Search Results (169)

Electric vehicle (EV) transport by ship is expanding beyond industrial logistics centred on automobile production, trade, and pure car and truck carriers (PCTCs) into daily transportation for island tourism, commuting, and essential mobility. According to Korea Maritime Transportation Safety Authority (KOMSA) vessel status data as of March 2026, 104 of 146 domestic passenger ships were car-ferry passenger ships, accounting for 71.2% of the fleet and operating on 75 of 99 designated routes nationwide. Korea Shipping Association (KSA) operational records show that the EV transport rate on these routes increased from 0.76% in 2024 to 1.21% in 2025, with some routes exceeding 2.0–4.7%. Unlike enclosed multi-deck PCTC vehicle spaces, Korean coastal car-ferry passenger ships generally have single-tier open vehicle decks and bow ramp gates. Crosswinds on open decks may reduce smoke detector activation probability by 60–75%. Although Article 97 of the Standard for Ship Fire-Fighting Appliance newly requires dedicated EV fire-fighting equipment for car-ferry ships, it remains primarily equipment-prescriptive and does not yet provide open-deck-specific performance requirements for wind-resistant detection, fixed EV-zone cooling, EV-designated stowage arrangements, or passenger–operator safety management obligations. This study applies the five-step International Maritime Organization (IMO) Formal Safety Assessment (FSA) procedure to support improvements to EV fire-fighting equipment standards for coastal car-ferry passenger ships. Hazard identification (HAZID) was conducted with a 15-member advisory panel, and probability elicitation was performed through a Delphi survey with 10 core experts, showing strong consensus (Kendall’s W = 0.74, p < 0.01). Fault tree analysis (FTA) and event tree analysis (ETA) probabilities were derived from the Delphi results and the international literature. H-07, representing wind-induced smoke dilution, was identified as the dominant single-point vulnerability within the detection-failure branch. Monte Carlo-based FTA–ETA analysis (n = 10,000) estimated annual fire frequencies of 5.9 × 10⁻², 1.8 × 10⁻¹, and 2.9 × 10⁻¹ yr⁻¹ at EV loading ratios of 10%, 30%, and 50%, respectively, with 2.47 expected fatalities per fire. Risk entered the IMO ALARP band above a 30% EV loading ratio and exceeded the maximum tolerable crew risk above 50%. The combined application of risk control options (RCOs) 2, 3, and 4 reduced annual expected fatalities by 85.6%. Based on these results, six RCOs and institutional recommendations are proposed, including strengthened safety management obligations for passenger ship operators. Full article

Patients presenting with conditions attributed to environmental exposures face complex, multi-toxicant burdens, yet the stability of multivariate toxicant patterns under demographic adjustment remains poorly evaluated. This study assessed the sensitivity of principal component analysis (PCA) structures to demographic confounding and variable composition in 551 patients (aged <1–86 years) with environmentally related conditions. Nine urinary biomarkers were analysed using PCA with Varimax and Promax rotation on both raw data and residuals adjusted for age and sex. Among the identified patterns, the solvent/industrial cluster (xylene and styrene metabolites) was the most stable, persisting across both raw and residual analyses regardless of rotation method. However, overall component structures were sensitive to preprocessing: the clustering pattern of the herbicide 2,4-dichlorophenoxyacetic acid shifted markedly after demographic adjustment, illustrating indirect confounding whereby demographic effects on co-variables altered apparent biomarker associations. Notably, inclusion of an endogenous mitochondrial marker (tiglylglycine) alongside exogenous toxicant biomarkers produced Heywood cases (loadings > 1), violating factor analysis assumptions and indicating that mixing exposure and response variables destabilises the model. Cumulative variance explained was modest, consistent with the weak inter-biomarker correlations observed (KMO ≈ 0.52). These findings do not support the identification of robust, demographically stable clustering patterns in this cohort. Instead, they demonstrate that PCA-derived structures in heterogeneous clinical data are vulnerable to demographic confounding, variable selection and marker type, and caution against interpreting transient clustering patterns as definitive exposure signatures without rigorous validation. It should be noted that the absence of a non-clinical matched control group means that whether the identified co-exposure signatures are distinctive to symptomatic patients or reflective of general population exposure patterns cannot be determined; future studies should incorporate matched controls to address this directly. Full article

