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• Micro-sized PS accumulated in zebrafish and affected fish at sub-individual level. • Micro-sized PS caused disorders and diversity changes of gut microbiota in fish. • Micro-sized PS adsorbed chlorpyrifos and transferred it to zebrafish. • PS adsorbing chlorpyrifos caused greater impacts on zebrafish than its action alone. Microplastics often co-occur with a variety of organic contaminants in aquatic environment and pose combined risks to aquatic wildlife. Here, we investigated joint effects of micro-sized polystyrene (mPS, 5 µm) and an organophosphate pesticide chlorpyrifos on zebrafish, using multiple endpoints at both fish individual and gut microbiota levels. It was revealed that mPS ingested by zebrafish accumulated in gut and liver, and caused oxidative stress, hyperactive swimming performance and histological damages in fish, and induced disorders and diversity alterations of the gut microbial community. More importantly, mPS exhibited considerable adsorption capacity against chlorpyrifos, and those adsorbing chlorpyrifos presented greater effects on fish individuals but no different effects on gut microbiota compared to single mPS exposure. Together with body residues of chlorpyrifos in zebrafish, it was proposed that the joint effects between mPS and chlorpyrifos were attributed to the chlorpyrifos released from mPS within zebrafish. The present results provided a comprehensive understanding of joint effects of mPS and contaminants co-occurring in the environment and emphasized the importance of considering the adsorbed chemicals in toxicological studies of microplastics. [Display omitted] [ABSTRACT FROM AUTHOR]

In light of the depletion of petrochemical resources and increase in environmental pollution, there has been a significant focus on utilizing natural biomass, specifically lignin, to develop sustainable and functional materials. This research presents the development of a lignin-based polyurethane (DLPU) with photothermal-responsiveness by incorporating lignin and oxime-carbamate bonds into polyurethane network. The abundant hydrogen bonds between lignin and the polyurethane matrix, along with its cross-linked structure, contribute to DLPU's excellent mechanical strength (30.2 MPa) and toughness (118.7 MJ·m−3). Moreover, the excellent photothermal conversion ability of DLPU (54.4 %) activates dynamic reversible behavior of oxime-carbamate bonds and hydrogen bonds, thereby endowing DLPU with exceptional self-healing performance. After 15 min of near-infrared irradiation, DLPU achieves self-healing efficiencies of 96.0 % for tensile strength and 96.3 % for elongation at break. Additionally, DLPU exhibits photocontrolled solid-state plasticity as well as an excellent phototriggered shape-memory effect (70 s), with shape fixity and recovery ratios reaching 98.8 % and 95.3 %, respectively. By exploiting the spatial controllability and photothermal-responsiveness of DLPU, we demonstrate multi-dimensional responsive materials with self-healing and shape-shifting properties. This work not only promotes the development of multi-functional polyurethanes but also provides a pathway for the high-value utilization of lignin. [Display omitted] • A photothermal-responsive lignin-based polyurethane was fabricated using lignin. • The polyurethane has a dynamic cross-linked network containing oxime-carbamate bonds and hydrogen bonds. • The existence of hydrogen bonds and cross-linked structure lead to mechanically robustness. • The polyurethane exhibits efficient self-healing, solid-state plasticity and shape-memory performance under near-infrared irradiation. [ABSTRACT FROM AUTHOR]

We study online auctions with production costs using an online primal dual framework. The seller allocates items to buyers and can produce multiple copies of each item subject to a non-decreasing marginal cost per copy. The buyers have arbitrary valuation functions and arrive one by one online in some arbitrary order. The goal is to design an online mechanism that maximizes the social welfare, that is, the sum of the buyers' values less the total production cost. For any strictly convex and differentiable production cost function, we characterize the optimal competitive ratio achievable by online mechanisms and, more generally, algorithms without incentive guarantees. We show that online posted pricing mechanisms, which are incentive compatible, can achieve competitive ratios arbitrarily close to the optimal, and construct lower bound instances on which no online algorithms, not necessarily incentive compatible, can do better. [ABSTRACT FROM AUTHOR]

[Display omitted] • A high-strength and recyclable lignin-based polyurethane elastomer was prepared using lignin. • The elastomer has a multi-level dynamic cross-linking structure. • The rearrangement of hydrogen bonds and strain-induced crystallization lead to self-reinforcement. • The elastomer exhibits excellent and stable photothermal conversion capacity. The preparation of lignin-based polyurethanes with excellent mechanical properties and recyclability remains extremely challenging because of the poor compatibility of lignin and matrix. Inspired by the multiple structures of spider silk and pearl layers, high-strength, heat-resistant and recyclable lignin-based polyurethanes (LPUs) were prepared by introducing weak hydrogen bonds, strong hydrogen bonds and dynamic silyl ether covalent bonds. Multiple hydrogen bonds provided a large energy dissipation mechanism. The covalent linkage between lignin and matrix promoted their compatibility and formed a robust cross-linking network. Depending on the multi-level dynamic interactions, the prepared elastomer exhibited a mechanical strength of 73 MPa, which was 1.9 times higher than that of the blank sample, with an initial thermal decomposition temperature of 310.5 °C and a good recyclability. Especially, the mechanical strength of LPUs was up to 99 MPa after mechanical training. In addition, the abundant conjugated structure of lignin endowed LPUs with an excellent photothermal conversion ability. Under the irradiation of NIR light with 4000 w m−2, the surface temperature increased to 95 °C within two minutes, exhibiting a stable photothermal performance even after 6 cycles. This work showed the application prospects of LPUs in the fields of high strength engineering materials and photo-induced actuators. [ABSTRACT FROM AUTHOR]

In computational biology, biological database search has been playing a very important role. Since the COVID-19 outbreak, it has provided significant help in identifying common characteristics of viruses and developing vaccines and drugs. Sequence alignment, a method finding similarity, homology and other information between gene/protein sequences, is the usual tool in the database search. With the explosive growth of biological databases, the search process has become extremely time-consuming. However, existing parallel sequence alignment algorithms cannot deliver efficient database search due to low utilization of the resources such as cache memory and performance issues such as load imbalance and high communication overhead. In this paper, we propose an efficient sequence alignment algorithm on Sunway TaihuLight, called ESA, for biological database search. ESA adopts a novel hybrid alignment algorithm combining local and global alignments, which has higher accuracy than other sequence alignment algorithms. Further, ESA has several optimizations including cache-aware sequence alignment, capacity-aware load balancing and bandwidth-aware data transfer. They are implemented in a heterogeneous processor SW26010 adopted in the world's 6th fastest supercomputer, Sunway TaihuLight. The implementation of ESA is evaluated with the Swiss-Prot database on Sunway TaihuLight and other platforms. Our experimental results show that ESA has a speedup of 34.5 on a single core group (with 65 cores) of Sunway TaihuLight. The strong and weak scalabilities of ESA are tested with 1 to 1024 core groups of Sunway TaihuLight. The results show that ESA has linear weak scalability and very impressive strong scalability. For strong scalability, ESA achieves a speedup of 338.04 with 1024 core groups compared with a single core group. We also show that our proposed optimizations are also applicable to GPU, Intel multicore processors, and heterogeneous computing platforms. • In this paper, we propose and implement an efficient sequence alignment algorithm, ESA, for biological database search on SW26010 heterogeneous processors. This algorithm adopts both local and global alignments for biological database search with several optimizations. ESA achieves high computational performance without sacrificing accuracy. To the best of our knowledge, this is the first attempt to parallelize hybrid sequence alignment on Sunway TaihuLight using multi-level optimizations. • We propose three optimization strategies in ESA: cache-aware sequence alignment, capacity-aware load balancing and bandwidth-aware data transfer. Cache-aware sequence alignment effectively reduces the size of the data structure for sequence alignment and fully utilizes the vectorization of the slave cores of SW26010. With capacity-aware load balancing, we distribute the workload evenly among the cores of SW26010. With bandwidth-aware data transfer, ESA reduces the communication overhead by using asynchronous DMA transmission and RLC. • We evaluate the performance of ESA using the Swiss-Prot database on Sunway TaihuLight. Our experimental results show that ESA achieves a speedup of 34.5 times on a single CG over the manager core. Compared with a serial implementation on Intel (R) Xeon (R) CPU E5-2620 v4 processor, ESA achieves a speedup of 21.6 on a single CG. We also demonstrate that ESA has linear weak scalability and very competitive strong scalability. Finally, we compare ESA with mainstream algorithms on the CPU+GPU platform and achieve the highest GCUPS of 228.91. [ABSTRACT FROM AUTHOR]

