Search Results (2,163)

The growing volume of construction and demolition waste has made discarded concrete a major source of urban solid waste, placing increasing pressure on land resources and the environment. Recycling waste concrete into recycled aggregate concrete (RAC) offers an effective solution for resource conservation and carbon reduction, aligning with the goals of sustainable development. However, due to the residual mortar, high porosity, and microcracks of recycled aggregates, RAC generally exhibits lower compactness, strength, and durability than conventional concrete, particularly under freeze–thaw conditions where degradation accelerates and service life decreases. To address these challenges, this study investigates the optimization of RAC mix design and its frost resistance performance for pavement base applications. An orthogonal experimental design was employed, with the water-to-binder ratio, recycled aggregate replacement ratio, and air-entraining agent dosage as key variables, while 7-day compressive strength, permeability coefficient, and rebound modulus served as evaluation indices. The influence and interaction of these factors were analyzed to determine an optimal mix meeting both mechanical and durability requirements. Rapid freeze–thaw cycling tests were then conducted to examine the variations in mass loss, relative dynamic modulus, and compressive strength retention, followed by exponential and damage variable modeling to characterize the degradation process. Results show that the water-to-binder ratio primarily governs strength, the replacement ratio affects stiffness and permeability, and the air-entraining agent significantly enhances frost resistance by improving pore structure. The optimized mix retained over 70% of its relative dynamic modulus after 300 freeze–thaw cycles, exhibiting superior durability. This work establishes a systematic framework for multi-factor optimization and durability evaluation of RAC, providing theoretical and practical guidance for its application in cold-region pavement bases. Full article

Hyperspectral unmixing aims to estimate endmember spectra and their corresponding abundance fractions at the subpixel scale, which is a critical preprocessing step for quantitative analysis of hyperspectral remote sensing imagery. While deep learning-based methods have achieved remarkable progress, three fundamental challenges remain: (i) reliance on a single shared spatial prior that cannot decouple the heterogeneous spatial patterns of different land covers; (ii) the lack of synergy in jointly optimizing endmember extraction and abundance estimation; (iii) the poor robustness of unsupervised training to complex mixtures, noise, and class imbalance. To address these issues, we propose a novel unsupervised unmixing framework that integrates adaptive orthogonal multi-faceted graph representation with curriculum learning. Specifically, we design an Adaptive Orthogonal Multi-Faceted Graph Generator (AOMFG) to learn a set of independent orthogonal graph structures, achieving spatially informed decoupling of land cover patterns. Then, a dual-branch collaborative optimization network is constructed: a Graph Convolutional Network (GCN) branch that incorporates the learned spatial topological priors for abundance estimation, and a 1D Convolutional Neural Network (1DCNN) branch that employs a query-attention mechanism to adaptively aggregate pure spectral features for endmember extraction. Finally, we introduce a three-stage curriculum learning strategy that progressively fine-tunes the model, which significantly enhances its performance. Extensive experiments on three widely used real-world benchmark datasets demonstrate that our proposed framework consistently outperforms state-of-the-art methods in both endmember extraction and abundance estimation accuracy. Comprehensive ablation studies, parameter sensitivity analysis, and noise robustness tests further validate the effectiveness of each core component. Full article

Accurately determining digging resistance during bucket–soil interaction is crucial for optimizing excavator working devices and power systems. To address measurement difficulties, a numerical simulation model based on the arbitrary Lagrangian–Eulerian (ALE) method was established and verified through excavation tests. Through orthogonal experiments, the influence of excavation parameters was studied, and the optimal compound digging trajectory was determined. The results show that increasing the excavation angle from 36° to 48° decreases the X-direction resistance and moment by 39.48% and 38.85%, respectively, though specific energy consumption (SE) increases. Additionally, optimizing arm movement speed reduces the X-direction resistance and moment. While ensuring the bucket load factor is suitable, reducing arm speed and a horizontal soil push during compound excavation effectively decreases SE. Finally, the optimal balance of digging resistance and SE can be achieved with a 300 mm bucket hydraulic cylinder displacement, a 1.5 s interval for initial arm and bucket movements, and an arm-to-bucket speed ratio of 5.5 for hydraulic cylinders. Full article