The economic viability of second-generation (2G) bioethanol production depends on the availability of robust, multistress-tolerant yeast strains capable of withstanding harsh industrial conditions. This study investigates animal manure as a novel ecological niche for discovering such strains, as microbes in these environments naturally adapt to high organic loading and fluctuating temperatures. From eighty-six initial isolates, twenty-nine demonstrated superior xylose fermentation at 37 °C. Eight high-performing isolates (C2-1, B1-2, B1-6, B2-6, B2-8, G1-4, G1-5, and G2-4) exhibited exceptional tolerance to ethanol, high temperatures, and lignocellulosic-derived inhibitors (acetic acid, formic acid, furfural, and vanillic acid). Molecular identification classified isolate C2-1 as Pichia kudriavzevii and the remaining seven as Candida tropicalis. In synthetic media, C. tropicalis B2-8 produced up to 16.33 g/L of ethanol using xylose (60 g/L) as the sole carbon source. While the undetoxified, highly acidic sugarcane bagasse hydrolysate completely inhibited yeast growth, the industrial potential of these strains was successfully validated using the concentrated, undetoxified enzymatic hydrolysate derived from the acid-pretreated sugarcane bagasse solids, which contained 30.15 g/L glucose and 25.58 g/L xylose. P. kudriavzevii C2-1 achieved ethanol titers of 6.02 g/L and 5.71 g/L at 37 °C and 40 °C, respectively. The C. tropicalis strains outperformed P. kudriavzevii, yielding 6.12–6.35 g/L at 37 °C and maintaining 5.75–6.19 g/L at 40 °C. These findings underscore the potential of manure-derived yeasts as resilient biocatalysts. Although their fermentation yields remain relatively low and require further metabolic optimization, their ability to survive and ferment in this concentrated, undetoxified enzymatic hydrolysate at elevated temperatures makes them promising candidates for further development in high-temperature ethanol fermentation (HTEF), offering a potential pathway toward reducing cooling costs associated with 2G biorefineries. Full article

The Santiago River is a highly anthropogenically impaired lotic system globally, yet the mechanisms governing its contaminant transport remain poorly understood under static monitoring paradigms. This study evaluates how hydrological forcing dictates the mobilization and bioavailability of trace metals by integrating a 15-year public hydrochemical database from 10 monitoring nodes with SAR-derived discharge estimates and thermodynamic metal modeling (PHREEQC). To validate the structural integrity of the mass load estimates against hydrometric uncertainties, a deterministic boundary-sensitivity analysis was conducted. Results empirically refute the classical dilution paradigm, introducing the “Anthropogenic Pulse” to describe the non-linear acceleration of pollutant export during high-flow events (discharge Q surging from 36.62 to 286.13 m³/s). While climate-driven parameters follow seasonal cycles, industrial stressors (COD, Pb, Cd) remain in a chronic steady state, decoupling from volumetric dilution. Based on coupled × CQ × C (discharge × concentration) estimates, this dynamic induces a synchronized flushing of toxic burdens, exporting monthly peak loads exceeding 51,000 kg of Zinc, 6500 kg of Lead, and 3100 kg of Cadmium. Thermodynamic simulations reveal that this hydrological flushing functions as a chemical activator; the seasonal dilution of natural Alkalinity and Hardness suppresses the river’s theoretical buffered pH (from 8.5 to 7.0), maintaining metals in their uncomplexed free-ion states (Me²⁺). Modeling indicates that nearly 90% of the exported Cadmium remains in this highly labile, toxic form due to a dual coupling with both river Discharge (r_s = 0.87) and pH (r_s = 0.79). The identification of stochastic arsenic peaks 100 times above regulatory limits at Paso de Guadalupe (RS-08) underscores the failure of concentration-based monitoring. Our findings suggest that restoration strategies should shift toward mass-loading-based regulatory frameworks and targeted sediment management at critical nodes to mitigate the chronic export of bioavailable industrial waste. Full article

