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ABSTRACT Floating offshore wind (FOW) is a renewable energy source that is set to play an essential role in addressing climate change and the need for sustainable development. However, due to the increasing threat of climate emergency, more wind turbines are required to be deployed in deep water locations, further offshore. This presents heightened challenges for accessing the turbines and performing maintenance, leading to increased costs. Naturally, methods to reduce operational expenditure (OpEx) are highly desirable. One method that shows potential for reducing OpEx of FOW is LIDAR‐assisted pitch control. This approach uses wind velocity measurements from a nacelle‐mounted LIDAR to enable feedforward control of floating offshore wind turbines (FOWTs) and can result in reductions to the variations of structural loads. Results obtained from a previous study of combined feedforward collective and individual pitch control (FFCPC + FFIPC) are translated to OpEx reductions via reduced component failure rates for future FOW developments, namely, in locations awarded in the recent ScotWind leasing round. The results indicate that LIDAR‐assisted pitch control may allow for an up to 5% reduction in OpEx, increasing to up to 11% with workability constraints included. The results varied across the three ScotWind sites considered, with sites furthest from shore reaping the greatest benefit from LIDAR‐assisted control. This work highlights the potential savings and reduction in the overall levelised cost of energy for future offshore wind turbine projects deliverable through the implementation of LIDAR‐assisted pitch control.

Abstract Offshore wind turbines have rapidly scaled up in recent years, with plans to construct turbines up to 22 MW in the next decade. However, the operations and maintenance (O&M) requirements for these ‘next‐generation turbines’ remain largely unknown. In this study, the total O&M costs are calculated, using a bench‐marked O&M model, for a hypothetical 10 MW turbine scenario using two drive train configurations, based on known failure rates of smaller turbines. The O&M costs of the 10 MW turbines are compared with those of existing 3 MW turbines in two case studies: a North Sea wind farm and an East Coast US wind farm. Overall, direct drive 10 MW turbines performed better depending on the site's climate conditions. The study indicated that the two‐stage drive train configuration may be more suitable for the US site than the North Sea, depending on the turbine's failure rate. The US site benefited from increased availability due to more favourable weather windows, resulting in lower lost revenue for the two‐stage configuration despite high transport costs. The study found that the failure rate of 10 MW offshore wind turbines in the North Sea with a two‐stage gearbox can increase by as much as 30% compared to the 3 MW failure rates without increasing direct O&M costs. These findings are crucial for the offshore wind energy industry, particularly for OEMs, developers and maintenance providers, as they provide insights into the required reliability for next generation turbines to reduce O&M costs compared to existing 3 MW turbines.

Accurate predictions for remaining useful life (RUL) of wind turbine drivetrains are crucial in reducing downtime, optimising maintenance strategies, extending operational life and improving costs. The purpose of this study is to present a method which utilises SCADA data for RUL estimation. It aims to answer whether, by only having the basic SCADA data, normally collected from all wind turbines, valuable RUL prediction results can be obtained. The work intends to develop a reliable, accurate, user-friendly and informative tool to enable observation of any growing trends in the proposed metrics, such as temperature difference, cumulative sum of temperature difference and the moving average of the cumulative sum. These metrics are defined based on the differences between the actual temperatures of the components, that might have undergone some damage and the model predicted temperatures of those components if they would have remained healthy, throughout years of operation. A machine learning model is used, along with selected SCADA input parameters, to predict the healthy state temperature of components. The proposed method is implemented on SCADA data collected from an actual wind farm over seven years. The results of this study show that while the SCADA data analysis can contribute to fault detection, the estimation of RUL purely based on SCADA appears to be uncertain. Such tools however, can be used as a monitoring method, during operation, to record abnormality trend of the components over the years and can be used as important inputs for the life extension evaluation of wind turbine drivetrains.

Abstract This work introduces and explores the use of secondary rotors for vertical axis wind turbine power take‐off. A parametric framework based on optimally designed secondary rotors is developed which calculates the maximum achievable efficiency of power conversion between the primary and secondary rotors. It is shown that practicable rotor designs can convert between 87% and 90% of primary rotor power to the secondary rotors whilst facilitating nacelle mass reductions between 85% and 87% compared to traditional reference turbine drivetrains.

