online.

Description: Circular RNAs (circRNAs) are widely expressed in highly diverged eukaryotes. Although circRNAs have been known for many years, their function remains unclear. Interaction with RNA-binding protein (RBP) to influence post-transcriptional regulation is considered to be an important pathway for circRNA function, such as acting as an oncogenic RBP sponge to inhibit cancer. In this study, we design a deep learning framework, CRPBsites, to predict the binding sites of RBPs on circRNAs. In this model, the sequences of variable-length binding sites are transformed into embedding vectors by word2vec model. Bidirectional LSTM is used to encode the embedding vectors of binding sites, and then they are fed into another LSTM decoder for decoding and classification tasks. To train and test the model, we construct four datasets that contain sequences of variable-length binding sites on circRNAs, and each set corresponds to an RBP, which is overexpressed in bladder cancer tissues. Experimental results on four datasets and comparison with other existing models show that CRPBsites has superior performance. Afterwards, we found that there were highly similar binding motifs in the four binding site datasets. Finally, we applied well-trained CRPBsites to identify the binding sites of IGF2BP1 on circCDYL, and the results proved the effectiveness of this method. In conclusion, CRPBsites is an effective prediction model for circRNA-RBP interaction site identification. We hope that CRPBsites can provide valuable guidance for experimental studies on the influence of circRNA on post-transcriptional regulation.

Description: Intratumoral heterogeneity is a well-documented feature of human cancers and is associated with outcome and treatment resistance. However, a heterogeneous tumor transcriptome contributes an unknown level of variability to analyses of differentially expressed genes (DEGs) that may contribute to phenotypes of interest, including treatment response. Although current clinical practice and the vast majority of research studies use a single sample from each patient, decreasing costs of sequencing technologies and computing power have made repeated-measures analyses increasingly economical. Repeatedly sampling the same tumor increases the statistical power of DEG analysis, which is indispensable toward downstream analysis and also increases one’s understanding of within-tumor variance, which may affect conclusions. Here, we compared five different methods for analyzing gene expression profiles derived from repeated sampling of human prostate tumors in two separate cohorts of patients. We also benchmarked the sensitivity of generalized linear models to linear mixed models for identifying DEGs contributing to relevant prostate cancer pathways based on a ground-truth model.

Description: Efforts to elucidate protein–DNA interactions at the molecular level rely in part on accurate predictions of DNA-binding residues in protein sequences. While there are over a dozen computational predictors of the DNA-binding residues, they are DNA-type agnostic and significantly cross-predict residues that interact with other ligands as DNA binding. We leverage a custom-designed machine learning architecture to introduce DNAgenie, first-of-its-kind predictor of residues that interact with A-DNA, B-DNA and single-stranded DNA. DNAgenie uses a comprehensive physiochemical profile extracted from an input protein sequence and implements a two-step refinement process to provide accurate predictions and to minimize the cross-predictions. Comparative tests on an independent test dataset demonstrate that DNAgenie outperforms the current methods that we adapt to predict residue-level interactions with the three DNA types. Further analysis finds that the use of the second (refinement) step leads to a substantial reduction in the cross predictions. Empirical tests show that DNAgenie’s outputs that are converted to coarse-grained protein-level predictions compare favorably against recent tools that predict which DNA-binding proteins interact with double-stranded versus single-stranded DNAs. Moreover, predictions from the sequences of the whole human proteome reveal that the results produced by DNAgenie substantially overlap with the known DNA-binding proteins while also including promising leads for several hundred previously unknown putative DNA binders. These results suggest that DNAgenie is a valuable tool for the sequence-based characterization of protein functions. The DNAgenie’s webserver is available at http://biomine.cs.vcu.edu/servers/DNAgenie/.

