Search Results (9,754)

Implicit-emotional expressions are common in college students’ social media posts, where literal meanings may contradict underlying emotions. Bidirectional Encoder Representations from Transformers (BERT)-based models face two coupled challenges in this setting: feature confusion between semantic content and expression style, and coarse-grained feature fusion. We propose CSD-IFRN (Content–Style Disentanglement with Conditional Fusion for Implicit Emotion Recognition Network), which disentangles content and style using dual non-shared BERT encoders, combines gradient-reversal adversarial training with a content–style orthogonality regularizer (L_orth), and applies conditional layer normalization (CLN) for adaptive fusion. On a dedicated dataset of 11,154 triple-annotated texts, averaged over five random seeds, CSD-IFRN achieves 88.58% accuracy and 88.43% macro-F1, improving over BERT-base-chinese by 6.99 points and over the strongest SOTA baseline by 2.61 points. The main gains remain significant after Holm correction (p < 0.01) and also hold on a public benchmark. A frozen style probe trained on content features falls to 50.40% balanced accuracy, close to chance level, supporting effective disentanglement. Among fusion strategies, CLN achieves the best accuracy with low seed-to-seed variance. These results suggest that CSD-IFRN can provide an auxiliary signal for university mental-health monitoring, rather than a clinical diagnostic tool. Full article

Existing target sorting methods suffer severe performance degradation or even failure under inherent severe spectrum overlap, homogeneous protocol parameters, and scarce single-source points in Synchronous Non-Orthogonal Frequency Hopping (SNOFH) scenarios. To address this challenge, this paper proposes a passive sorting framework for SNOFH UAV swarm signals based on multi-station relative hopping time difference. The proposed framework constructs a spatial-location-driven sorting feature system, designs a kernel joint distribution adaptation module to eliminate inter-station measurement discrepancies, and develops a multi-scale wavelet-based method to achieve sub-sampling level hopping time extraction, reducing the dependence on prior FH parameters and hardware radio frequency fingerprints. Experimental comparisons between the proposed and reference sorting methods are conducted on a simulated SNOFH dataset to validate the performance of the proposed sorting framework. The experimental results show that the proposed method achieves the highest sorting accuracy of 98%, outperforming adopted baselines in most SNOFH cases. The proposed method exhibits favorable robustness with noise interference, clock-synchronization error, carrier-frequency offset and multipath influence. It is a suitable choice for UAV swarm sorting under regular and slow-varying UAV formations. Full article

The integument of elasmobranchs is a complex biomechanical system in which epithelial, connective tissue and mineralised components form a functionally specialised body surface. Despite extensive research on dermal denticles, the spatial configuration of the whole integument, especially the regional arrangement of collagen fibres in benthic species, remains poorly understood. This study provides an integrated characterisation of the skin microarchitecture of the Arabian carpetshark (Chiloscyllium arabicum), with emphasis on regional variability in dermal denticles and collagen matrix structuring. Skin samples were analysed using haematoxylin and eosin, Masson–Goldner, PAS–Alcian blue and picrosirius red staining, together with transmitted and polarised light microscopy and morphometric analysis. The skin of this species showed a hierarchical organisation comprising a stratified epithelium and a complex fibrous dermis. Dermal denticles displayed typical elasmobranch anatomy, but their morphology, orientation, and density varied regionally, allowing five morphotypes to be distinguished. Collagen fibres formed ordered, plywood-like and orthogonal arrangements showing spatial correspondence with local denticle distribution. These findings indicate regional structural specialisation of the integument and suggest a structural association, and a possible functional relationship, between denticle morphology and collagen architecture. Full article

Urban congestion is difficult to alleviate through road expansion alone, making it necessary to improve existing road-network performance while maintaining public-transport priority. Under transit-priority policies, buses and private vehicles share limited road space, and bus lanes, stops, and lane-changing interactions may either improve or reduce network efficiency. This study treats capacity improvement as a constrained network-performance objective that preserves feasible bus operation and supports higher-occupancy, lower-emission public transport. A simulation-based capacity evaluation framework was developed using the three-dimensional macroscopic fundamental diagram (3D-MFD). A grid network was built in Simulation of Urban Mobility (SUMO), and 16 scenarios were designed by varying four factors: dedicated bus-lane proportion, average bus dwell time, driver lane-changing willingness, and bus-to-private-vehicle ratio. The 3D-MFD was fitted by nonlinear least squares, and range analysis and analysis of variance were used to identify significant factors. Results show that bus-lane proportion and bus-to-private-vehicle ratio dominate capacity variation. A supplementary simulation-based transferability assessment on a Beijing subnetwork further showed that the 4% bus-lane share and 7% bus-to-private-vehicle ratio produced the highest tested capacity response. The findings provide an assumption-bounded basis for screening bus-priority parameters rather than universal design values. Full article

