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The wear of a cutting tool can lead to tool failure and product quality deterioration, and thus timely maintenance of tools is crucial. Meanwhile, the wear of tools from a same population usually exhibits heterogeneous patterns. Therefore, this paper proposes a dynamic condition-based maintenance (CBM) policy for heterogeneous-wearing tools with considering the product quality deterioration caused by tool wear. The tool wear is modelled by an Inverse Gaussian (IG) process, and the wear rate is assumed to be a random variable to characterise the heterogeneity among tool wear processes. The posterior distribution of reciprocal of tool wear rate is dynamically estimated using the online wear data based on a Bayesian approach. Moreover, the impact of tool wear on product quality deterioration is modelled. The IG process is discretized into a discrete time Markov chain (DTMC). Under the frame of the DTMC, a cost function, containing product quality loss, preventive maintenance (PM) cost and corrective maintenance cost, is developed to determine the optimal PM threshold. The cost function updates dynamically with the dynamic estimation of tool wear rate and thus enables the optimal PM threshold to be dynamically revised. The effectiveness of the proposed CBM policy is demonstrated through a case study. [ABSTRACT FROM AUTHOR]

This paper aims to propose a Novel Condition-based maintenance (CBM) decision aid model for optimising the maintenance of complex multi-component systems. As the degradation level of each component is assumed to be independent and stochastic, it follows a specific probability distribution determined from historical data of experimental observations and inspection. The main objective is to optimise the total cost for providing maintenance actions and reducing the excess of spare parts usage. The decision support model consists of determining measurements on components with the aim of estimating the instant of time of removing predictively one or a group of components before they fail. The measurement model includes the mean residual lifetime (MRL) and some extensions developed for this purpose. For demonstrating the pertinency of the proposed model, we use a preventive maintenance strategy for one-component systems and a grouping/opportunistic maintenance for multi-component systems. Besides, a numerical comparative study performing these measurements is carried out using several examples and a case study from Electric energy distribution systems. The solution is illustrated as a decision-making optimal model for optimising the maintenance operations' costs and the total number of spare parts. The numerical results and the comparison show the efficiency of the proposed approach. [ABSTRACT FROM AUTHOR]

With the aging of production systems, failure modes become more common resulting in additional maintenance costs. To reduce these costs excess, flexible and smart maintenance strategies should be considered. This article proposes an integrated condition-based maintenance method associating opportunistic and importance measure concepts (IMC). The objective of an IMC-based model is to determine the contribution of the system's components according to their criticality degree for reliability improvement and maintenance planning. The identification of the best maintenance planning consists of determining the expected minimal cost that guarantees the repair actions of a group of critical components in one shot. Therefore, determining IMC values is not so easy for complex systems, it requires knowing their operational structure, the determination of the reliability value of each configuration and finally calculating each component's IMC degree and ranking them for the prioritisation selection. For testing the proposed method, an industrial case study has been used, regarding a complex system whose components fail at random times. The system undergoes minimal repairs if one or more components fail accidentally or by decision after inspection actions. The numerical results show that the developed approach incurs minimal maintenance costs, and can be integrated as a decision-aid solution for manufacturers. [ABSTRACT FROM AUTHOR]

To optimise production planning, maintenance strategies, and quality control in imperfect manufacturing systems, most existing works set the buffer stocking time at the cycle start time. However, excess inventory holding costs will be incurred if the buffer is stocked too early. This means that while the abovementioned setting can make the optimisation model simple, it might increase costs. Taking the uncertain practical buffer stocking time of an imperfect manufacturing system into account, this study aimed to find the production cycle, maintenance frequency, quality inspection cycle, and number of inspections that will minimise the expectation unit cost of the system. To this end, we developed a new model to optimise production, maintenance, and quality control considering timely replenishment. First, excess inventory holding and shortage costs were considered and the production process was divided into five scenarios based on the buffer stocking time and inspection time for assignable causes. Second, the link between production, maintenance, and quality was addressed by capturing the dynamic and random behaviour of production systems. An x-bar control chart was integrated into the model to monitor quality. A case study and sensitivity analysis were undertaken to verify the effectiveness and superiority of the proposed optimisation strategy. [ABSTRACT FROM AUTHOR]

In the maintenance optimisation framework, grouping maintenance is a promising solution for maintenance planning of multi-component systems, in which maintenance activities are performed together to reduce maintenance costs. One of the most widely identified challenges in real applications of grouping maintenance is that it may disturb the maintenance workload balance (smoothness), causing many difficulties in production and/or labour scheduling and inventory management. In this study, we propose a joint optimisation approach for maintenance grouping and workload balancing to address the above challenge. First, a mathematical model of the joint optimisation problem was derived. A multi-objective grouping optimisation approach based on the Weighted Sum model and Genetic Algorithm was implemented to determine the Pareto-optimal grouping solution. The proposed approach was applied to a real case study of an automotive plant comprising 40 production lines with 1090 components. The results highlighted the advantages, effectiveness, and flexibility of the proposed maintenance approach in real-world applications. [ABSTRACT FROM AUTHOR]

A maintenance model is specially developed for the work rolls equipped in the hot finishing mill group, with the purpose to decrease the total maintenance cost for the roll group under the constraint of the production batches and the roll thermal character. During the maintenance modelling at roll level, the impacts of the surface wear of the work roll on its failure rate state, the production quality and the preventive maintenance cost are deeply discussed. Then, a preventive maintenance grouping method is further proposed at roll group level, with the mutual restriction between the adjustable replacement time of the roll group and the optimal maintenance time of each work roll involved. The case study shows that the proposed maintenance model is practical for the maintenance scheduling of the work rolls in the real rolling production scenario. The total maintenance cost under the proposed maintenance method is always lower that the ones under the individual OM method and the maintenance window-based OM method. [ABSTRACT FROM AUTHOR]

In a multi-state manufacturing system, preventive maintenance is performed on other machines once the performance of a machine falls below a threshold state. Many investigations have been performed on the maintenance of manufacturing systems. However, studies on preventive maintenance for manufacturing systems that account for the different costs involved are limited. This paper focuses on the analysis of multi-state machines in a manufacturing system considering different costs. Subsequently, a cost-based preventive maintenance prioritisation method for multi-state machines is proposed. Based on the buffer capacity, three maintenance policies on the machines are discussed. Different machine failures lead to different maintenance priorities. The real-time buffer capacity greatly influences the selection of machines for preventive maintenance. The set of the optimal machines for preventive maintenance is determined based on the constraints of machine running time and different preventive maintenance costs. Finally, a numerical example is used to demonstrate the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