Highlights • Explores the importance of coverage and density of a PMC-space for power modelling. • Shows a k-nearest neighbour search can be used to estimate power from the PMC-space. • Discusses the simplicity and usability of the Manila modelling methodology. • Demonstrates our model is portable across system architectures. • Examines the scalability of monitoring cluster power. Abstract In this paper, we propose a new power modelling method, called Manila, that can largely reduce the effort of PMC-based power modelling using multi-dimensional k-nearest neighbour searching, without the use of model tuning and domain-specific knowledge. This method helps improve the accuracy of PMC-based power modelling and widen its scope of use. Specifically, Manila uses a densely populated PMC-space, generated from a large variety of computing workloads, which is essential for our improvement. This is in contrast to current PMC-based power models, that have a sparse PMC-space, due to using a predefined benchmark suite. A parameterised micro-benchmark is used to bootstrap the PMC-space, which is independent from any applications and can generate the generic computing workloads of many applications. Our method is more widely extendable and applicable than the existing methods. Manila can efficiently search the dense PMC-space for power estimates using a nearest neighbour search algorithm. Experimental results demonstrate that Manila is more accurate in power measurements for a wide range of parallel benchmarks, with an average mean absolute error of 2.8%. [ABSTRACT FROM AUTHOR]

This paper presents a dynamic representation-based tracker (DRT) to handle occlusions in the long-term pedestrian tracking of a single target. In our DRT, an adaptive representation network (ARN) is first constructed to extract multiple features, including classical features such as appearance and pose as well as some vector-format deep features. These features are then stacked to form a dynamic representation so as to convert the target tracking into a matching problem between the target features and candidate features, where the Euclidean distance (ED) and locality-constrained linear coding (LLC) are used as measurements in the decision-making. Next, the target state is determined through a voting procedure according to the feature matching error. Finally, a pose supervised module (PSM) and an IOU filtering module (IFM) are applied, respectively, to refine the target state and to filter out some invalid candidate targets that have been detected. Experimental results on public benchmark datasets show that our DRT is quite robust to complex environments with long-term pedestrian occlusions, and outperforms several existing state-of-the-arts trackers as it produces the best performance on both the pedestrian tracking dataset with occlusion (PTDO) and the pedestrian tracking dataset with occlusion plus (PTDO Plus). [ABSTRACT FROM AUTHOR]

• Ca 3 Zn 3 Te 2 O 12 :Bi3+, Ca 3 Zn 3 Te 2 O 12 :Eu3+, Ca 3 Zn 3 Te 2 O 12 :Eu3+/Bi3+ are prepared successfully in air. • The green emission of Ca 3 Zn 3 Te 2 O 12 :Bi3+ phosphor with excitation at 314 nm is observed. • Under excitation at 395 nm, the red emission of Ca 3 Zn 3 Te 2 O 12 :Eu3+ is observed. • The adjustable color light of Ca 3 Zn 3 Te 2 O 12 :Eu3+, Bi3+ phosphor with excitation at 314 nm is revealed. Ca 3 Zn 3 Te 2 O 12 :Bi3+, Ca 3 Zn 3 Te 2 O 12 :Eu3+, Ca 3 Zn 3 Te 2 O 12 :Eu3+/Bi3+ are prepared successfully in air. The crystal phase, microscopic image, and optical properties of samples are investigated by the X-ray difractometer, scanning electron microscopy, and edinburgh steady-state FLS 980 spectrograph. The green emission of Ca 3 Zn 3 Te 2 O 12 :Bi3+ phosphor with excitation at 314 nm is observed and its broad emission spectrum in the range of 360 - 790 nm is attributed to the 3 P 1 → 1 S 0 transition of Bi3+ ion. Under excitation at 395 nm, the red emission of Ca 3 Zn 3 Te 2 O 12 :Eu3+ is found, the chromaticity coordinates are (0.6392, 0.3605), and its PL spectrum is in the rang of 575 - 750 nm because of the 5 D 0 → 7 F 0 ,7 F 1 , 7 F 2 , 7 F 3 , and 7 F 4 transations of Eu3+ ion. The doping Bi3+ ion can affect the luminescence properties of Ca 3 Zn 3 Te 2 O 12 :Eu3+ phosphor. The adjustable color light from green to yellow of Ca 3 Zn 3 Te 2 O 12 :Eu3+, Bi3+ phosphor with excitation at 314 nm is revealed when Eu3+ concentration is changed from 0 to 12 mol%. When the excitation wavelength is 395 nm, Ca 3 Zn 3 Te 2 O 12 :Eu3+, Bi3+ phosphor only emits red light with emission spectrum in the rang of 575 - 750 nm. The energy level diagrams of Eu3+ and Bi3+ in Ca 3 Zn 3 Te 2 O 12 :Eu3+, Bi3+ are drawn, which is used to analyze the luminous mechanism of samples. [Display omitted] [ABSTRACT FROM AUTHOR]

$k$ Nearest Neighbors ($k$ -NN) search is a widely used category of algorithms with applications in domains such as computer vision and machine learning. Despite the desire to process increasing amounts of high-dimensional data within these domains, $k$ -NN algorithms scale poorly on multicore systems because they hit a memory wall. In this paper, we propose a novel data filtering strategy for $k$ -NN search algorithms on multicore platforms. By excluding unlikely features during the $k$ -NN search process, this strategy can reduce the amount of computation required as well as the memory footprint. It is complementary to the data selection strategies used in other state-of-the-art $k$ -NN algorithms. A Subspace Clustering for Filtering (SCF) method is proposed to implement the data filtering strategy. Experimental results on four $k$ -NN algorithms show that SCF can significantly improve their performance on three modern multicore platforms with only a small loss of search precision. [ABSTRACT FROM AUTHOR]

