Search

Enhancing crop water use efficiency (WUE) is a key target trait for climatic resilience and expanding cultivation on marginal lands. Engineering lower stomatal density to reduce stomatal conductance (g s) has improved WUE in multiple C3 crop species. However, reducing g s in C3 species often reduces photosynthetic carbon gain. A different response is expected in C4 plants because they possess specialized anatomy and biochemistry which concentrates CO2 at the site of fixation. This modifies the relationship of photosynthesis (A N) with intracellular CO2 concentration (c i), such that photosynthesis is CO2 saturated and reductions in g s are unlikely to limit A N. To test this hypothesis, genetic strategies were investigated to reduce stomatal density in the C4 crop sorghum. Constitutive expression of a synthetic epidermal patterning factor (EPF) transgenic allele in sorghum led to reduced stomatal densities, reduced g s, reduced plant water use, and avoidance of stress during a period of water deprivation. In addition, moderate reduction in stomatal density did not increase stomatal limitation to A N. However, these positive outcomes were associated with negative pleiotropic effects on reproductive development and photosynthetic capacity. Avoiding pleiotropy by targeting expression of the transgene to specific tissues could provide a pathway to improved agronomic outcomes. [ABSTRACT FROM AUTHOR]

Bearing component damage contributes significantly to rotating machinery failures. It is vital for the rotor-bearing system to be in good condition to ensure the proper functioning of the machine. Over recent decades, extensive research has been devoted to the condition monitoring of rotational machinery, with a particular focus on bearing health. This paper provides a comprehensive literature review of recent advancements in intelligent condition monitoring technologies for rolling element bearings. Fundamental monitoring strategies are introduced, covering various sensing, signal processing, and feature extraction techniques for detecting defects in rolling element bearings. While vibration-based monitoring remains prevalent, alternative sensor types are also explored, offering complementary diagnostic capabilities or detecting different defect types compared to accelerometers alone. Signal processing and feature extraction techniques, including time domain, frequency domain, and time–frequency domain analysis, are discussed for their ability to provide diverse perspectives for signal representation, revealing unique insights relevant to condition monitoring. Special attention is given to information fusion methodologies and the application of intelligent algorithms. Multisensor systems, whether homogeneous or heterogeneous, integrated with information fusion techniques hold promise in enhancing accuracy and reliability by overcoming limitations associated with single-sensor monitoring. Furthermore, the adoption of AI techniques, such as machine learning, metaheuristic optimisation, and deep-learning methods, has led to significant advancements in condition monitoring, yielding successful outcomes with improved accuracy and robustness in various studies. Finally, avenues for further advancements to improve monitoring accuracy and reliability are identified, offering insights into future research directions. [ABSTRACT FROM AUTHOR]

Pipeline integrity and safety depend on the detection and prediction of stress corrosion cracking (SCC) and other defects. In oil and gas pipeline systems, a variety of corrosion-monitoring techniques are used. The observed data exhibit characteristics of nonlinearity, multidimensionality, and noise. Hence, data-driven modeling techniques have been widely utilized. To accomplish intelligent corrosion prediction and enhance corrosion control, machine learning (ML)-based approaches have been developed. Some published papers related to SCC have discussed ML techniques and their applications, but none of the works has shown the real ability of ML to detect or predict SCC in energy pipelines, though fewer researchers have tested their models to prove them under controlled environments in laboratories, which is completely different from real work environments in the field. Looking at the current research status, the authors believe that there is a need to explore the best technologies and modeling approaches and to identify clear gaps; a critical review is, therefore, required. The objective of this study is to assess the current status of machine learning's applications in SCC detection, identify current research gaps, and indicate future directions from a scientific research and application point of view. This review will highlight the limitations and challenges of employing machine learning for SCC prediction and also discuss the importance of incorporating domain knowledge and expert inputs to enhance the accuracy and reliability of predictions. Finally, a framework is proposed to demonstrate the process of the application of ML to condition assessments of energy pipelines. [ABSTRACT FROM AUTHOR]

