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This study validates a novel structural reliability method, particularly suitable for high‐dimensional green energy harvesting device dynamic systems, versus a well‐established bivariate statistical method, known to accurately predict two‐dimensional system extreme response contours. Classic reliability methods dealing with time series do not always have an advantage of dealing easily with dynamic system high dimensionality, along with complex cross‐correlations among different system components. Energy harvesters constitute an important part of modern offshore green energy engineering; hence, proper experimental study along with safety and reliability analysis are of practical design and engineering importance. To study the performance of galloping energy harvesters, a series of laboratory wind tunnel tests have been conducted, selecting different wind speeds. This study illustrates the usage of the advocated novel reliability method, by analyzing bivariate statistics of experimental galloping energy harvester's dynamics. The bivariate statistics was extracted from available experimental results, more specifically for the device's voltage‐force dataset. Advantage of the proposed methodology being that relatively short experimental data record may still yield meaningful design results, provided proper statistical methods have been applied. Safety and reliability are important engineering concerns for all kinds of green energy devices. In the case of measured device's structural response, an accurate prediction of system failure or damage probability is possible, as illustrated in this study. Distinctive advantage of advocated novel semi‐analytical reliability methodology being the fact that it can tackle dynamic systems with practically unlimited number of dimensions (or components), along with complex nonlinear cross‐correlations between different system key components. The paper validates a novel structural reliability method, particularly suitable for multidimensional energy harvesting device responses, versus a well‐established bivariate statistical method that is known to accurately predict two‐dimensional system extreme response levels. Classic reliability methods, dealing with time series, do not have an advantage of dealing easily with system high dimensionality and cross‐correlation among different dimensions. Energy harvesters constitute an important part of modern offshore green energy engineering, therefore proper experimental study along with safety and reliability analysis are of practical engineering importance. To study performance of galloping energy harvesters various wind tunnel tests have been conducted with different wind speeds. This paper studies bivariate statistics of extreme experimental galloping energy harvester dynamic response. The bivariate statistics was extracted from available experimental results, more specifically for the voltage‐force dataset. Advantage of the proposed methodology is that relatively short experimental data record can still yield meaningful statistical and design results, provided proper statistical methods are applied. Safety and reliability are important engineering concerns for all kinds of green energy offshore installations. Unlike other reliability methods, the new method does not require to restart simulation each time the system fails, in the case of numerical simulation. In the case of measured structural response, an accurate prediction of system failure probability is also possible as illustrated in this study. Moreover, classic reliability methods dealing with time series do not have an advantage of dealing easily with system high dimensionality and cross‐correlation among different dimensions.

Green renewable energy is produced by floating offshore wind turbines (FOWT), a crucial component of the modern offshore wind energy industry. It is a safety concern to accurately evaluate excessive weights while the FOWT operates in adverse weather conditions. Under certain water conditions, dangerous structural bending moments may result in operational concerns. Using commercial FAST software, the study's hydrodynamic ambient wave loads were calculated and converted into FOWT structural loads. This article suggests a Monte Carlo‐based engineering technique that, depending on simulations or observations, is computationally effective for predicting extreme statistics of either the load or the response process. The innovative deconvolution technique has been thoroughly explained. The suggested approach effectively uses the entire set of data to produce a clear but accurate estimate for severe response values and fatigue life. In this study, estimated extreme values obtained using a novel deconvolution approach were compared to identical values produced using the modified Weibull technique. It is expected that the enhanced new de‐convolution methodology may offer a dependable and correct forecast of severe structural loads based on the overall performance of the advised de‐convolution approach due to environmental wave loading. This article promotes a computationally effective engineering technique based on Monte Carlo for predicting extreme statistics of either the load or the response process, depending on either simulations or observations. The innovative deconvolution technique has been thoroughly explained. The suggested technique effectively makes use of the entire collection of data while providing a straightforward and accurate extreme value forecast.

PurposeTo identify the indications for hepatocellular carcinoma (HCC) irradiated by intensity-modulated photon radiotherapy (IMRT), proton radiotherapy (PRT) or carbon-ion radiotherapy (CIRT) by comparing of dosimetric parameters and incidences of classic radiation-induced liver disease (RILD).MethodsIn all, 40 HCCs were divided into group A (tumors located > 1 cm away from gastrointestinal [GI] tract), and group B (tumors located < 1 cm away from GI tract). The prescribed curative doses were 60 Gy (relative biological effectiveness [RBE]) in 10 fractions for group A, and 67.5 Gy (RBE) in 15 fractions for group B. IMRT, PRT and CIRT plans were separately generated to reach the curative doses and coverage. Dosimetric parameters evaluated were mean dose to normal liver (MDTNL) and the volume of normal liver receiving more than 1 Gy (RBE) (V1). Lyman–Kutcher–Burman model was used to determine the incidences of classic RILD, and Power model of non-linear regression, to estimate the tumor volume that could be irradiated with the curative doses within dose constraint of MDTNL.ResultsWith comparable target doses, the MDTNL (Gy [RBE]) were 18.8 ± 3.7, 13.5 ± 3.1 and 12.8 ± 2.7 in group A and 24.9 ± 7.1, 18.2 ± 3.7 and 17.5 ± 3.7 in group B, respectively, for IMRT, PRT and CIRT. The classic RILD incidences (%) were 22.3 ± 30.0 in IMRT, 2.3 ± 4.9 in PRT and 1.2 ± 2.4 in CIRT. V1 (%) were 89.9 ± 8.8, 43.0 ± 10.2 and 45.9 ± 8.8, respectively, for IMRT, PRT and CIRT.ConclusionsPRT and CIRT could spare the liver more than IMRT. IMRT could deliver the curative doses to HCC up to a diameter of 7.9 cm; PRT, up to 13.2 cm; and CIRT, up to 14.8 cm.