Sodium-ion batteries (SIBs), owing to their cost-effectiveness and outstanding thermal safety, show great promise for energy storage applications, which is essential for improving the comprehensive utilization of renewable energy and advancing sustainable energy development. However, the thermal management technologies for SIBs have failed to attract enough attention for further research. Herein, temperature rise experiments of SIBs were carried out to explore their heat generation and transfer characteristics. The voltage and temperature rise characteristics, internal resistance, and entropy heat coefficient were investigated under various environmental temperatures and charge/discharge rates. Based on these findings, a thermal model was established according to the Bernardi theory. This model accurately describes the thermal behavior during the discharging process, enabling the prediction of heat generation in SIBs. The optimal air-cooled structure and operating condition parameters for the SIB pack were obtained using an orthogonal numerical optimization design. After optimization, the maximum temperature of the single cell is reduced by 7.76 °C, which is followed by a decrease of 21.33%. The average temperature difference of the SIB pack is 0.97 °C, which is reduced by 73.30%. This research is conducive to effectively controlling battery temperature within an optimal range to prevent combustion, explosion, and other thermal runaway events, providing a certain support for thermal management design for SIB packs in practical applications. Full article

Emotional design is increasingly recognised within landscape architecture, particularly in the context of sustainable and user-centred environments; however, systematic and data-driven approaches that translate users’ emotional responses into concrete design parameters remain limited. To address this gap, the aim of this study is to systematically integrate users’ emotional expectations into landscape design by applying Kansei Engineering, using ornamental pools as a case study. A semantic differential survey was conducted with 91 participants, including landscape design students and experts. The experimental stimuli were developed based on a Taguchi L8 orthogonal array, enabling the systematic evaluation of five design factors (depth, interior surface colour, surface planting, form, and motion) across eight configurations. The collected data were analysed using the Taguchi method and Analysis of Variance (ANOVA) to identify optimal design combinations and quantify the relative influence of each factor. The results reveal that surface planting is the dominant factor influencing perceptions such as captivating and legible, while motion plays a key role in shaping mental restoration. The optimal configuration, characterised by shallow depth, light colour, vegetation, natural form, and dynamic water, evoked strong positive responses including captivating, aesthetically pleasing, and satisfying. This study proposes a data-driven framework for linking emotional perception with landscape design variables, contributing to the development of more socially and psychologically sustainable, user-centred, and emotionally responsive landscape environments. Full article

Coal gasification slag (CGS), a massive industrial solid waste, possesses inherent adsorptive potential that remains underutilized due to pore blockage by amorphous siliceous phases. Conventional modification strategies typically rely on energy-intensive high-temperature processes. Herein, we report a facile, low-temperature alkaline activation approach to transform CGS into a high-efficiency adsorbent (denoted NCGS) for Cd²⁺ removal. Sodium hydroxide (NaOH) solution was employed under mild conditions (90 °C) to selectively etch siliceous species, thereby generating a porous architecture and enriching surface oxygen-containing functionalities. Orthogonal experimental design identified optimal synthesis parameters (1 mol/L NaOH, solid–liquid ratio of 1:30 g/mL, 12 h), yielding NCGS with significantly enhanced textural properties. The adsorption isotherm was well described by the Langmuir model, with a maximum capacity of 87.06 mg/g at pH 6.0, while kinetic studies indicated the adsorption process could be described by pseudo-second-order kinetic model. Comprehensive characterization via SEM-EDS, FTIR, and XPS elucidated a multi-mechanistic adsorption pathway mainly involving ion exchange (Na⁺/Cd²⁺) and coordination complexation. Life cycle assessment analysis revealed that NCGS production generates 11.23 kg CO₂ eq emissions, with transportation accounting for 88%. This study presents an energy-saving and environmentally friendly strategy to unlock the adsorptive potential of CGS, providing a highly promising waste-based adsorption material for the remediation of Cd²⁺-contaminated water. Full article

