Search Results (2,146)

Silage maize (Zea mays L.) serves as a key source of high-quality roughage for ruminants, yet its production and the development of the silage maize industry in Xinjiang are severely constrained by saline–alkali stress. In this study, root growth phenotypes, root energy metabolism, cell membrane stability, osmotic regulatory substances, and reactive oxygen species (ROS) metabolism were examined to elucidate the mechanisms by which iron chlorin e6 (ICe6) enhances saline–alkali tolerance in maize roots. The results showed that saline–alkali stress significantly suppressed root growth in maize seedlings, leading to increased malondialdehyde (MDA) content and relative conductivity. This suggests that membrane lipid peroxidation has intensified, resulting in increased cell membrane permeability. Meanwhile, ICe6 enhanced antioxidant enzyme (SOD, POD, CAT, and APX) activities, scavenged H₂O₂ accumulation, reduced MDA content, and stabilized cell membrane integrity, as indicated by reduced ion leakage. Moreover, ICe6 optimized root respiratory pathways, improved root vigor, and ATP synthesis to provide adequate energy for growth, while decreasing free proline accumulation to maintain cellular osmotic balance. These findings demonstrate that ICe6 mitigates saline–alkali stress in silage maize roots through coordinated regulation of energy metabolism, antioxidant defense, and osmotic adjustment. Full article

The aviation industry is adopting liquid hydrogen (LH₂) for sustainable flight, requiring robust safety systems. This work is an example of adaptation of a Micro-Electro-Mechanical Systems (MEMS)-based structural health monitoring (SHM) system for LH₂ tanks, developed in the H2ELIOS project. It uses a multisensor approach that combines MEMS sensors to monitor vibration and acceleration, shape memory alloy (SMA) strain sensors for measuring tank expansion, and hydrogen leakage sensors to prevent false alarms. This SHM technology detects cracks and delamination of material and coating, enabling predictive maintenance via digital twins and ensuring structural integrity. Full article

Public concrete datasets often contain duplicate records, coupled variables, and cross-source distribution shifts, which may lead to overly optimistic model evaluation. Based on a deduplicated UCI high-performance concrete dataset (1005 samples), this study develops a leakage-controlled data-driven workflow with applicability-domain assessment. TabPFN, SHAP, and NSGA-II are used for compressive strength prediction, model-response attribution, and scenario-based candidate mix screening, respectively. Model evaluation follows a unified data split, inner training-set cross-validation, and an independent test-set protocol. In addition, 502 non-overlapping records from the Mendeley PCC dataset are used as an external benchmark to examine cross-source transferability and sensitivity to distribution shift. The results show that TabPFN achieves the highest R² and the lowest RMSE, MAE, and MAPE on the internal UCI test set, with values of 0.953, 3.744 MPa, 2.265 MPa, and 7.580%, respectively; however, its advantage over strong baselines such as CatBoost is limited. On the external Mendeley PCC dataset, TabPFN remains competitive, with R², RMSE, and MAE values of 0.490, 15.175 MPa, and 11.457 MPa, respectively, but its performance is close to that of random forest, XGBoost, and CatBoost. The 5NN applicability-domain stratification shows that external samples located within the 95% 5NN applicability domain achieve improved performance (R² = 0.634 and RMSE = 12.367 MPa), suggesting that external prediction errors are associated with the distance from the source-domain distribution. SHAP results indicate that cement, ground granulated blast-furnace slag, curing age, and water are the main attribution variables in the model output; their response directions should be interpreted as statistical attributions rather than material causal mechanisms. The Pareto candidate mixes generated by NSGA-II satisfy basic engineering constraints. Nevertheless, because the external benchmark reveals sensitivity to cross-source distribution shift, the resulting mix proportions should be treated as pre-experimental screening candidates rather than engineering-validated low-GWP concrete mix proportions. Full article