With the current trends of wind energy already playing a major part in the Scottish energy supply, the capacity of wind farms is predicted to grow exponentially and reach further depths offshore. However, a key challenge that presents itself is the integration of large producing assets into the current UK grid. One potential solution to this is green hydrogen production, which is being heavily researched in industry, with many concepts being investigated for large-scale purposes. However, the operations and maintenance (O&M) costs and availability of green hydrogen systems need to be quantified to ensure economical and technical viability, which is sparse in the available literature. The study presented in this paper investigated the availability and O&M costs of coupled wind–hydrogen systems by attempting to quantify the failure rates, repair times, repair costs and number of technicians required for key green hydrogen components. This study also utilised an O&M model created by the University of Strathclyde, which uses Monte Carlo Markov chain simulations to produce the O&M outputs. A number of assumptions were made throughout the study in relation to the O&M model inputs, and the baseline availability for the coupled wind–hydrogen system was 85.24%. Whilst the wind turbine still contributed a major part to the downtime seen in the simulations, the combined hydrogen system also contributed a significant amount, a total of 37%, which could have been due to the technology readiness levels of some the components included in the hydrogen system.

Summary: Background: Mucositis is a common and highly impactful side effect of conventional and emerging cancer therapy and thus the subject of intense investigation. Although common practice, mucositis assessment is heterogeneously adopted and poorly guided, impacting evidence synthesis and translation. The Multinational Association of Supportive Care in Cancer (MASCC) Mucositis Study Group (MSG) therefore aimed to establish expert recommendations for how existing mucositis assessment tools should be used, in clinical care and trials contexts, to improve the consistency of mucositis assessment. Methods: This study was conducted over two stages (January 2022–July 2023). The first phase involved a survey to MASCC-MSG members (January 2022–May 2022), capturing current practices, challenges and preferences. These then informed the second phase, in which a set of initial recommendations were prepared and refined using the Delphi method (February 2023–May 2023). Consensus was defined as agreement on a parameter by >80% of respondents. Findings: Seventy-two MASCC-MSG members completed the first phase of the study (37 females, 34 males, mainly oral care specialists). High variability was noted in the use of mucositis assessment tools, with a high reliance on clinician assessment compared to patient reported outcome measures (PROMs, 47% vs 3%, 37% used a combination). The World Health Organization (WHO) and Common Terminology Criteria for Adverse Events (CTCAE) scales were most commonly used to assess mucositis across multiple settings. Initial recommendations were reviewed by experienced MSG members and following two rounds of Delphi survey consensus was achieved in 91 of 100 recommendations. For example, in patients receiving chemotherapy, the recommended tool for clinician assessment in clinical practice is WHO for oral mucositis (89.5% consensus), and WHO or CTCAE for gastrointestinal mucositis (85.7% consensus). The recommended PROM in clinical trials is OMD/WQ for oral mucositis (93.3% consensus), and PRO-CTCAE for gastrointestinal mucositis (83.3% consensus). Interpretation: These new recommendations provide much needed guidance on mucositis assessment and may be applied in both clinical practice and research to streamline comparison and synthesis of global data sets, thus accelerating translation of new knowledge into clinical practice. Funding: No funding was received.

Abstract Junctions between the endoplasmic reticulum (ER) and the plasma membrane (PM) are specialized membrane contacts ubiquitous in eukaryotic cells. Concentration of intracellular signaling machinery near ER-PM junctions allows these domains to serve critical roles in lipid and Ca2+ signaling and homeostasis. Subcellular compartmentalization of protein kinase A (PKA) signaling also regulates essential cellular functions, however, no specific association between PKA and ER-PM junctional domains is known. Here, we show that in brain neurons type I PKA is directed to Kv2.1 channel-dependent ER-PM junctional domains via SPHKAP, a type I PKA-specific anchoring protein. SPHKAP association with type I PKA regulatory subunit RI and ER-resident VAP proteins results in the concentration of type I PKA between stacked ER cisternae associated with ER-PM junctions. This ER-associated PKA signalosome enables reciprocal regulation between PKA and Ca2+ signaling machinery to support Ca2+ influx and excitation-transcription coupling. These data reveal that neuronal ER-PM junctions support a receptor-independent form of PKA signaling driven by membrane depolarization and intracellular Ca2+, allowing conversion of information encoded in electrical signals into biochemical changes universally recognized throughout the cell.