This paper introduces the research progress of path planning, trajectory tracking control, and multi-machine collaborative operation systems for agricultural robots. It summarizes the development laws of 3D terrain modeling and adaptive path planning algorithms for complex agricultural environments such as hills and mountains, and analyzes the dynamic disturbance characteristics of agricultural machinery under slip, sideslip, and dynamic load changes. Through comprehensive analysis, it is found that traditional kinematic control models have limitations in complex and unstructured environments. Combining soil mechanics mechanisms, variable load identification, and robust control strategies is key to improving trajectory tracking stability and operational quality. In terms of multi-machine collaboration, this paper discusses master–slave collaboration, distributed control, and task allocation modes. It further identifies that the stability of collaboration and interoperability standards between devices in weak network environments are currently the main bottlenecks limiting the large-scale application of this technology. Finally, this paper provides prospects for future research directions and suggests strengthening the closed-loop integration of perception, decision-making, and dynamic models, establishing industry unified standards, and enhancing the safety of the entire lifecycle of operations, providing suggestions for the unmanned application of agricultural robots. Full article

This study presents a simulation-assisted engineering framework intended to support comparative machining parameter selection for quartz sintered materials. The approach integrates CAD/CAM-based analysis, an illustrative Design of Experiments (DOE) framework, and preliminary experimental validation to improve process planning and machining quality. The analysis focuses on key technological parameters, including cutting speed (vc), feed rate (f), and depth of cut (ap), evaluated across cutting, milling, and finishing stages. The results indicate that feed rate is the dominant parameter influencing process stability, surface quality, and edge integrity. A practical transition region of approximately 1200 mm/min was identified, above which increased vibration, defect formation, and surface degradation occur. The complementary DOE analysis confirms the relative importance of process parameters and reveals interaction effects, particularly between feed rate and depth of cut, which significantly influence defect formation under high-load conditions. Preliminary industrial observations provide trend-oriented support for the simulation-predicted process behavior. Based on the integrated analysis, a preliminary technological operating region was identified (vc = 1080–1320 m/min, f = 800–1200 mm/min, ap = 0.5–1.0 mm), suggesting a practical compromise between machining efficiency and surface integrity. The proposed methodology provides preliminary engineering support for comparative process planning and defect-reduction-oriented parameter selection in the machining of brittle materials. The novelty of this work lies in the integration of CAD/CAM simulation, DOE-based interaction analysis, and experimental validation for supporting the identification of a practical technological operating region for machining brittle materials. The presented results should therefore be interpreted as engineering-oriented comparative process-planning guidelines rather than statistically generalized machining laws. The presented study should be interpreted as an exploratory simulation-assisted engineering investigation intended to support comparative process planning rather than as a fully experimentally validated machining model. Full article

Three-phase induction motors account for nearly two-thirds of industrial electricity consumption, making accurate parameter identification essential for efficiency optimization, predictive maintenance, and digital twin calibration. This paper introduces the stochastic spheric navigator algorithm (SSNA) for estimating the equivalent circuit parameters (stator and rotor resistances, leakage reactances, and magnetizing reactance) of induction motors by minimizing the normalized squared error between manufacturer-provided torque characteristics (starting, peak, and full-load) and their analytical counterparts derived from the steady-state Thévenin model. The SSNA employs an adaptive spherical search mechanism with a decaying radius schedule that progressively narrows the exploration neighborhood, enabling a balanced transition from global exploration to local refinement. Validated on 5 hp and 25 hp motors against the genetic algorithm (GA), particle swarm optimizer (PSO), hybrid GA-PSO, and sine–cosine algorithm (SCA), the SSNA demonstrates distinct advantages. For the 5 hp motor, it achieves the lowest errors in maximum torque ($1.34\times {10}^{-4}\%$) and full-load torque ($5.08\times {10}^{-4}\%$). For the previously unreported 25 hp motor, the SSNA yields an objective function value of $4.68\times {10}^{-12}$—six orders of magnitude lower than the SCA—and reduces magnetizing reactance estimation error from 46.55% (SCA) to 16.18%. Statistical analysis over 100 independent runs reveals that the SSNA uniquely combines the lowest minimum (best) value, the lowest maximum (worst) value, and the lowest standard deviation, demonstrating superior accuracy, reliability, and consistency. These results position the SSNA as a highly competitive optimization framework for induction motor parameter identification, with particular suitability for applications demanding high precision and robust performance. Full article