Plastic waste presents a significant environmental and public health concern in Malawi, where rapid urban growth, limited waste collection services, and informal disposal practices contribute to persistent plastic waste hotspots. In Lilongwe City, the waste collection rate has been reported ranges from 10% to 30%. This means that out of the 500 to 600 tons of municipal solid waste produced each day, only about 50 to 150 tons are collected daily. These hotspots occur in settings such as drains, markets, settlement edges, riverbanks, and lakeshore environments. They intensify health-relevant exposure pathways by encouraging stagnant water, increasing flood risk, facilitating open burning, and supporting the formation of plastisphere biofilms that can contain pathogenic and antimicrobial resistant organisms. This research synthesises evidence on the main sources of plastic waste in Malawi, the mechanisms of leakage across different environments, and the associated health implications. It uses a scoping approach aligned with PRISMA-ScR guidance and is informed by the UK Research and Innovation (UKRI) funded Sustainable Plastic Attitudes to benefit Communities and their Environments (SPACES project), which highlights the influence of behavioural, governance, and environmental factors on plastic pollution. A two phase, risk-based decision framework to support targeted management of plastic waste hotspots is described. Phase 1 focuses on rapid harm reduction through the identification and ranking of hotspots according to risk severity, spatial extent, and feasibility, guiding timely interventions such as drain clearance, waste capture, and temporary stabilisation. Phase 2 addresses longer term prevention by tackling upstream drivers through policy measures, improved services, reuse and reduction schemes, and community engagement. The framework has been developed using evidence from Malawi; however, its methodology could be applied to other low- and middle-income countries that experience similar constraints and exposure pathways. The framework offers a transparent and practical tool for decision makers seeking to allocate limited resources effectively while reducing environmental and health risks associated with plastic waste. Full article

This study focused on burn-through leakage at girth welds of mechanically lined pipe (MLP) during field service. Field failure analysis, experimental tests, and numerical simulation were combined to investigate the process parameters of seal welding and multi-pass girth butt welding. Macroscopic metallography and energy dispersive spectroscopy (EDS) of failed specimens showed that excessive welding heat input (high current) caused severe expansion of the heat-affected zone (HAZ) and significant element dilution. The results indicated that the HAZ width of the solid-wire girth weld increased markedly from 1.312 mm to 2.247 mm under high-current conditions. Meanwhile, the Fe mass fraction in the root pass sharply increased to 33.66%, while key corrosion-resistant elements such as Cr and Ni were greatly reduced, which directly led to local pitting corrosion and perforation leakage. In addition, a moving heat source model was established in Abaqus 2024 to simulate the multi-pass welding process. The results showed that strong stress concentration developed at the groove root and the interface between the backing steel pipe and corrosion-resistant liner during repeated thermal cycles. The maximum von Mises stress reached 686.56 MPa during the second butt welding pass. After final cooling, the residual hoop tensile stress and axial tensile stress at the center of the inner surface reached 500–550 MPa and 480–510 MPa, respectively. By correlating microscopic compositional evolution with the macroscopic residual stress field, this study revealed the weld failure mechanism of MLP joints. The proposed finite element method can also be used as an efficient tool to predict the effects of welding speed, current, and voltage on residual stress, providing guidance for field welding procedure optimization and pipeline structural integrity assessment. Full article

Employee attrition is a significant organizational challenge associated with substantial financial costs and the erosion of institutional knowledge. This study presents an AI-based Management Information System (MIS) that integrates machine learning (ML) models to forecast employee turnover and support technical interpretability for HR decision-making. Using the IBM HR Analytics Dataset comprising 1480 employee records with 38 features, we implemented a rigorous preprocessing pipeline—including Synthetic Minority Over-sampling Technique (SMOTE) applied exclusively within training folds to prevent data leakage, one-hot encoding, Z-score normalization, and mean-value imputation. Four ML classifiers—Logistic Regression (LR), Random Forest (RF), Multi-Layer Perceptron (MLP), and XGBoost—were evaluated under a stratified 80/20 split with 5-fold cross-validation. XGBoost achieved the highest performance, attaining an accuracy of 87.83%, a ROC-AUC of 0.94, a PR-AUC of 0.96, and an F1-score of 93.04%, attributed to its sequential boosting mechanism and built-in L1/L2 regularization. Beyond predictive performance, the system incorporates SHapley Additive exPlanations (SHAP) to deliver feature-level transparency, enabling HR professionals to engage in proactive, informed retention interventions while retaining full decision-making authority. Within-dataset comparisons confirm that the proposed framework outperforms prior methods evaluated on the same benchmark; cross-study accuracy comparisons are reported as contextual reference only, given differences in datasets and experimental protocols. The system facilitates human oversight by positioning AI as a decision-support collaborator rather than an autonomous replacement in workforce management. Future work will address real-time deployment, controlled user studies with HR practitioners, and validation with actual organizational HR data. Full article