Abstract Purpose To establish the incidence of pancreatic trauma in North Queensland to the region’s only tertiary paediatric referral centre, and to determine the patient’s outcomes based on their management. Methods A single centre, retrospective cohort study of patients

The government has set an ambitious target of renewable energy development in Malaysia. As a promising renewable energy source, wind energy plays an important role in the Malaysia renewable energy roadmap. Compared to onshore wind energy, offshore wind resources with better quality can be provided in the areas away from the coast, which has greater potential to contribute to electricity generation. Wind and bathymetry conditions are two important factors that determine the feasibility and economics of offshore wind turbines. In this paper, an investigation is conducted on wind and bathymetry conditions around Malaysia. The data source mainly originates from the Global Wind Atlas. The conditions of the coastal areas in different states and federal territories of both Peninsular Malaysia and East Malaysia are analysed, with a specific focus on wind speed, wind energy density, and bathymetry conditions in high-wind-speed regions. The data and survey are verified and compared with the past published literature. This paper aims to investigate the wind and bathymetry conditions around Malaysia, assess the potential of offshore wind energy, discuss the feasibility of offshore wind turbines, and provide references for offshore wind development in Malaysia.

Abstract Background Acute pancreatitis as a trigger of Takotsubo cardiomyopathy has been infrequently described in the literature. Misdiagnosis of this phenomenon can often occur due to overlap in symptomology, particularly in those outside of the usual patient demographic. Case presentation A 27-year-old man with a history of alcohol abuse presented with epigastric and chest pain. Electrocardiography showed ischemic changes, and laboratory workup revealed elevated lipase and troponin. He was diagnosed with acute pancreatitis and managed presumptively as acute coronary syndrome. Subsequent coronary angiography was negative for obstructive coronary artery disease, and left ventriculography demonstrated basal hyperkinesis and apical akinesis, characteristic of Takotsubo cardiomyopathy. Conclusions Takotsubo cardiomyopathy is a rare complication of acute pancreatitis. Increased awareness of this phenomenon is required to prevent delays in diagnosis and avoid unnecessary interventions and complications.

Background: The development of an integral and global treatment to improve the quality of life in those with fibromyalgia syndrome (FMS) is challenging. The aim of this study is to investigate the impact of whole-body photobiomodulation (PBM) on pain perception, functionality, quality of soft tissue, central sensitisation and psychological factors in patients suffering with FMS. Methods: This study is a randomised, placebo-controlled clinical trial. A total of 44 participants will be recruited in a private care practice and randomised to receive either a whole-body PBM therapy programme or placebo in the same care centre. The parameters of the PBM programme are as follows: wavelengths of red and near-infrared LEDs 50:50 ratio with 660–850 nanometers; fluence of 25.2 J/cm 2 ; treatment time of 1200 s and a total power emitted of 967 W. Treatment sessions will be 3 times weekly for a period of 4 weeks, totalling 12 treatment sessions. Primary outcome will be pain (Numeric Pain Rating Scale; Widespread Pain Index; Symptom Severity Score). Secondary outcomes will be functionality (Fibromyalgia Impact Questionnaire; the Leisure Time Physical Activity Instrument), quality of soft tissue (elastography), central sensitisation (pain pressure threshold and the Autonomic Symptom Profile) and psychological factors (Pain Catastrophising scale, Tampa Scale, Self-Efficacy questionnaire). Assessments will be at baseline (T1), after session 6 (T2), after treatment (T3) and 2 weeks (T4), 3 (T5) and 6 (T6) month follow-up. Discussion: PBM therapy has been shown to reduce pain and inflammation and to increase the rate of tissue repair for a wide range of conditions, but its potential use as a whole-body treatment in FM is yet to be explored. This trial will investigate whether whole-body PBM therapy is effective at reducing pain intensity, improving functionality, quality of soft tissue, central sensitisation symptoms and psychological measurements. Furthermore, 3- and 6-month follow-up will investigate long-term efficacy of this treatment. Trial registration: NCT04248972. Registered on January 29, 2020, https://clinicaltrials.gov/ct2/show/NCT04248972?term=navarro-ledesma+santiago&draw=2&rank=2 .

