Search Results (800)

Additive manufacturing (AM) using fused filament fabrication (FFF) offers a responsive solution to maritime supply chain delays through on-demand, shipboard component fabrication. However, the operational reliability of non-structural polylactic acid (PLA) assets over extended lifecycles remains critical. This paper investigates the 6-month atmospheric degradation behavior of FFF-printed PLA components across varying infill densities (33%, 66%, and 100%). Utilizing non-destructive parallel-plate dielectric spectroscopy across an alternating current spectrum (1 kHz to 20 kHz), experimental results reveal that the decay in the relative dielectric constant (ε_r) is most prominent at the 1 kHz window—dropping from 3.70 to 3.63 for solid structures. At higher frequencies (10–20 kHz), this aging signature is heavily attenuated due to polymer dipole immobilization, causing the dielectric response to converge toward a baseline polarization value. To map these state transitions, a multi-variable surface linear regression framework with an ordinary least squares metric was implemented. The bilinear model successfully captures the coupled density–frequency interaction, achieving a Coefficient of Determination (R²) of 0.8619 and a Root Mean Square Error (RMSE) of 0.00199 month⁻¹, providing a quantitative screening tool for decentralized maritime digital inventories. Full article

In complex industrial environments, vibration and acoustic signals are often corrupted by strong noise, resulting in non-stationary and low signal-to-noise ratio (SNR) characteristics that hinder effective feature extraction and degrade model generalization under limited samples. This study proposes a frequency-enhanced multi-scale adaptive state space Mamba (MSA-Mamba) model for motor fault diagnosis. A CNN Stem is first introduced for local feature extraction and sequence compression, followed by an adaptive frequency-domain filtering module to suppress noise and enhance fault-related frequency components. An MSA-Mamba Block combining multi-scale convolutional attention and parallel state-space modeling is then developed for adaptive feature fusion. In addition, Mixup and label smoothing strategies are employed to improve small-sample generalization. Experimental results show that MSA-Mamba achieves accuracies of 88.28%, 98.44%, and 99.48% on single-, three-, and four-channel datasets, respectively, and maintains 91.41% accuracy under strong-noise conditions. The proposed method demonstrates superior robustness and generalization for motor fault diagnosis. Full article

Surfactin is an amphiphilic lipopeptide, composed of a cyclic heptapeptide linked to a β-hydroxy fatty acid. This structural configuration gives rise to its remarkable biosurfactant properties. The synthesis is performed by a non-ribosomal peptide synthesis mechanism that involves surfactin synthetase, a multi-enzyme complex encoded by the srfA operon. The expression of this operon and surfactin synthetase activity are regulated by quorum sensing pathways such as Rap-Phr and ComXQPA controlling natural competence, YcxA and YerP efflux pumps, sporulation phosphorelay and degradative enzymes as well as surfactin-regulated pathways. The objective of this manuscript is to review well-studied regulatory metabolic pathways of Bacillus subtilis that control surfactin biosynthesis and to construct a global metabolic regulatory network in order to design the most efficient metabolic engineering strategies to improve surfactin production while maintaining strain stability. The surfactin synthesis regulatory network was built using KEGG pathway maps for two-component systems, quorum sensing, bacterial secretion, and ABC transporters. Most genes and interactions were based on Bacillus subtilis subsp. subtilis str. 168. Gene details were obtained from the NCBI database. The network map was created manually using Canva’s flowchart tool. Modifications in amino acid and fatty acid precursor pathways did not have a significant impact on surfactin yield despite an increase in amino acid availability. The modification of genes involved in quorum sensing pathways has been associated with the regulation of natural competence development and cell cycle progression. For example, srfA promoter replacement and comA overexpression resulted in a significant enhancement of srfA operon expression and surfactin production. The overexpression of efflux pumps ycxA and yerP resulted in enhanced surfactin secretion and increased self-resistance to surfactin. In contrast the deletion of sporulation genes resulted in an increase in surfactin yield per biomass; however, it caused inhibition of growth. Knockout of biofilm-related genes and competing lipopeptide pathways resulted in an increase in surfactin yield. A hypothesis of triple-mutant strain combining comA overexpression, ycxA and yerP enhanced efflux transport, and kinC deletion was proposed based on the constructed regulatory metabolic network that theoretically can improve surfactin yield and strain stability; however, further validation is required to assess potential unintended effects. Full article