To characterize the distinct expression profiles of microRNAs (miRNAs) in serum and tissue and to delineate the heterogeneity of their regulatory mechanisms in early-stage ovarian cancer (EOC), thereby identifying candidate biomarkers for non-invasive early diagnosis. Differentially expressed miRNAs were identified by integrating publicly available datasets of EOC tissues and serum samples from the Gene Expression Omnibus (GEO) and The Cancer Genome Atlas (TCGA). Core miRNAs were subsequently screened through integrated differential expression analysis, weighted gene co-expression network analysis (WGCNA), and feature importance ranking derived from optimized machine learning models. Protein–protein interaction (PPI) networks and functional enrichment analyses (GO and KEGG) were performed on predicted target genes to systematically compare the functional discrepancies between serum- and tissue-derived miRNAs. No overlapping core miRNAs were observed between the two compartments. Serum miRNAs exhibited an overall up-regulated trend, whereas tissue miRNAs were predominantly down-regulated. Although the regulatory pathways demonstrated significant heterogeneity, they ultimately converged on the cell cycle and the PI3K-Akt signaling pathway, indicating high functional homology. Furthermore, serum miRNAs are not merely passive leakage products from tissues; current evidence suggests they may be selectively packaged into exosomes to participate in tumor regulation. Despite divergent expression profiles, serum and tissue miRNAs share homologous regulatory functions in EOC. These findings suggest that serum miRNAs accurately reflect the core molecular status of tumor tissues, providing a robust molecular foundation for liquid biopsy-based early detection strategies. Full article

Leakage detection in water distribution networks is a core component of smart water management. Addressing the limitations of traditional acoustic detection methods, which heavily rely on manual expertise, and the inadequate feature extraction and low recognition rates for minor leaks of existing deep learning models in complex noise environments, this study proposes a novel hybrid architecture CNN model named Incep-ResNet. The model innovatively integrates multi-scale feature extraction and deep residual learning, incorporating an SE attention mechanism to achieve adaptive recalibration of feature channels. Experimental results demonstrate that the model achieves a leakage identification accuracy of 96.6%, representing improvements of 6.7% and 7% compared to ResNet18 and GoogLeNet, respectively. It exhibits excellent noise resistance and feature extraction capabilities, providing a new technical solution for intelligent leakage detection. Full article

To address the application bottlenecks of organic phase change materials characterized by low thermal conductivity and susceptibility to liquid leakage, this study utilized natural poplar wood as a raw material to construct a three-dimensional carbon/silver heterogeneous porous skeleton via delignification, gradient carbonization, and in situ electroless silver plating. Polyethylene glycol (PEG) was then vacuum-encapsulated within this structure to prepare form-stable composite phase change materials (CPCMs). The regulatory effects of carbonization temperature and metal interface modification on the microscopic morphology and thermophysical properties of the materials were systematically investigated. The results indicate that the skeleton carbonized at 800 °C achieves an optimal balance between pore distribution and skeleton rigidity, ensuring the uniform conformal growth of silver nanoparticles and endowing the material with excellent anti-leakage performance. The thermal conductivity of the optimal sample reaches as high as 0.683 W/(m·K), with the melting latent heat maintained at 133.9 J/g, while also demonstrating an agile and stable photothermal conversion response. Non-equilibrium molecular dynamics (NEMD) simulations further confirm that the silver nanoparticle modification layer smooths the phonon vibration frequency mismatch between the carbon substrate and organic segments, significantly reducing the interfacial thermal resistance. This research provides an important reference for the structural design and microscopic heat transfer mechanism analysis of high-performance phase change energy storage materials. Full article