Insulation-material manufacturers face increasing pressure to improve productivity, cost efficiency, energy performance and worker safety while maintaining stable quality in highly constrained production environments. Existing lean and smart-manufacturing studies often examine isolated tools, individual monitoring technologies or material-level sustainability, but fewer studies provide conservative plant-level validation of an integrated intervention in insulation-material production. This study therefore examines the optimization of insulation-material production in a human-supervised cyber–physical manufacturing system through an industrial before–after intervention. The framework combines bottleneck identification, value stream mapping, SMED, selective automation, preventive maintenance and KPI-based digital monitoring. The baseline system was constrained by manual crusher loading, long changeovers, inefficient pallet transport, repeated breakdowns, scrap and limited real-time visibility. After implementation, productivity increased from 7864 to 9000 kg/day (+14.5%), monthly production costs decreased from EUR 200,000 to EUR 180,000 (−10%), breakdown frequency fell from 5 to 3 events/month (−40%), scrap decreased from 5% to 3% (−40%), crusher loading time fell from 30 to 10 min/pallet (−66%), annual energy use dropped from 500 to 450 MWh (−10%) and reported safety incidents decreased to zero during the 12-month post-implementation observation period. An OEE-based surrogate model yielded pre- and post-state theoretical capacity estimates differing by less than 1%, supporting internal consistency. The results are interpreted as descriptive and practically meaningful before–after differences because the full raw monthly dataset is commercially sensitive and classical inferential testing was not performed. The study contributes by presenting a reproducible, conservative and human-supervised CPS-oriented plant-intervention protocol rather than by claiming a fully autonomous closed-loop CPS. Full article

False data injection (FDI) cyberattacks pose a growing threat to modern power distribution systems in smart cities by manipulating state-estimation processes and provoking covert voltage violations that traditional defense mechanisms fail to detect. Recent industry data indicate that coordinated FDI attacks can distort measurement sets by as little as 3–7%, yet trigger voltage deviations exceeding 10% in vulnerable feeders, resulting in operational instability, unnecessary load curtailments, and elevated outage risk. To address these challenges, this paper proposes a quantum-accelerated digital twin (QDT) framework that integrates quantum optimization algorithms with a high-fidelity digital twin (DT) of the distribution system to detect, localize, and remediate FDI-induced cyberattacks in real time. The rationale behind the approach lies in the superior combinatorial search capability of quantum solvers, which accelerates the identification of falsified measurement vectors and optimal corrective control actions compared with classical methods. In addition, the framework introduces an innovative Bitcoin-mining-oriented virtual energy storage (BMOVES) mechanism that treats mining facilities as dynamically controllable, fast-response electrical loads within smart city demand–response programs. By modulating mining power consumption with sub-second granularity, the proposed BMOVES resource provides up to 18–45% flexible capacity during attack scenarios, enabling voltage restoration without relying on conventional energy storage assets. The unified QDT + BMOVES architecture is validated using the 136-bus Brazilian distribution system, a realistic benchmark for cyber–physical resilience studies. Simulation results demonstrate over 99% FDI detection accuracy, up to an 82% reduction in peak voltage violations, and restoration of operational limits 11 times faster than state-of-the-art classical methods. These findings highlight the transformative potential of integrating quantum computing, digital twins, and nontraditional flexible assets to enhance cyber-resilient power infrastructure in future smart cities. Full article