Multi-layer perceptron (MLP) is a class of Artificial Neural Networks widely used in regression, classification, and prediction. To accelerate the training of MLP, more cores can be used for parallel computing on many-core systems. However, with the increasing number of cores integrated into the chip, the communication bottleneck in the training of MLP on electrical network-on-chip (ENoC) becomes severe, degrading MLP training performance. Replacing ENoC with optical network-on-chip (ONoC) can break the communication bottleneck in MLP training. To facilitate the development of ONoC for MLP training, it is necessary to compare and model the MLP training performance of ONoC and ENoC in advance. This paper first analyzes and compares the differences between ONoC and ENoC. Then, we formulate the performance and energy model of MLP training on ONoC and ENoC by analyzing the communication and computation time, static energy, and dynamic energy consumption, respectively. Furthermore, we conduct extensive simulations to compare their MLP training performance and energy consumption with our simulation infrastructure. The experimental results show the MLP training time of ONoC has been reduced by 65.16% and 52.51% on average in different numbers of cores and batch sizes compared with ENoC. The results also exhibit that ONoC overall has 54.86% and 43.13% on average energy reduction in different numbers of cores and batch sizes compared with ENoC. However, with a small number of cores (e.g., less than 50) in MLP training, ENoC consumes less energy than ONoC. These experiments confirm that generally ONoC is a good replacement for ENoC when using a large number of cores in terms of performance and energy consumption for MLP training. [ABSTRACT FROM AUTHOR]

Highlights: [•] Performance evaluation of the View-Oriented Transactional Memory (VOTM) system on a modern 64-core machine. [•] VOTM reduces contention among transactions of the application using Restricted Admission Control (RAC). [•] VOTM allows fine-grained optimization of memory accesses by partitioning shared data into multiple independent views. [•] VOTM can relieve contention on TM metadata through view partitioning and RAC in memory-intensive applications. [ABSTRACT FROM AUTHOR]

Large-scale genotype-phenotype screens provide a wealth of data for identifying molecular alterations associated with a phenotype. Epistatic effects play an important role in such association studies. For example, siRNA perturbation screens can be used to identify combinatorial gene-silencing effects. In bacteria, epistasis has practical consequences in determining antimicrobial resistance as the genetic background of a strain plays an important role in determining resistance. Recently developed tools scale to human exome-wide screens for pairwise interactions, but none to date have included the possibility of three-way interactions. Expanding upon recent state-of-the-art methods, we make a number of improvements to the performance on large-scale data, making consideration of three-way interactions possible. We demonstrate our proposed method, Pint, on both simulated and real data sets, including antibiotic resistance testing and siRNA perturbation screens. Pint outperforms known methods in simulated data, and identifies a number of biologically plausible gene effects in both the antibiotic and siRNA models. For example, we have identified a combination of known tumour suppressor genes that is predicted (using Pint) to cause a significant increase in cell proliferation. Author summary: In recent years, large-scale genetic data sets have become available for analysis. These large data sets often stretch the limits of classic computational methods, requiring too much memory or simply taking a prohibitively long time to run. Due to the enormous number of potential interactions, each gene or variation in the data is often modelled on its own, without considering interactions between them. Recently, methods have been developed to solve regression problems that include these interacting effects. Even the fastest of these cannot include three-way interactions, however. We improve upon one such method, developing an approach that is significantly faster than the current state of the art. Moreover, our method scales to three-way interactions among thousands of genes, while avoiding a number of the limitations of previous approaches. We analyse large-scale simulated data, antibiotic resistance, and gene-silencing data sets to demonstrate the accuracy and performance of our approach. [ABSTRACT FROM AUTHOR]

Soft soils are usually treated to mitigate their engineering problems, such as excessive deformation, and stabilization is one of most popular treatments. Although there are many creep models to characterize the deformation behaviors of soil, there still exist demands for a balance between model accuracy and practical application. Therefore, this paper aims at developing a Mechanistic-Empirical creep model (MEC) for unsaturated soft and stabilized soils. The model considers the stress dependence and incorporates moisture sensitivity using matric suction and shear strength parameters. This formulation is intended to predict the soil creep deformation under arbitrary water content and arbitrary stress conditions. The results show that the MEC model is in good agreement with the experimental data with very high R-squared values. In addition, the model is compared with the other classical creep models for unsaturated soils. While the classical creep models require a different set of parameters when the water content is changed, the MEC model only needs one set of parameters for different stress levels and moisture conditions, which provides significant facilitation for implementation. Finally, a finite element simulation analysis of subgrade soil foundation is performed for different loading levels and moisture conditions. The MEC model is utilized to predict the creep behavior of subgrade soils. Under the same load and moisture level, the deformation of soft soil is largest, followed by lime soil and RHA–lime-stabilized soil, respectively. [ABSTRACT FROM AUTHOR]

The occurrence of fault fracture zones is a frequently encountered geological impediment while carrying out shield-driven twin tunnels, which presents significant hazards to the construction process. To examine the impact of these zones on shield-driven twin tunnels, an improved three-dimensional numerical model is utilized, based on the three-dimensional discrete fracture network (DFN) approach. This model assesses the surrounding rock’s deformation, the lining response, and the excavation plastic zone (EPZ). The joint orientation, joint density, and lagging distance are systematically analyzed. The research findings indicate that the surrounding rock response occurs mainly during the passage of the shield machine, with twin-tunnel excavation leading to the increased response of the surrounding rock and lining. Joint orientation poses complexities for twin-tunneling excavation, as discontinuities intersect the tunnel axis obliquely, leading to elevated construction risk. Large joint density weakens the overall strength of the surrounding rock, rendering it more akin to a soft equivalent homogeneous medium. Lagging distance primarily influences the surrounding rock’s deformation and lining response, emphasizing the need for a larger lagging distance in twin-tunnel construction. These results establish a solid theoretical basis for shield-driven twin tunnels crossing fault fracture zones construction. [ABSTRACT FROM AUTHOR]

Thermal damage assessment of cementitious composites is essential for evaluating post-fire health conditions of the engineering structures, as well as the basis for reinforcement and repair after fires. Fibers and fly ash are widely used in cementitious composites due to their excellent properties. However, quantifying and predicting the thermal damage of hybrid fibers reinforced cementitious composites blended with fly ash at high temperatures is still inexplicit. Hence, this study aims to realize multiscale prediction of thermal damage for hybrid fibers reinforced cementitious composites blended with fly ash at high temperatures. First, the volumes of the phase compositions during hydration and dehydration are calculated by the hydration of cement and fly ash and the dehydration of hydration products. Then, a multiscale model is established to predict the thermal damage of hybrid fibers reinforced cementitious composites and verified by the experimental data. At last, the temperature field of tunnel lining structure in fires is obtained by numerical modeling and employing it to predict thermal damage at different thicknesses and moments. Results show that the heating rate determines the dehydration degree of hydration products and the volumes of the phase composites at high temperatures. The proposed multiscale model can reflect the thermal microcracking of cement paste, the interfacial thermal damage between aggregates and the cement paste, and the deterioration of elastic modulus of fibers. After three hours of exposure to fires, serious damage appears at the surface and the thickness of 2 cm and 5 cm of the lining, while there is nearly no damage at a thickness of 30 cm or more. [ABSTRACT FROM AUTHOR]

Hunting for blocked objects is complicated in crowded scenes due to the frequent occlusions. However, creating an effective occlusion detector remains challenging for the following inherent reasons: (1) the limited feature extraction capacity of encoders and (2) the loss of highly overlapped objects by decoders. We propose a spectral-angular ensemble-based Transformer network, OBhunter, to address these two issues. In OBhunter, an effective encoder with robust feature extraction performance is constructed through the ensemble spectral-angular self-attention (ESA) mechanism, extending the original softmax-based attention to the spectral characteristic dimension. To tackle the second issue, we branch the decoder using our crowded region generator (CRG). These two branches undergo differential processing by ensemble spectral-angular region (ESR) loss, a multi-task training loss function, to prevent erroneous suppression of proposal boxes. Extensive experiments demonstrate that our OBhunter is effective in occlusion detection based on CrowdHuman, CityPersons, and Caltech-Pedestrians datasets. With OBhunter, the occlusion detection performance achieves 33.52% MR−2. Additionally, we validate the robustness of our OBhunter on a less crowded dataset such as MS-COCO. • Transformer's potential for occlusion detection is demonstrated. • The ESA mechanism extends the attention to the spectral characteristic dimension. • The CRG parts two branches to adapt crowded and sparse ROIs in decoder-stage. • The ESR Loss yields complementary outputs by these two branches separately. [ABSTRACT FROM AUTHOR]