Tracked agricultural robots operating on soft ground may experience asymmetric left–right track slip, while two-dimensional global navigation satellite system (GNSS) observations may contain a dominant anomaly in one coordinate. This simulation study evaluates a robust multi-sensor localization method for this coupled degradation. Independent left–right slip ratios are introduced into a six-state extended Kalman filter (EKF) to improve motion prediction. A coordinate-adaptive Huber extended Kalman filter (CAH-EKF) is then developed for GNSS updating. It combines component-wise normalized innovations and innovation jumps for coordinate-level anomaly detection, maps Huber weights to effective measurement covariance, and uses finite-state switching to confirm, maintain, and recover robust processing while retaining information from the relatively reliable coordinate. Simulation experiments on S-shaped and multi-U paths separate prediction-stage and update-stage effects. The independent-slip model improves over the common-slip model by 16.99–34.34% in position root mean square error (RMSE) and 29.11–36.27% in heading RMSE. Under sustained single-coordinate GNSS anomalies, CAH-EKF outperforms IS-EKF and remains competitive with Huber-IEKF. Under the main joint degradation experiments, the combined method yields 42.78–77.43% position and 27.05–76.33% heading improvements. Additional simulation checks covering slip excitation, path and motion changes, GNSS-degradation variants, and runtime further characterize the operating range of the method. These results support robust low-cost localization using encoders, a yaw-rate gyroscope, and a two-dimensional GNSS within the tested simulation conditions. Full article

This study investigates the combined influence of multi-walled carbon nanotube (MWCNT) concentration and structural architecture on the compressive behavior of fused filament fabrication (FFF)-printed PLA components. Neat PLA and PLA reinforced with 1.0 and 2.0 wt% carboxyl-functionalized MWCNTs were characterized by Raman spectroscopy, DSC, TGA, compression testing, statistical analysis, and scanning electron microscopy. Thermal characterization showed that MWCNT incorporation caused only minor changes in PLA thermal degradation while altering its crystallization behavior. For nearly solid specimens (90% infill), compressive strength increased from 53.4 MPa for neat PLA to 73.6 MPa at 2.0 wt% MWCNTs, although differences among MWCNT concentrations were not statistically significant. Honeycomb structures exhibited the highest mechanical performance at 1.0 wt% MWCNTs, reaching a compressive strength of 33.3 MPa, approximately 59% higher than that of neat PLA, with significant improvements in both compressive strength and elastic modulus. Two-way ANOVA revealed significant interactions between structural architecture and MWCNT concentration for both compressive strength and elastic modulus, demonstrating an architecture-dependent reinforcement response. SEM provided complementary morphological evidence consistent with the observed mechanical trends. These findings demonstrate that the most effective MWCNT concentration depends on structural architecture, highlighting the importance of simultaneously optimizing material composition and geometry in FFF-manufactured polymer nanocomposites. Full article

This review examines recent advances in hydrated lime-activated systems using hydrated lime (HL) as the sole alkaline activator, with particular emphasis on the effects of reactive precursors and auxiliary materials on mechanical properties and carbonation behavior. Compared with conventional strong alkali activators, HL provides a milder alkaline environment and an additional Ca source, promoting precursor reactions and the formation of C–S–H, C–(A)–S–H, and other calcium-containing hydration products. Precursor characteristics, multicomponent mixture design, water-to-binder ratio, and curing conditions strongly influence reaction kinetics, strength development, hydration products, and microstructure. Carbonation is a key process that distinguishes hydrated lime-activated systems from conventional strong alkali-activated systems and plays an important role in their microstructural and mechanical development. It has a dual effect on material performance, as it can contribute to matrix densification but may also lead to the degradation of calcium-containing binding phases as carbonation progresses. Therefore, material composition and curing conditions should be considered together to balance mechanical performance and carbonation behavior. From a sustainability perspective, hydrated lime-activated systems show promise in by-product utilization, reduced use of conventional strong alkalis, substitution of OPC, and CO₂ uptake through mineral carbonation. Further systematic studies are needed to evaluate these potential sustainability benefits. Full article

