Search Results (2,156)

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

Inertial measurement units (IMUs) in low-cost navigation systems suffer from significant drift and noise errors due to sensor biases, scale factor instability, and nonlinear stochastic noise. This paper proposes a hybrid error compensation approach that combines Fast Orthogonal Search (FOS), a nonlinear system identification technique, with deep Long Short-Term Memory (LSTM) neural networks to improve IMU signal accuracy in GNSS-denied navigation. The FOS algorithm efficiently models deterministic error patterns (such as bias drift and scale factor errors) using a small training dataset, while the LSTM learns the IMU’s complex time-dependent error dynamics from much longer training data. In the proposed method, FOS is first used to predict the output of a high-end IMU based on that of a low-end IMU, and the trained FOS model is then used to extend the training data for an LSTM-based predictor. We demonstrate the efficacy of this FOS–LSTM hybrid on real vehicular IMU data by training with a limited segment of high-precision reference measurements and testing on extended operation periods. The hybrid model achieves high predictive accuracy for predicting the high-end signal based on the low-end signal, with a mean squared error below 0.1% and yields more stable velocity estimates than models using FOS or LSTM alone. Although long-term position drift is not fully eliminated, the proposed method significantly reduces short-term uncertainty in the inertial solution. These results highlight a promising synergy between model-based system identification and data-driven learning for sensor error calibration in navigation systems. Key contributions include FOS-based pseudo-label bootstrapping for data-efficient LSTM training and a navigation-level evaluation illustrating how signal correction impacts dead reckoning drift. Full article

In order to reduce the environmental pollution caused by waste white mud from the papermaking process, this paper proposes a new method of preparing lightweight concrete using waste white mud and shale ceramsite, aiming to provide a new approach for the recycling of papermaking waste. The main objective of this study is to investigate the feasibility of utilizing paper-making white clay as a cement replacement in lightweight concrete and to systematically evaluate the influence of key parameters, such as white clay content, on its fundamental mechanical properties. Based on lightweight ceramsite concrete, paper-making white clay was used to replace cement in preparing white clay lightweight concrete. Through orthogonal tests, mix proportion design and optimization were carried out, and the effects of factors like water–binder ratio and white clay content on the compressive strength, splitting tensile strength, and early-age cracking resistance of the concrete were studied. The results show that with the increase in white clay content, the cube compressive strength of concrete first increases and then decreases. When the white clay content is 5%, the splitting tensile strength of the concrete is the highest at all ages, and when the white clay content is 15%, the internal structural compactness of the concrete is optimal. Full article

In response to the shortcomings still observed in polyethylene (PE)/ethylene-vinyl acetate (EVA)/styrene-butadiene-styrene (SBS) composite modified bitumen regarding storage stratification and low-temperature performance, this paper further introduces furfural extract, elemental sulphur, stabilisers and Z-6036 into this ternary system, and employs orthogonal design to screen the additive ratios. Tests were conducted on conventional physical properties, rotational viscosity, dynamic shear rheology and bending beam rheology, focusing on the material’s temperature sensitivity, rheological behaviour, low-temperature creep resistance and phase characteristics. The modification effects were analysed using fluorescence microscopy, scanning electron microscopy and infrared spectroscopy. Compared with the control group composed of 4% PE, 4% EVA and 2% SBS, the samples obtained from the orthogonal design showed an increase in elongation at 5 °C ranging from 52.5% to 213.9%; the difference in softening points decreased from 35.2 °C to a minimum of 0.1 °C, indicating improved storage stability. The temperature sensitivity of all sample groups was reduced, with the optimal group achieving a VTS of −0.4413, representing a 46.7% improvement over the control group. At −12 °C, the m-values of all nine orthogonal samples were higher than those of the control group, with seven groups reaching m ≥ 0.3, indicating improved low-temperature stress relaxation capability. A comprehensive analysis of the experimental results indicates that the selected chemical additives are beneficial for optimising the dispersion state and compatibility of the SBS/PE/EVA ternary modified bitumen, whilst also balancing rheological properties and low-temperature crack resistance to a certain extent. Microscopic and spectroscopic analyses further suggest that internal interactions within the system have been enhanced and the phase distribution has become more uniform; however, the current evidence is insufficient to conclusively determine that a specific form of chemical cross-linking reaction has occurred. Full article