In practical engineering applications, rotor–bearing systems inevitably exhibit inherent or residual faults such as imbalance and shaft-bow, originating from manufacturing tolerances, thermal deformation, or operational loading. Accurate monitoring of these faults and their evolution is fundamental to the effectiveness of modern prognostics and health management (PHM) frameworks. However, if such inherent faults are not identified at an early stage, substantial deviations in fault diagnosis may occur, thereby compromising the accuracy of subsequent prognostic assessments and maintenance strategies. This study presents a hybrid diagnostic methodology that integrates a physics-based model with neural network techniques to enhance rotor fault diagnosis. A Jeffcott rotor subjected to simultaneous disk imbalance and shaft-bow is used to demonstrate the methodology, and the results proves its superior capability for simultaneous fault identification. Nonetheless, discrepancies between model predictions and experimental results are observed, attributed to the presence of inherent faults within the rotor system. To address this issue, algorithms for inherent fault identification and compensation, supported by experimental verification, are developed. Following compensation, the accuracy in simultaneously diagnosing and estimating the parameters of imbalance and shaft-bow is significantly improved. The proposed methodology is designed for seamless integration into real-time monitoring systems of industrial rotating machinery. Full article

This article presents a comprehensive assessment of sediment contamination in the Irtysh River within the industrial zone of the city of Ust-Kamenogorsk, using the Specific Combinatorial Sediment Pollution Index (SCSPI). This study includes a set of priority chemical elements characteristic of the region’s technogenic load (Be, Cu, Zn, As, Se, Cd, Te, Hg, Pb), taking into account their hazard class, persistence in bottom sediments, and ability to accumulate in fine-grained (pelitic) fractions. The assessment was carried out based on the calculation of the frequency index of background exceedance (S_α) and the exceedance multiplicity index (S_β), relative to the effective local background value, followed by the determination of the partial pollution indices (Ki) and the integral SCSPI indicator. It was established that, for most elements, the frequency of exceedance ranges from 75% to 100%, indicating widespread surpassing of the effective local background. The partial indices vary within 4–7 points, with cadmium and zinc making the greatest contribution to the formation of integrated pollution due to the presence of local accumulation zones. Correlation analysis showed that the proportion of the pelitic fraction (<0.01 mm) is most strongly associated with the accumulation of Cd (r = 0.67) and Se (r = 0.66), indicating the preferential accumulation of these elements in fine-grained sediments. Principal component analysis revealed stable geochemical associations among the elements. For the <2.0 mm fraction, the first three principal components explain 73.57% of the total variance, with PC1 mainly associated with Pb, Se, and Cd. For the <0.2 mm fraction, the first three components explain 72.44% of the total variance, and PC1 is characterized by high loadings of Zn, Cd, As, and Se, reflecting the strengthening of the technogenic association in fine-grained material. The SCSPI values across the studied cross-sections range from 5.0 to 5.6, corresponding to a moderately polluted state of bottom sediments (Classes 3a–3b). The spatial distribution of the index reflects the combined influence of technogenic sources and hydrodynamic processes responsible for the redistribution of fine-grained material. The obtained results confirm the applicability of the Specific Combinatorial Sediment Pollution Index (SCSPI) for an integrated assessment of the ecological condition of bottom sediments and for identifying zones of increased technogenic load. A comprehensive approach to the analysis of bottom sediment pollution is proposed, enabling a more accurate identification of spatial distribution patterns of contaminants and their accumulation zones. This provides a scientific basis for the development of adaptive strategies for monitoring and management of aquatic ecosystems. This study is of significant practical importance for advancing sustainable environmental management and the rational use of natural resources under increasing anthropogenic impact. Full article

Monitoring the condition of rolling bearings is critical for industrial reliability, yet traditional contact-based accelerometers can be impractical in confined or hazardous environments. This study investigates the use of microphones as a non-invasive diagnostic alternative, focusing on the impact of sensor distance and spatial placement on fault detection sensitivity across various rotational speeds and load conditions. Using an accelerometer mounted directly on the bearing as a benchmark, acoustic data were acquired on a test bench under different speed and load conditions. The experimental setup evaluated three distinct microphone positions and five distances relative to the source to assess spatial influence. Analysis was conducted comparing scalar indicators, such as Root Mean Square (RMS), kurtosis and Crest Factor (CF) values, with advanced diagnostic techniques, specifically the High-Frequency Resonance Technique (HFRT) for envelope spectrum extraction. Results indicate that while the signal-to-noise ratio (SNR) predictably decreases with distance, diagnostic performance is significantly compromised by acoustic shielding effects caused by bearing housing. Moreover, while simple statistical factors (RMS, kurtosis, CF) show limited reliability across varying distances and noise floors, HFRT-based envelope analysis yields robust fault identification even at the maximum sensor distance. The study concludes that optimal microphone placement is essential for reliable remote monitoring. Particularly, these findings suggest that a preliminary spatial characterization of the acoustic field can significantly enhance the effectiveness of non-contact diagnostic systems in industrial applications. Full article