Taking care of people with dementia is stressful for informal caregivers. Respite care can provide a break for them. But it is often underused, and its acceptability can be low in China. One reason is that it fails to meet their needs. This study aimed to explore the preference of caregivers of people with dementia for respite care services. A discrete choice experiment was used. Attributes and levels of respite care were informed by a literature review and expert consultations. A fractional factorial design was used to reduce the number of choice sets. A questionnaire survey was conducted in Tianjin, China. A mixed logit model was used to compute coefficients for attributes and levels. Respite care packages differed in five attributes, which were content, provider, duration, frequency, and cost. 322 caregivers completed valid discrete choice experiment questionnaires. All five attributes were statistically significant. Two most preferred attributes were "providers" (coefficient 1.297, p < 0.001) and "frequency" (coefficient 1.169, p < 0.001). Caregivers prefer highly qualified service providers and frequent respite care. The desirable respite care for caregivers would be assistance with personal care for people with dementia which was provided by an experienced worker with attitudes of understanding and respect towards people with dementia more than 3 times per week regularly and more than 4 h every time with a lower cost. This study offered new information on Chinese dementia caregivers' preferences for respite care. Policymakers should pay attention to the frequency and quality of providers when designing respite care to improve service uptake and ultimately lighten the burden on caregivers of people with dementia. [ABSTRACT FROM AUTHOR]

This paper focuses on developing maintenance policies for critical assets to improve the production performance based on life cycle cost (LCC) analysis. A general approach is adopted for conducting the LCC analysis. The investigation is based on a case study to demonstrate how an optimum maintenance policy is determined. The relevant LCC structure in the case study is defined for the decision process which involves determination of the optimum life, repair limit and selection of materials, and trade-off between repair and replacement. The LCC analysis is based on statistical data modelling which facilitates decision-making on the optimal replacement of an asset and its remaining life. Based on the optimization and remaining life criterion, the optimal maintenance policy can be made. The results obtained from this case study include selection of the best lining material for use, determination of the optimal time for refractory lining replacement, the hot repair sequence required for maintaining the optimum condition and the repair limit for doing cold repairs before replacement, for one type of electric arc furnaces used in the steel industry. [ABSTRACT FROM PUBLISHER]

Wind energy is one of the most important renewable energy sources in the world. Accurate wind power prediction is of great significance for achieving reliable and economical power system operation and control. For this purpose, this paper is focused on wind power prediction based on a newly proposed shuffle–and–fusion interaction network (SFINet). First, a channel shuffle is employed to promote the interaction between timing features. Second, an attention block is proposed to fuse the original features and shuffled features to further increase the model's sequential modeling capability. Finally, the developed shuffle–and–fusion interaction network model is tested using real-world wind power production data. Based on the results verified, it was proven that the proposed SFINet model can achieve better performance than other baseline methods, and it can be easily implemented in the field without requiring additional hardware and software. [ABSTRACT FROM AUTHOR]

Caregivers of people with dementia (PwD) frequently experience poor physical, mental and social health. Over the past decade, several guidelines have offered recommendations on supporting caregivers to improve their health outcomes. This systematic review aimed to assess the quality and evidence base of guidelines on supporting caregivers of PwD and review the content of their recommendations on caregiver support to facilitate the adoption and implementation of a specific guideline among policymakers and practitioners. A systematic search for guidelines on supporting caregivers of PwD was performed using journal databases, websites of guideline development organisations and professional dementia‐related societies from inception to 30 June 2020. Four independent reviewers performed the quality assessment of the included guidelines using the Appraisal of Guidelines for Research and Evaluation II (AGREE II) tool. A thematic synthesis was used to analyse the content of recommendations on caregiver support extracted from the included guidelines by two independent reviewers. The strength of recommendations and level of evidence were reclassified and analysed. Literature searches identified nine guidelines. Their quality was variable. The applicability domain on AGREE II yielded the lowest median score (62.2%; range: 18.6%–86.6%), suggesting that some guidelines lacked applicability in certain fields, leaving them open to interpretation. Most evidence underlying the extracted recommendations was rated as Levels 3 (51.3%) and 4 (31.1%). High‐quality evidence is required to fortify these recommendations. The nine guidelines covered six themes—assessment, education, skill training, information provision, psychosocial support and general support policies—which can guide future policies and interventions of caregiver support. The recommendations extracted were consistent across guidelines, but each guideline failed to cover all six themes. Thus, more applicable and comprehensive guidelines that fully address all themes of support for caregivers of PwD are needed. [ABSTRACT FROM AUTHOR]

