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Mustard (Brassica juncea) is a cruciferous vegetable used as a food spice and folk medicine worldwide. Mustard contains numerous phytochemicals such as: vitamins, minerals, dietary fiber, chlorophylls, glucosinolates (and their degradation products), polyphenols and volatile components(allyl isothiocyanate, 3-butyl isothiocyanate, etc.). The content and exact chemical composition of these phytochemicals is affected by plant variety, growth environment, extraction process and food processing methods. In addition, mustard may possess a plethora of pharmacological activities including anti-oxidation, anti-inflammation, and bacteriostatic and antiviral activity. Mustard has also been used to combat several illnesses such as cancer, obesity, depression, diabetes, and cataracts. This review provides an overview of plant characteristics, types, origins, distribution, and consumption methods of Mustard, as well as its phytochemicals and biological activities. The findings of this paper may serve as references for the development and utilization of Mustard resources.

This paper proposes a flexible sensor for detecting cracks on bridges. Strain and deflection sensing modules are integrated on the film that is made of composite conductive materials. By optimizing the preparation ratio and internal structure, the strain detection accuracy and sensitivity of the sensor have been improved. The bridge crack detection accuracy reached 91%, which is higher than current sensors. Experimental results show that the composite material containing 2.23 wt% carbon black (CB) mixed hybrid filler has good linearity, higher accuracy than sensors in use, excellent stretchability (>155%), high gauge factor (GF ~ 43.3), and excellent durability over 2000 stretching-releasing cycles under 10 N. The designed flexible sensor demonstrates the practicality and effectiveness of bridge crack detection and provides a feasible solution for accurate bridge health monitoring in the future.

As global energy networks expand and smart grid technology evolves rapidly, the volume of historical power accident data has increased dramatically, containing valuable risk information that is essential for building efficient public safety early warning systems. This paper introduces an innovative text analysis method, the Sparse Coefficient Optimized Weighted FP-Growth Algorithm (SCO-WFP), which is designed to optimize the processing of power accident-related textual data and more effectively uncover hidden patterns behind accidents. The method enhances the evaluation of sparse risk factors by preprocessing, clustering analysis, and calculating piecewise weights of power accident data. The SCO-WFP algorithm is then applied to extract frequent itemsets, revealing deep associations between accident severity and risk factors. Experimental results show that, compared to traditional methods, the SCO-WFP algorithm significantly improves both accuracy and execution speed. The findings demonstrate the method’s effectiveness in mining frequent itemsets from text semantics, facilitating a deeper understanding of the relationship between risk factors and accident severity.

In a distributed photovoltaic system, photovoltaic data are affected by heterogeneity, which leads to the problems of low adaptability and poor accuracy of photovoltaic power prediction models. This paper proposes a distributed photovoltaic power prediction scheme based on Personalized Federated Multi-Task Learning (PFL). The federal learning framework is used to enhance the privacy of photovoltaic data and improve the model’s performance in a distributed environment. A multi-task module is added to PFL to solve the problem that an FL single global model cannot improve the prediction accuracy of all photovoltaic power stations. A cbam-itcn prediction algorithm was designed. By improving the parallel pooling structure of a time series convolution network (TCN), an improved time series convolution network (iTCN) prediction model was established, and the channel attention mechanism CBAMANet was added to highlight the key meteorological characteristics’ information and improve the feature extraction ability of time series data in photovoltaic power prediction. The experimental analysis shows that CBAM-iTCN is 45.06% and 42.16% lower than a traditional LSTM, Mae, and RMSE. Compared with FL, the MAPE of the PFL proposed in this paper is reduced by 9.79%, and for photovoltaic power plants with large data feature deviation, the MAPE experiences an 18.07% reduction.

This paper presents a hydrogel-based flexible sensor array to detect plantar pressure distribution and recognize the gait patterns to assist those who suffer from gait disorders to rehabilitate better. The traditional pressure detection array is composed of rigid metal sensors, which have poor biocompatibility and expensive manufacturing costs. To solve the above problems, we have designed and fabricated a novel flexible sensor array based on AAM/NaCl (Acrylamide/Sodium chloride) hydrogel and PI (Polyimide) membrane. The proposed array exhibits excellent structural flexibility (209 KPa) and high sensitivity (12.3 mV·N−1), which allows it to be in full contact with the sole of the foot to collect pressure signals accurately. The Wavelet Transform-Random Forest (WT-RF) algorithm is introduced to recognize the gaits based on the plantar pressure signals. Wavelet transform realizes the signal filtering and normalization, and random forest is responsible for the classification of the processed signals. The classification accuracy of the WT-RF algorithm reaches 91.9%, which ensures the precise recognition of gaits.