Description: Accurate prediction of immunogenic peptide recognized by T cell receptor (TCR) can greatly benefit vaccine development and cancer immunotherapy. However, identifying immunogenic peptides accurately is still a huge challenge. Most of the antigen peptides predicted in silico fail to elicit immune responses in vivo without considering TCR as a key factor. This inevitably causes costly and time-consuming experimental validation test for predicted antigens. Therefore, it is necessary to develop novel computational methods for precisely and effectively predicting immunogenic peptide recognized by TCR. Here, we described DLpTCR, a multimodal ensemble deep learning framework for predicting the likelihood of interaction between single/paired chain(s) of TCR and peptide presented by major histocompatibility complex molecules. To investigate the generality and robustness of the proposed model, COVID-19 data and IEDB data were constructed for independent evaluation. The DLpTCR model exhibited high predictive power with area under the curve up to 0.91 on COVID-19 data while predicting the interaction between peptide and single TCR chain. Additionally, the DLpTCR model achieved the overall accuracy of 81.03% on IEDB data while predicting the interaction between peptide and paired TCR chains. The results demonstrate that DLpTCR has the ability to learn general interaction rules and generalize to antigen peptide recognition by TCR. A user-friendly webserver is available at http://jianglab.org.cn/DLpTCR/. Additionally, a stand-alone software package that can be downloaded from https://github.com/jiangBiolab/DLpTCR.

Description: Motivation The investigation of the structure of biological systems at the molecular level gives insights about their functions and dynamics. Shape and surface of biomolecules are fundamental to molecular recognition events. Characterizing their geometry can lead to more adequate predictions of their interactions. In the present work, we assess the performance of reference shape retrieval methods from the computer vision community on protein shapes. Results Shape retrieval methods are efficient in identifying orthologous proteins and tracking large conformational changes. This work illustrates the interest for the protein surface shape as a higher-level representation of the protein structure that (i) abstracts the underlying protein sequence, structure or fold, (ii) allows the use of shape retrieval methods to screen large databases of protein structures to identify surficial homologs and possible interacting partners and (iii) opens an extension of the protein structure–function paradigm toward a protein structure-surface(s)-function paradigm. Availabilityand implementation All data are available online at http://datasetmachat.drugdesign.fr. Supplementary information Supplementary data are available at Bioinformatics online.

Description: The widespread use of artificial intelligence (AI) in civil engineering has provided civil engineers with various benefits and opportunities, including a rich data collection, sustainable assessment, and productivity. The trend of construction is diverted toward sustainability with the aid of digital technologies. In this regard, this paper presents a systematic literature review (SLR) in order to explore the influence of AI in civil engineering toward sustainable development. In addition, SLR was carried out by using academic publications from Scopus (i.e., 3478 publications). Furthermore, screening is carried out, and eventually, 105 research publications in the field of AI were selected. Keywords were searched through Boolean operation “Artificial Intelligence” OR “Machine intelligence” OR “Machine Learning” OR “Computational intelligence” OR “Computer vision” OR “Expert systems” OR “Neural networks” AND “Civil Engineering” OR “Construction Engineering” OR “Sustainable Development” OR “Sustainability”. According to the findings, it was revealed that the trend of publications received its high intention of researchers in 2020, the most important contribution of publications on AI toward sustainability by the Automation in Construction, the United States has the major influence among all the other countries, the main features of civil engineering toward sustainability are interconnectivity, functionality, unpredictability, and individuality. This research adds to the body of knowledge in civil engineering by visualizing and comprehending trends and patterns, as well as defining major research goals, journals, and countries. In addition, a theoretical framework has been proposed in light of the results for prospective researchers and scholars.

Description: Over the past decade, genome-wide assays for chromatin interactions in single cells have enabled the study of individual nuclei at unprecedented resolution and throughput. Current chromosome conformation capture techniques survey contacts for up to tens of thousands of individual cells, improving our understanding of genome function in 3D. However, these methods recover a small fraction of all contacts in single cells, requiring specialised processing of sparse interactome data. In this review, we highlight recent advances in methods for the interpretation of single-cell genomic contacts. After discussing the strengths and limitations of these methods, we outline frontiers for future development in this rapidly moving field.

