Search Results (989)

This study presents a comparative Failure Modes and Effects Analysis (FMEA) of electro-hydraulic braking (EHB) and electro-mechanical braking (EMB) systems within brake-by-wire architectures. The analysis integrates both the conventional Risk Priority Number (RPN) approach and the AIAG–VDA Action Priority (AP) methodology, enabling a structured comparison of risk prioritization strategies applied to identical failure modes. A consistent system-level framework is developed to harmonize severity (S), occurrence (O), and detection (D) assessments across both architectures, allowing direct evaluation of methodological differences. The results demonstrate systematic divergences between RPN and AP approaches, particularly in high-severity scenarios, where AP provides more safety-oriented prioritization. The study further identifies key limitations of traditional RPN-based evaluation in safety-critical systems and highlights the advantages of rule-based prioritization frameworks. In addition, corrective measures are proposed and their impact on occurrence and detection ratings is quantified, illustrating practical pathways for risk reduction. Beyond methodological comparison, the work introduces a novel integration of reliability engineering with advanced manufacturing strategies, demonstrating how laser and plasma-based surface engineering can mitigate failure mechanisms by reducing occurrence and improving system robustness. The proposed approach establishes a conceptual and physically grounded bridge between system-level risk assessment and material-level optimization, contributing to the development of more reliable next-generation brake-by-wire systems. Full article

This study investigated the effects of dietary Bacillus cereus, administered alone or in combination with biofloc technology, on the growth performance, immune response, disease resistance, and intestinal microbiota of Litopenaeus vannamei. Shrimp fed diets supplemented with B. cereus, either directly or via biofloc systems, exhibited significantly increased final body weight and specific growth rate, together with a reduced feed conversion ratio compared with the control group. The expression levels of key hepatopancreatic immune-related genes, including lysozyme, prophenoloxidase, superoxide dismutase, Toll, immune deficiency, and Relish, were significantly upregulated in probiotic-associated treatments. Following challenge with Vibrio parahaemolyticus, cumulative mortality was markedly lower in all treatments involving B. cereus or biofloc compared with the control. Although alpha diversity indices were not significantly affected, beta diversity analysis demonstrated that supplementation frequency and delivery mode altered intestinal microbial community structure. The phyla Bacteroidota, Firmicutes, and Proteobacteria predominated across treatments, while members of Marinilabiliaceae and Shewanellaceae were enriched under probiotic-associated conditions, suggesting enhanced nutrient transformation potential. Co-occurrence network analysis further revealed increased microbial network complexity and positive interactions in probiotic and biofloc treatments, indicating improved community stability. These findings demonstrate that the synergistic application of B. cereus and biofloc technology enhances growth performance, immune capacity, and intestinal microbial resilience in intensive shrimp culture, and that supplementation strategy plays a critical role in optimizing probiotic efficacy. Full article

The increasing complexity and variability of operating conditions, along with the occurrence of low-probability cascading failures, imposes more stringent requirements on data-driven intelligent methods for power system stability analysis. This paper proposes an intelligent enhancement approach for transient voltage stability assessment in modern power systems, considering improved conditional generative adversarial network (CGAN)-based sample balancing. Firstly, an improved CGAN incorporating an enhanced feature-distance metric is developed to accurately capture the distribution characteristics of real samples, effectively alleviating training issues such as gradient vanishing and mode collapse during adversarial learning. Secondly, an intelligent sample enhancement method for transient voltage stability is established based on the improved CGAN, which effectively complements the initial dataset and ensures the predictive performance of intelligent models under extreme operating conditions. Finally, a transient voltage stability assessment framework integrating a convolutional neural network and a transformer is proposed to enable efficient extraction of low-dimensional features and achieve accurate evaluation of transient voltage stability states. Full article

In this paper, a cold standby repairable system comprising two heterogeneous components, each characterized by multiple types of mutually independent failure modes, is investigated. The operational lifetimes of the components follow exponential distributions, while their repair times after failure are governed by general distributions. By applying the theory of the Markov renewal process together with the Laplace and the Laplace–Stieltjes transform techniques, we derive analytical expressions for the time to the first system failure, system availability, and the rate of occurrence of system failures. Some results for these reliability measures under several special cases are also presented. Finally, numerical examples are provided under different repair time distributions to analyze the influence of model parameters on the system’s reliability performance. Full article

