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The orthogonal basis functions (OBFs) method is a prevailing choice for the detection of undersurface magnetic anomaly targets. However, it requires the detecting platform or target to move uniformly along a straight path. To circumvent the restrictions, a new adaptive basis functions (ABFs) approach is proposed in this article. It permits the detection platform to search for a possible target at different speeds along any course. The ABFs are constructed using the real-time data of the onboard triaxial fluxgate, GPS module, and attitude gyro. Based on the pseudo-energy of an apparent target signal, the constant false alarm rate (CFAR) method is employed to judge whether a target is present. Moreover, by defining the pixel as a relative possibility for a target at a geographic location, a magnetic anomaly target imaging scheme is introduced by displaying the pixels onto the searching area. On-site experimental data are utilized to demonstrate the proposed approach. Compared with the traditional OBFs method, the present ABFs approach can substantially improve the detection possibility and reduce false alarms. [ABSTRACT FROM AUTHOR]

Accurately predicting the State of Health (SOH) of lithium-ion batteries is essential for ensuring their safe and reliable operation, and reducing maintenance and service costs for associated equipment. Nevertheless, the aging data of lithium-ion batteries displays pronounced nonlinearity and is plagued by issues such as capacity regeneration. To address this issue, this study proposes a framework for SOH prediction of lithium-ion batteries based on Variational Mode Decomposition (VMD) and CNN-Transformer. First, the original data undergoes a VMD smoothing process to eliminate capacity regeneration and a portion of the noise signals. Subsequently, Convolutional Neural Networks (CNN) is utilized for feature extraction. Then, a modified Transformer model is employed to capture the inherent correlations in the time series and map the features to future SOH values. An iterative strategy is adopted to predict SOH for each charge-discharge cycle. The experimental results on the CALCE dataset demonstrate that the proposed method can accurately predict the SOH of lithium-ion batteries using just 5 %–6 % of the complete cycle's aging data. Additionally, comparative results on the NASA dataset show that, compared to the latest relevant literature, the proposed method achieves high prediction accuracy while maintaining exceptional generalization. • Apply VMD to remove the effect of irregular noise on model performance. • Use CNN-transformer model for feature extraction and long dependency learning. • Iterative prediction strategies require only a small amount of data. [ABSTRACT FROM AUTHOR]

Zinc oxide (ZnO) nanorods and acetylene black (AB) doped ZnO (AB-ZnO) nanorods were fabricated by the hydrothermal method, and characterized by various methods (XRD, EDS, SEM, BET, FT-IR, Zeta potential). The photo-catalytic degradation ability of AB-ZnO/PDS/vis system on simulated dyeing wastewater was studied with methyl orange (MO) as the target pollutant. The effects of AB doping ratio, AB-ZnO dosage, PDS concentration, initial pH, and temperature on MO degradation were comprehensively investigated. The AB-ZnO/PDS/vis system has a wide range of pH applications from 3 to 11. The prepared material of AB-ZnO had excellent stability that the degradation efficiency on MO remained above 90 % after 4 cycles. The theoretical optimal removal rate of MO was 98.59 % when AB-ZnO dosage was 1.0 g/L, PDS concentration was 2.0 mmol/L, initial pH was 5, and temperature was 50 °C. Furthermore, degradation kinetic model, the orthogonal experiment and the degradation mechanisms were elaborated. By using methanol and tert-butanol as free radical inhibitors, it was confirmed that SO 4 ·- and ·OH were the main active free radicals in AB-ZnO/PDS/vis system. • The prepared AB-ZnO nanorods have better photo-catalytic performance than ZnO. • The degradation rate of MO by AB-ZnO/PDS/vis system exceeded 98 %. • AB-ZnO/PDS/vis system widens the application range of pH. • AB-ZnO nanorods maintain high photo-catalytic activity after repeated usage. • AB-ZnO/PDS/vis system operates efficiently and stably. [ABSTRACT FROM AUTHOR]

Photovoltaic (PV) power generation prediction is a significant research topic in photovoltaics due to the clean and pollution-free characteristics of solar energy, which have contributed to its popularity worldwide. Photovoltaic data, as a type of time series data, exhibit strong periodicity and volatility. Researchers typically employ time–frequency signal processing methods, like empirical mode decomposition (EMD), to smooth the data during the feature engineering stage. However, improper operations at this stage could result in information leakage. Unfortunately, many existing studies on photovoltaic prediction fail to provide sufficient details on how signal processing methods are used during model training. To address this issue, this paper proposes the similarity-day extension EMD that avoids information leakage. The proposed method is validated through experiments conducted on the PV dataset of the Desert Knowledge Australia Solar Center, using mainstream models such as GRU, LSTM, CNN-LSTM, LSTN-CNN, and Bi-LSTM. The experimental results demonstrate an average improvement of 3.67% in MAE and 5.71% in RMSE when using this method, thus verifying its feasibility and effectiveness. Moreover, the proposed method can be applied to other data processing methods that may suffer from information leakage. [ABSTRACT FROM AUTHOR]

A novel phosphorus‐doped graphitic carbon nitride nanosheets (PS‐CN) photocatalyst was prepared by thermal polycondensation and thermal stripping method, and its physicochemical properties were characterized. The results show that PS‐CN formed a thin sheet and multi‐cavity structure by thermal delamination that could enhance the surface area, light reception, and adsorption capacity. The prepared PS‐CN activated sodium percarbonate system (PS‐CN/SPC) was developed for the degradation of dyeing wastewater with methylene blue (MB) as the target pollutant and the degradation mechanism was analyzed. At room temperature (20 °C), the degradation rate of MB in PS‐CN/SPC system within 10 min (88.13 %) was three times that of pure g‐C3N4. In addition, the MB was completely degraded under visible light within 60 min. Moreover, PS‐CN exhibited excellent reusability that the degradation rates of MB have remained above 96 % in five consecutive cycles. The free radical quenching experiments showed that hole (h+), hydroxyl radical (•OH) and superoxide radical (•O2−) all participated in MB degradation, and •OH was the main active species. The PS‐CN/SPC system would provide a high‐value solution for the treatment of dye wastewater due to the low cost of raw materials, superior photocatalytic activity, simple synthesis method, and reusability of the catalyst. [ABSTRACT FROM AUTHOR]