Description: Good knowledge of a peptide’s tertiary structure is important for understanding its function and its interactions with its biological targets. APPTEST is a novel computational protocol that employs a neural network architecture and simulated annealing methods for the prediction of peptide tertiary structure from the primary sequence. APPTEST works for both linear and cyclic peptides of 5–40 natural amino acids. APPTEST is computationally efficient, returning predicted structures within a number of minutes. APPTEST performance was evaluated on a set of 356 test peptides; the best structure predicted for each peptide deviated by an average of 1.9Å from its experimentally determined backbone conformation, and a native or near-native structure was predicted for 97% of the target sequences. A comparison of APPTEST performance with PEP-FOLD, PEPstrMOD and PepLook across benchmark datasets of short, long and cyclic peptides shows that on average APPTEST produces structures more native than the existing methods in all three categories. This innovative, cutting-edge peptide structure prediction method is available as an online web server at https://research.timmons.eu/apptest, facilitating in silico study and design of peptides by the wider research community.

Description: Deep generative models have been an upsurge in the deep learning community since they were proposed. These models are designed for generating new synthetic data including images, videos and texts by fitting the data approximate distributions. In the last few years, deep generative models have shown superior performance in drug discovery especially de novo molecular design. In this study, deep generative models are reviewed to witness the recent advances of de novo molecular design for drug discovery. In addition, we divide those models into two categories based on molecular representations in silico. Then these two classical types of models are reported in detail and discussed about both pros and cons. We also indicate the current challenges in deep generative models for de novo molecular design. De novo molecular design automatically is promising but a long road to be explored.

Description: DNA methylation may be regulated by genetic variants within a genomic region, referred to as methylation quantitative trait loci (mQTLs). The changes of methylation levels can further lead to alterations of gene expression, and influence the risk of various complex human diseases. Detecting mQTLs may provide insights into the underlying mechanism of how genotypic variations may influence the disease risk. In this article, we propose a methylation random field (MRF) method to detect mQTLs by testing the association between the methylation level of a CpG site and a set of genetic variants within a genomic region. The proposed MRF has two major advantages over existing approaches. First, it uses a beta distribution to characterize the bimodal and interval properties of the methylation trait at a CpG site. Second, it considers multiple common and rare genetic variants within a genomic region to identify mQTLs. Through simulations, we demonstrated that the MRF had improved power over other existing methods in detecting rare variants of relatively large effect, especially when the sample size is small. We further applied our method to a study of congenital heart defects with 83 cardiac tissue samples and identified two mQTL regions, MRPS10 and PSORS1C1, which were colocalized with expression QTL in cardiac tissue. In conclusion, the proposed MRF is a useful tool to identify novel mQTLs, especially for studies with limited sample sizes.

Description: Motivation The mathematically optimal solution in computational protein folding simulations does not always correspond to the native structure, due to the imperfection of the energy force fields. There is therefore a need to search for more diverse suboptimal solutions in order to identify the states close to the native. We propose a novel multimodal optimization protocol to improve the conformation sampling efficiency and modeling accuracy of de novo protein structure folding simulations. Results A distance-assisted multimodal optimization sampling algorithm, MMpred, is proposed for de novo protein structure prediction. The protocol consists of three stages: The first is a modal exploration stage, in which a structural similarity evaluation model DMscore is designed to control the diversity of conformations, generating a population of diverse structures in different low-energy basins. The second is a modal maintaining stage, where an adaptive clustering algorithm MNDcluster is proposed to divide the populations and merge the modal by adjusting the annealing temperature to locate the promising basins. In the last stage of modal exploitation, a greedy search strategy is used to accelerate the convergence of the modal. Distance constraint information is used to construct the conformation scoring model to guide sampling. MMpred is tested on a large set of 320 non-redundant proteins, where MMpred obtains models with TM-score≥0.5 on 291 cases, which is 28% higher than that of Rosetta guided with the same set of distance constraints. In addition, on 320 benchmark proteins, the enhanced version of MMpred (E-MMpred) has 167 targets better than trRosetta when the best of five models are evaluated. The average TM-score of the best model of E-MMpred is 0.732, which is comparable to trRosetta (0.730). Availability and implementation The source code and executable are freely available at https://github.com/iobio-zjut/MMpred. Supplementary information Supplementary data are available at Bioinformatics online.