Rotary kiln-based activated carbon production combines high-temperature operation with flammable/reducing gases, carbonaceous dust, and downstream off-gas treatment and acid/base washing, creating complex escalation pathways. This study prioritizes safety improvements by applying classical failure modes and effects analysis (FMEA) and a transparent Fuzzy-FMEA framework to 18 representative failure modes (six each for kiln/activation, acid/base handling, and atmosphere/control). Five experts evaluated Severity, Occurrence, and Detection on a 10-point scale. The fuzzy model used triangular membership functions (L/M/H), a monotonic 27-rule base, Mamdani max–min inference, and centroid defuzzification to compute a continuous fuzzy risk priority number (FRPN, 0–10). Classical FMEA identified dust explosion (RPN = 405), temperature control failure (RPN = 378), and off-gas leakage (RPN = 324) as the highest-ranked risks. Fuzzy-FMEA preserved the top-risk group while more strongly highlighting barrier-related risks, placing off-gas leakage, instrumentation/interlock failure, and electrostatic ignition control alongside dust explosion (FRPN 9.221–9.332). The rankings were strongly correlated (Spearman ρ = 0.871; Kendall τ = 0.752), yet mid-risk items were rearranged (mean |Δrank| = 2.06; max = 5), improving discrimination within tied RPN clusters. The five highest-priority scenarios were reconstructed into actionable engineering packages, including dust and ignition control, off-gas integrity linked to shutdown logic, interlock proof testing and bypass management, and independent protection layers for kiln temperature control. Full article

Five-axis Computerized Numerical Control (CNC) machine tools play a pivotal role in the precision manufacturing of aeroengine turbine blades, where ultra-high reliability and accuracy are essential. Failure Mode, Effects and Criticality Analysis (FMECA) has been widely applied in the reliability assessment of such advanced machining systems due to its systematic evaluation of potential failure modes. However, traditional FMECA approaches often overlook the ambiguity of human cognition and the interdependence among expert evaluations, limiting their effectiveness in complex aerospace manufacturing environments. To address these issues, this paper proposes a novel FMECA framework based on generalized intuitionistic linguistic theory. A new Generalized Intuitionistic Linguistic Weighted Geometric Average (GILWGA) operator is introduced to couple multi-source expert information and quantify the fuzziness inherent in subjective assessments. Additionally, an intuitionistic linguistic entropy-based weighting scheme is developed to dynamically evaluate key risk factors, including severity, occurrence, detectability, and controllability. The proposed framework is applied to a case study involving the spindle system of a five-axis CNC machine tool used in aeroengine blade production. The results demonstrate that the proposed method offers more robust and consistent failure mode prioritization, providing effective decision support for reliability-centered maintenance in aerospace equipment manufacturing. Full article

Drought is a complex natural hazard with severe impacts on ecosystems, agriculture, water resources, and socio-economic stability. Understanding its spatiotemporal evolution is critical for effective drought monitoring and prevention. This study analyzed drought characteristics in Henan province from 1961 to 2023 using the Standardized Precipitation Evapotranspiration Index (SPEI), calculated from daily meteorological data at 111 meteorological stations. Drought was examined at annual and seasonal scales across multiple time scales, including the 1-month time scale (SPEI1), 3-month time scale (SPEI3), and 12-month time scale (SPEI12), and future trends were assessed using Theil–Sen Median and Hurst exponent analyses. Key findings revealed the following: (1) Drought frequency showed a non-significant increasing trend overall, but drought intensity increased significantly, with severe and extreme droughts becoming more frequent. Most areas are projected to continue aridification. (2) Winter recorded the highest frequency and occurrence of droughts, followed by autumn and summer. Except for summer, moderate and severe droughts increased across all seasons. Extreme droughts increased significantly across all seasons, especially in spring and autumn. (3) High annual drought frequency was concentrated in the northwest, north, and east. Spatial patterns varied by drought severity: slight droughts were more common in the north, moderate droughts in the central–east, severe droughts in the west and south, and extreme droughts in the southwest and north. (4) Empirical Orthogonal Function (EOF) analysis revealed three main spatial modes: a uniform regional pattern, a southeast–northwest contrast, and a central–eastern opposition. Shorter time scales provided more detailed spatial patterns, while longer scales better reflected interannual characteristics of drought and flood variations. This study offers valuable insights for improving drought assessment and supporting risk management and policy decisions. Full article

Background: The term dyskinesia describes involuntary movements of the face, body and extremities. Frequently, they appear following and in relation with prior oral long-lasting and high-dose levodopa therapy in Parkinson’s disease patients. Onset of these motion sequences causes patient distress and caregiver embarrassment with declined quality of life. Continuity of nigrostriatal postsynaptic dopamine receptor stimulation delays occurrence of dyskinesia. A pulsatile pattern with temporary too high dopamine receptor excitation promotes manifestation of dyskinesia. Methods: This narrative review describes past pharmacologic approaches for therapy of dyskinesia, such as the principle of continuous dopamine receptor stimulation. Discussion and Conclusions: Novel concepts were tested. They influenced neurotransmission of serotonin and altered stimulation of dopamine receptor subtypes. The translation of successful experimental research outcomes into valuable clinical trial results with consecutive approval of drugs with a new mode of action under the indication “antidyskinetic” repeatedly failed. An exception is the open-channel blocker of the N-methyl-D-aspartate receptor and dopamine reuptake inhibitor amantadine with its moderate dyskinesia-reducing effects, particularly in its extended-release formulation. This antiviral compound also improves impaired motor behavior and reduces “OFF” intervals. Therefore, amantadine is currently experiencing a certain resurgence in regions where its extended-release formulations are marketed for therapy of levodopa-induced dyskinesia. Full article