Short-chain fatty acids (SCFAs) are key biomarkers of gut microbiota activity; however, routine quantification in fecal samples relies largely on chromatography, which is instrument-intensive and throughput-limited chromatography techniques. Herein, we present a rapid machine-learning-assisted electroanalysis platform for SCFAs profiling that integrates a disposable three-electrode planar gold chip with voltammetric fingerprinting and artificial neural network (ANN)-based signal decoupling. To generate orthogonal chemical information and improve the discrimination of structurally similar species, a dual pretreatment strategy combining acid-catalyzed esterification and alkaline dissociation was employed prior to electrochemical analyses. Differential pulse voltammetry (DPV) and cyclic voltammetry (CV) were employed to acquire high-dimensional fingerprints, from which current-, potential-, and area-based descriptors were extracted using a cross-information feature strategy. A hierarchical modeling framework improved total SCFAs prediction by incorporating ANN-predicted propionate and butyrate concentrations as auxiliary inputs. While linear calibration was achievable in standard mixtures, direct linear models performed poorly in real fecal matrices due to strong sample-dependent matrix interference. In contrast, the ANN captured nonlinear relationships among multifeature inputs and suppressed matrix effects. Validation against gas chromatography–mass spectrometry in an independent fecal test cohort (n = 30) demonstrated excellent agreement and low prediction errors, with mean absolute error/root mean square error values of 0.063/0.072 mM (propionic acid), 0.029/0.034 mM (butyric acid), and 0.135/0.202 mM (total SCFAs). The DPV/CV acquisition requires only minutes per sample, whereas pretreatment takes 1~3 h depending on the target route but can be performed in parallel for batch processing; thus, overall throughput is determined mainly by batch pretreatment rather than per-sample instrument time. This electrochemical–ANN workflow provides a portable, high-throughput alternative to chromatography for fecal SCFAs profiling in clinical screening and microbiome research. Full article

Postharvest processing plays an important role in improving the quality and storage stability of mature fresh Amomum villosum Lour. (A. villosum). We investigated the effects of seven common drying methods (electric baking drying (EBD), heat pump drying, sun drying after heat pump drying, shade drying, hot air drying, sun drying, and freeze drying) on the volatile components of Amomum villosum. To discriminate different samples and identify key markers, chemometric techniques, including principal component analysis (PCA) and orthogonal partial least squares discriminant analysis (OPLS-DA), were applied to Chromatography–Mass Spectrometer (GC-MS) data of 70 identified metabolites. As an unsupervised method, PCA was first utilized to observe the overall clustering tendency of 21 samples, showing clear dispersion among seven groups with a slight overlap in the samples from sun drying after heat pump drying and hot air drying. To improve discrimination accuracy, the OPLS-DA model was further established as a supervised method. Its reliability was verified by permutation tests and cross-validation, which confirmed the absence of overfitting (R² and Q² intercepts with the vertical axis were <1 and <0, respectively). S-plots combined with variable importance in projection (VIP) values greater than 1 were used to screen differential metabolites, and camphor, borneol, and bornyl acetate were identified as the key discriminant markers for the samples obtained by different drying methods. Consequently, camphor, borneol and bornyl acetate, which are regarded as quality markers of A. villosum, were determined by gas chromatography (GC) to identify the optimal drying method for fresh A. villosum. The results showed that the content of the quality markers in A. villosum obtained by the seven drying methods outclass the standards of the Chinese Pharmacopoeia.Comprehensively considering the experimental results and the convenience and operability of the drying process, EBD is the most suitable drying process of A. villosum for popularization and application. It is on account of the shortest drying time among the seven drying methods, which only took 21.63 h to complete the drying of fresh A. villosum. Besides that, the quality control parameters in the content of bornyl acetate, camphor, borneol and the essential oil of A. villosum obtained by EBD were far more than the standards stipulated in the pharmacopeia. Full article

In this study, a hybrid multi-layer scheme for reversible color image encryption is proposed, ensuring lossless reconstruction and strong cryptographic security concurrently. This method consists of three main stages. First, session-specific keys are generated using HKDF-SHA256 along with a timestamp-based mechanism to prevent replay attacks and support dynamic key management. Second, a four-layer confusion–diffusion structure is applied. It uses Gram–Schmidt orthogonal matrices, integer-based PWLCM chaotic mapping, the Hill cipher, and dynamically created S-Boxes. These operations rely on integer modular arithmetic ${\mathbb{Z}}_{256}$ and Q16.16 fixed-point precision. Finally, ChaCha20 stream encryption with HMAC-SHA256 authentication is used to secure data transmission in distributed environments. Experimental tests conducted on standard images show strong cryptographic performance, including near-ideal entropy (7.9993 bits), a significant avalanche effect ($NPCR99.6\mathrm{\%}$, $UACI33.4\mathrm{\%}$), and very low pixel correlation. The method achieves perfect lossless reconstruction and provides an effective key space $\ge 2¹²⁸$. These results confirm the suitability of the proposed scheme for secure image protection in applications requiring bit-exact recovery, such as medical imaging, digital forensics, and satellite communications. Full article