This paper focuses on the combined maintenance and routing optimisation problem in a multi-period environment that consists of scheduling maintenance operations for a set of geographically distributed machines, subject to non-deterministic failures with a set of technicians that perform preventive maintenance and corrective operations. This study uses a two-step approach based on column generation. The first step consists of a maintenance model that determines the optimal time until the next preventive maintenance operation, and each machine's maintenance frequency while minimising the total expected maintenance costs. The second step involves a routing model that assigns and schedules maintenance operations for each technician over the planning horizon while minimising the routing and maintenance costs. We formulate the second step problem as a periodic vehicle routing problem and to solve it, we propose a column generation approach. The master problem is a set partitioning formulation that indicates which machines must be visited in the planning horizon. This decomposition has two auxiliary problems: the first one is an elementary shortest-path problem to find a feasible path for each period and the second one finds a set of visiting periods for each machine. Our approach balances the maintenance cost, routing cost, and failure probabilities. [ABSTRACT FROM AUTHOR]

In this article, maintenance operations were incorporated to the sequence-dependent setup blocking flow shop to minimise total completion time subject to total maintenance costs. A mixed integer linear programming and procedures to incorporate maintenance to job sequence were developed. A constructive heuristic and three metaheuristics, greedy randomised adaptative search procedure (GRASP), discrete artificial bee colony (DABC), variable block insertion heuristic (VBIH) and iterated greedy algorithm (IG), designed to the blocking flow shop with total completion time minimisation were adapted to minimise total maintenance costs and the hierarchical function, respectively.All the methods were applied to solve small and medium and large size instance sets, with respective 1920 and 2200 problems. Experimental results shows that for small size instances set, DABC with α = 20 (DABC(20)) obtained the best trade-off between effectiveness and efficiency. For medium and large size instances set, DABC(20) VBIH with α = 20 (VBIH(20)) generated the best trade-off between quality of solution and computational time. Considering both instances set together, both DABC(20) and VBIH(20) obtained the best performance between quality of solution and computational time. [ABSTRACT FROM AUTHOR]

Ceramic Matrix Composites (CMCs), specifically SiC/SiC composites, represent a significant innovation in aerospace material technology, offering superior performance over traditional nickel-based superalloys in high-temperature turbine blade applications. This study presents a novel techno-economic assessment, filling a critical gap in the literature by directly comparing the economic and technical viability of CMCs versus superalloys. Unlike previous studies, which primarily focus on technical performance or cost analysis independently, this work integrates both aspects, providing a holistic comparison across key economic metrics, including acquisition, machining, maintenance, and recycling costs. The results demonstrate that SiC/SiC blades offer a 15–20% higher Net Present Value (NPV) and a 17% greater Internal Rate of Return (IRR) over a 20-year lifecycle than superalloys. Despite higher initial costs, CMCs achieve an estimated 2 to 3 years reduction in payback period, mainly due to their superior thermal and creep resistance, leading to fewer maintenance interventions and longer operational lifetimes. Although machining costs for CMCs are higher, these are more than offset by the long-term savings achieved through improved fuel efficiency and lower maintenance costs. A comprehensive sensitivity analysis, incorporating fluctuations in discount rates and material costs, further validates the economic robustness of CMCs in various operational scenarios. This study is the first to compare CMCs and superalloys, offering new insights into the financial implications of material selection in aerospace manufacturing. The findings present critical engineering recommendations that empower aerospace manufacturers and decision-makers to optimise material selection for improved efficiency and cost-effectiveness in high-performance turbine applications. [ABSTRACT FROM AUTHOR]

While dynamic regulation of photosynthesis in fluctuating light is increasingly recognized as an important driver of carbon uptake, acclimation to realistic irradiance fluctuations is still largely unexplored. We subjected Arabidopsis thaliana (L.) wild‐type and jac1 mutants to irradiance fluctuations with distinct amplitudes and average irradiance. We examined how irradiance fluctuations affected leaf structure, pigments and physiology. A wider amplitude of fluctuations produced a stronger acclimation response. Large reductions of leaf mass per area under fluctuating irradiance framed our interpretation of changes in photosynthetic capacity and mesophyll conductance as measured by three separate methods, in that photosynthetic investment increased markedly on a mass basis, but only a little on an area basis. Moreover, thermal imagery showed that leaf transpiration was four times higher under fluctuating irradiance. Leaves growing under fluctuating irradiance, although thinner, maintained their photosynthetic capacity, as measured through light‐ and CO2‐response curves; suggesting their photosynthesis may be more cost‐efficient than those under steady light, but overall may incur increased maintenance costs. This is especially relevant for plant performance globally because naturally fluctuating irradiance creates conflicting acclimation cues for photosynthesis and transpiration that may hinder progress towards ensuring food security under climate‐related extremes of water stress. Summary statement: We investigated photosynthesis acclimation and dynamics of leaf transpiration under specific light fluctuations in A. thaliana. The response was stronger for more intense fluctuations and showed a divergent response whereby photosynthesis is reduced but transpiration rose compared to steady light. [ABSTRACT FROM AUTHOR]

The assessment of rolling bearing reliability is vital for ensuring mechanical operational safety and minimizing maintenance costs. Due to the difficulty in obtaining data on the performance degradation and failure time of rolling bearings, traditional methods for reliability assessment are challenged. This paper introduces a novel hybrid method for the reliability assessment of rolling bearings, combining the convolutional neural network (CNN)‐convolutional block attention module (CBAM)‐ bidirectional long short‐term memory (BiLSTM) network with the Wiener process. The approach comprises three distinct stages: Initially, it involves acquiring two‐dimensional time‐frequency representations of bearings at various operational phases using Continuous Wavelet Transform. Subsequently, the CNN‐CBAM‐BiLSTM network is employed to establish health index (HI) for the bearings and to facilitate the extraction of deep features, serving as input for the Wiener process. The final stage applies the Wiener process to evaluate the bearings' reliability, characterizing the HI and quantifying uncertainties. The experiment is performed on bearing degradation data and the results indicate the effectiveness and superiority of the proposed hybrid method. [ABSTRACT FROM AUTHOR]

Most industries dealing with pipelines face problems resulting from the buildup of deposits therein, such as reduced efficiency, downtime and increased maintenance costs. Although solutions to this issue have been sought for decades, no widely employed technique for monitoring growth of inorganic deposits (or 'scaling') in pipelines exists. In this research, a means of detecting the onset of scaling growth, by processing resonance testing data, was sought. For the resonance testing measurements the pipeline segment of interest is equipped with acceleration sensors which record signals generated by impacting the pipeline with a steel tip. The signals are Fourier transformed and analysed in the frequency domain, where a clear shift in frequency peak positions can be observed as the scaling thickness changes. How best to extract quantitative information from the generated frequency data is an open question. In this research, two data analysis approaches for scaling thickness prediction are compared: a supervised (binary classification) machine learning approach as well as a comparison-based change detection approach using cross-correlation. The supervised machine learning approach yields generalizable results for different acceleration sensors and impactor diameters whilst the change detection approach is sensitive from a scaling thickness of 0.5 mm. Whilst this research is specific to the pipe–scaling geometry—and type used in the experiments conducted, resonance testing can be applied to any pipe–scaling combination. The robustness of the data processing approaches presented in this work, when applied to other pipe–scaling materials and geometries, is the next point of research. [ABSTRACT FROM AUTHOR]

