Search Results (2,848)

GhPEL48_Dt, a Pectate lyase (PEL, EC4.2.2.2), is a crucial enzyme involved in cell-wall modification and pectin degradation. Studies have shown that the GhPEL48_Dt also plays a significant role in cotton-fiber development; however, the specific function and regulatory mechanism of GhPEL48_Dt in cotton-fiber development are still not fully understood. Here, we found that the histone deacetylase inhibitor-Trichostatin A significantly reduces the transcript levels of GhPEL48_Dt and its enzyme activity. Further, silencing of GhPEL48_Dt significantly inhibits the initiation and elongation of cotton fibers by promoting pectin degradation, and the heterologous expression of GhPEL48_Dt promotes the development of trichomes and root hairs in Arabidopsis, which suggests that GhPEL48_Dt plays a positive and conserved role in single cell i.e., fiber, root hair, and leaf trichome development. Collectively, this paper provides a comprehensive analysis of the fundamental characteristics and functions of GhPEL48_Dt in fiber development, including the regulatory role of histone acetylation on GhPEL48_Dt, which contributes to the understanding of pectin degradation pathways and establishes a theoretical foundation for elucidating its regulatory mechanism. Full article

Digital Twin is becoming an increasingly powerful resource in the field of building production, replacing traditional processes in the Architecture, Engineering, Construction and Operations sector. This study is concerned with the development of a DT, enabled by Building Information Modeling, artificial intelligence, machine learning, and the Internet of Things to implement space management strategies. It proposes an application case for the Lazio Region headquarters, which has partly adopted smart working typology post-COVID-19. The aim is to create an accurate digital replica of the building based on BIM, integrated with real-time data. This will help to improve the use of space, the management of resources, and the quality of services provided to the community. It also improves energy efficiency, reducing consumption by 530.40 MWh per year and reducing greenhouse gas emissions by 641.32 tons of CO₂ per year. The research proposes a holistic framework for the implementation of innovative solutions in the context of public infrastructure space management through the use of digital technology, facilitating the promotion of efficiency and sustainability in decision-making and operational processes through the application of a digital methodology. Full article

Background/Objectives: This study looks at how a kinematic chain exercise regimen that targets the lower, core, and upper body affects university shot put participants’ shoulder muscle strength and throwing efficiency. This study fills an apparent research void on shot put training approaches by presenting a comprehensive kinematic chain workout program. It was anticipated that this method would improve performance the most, considering the complex biomechanical requirements of the sport. Methods: Eighty athletes aged (19.87 ± 1.31 years), were assigned into two groups at random: experimental (n = 40) and control (n = 40). While the control group carried on with their usual training, the experimental group participated in an 8-week kinematic chain training program. Pre- and post-training evaluations were carried out to evaluate shot put-throwing ability, shoulder muscle strength, and participant satisfaction with the exercise regimen. Results: The analyses were performed to evaluate the between- and within-group effects in the 10-week intervention period using a two-way ANOVA. This study demonstrated that, when compared to the control group, the athletes in the kinematic chain program had significantly increased throwing distance (p = 0.01) and shoulder muscle strength (p = 0.01). Furthermore, there was a significant increase (p = 0.005) in the athletes’ satisfaction levels with the workout program among those in the experimental group. Conclusions: In shot put athletes, this study suggests that a kinematic chain-focused strategy can improve throwing performance and shoulder muscle strength. The findings suggest that incorporating kinematic chain workouts into shot put training programs could be beneficial. However, conclusions should be drawn with caution, and further research is necessary to confirm the effectiveness of kinematic chain-based approaches across various sports and to understand their broader implications in sports science. Full article

Incorporating AC-type flying capacitors (FC) between series-connected devices is an effective way to enhance the rated voltage for high-power applications based on current source converters (CSCs). Through appropriate modulation and FC voltage control, it is possible to achieve improved DC bus voltage quality with reduced common-mode voltage (CMV) and low dv/dt. On the other hand, the parallel CSC is a popular choice for increasing the system’s rated current to accommodate higher power applications. The use of interleaved modulation techniques can improve the harmonic performance of parallel converters while reducing the need for passive filters. The modular flying capacitor clamped (FCC)-CSC structure can combine these advantages, achieving higher rated power along with improved power quality on both the DC and AC sides. Moreover, the enhanced AC quality contributes to the regulation of FC voltage and further improves the DC-side voltage quality. This paper analyzes the operation principle of the parallel FCC-CSC structure and proposes an interleaved space vector modulation (SVM) method to enhance the harmonic performance of the AC output. Additionally, an optimized zero-state replacement (ZSR) based FC voltage control and a DC-link current balance strategy built on this control are introduced. Simulation and experimental results validate the effectiveness of the proposed methods. Full article