Background To estimate cardiovascular and cancer death rates by regions and time periods. Design Novel statistical methods were used to analyze clinical surveillance data. Methods A multicenter, population‐based medical survey was performed. Annual recorded deaths from cardiovascular diseases were analyzed for all 195 countries of the world. It is challenging to model such data; few mathematical models can be applied because cardiovascular disease and cancer data are generally not normally distributed. Results A novel approach to assessing the biosystem reliability is introduced and has been found to be particularly suitable for analyzing multiregion environmental and healthcare systems. While traditional methods for analyzing temporal observations of multiregion processes do not deal with dimensionality efficiently, our methodology has been shown to be able to cope with this challenge. Conclusions Our novel methodology can be applied to public health and clinical survey data. A novel health system reliability method has been developed and applied to cardio and cancer death rate data. Accurate disease multiregional prediction is done.

Background Emotional responses of a single individual or a group (team) of inter-connected individuals can be viewed as a multi-dimensional nonlinear dynamic system, continuously developing in time, rather than just a discretized catalogue of scenarios. Emotional dynamics encompasses theoretical, experiential and physiological qualitative aspects. Emotions can be conceptualized as physiological, perceptual, and behavioral reactions to certain personally relevant circumstances within the consensual, multi-componential paradigm. New method Current investigation regards the human emotional dynamic system as affected by various kinds of emotion-evoking stimuli (covariates), linked to consistent and distinct human response patterns, as well as how those response trajectories can be quantified. Results This case study explores multi-modal relationships between measured bio-signals + correlated emotion expressions by employing professional actors. The latter facilitated the investigation of intrinsic physiological multi-modal patterns. Current research advocates a state-of-art hypersurface multi-variate human reliability approach, that can contribute to e.g., human error diagnostics, Health, Safety & Environment (HSE) studies within a wide range of industries, affected by human-related (emotional) risks. Comparison with existing methods Advocated method outperforms existing reliability methods as it is able to treat systems with dimensionality above 2. Conclusions The presented multi-modal hypersurface reliability methodology possesses a generic nature, thus it is not limited to the specific case, presented in this case study. Plain language summary The presented case study considers human behavioral dynamics, to be important for the safety and reliability of human-operated processes, e.g., those that can lead to environmental and technogenic accidents. Key points The current state of research: collective emotional state quantification constitutes both theoretical as well as experimental challenges. Experimental component: a range of experimental studies was conducted, attempting to quantify human emotional dynamics. Theory: human emotions are qualitatively conceptualized, but the quantitative aspect still lagging. What this case study adds up: advocates state-of-the-art multi-modal hypersurface risk prognostics approach. Modelling approach: collective emotional dynamics was modelled as a multi-variate system with nonlinear hypersurface failure mode. What was achieved in this research: prognostics of collective emotional excess risks.

Distributed acoustic sensing (DAS) technology is a fiber-optic based distributed sensing technology. It achieves real-time monitoring of acoustic signals by detecting weak disturbances along the fiber. It has advantages such as long measurement distance, high spatial resolution and large dynamic range. Artificial intelligence (AI) has great application potential in DAS technology, including data augmentation, preprocessing and classification and recognition of acoustic events. By introducing AI algorithms, DAS system can process massive data more automatically and intelligently. Through data analysis and prediction, AI-enabled DAS technology has wide applications in fields such as transportation, energy and security due to its accuracy of monitoring data and reliability of intelligent decision-making. In the future, the continuous advancement of AI technology will bring greater breakthroughs and innovations for the engineering application of DAS technology, play a more important role in various fields, and promote the innovation and development of the industry.