The state goes the extra mile to recover the assets and reduce the financial loss caused by corruption, which is to execute the assets from the perpetrators who use or get by corruption activity, causing state financial loss. This research aims to identify asset depreciation as one of the economic problems of asset recovery, impacting the asset value and cost of asset maintenance. This study methodology used socio-legal research with a financial analysis from a legal perspective. The results show that asset depreciation could affect fixed asset value and asset recovery activity. Economic productive perspective implementation to the assets could benefit state financial income, reduce the cost of the maintenance of the asset, and maximize asset recovery. This research could contribute to and encourage the government to create asset management and regulation with an economic overview. [ABSTRACT FROM AUTHOR]

The research aims to investigate the effects of proportion and age of maintenance on the strength and economy of filling materials, in order to provide theoretical guidance and technical support for the management of coal mined-out area. Firstly, adjustments are made to the proportioning and maintenance age of the fill materials, and experiments are conducted to explore the strength performance of materials under different proportions. Secondly, GANs is utilized to expand the dataset, and an MLP-LSTM network is constructed to predict the strength of materials experiencing different maintenance ages under various proportions. Finally, integrating practical engineering applications, an economic viability prediction analysis is conducted to examine the cost of fill materials under different proportions and maintenance ages, along with their influencing factors. The results indicate that appropriate adjustments to proportioning can effectively enhance the strength of fill materials, whereas excessively high or low proportions may lead to unstable or surplus strength. Extending the maintenance age can to some extent improve the material's strength, but it also increases maintenance costs, necessitating a comprehensive balance in terms of economic viability. Consequently, this research offers a theoretical foundation and practical guidelines for optimizing mixture proportions and selecting appropriate curing ages, providing valuable insights for enhancing the efficiency and cost-effectiveness of coal mined-out area filling treatments. [ABSTRACT FROM AUTHOR]

The competitiveness between photovoltaic conversion efficiency (PCE) and transparency would be the bottleneck of transparent device for actual applications, as well as the high maintenance cost. In this work, the self-cleaning transparent device in interfacial Tb:Bi 2 Sn 2 O 7 QDs modified Cu 2 Y 2 O 5 /Bi 2 O 3 pn junction is prepared via a simple approach of sol-gel-hydrothermal method. The obtained Cu 2 Y 2 O 5 /Tb:Bi 2 Sn 2 O 7 QDs/Bi 2 O 3 exhibits high transmittance of ∼85 %, obvious photovoltaic enhancement of ∼2.0 × 103-folds (PCE of ∼1.17 %), stable output in 5 months cycles and a good self-cleaning via the hydrophobicity (contact angle of ∼123.4°). It's mainly attributed to the Tb:Bi 2 Sn 2 O 7 QDs and lamellar Bi 2 O 3 array. Besides appropriate potential and high quantum yield, the Tb:Bi 2 Sn 2 O 7 QDs, with the synergism of up-conversion fluorescence and interface/surface optimization, can improve the carrier kinetic equilibrium for achieving PCE-transparency balance, as well as increasing p-type conductivity through the synergism of Cu+/Cu2+ and interstitial oxygen. Moreover, the regular lamellar array can enhance solar efficiency via optimized surface, meanwhile achieving self-cleaning via the surface hydrophobicity and enhancing structural stability via the regular interval, making it being advantageous in actual applications of energy and information field, including windows, smart devices, etc. [ABSTRACT FROM AUTHOR]

Rolling bearing fault diagnosis enhances equipment reliability, reduces maintenance costs, and enables effective non-destructive testing (NDT). However, current research often emphasizes model design and performance optimization, overlooking the long-term dependencies of fault signals and the need for interpretability. This study proposes a rolling bearing fault diagnosis model utilizing a time-series fusion transformer with interpretability analysis. The model introduces multi-scale feature adaptive fusion to automatically capture and integrate features across different scales, enhancing the global pattern detection in time-series data. A dynamic patch auto-encoder module transforms feature embeddings into a low-dimensional space to better retain local information. The model’s design, particularly the decoding layer of the time-series Transformer, is optimized with a multi-head self-attentive mechanism, and multi-dimensional attention weights visualization methods are employed to clarify the fault feature extraction process. Quantitative visualizations throughout training improve interpretability and insight into learning dynamics. Experimental results indicate that this model surpasses state-of-the-art approaches on benchmark datasets, proving its generalizability and robustness in diverse testing scenarios. [ABSTRACT FROM AUTHOR]

Hydraulic systems are critical components of mechanical equipment, and effective fault diagnosis is essential for minimizing maintenance costs and enhancing system reliability. In practical applications, data from hydraulic systems are collected with varying sampling frequencies, coupled with complex interdependencies within the data, which poses challenges for existing fault diagnosis algorithms. To solve the above problems, this paper proposes an intelligent fault diagnosis of a hydraulic system based on a multiscale one-dimensional convolution neural network with a multiattention mechanism (MA-MS1DCNN). The proposed method first extracts features from multirate data samples using a parallel 1DCNN with different receptive fields. Next, a Hybrid Attention Module (HAM) is proposed, consisting of two submodules: the Correlation Attention Module (CAM) and the Importance Attention Module (IAM), which aim to meticulously and comprehensively model the complex relationships between channel features. Subsequently, to effectively utilize the feature information of different frequencies, the HAM is integrated into the 1DCNN to form the MA-MS1DCNN. Finally, the proposed method is evaluated and experimentally compared using the UCI hydraulic system dataset. The results demonstrate that, compared to existing methods such as Shapelet, MCIFM, and CNNs, the proposed method shows superior diagnostic performance. [ABSTRACT FROM AUTHOR]

A scientific and reasonable microseismic monitoring sensor network is crucial for the prevention and control of rockmass instability disasters. In this study, three feasible sensor network layout schemes for the microseismic monitoring of Sanshandao Gold Mine were proposed, comprehensively considering factors such as orebody orientation, tunnel and stope distributions, blasting excavation areas, construction difficulty, and maintenance costs. To evaluate and validate the monitoring effectiveness of the sensor networks, three layers of seismic sources were randomly generated within the network. Four levels of random errors were added to the calculated arrival time data, and the classical Geiger localization algorithm was used for locating validation. The distribution of localization errors within the monitoring area was analyzed. The results indicate that when the arrival time data are accurate or the error is between 0% and 2%, scheme 3 is considered the most suitable layout; when the error of the arrival time data is between 2% and 10%, scheme 2 is considered the optimal layout. These research results can provide important theoretical and technical guidance for the reasonable design of microseismic monitoring systems in similar mines or projects. [ABSTRACT FROM AUTHOR]