In this article, we propose a new path-conservative discontinuous Galerkin (DG) method to solve non-conservative hyperbolic partial differential equations (PDEs). In particular, the method here applies the one-stage ADER (Arbitrary DERivatives in space and time) approach to fulfill the temporal discretization. In addition, this method uses the differential transformation (DT) procedure rather than the traditional Cauchy–Kowalewski (CK) procedure to achieve the local temporal evolution. Compared with the classical ADER methods, the current method is free of solving generalized Riemann problems at inter-cells. In comparison with the Runge–Kutta DG (RKDG) methods, the proposed method needs less computer storage, thanks to the absence of intermediate stages. In brief, this current method is one-step, one-stage, and fully-discrete. Moreover, this method can easily obtain arbitrary high-order accuracy both in space and in time. Numerical results for one- and two-dimensional shallow water equations (SWEs) show that the method enjoys high-order accuracy and keeps good resolution for discontinuous solutions. Full article

The growing interest in solar energy stems from its potential to reduce greenhouse gas emissions. Global horizontal irradiance (GHI) is a crucial determinant of the productivity of solar photovoltaic (PV) systems. Consequently, accurate GHI forecasting is essential for efficient planning, integration, and optimization of solar PV energy systems. This study evaluates the performance of six machine learning (ML) regression models—artificial neural network (ANN), decision tree (DT), elastic net (EN), linear regression (LR), Random Forest (RF), and support vector regression (SVR)—in predicting GHI for a site in northern Saudi Arabia known for its high solar energy potential. Using historical data from the NASA POWER database, covering the period from 1984 to 2022, we employed advanced feature selection techniques to enhance the predictive models. The models were evaluated based on metrics such as R-squared (R²), Mean Squared Error (MSE), Root Mean Squared Error (RMSE), Mean Absolute Percentage Error (MAPE), and Mean Absolute Error (MAE). The DT model demonstrated the highest performance, achieving an R² of 1.0, MSE of 0.0, RMSE of 0.0, MAPE of 0.0%, and MAE of 0.0. Conversely, the EN model showed the lowest performance with an R² of 0.8396, MSE of 0.4389, RMSE of 0.6549, MAPE of 9.66%, and MAE of 0.5534. While forward, backward, and exhaustive search feature selection methods generally yielded limited performance improvements for most models, the SVR model experienced significant enhancement. These findings offer valuable insights for selecting optimal forecasting strategies for solar energy projects, contributing to the advancement of renewable energy integration and supporting the global transition towards sustainable energy solutions. Full article

The aim of this study was to determine the most appropriate advanced methods for distinguishing the gait of healthy children (CO) from the gait of children with cerebral palsy (CP) based on electromyography (EMG) parameters and coactivations. An EMG database of 22 children (aged 4–11 years) was used in this study, which included 17 subjects in the CO group and 5 subjects in the CP group. EMG time parameters were calculated for the biceps femoris (BF) and semitendinosus (SE) muscles and coactivations for the rectus femoris (RF)/BF and RF/SE muscle pairs. To obtain a more accurate classification result, data augmentation was performed, and three classification algorithms were used: support vector machine (SVM), k-nearest neighbors (KNNs), and decision tree (DT). The accuracy of the root-mean-square (RMS) parameter and KNN algorithm was 95%, the precision was 94%, the sensitivity was 90%, the F1 score was 92%, and the area under the curve (AUC) score was 98%. The highest classification accuracy based on coactivations was achieved using the KNN algorithm (91–95%). It was determined that the KNN algorithm is the most effective, and muscle coactivation can be used as a reliable parameter in gait classification tasks. Full article