Offshore wind is poised for huge growth in the coming years, the UK government has set targets of 40 GW of offshore wind, including 1 GW of floating wind, to be installed in the UK by 2030. Many proposed wind development sites are in deeper waters, farther from shore and will therefore need to be developed as floating wind sites. Developing sites in deeper waters provides access to higher, more consistent wind speeds, however this also means increased wave heights and tougher operating conditions. This makes the challenge of site accessibility critical to the goal of lowering the costs of offshore wind. Accessibility is the amount of time that vessels can safely access a given site. The objective of this study was to make a comparison of the accessibility of potential future floating wind sites to existing fixed bottom wind farm sites. Accessibility was calculated by developing Matlab code using established techniques and definitions found in the literature. A case study was then completed using sites in Scottish waters proposed for development as part of the Scotwind leasing plan. The majority of the Scotwind sites will need to be developed as floating wind sites due to the large water depths. This study assesses the accessibility of the Scotwind leasing sites and compares them to a typical fixed bottom site. The study found that accessibility will be a greater challenge for floating farm wind sites compared to fixed bottom sites. Increased access to vessels that can operate in larger wave heights will likely be necessary to operate and maintain floating wind farm sites.

This study analysed circadian variation changes in blood pressure (BP), the pain pressure threshold (PPT) and the elasticity of tissue in patients with fibromyalgia (FM) after a whole-body photobiomodulation (PBM) treatment. This was a tripled-blinded randomized clinical trial including forty participants with FM. Participants using validated self-measurement BP devices attained readings that were used to calculate the circadian variation. Additionally, a standard pressure algometer of 1cm2 was used to assess 13 tender points by exerting a pressure of up to 4 kg, and strain elastography assessed the elasticity of tissue. Circadian variations in BP showed significant differences after the PBM intervention (p = 0.036). When comparing PPT between groups, statistically significant differences were found in the occiput (p = 0.039), low cervical (p = 0.035), trapezius (p = 0.037), second rib (p < 0.001) and medial epicondyle points (p = 0.006). Furthermore, there were statistically significant differences in both the trapezius and the forearm at the distal dorsal third SEL values (p ≤ 0.001) when comparing groups. Whole-body PBM produces changes in circadian blood pressure, the pain pressure threshold and the elasticity of tissue after a treatment program was carried out. However, more studies are needed to corroborate our findings as well as to better understand the underlying mechanisms.

Class A G-protein-coupled receptors (GPCRs) normally function as monomers, although evidence from heterologous expression systems suggests that they may sometimes form homodimers and/or heterodimers. This study aims to evaluate possible functional interplay of endogenous µ- and δ-opioid receptors (MORs and DORs) in mouse neurons. Detecting GPCR dimers in native tissues, however, has been challenging. Previously, MORs and DORs co-expressed in transfected cells have been reported to form heterodimers, and their possible co-localization in neurons has been studied in knock-in mice expressing genetically engineered receptors fused to fluorescent proteins. Here, we find that single cholinergic neurons in the mouse striatum endogenously express both MORs and DORs. The receptors on neurons from live brain slices were fluorescently labeled with a ligand-directed labeling reagent, NAI-A594. The selective activation of MORs and DORs, with DAMGO (µ-agonist) and deltorphin (δ-agonist) inhibited spontaneous firing in all cells examined. In the continued presence of agonist, the firing rate returned to baseline as the result of receptor desensitization with the application of deltorphin but was less observed with the application of DAMGO. In addition, agonist-induced internalization of DORs but not MORs was detected. When MORs and DORs were activated simultaneously with [Met5]-enkephalin, desensitization of MORs was facilitated but internalization was not increased. Together, these results indicate that while MORs and DORs are expressed in single striatal cholinergic interneurons, the two receptors function independently.

A battolyser is a combined battery electrolyser in one unit. It is based on flow battery technology and can be adapted to produce hydrogen at a lower efficiency than an electrolyser but without the need for rare and expensive materials. This paper presents a method of determining if a battolyser connected to a wind farm makes economic sense based on stochastic modelling. A range of cost data and operational scenarios are used to establish the impact on the NPV and LCOE of adding a battolyser to a wind farm. The results are compared to adding a battery or an electrolyser to a wind farm. Indications are that it makes economic sense to add a battolyser or battery to a wind farm to use any curtailed wind with calculated LCOE at £56/MWh to £58/MWh and positive NPV over a range of cost scenarios. However, electrolysers, are still too expensive to make economic sense.

Data-driven normal behaviour models have gained traction over the last few years as a convenient way of modelling turbine operational health to detect anomalies. By leveraging high-dimensional operational relationships, temperature thresholds can be automatically calculated based on each individual turbine unique operating envelope, in theory minimising false alarms and providing more reliable diagnostics. The aim of this work is to provide further insight into practical uses and limitations of implementing normal behaviour temperature models in practice, to inform practitioners, as well as assist in improving wind turbine generator fault detection systems. Results suggest that, on average, as little as two months of data are adequate to produce stable temperature alarm thresholds, with the worst case example requiring approximately 200–290 days of data depending on the component and desired convergence criteria.