The strength of Xigeda strata decreases significantly upon contact with water, and the shear strength between sandstone and mudstone layers is lower than that within the individual layers. Therefore, the interlayer dip angle plays an important role in determining the failure mode of rainfall-induced landslides. To investigate the effect of interlayer dip angle on the mechanical response of Xigeda sandstone–mudstone slopes under rainfall conditions, a total of five model slope tests were conducted. Different ratios of model materials were selected for the sandstone and mudstone, and artificial rainfall with intensities representative of the Panxi region was simulated using a calibrated rainfall device. A combination of photography and instrument measurements was employed to study the seepage field, deformation field, and slope failure characteristics at five interlayer dip angles. It is shown that when the interlayer dip angle is smaller than the slope angle, an increase in the interlayer dip angle accelerates the movement of the wetting front along the weak interlayer plane. At the same time, this increase shortens the time to the occurrence of abrupt displacement and increases the corresponding displacement magnitude, which makes slope failure prediction more challenging. The shoulders of all slopes experienced displacement earliest and exhibited the largest displacement amplitude. The slope failure mode transitioned from shallow surface sliding to interlayer sliding. When the interlayer dip angle surpassed the slope angle, the weak interlayer plane was no longer the dominant control surface. Slope stability was thereby moderately enhanced, with the failure mode shifting to through-layer sliding. Full article

Existing ferroresonance fault identification methods often suffer from high misclassification rates, strong threshold dependency, and insufficient noise resistance. To bridge this gap, we propose a novel ferroresonance fault recognition method based on the Markov transition field (MTF) and three-branch Gaussian clustering (TBGC). Firstly, a symplectic geometric algorithm is employed to denoise the resonance feature signal, extract effective dominant modes, and reshape the series. Secondly, the reshaped feature series is converted into a Pixel matrix image employing the MTF. Subsequently, the gray-level co-occurrence matrix (GLCM) is utilized to extract the two-dimensional texture features of MTF images corresponding to different resonance types and construct corresponding TBGC models. Finally, the overvoltage sequence to be recognized is input into the TBGC model after feature extraction, and accurate discrimination of ferroresonance types is achieved based on cosine similarity. The analysis of fault recording data indicates that this method achieves 100% discrimination accuracy in eight test cases, surpassing the comparative method (maximum accuracy of 62.5%) by 37.5%, thereby validating its effectiveness and accuracy in ferroresonance identification. Full article

To investigate the relationship between operator fatigue and collision risk under human–machine interaction (HMI) in intelligent tower crane operations, and to reveal the mitigating effects of HMI on fatigue-induced collision risks, a comprehensive data acquisition approach integrating eye-tracking signals, risk indicators, and fatigue scale assessments was proposed and validated through scenario-based experiments. First, two experimental scenarios—traditional mechanical operation and HMI operation—were established. Based on a review of existing studies, representative eye-movement metrics and fatigue scale indicators were selected. Subsequently, operator fatigue states were classified into three levels: low fatigue, moderate fatigue, and high fatigue. A total of 28 participants were recruited to complete fatigue assessments and subsequently perform tower crane lifting tasks under both experimental scenarios. Finally, collision risk under different scenarios was quantitatively evaluated using the safety distance between the crane hook and the rigger, as well as the frequency of collision alarms. The results indicate that, under traditional mechanical operation, increasing fatigue levels were associated with a significant reduction in safety distance between the crane hook and the rigger, accompanied by a marked increase in collision alarm occurrences, resulting in a relatively high overall collision risk. In contrast, under the HMI operation scenario, participants demonstrated superior operational control at equivalent fatigue levels. Specifically, under moderate fatigue, collision risk was reduced from low risk to no risk, while under high fatigue, collision risk decreased from high risk to low risk. These results indicate that, under laboratory-simulated conditions, human–machine interaction can mitigate, to a certain extent, the increasing trend of collision risk when operators perform tower crane lifting operations under fatigue. These findings provide a scientific basis for further optimization of intelligent tower crane operational modes and the development of enhanced safety management strategies. Full article