To address the issues of high cutting temperatures and severe tool wear during titanium alloy machining, this study proposes a hybrid surface modification strategy combining micro-textures and multicomponent titanium nitride (TiN)-based coatings on cemented carbide tools. Using YG8 cemented carbide as the substrate, micro-dimple textures were fabricated by fiber laser, and three coatings with different architectures (TiAlSiN, TiSiN/TiAlN, and TiSiN/TiAlSiN/TiAlN) were deposited via multi-arc ion plating technology. Based on a two-factor (texture diameter and texture spacing) and three-level orthogonal experiment, the evolution behaviors of surface morphology, phase composition, and mechanical properties of the textured multicomponent TiN-based coatings were systematically characterized and comparatively analyzed. The results reveal that: compared to the monolithic-structured TiAlSiN coating, the TiSiN/TiAlSiN/TiAlN and TiSiN/TiAlN composite coatings with multilayered composite structures can effectively relieve the residual stress inside the film–substrate system, and significantly suppress the phenomena of coating cracking and localized spallation caused by irregular protrusions of the recast layer at the micro-texture edges. X-ray diffraction (XRD) and crystallite size analyses indicate that the amorphous Si₃N₄ phase promoted by the Si element in the composite coatings effectively impedes the growth of TiN columnar crystals, achieving significant grain refinement. Mechanical property tests confirm that the existence of multicomponent composite interfaces effectively hinders dislocation movement. Among them, the textured TiSiN/TiAlSiN/TiAlN composite coating exhibits the optimal comprehensive performance; its microhardness, nanohardness, and H/E ratio (characterizing the resistance to plastic deformation) are increased by 17.94%, 8%, and approximately 45%, respectively, compared to those of the textured TiAlSiN coating. This study deeply elucidates the synergistic strengthening and toughening mechanisms between micro-texture parameters and the internal structures of the coatings, providing important theoretical guidance and experimental data support for the surface design of long-lifespan tools oriented towards the high-efficiency machining of titanium alloys. Full article

This study develops a semiparametric causal inference framework to quantify the effect of Artificial Intelligence (AI) adoption on enterprise labor structure under high-dimensional confounding. We employ the Double Machine Learning (DML) estimator proposed , which combines Neyman orthogonality and cross-fitting to achieve reliable causal identification in settings where conventional regression methods are prone to bias from high-dimensional controls and nonlinear confounding. Nuisance functions are estimated using Lasso and Random Forests, enabling flexible modeling of complex relationships between control variables and outcomes. Using an unbalanced panel of Chinese A-share listed companies spanning 2006 to 2023, we identify a significant positive average treatment effect of AI adoption on the share of high-skilled labor (estimate: 0.118; 95% CI: [0.073, 0.163]), indicating that complementarity between AI and skilled workers dominates substitution at the firm level. Heterogeneity analysis reveals that the effect is stronger in manufacturing (0.183) than in services (0.071), and more pronounced in Eastern China (0.142) than in Central and Western regions (0.079). Quantile regression further shows that the complementarity effect intensifies at higher skill quantiles. A Panel Smooth Transition Regression (PSTR) model identifies a digitalization threshold beyond which AI–skill complementarity further strengthens. Mediation analysis confirms that productivity enhancement, digital transformation, and innovation activities together account for the majority of the total effect, with productivity improvement alone contributing approximately 34%. Placebo tests and propensity score weighting validate the robustness of our findings. Full article

In this study, an L₁₆(4³) orthogonal experimental design was employed to optimize the preparation process of rubber-modified asphalt, and a series of rheological tests were conducted using a dynamic shear rheometer to systematically investigate the compatibility mechanisms among the four components: base asphalt and rubber particles. The results indicate that process parameters exert varying degrees of influence on performance. The optimal combination determined was: base bitumen temperature of 170 °C, shear rate of 4000 r/min, and shear time of 40 min, followed by isothermal curing at 170 °C for 60 min. Rheological analysis indicates that resin and asphalt are the key components determining the high-temperature rheological properties of rubber-modified asphalt; notably, L74, which has the highest asphalt content, exhibits excellent high-temperature performance. Grey correlation analysis shows that the correlation coefficient between resin content and creep recovery capacity is 0.82, while the correlation coefficient between asphalt content and resistance to permanent deformation is 0.86. Furthermore, the goodness-of-fit value of the multiple regression model exceeded 0.99, further confirming the reliability of the research results. This study provides a precise characterization of compatibility, thereby offering a theoretical foundation and technical support for material selection and process control in the application of rubber-modified asphalt. Full article