Data on adverse events from research bronchoscopy with bronchoalveolar lavage (BAL) in patients with cystic fibrosis (CF) is lacking. As research bronchoscopy with BAL is useful for isolation of immune cells and investigation of CF lung microbiome, we sought to investigate the safety of bronchoscopy in adult patients with CF. Between November 2016 and September 2019, we performed research bronchoscopies on CF subjects (32) and control subjects (82). Control subjects were nonsmokers without respiratory disease. CF subjects had mild or moderate obstructive lung disease (FEV1 > 50% predicted) and no evidence of recent CF pulmonary exacerbation. There was no significant difference in the age or sex of each cohort. Neither group experienced life threatening adverse events. The number of adverse events was similar between CF and control subjects. The most common adverse events were sore throat and cough, which occurred at similar frequencies in control and CF subjects. Fever and headache occurred more frequently in CF subjects. However, the majority of fevers were seen in CF subjects with FEV1 values below 65% predicted. We found that CF subjects had similar adverse event profiles following research bronchoscopy compared to healthy subjects. While CF subjects had a higher rate of fevers, this adverse event occurred with greater frequency in CF subjects with lower FEV1. Our data demonstrate that research bronchoscopy with BAL is safe in CF subjects and that safety profile is improved if bronchoscopies are limited to subjects with an FEV1 > 65% predicted.

Lung cancer is the leading cause of cancer-related mortality worldwide. Epidermal growth factor receptor (EGFR) tyrosine kinase inhibitor (TKI) therapies, based on the evaluation of EGFR mutations, have shown dramatic clinical benefits. EGFR mutation assays are mainly performed on tumor biopsies, which carry risks, are not always successful and give results relevant to the timepoint of the assay. To detect secondary EGFR mutations, which cause resistance to 1st and 2nd generation TKIs and lead to the administration of a 3rd generation drug, effective and non-invasive monitoring of EGFR mutation status is needed. Liquid biopsy analytes, such as circulating tumor cells (CTCs) and circulating tumor DNA (cfDNA), allow such monitoring over the course of the therapy. The aim of this study was to develop and optimize a workflow for the evaluation of cfDNA and CTCs in NSCLC patients all from one blood sample. Using Vortex technology and EntroGen ctEGFR assay, EGFR mutations were identified at 0.5 ng of DNA (∼83 cells), with a sensitivity ranging from 0.1 to 2.0% for a total DNA varying from 25 ng (∼4 CTCs among 4000 white blood cells, WBCs) to 1 ng (∼4 CTCs among 200 WBCs). The processing of plasma-depleted-blood provided comparable capture recovery as whole blood, confirming the possibility of a multimodality liquid biopsy analysis (cfDNA and CTC DNA) from a single tube of blood. Different anticoagulants were evaluated and compared in terms of respective performance. Blood samples from 24 NSCLC patients and 6 age-matched healthy donors were analyzed with this combined workflow to minimize blood volume needed and sample-to-sample bias, and the EGFR mutation profile detected from CTCs and cfDNA was compared to matched tumor tissues. Despite the limited size of the patient cohort, results from this non-invasive EGFR mutation analysis are encouraging and this combined workflow represents a valuable means for informing therapy selection and for monitoring treatment of patients with NSCLC.