The paper presents the Failure Mode and Effects Analysis (FMEA) method applied to a process-based case study, together with an approach for implementing the AIAG & VDA harmonized FMEA standard by using modern digital tools. While classical FMEA is widely used in the industry, risk assessment based on the Risk Priority Number (RPN) often leads to the inconsistent ranking of failures and unclear prioritization of corrective actions. This paper explores the shift from the traditional Risk Priority Number (RPN) approach to the Action Priority (AP) concept introduced in the AIAG & VDA FMEA Handbook and explains why this change leads to clearer, more consistent risk-based decisions. Rather than focusing only on the methodological differences, the paper also outlines a practical framework for full implementation, showing how Industry 4.0 technologies can strengthen traceability, improve response time, and ensure greater consistency in PFMEA development. It also examines how Artificial Intelligence (AI) and Large Language Models (LLMs) can support engineers in everyday practice—for example, by helping identify potential failure modes, standardizing documentation, and guiding the definition of prevention and detection controls. In parallel, IoT-based monitoring and real-time data collection can provide valuable feedback to validate occurrence and detection ratings. Over time, this data-driven feedback loop can improve the accuracy and reliability of risk assessments. The proposed framework contributes to improved responsiveness in process optimization activities, reduces the probability of recurring failures, and supports continuous quality improvement in manufacturing organizations. The solution is discussed in relation to classical FMEA practices and recent trends in the digital transformation of quality management systems. Full article

Water reuse may pose risks to the environment and human health due to pathogens or chemical pollutants (hazards) in reclaimed water arising from treatment or distribution system failures (hazardous events). In this context, the European Regulation EU 2020/741 requires the development of a Risk Management Plan (RMP) from the source to the irrigated fields. This study proposes a methodology to assess and manage the risk to guarantee a reliable treatment able to produce an effluent adequate for reuse. It combines Failure Mode and Effect Analysis (FMEA) with a Risk Priority Number (RPN) approach. FMEA identifies failure modes for the treatment components (hazardous events), their consequences for the system, and the hazards for environment and human health. The RPN measures the failure risk by the product of the likelihood of occurrence L, magnitude of effects M and ease of detection D for each failure. Due to a lack of data, L, M and D are estimated through scores. Failure risks are classified as low, medium, high and very high. The last step is the revision of existing corrective actions or the adoption of new ones to reduce the risk of critical failures (highest RPN). This methodology is applied to a large wastewater treatment plant (Class A technology, according to EU 2020/741). Out of the 303 failure modes identified for the 86 components, 12 are the most critical (very high risk) and the suggested additional corrective actions reduce L and/or D and thus M. This methodology supports an RMP for similar or more complex treatment plants. Full article

Regional production-consumption imbalances and deficient multimodal connectivity in grain circulation systems have rendered traditional segmented transport inefficient, loss-intensive, and costly, constraining overall supply chain performance. In China, the persistent north-to-south and west-to-east grain transfer patterns, driven by regional production–consumption imbalances, have imposed significant challenges on the grain circulation system, making multimodal transport optimization a critical priority for national food security. Multimodal transport, a critical logistics optimization strategy, integrates diverse transport modes and hub nodes to enable end-to-end coordination, thereby enhancing circulation efficiency and food security. This study systematically reviews the transport configurations and modal characteristics of grain multimodal transport, and employs bibliometric analysis with the VOSviewer tool to map publication trends and keyword co-occurrence networks. Subsequently, recent advances in transshipment hub location selection and route optimization in multimodal transport systems are examined. Finally, existing technical bottlenecks are summarized, and future research directions are outlined from the perspectives of intelligent logistics, green and low-carbon development, coordinated operations, and supply chain resilience. Full article

Wire-arc coatings have received substantial attention for corrosion protection; however, poor bonding often leads to delamination, corrosion initiation, and costly re-coating of structural components. This review combines bibliometric mapping with a focused technical synthesis to clarify how bonding performance has been studied in wire-arc coatings. Specifically, publication trends, keyword co-occurrence networks, and country-level co-authorship maps are used to map the evolution of the field and position adhesion-related studies within the broader literature. The analysis of 762 wire-arc coating publications from Web of Science (among 13,314 thermal spray coating records) reveals that research is centered on microstructure, mechanical properties, and corrosion resistance, with growing links to wire-based additive manufacturing. Keyword co-occurrence networks demonstrate clear process–structure–property relationships, while country-level collaboration maps highlight the leadership of China, the USA, and Germany. Critical to note, only eight publications systematically investigate the combined effects of substrate roughness, coating thickness, and Zn-Al coating composition on bond strength—representing less than 0.01% of the thermal spray literature. This pronounced research gap underscores the novelty of the present review, which synthesizes existing knowledge on adhesion mechanisms, identifies key process parameters, and establishes a research agenda to optimize wire-arc coatings for infrastructure corrosion protection. The technical synthesis highlights that adhesion is governed by the coupled effects of surface preparation (roughness and topography), coating build-up (thickness), and spray conditions (e.g., standoff distance and substrate preheating), which together influence coating microstructure and failure modes. These findings provide a structured framework to guide parameter selection for durable coatings. Full article