Description: Antimicrobial resistance (AMR) poses a threat to global public health. To mitigate the impacts of AMR, it is important to identify the molecular mechanisms of AMR and thereby determine optimal therapy as early as possible. Conventional machine learning-based drug-resistance analyses assume genetic variations to be homogeneous, thus not distinguishing between coding and intergenic sequences. In this study, we represent genetic data from Mycobacterium tuberculosis as a graph, and then adopt a deep graph learning method—heterogeneous graph attention network (‘HGAT–AMR’)—to predict anti-tuberculosis (TB) drug resistance. The HGAT–AMR model is able to accommodate incomplete phenotypic profiles, as well as provide ‘attention scores’ of genes and single nucleotide polymorphisms (SNPs) both at a population level and for individual samples. These scores encode the inputs, which the model is ‘paying attention to’ in making its drug resistance predictions. The results show that the proposed model generated the best area under the receiver operating characteristic (AUROC) for isoniazid and rifampicin (98.53 and 99.10%), the best sensitivity for three first-line drugs (94.91% for isoniazid, 96.60% for ethambutol and 90.63% for pyrazinamide), and maintained performance when the data were associated with incomplete phenotypes (i.e. for those isolates for which phenotypic data for some drugs were missing). We also demonstrate that the model successfully identifies genes and SNPs associated with drug resistance, mitigating the impact of resistance profile while considering particular drug resistance, which is consistent with domain knowledge.

Description: Protein engineering and design principles employing the 20 standard amino acids have been extensively used to achieve stable protein scaffolds and deliver their specific activities. Although this confers some advantages, it often restricts the sequence, chemical space, and ultimately the functional diversity of proteins. Moreover, although site-specific incorporation of non-natural amino acids (nnAAs) has been proven to be a valuable strategy in protein engineering and therapeutics development, its utility in the affinity-maturation of nanobodies is not fully explored. Besides, current experimental methods do not routinely employ nnAAs due to their enormous library size and infinite combinations. To address this, we have developed an integrated computational pipeline employing structure-based protein design methodologies, molecular dynamics simulations and free energy calculations, for the binding affinity prediction of an nnAA-incorporated nanobody toward its target and selection of potent binders. We show that by incorporating halogenated tyrosines, the affinity of 9G8 nanobody can be improved toward epidermal growth factor receptor (EGFR), a crucial cancer target. Surface plasmon resonance (SPR) assays showed that the binding of several 3-chloro-l-tyrosine (3MY)-incorporated nanobodies were improved up to 6-fold into a picomolar range, and the computationally estimated binding affinities shared a Pearson’s r of 0.87 with SPR results. The improved affinity was found to be due to enhanced van der Waals interactions of key 3MY-proximate nanobody residues with EGFR, and an overall increase in the nanobody’s structural stability. In conclusion, we show that our method can facilitate screening large libraries and predict potent site-specific nnAA-incorporated nanobody binders against crucial disease-targets.

Description: Over the past few years, meta-analysis has become popular among biomedical researchers for detecting biomarkers across multiple cohort studies with increased predictive power. Combining datasets from different sources increases sample size, thus overcoming the issue related to limited sample size from each individual study and boosting the predictive power. This leads to an increased likelihood of more accurately predicting differentially expressed genes/proteins or significant biomarkers underlying the biological condition of interest. Currently, several meta-analysis methods and tools exist, each having its own strengths and limitations. In this paper, we survey existing meta-analysis methods, and assess the performance of different methods based on results from different datasets as well as assessment from prior knowledge of each method. This provides a reference summary of meta-analysis models and tools, which helps to guide end-users on the choice of appropriate models or tools for given types of datasets and enables developers to consider current advances when planning the development of new meta-analysis models and more practical integrative tools.

Description: A gemini surfactant imidazoline type, namely N-(3-(2-fatty-4,5-dihydro-1H-imidazol-1-yl) propyl) fatty amide, has been obtained from the fatty acids contained in the mango seed and used as a CO2 corrosion inhibitor for API X-120 pipeline steel. Employed techniques involved potentiodynamic polarization curves, linear polarization resistance, and electrochemical impedance spectroscopy. These tests were supported by detailed scanning electronic microscopy (SEM) and Raman spectroscopy studies. It was found that obtained gemini surfactant greatly decreases the steel corrosion rate by retarding both anodic and cathodic electrochemical reactions, with an efficiency that increases with an increase in its concentration. Gemini surfactant inhibits the corrosion of steel by the adsorption mechanism, and it is adsorbed on to the steel surface according to a Langmuir model in a chemical type of adsorption. SEM and Raman results shown the presence of the inhibitor on the steel surface.

