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• All dispersants considered acted in synergy with resins in peptizing asphaltenes. • Ionic liquid yielded the best dispersing power and the highest self-association. • Significant electron-withdrawing sites were main contributors to dispersing power. • Steric hinderance associated with molecular structure affected dispersing behavior. In confronting the global climate change, reusing waste asphalt materials stands out as a key strategy for the transportation sector to preserve natural resources and reduce the carbon footprint. Recycling additives or rejuvenators are usually required in this practice, especially for boosting the recycling rate. Given the imperative need for a general compositional design principle of rejuvenators, we previously proposed an innovative formulation concept by combining softeners with asphaltene dispersants, and reported that the use of dispersants largely reduced the total quantity of rejuvenators needed and thus would minimize the environmental impacts. The present study is aimed for probing the molecular mechanisms underlying this observation, with additional interest focused on the synergistic effect of the dispersants with resins and also on the impacts due to their self-association. Selection of representative materials consisted of three types of dispersants including an ionic liquid, a phenolic amphiphile, and an ester plasticizer, in combination with an aromatic oil as the softener. Long-time molecular dynamics (MD) simulation was employed to inspect the aggregation of asphaltenes. Quantum chemical (QC) computation was performed to provide insights into the source of different dispersing behaviors. The results indicated that the three dispersants all acted in concert with resins in dispersing asphaltenes. The dispersion characteristics of asphaltenes could be viewed as a net result of the two competing mechanisms of the dispersing capability and self-association of the dispersant used. The ionic liquid exhibited much higher propensity to self-association at increasing concentrations than the phenolic dispersant, and yet the optimum performance was achieved for both when they were added at low dosages. The ester plasticizer showed minimal self-association and yielded improved dispersion with increasing concentrations. In all cases, the dispersing functionality mainly originated from the presence or formation of significant electron-withdrawing sites, collectively due to the highly electronegative cation and anion, hydrogen bonding, and polar groups. Comparison of the dispersing power was also conducted from different perspectives. The findings are expected to provide molecular insights for formulating versatile and more environmentally benign asphalt rejuvenators. [ABSTRACT FROM AUTHOR]

• Semi-analytic methods are used to solve 3D DPL problems. • Compared with FEM, the semi-analytic method has higher computational efficiency. • The phase lag of heat flow and the phase lag of temperature gradient have influence on the temperature response speed and the formation of temperature field. With the development of picosecond laser technology, the dual-phase-lagging (DPL) model is more suitable to describe the heat transfer problem with micro-scale effect and micro-time effect. In this paper, a three-dimensional dual-phase-lagging heat transfer model for picosecond laser processing is established. Then Fourier transform (FT) and Laplace transform (LT) are used to derive the frequency response functions (FRFs) of the problem in the frequency domain, and the inverse Laplace transform (ILT) and discrete-convolution fast Fourier transform (DC-FFT) algorithm are used to solve the temperature field. The calculation accuracy of this method is verified and the effect of phase lag constants on temperature field is studied. The results show that the semi-analytical method has a high accuracy for solving the dual-phase-lagging heat conduction model, and the phase lag of heat flux will affect the heating rate in the heating stage, and the phase lag of temperature gradient will delay the formation of temperature field. The effect of phase lag constants on temperature-time history is investigated and the related phenomena are explained by using the two-step heat transfer theory. This semi-analytical method provides a new approach to solve the problem of three-dimensional dual-phase-lagging heat transfer. [ABSTRACT FROM AUTHOR]

[Display omitted] • CB/CNT hybrids and elastic TPU are combined to assemble resistive strain sensor. • The hierarchical conductive network endows the sensor with desirable sensing capabilities. • CB/CNT@TPU strain sensor shows great application in personalized health monitoring. • Multiple sensors are integrated for robotic arm control by capturing human motions. Flexible strain sensors are strongly demanded in fields of personal healthcare monitoring and human–machine interface, as they can accurately perceive external action to produce electrical output signals. Aiming at the existing problems, such as the balanced sensitivity and sensing range, durability as well as expanded application, this paper proposes a flexible strain sensor prepared based on electrospun thermoplastic polyurethane (TPU) membrane, followed by anchoring mixed conductive materials (carbon black (CB)/carbon nanotubes (CNT)) on its surface via an effective ultrasonic-assisted method. The resulting CB/CNT@TPU strain sensor delivers a desirable integration of sensing performances: good sensitivity, wide work range, fast response speed (<150 ms) and reliable cyclic stability (5000 cycles). Ascribed to the sensing behavior and additional breathability, strain sensor can be applied well to monitor human health, including human joints movements (fingers and wrists) and physiological signals (pulse and laryngeal vibration recognition). Finally, a multiaxial robotic arm control system is constructed, which provides a feasible strategy for intelligent robotic arms. [ABSTRACT FROM AUTHOR]