Faults in the wafer transfer robots (WTRs) used in semiconductor manufacturing processes can significantly affect productivity. This study defines high-risk components such as bearing motors, ball screws, timing belts, robot hands, and end effectors, and generates fault data for each component based on Fluke's law. A stacking classifier was applied for fault prediction and severity classification, and logistic regression was used to identify fault components. Additionally, to analyze the frequency bands affecting each failed component and assess the severity of faults involving two mixed components, a hybrid explainable artificial intelligence (XAI) model combining Shapley additive explanations (SHAP) and local interpretable model-agnostic explanations (LIME) was employed to inform the user about the component causing the fault. This approach demonstrated a high prediction accuracy of 95%, and its integration into real-time monitoring systems is expected to reduce maintenance costs, decrease equipment downtime, and ultimately improve productivity. [ABSTRACT FROM AUTHOR]

Mechanistic explanations of the impact of climate change on fish growth are currently under debate. However, critical assessments of even the most prominent theories are not always based on accurate interpretations of their underlying mechanistic models. This contribution addresses some of the major misunderstandings still causing the Gill-Oxygen Limitation Theory (GOLT) from being examined based on its actual structuring elements and assumptions, rather than erroneous perceptions. As we argue, recent critiques of the GOLT are based on implausible interpretations of respirometry data that are invoked to distinguish maintenance costs and overhead costs of growth. Discussing the current state of the debate, we emphasize the fact that fasting young and, thus, growing fish for short periods of time is not sufficient to suppress energy (i.e., oxygen) allocation to growth. In the process of dealing with these issues, several cases of apparent 'counter-evidence' are discussed. Highlighting the need to base critical discussions and examinations of the GOLT on its actual predictions, we recommend that testing the theory should focus on broad reviews or meta-analyses, e.g., on datasets of gill surface area and the relationship of these data to growth performance under different temperature regimes. [ABSTRACT FROM AUTHOR]

Spatiotemporal big data contain information in multiple dimensions such as space and time. Spatiotemporal data have the characteristics of large volume, intricate spatiotemporal relationship, and uneven spatiotemporal distribution. Index structure is one of the most important technologies used to improve system data analysis and workload. However, it is difficult to dynamically adjust with data density, resulting in increased maintenance costs and retrieval complexity. At the same time, maintaining the proximity of spatiotemporal data in spatial or temporal dimensions is crucial for efficient spatiotemporal analysis. To address these challenges, this paper proposes a learned index method, GLMI (Geohash and piecewise linear model-based index for spatiotemporal data). GLMI uses dynamic space partitioning based on the Hilbert curve to reduce the impact of data skew on index performance. In the time dimension, a piecewise linear model was constructed using the ShrinkingCone algorithm, and a buffer was designed to support the fast writing of spatiotemporal data. Compared with the current mainstream traditional high-dimensional indexes and the ZM index, GLMI has a smaller space consumption and shorter construction time compared to high-dimensional learned indexes on real traffic itinerary and trajectory record datasets. Meanwhile, GLMI also has an advantage in query efficiency. [ABSTRACT FROM AUTHOR]

Purpose: The purpose of this study is to optimize the operational efficiency of the entire system by developing a reasonable maintenance strategy for wind turbines that improves component reliability and safety while reducing maintenance costs. Design/methodology/approach: A hybrid incomplete preventive maintenance (PM) model based on boundary intensity process is established to give dynamic PM intervals for wind turbines using an iterative method with reliability as a constraint; the selection method of PM and replacement is given based on the cost-effectiveness ratio, which in turn determines the optimal number of PM for wind turbines. Findings: The reliability is used to obtain the components' maintenance cycle, and the cost-effectiveness ratio is used to select the number of maintenance times, thus, getting the optimal maintenance strategy. The validity of this paper's method is verified by arithmetic cases, which provides a new method for formulating a reasonable PM strategy for wind turbines. Practical implications: The wind turbine preventive maintenance strategy for Boundary intensity process proposed in this paper can scientifically formulate the maintenance strategy, optimize the cost-effectiveness per unit of time of the wind power generation system, and solve the problems of difficulty in formulating a reasonable maintenance strategy for the wind turbine components and high operation and maintenance costs. Originality/value: In this paper, the authors describe the failure pattern by a Boundary intensity process, establish a hybrid incomplete PM model by introducing a failure intensity increment factor and an age reduction factor and establish a maintenance strategy optimization model with comprehensive consideration of reliability and cost-effectiveness ratio. Finally, the validity of the model in this paper is verified by arithmetic case analysis. Peer review: The peer review history for this article is available at: https://publons.com/publon/10.1108/ILT-05-2024-0153/ [ABSTRACT FROM AUTHOR]

Purpose: Technological advances in instruments have greatly promoted the development of positron emission tomography (PET) scanners. State-of-the-art PET scanners such as uEXPLORER can collect PET images of significantly higher quality. However, these scanners are not currently available in most local hospitals due to the high cost of manufacturing and maintenance. Our study aims to convert low-quality PET images acquired by common PET scanners into images of comparable quality to those obtained by state-of-the-art scanners without the need for paired low- and high-quality PET images. Methods: In this paper, we proposed an improved CycleGAN (IE-CycleGAN) model for unpaired PET image enhancement. The proposed method is based on CycleGAN, and the correlation coefficient loss and patient-specific prior loss were added to constrain the structure of the generated images. Furthermore, we defined a normalX-to-advanced training strategy to enhance the generalization ability of the network. The proposed method was validated on unpaired uEXPLORER datasets and Biograph Vision local hospital datasets. Results: For the uEXPLORER dataset, the proposed method achieved better results than non-local mean filtering (NLM), block-matching and 3D filtering (BM3D), and deep image prior (DIP), which are comparable to Unet (supervised) and CycleGAN (supervised). For the Biograph Vision local hospital datasets, the proposed method achieved higher contrast-to-noise ratios (CNR) and tumor-to-background SUVmax ratios (TBR) than NLM, BM3D, and DIP. In addition, the proposed method showed higher contrast, SUVmax, and TBR than Unet (supervised) and CycleGAN (supervised) when applied to images from different scanners. Conclusion: The proposed unpaired PET image enhancement method outperforms NLM, BM3D, and DIP. Moreover, it performs better than the Unet (supervised) and CycleGAN (supervised) when implemented on local hospital datasets, which demonstrates its excellent generalization ability. [ABSTRACT FROM AUTHOR]