The highly conserved QseB/QseC two-component system (TCS) in Glaesserella parasuis (G. parasuis) regulates gene expression, with QseC as a histidine kinase and QseB as a response regulator. This study employed data-independent acquisition (DIA) quantitative proteomics to investigate the effects of qseB deletion on bacterial morphology, proteome, and molecular regulation. Electron microscopy revealed that the ΔqseB mutant exhibited ultrastructural damage, including cell shrinkage, membrane impairment, cell wall separation, and protoplast dissolution, indicating that the loss of qseB resulted in severe compromise of cellular structural integrity. A total of 1493 proteins were identified by DIA quantitative proteomics. Fifty-five proteins were differentially expressed between the ΔqseB mutant and the wild-type strain SC1401. Among them, 24 were upregulated and 31 were downregulated. Upregulated proteins were mainly heat shock proteins (DnaK, HslV/U, ClpB), molecular chaperones (GroL, HtpG, HslO), and co-chaperone GroES, along with translation inhibitors RaiA and RsfS. These proteins participate in stress response, protein folding, and proteostasis maintenance. The downregulated proteins included QseC, LolB, CarB, ThiL, and Tgt, which are linked to TCSs, quorum sensing, and transport functions. KEGG analysis indicated that the differentially expressed proteins are involved in RNA degradation, amino acid metabolism, and ABC transporters. Protein–protein interaction (PPI) network analysis identified 10 core hub proteins, including GroEL, HslU, and QseC. These proteins may play key roles in maintaining cellular homeostasis. Integrated multi-omics analysis identified three genes (dnaK, hslU, hslO) that were differentially expressed in both transcriptomic and proteomic data. qRT-PCR results confirmed these findings. In summary, loss of qseB disrupts the QseB/QseC system. This affects stress response, metabolism, and cell structure. These changes may affect bacterial fitness and host adaptation, providing clues for understanding the pathogenic mechanisms of G. parasuis. Full article

This paper concerns the optimization of the tribological properties of carbonitrided steel, a matter of particular significance in the design of highly stressed, hardened mechanical components. The study aims to explore the simultaneous treatment of microhardness, normal load and number of passes as jointly optimized design variables for the multi-pass scratch friction response of carbonitrided AISI 4130 steel. It employs a full-factorial response-surface (RSM) approach, in conjunction with a desirability-function optimization and an artificial neural network (ANN) model, with the objective of both explaining and predicting this response. The microstructural and mechanical properties of carbonitrided AISI 4130 steel were first examined by looking at how the steel wears in tribological applications. The microstructure of the carbonitrided steel was analyzed using optical microscopy and X-ray diffraction (XRD), with varying carbon-potential and tempering parameters. As the tempering temperature increased, the carbonitrided steel demonstrated a decrease in microhardness, consistent with progressive relief of quenching-induced stresses and decomposition of retained austenite. The surface microhardness ranged from 630 HV0.1 (C12, tempered at 550 °C) to 980 HV0.1 (C2), against 270 HV0.1 for untreated steel. To investigate the friction coefficient, a multi-pass scratching approach was employed, utilizing a full-factorial design of experiments (4 × 4 × 4 combinations of hardness, normal load and number of passes). The experimental data were analyzed by response surface methodology (RSM) with a desirability-function approach, and by an artificial neural network (ANN) trained with the standard back-propagation algorithm. The correlation coefficient (R²) of 0.993 demonstrates a strong agreement between the model predictions and the experimental results. The findings establish a validated, transferable quantitative relationship between carbonitriding-induced hardness gradients and adhesive-wear friction behavior, providing a predictive framework that can be extended to other case-hardened low-alloy steels subjected to multi-pass sliding degradation. Full article

The mooring system is the dominant single point of failure on a floating offshore wind turbine, and inspecting it drives much of its operations and maintenance cost. Data-driven damage classifiers could ease that burden, but they are usually trained and tested on one sea-state distribution, so accuracy drops for the severe conditions that lie outside it. This work presents a domain-adversarial gated recurrent unit (GRU) that identifies eleven mooring conditions from the platform’s six rigid-body motions and transfers, under a semi-supervised protocol, to sea states for which no damage labels are available in training. The conditions span a healthy baseline, single-line and multi-line stiffness loss, non-uniform degradation, and two biofouling severities; all are sub-failure damage cases whose motion signatures are weak and easily confused. The model is trained on operational sea states and tested on unseen extreme storm states (22–26 m/s wind, 8.0–9.5 m significant wave height), simulated in OpenFAST with MoorDyn on the IEA 15 MW UMaine VolturnUS-S platform. A three-phase schedule combining a labeled intermediate near-target domain with adversarial alignment of the training and test feature distributions raises per-simulation macro-F1 across three seeds to 0.87, against 0.29 for a source-only model, 0.47 for the near-target bridge alone and 0.73 for adversarial alignment alone. Neither component reaches this level by itself: alignment supplies the larger share of the gain and the bridge stabilizes it, reducing seed-to-seed variability roughly fourfold. The gain persists under sensor noise up to 20% of the per-channel standard deviation, and a parameter-matched 1D-CNN backbone confirms it is not tied to the recurrent architecture. The setting is semi-supervised, with respect to which target domain labels are available for the source and the intermediate near-target range, while the target sea states contribute unlabeled windows only. Because it requires no labels at the target sea states and only the standard six-degree-of-freedom motion sensors, the method can be extended to the severe, unseen simulated sea states that an asset meets in service; labeled near-target simulations from a calibrated platform model are still required. The present study is a simulation-based feasibility study: all evidence derives from OpenFAST and MoorDyn simulations of a single platform, and validation against tank-test or field measurements remains to be carried out. Full article