In the face of escalating credit card fraud due to the surge in e-commerce activities, effectively distinguishing between legitimate and fraudulent transactions has become increasingly challenging. To address this, various machine learning (ML) techniques have been employed to safeguard cardholders and financial institutions. This article explores the use of the Ensemble Hidden Markov Model (EHMM) combined with two distinct feature extraction methods: principal component analysis (PCA) and a proposed statistical feature set termed MRE, comprising Mean, Relative Amplitude, and Entropy. Both the PCA-EHMM and MRE-EHMM approaches were evaluated using a dataset of European cardholders and demonstrated comparable performance in terms of recall (sensitivity), specificity, precision, and F1-score. Notably, the MRE-EHMM method exhibited significantly reduced computational complexity, making it more suitable for real-time credit card fraud detection. Results also demonstrated that the PCA and MRE approaches perform significantly better when integrated with the EHMM in contrast to the conventional HMM approach. In addition, the proposed MRE-EHMM and PCA-EHMM techniques outperform other classic ML models, including random forest (RF), linear regression (LR), decision trees (DT) and K-nearest neighbour (KNN). Full article

Sperm cryopreservation is a fundamental tool for the conservation of avian genetic resources; however, avian spermatozoa are susceptible to this process. To cope with the high production of reactive oxygen species (ROS), the addition of exogenous antioxidants is beneficial. Pectoliv30 is a substance derived from alperujo, and in this study, its effect was analyzed on seminal quality after its addition to the cryopreservation extender of roosters at different concentrations. For this purpose, 16 Utrerana breed roosters were used, and seminal collection was performed in six replicates, creating a pool for each working day with ejaculates of quality. After cryopreservation, one sample per treatment and replicate was thawed, and several seminal quality parameters were evaluated. Statistical analysis revealed numerous correlations between these variables, both positive and negative according to the correlation matrix obtained. Furthermore, the chi-squared automatic interaction detection (CHAID) decision tree (DT) reported significant differences in the hypo-osmotic swelling test (HOST) variable between groups. Moreover, results for this parameter were more desirable at high concentrations of Pectoliv30. The application of this substance extracted from the by-product alperujo as an antioxidant allows the improvement of the post-thawing seminal quality in roosters and facilitates optimization of the cryopreservation process as a way to improve the conservation programs of different endangered poultry breeds. Full article

For lithium-ion batteries and supercapacitors in hybrid power storage facilities, both steady degradation and random shock contribute to their failure. To this end, in this paper, we propose to introduce the degradation-threshold-shock (DTS) model for their remaining useful life (RUL) prediction. Non-homogeneous compound Poisson process (NHCP) is proposed to simulate the shock effect in the DTS model. Considering the long-range dependence and heavy-tailed characteristics of the degradation process, fractional Weibull process (fWp) is employed in the diffusion term of the stochastic degradation model. Furthermore, the drift and diffusion coefficients are constantly updated to describe the environmental interference. Prior to the model training, steady degradation and shock data must be separated, based on the three-sigma principle. Degradation data for the lithium-ion batteries (LIBs) and ultracapacitors are employed for model verification under different operation protocols in the power system. Recent deep learning models and stochastic process-based methods are utilized for model comparison, and the proposed model shows higher prediction accuracy. Full article

Diagnosing agricultural machinery faults is critical to agricultural automation, and identifying vibration signals from faulty bearings is important for agricultural machinery fault diagnosis and predictive maintenance. In recent years, data–driven methods based on deep learning have received much attention. Considering the roughness of the attention receptive fields in Vision Transformer and Swin Transformer, this paper proposes a Shift–Deformable Transformer (S–DT) network model with multi–attention fusion to achieve accurate diagnosis of composite faults. In this method, the vibration signal is first transformed into a time–frequency graph representation through continuous wavelet transform (CWT); secondly, dilated convolutional residual blocks and efficient attention for cross–spatial learning are used for low–level local feature enhancement. Then, the shift window and deformable attention are fused into S–D Attention, which has a more focused receptive field to learn global features accurately. Finally, the diagnosis result is obtained through the classifier. Experiments were conducted on self–collected datasets and public datasets. The results show that the proposed S–DT network performs excellently in all cases. With a slight decrease in the number of parameters, the validation accuracy improves by more than 2%, and the training network has a fast convergence period. This provides an effective solution for monitoring the efficient and stable operation of agricultural automation machinery and equipment. Full article