This study experimentally investigates the thermal performance of wraparound loop heat pipes (WLHP) using R134a as the working fluid and copper tubing with an outer diameter of 8.5 mm. A dedicated experimental apparatus was developed to evaluate thermal resistance under varying heat loads (200–500 W), inclination angles (15° and 30°), and coolant temperatures (5–15 °C) at a constant coolant flow rate of 3.2 L/min. Key performance metrics, including evaporator wall temperature and overall thermal resistance, were analyzed to identify optimal operating conditions. The results reveal that increasing the heat load significantly reduces thermal resistance, reaching a minimum of 0.056 °C/W at 500 W. An inclination angle of 30° improved heat transfer, lowering the evaporator temperature by approximately 5 °C compared to 15°. Moreover, lower coolant temperatures enhanced the temperature gradient between the evaporator and condenser, further improving heat transfer. Principal component analysis (PCA) was employed for dimensionality reduction and identification of the dominant thermal variables affecting system performance. Based on the experimental dataset, a regression model was developed to predict thermal resistance, achieving a coefficient of determination of R² = 0.96. These findings confirm the WLHP’s potential as an efficient and reliable passive thermal management system for medium- to high-power applications in tropical and industrial environments. Full article

Identifying the specific sources of heavy metal accumulation in intensive agricultural landscapes is essential for ensuring soil sustainability and food security. In this study, we independently carried out a high-density regional geochemical survey and high-resolution field sampling in the Yangtze–Huaihe Watershed, Eastern China, and used the original sample dataset to distinguish between geogenic backgrounds and anthropogenic enrichments. By employing the APCS-MLR model, four distinct pollution sources were quantitatively identified: natural pedogenesis, agricultural activities, traffic emissions, and industrial inputs. Results demonstrated that while most heavy metal concentrations remained below national safety thresholds, Cd and Hg exhibited significant topsoil enrichment, signaling potential ecological risks. Source apportionment revealed that natural sources primarily controlled As, Cr, Ni, and Pb, with the contribution ranging from 41% to 70%. In contrast, traffic emissions (e.g., tire wear and fuel combustion) emerged as the dominant source for Cd (68%), Zn (55%), and Cu (34%), while industrial activities accounted for a substantial 89% of Hg accumulation via atmospheric deposition. Notably, despite the region’s intensive cultivation, agricultural practices played a surprisingly minor role in heavy metal accumulation. These findings highlight that the accumulations from traffic and industry now account for approximately 50% of the total heavy metal load in the region. Our results underscore the critical importance of high-resolution spatial data for precise source identification and suggest that implementing vegetative buffer zones and stricter industrial emission controls are imperative to mitigate further soil degradation in similar agricultural watersheds. Full article

This study addresses the problem of improving the efficiency of fine grinding of bulk materials in a spring-rotor mill. The objective is to determine technologically sound operating parameters based on mathematical modeling, design of experiments, and multi-objective optimization. The methodology relies on a full-factorial experimental design according to the Hartley plan, with five control factors: rotor rotational speed, material loading ratio, overlap of the working zones, grinding chamber clearance, and grinding duration. The analyzed responses include grinding fineness, throughput, power consumption, specific energy consumption, and specific metal intensity. Based on experimental data, adequate second-order polynomial regression models were obtained for all response variables using the least-squares method. Statistical analysis showed that grinding time and rotational speed had the most significant influence on the process. Multi-objective optimization using the weighted-sum method enabled the identification of optimal operating conditions that balance product quality, throughput, and energy consumption. Verification experiments confirmed the adequacy of the developed models. Practical implementation of the optimized regimes increases throughput by 15–20% while simultaneously reducing energy consumption by 8–12% compared with empirically selected operating conditions. The proposed models and recommendations provide a quantitative basis for tuning and controlling grinding equipment in processing industries. Full article