This paper presents a consecutive k$k$‐out‐of‐n$n$: F$F$ balance system in a shock environment, where the state transition of component is induced by external shocks. If a predetermined threshold number of effective shocks are applied to component in a critical state, the component will fail. The state of the system is defined by the number of consecutive failing component groups in the system, which leads to system failure when a critical number of consecutive failing components is reached. To minimize maintenance costs, we propose a preventive maintenance method with an optimization model. We use finite Markov chain imbedding and Phase‐type distribution to calculate component group failure rates and associated probability functions in discrete and continuous time, respectively. The validity and accuracy of the model are confirmed by numerical examples and Monte Carlo simulations. [ABSTRACT FROM AUTHOR]

The state of a machine is modeled as a controlled continuous-time Markov chain. Moreover, the machine is being serviced at random times. The aim is to maximize the time until the machine must be repaired, while taking the maintenance costs into account. The dynamic programming equation satisfied by the value function is derived, enabling optimal decision-making regarding inspection rates, and special problems are solved explicitly. This approach minimizes direct maintenance costs along with potential failure expenses, establishing a robust methodology for determining inspection frequencies in reliability-centered maintenance. The results contribute to the advancement of maintenance strategies and provide explicit solutions for particular cases, offering ideas for application in reliability engineering. [ABSTRACT FROM AUTHOR]

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Copyright of Przegląd Elektrotechniczny is the property of Przeglad Elektrotechniczny and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The simple design, compactness, simultaneous treatment of multiple contaminants, absence of chemical usage, minimal sludge formation (reducing secondary pollution), low maintenance cost, and versatility to operate in both batch and continuous modes make electrocoagulation (EC) a promising choice for treating various types of industrial wastewater. In this study, EC was employed in batch mode to treat produced water obtained from an oil drilling site, to reuse it for injection purposes in the reservoir. Produced water typically contains high levels of total dissolved solids (TDS), turbidity, chemical oxygen demand (COD), biological oxygen demand (BOD), and oil & grease (O&G) contaminants. High-performing aluminum (Al) electrodes were chosen due to their stability, conductivity, and, most significantly, their high capacity for generating aluminum hydroxide ([Al(OH)₃]ₙ) flocs, which serve as carriers for contaminant capture. This compound has demonstrated remarkable effectiveness in trapping the aforementioned contaminants from produced water under various operating conditions, including the number of electrodes, supplied current, and electrode configuration (bipolar and monopolar). The impact of several factors, including the number of electrodes (varying from 4 to 8), current density (varying as 16, 79, 158 A/m2), and electrode configuration (bipolar and monopolar), was studied at room temperature and 250 rpm agitation speed. Initial turbidity, COD, BOD, and O&G concentrations were measured at 38 NTU, 700.7 ppm, 120 ppm, and 32.8 ppm, respectively. The EC treatment exhibited removal efficiencies of 51% for TDS, 85% for turbidity, 78% for COD, 80% for BOD, and 85% for O&G using a monopolar configuration with 8 electrodes, and 59% for TDS, 90% for turbidity, 85% for COD, 84% for BOD, and 86% for O&G using a bipolar configuration with eight electrodes. Additionally, cost estimation, considering electrode dissolution rate and power requirements, was conducted for the operation of both configurations. [ABSTRACT FROM AUTHOR]

In the petrochemical industry, effective maintenance management is critical to operational efficiency and safety. Key indicators such as overall equipment effectiveness (OEE), reliability, safety incidents, and maintenance costs are commonly used by management to evaluate maintenance system performance. Increasing equipment availability and product quality can boost OEE and lead to fewer safety incidents and improved overall performance. Studies in Iran's petrochemical sector have shown that investing in maintenance and training can bring significant benefits. For example, data from 42 petrochemical complexes in Iran, covering 2015 to 2021, show an increase in equipment effectiveness and reliability over time. However, there was a slight slowdown in 2021 due to the impact of the Covid19 pandemic. Improvements in maintenance performance, the use of excellence models along with the necessary training have been associated with increased product realization and productivity growth. Research also shows that increased reliability can positively impact the reduction of safety incidents, environmental issues, and overall operating costs. By prioritizing maintenance excellence and adopting best practices, organizations can strengthen their competitiveness and sustainability in a dynamic business landscape. [ABSTRACT FROM AUTHOR]

Spare parts management is a critical aspect of high-speed train health management, playing a vital role in maximizing in-service time and minimizing maintenance costs. However, traditional spare parts management methods, which rely solely on historical experience and suggest spare parts quantities or ratios in equipment manuals, often lack practicality and fail to meet real-world demands. To address these limitations, this paper proposes a performance prediction-based spare parts management strategy for high-speed trains. The strategy comprises three main components. First, a performance degradation model is developed using performance evaluation results to define a performance degradation envelope. Next, the required quantity or ratio of spare parts for multiple devices in different performance states is determined using the expected performance score method. Finally, the timing of spare parts orders is scientifically optimized by accounting for production and transportation lead times. To demonstrate the effectiveness of the proposed strategy, we conducted experiments using the spare parts management of a specific high-speed train running gear as a case study and compared it with existing spare parts management methods. [ABSTRACT FROM AUTHOR]

The maintenance and upkeep costs of wind farms and their internal wind turbines have been increasing annually. Therefore, a systematic evaluation of their operating status is of great importance in guiding reductions in maintenance and upkeep costs. In this aspect, this article proposes a three-level service quality index system of "key component–wind turbine–wind farm" based on the fuzzy comprehensive evaluation method. Firstly, raw data on the wind farm are preprocessed to avoid the impact of abnormal data on the evaluation results. Then, the data types are classified and the degradation degree of each indicator is calculated. Based on the entropy weight method, the weight of each indicator is weighted and summed to obtain the overall membership degree. Finally, the overall health level is determined according to the "maximum membership degree", which is the evaluation result. This article conducts an evaluation experiment based on the actual operating data of Gansu Huadian Nanqiu Wind Farm. The example shows that the proposed strategy can systematically evaluate the health level of wind farms and predict the future trends of health status changes. The research results can provide reference for the reasonable arrangement of unit scheduling, operation, and maintenance plans in wind farms. [ABSTRACT FROM AUTHOR]

Web‐based test automation heavily relies on accurately finding web elements. Traditional methods compare attributes but do not grasp the context and meaning of elements and words. The emergence of large language models (LLMs) like GPT‐4, which can show human‐like reasoning abilities on some tasks, offers new opportunities for software engineering and web element localization. This paper introduces and evaluates VON Similo LLM, an enhanced web element localization approach. Using an LLM, it selects the most likely web element from the top‐ranked ones identified by the existing VON Similo method, ideally aiming to get closer to human‐like selection accuracy. An experimental study was conducted using 804 web element pairs from 48 real‐world web applications. We measured the number of correctly identified elements as well as the execution times, comparing the effectiveness and efficiency of VON Similo LLM against the baseline algorithm. In addition, motivations from the LLM were recorded and analysed for 140 instances. VON Similo LLM demonstrated improved performance, reducing failed localizations from 70 to 40 (out of 804), a 43% reduction. Despite its slower execution time and additional costs of using the GPT‐4 model, the LLM's human‐like reasoning showed promise in enhancing web element localization. LLM technology can enhance web element localization in GUI test automation, reducing false positives and potentially lowering maintenance costs. However, further research is necessary to fully understand LLMs' capabilities, limitations and practical use in GUI testing. [ABSTRACT FROM AUTHOR]