The recent developments in electric vehicle (EV) necessities the requirement of a human intervention free charging system for safe and reliable operation. Wireless power transfer (WPT) technology shows promising options to automate the charging process with user convenience. However, the operation of the WPT system is designed to operate at a high-frequency (HF) range, which requires proper control and modulation technique to improve the performance of power electronic modules. This paper proposes a dead-time (DT) integrated Pulse Density Modulation (PDM) technique to provide better control with minimal voltage and current ripples at the switches. The proposed technique is investigated using a LCC-LCL compensated WPT system, which predominantly affects the high-frequency voltage and current ripples. The performance analysis is studied at different density conditions to explore the impact of the integrated PDM approach. Moreover, the PDM technique gives better control over the power transfer at different levels of load requirement. The simulation and experimental analysis was performed for a 3.7 kW WPT prototype test system under different modes of operation of the high-frequency power converters. Both the simulated and experimental results demonstrate that the proposed PDM technique effectively enhances the efficiency of the HF inverter while significantly reducing output current ripples, power dissipation and improving the overall WPT system efficiency to 92%, and leading to a reduction in the power loss in the range of 10% to 20%. This leads to improved overall system control and performance. Full article

Digital Twins (DT) play a key role in Industry 4.0 applications, and the technology is in the process of being mature. Since its conceptualisation, it has been heavily contextualised and often misinterpreted as being merely a virtual model. Thus, it is crucial to define it clearly and have a deeper understanding of its architecture, workflow, and implementation scales. This paper reviews the notion of a Digital Twin represented in the literature and analyses different kinds of descriptions, including several definitions and architectural models. A new fit-for-all definition is proposed which describes the underlying technology without being context-specific and also overcomes the pitfalls of the existing generalised definitions. In addition, the existing three-dimensional and five-dimensional models of the DT architecture and their characteristic features are analysed. A new simplified two-branched model of DT is introduced, which retains a clear separation between the real and virtual spaces and outlines the latter based on the two key modelling approaches. This model is then extended for condition monitoring of electronic components and systems, and a hybrid approach to Prognostics and Health Management (PHM) is further elaborated on. The proposed framework, enabled by the two-branched Digital Twin model, combines the physics-of-degradation and data-driven approaches and empowers the next generation of reliability assessment methods. Finally, the benefits, challenges, and outlook of the proposed approach are also discussed. Full article

Hydraulic tomography (HT) is a promising technique for high-resolution imaging of subsurface heterogeneity, which addresses the limitations of traditional methods, such as borehole drilling and geophysical surveys. This study focuses on the application of HT to detect and characterize fractured zones in bedrock and addresses the gap in the understanding of the role of distributed flux data in the joint inversion of hydraulic head and flux data. By conducting synthetic injection tests and using sequential successive linear estimators for inversion, the study explores the effectiveness of combining limited head data with distributed temperature sensing (A-DTS)-derived flux data. The findings highlight the fact that integrating flux data significantly enhances the accuracy of identifying fracture permeability characteristics, even when head data is sparse. This approach not only improves the resolution of hydraulic conductivity fields but also offers a cost-effective strategy for practical field applications. The results underscore the potential of HT to enhance our understanding of groundwater flow and contaminant transport in fractured media, which has important implications for carbon capture, enhanced geothermal systems, and radioactive waste disposal. Full article

Risk management in electronic component selection is crucial for ensuring inherent system quality dependability in aerospace equipment research and development (R&D). Therefore, it is of great significance to conduct rapid and accurate risk assessment research of electronic components based on engineering practice. This article utilizes the extreme gradient boosting (XGBoost) algorithm and domain knowledge to assess electronic component selection risk. Firstly, an innovative risk assessment system is established for electronic component selection based on business materials analysis and investigation by questionnaire. Then, the values of factors in the system are quantified based on domain knowledge and empirical formulae. Finally, an XGBoost-based risk assessment model is constructed that can explore learning strategies and develop latent features by integrating multiple decision trees. The model is compared against the random forest (RF), support vector machine (SVM) and decision tree (DT) algorithms. Accuracy, precision, recall, and F1 score are used as evaluation indexes. The results obtained from the above algorithms illustrate the effectiveness of the proposed method in electronic component selection risk assessment. Full article