Rotational friction welding offers an adhesive-free approach for producing wood dowel joints, but pull-out performance and process consistency are strongly affected by the welding parameters. This study investigated the effects of the hole-to-dowel diameter ratio, rotational speed, and plunging rate on rotationally friction-welded beech (Fagus sylvatica L.) dowel joints. An L9 orthogonal design was combined with supplementary testing, curve-based validity assessment, post-peak analysis, post-pull-out surface imaging, and independent validation. Range analysis ranked the parameter effects as plunging rate, hole-to-dowel diameter ratio, and rotational speed. Type III analysis of variance confirmed significant effects of the hole-to-dowel diameter ratio and plunging rate, whereas rotational speed was not significant within 1600–2000 rpm. The predicted combination was a ratio of 0.80, 1800 rpm, and 14 mm·s⁻¹. The validation group reached 2567.22 N, 34.96% above T3, but its coefficient of variation of 35.93% showed that considerable variability remained. All joints failed by complete dowel withdrawal; the exposed dowel surfaces indicated mixed interfacial separation, sliding, and localized wood-fiber tearing. Darkened regions occurred at different speed levels, without consistent evidence of extensive burning at 2000 rpm. High-capacity joints also showed more abrupt post-peak degradation, indicating a trade-off between capacity, consistency, and failure suddenness. Full article

Indoor companion, rehabilitation, logistics, laboratory transport, and service robot scenarios require mobile platforms that can follow a human operator safely and flexibly under lighting changes, occlusion, texture-poor corridors, and dynamic pedestrian environments. Vision-, LiDAR-, and UWB-based following systems can provide high perception capability, but their deployment cost, environmental dependence, and sensing complexity remain limiting factors for low-perception-dependence applications. This paper presents a passive following system for a Mecanum-wheel mobile platform based on gimbal posture perception and orthogonal odometry fusion. A rope-tensioned two-axis gimbal is mounted above a 300 mm × 300 mm × 150 mm omnidirectional chassis, and a six-axis inertial sensor installed at the top of the gimbal detects pitch and roll changes induced by user traction. A piecewise posture-to-velocity mapping model with a dead zone, saturation, low-pass filtering, and acceleration limiting converts the user’s traction intention into planar velocity commands in the vehicle coordinate frame. To reduce pose errors caused by Mecanum-wheel slip and discontinuous roller-ground contact, two orthogonal passive odometry wheels and inertial attitude estimation are fused to provide planar position feedback for closed-loop following. A prototype was implemented using an Infineon TRAVEO CYT4BB77 controller, TI DRV8701E motor drivers, six-axis IMUs, magnetic encoders, and an embedded display interface. Experiments evaluated attitude estimation accuracy, planar localization accuracy, passive following performance, gyroscope compensation, and open-loop/closed-loop following. The compensated attitude module achieved a static yaw drift of 0.45 deg/h and a dynamic attitude RMSE below 0.56 deg. Orthogonal odometry fusion produced an average positioning error of 3.8 mm over a 3000 mm linear displacement, reducing error by approximately 84.6% compared with pure Mecanum-wheel drive odometry. In a 5000 mm forward traction task, closed-loop following reduced the average distance error from 38.6 mm to 11.5 mm compared with open-loop attitude mapping. The results indicate that the proposed gimbal-orthogonal odometry architecture provides a compact, intuitive, and environment-robust solution for passive following on omnidirectional mobile platforms. Full article

Conventional wheat planters often suffer from poor uniformity and stability in sowing depth. To address these issues, a new sowing depth adjustment method based on soil coverage regulation was proposed, and the corresponding working parameters were established for a twin-shaft rotary tillage wheat planter. Following the “shallow rotation + deep rotation” twin-shaft plot preparation scheme, the sowing depth adjustment unit’s mechanical structure was designed, and the dynamic adjustment unit of sowing depth and the twin-shaft rotary planter were designed and verified. The experiments primarily involved parameter optimization testing of the sowing depth adjustment unit to determine both its optimal structural configuration and range of operating parameters. Using quadratic regression orthogonal testing, the optimal operational parameters were determined as follows: a secondary rotary tillage knife group rotation speed of 330 r/min, an operational speed of 4 km/h, and a soil coverage adjustment mechanism distance of 610 mm. The highest qualified rate for seeding depth reached 99.246%, while the minimum seeding depth variation coefficient was 6.521%. Field trials indicated that with a secondary rotary tillage knife group rotation speed of 330 r/min, a working speed of 4 km/h, and a compaction roller adjustment distance of 610 mm, the seeding depth qualified rate was 98.803%, and the variation coefficient of seeding depth was 6.881%. This method enables precise regulation of sowing depth in twin-shaft rotary planters, significantly enhancing the quality of wheat sowing operations. Full article