Fermented pepper is one of the traditional Chinese fermented vegetables. The production mainly relies on the fermentation by natural microorganisms. This fermentation system is a unique and dynamic microecological environment, and involved microbial communities are very complex. In this study, 454 pyrosequencing was first used to investigate the fungal communities in fresh pepper and different fermentation phases. The results showed that fungal communities in fresh pepper (sample M_0) were more abundant than later fermented phases. Taxa in proportions >0.01% could be assigned to 21 different genera. Taxa in proportions >1% were Trichosporon 24.11%, Rhodotorula 7.4%, Cladosporium 4.26%, Debarvomvces 3.94%, Mucor 2.51% and Cryptococcus 1.86%. There were a large number of unknown fungi (47.99%) in the sample waiting to be identified. Along with the fermentation, microbial communities became less diverse. Hanseniaspora and Pichia became the dominant fungal genera, while Trichosporon decreased from a maximum 24.11% to a minimum 0.1%. On the seventh fermentation day, the percentage of Hanseniaspora reached 89.3%. On the 20th fermentation day, taxa in proportions >1% were Hanseniaspora 69.25%, Unclassified 12.23%, Pichia 8.95%, Debaryomyces 6.22% and Rhodotorula 1.31%.

To address the challenges of prolonged current isolation times and high dependency on varistors in traditional flexible short-circuit fault isolation schemes for DC systems, this paper proposes a rapid fault isolation circuit design based on an adaptive solid-state circuit breaker (SSCB). By introducing an adaptive current-limiting branch topology, the proposed solution reduces the risk of system oscillations induced by current-limiting inductors during normal operation and minimizes steady-state losses in the breaker. Upon fault occurrence, the current-limiting inductor is automatically activated to effectively suppress the transient current rise rate. An energy dissipation circuit (EDC) featuring a resistor as the primary energy absorber and an auxiliary varistor (MOV) for voltage clamping, alongside a snubber circuit, provides an independent path for inductor energy release after faults. This design significantly alleviates the impact of MOV capacity constraints on the fault isolation process compared to traditional schemes where the MOV is the primary energy sink. The proposed topology employs a symmetrical bridge structure compatible with both pole-to-pole and pole-to-ground fault scenarios. Parameter optimization ensures the IGBT voltage withstand capability and energy dissipation efficiency. Simulation and experimental results demonstrate that this scheme achieves fault isolation within 0.1 ms, reduces the maximum fault current-to-rated current ratio to 5.8, and exhibits significantly shorter isolation times compared to conventional approaches. This provides an effective solution for segment switches and tie switches in millisecond-level self-healing systems for both low-voltage (LVDC, e.g., 750 V/1500 V DC) and medium-voltage (MVDC, e.g., 10–35 kV DC) smart DC distribution grids, particularly in applications demanding ultra-fast fault isolation such as data centers, electric vehicle (EV) fast-charging parks, and shipboard power systems.

The load forecasting research for an NPS faces challenges including a high model accuracy, non-sharing of data, and a high communication cost. This paper proposes a load forecasting method for an NPS, based on efficient federated transfer learning (FTL). The adversarial feature extractor is added on the basis that FTL can effectively transfer the parameter features of the non-mask load to the local load data, and make up for the loss of mask load prediction accuracy. In order to improve the efficiency of the gradient compression of federated learning (FL), a depth dynamic threshold compression sensing method is proposed, which replaces the sparse signal in compressed sensing via the U-Net model and achieves an observation dimension reduction through a convolutional neural network (CNN). The experimental results show that the mean absolute percentage error (MAPE) and the root-mean-square error (RMSE) of the load forecasting method proposed in this paper are reduced by 9.6% and 2.31 kW, on average, when the load data are covered up to different degrees. Compared with the traditional FL model, the proposed compression algorithm saves 23.5% of the communication cost, without changing the accuracy of the model. The proposed prediction framework is easily interpretable, and robust under different validation metrics.

With the present fault detection method for low-voltage distribution networks, it is difficult to detect single-phase grounding faults under complex working conditions. In this paper, a particle swarm optimization (PSO) support vector machine (SVM)-based grounding fault detection method is proposed for distribution networks. By improving the inertia weight value and introducing a flight-time factor, the PSO algorithm can be improved. The parameters C and g of the SVM can be optimized based on the improved PSO algorithm. Based on the PSO-SVM-based method, a grounding fault detection method can be established. By testing the proposed model in the simulation and experiment, its effectiveness and detection accuracy is validated.

In order to solve the problem of the large-scale integration of new energy into power grid output fluctuations, this paper proposes a new energy microgrid optimization scheduling algorithm based on a two-stage robust optimization and improved grey correlation theory. This article simulates the fluctuation of the outputs of wind turbines and distributed photovoltaic power plants by changing their robustness indicators, generates economic operating cost data for microgrids in multiple scenarios, and uses an improved grey correlation theory algorithm to analyze the correlation between new energy and various scheduling costs. Subsequently, a weighted analysis is performed on each correlation degree to obtain the correlation degree between new energy and total scheduling operating costs. The experimental results show that the improved grey correlation theory optimization scheduling algorithm for new energy microgrids proposed obtains weighted correlation degrees of 0.730 and 0.798 for photovoltaic power stations and wind turbines, respectively, which are 3.1% and 4.6% higher than traditional grey correlation theory. In addition, the equipment maintenance costs of this method are 0.413 and 0.527, respectively, which are 25.1% and 5.4% lower compared to the traditional method, respectively, indicating that the method effectively improves the accuracy of quantitative analysis.