Visual–inertial odometry (VIO) on industrial stereo camera–IMU platforms suffers from frame-dependent timing errors and degraded feature tracking under auto-exposure, auto-gain adjustment, and rapid motion. This paper presents Hardware-Synchronized Exposure-State-Aware VINS-Fusion (HS-ES-VINS), a hardware-synchronized, exposure-aware extension of VINS-Fusion. The effective sampling time of each global-shutter image is reconstructed at the exposure midpoint using synchronized trigger timestamps, the camera debounce delay, and the frame-wise exposure duration. An exposure–gain–motion risk model adaptively adjusts feature detection criteria, Kanade–Lucas–Tomasi (KLT) tracking parameters, and correspondence filtering, while IMU-integrated rotation supplies initial predictions for feature tracking. On hardware-synchronized indoor and outdoor datasets with independent ground truth, controlled experiments show that the exposure-midpoint timestamp removes the frame-dependent exposure component of the camera–IMU offset that neither fixed nor online scalar compensation can remove, reducing the ATE RMSE by 28.6% (fixed offset) and 22.7% (online estimation) relative to the exposure-start timestamp under 20 ms low-light exposure. Compared with baseline VINS-Fusion, the complete adaptive front end reduces the ATE RMSE by 16.8% and 12.9% on indoor multi-floor and outdoor cycling sequences, and the risk score explains frame-wise tracking quality (correlation of 0.587 with the inlier ratio). The front end runs faster than the baseline (17.1 ms per frame), confirming real-time operation and improved robustness against variable illumination and rapid motion. Full article

Accurate state-of-health (SOH) prediction is essential for the safe and cost-effective operation of lithium-ion battery systems. However, current data-driven methods still struggle to jointly extract multiscale degradation information, capture long-range dependencies, fuse heterogeneous health indicators, and provide transparent model decisions. This study proposes an interpretable multi-feature SOH prediction framework that integrates improved complete ensemble empirical mode decomposition with adaptive noise (ICEEMDAN), Autoformer, and a bidirectional temporal convolutional network (BiTCN). ICEEMDAN adaptively decomposes capacity-related sequences into intrinsic mode functions and residual components, thereby suppressing noise and representing degradation dynamics at multiple temporal scales. Autoformer captures long-term trends and periodic patterns through series decomposition and auto-correlation, whereas BiTCN models local dynamic fluctuations through bidirectional temporal convolutions. The decomposed components and health indicators are then fused for battery health-state prediction. Shapley additive explanations (SHAP) are used to quantify feature contributions, and interval prediction is introduced to evaluate predictive uncertainty. Experiments on CALCE lithium-ion battery datasets show that the proposed ICEEMDAN-Autoformer-BiTCN model outperforms the tested baseline combinations, achieving an MAE of 6.6646 and an R² of 0.9896 under ICEEMDAN decomposition. SHAP analysis further indicates that residual components, SOH-related information, cycle number, and internal resistance make consistent contributions across battery samples. These results suggest that the proposed framework improves prediction accuracy while providing a more interpretable and uncertainty-aware approach to battery health assessment. Full article

Predictive opportunistic maintenance can improve maintenance coordination in multi-component systems by jointly exploiting health information and shared maintenance opportunities. However, redundancy and component heterogeneity complicate maintenance timing and component selection. Consequently, this paper proposes a reliability-centered predictive opportunistic maintenance framework for k-out-of-n systems with heterogeneous two-stage degrading components. Firstly, a two-stage Wiener process is used to characterize heterogeneous component degradation, with preventive maintenance restricted to defective-stage components. Secondly, future component reliabilities are aggregated according to the k-out-of-n structure to incorporate system redundancy into maintenance triggering. Thirdly, a hierarchical maintenance mechanism distinguishes necessary reliability-restoration actions from opportunistic replacements: failed components are correctively maintained, a minimum-cost necessary preventive-maintenance set is selected when required, and additional high-risk defective components are opportunistically maintained within the same maintenance event. Finally, the state-assessment interval, system maintenance threshold, and opportunistic-maintenance threshold are jointly optimized under a long-run average cost criterion. Numerical results confirm the economic advantage of the proposed policy over the benchmark strategies. Full article