The effects of cenospheres, an industrial waste residue, on the compressive strength, flexural strength, toughness, ductility, chemical component, microstructures, and micromechanics of lightweight toughness cement-based composites (LTCCs) by comprehensive experimental tests are explored in this paper. The results indicate that an increase in the amount of cenospheres leads to a decrease in the compressive and flexural strength of LTCCs. However, the specific strength of LTCCs increases with increasing cenosphere content. LTCCs containing 20% cenospheres and 1% fiber volume have the best toughness and ductility. Significant strain hardening occurs during the four-point bending and uniaxial tensile process. Furthermore, the incorporation of cenospheres promotes the hydration reaction of LTCCs due to its high pozzolanic activity. The LTCC cement paste has a low bonding strength to the fiber, which helps the fiber to be pulled out to produce greater bending deformation and tensile strain. The elastic modulus and hardness of the LTCC cement paste decrease linearly with increasing cenosphere content, which also causes the LTCC microstructure to become loose and more ettringite to generate. The weak interfacial transition zone between the cenospheres and the cement matrix is the important reason for the decreasing compressive strength of the LTCC. In conclusion, LTCC incorporating cenospheres is suitable for long-span steel deck pavements due to its light weight and excellent toughness. The successful application of cenospheres in engineering construction can save natural resources and contribute to sustainable development. [ABSTRACT FROM AUTHOR]

With increased awareness of environmental protection, the output of traditional curing agents such as cement and lime is less and less, so it is urgent to develop new curing agents with high efficiency and environmental benefits. Thus, this study aims at investigating the application of rice husk ash (RHA) from agricultural waste to the soft soil stabilization. A series of tests are conducted to analyze the strength development process and soil–water characteristics of rice husk ash–lime (RHA–lime) stabilized soils. The results of the strength tests showed that by increasing the content of RHA, the unconfined compressive strength (UCS) and splitting strength of stabilized soils increased first and then decreased. The effective shear strength indexes of the three soil types (soft soil, lime-stabilized soil, and RHA–lime soil) are measured and compared. It is found that RHA can effectively improve the shear resistance and water resistance of stabilized soil. The results of methylene blue test demonstrated that RHA can also promote the reduction of the specific surface area and swelling potential energy of lime-stabilized soil. In addition, the influence of RHA on mineral composition and morphology change in stabilized soils is studied at the microscopic level. The X-ray diffraction tests and scanning electron microscope (SEM) tests showed that strength development and change of soil–water properties of RHA–lime stabilized soil are attributed to enhanced cohesion by cementation and pores filling with agglomerated mineral. [ABSTRACT FROM AUTHOR]

In the coastal areas of southeastern China, high temperatures and humidity in the summer and microfreezing in the winter, as well as a high concentration of salt spray in the environment, seriously deteriorate the durability of asphalt mixtures. Therefore, the microcharacteristics of asphalt mastics (asphalt mixed with mineral filler) under the effect of chlorine salt and "dry–wet and freeze–thaw" (DW-FT) cycles were investigated by Fourier-transform infrared (FTIR) spectroscopy, gel permeation chromatography (GPC), and atomic force microscopy (AFM) techniques. Two factors, including asphalt mastic types (base and styrene-butadiene-styrene (SBS)-modified mastics) and numbers of DW-FT cycles, were considered based on the natural environment. Regression functions were established to explore the relationship between the FTIR, GPC, and AFM indexes. The results indicate that there were no chemical reactions between the asphalt and filler because the infrared spectrum of the base and SBS-modified mastics were similar. With the increase of the salt "DW-FT" cycle numbers, the sulfoxide index and large molecular size ratios ( L M S % ) increased, and the surface roughness ( R q and R a ) of the morphology decreased, as illustrated by a flatting mastics surface phenomenon in the AFM test. Regression analysis confirmed that there was a high correlation between the FTIR, GPC, and AFM indexes, and formation of the bee structures was closely related to the long chain index. The SBS-modified mastics had a better antiaging performance with a lower increase in the sulfoxide index after the salt "DW-FT" cycles in the coastal environment. [ABSTRACT FROM AUTHOR]

The prevalent overreliance on silica sand within strain-hardening cement-based composites (SHCC) underscores the urgency to address the depletion of natural resource. To address this concern, a novel approach involving the utilization of waste marine clay (WMC), an underutilized construction waste residue, is explored. This study marks the first application of high-volume calcined WMC as a substitute for silica sand within sustainable and cost-effective strain-hardening cement-based composites (SC-SHCC). The investigation encompasses a comprehensive analysis of SC-SHCC, covering mechanical properties, hydration, shrinkage, and cost. The results showed that the integration of calcined WMC instead of silica sand had noticeable improvements in the mechanical properties of SC-SHCC due to the notable pozzolanic activity and filling effect of calcined WMC. The developed SC-SHCC replacing two-thirds of the silica sand with calcined WMC exhibited a compressive strength of 76.34 MPa, a tensile strength of 14.63 MPa, and a tensile strain capacity of 5.99%, which successfully pushed the performance of conventional SHCC. Key indicators such as compressive strength, flexural strength, and tensile strength exhibited promising enhancements as calcined WMC dosage increased. The incorporation of calcined WMC contributed to heightened ettringite formation, thereby improving the tensile strength, and tensile strain energy of SC-SHCC. Besides, SC-SHCC exhibited pronounced drying shrinkage, primarily attributable to the free water evaporation and capillary pore development associated with calcined WMC. Furthermore, substituting calcined WMC for silica sand led to reducing the costs within SC-SHCC production. Therefore, the findings of this study hold promise for advancing the field of SHCC while promoting the environmentally friendly utilization of waste materials. • Calcined waste marine clay (WMC) is used as fine aggregates in strain-hardening cement composites (SHCC). • SHCC containing calcined WMC exhibits excellent mechanical performance. • Bond between fibers and SHCC matrix is enhanced. • The enhanced performance can be attributed to filler effect and pozzolanic activity of calcined WMC. [ABSTRACT FROM AUTHOR]

Wearable electronics based on conductive hydrogels (CHs) easily suffer from prolonged response times, reduced wearing comfort, shortened service lives, and impaired signal accuracy in cold environments, because conventional CHs tend to freeze at subzero temperatures and lose their flexibility, adhesion, transparency, and conductivity, which will limit their applications in extreme environments. Inspired by the way psychrotolerant creatures and superabsorbent materials interfere with the hydrogen bonding networks of water, a freeze‐resistant conductive organohydrogel (COH) is facilely fabricated. The synergy effect between charged polar terminal groups and a binary solvent system of water–ethylene glycol weakens the hydrogen bonding between water molecules and endows the COH with remarkable freezing tolerance (−78 °C). Additionally, the obtained COH is ultra‐stretchable (≈6185%), tough (9.2 MJ m−3), highly transparent (≈99%), self‐adhesive (10.2–27.8 kPa), and biocompatible. This versatile COH is assembled into a strain sensor and a well‐designed bracelet electrocardiogram sensor. Benefiting from the exceptional low‐temperature tolerance of the prepared COH, these devices exhibit fast response with delay‐free signals even at −40 °C. Overall, this work proposes a strategy to develop multifunctional COHs for supporting human health in cold environments. [ABSTRACT FROM AUTHOR]