• A novel 3D anisotropic random porous structure based on anisotropic Voronoi diagrams with high strength and high stiffness. • The parallel 3D anisotropic Voronoi diagram algorithm which eliminates the need for iterative calculations and is applicable to complex 3D shapes, including intricate concave shapes and structures with non-zero genus. • The stress driven strategy in which the distribution of Voronoi cells is related with the spatial distribution of stress magnitudes, while the cell orientations are controlled by the direction of stress tensor. Porous structures exhibit excellent mechanical properties and are widely utilized in engineering design. However, there is still substantial potential for enhancing the mechanical performance of these structure. By customizing the anisotropic design of each pore in the porous structure, the strength and stiffness of the structure can be significantly enhanced. In this work, we present a novel method for designing 3D anisotropic Voronoi porous structures (AVPSs). To construct these structures, we propose a novel algorithm for generating 3D anisotropic Voronoi diagrams (AVDs) based on Riemannian distance. The geometries of Voronoi cells are controlled to conform with both the stress distribution and the principal stress directions. The distribution of Voronoi cells are graded using a stress-based site sampling strategy, the orientation of Voronoi cells are modified by introducing stress-mapped Riemann tensor fields. The AVPSs are then modeled by computing implicit surfaces based on AVDs. The AVPSs conform closely to the geometric profile of the input model by clipping AVDs using the tetrahedral mesh. Furthermore, the results obtained from numerical simulations and experimental tests indicate that the AVPSs possess both high strength and high stiffness. [ABSTRACT FROM AUTHOR]

• We projected shifts in ecological supply and consumption patterns among scenarios. • Over one-third of Nepal's districts are at risk of ecological overload by 2030. • Climate change sensitivity and increasing human demands challenge overloaded areas. • Multiple strategies were proposed for Nepal's ecoresource management. As global demand for ecological resources continues to surpass ecosystem capacity, predicting future changes in ecological supply, consumption, and carrying capacity is crucial for informed decision-making regarding sustainable land use and ecological security. This study forecasts ecological carrying states in Nepal, a representative region in the southern Himalayan foothills, from 2020 to 2030. We employ three alternative scenarios from the Shared Socioeconomic Pathway and Representative Concentration Pathway (SSP-RCP): SSP2-RCP4.5 (BAU), SSP1-RCP2.6 (TSS) and SSP5-RCP8.5 (SSS). Our findings indicate a continuous rise in ecological consumption, particularly in densely populated tropical areas such as the hilly region (HR) and Terai region (TR). Under the BAU and SSS scenarios, ecological supply decreases rapidly in these regions. Alarmingly, over one-third of Nepal's districts, mainly in central and eastern HR and TR, are projected to experience overloaded ecological carrying states by 2030. Climate change sensitivity and escalating consumption due to improved living standards and population growth pose significant challenges for these regions to reverse ecological deficits. This study offers a scientific foundation for reconciling the conflict between ecological protection and societal demands, facilitating the preservation, restoration, and sustainable use of terrestrial ecosystems while striving to achieve the Sustainable Development Goals by 2030. [ABSTRACT FROM AUTHOR]

The government service platform, exemplified by the government hotline, has to handle extensive volumes of business documents that contain rich and timely public opinion information and citizens' demands. However, manual processing struggles to process large-scale text data, adversely impacting operating costs and the quality of government services. This study proposes a comprehensive system for business document classification of the government hotline (BDCGHS) in China to address these challenges. BDCGHS leverages information entropy fused with term frequency-inverse document frequency (TF-IDF) weight to mine new words from business documents of the government hotline, and store them in a new word repository. These new words optimize Chinese word segmentation and text representation for text classification. We introduce a novel data structure called nested balanced binary tree to expedite new word mining, yielding a computational speed of almost five times than the Trie trees. Comparative experiments on the THUNews and government hotline datasets validate our proposed improvement BDCGHS algorithm's superior performance 3 % over text classification algorithms. Compared to the latest bidirectional encoder representations from the transformers (BERT) model, BDCGHS enhances the accuracy of order dispatch based on business documents by almost 3 %. It has also demonstrated stable operations in two Chinese cities for over a year, yielding favorable results. [Display omitted] • An embedded balanced binary tree structure is proposed for new word discovery. • A new word database is constructed for the government hotline. • The effects of mainstream classification algorithms are compared based on a new word database. • An intelligent text classification system for the government hotline is constructed and the results have promising results. [ABSTRACT FROM AUTHOR]

Gas chromatography-mass spectrometry (GC-MS) was used to analyze the soluble portions (SPs) of eight lignites which were obtained by extracting using cyclohexane, methanol and ethanol, respectively, as the solvent. For thermal dissolution at 300 °C using cyclohexane, the intermolecular interactions between mobile phase and macromolecules in coal were weakened. Consequently, non- and low-polar compounds such as alkanes and aromatics were well dissolved. Methanol and ethanol were more likely to closely interact with oxygen-containing functional groups and promoted the breakage of hydrogen bonds and weak covalent bonds. Hierarchical cluster analysis classified all the 24 SPs, and the SPs from cyclohexane and ethanol solvents exhibited more similarity. Partial least squares discriminant analysis was established to reproduce the classification results of SPs entirely and accurately. The SPs were further classified by combining a small number of geochemical characteristic compounds rather than massive GC-MS data. • Three low-rank coals retain the biological properties of numerous aquatic plants. • Data mining provides convenient and accurate classification for coal samples. • PLS-DA classification is determined by differential geochemical characteristic compounds. [ABSTRACT FROM AUTHOR]