Neuropathic pain (NP) can be challenging to treat effectively as analgesic pharmacotherapy (MED) can reduce pain, but the majority of patients do not experience complete pain relief. Our pilot approach is to assess the feasibility and efficacy of an evidence-based photobiomodulation (PBM) intervention protocol. This would be as an alternative to paralleled standard analgesic MED for modulating NP intensity-related physical function and quality of life (QoL) prospectively in a mixed neurological primary burning mouth syndrome and oral iatrogenic neuropathy study population (n = 28). The study group assignments and outcome evaluation strategy/location depended on the individual patient preferences and convenience rather than on randomisation. Our prospective parallel study aimed to evaluate the possible pre/post-benefit of PBM and to allow for a first qualitative comparison with MED, various patient-reported outcome measures (PROMs) based on Initiative on Methods, Measurement, and Pain Assessment in Clinical Trials (IMMPACT-II) were used for up to a nine-month follow-up period in both intervention groups (PBM and MED). The PBM protocol applied to the PBM group was as follows: λ810 nm, 200 mW, 0.088 cm2, 30 s/point, 9 trigger and affected points, twice a week for five consecutive weeks, whereas the MED protocol followed the National Institute of Clinical Excellence (NICE) guidelines. Our results showed that despite the severe and persistent nature of the symptoms of 57.50 ± 47.93 months at baseline in the PBM group, a notably rapid reduction in PISmax on VAS from 7.6 at baseline (T0) to 3.9 at one-month post-treatment (T3) could be achieved. On the other hand, mean PISmax was only reduced from 8.2 at baseline to 6.8 at T3 in the MED group. Our positive PBM findings furthermore support more patients’ benefits in improving QoL and functional activities, which were considerably impaired by NP such as: eating, drinking and tasting, whereas the analgesic medication regimens did not. No adverse events were observed in both groups. To the best knowledge of the authors, our study is the first to investigate PBM efficacy as a monotherapy compared to the gold standard analgesic pharmacotherapy. Our positive data proves statistically significant improvements in patient self-reported NP, functionality, psychological profile and QoL at mid- and end-treatment, as well as throughout the follow-up time points (one, three, six and nine months) and sustained up to nine months in the PBM group, compared to the MED group. Our study, for the first time, proves the efficacy and safety of PBM as a potent analgesic in oral NP and as a valid alternative to the gold standard pharmacotherapy approach. Furthermore, we observed long-term pain relief and functional benefits that indicate that PBM modulates NP pathology in a pro-regenerative manner, presumably via antioxidant mechanisms.

Wind turbine pitch system condition monitoring is an active area of research, and this paper investigates the use of the Isolation Forest Machine Learning model and Supervisory Control and Data Acquisition system data for this task. This paper examines two case studies, turbines with hydraulic or electric pitch systems, and uses an Isolation Forest to predict failure ahead of time. This novel technique compared several models per turbine, each trained on a different number of months of data. An anomaly proportion for three different time-series window lengths was compared, to observe trends and peaks before failure. The two cases were compared, and it was found that this technique could detect abnormal activity roughly 12 to 18 months before failure for both the hydraulic and electric pitch systems for all unhealthy turbines, and a trend upwards in anomalies could be found in the immediate run up to failure. These peaks in anomalous behaviour could indicate a future failure and this would allow for on-site maintenance to be scheduled. Therefore, this method could improve scheduling planned maintenance activity for pitch systems, regardless of the pitch system employed.

Advancements in wind turbine condition monitoring systems over the last decade have made it possible to optimise operational performance and reduce costs associated with component failure and other unplanned maintenance activities. While much research focuses on providing more automated and accurate fault diagnostics and prognostics in relation to predictive maintenance, efforts to quantify the impact of such strategies have to date been comparatively limited. Through time-based simulation of wind farm operation, this paper quantifies the cost benefits associated with predictive and condition-based maintenance strategies, taking into consideration both direct O&M costs and lost production. Predictive and condition-based strategies have been modelled by adjusting known component failure and repair rates associated with a more reactive approach to maintenance. Results indicate that up to 8% of direct O&M costs can be saved through early intervention along with up to 11% reduction in lost production, assuming 25% of major failures of the generator and gearbox can be diagnosed through advanced monitoring and repaired before major replacement is required. Condition-based approaches can offer further savings compared to predictive strategies by utilising more component life before replacement. However, if weighing up the risk between component failure and replacing a component too early, results suggest that it is more cost effective to intervene earlier if heavy lift vessels can be avoided, even if that means additional major repairs are required over the lifetime of the site.

This study details the development of a mathematical thermal model of a small wind turbine gearbox for use in condition monitoring. The model was optimised and partially validated using experimental data from a wind turbine drivetrain test rig. The model was then used to mimic bearing faults, by simulating additional heat losses at respective faulty components. The extent to which the thermal behaviour changed as a result of a fault was studied, with a view to use such an approach to detect and locate faults.

Innovations which help facilitate predictive maintenance strategies have the potential to greatly reduce costs associated with wind turbine O&M by driving efficiency and increasing wind turbine availability. This study uses multiple examples of the same generator bearing failure to provide insight into how condition monitoring systems can be used in to train machine learning algorithms with the ultimate goal of predicting failure and remaining useful life. Results show that by analysing high-frequency vibration data and extracting key features to train support vector machine algorithms, an accuracy of 67% can be achieved in successfully predicting failure 1–2 months before occurrence. This study reflects on the limitations surrounding a generalised training approach, taking advantage of all available data, showing that if too many different examples are considered of different wind turbines and operating conditions, the overall accuracy can be diminished.