Strain hardening cementitious composites (SHCCs) are widely used in projects due to their excellent deformation resistance and large energy absorption capacity. However, determining tensile strain capacity is still a challenge for engineers. The current popular approach is to use inverse methods to predict the tensile behavior of SHCCs, such as the UM method (Qian and Li) and the JCI (Japan Concrete Institute) method. The key to these two approaches is to acquire the exact relationship between the bending and the uniaxial response. In this paper, a reasonable linear constitutive model of the SHCCs is modified. Initially, the moment-curvature diagrams are discussed by material parameters. The results reveal that the moment-curvature response is quite sensitive to the variations in the parameter of transition strain α, post-cracking tensile stiffness η, and strain softening stiffness μ, however, for the compressive parameters, the moment-curvature responses influence on flexural behavior is insignificant. Moreover, the load-deflection curve in the mid-span of SHCC, based on the consideration of shear effect, is simulated under a four-point bending test (FPBT). The results show a remarkable consistency with the experimental data when compared to the previous simulations. It is expected that this modified method can enhance an accurate program in order to obtain the tensile capacity. [ABSTRACT FROM AUTHOR]

Vitamin A (VitA) is a micronutrient that is crucial for maintaining vision, promoting growth and development, and protecting epithelium and mucus integrity in the body. VitA is known as an anti-inflammation vitamin because of its critical role in enhancing immune function. VitA is involved in the development of the immune system and plays regulatory roles in cellular immune responses and humoral immune processes. VitA has demonstrated a therapeutic effect in the treatment of various infectious diseases. To better understand the relationship between nutrition and the immune system, the authors review recent literature about VitA in immunity research and briefly introduce the clinical application of VitA in the treatment of several infectious diseases. [ABSTRACT FROM AUTHOR]

Ageing and healing are both important characteristics of asphalt binder. The effect of ageing on healing is often studied, but the quantitative relationship between them is not clear. Thus, this study aims at explaining and formulating the inner relationship between healing and ageing based on a kinetics-based approach, expressed as healing and ageing activation energy, respectively. The healing activation energy is obtained based on the healing kinetics approach using recoverable strain energy (RSE). The ageing activation energy is calculated based on the kinetics-based ageing model. After that, further analysis is performed to explore the relationship between healing and ageing kinetic rates of asphalt binders, and the interpretation of the discovered relationship is proposed. The results indicate that the healing activation energy of aged asphalt binder has no big difference from the sum of ageing activation energy plus the healing activation energy of unaged asphalt binder. Define the ratio of initial temperature kinetic rate to the final temperature kinetic rate of healing/ageing as the healing/ageing rate ratio. It indicates that the product of the healing rate ratio of unaged asphalt binder and the ageing rate ratio of the same binder is close to the healing rate ratio of aged asphalt binder. [ABSTRACT FROM AUTHOR]

Lignin is a natural polymer with abundant functional groups with great application prospects in lignin-based polyurethane elastomers with self-healing abilities. In this study, a lignin self-healing polyurethane (PUDA-L) was specially designed using lignin as the raw material of polyurethane, combining lignin with Diels-Alder (DA) bond and hydrogen bonds. The experimental results showed that PUDA-L was prepared with good thermal stability, fatigue resistance, shape memory effect, excellent mechanical strength, and self-healing ability by partially replacing the crosslinking agents with bio-based lignin and hydroxylated modified lignin to increase the hydroxyl content. Polyurethane has a tensile strength of up to 29 MPa and an elongation at break of up to 500 %. The excellent self-healing ability of PUDA-L originates from the internal DA bonds and cross-linked hydrogen bonds. After the dumbbell sample was fused and heated at 130 °C for 4 h, the elastomer could be completely healed, the tensile strength was restored to 29 MPa, and the self-healing efficiency was up to 100 %. The developed PUDA-L elastomer has promising applications in sensors and smart skins. [Display omitted] [ABSTRACT FROM AUTHOR]

Detecting small objects is critical to many submissions, such as automatic drive and lung nodule detection. However, small object detection is challenging with low-resolution features. Therefore, the linchpin of small object detection is to design an effective encoder that can extract subtle features. In this paper, we present a powerful encoder, called Ensemble Transformer with Attention Modules (ETAM) encoder, for abstracting the subtle small object features without sacrificing the capability of larger object detection. In ETAM, a Magnifying Glass (MG) module is proposed to focus on representative features of small objects. Then, the Quadruple Attention (QA) is designed to enrich the small object features with width and height in addition to channel and position. To accommodate both small and large objects, we use ensemble learning in our ETAM encoder, which has two branches. Experimental results show that ETAM significantly improves small object detection based on PASCAL VOC, MS-COCO, VisDrone2019, and LIDC-IDRI. With ETAM, the m AP for small objects is improved up to 91.7% based on the four datasets. • Transformer's potential for small object detection is demonstrated. • The MG can forecast small objects' wide positions on shallow features. • The QA extends the attention to two extra dimensions, height and width. • ETAM has two branches to adapt small and larger object detection. [ABSTRACT FROM AUTHOR]

Nanostructures made entirely of DNAs display great potential as chemotherapeutic drug carriers but so far cannot achieve sufficient clinic therapy outcomes due to off‐target toxicity. In this contribution, an aptamer‐embedded hierarchical DNA nanocluster (Apt‐eNC) is constructed as an intelligent carrier for cancer‐targeted drug delivery. Specifically, Apt‐eNC is designed to have a built‐in reserve pool in the interior cavity from which aptamers may move outward to function as needed. When surface aptamers are degraded, ones in reserve pool can move outward to offer the compensation, thereby magically preserving tumor‐targeting performance in vivo. Even if withstanding extensive aptamer depletion, Apt‐eNC displays a 115‐fold enhanced cell targeting compared with traditional counterparts and at least 60‐fold improved tumor accumulation. Moreover, one Apt‐eNC accommodates 5670 chemotherapeutic agents. As such, when systemically administrated into HeLa tumor‐bearing BALB/c nude mouse model, drug‐loaded Apt‐eNC significantly inhibits tumor growth without systemic toxicity, holding great promise for high precision therapy. [ABSTRACT FROM AUTHOR]

In this work, the effect of graphene oxide (GO) on the physical performance related properties of an asphalt binder was investigated in terms of penetration, softening point, viscosity (110–185°C), and ductility as well as temperature performance indices. Subsequently, response surface methodology (RSM) was used to examine the influence of GO and asphalt contents on the volumetric and strength properties of the asphalt mixture. Marshall stability/flow and indirect tensile strength (ITS) tests were performed on the compacted mixtures. Then, the RSM based on the central composite design (CCD) was utilised to analyse the effects of the preparation parameters and conduct mathematical modelling for the response variables' predictions. According to the results, the addition of GO significantly enhanced the physical properties of the asphalt. The RSM analysis showed that the tensile strength, stability, and plastic flow, coupled with the volumetric characteristics of the asphalt mixture were highly influenced by variations in the preparations. The proposed models were well-fitted to the actual results with a high degree of correlation. Furthermore, numerical optimisation results presented 1.8% of the GO and 5.3% of the asphalt content as the optimum proportions of the mixture to achieve the maximum desired responses. [ABSTRACT FROM AUTHOR]