An accurate estimate of scour depth downstream of culvert outlets is essential for culvert design integrity. Inadequate designs can result in structural failures, leading to increased costs for maintenance and rehabilitation. The present research evaluates the efficacy of numerical models in predicting scour depth and its location downstream of circular culverts under variable flow conditions. Two hydrographs were created for unsteady flow, featuring nine different flow discharges, while steady flow conditions were analysed at flow rates of 14 l/s and 22 l/s. The study investigated circular culverts with inlet blockages of 0%, 15%, and 30%, comparing outcomes with predictions from the Flow-3D software using the renormalisation group (RNG) turbulence model. Extensive experimental data on circular culverts were utilised, with simulations performed using commercial software. This involved analysing the scour's downstream profile, its maximum depth, and its location, and comparing these metrics with actual observed data. The results revealed that the numerical model predictions closely corresponded to the experimental data, even though the simulated scour was generally less than that observed for steady and unsteady flows. The results showed that in unsteady flow conditions and for the discharge of 22 l/s, 30% blockage increased scour by 6.8% and 14.2%, respectively, compared to 15% blockage and non-blocked flow. This increase was 22% and 9.5% for the discharge of 14 l/s, respectively. In the steady case, when the flow rate was adjusted from 14 l/s to 22 l/s, there was a noticeable increase in scour depth downstream of the culvert. While blockage rates impacted the scour patterns significantly in unsteady flow scenarios, escalating blockage percentages did not lead to uniformly proportional increases in scour depth within steady flow environments. [ABSTRACT FROM AUTHOR]

Hydrocyclones utilize centrifugal sedimentation to separate dispersed phases from a continuous liquid phase, offering advantages such as the absence of moving parts, ease of operation, and low maintenance costs. They are widely used in the mineral, petrochemical, and biotechnological industries, with their performance being significantly influenced by geometric and operational variables. This review examines the impact of key geometric (e.g. inlet, cone, vortex finder, overflow, outflow, and cylinder) and operational (e.g. particle size, solid concentration, feed flow rate, and pressure drop) parameters on hydrocyclone performance. Recent innovations, new developments, and perspectives to address specific industrial demands are also discussed. [ABSTRACT FROM AUTHOR]

Background: HIV treatment programs in Africa have implemented centralized testing for routine viral load monitoring (VLM), which may result in specimen processing delays inhibiting timely return of viral load results. Decentralized, point-of-care (PoC) VLM is a promising tool for expediting HIV clinical decision-making but remains unavailable in most African settings. We qualitatively explored the perceived feasibility and appropriateness of PoC VLM to address gaps along the viral load monitoring continuum in rural Uganda. Methods: Between May and September 2022, we conducted 15 in-depth interviews with HIV clinicians (facility in-charges, clinical officers, nurses, counselors) and six focus group discussions with 47 peer health workers from three south-central Ugandan districts. Topics explored centralized VLM implementation and opportunities/challenges to optimizing routine VLM implementation with PoC testing platforms. We explored perspectives on PoC VLM suitability and feasibility using iterative thematic analysis. Applying the Framework Method, we then mapped salient constraints and enablers of PoC VLM to constructs from the Consolidated Framework for Implementation Research. Results: Clinicians and peers alike emphasized centralized viral load monitoring's resource-intensiveness and susceptibility to procedural/infrastructural bottlenecks (e.g., supply stockouts, testing backlogs, community tracing of clients with delayed VLM results), inhibiting timely clinical decision-making. Participants reacted enthusiastically to the prospect of PoC VLM, anticipating accelerated turnarounds in specimen processing, shorter and/or fewer client encounters with treatment services, and streamlined efficiencies in HIV care provision (including expedited VLM-driven clinical decision-making). Anticipated constraints to PoC VLM implementation included human resource requirements for processing large quantities of specimens (especially when machinery require repair), procurement and maintenance costs, training needs in the existing health workforce for operating point-of-care technology, and insufficient space in lower-tier health facilities to accommodate installation of new laboratory equipment. Conclusions: Anticipated implementation challenges, primarily clustering around resource requirements, did not diminish enthusiasm for PoC VLM monitoring among rural Ugandan clinicians and peer health workers, who perceived PoC platforms as potential solutions to existing inefficiencies within the centralized VLM ecosystem. Prioritizing PoC VLM rollout in facilities with available resources for optimal implementation (e.g., adequate physical and fiscal infrastructure, capacity to manage high specimen volumes) could help overcome anticipated barriers to decentralizing viral load monitoring. [ABSTRACT FROM AUTHOR]

Improving wind power generation efficiency and lowering maintenance and operational costs are possible through the early and efficient diagnosis and repair of surface defects in wind turbines. To solve the lightweight deployment difficulty and insufficient accuracy issues of the traditional detection methods, this paper proposes a high-precision PC-EMA block based on YOLOv8 using partial convolution (PConv) combined with an efficient multiscale attention (EMA) channel attention mechanism, which replaces the bottleneck layer of the YOLOv8 backbone network to improve the extraction of target feature information from each layer of the network. In the feature fusion phase, GSConv, which can retain more channel information, is introduced to balance the model's complexity and accuracy. Finally, by merging two branches and designing the PConv head with a low-latency PConv rather than a regular convolution, we are able to effectively reduce the complexity of the model while maintaining accuracy in the detection head. We use the WIoUv3 as the regression loss for the improved model, which improves the average accuracy by 5.07% and compresses the model size by 32.5% compared to the original YOLOv8 model. Deployed on Jetson Nano, the FPS increased by 11 frames/s after a TensorRT acceleration. [ABSTRACT FROM AUTHOR]

The automation of rail track inspection addresses key issues in railway transportation, notably reducing maintenance costs and improving safety. However, it presents numerous technical challenges, including sensor selection, calibration, data acquisition, defect detection, and storage. This paper introduces a compression method tailored for laser triangulation scanners, which are crucial for scanning the entire rail track, including the rails, rail fasteners, sleepers, and ballast, and capturing rail profiles for geometry measurement. The compression technique capitalizes on the regularity of rail track data and the sensors' limited measurement range and resolution. By transforming scans, they can be stored using widely available image compression formats, such as PNG. This method achieved a compression ratio of 7.5 for rail scans used in the rail geometry computation and maintained rail gauge reproducibility. For the scans employed in defect detection, a compression ratio of 5.6 was attained without visibly compromising the scan quality. Lossless compression resulted in compression ratios of 5.1 for the rail geometry computation scans and 3.8 for the rail track inspection scans. [ABSTRACT FROM AUTHOR]