Description: Motivation Co-evolution analysis can be used to accurately predict residue–residue contacts from multiple sequence alignments. The introduction of machine-learning techniques has enabled substantial improvements in precision and a shift from predicting binary contacts to predict distances between pairs of residues. These developments have significantly improved the accuracy of de novo prediction of static protein structures. With AlphaFold2 lifting the accuracy of some predicted protein models close to experimental levels, structure prediction research will move on to other challenges. One of those areas is the prediction of more than one conformation of a protein. Here, we examine the potential of residue–residue distance predictions to be informative of protein flexibility rather than simply static structure. Results We used DMPfold to predict distance distributions for every residue pair in a set of proteins that showed both rigid and flexible behaviour. Residue pairs that were in contact in at least one reference structure were classified as rigid, flexible or neither. The predicted distance distribution of each residue pair was analysed for local maxima of probability indicating the most likely distance or distances between a pair of residues. We found that rigid residue pairs tended to have only a single local maximum in their predicted distance distributions while flexible residue pairs more often had multiple local maxima. These results suggest that the shape of predicted distance distributions contains information on the rigidity or flexibility of a protein and its constituent residues. Supplementary information Supplementary data are available at Bioinformatics online.

Description: Motivation The well-known fact that protein structures are more conserved than their sequences forms the basis of several areas of computational structural biology. Methods based on the structure analysis provide more complete information on residue conservation in evolutionary processes. This is crucial for the determination of evolutionary relationships between proteins and for the identification of recurrent structural patterns present in biomolecules involved in similar functions. However, algorithmic structural alignment is much more difficult than multiple sequence alignment. This study is devoted to the development and applications of DAMA—a novel effective environment capable to compute and analyze multiple structure alignments. Results DAMA is based on local structural similarities, using local 3D structure descriptors and thus accounts for nearest-neighbor molecular environments of aligned residues. It is constrained neither by protein topology nor by its global structure. DAMA is an extension of our previous study (DEDAL) which demonstrated the applicability of local descriptors to pairwise alignment problems. Since the multiple alignment problem is NP-complete, an effective heuristic approach has been developed without imposing any artificial constraints. The alignment algorithm searches for the largest, consistent ensemble of similar descriptors. The new method is capable to capture most of the biologically significant similarities present in canonical test sets and is discriminatory enough to prevent the emergence of larger, but meaningless, solutions. Tests performed on the test sets, including protein kinases, demonstrate DAMA’s capability of identifying equivalent residues, which should be very useful in discovering the biological nature of proteins similarity. Performance profiles show the advantage of DAMA over other methods, in particular when using a strict similarity measure QC, which is the ratio of correctly aligned columns, and when applying the methods to more difficult cases. Availability and implementation DAMA is available online at http://dworkowa.imdik.pan.pl/EP/DAMA. Linux binaries of the software are available upon request. Supplementary information Supplementary data are available at Bioinformatics online.

Description: Genetic algorithms (GA’s) are mostly used as an offline optimisation method to discover a suitable solution to a complex problem prior to implementation. In this paper, we present a different application in which a GA is used to progressively adapt the collective performance of an ad hoc collection of devices that are being integrated post-deployment. Adaptive behaviour in the context of this article refers to two dynamic aspects of the problem: (a) the availability of individual devices as well as the objective functions for the performance of the entire population. We illustrate this concept in a video surveillance scenario in which already installed cameras are being retrofitted with networking capabilities to form a coherent closed-circuit television (CCTV) system. We show that this can be conceived as a multi-objective optimisation problem which can be solved at run-time, with the added benefit that solutions can be refined or modified in response to changing priorities or even unpredictable events such as faults. We present results of a detailed simulation study, the implications of which are being discussed from both a theoretical and practical viewpoint (trade-off between saving computational resources and surveillance coverage).