Modern multicore computers often adopt a multisocket multicore architecture with shared caches in each socket. However, traditional work-stealing schedulers tend to pollute the shared cache and incur more cache misses due to their random stealing. To relieve this problem, this paper proposes an Adaptive Cache-Aware Bi-tier work-stealing (A-CAB) scheduler. A-CAB improves the performance of memory-bound applications by reducing memory footprint and cache misses of tasks running inside the same CPU socket. A-CAB adaptively uses a DAG partitioner to divide an execution Directed Acyclic Graph (DAG) into the intersocket tier and the intrasocket tier. Tasks in the intersocket tier are scheduled across sockets while tasks in the intrasocket tier are scheduled within the same socket. Experimental results tell us that A-CAB can improve the performance of memory-bound applications up to 74.4 percent compared with the traditional work-stealing. [ABSTRACT FROM PUBLISHER]

Benefiting from deep learning, Salient Object Detection (SOD) has made much progress. However, most existing methods adopt the same strategy to extract salient cues from different feature levels without fully considering their differences in the feature extraction stage and/or suffer from the accumulation of noise and dilution of spatial details in the feature fusion stage. These two problems hinder the further improvement in performance. In this paper, we propose an effective SOD model, PiNet, which can address the above problems via two novel mechanisms in the network: level-specific feature extraction and progressive refinement of saliency. We have designed the customized feature extraction components for each level of features—enabling us to extract better saliency cues from multi-level features. The saliency feature refinement in the branches follows a coarse-to-fine process, where the refined features progressively contain more location cues, internal and boundary details. Through short connections, the extracted saliency cues in different branches are selectively transmitted and integrated, which well mitigates the accumulation of noisy information and the dilution of detailed information. By using four different backbones, we verify our model has good adaptability and can make accurate saliency predictions under different pretrained models. Extensive experiments on five public datasets demonstrate that PiNet outperforms 19 state-of-the-art (SOTA) methods in SOD, with its small model size (56.1 MB) and fast inference speed (47 FPS). • An effective model, PiNet, is proposed for SOD with two novel mechanisms. • A Smoothed Feature Attention Module is designed to consider the location of salient objects. • A Feature Interaction Module is designed to guarantee the consistency in the interior of salient objects. • A Progressive Fusion Module is designed to effectively aggregate edge cues and saliency features. • PiNet outperforms 19 state-of-the-art methods and reaches a real-time speed for SOD. [ABSTRACT FROM AUTHOR]

[Display omitted] • Cyanamide molecules are successfully decorated onto the surface of sulphide photocatalysts. • The cyanamide decorated sulphide is among the most active photocatalysts for CO 2 reduction. • Cyanamide groups make the neighboring Zn atoms effective in activating and reducing CO 2. • Our work opens a new pathway for developing highly efficient catalysts for targeted reactions. The using of reductive cocatalysts to dictate the transfer path of electrons towards targeting products is indispensable for photocatalytic CO 2 reduction reaction (CRR). However, the development of low-cost and highly selective cocatalysts for photocatalytic CRR remains a daunting challenge. Herein, a functional molecule decoration strategy is proposed to tune the surface electronic structures of semiconductor photocatalysts, in order to promote their photocatalytic performance in CRR. We employ cyanamide groups as the chemical modifiers to regulate the CRR performance of solid solution metal sulphide photocatalysts. The synthesized photocatalysts exhibit excellent photocatalytic performance in CRR, and the highest CO evolution rate exceeds 1400 μmol g−1h−1, being among the best results reported to date. Multiple characterizations in combination with theoretical simulations further demonstrate that the decorated cyanamides do not act as active sites, but changes the electronic structures of the neighboring surface lattice metal atoms and decreases the kinetic barrier of the rate determining *CO 2 →*COOH step, thereby making the surface much more active for photocatalytic CRR. These findings explicitly verify the effectiveness of the functional molecule decoration strategy in promoting the CRR performance of photocatalysts, and also open up a new pathway for the development of highly efficient and selective catalysts for specific catalytic reactions. [ABSTRACT FROM AUTHOR]

The fabrication of flexible surface‐enhanced Raman scattering (SERS) substrates for sensitive detection on uneven or irregular surfaces is challenging. In this study, a flexible dual plasmonic SERS (FDPS) substrate rationally constructed using Au nanoparticle (AuNP) arrays/aligned Ag nanowires (AgNWs) and elastic polyurethane (PU) is demonstrated. It exhibits high sensitivity (detection limit of 10−8m for melamine and 10−10m for malachite green) and excellent reproducibility. The well‐designed structure of AuNP arrays/aligned AgNWs fabricated using block copolymer self‐assembly and oil–water–air interfacial self‐assembly successfully enhances the electromagnetic field through plasmonic coupling. In addition, the FDPS substrate retains a high SERS sensitivity after exposure to air at room temperature for 30 days because of the high stability of AuNP arrays and antioxidation characteristic of the PU covered on the aligned AgNWs. Even after undergoing stretching, bending, and twisting for 100 cycles, the FDPS substrate maintains a stable SERS activity owing to the introduction of the elastic PU. This study demonstrates a potential application of SERS detection under practical conditions for irregular surfaces and may be helpful in the development of flexible sensors. [ABSTRACT FROM AUTHOR]

Herein, benefiting from effective energy-dissipation mechanism, the high-performance thermoplastic elastomers (ZASM/SEBS composites) with excellent mechanical strength-toughness combination were prepared by blending 3-Amino-1,2,4-triazole (ATA), maleic anhydride grafted styrene-ethylene/butadiene-styrene triblock copolymer (SEBS-g-MA), zinc acetate and styrene-ethylene/butadiene-styrene triblock copolymer (SEBS) successively. The Zn2+-carboxyl coordination and hydrogen bonding dual-dynamic network of composites was proved by Fourier transform infrared spectroscopy and X-ray photoelectron spectroscopy. The mechanical performance of ZASM/SEBS composites was investigated by tensile test. With the decreasing mass ratio of ZASM in ZASM/SEBS composites, the tensile strength and elongation at break increased firstly and then decreased. When the mass ratio of ZASM in composites was 30%, the tensile strength reached 32.1 MPa along with the elongation at break of 1383%, attaining the best mechanical strength-toughness combination. It is demonstrated that the ZASM/SEBS composites could be a promising substitute for traditional vulcanized rubber. [ABSTRACT FROM AUTHOR]

This paper proposes a Restricted Admission Control (RAC) scheme for View-Oriented Transactional Memory. The scheme can control the number of threads concurrently accessing a view in order to reduce the number of aborts of transactions. The RAC scheme has the merits of both the locking mechanism and the transactional memory. A theoretical model is proposed to analyze the performance of the RAC scheme and to provide guidance for dynamic adjustment of the number of concurrent threads accessing the same view. Experimental results demonstrate that theoretical RAC model can mostly provide correct guidance to transactional concurrency control. Our RAC implementation shows that RAC can optimize concurrency control of transactions and performs much better than conventional transactional memory systems such as TinySTM that have no dynamic admission control. [ABSTRACT FROM AUTHOR]