This paper aims to develop a method to construct an asymmetric copula, based on which a closed form of the cumulative bivariate failure rate can be obtained. The construction method differs from existing ones. This new method can facilitate the derivation of some results such as the estimation of the expected number of occurrences for a system whose failure process is modelled by a bivariate stochastic process or the expected cost in optimisation of maintenance policies. [ABSTRACT FROM AUTHOR]

ABSTRACT Objectives Material and Methods Results Conclusions To assess the costs and efforts of maintenance therapy following implant treatment with fixed restoration over an observation period of 10 years.This randomized controlled clinical trial included 64 patients who were randomly assigned to receive one of two implant systems (AST or STM) and fixed restoration. Patients were included in a regular maintenance program and were examined at loading, 1, 3, 5, 8, and 10 years. Outcome measures included technical and biological complications, time, efforts, and costs to resolve them.A total of 97 implants were placed in 64 patients (AST: 54, STM: 43). Patient recall rates at 5 and 10 years were 89% and 67%. In general, technical complications were resolved within one to two appointments (mean = 1.5), and biological complications required a mean of 1.3 appointments. The overall regular maintenance time for the period of 10 years amounted to 77 min per year. Technical complications occurred in 39.5% of the patients, with screw‐loosening being the most common one (43.4% of all complications). The most time‐consuming technical complication was abutment fracture (94 min ± 68), followed by screw fracture (84 min ± 38). The prevalence of peri‐implant mucositis on the patient level was 30.2%, and it was 9.3% for peri‐implantitis. The average annual maintenance costs amounted to 9% of the initial cost of the implant treatment over the period of 10 years.Additional regular maintenance costs and costs due to the treatment of potential complications have to be taken into consideration when placing dental implants. The majority of technical complications could be resolved within one appointment, whereas the time needed to treat biological complications varied between one and three appointments for peri‐implantitis. [ABSTRACT FROM AUTHOR]

The Vehicle On-Board Controller (VOBC) is a core component of urban rail transit trains, with high maintenance costs and serious consequences in case of failure. Ensuring long-term VOBC operation while minimizing maintenance costs is of paramount importance. This paper proposes a multi-objective preventive maintenance (PM) strategy and optimization considering system unavailability and maintenance costs. Firstly, we provide a statement of the problem and analyze the impact of different maintenance actions on VOBC. Next, the decreasing process parameter is introduced to adjust the PM interval dynamically. The mathematical models of system unavailability and maintenance costs are derived to construct a multi-objective PM strategy. Then, we develop a hybrid heuristic algorithm including multi-objective particle swarm optimization and genetic algorithm (MOPSO-GA), which is used to obtain the optimal Pareto frontier for maintenance strategies. In the study, we select maintenance strategies by introducing an aversion factor to fulfill the preferences of different decision-makers. A comprehensive sensitivity analysis is performed to illustrate the impact of several critical parameters on the multi-objective PM strategy. The case study on Beijing subway's VOBC is also executed. The outcomes indicate that the proposed method can dynamically adjust to match the preferences of diverse decision-makers and the MOPSO-GA algorithm has excellent robustness on most parameters. This approach allows for creating field maintenance strategies catering to the varied maintenance requirements of urban rail transit VOBC. [ABSTRACT FROM AUTHOR]

Based on Law no. 3 of 2020 article 140 concerning Mineral and Coal Mining which states that "Every holder of a Mining Business Permit (IUP) and Special Mining Business Permit (IUPK) is required to submit a reclamation plan and post-mining plan when applying for a Production Operation IUP or Production Operation IUPK as basis for placing reclamation guarantees. The aim of this research is to calculate the direct and indirect costs required for PT Bukit Asam's reclamation plan. The research method used in this research is using activities in the form of visits and direct observations in the field and also collecting articles as support. Direct costs include recountouring costs, revegetation costs and maintenance costs, which are planned at Rp. 12,889,010,229. Meanwhile for indirect costs, the amount is determined from the indirect cost graph, the plan is Rp. 3,054,695,424, so the total cost of the reclamation plan for the West Banko Pit is Rp. 15,943.,705,653 with an escalation calculation of 5.51%, it becomes IDR 19,758,945,354. [ABSTRACT FROM AUTHOR]

Introduction/purpose: The aim of this paper is to obtain quantitative and qualitative indicators of vehicle condition and reliability based on operational data which can be used to determine the optimal periodicity of preventive maintenance for special purpose vehicles, and to more accurately manage the maintenance process and the operational readiness of these vehicles. Methods: Based on operational failure data and statistical methods, a mathematical model of the reliability of special purpose vehicles was determined. Using this model and operational data, the periodicity of preventive maintenance for the vehicles was determined through multicriteria optimization, considering both readiness and minimum maintenance costs. The same methodology was applied to determine the optimal preventive maintenance periodicity for 15 components of the mechanical system of special purpose vehicles. A group analysis was conducted using Minitab 15 statistical software, based on the theory of similarity in preventive maintenance periodicity for the 15 components, and a statistical analysis of the conducted research was performed using Minitab 16 statistical software. Results: Models for preventive maintenance of special purpose vehicles were developed based on the recorded vehicle failures and the failures of fifteen vital components. A group analysis grouped the fifteen components into optimal maintenance groups, similar in terms of working time between failures. The statistical analysis of the research determined a functional relationship for the optimal periodicity of preventive maintenance for special purpose vehicles. Conclusion: The maintenance periodicity obtained through multi-criteria analysis is optimal, as it achieves satisfactory vehicle readiness with optimal maintenance costs. The statistical analysis of the research concluded that the maintenance periodicities of the vehicle components are different. In both fleets, the engine and the transmission block have the longest maintenance intervals. The research results can be used to rationalize the existing preventive maintenance concept. [ABSTRACT FROM AUTHOR]

Aiming at the contract period of Shared Water Saving Management Contract (WSMC), this paper establishes a contract period optimization model based on win-win. Firstly, the water-saving investment and revenue are modeled as stochastic processes considering the fluctuation of uncertain variables such as operation and maintenance cost, water-saving amount and water price. Secondly, the profits of the water owner and Water-Saving Service Company (WSCO) are portrayed separately based on the WSMC market service mechanism. Then, the contract period model is constructed by minimizing the difference between the NPV of the two parties involved in the project as a constraint. Finally, the model is applied to two real cases, where the optimized contract period is 9 years in Case 1 and 12 years in Case 2. In addition, several variables affecting the model are analyzed, allowing the model to expand its applicability. [ABSTRACT FROM AUTHOR]