Background: Distinguishing genuine kinase–substrate motifs from background noise is a growing challenge, as mass spectrometry (MS)-based global phosphoproteomics identifies a rapidly expanding set of phosphorylation sites. One of the major limitations is selecting an appropriate background model that systematically controls both technical and biological sources of bias. Although using the entire proteome as a background in a FASTA format considers the overall amino acid composition, it is still prone to biases from protein abundance and the uneven distribution of sequence space (particularly around low-abundance proteins). By contrast, internal background methods can control experiment-specific detection biases, but they may not fully capture residue-specific compositions or general trends in phosphorylation. Methods: I develop a Dual-Background Enrichment (DBE) framework with a position-specific enrichment (PSE) strategy, which involves analyzing motif enrichment against two distinct background models: (1) A residue-heterogeneous internal background composed of phospho-motifs centered on the residue; e.g., phosphoserine (pS) motifs are tested relative to the pool of all detected phosphothreonine (pT) and phosphotyrosine (pY) motifs from the same experiment. (2) A FASTA background that includes all S, T, and Y residues in the UniProtKB proteome sequences. Results: Motifs are classified as high confidence if they meet statistical significance (q ≤ 0.05, fold enrichment > 1.5) against both background models. Conclusion: By applying the DBE strategy to a large-scale phosphoproteomics dataset, we distinguish motifs driven by amino acid composition (enriched in FASTA background only) from those reflecting kinase substrate specificity (enriched in both backgrounds). This dual-reference approach reduces false positives arising from sequence composition bias and enriches high-confidence candidate kinase recognition motifs. Full article

Circulating tumor DNA fraction (ctFraction) has emerged as an important biomarker for assessing tumor burden and monitoring treatment response in patients with cancer. In this study, we compared ctFraction estimates generated by ichorCNA, Fragle low-pass whole-genome sequencing (Fragle LP-WGS), Fragle off-target, and OTTER, a proprietary algorithm from Tempus AI. Plasma samples from 33 patients with advanced solid tumors were analyzed using a ctDNA assay targeting 150 cancer-associated genes, and ctFraction estimates generated by the different methods were compared. Fragle off-target demonstrated the highest concordance with Fragle LP-WGS (rho = 0.903), followed by OTTER (rho = 0.698) and ichorCNA (rho = 0.696), while OTTER and ichorCNA showed strong agreement (rho = 0.826). Mean VAF (mVAF) significantly correlated with all ctFraction estimates, with the strongest association observed for ichorCNA (rho = 0.910), followed by OTTER (rho = 0.865), Fragle LP-WGS (rho = 0.680), and Fragle off-target (rho = 0.658). Longitudinal analysis of 20 patients at baseline and after two cycles of treatment demonstrated strong correlations between changes in ctFraction (ΔctFraction) and mean ΔVAF for both ichorCNA and Fragle off-target (r = 0.955 and r = 0.906, respectively). Overall, these findings demonstrate that ctFraction estimates derived from copy-number- and fragmentomic-based approaches show strong concordance across advanced solid tumors and significantly correlate with mVAF, a commonly used measure of ctDNA abundance. Fragle off-target, in particular, provides an efficient strategy for ctFraction estimation directly from existing targeted sequencing data, eliminating the need for additional sequencing. Larger prospective studies are warranted to further evaluate Fragle off-target clinical utility for treatment monitoring and outcome prediction. Full article