This study evaluates the feasibility of the pencil beam scanning technique of carbon ion radiotherapy (CIRT) in the setting of hepatocellular carcinoma (HCC) and establishes the maximum tolerated dose (MTD) calculated by the Local Effect Model version I (LEM‐I) with a dose escalation plan. The escalated relative biological effectiveness‐weighted dose levels included 55, 60, 65, and 70 Gy in 10 fractions. Active motion management techniques were employed, and several measures were applied to mitigate the interplay effect induced by a moving target. CIRT was planned with the LEM‐I‐based treatment planning system and delivered by raster scanning. Offline PET/CT imaging was used to verify the beam range. Offline adaptive replanning was performed whenever required. Twenty‐three patients with a median tumor size of 4.3 cm (range, 1.7–8.5 cm) were enrolled in the present study. The median follow‐up time was 56.1 months (range, 5.7–74.4 months). No dose limiting toxicity was observed until 70 Gy, and MTD had not been reached. No patients experienced radiation‐induced liver disease within 6 months after the completion of CIRT. The overall survival rates at 1, 3, and 5 years were 91.3%, 81.9%, and 67.1% after CIRT, respectively. The local progression‐free survival and progression‐free survival rates at 1, 3 and 5 years were 100%, 94.4%, and 94.4% and 73.6%, 59.2%, and 37.0%, respectively. The raster scanning technique could be used to treat HCC. However, caution should be exercised to mitigate the interplay effect. CIRT up to 70 Gy in 10 fractions over 2 weeks was safe and effective for HCC. No dose‐limiting toxicity was observed in this phase I dose escalation trial, and the maximum tolerated dose had not been reached. After a median follow‐up time of 56.1 months (range, 5.7–74.4 months), we obtained excellent 5‐year survival rates (overall survival rate of 67.1% and local progression‐free survival rate of 94.4%). Our study demonstrated that the pencil beam scanning (raster scanning) technique was feasible for HCC, and carbon ion radiotherapy up to 70 Gy in 10 fractions over 2 weeks was safe and effective for HCC.

Sunflower is a globally important oilseed crop. Improving its fatty acid composition is crucial for enhancing oil quality and nutritional value. To dissect the genetic basis of quality traits, we performed genome resequencing on 203 sunflower inbred lines and conducted a genome-wide association study (GWAS) for five traits—oil content, stearic acid, palmitic acid, oleic acid, and linoleic acid—across three environments. We identified 103 significant single-nucleotide polymorphisms (SNPs) and 154 candidate genes. Notably, several associated loci were co-localized for multiple traits, suggesting pleiotropic effects or close genetic linkages. Integration with transcriptome data from developing seeds revealed that 66 candidate genes were expressed within 30 days after pollination, of which 12 showed significant differential expression between high- and low-oleic acid varieties. Functional characterization of a selected candidate, the ω-6 fatty acid desaturase gene (LOC110938218, designated HaDES8.11), demonstrated that the HaDES8.11-eGFP fusion protein localizes to the endoplasmic reticulum. Heterologous expression of HaDES8.11 in Arabidopsis thaliana significantly increased seed linoleic acid content while decreasing oleic acid content, confirming its role in fatty acid desaturation. Our study provides novel genetic insights and valuable candidate genes for the molecular breeding of sunflower with optimized oil quality.

Nissle 1917 (EcN) is one of the most extensively studied nonpathogenic Gram-negative probiotic strains worldwide. Recent research has highlighted the ability of EcN outer membrane vesicles (OMVs) to enhance the phagocytosis and proliferation of RAW264.7 macrophages. However, the impact of EcN-OMVs on M1/M2 polarization and metabolic modulation remains unknown. In this study, we evaluated the metabolic effects of EcN-OMVs on RAW264.7 macrophage polarization using metabolomic, transcriptomic, and fluxomic approaches. We found that the RAW264.7 macrophages phagocytosed EcN-OMVs, triggering upregulation of the HIF-1, mTORC1, and NF-κB signaling pathways. This metabolic reprogramming enhanced glycolysis, suppressed the TCA cycle, elevated intracellular reactive oxygen species (ROS), TNF-α, IL-6, IL-1β, ATP, and nitric oxide (NO) production, and promoted macrophage proliferation, migration, invasion, and M1-type polarization. In summary, this research establishes a theoretical foundation for utilizing probiotic OMVs in immunomodulatory therapeutic applications.

Background and purpose This study evaluates the dosimetric impact of tumor matching (TM) and bone matching (BM) in carbon ion radiotherapy for locally advanced non-small cell lung cancer. Materials and methods Forty patients diagnosed with locally advanced non-small cell lung cancer were included in this study. TM and BM techniques were employed for recalculation based on re-evaluation computed tomography (CT) images of the patients, resulting in the generation of dose distributions: Plan-T and Plan-B, respectively. These distributions were compared with the original dose distribution, Plan-O. The percentage of the internal gross tumor volume (iGTV) receiving a prescription dose greater than 95% (V95%) was evaluated using dose-volume parameters. Statistical analysis was performed using a paired signed-rank sum test. Additionally, the study investigated the influence of tumor displacement, volume changes, and rotational errors on target dose coverage. Results The median iGTV V95% values for the Plan-O, Plan-T, and Plan-B groups were 100%, 99.93%, and 99.60%, respectively, with statistically significant differences observed. TM demonstrated improved target dose coverage compared to BM. Moreover, TM exhibited better target coverage in case of larger tumor displacement. TM’s increased adjustability in rotation directions compared to BM significantly influenced dosimetric outcomes, rendering it more tolerant to variations in tumor morphology. Conclusion TM exhibited superior target dose coverage compared to BM, particularly in cases of larger tumor displacement. TM also demonstrated better tolerance to variations in tumor morphology.