This study presents a method of anomaly detection within a gearbox by way of standardising temperature data. Assessing measured parameters in isolation is not sufficient to detect faults within a wind turbine. This technique uses temperature, rotational speed, and generator torque to detect a bearing fault within the gearbox. Standardising data allows a parameter to be analysed which also takes into consideration the operating state of the wind turbine, therefore providing a more holistic view of the health of the wind turbine and component being monitored.

This research aims to bring together thermal modelling and machine learning approaches to improve the understanding on the operation and fault detection of a wind turbine gearbox. Recent fault detection research has focused on machine learning, black box approaches. Although it can be successful, it provides no indication of the physical behaviour. In this paper, thermal network modelling was applied to two datasets using SCADA (Supervisory Control and Data Acquisition) temperature data, with the aim of detecting a fault one month before failure. A machine learning approach was used on the same data to compare the results to thermal modelling. The results found that thermal network modelling could successfully detect a fault in many of the turbines examined and was validated by the machine learning approach for one of the datasets. For that same dataset, it was found that combining the thermal model losses and the machine learning approach by using the modelled losses as a feature in the classifier resulted in the engineered feature becoming the most important feature in the classifier. It was also found that the results from thermal modelling had a significantly greater effect on successfully classifying the health of a turbine compared to temperature data. The other dataset gave less conclusive results, suggesting that the location of the fault and the temperature sensors could impact the fault-detection ability.

The intelligent condition monitoring of wind turbines reduces their downtime and increases reliability. In this manuscript, a feature selection-based methodology that essentially works on regression models is used for identifying faulty scenarios. Supervisory control and data acquisition (SCADA) data with 1009 samples from one year and one month before failure are considered. Gearbox oil and bearing temperatures are treated as target variables with all the other variables used for the prediction model. Neighborhood component analysis (NCA) as a feature selection technique is employed to select the best features and prediction performance for several machine learning regression models is assessed. The results reveal that twin support vector regression (99.91%) and decision trees (98.74%) yield the highest accuracy for gearbox oil and bearing temperatures respectively. It is observed that NCA increases the accuracy and thus reliability of the condition monitoring system. Furthermore, the residuals from the class of support vector regression (SVR) models are tested from a statistical point of view. Diebold–Mariano and Durbin–Watson tests are carried out to establish the robustness of the tested models.

Anomaly detection for wind turbine condition monitoring is an active area of research within the wind energy operations and maintenance (O & M) community. In this paper three models were compared for multi-megawatt operational wind turbine SCADA data. The models used for comparison were One-Class Support Vector Machine (OCSVM), Isolation Forest (IF), and Elliptical Envelope (EE). Each of these were compared for the same fault, and tested under various different data configurations. IF and EE have not previously been used for fault detection for wind turbines, and OCSVM has not been used for SCADA data. This paper presents a novel method of condition monitoring that only requires two months of data per turbine. These months were separated by a year, the first being healthy and the second unhealthy. The number of anomalies is compared, with a greater number in the unhealthy month being considered correct. It was found that for accuracy IF and OCSVM had similar performances in both training regimes presented. OCSVM performed better for generic training, and IF performed better for specific training. Overall, IF and OCSVM had an average accuracy of 82% for all configurations considered, compared to 77% for EE.

Operations and Maintenance (O&M) can make up a significant proportion of lifetime costs associated with any wind farm, with up to 30% reported for some offshore developments. It is increasingly important for wind farm owners and operators to optimise their assets in order to reduce the levelised cost of energy (LCoE). Reducing downtime through condition-based maintenance is a promising strategy of realising these goals. This is made possible through increased monitoring and gathering of operational data. SCADA data are useful in terms of wind turbine condition monitoring. This paper aims to perform a comprehensive comparison between two types of normal behaviour modelling: full signal reconstruction (FSRC) and autoregressive models with exogenous inputs (ARX). At the same time, the effects of the training time period on model performance are explored by considering models trained with both 12 and 6 months of data. Finally, the effects of time resolution are analysed for each algorithm by considering models trained and tested with both 10 and 60 min averaged data. Two different cases of wind turbine faults are examined. In both cases, the NARX model trained with 12 months of 10 min average Supervisory Control And Data Acquisition (SCADA) data had the best training performance.