Background: Comprehensive genomic profiling (CGP) is increasingly used in precision oncology to identify actionable genomic alterations and guide targeted therapies in solid tumors. However, the clinical implementation of CGP assays requires rigorous analytical validation to ensure accurate and reproducible detection of diverse genomic alterations across heterogeneous tumor samples. Despite rapid advancements in next-generation sequencing technologies, there remains a need for validated CGP platforms that demonstrate reliable performance and readiness for routine clinical use. Methods: This study evaluated the analytical and clinical performance of a CGP assay capable of detecting multiple genomic alteration types, including single nucleotide variants (SNVs), insertions/deletions (Indels), copy number variations (CNVs), gene fusions, and tumor mutational burden (TMB). Validation was conducted using patient-derived 117 FFPE tumor samples, external proficiency testing materials, and reference standards. Assay performance was assessed through comparison with orthogonal methods and through evaluation of reproducibility, limit of detection, and TMB concordance. Results: The assay demonstrated excellent analytical performance, achieving 100% sensitivity, specificity, and accuracy for variant detection across evaluated samples. Strong concordance was observed for TMB estimation (R² = 0.9925), with consistent classification of TMB-high cases. The assay showed robust inter- and intra-run reproducibility and reliable detection of low-frequency variants. Limit-of-detection (LOD) analysis confirmed accurate SNV detection at approximately 1% variant allele frequency and reliable RNA fusion detection at low input levels. Conclusions: The validated CGP assay provides accurate, reproducible, and comprehensive detection of clinically relevant genomic alterations in solid tumors. These results support its suitability for routine clinical deployment, enabling reliable genomic profiling to inform precision oncology treatment decisions. Full article

In the fabrication process of pipelines for petrochemical and other industries, weld reinforcement is often excessive and adversely affects subsequent processes such as anticorrosion treatment and surface coating. Weld reinforcement must be removed through a grinding process. Welding deformation and fit-up errors often lead to highly irregular weld geometries, which makes robotic grinding difficult and causes the task to still heavily rely on manual operation. To address this issue, this study proposes an automatic weld recognition and grinding trajectory planning method based on 3D visualization and deep learning. A weld recognition network, termed WSR-Net, has been developed based on an improved PointNet++ architecture with a cross-attention mechanism, achieving a segmentation accuracy of 98.87% and a mean intersection over union of 90.71% on the test set. An intrinsic shape signature (ISS) key point selection algorithm with orthogonal slicing-based pruning optimization is developed to robustly extract key weld ridge points that characterize the weld trend on rugged weld surfaces. According to the height differences between the weld and the adjacent base metal surfaces, the grinding reference surface is fitted using the weld contour through the moving least-squares method. The ridge line points are projected onto the grinding reference surface along the local normal to generate the expected grinding trajectory points. The grinding trajectory that meets the process constraints is generated through reverse layer slicing. Grinding experiments demonstrate that the proposed WSR-Net achieves robust segmentation performance for both planar and curved surface welds. With the reverse layered trajectory planning method, the proposed method enables high-precision automatic grinding of complex spatially curved surface welds. The results show that the final grinding mean error is 0.316 mm, which satisfies the preprocessing requirements for subsequent processes. The proposed method provides a feasible technical method for the intelligent grinding of spatially curved surface welds. Full article

Glass artworks represent a significant component of cultural heritage, yet their surfaces are highly vulnerable to physicochemical weathering resulting from composition-dependent interactions with environmental factors. Understanding the complex and nonlinear relationships between glass composition and deterioration remains challenging using conventional, often invasive, analytical techniques. To address this issue, this study proposes an interpretable and non-destructive computational framework to analyze weathering patterns in historical glass based on oxide composition data. The framework combines statistical hypothesis testing (Chi-squared analysis), metric-based machine learning (Prototypical Networks), probabilistic modeling (Gaussian Mixture Models), multivariate statistical analysis (orthogonal partial least squares discriminant analysis), and information-theoretic methods (mutual information analysis) to identify key compositional features and inter-elemental relationships associated with surface degradation. The results show that lead-barium glass exhibits a higher susceptibility to weathering compared with high-potassium glass, with PbO, BaO, and SiO₂ identified as the most discriminative components. The Prototypical Network achieved 100% accuracy on most specific data partitions within the analyzed dataset, demonstrating its effectiveness in small-sample compositional classification. Meanwhile, mutual information network analysis revealed the complex interrelationships among chemical components involved in surface weathering behavior. These findings indicate that interpretable machine learning and statistical modeling can provide meaningful insights into composition-dependent patterns and support reproducible analysis for the sustainable conservation of cultural heritage glass. Full article

CRISPR–Cas9 has progressed from an experimental tool to a therapeutic modality, marked by the first regulatory approvals of an ex vivo-edited autologous CD34+ hematopoietic stem cell product that induces fetal hemoglobin (CASGEVY/exa-cel). In this narrative review, we synthesize modality-specific molecular diagnostic strategies used across early CRISPR clinical translation. In parallel, early clinical experience has begun to demonstrate the feasibility of in vivo editing, including subretinal delivery for CEP290-associated inherited retinal degeneration (EDIT-101 programme) and hepatocyte-targeted lipid nanoparticles (LNPs) for liver-derived targets such as transthyretin and plasma prekallikrein (KLKB1). As translation expands across hematologic, metabolic, ocular and oncology indications, development is increasingly constrained by the predictability and safety of editing outcomes, delivery-determined biodistribution and exposure time, and immune recognition of bacterial Cas9 orthologs and delivery components. We summarize diagnostic readouts for confirming patient genotype, quantifying on-target editing and expression changes, assessing off-target and structural outcomes using orthogonal assays, and monitoring clonal dynamics and immune responses during long-term follow-up. We also discuss how these readouts interface with CMC controls and regulatory expectations for advanced therapy medicinal products (ATMPs), highlighting the need for fit-for-purpose, standardized testing frameworks in early trials. Full article