Matching of multi-run magnetic flux leakage (MFL) in-line inspection data for oil and gas pipelines provides an essential basis for defect evolution analysis, corrosion growth assessment, and integrity management. However, in practical engineering applications, inconsistencies in total measured mileage, differences in the number of key points, and cumulative mileage errors across different inspection runs significantly increase the difficulty of data matching. To address these issues, this study proposes a report-level matching framework for multi-run MFL in-line inspection data that combines key-point alignment with defect matching. The proposed method improves the adaptability of defect matching under complex defect-size and spatial-distribution conditions through a dynamic-threshold mechanism and mitigates the influence of cumulative mileage errors on the matching results in later pipeline sections when large total mileage discrepancies exist between inspection runs through an adaptive anchor-based segmentation mechanism. Experiments based on multi-run MFL in-line inspection data from two actual pipelines demonstrate that the proposed method can achieve stable key-point and defect correspondence in scenarios with both small and large total mileage differences, thereby providing a basis for subsequent defect growth analysis. Full article

Quantum secure direct communication, as an important branch of quantum communication, possesses strict information-theoretic security and can achieve secure communication in channel environments with noise interference and eavesdropping threats. As voice communication is the most fundamental and widespread communication method in daily life, guaranteeing its security and efficiency has become an important research topic in current communication technology. One-way quantum secure direct communication technology can build an efficient and reliable security barrier for voice communication services, effectively preventing the leakage of private information in voice communication. This paper proposes a duplex voice communication scheme based on one-way quantum secure direct communication. By adopting a method combining multi-task parallel processing and stream processing, the communication rate and transmission delay performance of the system are significantly improved. Relying on quantum secure direct communication technology and the one-time-key encryption channel within the system, duplex voice communication is achieved securely. The real-time temperature drift compensation algorithm is introduced to ensure the long-term stable operation of the system. At the same time, through the real-time temperature drift prediction mechanism, the strategy selection during the call process is optimized to ensure the quality of the voice communication. To verify the feasibility and performance of this scheme, a one-way quantum secure direct communication duplex voice communication system was built in the laboratory environment, and comprehensive performance indicator tests were conducted. The test results show that the constructed one-way quantum secure direct communication system can fully meet the performance requirements of duplex voice communication. The realization of this system successfully achieves the goal of secure and efficient quantum voice communication, laying an important technical foundation for further expanding the practical application scenarios of quantum communication technology and promoting the industrialization development of quantum communication. Full article

In user–server authentication environments, persistent server-side verification tables, such as password verifiers, shared authentication records, or per-user secret tables, may become a critical point of failure once leaked. To address this problem in the post-quantum setting, this paper proposes an ML-DSA-specific verification-table-free authenticated key agreement (AKA) scheme based on the NIST-standardized Module-Lattice-Based Digital Signature Algorithm (ML-DSA). The main contribution is a protocol-level use of the signer-recoverable masking vector in ML-DSA as an on-demand reconstruction mechanism for user-related authentication material. This enables the server to reconstruct the required user-related authentication material from its own signature and long-term secret key. This architecture reduces the exposure associated with centralized verification-table leakage, but it should be understood as a storage-relocation tradeoff rather than a storage-free design, because each user must retain the issued signature and the corresponding hash-derived authentication value. By combining the recovered value with identity information through a quantum-resistant one-way hash function, the server can authenticate the user and establish a session key. Its security is analyzed within a Canetti–Krawczyk-style adversarial model and further discussed in the random-oracle setting through a sequence-of-games argument. The analysis supports session-key indistinguishability under the stated freshness and exposure assumptions, while explicitly excluding full forward secrecy under compromise of the server’s long-term ML-DSA secret key. In addition, an operation-level comparison is provided to clarify computational, storage, and communication tradeoffs relative to representative post-quantum AKA schemes. Since the present work does not include implementation-level benchmarking, the performance discussion should be interpreted as analytical rather than empirical validation. The proposed scheme is therefore most suitable for account-login-oriented applications in which reducing centralized verification-table leakage is a primary design objective and where user-side credential storage can be securely managed. Full article