The goal of the present paper is to achieve the diagnosis of an in-service 1.5 MW wind turbine equipped with a doubly-fed induction generator through current signature and vibration analyses. Real data from operating machines have rarely been analysed in the scientific literature through current signature analysis supported by vibrations. The wind turbine under study was originally misdiagnosed by the operator, where a healthy component was replaced and the actual failure continued progressing. The chronological evolution of both the electrical current and vibration spectra is presented to conduct an in-depth tracking of the fault. The diagnosis is achieved through spectral analysis of the stator currents, where fault frequency components related to rotor mechanical unbalance are identified. This is confirmed by the vibration analysis, which provides insightful information on the health of the drive train. These results can be implemented in condition monitoring strategies, which is of great interest to optimise operation and maintenance costs of wind farms.

In this study, a series of fourteen ring-substituted 8-hydroxyquinoline derivatives were prepared. The synthesis procedures are presented. The compounds were analyzed using RP-HPLC to determine lipophilicity. They were tested for their activity related to inhibition of photosynthetic electron transport (PET) in spinach (Spinacia oleracea L.) chloroplasts. Primary in vitro screening of the synthesized compounds was also performed against four mycobacterial strains and against eight fungal strains. Several compounds showed biological activity comparable with or higher than the standards isoniazid or fluconazole. For all the compounds, the relationships between the lipophilicity and the chemical structure of the studied compounds are discussed.

In this study, series of ring-substituted 2-styrylquinazolin-4(3H)-one and 4-chloro-2-styrylquinazoline derivatives were prepared. The syntheses of the discussed compounds are presented. The compounds were analyzed by RP-HPLC to determine lipophilicity. They were tested for their inhibitory activity on photosynthetic electron transport (PET) in spinach (Spinacia oleracea L.) chloroplasts. Primary in vitro screening of the synthesized compounds was also performed against four mycobacterial strains and against eight fungal strains. Several compounds showed biological activity comparable with or higher than that of the standard isoniazid. It was found that the electronic properties of the R substituent, and not the total lipophilicity of the compound, were decisive for the photosynthesis-inhibiting activity of tested compounds.

In the study, a series of twelve ring-substituted 4-hydroxy-1H-quinolin-2-one derivatives were prepared. The procedures for synthesis of the compounds are presented. The compounds were analyzed using RP-HPLC to determine lipophilicity and tested for their photosynthesis-inhibiting activity using spinach (Spinacia oleracea L.) chloroplasts. All the synthesized compounds were also evaluated for antifungal activity using in vitro screening with eight fungal strains. For all the compounds, the relationships between the lipophilicity and the chemical structure of the studied compounds are discussed, as well as their structure-activity relationships (SAR).

Acute prevertebral calcific tendonitis (APCT) is a rare condition, the exact incidence of which isunknown. It is of particular interest to the emergency physician owing to the other potentiallydevastating conditions in the differential diagnosis of neck stiffness and/or odynophagia (includingretropharyngeal abscess, infectious spondylitis, and meningitis.) In contrast, APCT has a benignclinical course and can be easily managed in the emergency department. We will present a case ofAPCT, followed by a brief discussion of the disease and current literature. [West J Emerg Med.2012;13(1):114–116.]

Low-level laser (light) therapy (LLLT) has been known since 1967 but still remains controversial due to incomplete understanding of the basic mechanisms and the selection of inappropriate dosimetric parameters that led to negative studies. The biphasic dose-response or Arndt-Schulz curve in LLLT has been shown both in vitro studies and in animal experiments. This review will provide an update to our previous ( Huang et al. 2009 ) coverage of this topic. In vitro mediators of LLLT such as adenosine triphosphate (ATP) and mitochondrial membrane potential show biphasic patterns, while others such as mitochondrial reactive oxygen species show a triphasic dose-response with two distinct peaks. The Janus nature of reactive oxygen species (ROS) that may act as a beneficial signaling molecule at low concentrations and a harmful cytotoxic agent at high concentrations, may partly explain the observed responses in vivo. Transcranial LLLT for traumatic brain injury (TBI) in mice shows a distinct biphasic pattern with peaks in beneficial neurological effects observed when the number of treatments is varied, and when the energy density of an individual treatment is varied. Further understanding of the extent to which biphasic dose responses apply in LLLT will be necessary to optimize clinical treatments.