• Investigated the reliability metrics of a product with heterogeneous age, degradation level and repair condition. • Redefined and well balanced the producer's and consumer's risks. • Utilize both non-destructive test and destructive test to explore the product's operational reliability metrics without discarding the product. • Proposed a sequential, degradation-based hybrid double stage acceptance sampling plan (DHDASP) under accelerated condition. • Recommended applicable scenarios of the proposed DHDASP. Units of a product, even though produced in the same batch, may perform differently due to a variety of randomness in raw materials, manufacturing process and repair actions (e.g., refurbishment). If these units are also stored for a long period of time, their key characteristics will degrade randomly during storage. As a result, the product will exhibit heterogeneous performance upon delivery. Such a heterogeneity significantly affects the product's acceptability and is one of the utmost concerns to both the producer and consumer. In this study, we propose a degradation-based hybrid double-stage acceptance sampling plan (DHDASP) to determine the acceptability for a product exhibiting heterogeneous performance. Compared to ordinary acceptance sampling plans, the new plan provides the producer with a higher likelihood of product acceptability and offers the consumer an opportunity to explore the product's operational reliability metrics without unnecessary discard of units. In practice, it will balance the benefits of the producer and consumer and meanwhile, accurately assess the product's reliability metrics under flexible testing scenarios. A numerical study verifies the advantages and higher efficiency of the DHDASP over commonly used alternatives in handling heterogeneous products. [ABSTRACT FROM AUTHOR]

Electroencephalogram (EEG)-based Brain–Computer Interfaces (BCIs) build a communication path between human brain and external devices. Among EEG-based BCI paradigms, the most commonly used one is motor imagery (MI). As a hot research topic, MI EEG-based BCI has largely contributed to medical fields and smart home industry. However, because of the low signal-to-noise ratio (SNR) and the non-stationary characteristic of EEG data, it is difficult to correctly classify different types of MI-EEG signals. Recently, the advances in Deep Learning (DL) significantly facilitate the development of MI EEG-based BCIs. In this paper, we provide a systematic survey of DL-based MI-EEG classification methods. Specifically, we first comprehensively discuss several important aspects of DL-based MI-EEG classification, covering input formulations, network architectures, public datasets, etc. Then, we summarize problems in model performance comparison and give guidelines to future studies for fair performance comparison. Next, we fairly evaluate the representative DL-based models using source code released by the authors and meticulously analyse the evaluation results. By performing ablation study on the network architecture, we found that (1) effective feature fusion is indispensable for multi-stream CNN-based models. (2) LSTM should be combined with spatial feature extraction techniques to obtain good classification performance. (3) the use of dropout contributes little to improving the model performance, and that (4) adding fully connected layers to the models significantly increases their parameters but it might not improve their performance. Finally, we raise several open issues in MI-EEG classification and provide possible future research directions. • 67 relevant studies are identified and included primarily through a systematic meta-analysis procedure. • A comprehensive review of Deep Learning-based Motor Imagery EEG classification from various perspectives. • 13 typical models are selected, evaluated and analysed to shed light on the development of this field. • Ablation studies investigate how specific design factors impact common network architectures. • Open issues and potential future directions in this field are discussed. [ABSTRACT FROM AUTHOR]

Targeted therapy has revolutionized cancer treatment, greatly improving patient outcomes and quality of life. Lung cancer, specifically non-small cell lung cancer, is frequently driven by the G12C mutation at the KRAS locus. The development of KRAS inhibitors has been a breakthrough in the field of cancer research, given the crucial role of KRAS mutations in driving tumor growth and progression. However, over half of patients with cancer bypass inhibition show limited response to treatment. The mechanisms underlying tumor cell resistance to this treatment remain poorly understood. To address above gap in knowledge, we conducted a study aimed to elucidate the differences between tumor cells that respond positively to KRAS (G12C) inhibitor therapy and those that do not. Specifically, we analyzed single-cell gene expression profiles from KRAS G12C-mutant tumor cell models (H358, H2122, and SW1573) treated with KRAS G12C (ARS-1620) inhibitor, which contained 4297 cells that continued to proliferate under treatment and 3315 cells that became quiescent. Each cell was represented by the expression levels on 8687 genes. We then designed an innovative machine learning based framework, incorporating seven feature ranking algorithms and four classification algorithms to identify essential genes and establish quantitative rules. Our analysis identified some top-ranked genes, including H2AFZ, CKS1B, TUBA1B, RRM2, and BIRC5, that are known to be associated with the progression of multiple cancers. Above genes were relevant to tumor cell resistance to targeted therapy. This study provides important insights into the molecular mechanisms underlying tumor cell resistance to KRAS inhibitor treatment. • The development of KRAS inhibitors has been a breakthrough in cancer research. • Single-cell gene expression profiles from KRAS G12C-mutant tumor cell models treated with KRAS G12C were deeply analyzed. • Genes were discovered to indicate the differences between tumor cells with positive response and no response. • The current evidences proved the reliability of the top genes. • Decision rules were built to indicate special expression patterns on tumor cells with positive response and no response. [ABSTRACT FROM AUTHOR]

The recovery property of asphalt binders plays an important role in the performance and service life of asphalt pavements. Since the internal stress is the driving force for the recovery of asphalt binders, the accurate measurement of the internal stress is full of significance. Based on this rationale, this paper aims to measure the internal stress of asphalt binders using a creep and step-loading recovery (CSR) test and characterizing the recovery behaviors by the internal stress. One base asphalt binder and one styrene–butadiene–styrene (SBS)-modified binder are selected in this study. The key elements of the CSR test are carefully designed and its accuracy is verified in three aspects, including the loading conditions, the effect of disturbance by step-loads, and accuracy of measured internal stress. Then, a kinetics-based recovery model is proposed to evaluate and predict the recovery properties of asphalt binders from its causal relationship. The constant-rate recovery activation energy indicates a major difference with nondestructive and destructive loading conditions, while the fast-rate recovery activation energy keeps almost constant regardless of the loading conditions. After that, the healing activation energy is calculated by using the kinetics-based recovery model and the results indicate that SBS modified asphalt binder shows better healing abilities than a base binder. [ABSTRACT FROM AUTHOR]

[Display omitted] • Perceived luminance is introduced to evaluate driver's perception in tunnel lighting. • An antagonism effect between luminance intensity and difference is discovered. • Antagonism effect is quantified with reaction speed and perceived luminance. • The correlation between luminance and visual performance is affected by uniformity. • The uniformity should match up the luminance, instead of separation in current codes. The present study aimed to investigate the comprehensive influence of the average luminance L av and luminance longitudinal uniformity U 1 on the diver's visual performance under the stage of driving in the tunnel's interior zone during the experiment. Specifically, we collected reaction time, pupil area change rate, and blink frequency from 40 participants through the indoor visual performance experiments. The present study quantitatively characterized the mechanism of the antagonism between luminance intensity and difference, and established the L av - U 1 curve which succeeds in satisfying the safety threshold of the driver's comprehensive visual performance. The results show that the driver's visual performance deteriorated continuously as the test time increases. Increasing the luminance was beneficial for accelerating the reaction speed, but also caused greater stimulation of light and dark changes. The change ratio of reaction speed and the increased amplitude of perceived luminance increased exponentially with the increase of luminance, resulting in an antagonism effect. The visual performance decreased with the increase of average luminance under low uniformity levels, and increased with the increase of average luminance under high uniformity levels. Furthermore, we found that the luminance longitudinal uniformity should match up the luminance in the interior zone, instead of separation under the current specification. Based on our findings, we propose specific recommendations for the luminance longitudinal uniformity under different average luminance levels. For the interior zone of tunnels with an average luminance of 2.5, 4.5, and 6 cd/m2, the luminance longitudinal uniformity should be increased from 0.6 to 0.67, 0.72, and 0.75, respectively. Current findings demonstrate that the implementation of the proposed U 1 standard in tunnel lighting design can significantly fasten drivers' reaction time, contribute to a decrease in traffic accidents, and thus improve the safety level of tunnel lighting design in the interior zone of tunnel. These results have significant implications for the design of tunnel lighting. [ABSTRACT FROM AUTHOR]