The converter is an important component in wind turbine power drive-train systems, and usually, it has a higher failure rate. Therefore, detecting the potential faults for prediction of its failure has become indispensable for condition-based maintenance and operation of wind turbines. This paper presents an approach to wind turbine converter fault detection using convolutional neural network models which are developed by using wind turbine Supervisory Control and Data Acquisition (SCADA) system data. The approach starts with the selection of fault indicator variables, and then the fault indicator variables data are extracted from a wind turbine SCADA system. Using the data, radar charts are generated, and the convolutional neural network models are applied to feature extraction from the radar charts and characteristic analysis of the feature for fault detection. Based on the analysis of the Octave Convolution (OctConv) network structure, an improved AOctConv (Attention Octave Convolution) structure is proposed in this paper, and it is applied to the ResNet50 backbone network (named as AOC–ResNet50). It is found that the algorithm based on AOC–ResNet50 overcomes the issues of information asymmetry caused by the asymmetry of the sampling method and the damage to the original features in the high and low frequency domains by the OctConv structure. Finally, the AOC–ResNet50 network is employed for fault detection of the wind turbine converter using 10 min SCADA system data. It is verified that the fault detection accuracy using the AOC–ResNet50 network is up to 98.0%, which is higher than the fault detection accuracy using the ResNet50 and Oct–ResNet50 networks. Therefore, the effectiveness of the AOC–ResNet50 network model in wind turbine converter fault detection is identified. The novelty of this paper lies in a novel AOC–ResNet50 network proposed and its effectiveness in wind turbine fault detection. This was verified through a comparative study on wind turbine power converter fault detection with other competitive convolutional neural network models for deep learning. [ABSTRACT FROM AUTHOR]

Existing nonlinear optimization-based algorithms for estimating Markov transition probability matrix (TPM) in bridge deterioration modeling sometimes fail to find optimum TPM values, and hence lead to invalid future condition prediction. In this study, a Metropolis-Hasting algorithm (MHA)-based Markov chain Monte Carlo (MCMC) simulation technique is proposed to overcome this limitation and calibrate the state-based Markov deterioration models (SBMDM) of railway bridge components. Factors contributing to rail bridge deterioration were identified; inspection data for 1,000 Australian railway bridges over 15 years were reviewed and filtered. The TPMs corresponding to a typical bridge element were estimated using the proposed MCMC simulation method and two other existing methods, namely, regression-based nonlinear optimization (RNO) and Bayesian maximum likelihood (BML). Network-level condition state prediction results obtained from these three approaches were validated using statistical hypothesis tests with a test data set, and performance was compared. Results show that the MCMC-based deterioration model performs better than the other two methods in terms of network-level condition prediction accuracy and capture of model uncertainties. [ABSTRACT FROM AUTHOR]

There are many real-life situations where exact failure times are not available or not easily available for reliability analysis. Motivated by this problem, this work is concerned with the analysis of reliability data without exact failure times. The unavailability of exact failure times has compelled us to develop a simplified version of the maximum likelihood (ML) estimation for reliability parameters and measures. The approach is applicable to single-parameter reliability models including the exponential reliability model and the Weibull reliability model with an assumed or known shape parameter value. In the latter case, it takes advantage of the fact that in many practical situations a reasonable estimate of the Weibull shape parameter is attainable by certain means. This is particularly valuable for reliability assessment of highly censored Weibull data with only a few failures, because in such situations it is highly desirable or necessary to exploit prior knowledge of the Weibull shape parameter to compensate for the limited information contained in the data in the hope of making a sound assessment. This is an application in a sense of the ideas and principles of the emerging paradigm of statistical engineering. The methods are developed by practitioners and for practitioners. We show that they offer practitioners a handy and efficient tool for reliability analysis, using hard disk drive product testing as examples. [ABSTRACT FROM AUTHOR]

A three-parameter distribution called extended Weibull distribution is investigated in this article. This model is generated by a method of introducing an additional parameter into a family of distributions by Marshall and Olkin (1997). It has two-parameter Weibull distribution as a special case. One of the merits of this distribution is that the hazard-rate can be increasing, decreasing, or initially increasing, then decreasing and eventually increasing. The model characterization based on the Weibull Probability Plot (WPP) is studied in this article. The WPP for actual data set can be concave, convex, or likely S-shaped. A procedure is provided for parameter estimation based on WPP. In addition, the maximum likelihood estimation is also presented. An example is shown to illustrate the procedure and application. [ABSTRACT FROM AUTHOR]

Firmware is embedded software in hardware devices and they play important role for many critical systems' function. Firmware failure rate in operation should be quite lower than the application software which is operating on it. Most of the study on software reliability deals with systems during development, and it is also important to study the integrated system during operation. Complex systems usually have a bathtub-shaped failure rate over the lifecycle of the product. This paper discusses the parametric analysis of model given by Haupt and Schäbe (1992), exponentiated Weibull distribution and models generated from Pareto and Weibull distribution, as well as their possible application to modeling firmware system failure. In addition, the Safety Integrity Levels (SIL) stipulated in IEC 61508 are taken into account in the modeling since the safety-critical systems in general are firmware-dominated. [ABSTRACT FROM AUTHOR]