In the mechanized pruning process of apple trees, reasonably matching cutting parameters is the key to reducing energy consumption and improving pruning quality. The conventional empirical parameter configuration usually ignores the vibration suppression effect of the branch support system, resulting in unstable cutting processes and poor cross-section quality. This study systematically investigated the influences of saw blade rotational speed, feed speed, and active support system on the sawing process of apple branches, aiming to obtain optimal operating parameters through a closed-loop research method of “simulation, optimization, and verification”. An explicit dynamic finite element model was established for multi-branch staggered sawing with three saw blades. The influence trends of each factor were analyzed via single-factor tests. A three-factor, three-level orthogonal experiment was designed based on the Box–Behnken method, and a response surface prediction model of sawing force was constructed. Regression analysis showed that the established model was extremely significant (p < 0.01). The order of factors affecting sawing force from primary to secondary was as follows: feed speed > number of support components > saw blade rotational speed. Multi-objective optimization yielded the optimal parameter combination: rotational speed of 2500 r/min, feed speed of 2 km/h, and five support components. A prototype was manufactured according to these parameters, and field verification tests were carried out in orchards. Taking the qualified rate of cross-section quality and the missed-cut rate as evaluation indexes, the qualified rate under optimized parameters reached 95.07%, which was significantly higher than 83.11% under traditional parameters, and the missed-cut rate decreased from 11.27% to 2.63%. Results indicate that the collaborative optimization mode of “medium-high rotational speed, moderate feed speed, and active support” enables the low-vibration and high-quality sawing of apple branches. The combined method of explicit dynamics, response surface methodology, and field verification provides a systematic solution for intelligent parameter configuration of orchard pruning equipment. Full article

Alkali-activated cementitious materials (AACMs) are recognized as promising green building materials and a viable alternative to traditional cement due to their low carbon footprint, high durability, and superior mechanical properties. These materials primarily utilize industrial by-products such as coal gangue, steel slag, and gasification slag. The alkali activation process offers an environmentally friendly pathway for the construction industry. To address the need for the large-scale utilization of bulk solid wastes, this study established a ternary solid waste synergy system comprising coal gangue, steel slag, and gasification slag. The preparation and performance optimization of AACMs based on this system were investigated. An optimal mix proportion was identified through orthogonal experiments, and the influence of various factors on the mechanical properties at different curing ages was analyzed. The results indicate that the fluidity of all AACMs meets the requirements for general backfilling applications. Among the alkali activators, Na₂SO₄ had the smallest effect on fluidity. Under single-activator conditions, sodium silicate (water glass) and sodium hydroxide exerted a greater influence on strength development compared to anhydrous sodium sulfate. For the composite activator system, the significance of parameters affecting compressive strength followed the order: silicate modulus > alkali activator content. The maximum 28-day unconfined compressive strength reached 7.653 MPa with a mix proportion of 55% coal gangue, 45% steel slag, and 5% gasification slag, as well as a silicate modulus of 1.2 and a water glass content of 8%. This represents increases of 540.95% and 299.25% compared to the non-activated group and single-activator groups, respectively. Microstructural analysis revealed that the enhanced integrity and strength of AACMs are attributed to pore-filling by hydration products, predominantly C–S–H and C–A–S–H gels. This study successfully developed high-performance AACMs based on a coal gangue–steel slag–gasification slag ternary system, elucidating the critical regulatory role of silicate modulus in composite activators and the underlying microstructural strengthening mechanisms. The findings provide a theoretical foundation and technical support for the high-value, large-scale utilization of bulk industrial solid wastes in building materials. Full article

Pyrolysis of refuse-derived fuel (RDF) is a promising waste-to-energy route, but its use in higher-value applications remains limited by tar carryover, benzene, toluene, ethylbenzene, and xylenes (BTEX), heteroatom-containing compounds, and pollutant accumulation in recirculated scrubber water. This study evaluated operating windows for RDF pyrolysis coupled with direct wet scrubbing and closed-loop water reuse, with the aim of identifying regimes suitable for different end-use tiers. A Taguchi L27 design of experiments (DOE), i.e., an orthogonal array comprising 27 experimental runs, was applied to evaluate the effects of pyrolysis temperature, residence time, scrubber liquid-to-gas ratio, and scrubber-water temperature, while sequential reuse of the same scrubber-water inventory was evaluated at 5, 10, and 15 cycles. Cleaned-gas pollutants were quantified by compound-resolved gas chromatography–mass spectrometry (GC–MS) after solid-phase adsorption (SPA) sampling, while phenolics and polycyclic aromatic hydrocarbons (PAHs) in scrubber water were determined by extraction followed by GC–MS. Feasibility within each end-use tier was defined as simultaneous satisfaction of tier-specific cleaned-gas thresholds (C_tar, C_BTEX, I_N, and I_S) and the corresponding water-loop hazard limit (I_tox), using literature-informed engineering screening criteria. The results showed that stronger scrubbing reduced gas-phase tar and BTEX burdens, whereas extended water reuse caused systematic accumulation of phenolics and PAHs and increased the composite water-loop hazard index. Boiler-grade operation remained feasible across a broad operating range, with 23 of the 27 tested conditions remaining robust, whereas internal combustion engine combined heat and power (ICE-CHP) feasibility was restricted to a narrow robust regime, and no robust microturbine-grade condition was identified. These findings show that operating windows for RDF pyrolysis must be defined jointly by gas cleanliness and water-loop management constraints. Full article