Approximate $k$ Nearest Neighbours (A $k$ NN) search is widely used in domains such as computer vision and machine learning. However, A$k$ NN search in high-dimensional datasets does not scale well on multicore platforms, due to its large memory footprint. Parallel A $k$ NN search using space subdivision for filtering helps reduce the memory footprint, but its loss of precision is unstable. In this paper, we propose a new data filtering method—PCAF—for parallel A$k$ NN search based on principal component analysis. PCAF improves on previous methods, demonstrating sustained, high scalability for a wide range of high-dimensional datasets on both Intel and AMD multicore platforms. Moreover, PCAF maintains highly precise A$k$ NN search results. [ABSTRACT FROM AUTHOR]

Background EEG signals are often contaminated with artefacts, particularly with large signals generated by eye blinks. Deletion of artefact can lose valuable data. Current methods of removing the eye blink component to leave residual EEG, such as blind source component removal, require multichannel recording, are computationally intensive, and can alter the original EEG signal. New method Here we describe a novel single-channel method using a model based on the ballistic physiological components of the eye blink. This removes the blink component, leaving uncontaminated EEG largely unchanged. Processing time allows its use in real-time applications such as neurofeedback training. Results Blink removal had a success rate of over 90% recovered variance of original EEG when removing synthesised eye blink components. Fronto-lateral sites were poorer (∼80%) than most other sites (92–96%), with poor fronto-polar results (67%). Comparisons with existing methods When compared with three popular independent component analysis (ICA) methods, our method was only slightly (1%) better at frontal midline sites but significantly (>20%) better at lateral sites with an overall advantage of ∼10%. Conclusions With few recording channels and real-time processing, our method shows clear advantages over ICA for removing eye blinks. It should be particularly suited for use in portable brain-computer-interfaces and in neurofeedback training. [ABSTRACT FROM AUTHOR]

• The macroscopic mechanical properties of SBFRCC decrease with temperature. • The pore structure is "coarsened" significantly with the increasing temperature. • The index with the highest correlation to uniaxial compressive strength at high temperatures is porosity. • Gel pore directly affects the generation of cracks while large pore affects the propagation of cracks. Steel-basalt hybrid fibers reinforced cementitious composites (SBFRCC) have excellent mechanical properties, and current studies mainly focused on the macroscopic or microscopic properties of SBFRCC at room temperature separately. However, the macroscopic and microscopic properties of SBFRCC subjected to elevated temperatures, especially their correlation relationships are still unclear. Hence, this study aims to investigate the macroscopic and microscopic properties and their correlation relationships of SBFRCC subjected to elevated temperatures. Firest, the mass loss rate, three-point bending, uniaxial compressive, mercury intrusion porosimetry (MIP) and scanning electron microscopy (SEM) tests were conducted subjected to elevated temperatures. Then, the correlations between mass loss rate, fracture, uniaxial compressive behavior and pore structure were analyzed based on a correlation analysis approach. The results show that steel fiber and basalt fiber can effectively restrict crack initiation and propagation subjected to elevated temperatures. Uniaxial compressive strength and elastic modulus decrease with temperature while peak strain increases below 600 °C and drops after 600 °C. With the increase of temperature, the gel pore and transitional pore decrease, the large pores increase, the porosity increases, and the pore structure is "coarsened" significantly. The correlation coefficient between uniaxial compression strength and porosity is the highest, and it is reasonable to use porosity to characterize the changes of uniaxial compressive strength subjected to elevated temperatures. Gel pore has the highest correlation with initiation fracture toughness while large pore has the highest correlation with mass loss rate, unstable fracture toughness, fracture energy, and elastic modulus. Gel pore directly affects the generation of cracks while large pore directly affects the propagation of cracks. [ABSTRACT FROM AUTHOR]

In this study, the flame retardant mechanism of hydrated lime (HL) on asphalt mastics was investigated via a range of analytical techniques, including cone calorimeter test, and thermogravimetry and differential scanning calorimetry analysis. Experimental results indicated that the flame retardant mechanism of HL is the result of comprehensive effects including blocking, cooling and dilution. As compared with magnesium hydroxide (MH), the HL shows a stronger condensed phase flame retardant effect, which leads to a better performance in reducing total heat release rate, CO yield and total smoke release rate. Furthermore, it is noted that in the process of asphalt combustion, the parallel reactions of dehydration and carbonation of HL will occur, wherein the carbonation runs through the first and second stage of asphalt combustion. The generated calcium carbonate promotes the asphalt mastic surface to form a barrier layer, which limits the first and second stage of asphalt combustion and postpones the char combustion to an additional third stage. This leads to that the combustion of asphalt mastic with HL shows typical characteristics of char- or residue-forming materials. [ABSTRACT FROM AUTHOR]

Performance monitoring counters (PMCs) are of great value to monitor the status of processors and their further analysis and modeling. In this paper, we explore a novel problem called PMC integration, i.e., how to combine a group of PMCs which are collected asynchronously together. It is well known that, due to hardware constraints, the number of PMCs that can be measured concurrently is strictly limited. It means we cannot directly acquire all the phenomenon features that are related with the system performance. Clearly, this source raw data shortage is extremely frustrating to PMCs based analysis and modeling tasks, such as PMCs based power estimation. To deal with this problem, we introduce a neighboring interval power values based PMC data integration approach. Based on the activity similarity of easily collected power dissipation values, the proposed approach can automatically combine distinct categories of PMC data together and hence realize the recovery of intact raw PMC data. In addition, the significance and effectiveness of the proposed approach are experimentally verified on a common task, the PMCs based power consumption modeling. [ABSTRACT FROM AUTHOR]

• Increasing fly ash properly can reduce the rate of strength degradation, but will reduce the compressive strength and increase the explosive spalling rates. • Hybrid fibers can significantly improve the explosive spalling behavior, flexural and splitting tensile strength. • The microstructures become loose and the porosities increase after exposure to high temperatures. The excellent performance of steel and basalt fiber at room and high temperatures makes steel-basalt hybrid fibers reinforced cementitious composites (SBFRCC) a promising solution for engineering structures with fire risk. In this study, the morphology, explosive spalling behavior, flexural, compressive and splitting tensile strength, and uniaxial tensile properties of SBFRCC with different substitution rates of fly ash after exposure to high temperatures were studied. In addition, the effects of high temperature on the microstructures of SBFRCC were investigated by the scanning electron microscopy (SEM), energy dispersive X-ray (EDX), and X-ray computed tomography (XCT) tests. The results show that the incorporation of steel fiber and basalt fiber can significantly improve the explosive spalling behavior, flexural and splitting tensile strength. After exposure to high temperatures, increasing the substitution rate of fly ash properly can reduce the rate of strength degradation to some extent, but will reduce the compressive strength and increase the explosive spalling rates. The microstructures of SBFRCC become loose and the porosities of SBFRCC increase under the combined effect of the decomposition of hydration products, the melting of fly ash, and the deterioration of steel fiber and basalt fiber. [ABSTRACT FROM AUTHOR]