Description: Within the context of structure deterioration studies, we propose a new numerical method based on the use of fragility curves. In particular, the present work aims to theoretically study the degradation of concrete bridge structures subjected to aggressive environments. A simple probabilistic method based on fragility curves is presented which allows the forecasting of the lifetime of the considered structural system and the best monitoring time. The method was applied to investigate the degradation of a concrete bridge used as a case study. A Monte Carlo numerical procedure was used to simulate the variation over time of the residual resistant section and the ultimate bending moment of the deck of the case study. Within this context, fragility curves are used as reliable indicators of possible monitoring scenarios. In comparison with other methods, the main advantage of the proposed approach is the small amount of computing time required to obtain rapid assessment of reliability and deterioration level of the considered structure.

Description: Greywater has been identified as a valuable alternative water source over recent years. Few practices (i.e., recycling and reuse) of greywater have attracted global attention in meeting the future water demand. However, essential parameters should be analyzed for reliable reuse and treatment. The present study addresses the possibilities of the alternative source with the treated greywater. Gravity—governed flow methods through a column containing gravel, sand, and activated carbon was applied. The quality of treated greywater from the university campus, which included physical, chemical, and biological parameters, was assessed to check non-potable reuse suitability. The reduction percentage of organics in biological oxygen demand and chemical oxygen demand was 64% and 42%, respectively. Similarly, the reduction percentage was obtained at 74% and 66% for turbidity and electrical conductivity. The removal efficiency was 57%, 77%, 48%, and 44% for total dissolved solids, alkalinity, chlorides, and total hardness. The pH of treated water samples was found in the neutral range suggesting its suitability for reuse. Hence, the proposed greywater treatment method is a cost-effective and straightforward approach to reuse greywater for irrigation, watering the lawns, and car washing. The greywater collected can be disinfected immediately and reused with minimal possibility of regrowth of microorganisms.

Description: Using automotive smartphone applications (apps) provided by car manufacturers may offer numerous advantages to the vehicle owner, including improved safety, fuel efficiency, anytime monitoring of vehicle data, and timely over-the-air delivery of software updates. On the other hand, the continuous tracking of the vehicle data by such apps may also pose a risk to the car owner, if, say, sensitive pieces of information are leaked to third parties or the app is vulnerable to attacks. This work contributes the first to our knowledge full-fledged security assessment of all the official single-vehicle management apps offered by major car manufacturers who operate in Europe. The apps are scrutinised statically with the purpose of not only identifying surfeits, say, in terms of the permissions requested, but also from a vulnerability assessment viewpoint. On top of that, we run each app to identify possible weak security practices in the owner-to-app registration process. The results reveal a multitude of issues, ranging from an over-claim of sensitive permissions and the use of possibly privacy-invasive API calls, to numerous potentially exploitable CWE and CVE-identified weaknesses and vulnerabilities, the, in some cases, excessive employment of third-party trackers, and a number of other flaws related to the use of third-party software libraries, unsanitised input, and weak user password policies, to mention just a few.

Description: A wave state related sea surface roughness parameterization scheme that takes into account the impact of sea foam is proposed in this study. Using eight observational datasets, the performances of two most widely used wave state related parameterizations are examined under various wave conditions. Based on the different performances of two wave state related parameterizations under different wave state, and by introducing the effect of sea foam, a new sea surface roughness parameterization suitable for low to extreme wind conditions is proposed. The behaviors of drag coefficient predicted by the proposed parameterization match the field and laboratory measurements well. It is shown that the drag coefficient increases with the increasing wind speed under low and moderate wind speed conditions, and then decreases with increasing wind speed, due to the effect of sea foam under high wind speed conditions. The maximum values of the drag coefficient are reached when the 10 m wind speeds are in the range of 30–35 m/s.