Self-healing systems have become essential in modern computing for ensuring continuous and secure operations while minimising downtime and maintenance costs. These systems autonomously detect, diagnose, and correct anomalies, with effective self-healing relying on accurate interpretation of system logs generated by operating systems (OSs). Manual analysis of these logs in complex environments is often cumbersome, time-consuming, and error-prone, highlighting the need for automated, reliable log analysis methods. Our research introduces an intelligent methodology for creating self-healing systems for multiple OSs, focusing on log classification using CountVectorizer and the Multinomial Naive Bayes algorithm. This approach involves preprocessing OS logs to ensure quality, converting them into a numerical format with CountVectorizer, and then classifying them using the Naive Bayes algorithm. The system classifies multiple OS logs into distinct categories, identifying errors and warnings. We tested our model on logs from four major OSs; Mac, Android, Linux, and Windows; sourced from Zenodo to simulate real-world scenarios. The model's accuracy, precision, and reliability were evaluated, demonstrating its potential for deployment in practical self-healing systems. [ABSTRACT FROM AUTHOR]

HVDC is a promising interconnection solution for connecting asynchronous systems and ensuring power control. In Indonesia, a remote industrial system in Sumatra is experiencing load growth and has the option to draw power from the Sumatra system. However, due to frequency differences, the use of HVDC is crucial. The Generation Expansion Planning has proposed six converters but not their interconnection points. This study will determine the most reliable interconnection locations. The chosen converters are modular multilevel converters (MMCs) with high modularity. The converter reliability modeling considers voltage levels, the number of modules, and redundancy strategies. This modeling is then implemented at the power system level to obtain the best placement at the available high-voltage (HV) substation options. Determining the best placement is based on the optimal reliability index. The optimal placement also includes the option to convert from HV to medium-voltage (MV) interconnection. MV interconnection offers higher flexibility but tends to be more expensive. The availability for HV converters is 99.69%, while for MV converters, it is slightly higher, at 99.81%. Additionally, converting from HV to MV reduces the SAIFI (system average interruption frequency index) from 0.2668 to 0.2284 occurrences per year, lowering the interruption cost from 7.804 million USD to 5.737 million USD per year. The sensitivity of interruption, investment, and maintenance costs shows that converting at least one HV converter to MV remains economical. In this case study, the optimal converter placement includes Area VI–2, recommended for conversion from HV to a more distributed MV configuration, improving reliability and economic efficiency. [ABSTRACT FROM AUTHOR]

Although the double-double (DD) laminates proposed by Tsai provide a promising option for achieving better structural performance with lower manufacturing and maintenance costs, the buckling performance of perforated DD laminates still remains clear. In this study, optimal ply angles, rotation angles, and the corresponding maximum buckling loads are determined for DD laminates with various cutouts, which are used for comparisons to evaluate the effects of cutout size and shape on the buckling behaviour of perforated DD laminates. Apart from conventional circular and elliptical cutouts, the use of a combined-shape cutout for DD laminates is also investigated. As a large number of optimisations are required to obtain the maximum buckling loads for different cases in this study, an efficient optimisation method for perforated DD laminates is proposed based on an artificial neural network (ANN) and a genetic algorithm (GA). Unlike conventional quadaxial (QUAD) laminates, the repetition of a four-ply sublaminate in DD laminates makes their layup to be represented by only two ply angles; hence, the application of ANN models for predicting the buckling behaviour of various perforated DD laminates is studied in this paper. The superior performance of the ANN models is demonstrated by comparisons with other machine learning models. Instead of using the time-consuming FEA, the developed ANN model is utilised within a GA to obtain the maximum buckling load of perforated DD laminates. Compared to the circular cutout, the use of elliptical and combined-shape cutouts leads to more noticeable changes in the optimal ply angles as the cutout size increases. Based on the obtained results, the use of the combined-shape cutout is recommended for DD laminates. [ABSTRACT FROM AUTHOR]

Sea level monitoring is of great significance to the life safety and daily production activities of coastal residents. In recent years, GNSS interferometric reflectometry (GNSS-IR) has gradually developed into a powerful complementary technique for sea level monitoring, with the advantages of wide signal spatial coverage and lower maintenance cost. However, GNSS-IR-retrieved sea level estimates suffer from a prominent error source, referred to as the dynamic height error due to the nonstationary sea level. In this study, the tidal analysis method, least squares method and cubic spline fitting method are used to correct the dynamic height error, and their performances are analyzed. These three methods are applied to multi-system and multi-frequency data from three coastal GNSS stations, MAYG, SC02 and TPW2, for three years, and the retrievals are compared and analyzed with the in situ measurements from co-located tide gauges to explore the applicability of the three methods. The results show that the three correction methods can effectively correct the sea level dynamic height error and improve the accuracy and reliability of the GNSS-IR sea level retrievals. The tidal analysis method shows the best correction performance, with an average reduction of 39.3% (10.7 cm) and 37.6% (6.7 cm) in RMSE at the MAYG and TPW2 stations, respectively. At station SC02, the cubic spline fitting method performs the best, with the RMSE reduced by an average of 39.3% (5.5 cm) after correction. Furthermore, the iterative process of the tidal analysis method is analyzed for the first time. We found the tidal analysis method could significantly remove the outliers and correct the dynamic height error through iterations, generally superior to the other two correction methods. With the dense preliminary GNSS-IR sea level retrievals, the smaller window length of the least squares method can yield more corrected retrievals and better correction performance. The least squares method and cubic spline fitting method, especially the former, are highly dependent on the amount of daily GNSS-IR sea level retrievals, but they are more suitable for dynamic height correction in storm events than the tidal analysis method. [ABSTRACT FROM AUTHOR]

Males of many species develop disproportionately large weapons, and frequently the weapon size increases more quickly with body size in small than in large males—a trend often considered to result from the depletion of developmental resources limiting male weapon exaggeration. Based on the cost‐minimization hypothesis, a shallower weapon‐body size allometric slope in large than in small males could be selected for if the energetic maintenance costs of having oversized weapons increase faster in large than in small males. Whether males of different size groups that differ in the weapon‐body size allometric relationship also differ in the energetic costs of maintaining their weapons has not yet been investigated. Consequently, we studied this using the stag beetle (Cyclommatus mniszechi), in which males are larger in size and have larger mandibles than do females and are divided into two morphs: majors are larger and have longer mandibles than minors. In both male morphs, mandible size increases disproportionately with their body size, but it increases more quickly in minors than in majors. We measured the standard metabolic rate (SMR) of the three types of these beetles (the two male morphs and females) to investigate whether the energetic maintenance cost of carrying weapons differs among them. Body weight explained 80% of the variation in the SMR in the three types of beetles, and SMR increased more quickly with body weight in major males than in minor males or females. Weapon and body size also jointly explained approximately 80% of the variation in the SMR. Whereas SMR increased with body size in all three types of beetles (with a higher rate of increase in major males than in minor males or females), only the major males' SMR increased with weapon size. Overall, being heavier and larger and carrying oversized weapons are energetically costly in major males, something which could constrain the exaggeration of their weapons. [ABSTRACT FROM AUTHOR]