Summary Biomass crops engineered to accumulate energy‐dense triacylglycerols (TAG or ‘vegetable oils’) in their vegetative tissues have emerged as potential feedstocks to meet the growing demand for renewable diesel and sustainable aviation fuel (SAF). Unlike oil palm and oilseed crops, the current commercial sources of TAG, vegetative tissues, such as leaves and stems, only transiently accumulate TAG. In this report, we used grain (Texas430 or TX430) and sugar‐accumulating ‘sweet’ (Ramada) genotypes of sorghum, a high‐yielding, environmentally resilient biomass crop, to accumulate TAG in leaves and stems. We initially tested several gene combinations for a ‘push‐pull‐protect’ strategy. The top TAG‐yielding constructs contained five oil transgenes for a sorghum WRINKLED1 transcription factor (‘push’), a Cuphea viscosissima diacylglycerol acyltransferase (DGAT; ‘pull’), a modified sesame oleosin (‘protect’) and two combinations of specialized Cuphea lysophosphatidic acid acyltransferases and medium‐chain acyl‐acyl carrier protein thioesterases. Though intended to generate oils with medium‐chain fatty acids, engineered lines accumulated oleic acid‐rich oil to amounts of up to 2.5% DW in leaves and 2.0% DW in stems in the greenhouse, 36‐fold and 49‐fold increases relative to wild‐type (WT) plants, respectively. Under field conditions, the top‐performing event accumulated TAG to amount to 5.5% DW in leaves and 3.5% DW in stems, 78‐fold and 58‐fold increases, respectively, relative to WT TX430. Transcriptomic and fluxomic analyses revealed potential bottlenecks for increased TAG accumulation. Overall, our studies highlight the utility of a lab‐to‐field pipeline coupled with systems biology studies to deliver high vegetative oil sorghum for SAF and renewable diesel production. [ABSTRACT FROM AUTHOR]

In wind power generation, one aim of wind turbine control is to maintain it in a safe operational status while achieving cost-effective operation. The purpose of this paper is to investigate new techniques for wind turbine fault detection based on supervisory control and data acquisition (SCADA) system data in order to avoid unscheduled shutdowns. The proposed method starts with analyzing and determining the fault indicators corresponding to a failure mode. Three main system failures including generator failure, converter failure and pitch system failure are studied. First, the indicators data corresponding to each of the three key failures are extracted from the SCADA system, and the radar charts are generated. Secondly, the convolutional neural network with ResNet50 as the backbone network is selected, and the fault model is trained using the radar charts to detect the fault and calculate the detection evaluation indices. Thirdly, the support vector machine classifier is trained using the support vector machine method to achieve fault detection. In order to show the effectiveness of the proposed radar chart-based methods, support vector regression analysis is also employed to build the fault detection model. By analyzing and comparing the fault detection accuracy among these three methods, it is found that the fault detection accuracy by the models developed using the convolutional neural network is obviously higher than the other two methods applied given the same data condition. Therefore, the newly proposed method for wind turbine fault detection is proved to be more effective. [ABSTRACT FROM AUTHOR]

In order to reduce operation and maintenance cost and improve fault diagnosis and detection accuracy for wind turbines, a study on advanced methods has been carried out. The purpose of this paper is to present a new method developed using radar chart and support vector machine (SVM) approach for fault diagnosis and prediction of wind turbine pitch system as it usually has a higher failure rate. In the study, the supervisory control and data acquisition (SCADA) system data are utilized as source data for SVM prediction. First of all, the characteristics of the indicator variable data collected by the SCADA system are analyzed, and the radar charts corresponding to the normal and faulty operation of the wind turbine pitch system are constructed using the indicator variable data. Secondly, the SVM method is used to extract the gray-level co-occurrence matrix (GLCM) features and histogram of oriented gradients (HOG) features of the radar charts, and the SVM classifier is trained. Then, the operational status is predicted, the classification effect is evaluated by the confusion matrix, and the prediction evaluation index is calculated. Thirdly, the support vector regression method is used to analyze the SCADA indicator variable data, the input and output of the regression model are determined, and the training prediction model is established, and the prediction accuracy of the test model is analyzed using the test sample data. Finally, the forecasting evaluation indexes obtained by the above two methods are compared. It proves that the proposed method using SVM to analyze the system radar charts has a higher prediction accuracy of 91.24% than the support vector regression method. The prediction accuracy is improved by 8.6%. Hence, it is verified that the new method using a radar chart and SVM approach has superiority over the support vector regression method. [ABSTRACT FROM AUTHOR]