The Vortex Ring State (VRS) poses a catastrophic aerodynamic threat to coaxial dual-rotor unmanned aerial vehicles (UAVs). Traditional reactive detection mechanisms provide insufficient altitude for recovery, while existing data-driven diagnostics are severely bottlenecked by data leakage, extreme class imbalance, and a lack of physical interpretability. To bridge these gaps, this paper proposes a physics-informed multimodal deep learning framework that transitions from post-occurrence detection to proactive early warning. We establish a 1.5 s precursor window—creating a three-class ordinal state space—to provide the flight control system with critical intervention time for differential rotor recovery. We developed a novel ViT-LSTM architecture (MTSF-Net) to fuse continuous seven-channel onboard-recorded data (comprising three-axis acceleration, three-axis angular velocity, and barometric vertical velocity), which are subsequently transformed into Continuous Wavelet Transform (CWT) spectrograms. To ensure real-time unidirectional inference while preserving absolute physical vibration scales across heterogeneous sensors, a Calibrated Benchmark Normalization (CBN) strategy is introduced. Furthermore, a Hybrid Ordinal Loss is proposed to mitigate the extreme sample imbalance (<0.5%) of the precursor state by penalizing asymmetric aerodynamic degradation. Evaluated under a strict sortie-based isolation protocol, the proposed system achieves an exceptional test accuracy of 98.26% and an unprecedented precursor recall of 100%. Notably, it completely eliminates fatal missed detections (VRS predicted as Normal) and false-positive VRS predictions triggered by precursor states. Finally, Gradient-weighted Class Activation Mapping (Grad-CAM) is utilized to verify that the multimodal sensor processing pipeline successfully anchors onto authentic physical vibration frequencies rather than artifactual noise, laying a rigorous, interpretable foundation for intelligent aviation safety systems. Full article

Linearly homomorphic signatures (LHSs) are widely used in scenarios such as network coding and the Internet of Things, but their security faces the serious threat of key leakage. To address this issue, this paper introduces a forward secure mechanism into LHSs, aiming to construct a linearly homomorphic signature (LHS) scheme that can resist the risk of key leakage. By combining the binary tree minimal cover set mechanism with lattice-based extension algorithms, we construct an LHS scheme that supports time-period key updates. We prove its forward secure unforgeability under the standard model (SM) by reducing it to the Short Integer Solution (SIS) problem. To the best of our knowledge, this scheme is the first provably secure lattice-based forward secure linearly homomorphic signature (FSLHS) scheme in the SM, filling a theoretical gap in existing research. Furthermore, we apply this scheme to a smart grid data acquisition system and verify its practicality through concrete performance analysis. Full article

In this study, we propose a hybrid cryptanalytic technique targeting the RSA cryptosystem when instantiated with small private exponents. By integrating the continued fraction approach with Coppersmith’s lattice-based technique, we formulate a novel vulnerability framework. Utilizing an innovative relationship extracted from continued fraction convergents, we deduce an improved upper bound for the secret key: $d<N^{1-\alpha /3-\gamma /2}$. In this context, $\alpha :={log}_{N}e$ and $\gamma :={log}_N|p+q-S|$, where S serves as a known approximation of the prime sum $p+q$. As an extension of our preliminary conference proceedings, this paper supplies comprehensive proofs for all theoretical propositions, performs a comprehensive parameter sensitivity evaluation, and provides bounds for partial prime exposure scenarios. Empirical evaluations confirm the theoretical mechanics of our framework, demonstrating that it offers improved bounds in specific partial leakage scenarios compared to traditional lattice-only baselines. Full article