Description: Collision between rigid three-dimensional objects is a very common modelling problem in a wide spectrum of scientific disciplines, including Computer Science and Physics. It spans from realistic animation of polyhedral shapes for computer vision to the description of thermodynamic and dynamic properties in simple and complex fluids. For instance, colloidal particles of especially exotic shapes are commonly modelled as hard-core objects, whose collision test is key to correctly determine their phase and aggregation behaviour. In this work, we propose the Oriented Cuboid Sphere Intersection (OCSI) algorithm to detect collisions between prolate or oblate cuboids and spheres. We investigate OCSI’s performance by bench-marking it against a number of algorithms commonly employed in computer graphics and colloidal science: Quick Rejection First (QRI), Quick Rejection Intertwined (QRF) and a vectorized version of the OBB-sphere collision detection algorithm that explicitly uses SIMD Streaming Extension (SSE) intrinsics, here referred to as SSE-intr. We observed that QRI and QRF significantly depend on the specific cuboid anisotropy and sphere radius, while SSE-intr and OCSI maintain their speed independently of the objects’ geometry. While OCSI and SSE-intr, both based on SIMD parallelization, show excellent and very similar performance, the former provides a more accessible coding and user-friendly implementation as it exploits OpenMP directives for automatic vectorization.

Description: Research on flexible unit systems (FUS) with the context of descriptive, predictive, and prescriptive analysis have remarkably progressed in recent times, being now reinforced in the current Industry 4.0 era with the increased focus on integration of distributed and digitalized systems. In the existing literature, most of the work focused on the individual contributions of the above mentioned three analyses. Moreover, the current literature is unclear with respect to the integration of degradation and upgradation models for FUS. In this paper, a systematic literature review on degradation, residual life distribution, workload adjustment strategy, upgradation, and predictive maintenance as major performance measures to investigate the performance of the FUS has been considered. In order to identify the key issues and research gaps in the existing literature, the 59 most relevant papers from 2009 to 2020 have been sorted and analyzed. Finally, we identify promising research opportunities that could expand the scope and depth of FUS.

Description: The Dynamic Facility Location problem is a generalization of the classic Facility Location problem, in which the distance metric between clients and facilities changes over time. Such metrics that develop as a function of time are usually called "evolving metrics", thus Dynamic Facility Location can be alternatively interpreted as a Facility Location problem in evolving metrics. The objective in this time-dependent variant is to balance the trade-off between optimizing the classic objective function and the stability of the solution, which is modeled by charging a switching cost when a client’s assignment changes from one facility to another. In this paper, we study the online variant of Dynamic Facility Location. We present a randomized O(logm+logn)-competitive algorithm, where m is the number of facilities and n is the number of clients. In the first step, our algorithm produces a fractional solution, in each timestep, to the objective of Dynamic Facility Location involving a regularization function. This step is an adaptation of the generic algorithm proposed by Buchbinder et al. in their work "Competitive Analysis via Regularization." Then, our algorithm rounds the fractional solution of this timestep to an integral one with the use of exponential clocks. We complement our result by proving a lower bound of Ω(m) for deterministic algorithms and lower bound of Ω(logm) for randomized algorithms. To the best of our knowledge, these are the first results for the online variant of the Dynamic Facility Location problem.

Description: We study the algorithmic complexity of solving subtraction games in a fixed dimension with a finite difference set. We prove that there exists a game in this class such that solving the game is EXP-complete and requires time 2Ω(n), where n is the input size. This bound is optimal up to a polynomial speed-up. The results are based on a construction introduced by Larsson and Wästlund. It relates subtraction games and cellular automata.

Description: Computational Fluid Dynamics simulations of planing hulls are generally considered less reliable than simulations of displacement hulls. This is due to the flow complexity around planing hulls, especially in the bow region, where the sprays are formed. The recent and constant increasing of computational capabilities allows simulating planing hull features, with more accurate turbulence models and advanced meshing procedures. However, mesh-based approaches based on the finite volume methods have shown to be limited in capturing all the phenomena around a planing hull. As such, the focus of this study is on evaluating the ability of the Smoothed Particle Hydrodynamics mesh-less method to numerically solve the 3-D flow around a planing hull and simulate more accurately the spray structures, which is a rather challenging task to be performed with mesh-based tools. A novel application of the DualSPHysics code for simulating a planing hull resistance test has been proposed and applied to the parent hull of the Naples warped planing hull Systematic Series. The drag and the running attitudes (heave and dynamic trim angle) are computed for a wide range of Froude’s numbers and discussed concerning experimental values.