To address the complex factors affecting the productivity of fractured horizontal wells in tight condensate gas reservoirs, as well as the high computational costs and opaque mechanism interpretation associated with traditional numerical simulations, this study proposes and implements a quantitative evaluation method for the main productivity-controlling factors. This method integrates a machine learning surrogate model with the Shapley additive explanations (SHAP) interpretability framework. First, based on 3D geological modeling and fracture propagation simulation, a high-dimensional parameter set encompassing reservoir geology, artificial fractures, and fluid properties was constructed. Subsequently, representative samples were generated through an orthogonal experimental design. On this basis, machine learning algorithms, including Support Vector Machines (SVM), Random Forests (RF), and eXtreme Gradient Boosting (XGBoost), were utilized to construct low-cost, high-precision surrogate models targeting initial productivity and Estimated Ultimate Recovery (EUR). These surrogate models effectively substituted the computationally expensive fully coupled numerical simulations. Furthermore, SHAP values were applied to the trained surrogate models to conduct both global and local interpretability analyses. This approach not only quantifies the magnitude and direction of each input parameter’s contribution to the productivity predictions, but also reveals their non-linear mechanisms and interaction effects. The results indicate that reservoir properties and gas saturation are the fundamental factors determining the productivity of fractured horizontal wells, while fracture conductivity and fracture half-length are the key engineering factors. Furthermore, there exist significant synergistic or antagonistic effects between the geological and engineering parameters. The integrated “parametric modeling–surrogate model construction—SHAP interpretability analysis” workflow established in this study provides a highly efficient, transparent, and physically insightful novel approach for the rapid optimization of fracturing designs and the mechanistic analysis of main productivity-controlling factors in tight condensate gas reservoirs. Full article

A rapid prediction framework for multi-physics field distributions in coal mine airways of variable lengths is presented. The framework integrates a Computational Fluid Dynamics model, a Proper Orthogonal Decomposition model, and machine learning techniques. The study first obtains multi-physics field distributions of temperature, velocity, species mass fraction, etc., in mining airways using CFD simulations under various operating parameters. It then constructs a POD model to decompose the high-dimensional raw snapshot data into mean field and pulsation field components, performing singular value decomposition on the pulsation field to obtain POD spatial modes and corresponding POD coefficients. Machine learning algorithms, including GA-BPNN and Bayes-XGBoost, are employed to construct predictive models of the POD coefficients. The results show that after fitting the relationship between operating parameters and POD coefficients, the multi-physics field distribution within the training parameter range can be rapidly predicted. When the cumulative energy contribution of POD modes exceeds 0.99 of the total energy, the Bayes-XGBoost model achieves minimum R² values of 0.9448, 0.9999, and 0.9996 for velocity, temperature, and oxygen mass fraction predictions, respectively. This work provides a practical engineering solution for real-time prediction of multi-physical fields in variable-length mine airways, and achieves fast and accurate prediction within the training parameter range. Full article

Background/Objectives: Pancreatic ductal adenocarcinoma (PDAC) is a highly aggressive malignancy characterized by limited treatment options and poor prognosis. Gemcitabine is a commonly used chemotherapy; however, gemcitabine resistance in PDAC poses a critical barrier to effective treatment, as the underlying mechanisms are not yet fully understood. Methods: This study employs an exploratory untargeted metabolomics approach to investigate metabolic differences in PDAC cells in the presence and absence of gemcitabine treatment. HPAF-II, MIA PaCa-2, and BxPC-3 cell lines were used as models for gemcitabine-resistant, moderately responsive, and permissive PDAC cells, respectively. Results: MTT assay results revealed that BxPC-3 cells are highly sensitive to gemcitabine treatment, HPAF-II cells are the most resistant, and MIA PaCa-2 cells exhibit moderate sensitivity. Orthogonal Partial Least Squares Discriminant Analysis (OPLS-DA) of the metabolomics data demonstrated clear differentiation of gemcitabine-treated and untreated (control) cells. When comparing the treated vs. control conditions, 170 metabolites matched to an in-house library of standards were significant (p < 0.05 or fold change ≥ 2 or VIP ≥ 1) differentiators in HPAF-II cells, whereas MIA PaCa-2 and BxPC-3 cells had 178 and 218 differentiating metabolites, respectively. HPAF-II cells treated with gemcitabine had significantly higher levels of N-acetylneuraminic acid and 7-dehydrocholesterol compared with the control group. In contrast, these metabolites were significantly lower or non-significant in BxPC-3 treated cells. Pathway analysis revealed that the steroid biosynthesis pathway was significantly perturbed in HPAF-II cells, whereas amino sugar and nucleotide sugar metabolism was predominantly altered in BxPC-3 cells. Conclusions: Overall, this exploratory study reveals metabolic differences between treated and untreated cells to derive targeted therapeutic strategies that could be used in the future to improve treatment outcomes for PDAC patients. Full article