Industrial effluents containing persistent synthetic dyes represent an important environmental challenge because of their high chemical stability, low biodegradability, and potential adverse effects on aquatic ecosystems. This study evaluates electrochemical advanced oxidation processes (EAOPs) as remediation strategies for dye-contaminated tanery dyes, with particular emphasis on conditions representative of complex industrial effluents. Electrochemical oxidation (EOx), electro-Fenton (EF), and photoelectro-Fenton (PEF) processes using boron-doped diamond (BDD) electrodes were comparatively investigated for the degradation of Violet S4B and a multicomponent mixture containing Violet S4B, Brown DR, and Black NT2. The effects of current density and pollutant concentration were assessed through discoloration, pseudo-first-order kinetics, chemical oxygen demand (COD) removal, and HPLC analysis of oxidation intermediates. For the multicomponent system, PEF achieved approximately 99% discoloration after 120 min, compared with nearly 97% for EF and 95% for EOx, with apparent rate constants increasing in the order EOx < EF < PEF. More importantly, COD analysis demonstrated extensive mineralization during PEF treatment, while HPLC revealed negligible accumulation of oxalic acid, indicating effective oxidation of refractory intermediates. The results demonstrate that combining anodic oxidation, electrochemically generated Fenton chemistry, and photo-assisted reactions enhances the remediation of complex dye mixtures. These findings support BDD-based EAOPs, particularly PEF, as promising technologies for reducing persistent organic pollution associated with tannery dye solutions. Full article

Electrified mobility regulations and lifecycle emissions targets have increased the demand for lightweight structural materials in vehicle architectures. Bioinspired composite materials offer microstructural configurations that alter conventional trade-offs among specific stiffness, crash energy absorption, and manufacturing energy requirements. This review evaluates the translation of biological structural archetypes including nacre, bamboo, cortical bone, and lotus leaves into load-bearing and functional automotive components. Quantitative benchmarks of continuous natural-fiber laminates, bio-cellular lattices, and mycelium-based acoustic cores are compared against high-strength steel and aluminum alloys. Key mechanical and functional metrics, including specific energy absorption (ranging from 35 to 48 kJ kg⁻¹ for bioinspired crash structures), dynamic loss factors, and Cassie-Baxter superhydrophobic surface stability, are evaluated alongside high-throughput manufacturing routes such as high-pressure resin transfer molding (HP-RTM) and additive manufacturing. Methodological parameters for ISO 14040/14044-compliant Life Cycle Assessment (LCA) are synthesized, emphasizing component-level functional units over gravimetric mass equivalence. Furthermore, operational boundaries, specifically hygrothermal interfacial degradation, matrix glass transitions (T_g < 120 °C), and multi-axial loading sensitivity, are systematically outlined to define design limits for automotive deployment. Full article

The ecological security of the Qinghai–Tibet Plateau (QTP) alpine transitional region plays a critical role in regional and global climate regulation. Conventional Remote Sensing Ecological Index (RSEI) assessments, however, rely on single-date imagery and often fail to account for cumulative climatic effects. To address this limitation, we refined the traditional RSEI through a multi-principal component weighting scheme that retains over 95% of the cumulative variance. Using imagery from five discrete periods between 2000 and 2020, we evaluated the spatiotemporal evolution of regional ecological quality in the alpine transitional region in Gannan. Results show an overall improvement: mean RSEI increased from 0.63 to 0.69, Good/Excellent areas expanded from 65.6% to 79.9%, and the interquartile range decreased from 0.19 to 0.14. Spatially, ~59% of the area improved, whereas ~41% degraded, primarily in western transition zones and urban peripheries. As an evaluation of internal index contributions, NDVI remained the dominant structural component. Climatic constraints shifted from temperature dominance between 2005 and 2010 to precipitation dominance by 2020, with all interactions exhibiting synergistic enhancement. Cumulative climatic variables during the growing season, with an optimal seven month accumulation window, substantially outperformed single month variables in explaining ecological variations. Specifically, mean q-values increased by 0.12 for temperature and 0.18 for precipitation, yielding a 15 to 25 percent relative improvement, which validates the necessity of incorporating cumulative effects. This framework provides a robust foundation for long-term monitoring and differentiated management of fragile alpine ecosystems. Full article