Vibration test fixtures are widely used to evaluate the dynamic characteristics of structures. However, their performance is often limited by their excessive weight and unintended resonances. Conventional optimization methods, such as genetic algorithms, have been applied to improve fixture design; however, they often require considerable computational effort and are inefficient for problems involving discrete design variables. To address these limitations, this study proposes a rib thickness optimization method based on an orthogonal array. The novelty of the proposed method lies in the introduction of an influence value that simultaneously reflects lightweighting effect and first natural frequency change. The proposed method generates orthogonal arrays for rib-thickness configurations, performs modal analyses, and applies analysis of means based on this influence value to identify ribs with low structural influence for thickness reduction. Its effectiveness was validated through comparison with a genetic algorithm under identical conditions. The results showed that the orthogonal array achieved rib reduction patterns similar to those of the genetic algorithm while requiring only 0.84% of the analyses and 1.14% of the computation time required by the genetic algorithm. These findings demonstrate that the orthogonal array provides an efficient and practical alternative for rib thickness optimization in vibration test fixtures. Full article

Alzheimer’s disease (AD) is characterized by progressive cognitive decline and the accumulation of amyloid-β (Aβ) plaques and tau neurofibrillary tangles. Beyond genetic and proteostatic mechanisms, infection- and dysbiosis-based models of AD have gained renewed attention, including the antimicrobial protection hypothesis, in which Aβ may participate in innate immune defense. Here, we reanalyzed ribosomal depleted (Ribo-Zero) RNA-seq data from dorsolateral prefrontal cortex (DLPFC) samples from the Mount Sinai Brain Bank cohort (GSE53697) to screen for non-human transcripts. Reads underwent quality control and adapter trimming, taxonomic classification with Kraken2, abundance re-estimation with Bracken, and differential abundance testing with edgeR. Across 17 samples (9 advanced AD and 8 controls), we detected low-biomass microbial signals, with Acinetobacter radioresistens showing enrichment in the AD group (FDR = 0.018). Several additional taxa showed suggestive group differences but did not remain significant after multiple testing correction, including Lactobacillus iners (FDR = 0.051). We also performed an exploratory in silico analysis of an A. radioresistens biofilm-associated protein homolog, identifying predicted amyloidogenic motifs and surface-exposed regions that may be relevant to cross-seeding hypotheses, although no mechanistic inference can be drawn without experimental validation. Given the technical challenges of inferring microbial signals from post-mortem brain RNA-seq data, including contamination risk, low microbial biomass, and overwhelming host background, these findings should be interpreted as hypothesis-generating and warrant orthogonal validation in larger, microbiome-aware cohorts. Full article

The inversion of wind and wave parameters in high-frequency ocean remote sensing radar relies heavily on the sea echo Doppler power spectrum. However, the accuracy of parameter inversion is often compromised by radio frequency interference (RFI), which distorts the Doppler spectral power distribution. Existing RFI suppression algorithms primarily focus on enhancing the signal-to-interference-plus-noise ratio post-mitigation, while insufficient attention has been paid to the spectral power fluctuations induced by these suppression processes. To address this issue, this study proposes a narrowband RFI suppression scheme that combines inter-pulse phase agility (IPA) with orthogonal projection (OP). An optimized aperiodic sequence is used to modulate the inter-pulse phases of the transmitted waveform, thus uniformly dispersing the sea echo power across the entire Doppler spectrum. Spatial OP is then applied to suppress RFI stripes on the range-Doppler spectrum, a process in which only the sea echo samples masked by the RFI stripes are affected. Finally, phase compensation restores the sea echo coherence and disperses residual RFI power uniformly into the Doppler domain, minimizing its localized impact. Simulations and semi-synthetic tests involving real-world interference verify that the proposed scheme effectively suppresses RFI while alleviating spectral distortion in the sea-echo Doppler spectrum. Full article