Conventionally, the output amplitude of triboelectric nanogenerators (TENGs) sensors is detected as the sensing output, where the accuracy and stability are easily affected by environmental ...interferences such as humidity, temperature and electrostatic coupling with surrounding objects. Meanwhile, the nature of pulse mode voltage output cannot provide information to further generate a detailed profile of the varying force with time interval when users press a typical TENG working in the contact-separation mode. These two critical issues hinder the TENGs sensors to be competitive with conventionally commercialized sensors. In this study, a switchable textile-triboelectric nanogenerator (S-TENG) is proposed to address these issues. By working on a switchable mode to generate resistor-capacitor (RC) discharging voltage, i.e, enabling capacitive sensing, the capacitance of the TENGs device is not affected by environmental interferences. Moreover, a high-frequency switching approach is investigated to generate a continuous profile of time-dependent capacitance as a function of force along time, referring to the continuous sensing parameter. Therefore, S-TENGs offer the sensory information which could not be achieved by any other TENGs so far.
With a high frequency operating switch, the continuous profile of the sensing target, such as applied force or bending angle, can be decoded from the time constant of the exponential waveform in the recoding data. It is, indeed, a self-powered capacitive sensing. Therefore, the environmental interference, such a humidity, can be eliminated. Display omitted
•A continuous sensing profile can be generated by the Switchable-textile-triboelectric nanogenerators (S-TENGs).•The interference from the environment, such a humidity, can be eliminated.•An algorithm is developed to correct the data when the sampling rate is high.
•Raman spectroscopy is a powerful tool for obtaining chemical information of a material.•Biological stains have unique Raman spectroscopic signatures.•A novel universal tool for semen identification ...was validated.•Random forest algorithm differentiated between body fluids and environmental interferences for semen traces.•Raman spectroscopy combined with chemometrics offers a nondestructive and rapid identification of semen traces.
Proper identification of body fluids at crime scenes is imperative to forensic investigations, as they yield crucial pieces of DNA evidence that lead to subsequent investigation resolution and criminal justice. Nevertheless, body fluid identification can prove a burdensome process, as several ubiquitous substances found at crime scenes often resemble the appearance of a specific body fluid. Therefore, it is extremely important to find a reliable method for body fluid discovery and differentiation among other potential environmental inferences (EIs). In this study, Raman spectroscopy was combined with advanced statistical analysis and used for semen identification. This method has already been proven to be a powerful tool for characterization of body fluid traces for forensic purposes. Twenty-seven EI substances that may (i) yield a false positive (FP) result on routine forensic tests for semen and/or (ii) resemble a semen stain at a crime scene were examined. Raman spectra of EI substances were compared to spectra of main body fluids: semen, blood, saliva, sweat, and vaginal fluid. The random forest algorithm was utilized for differentiating between body fluids and EIs commonly found for semen traces. Using the classification probability threshold of 70%, entire separation of classes was observed. None of the twenty-seven EI substances was classified as any body fluid. This confirms a great potential of Raman spectroscopy for nondestructive and rapid identification of semen traces found at a crime scene.
Handheld X-ray fluorescence (HH-XRF) has expanded its utilization areas according to recent technological developments. Most current applications, though, are still concentrated in traditional areas ...including mineral resource analysis and environmental regulation rather than forensic science for the purpose of investigating a nuclear security event involving nuclear material out of regulatory control. To apply HH-XRF to nuclear material analysis, it is necessary to first obtain calibration data using standard reference materials. Considering the difficulty in obtaining such standard reference materials as well as the high costs involved, one well-known alternative method is to use Monte Carlo simulation code. This study investigated the feasibility of employing Monte Carlo N-Particle transport 6 (MCNP6) simulation to provide calibration data through comparison with experimental measurements of pure solid samples of graphite, copper, SiO2, and UO2 using HH-XRF. The results showed that the MCNP6 simulation results were entirely consistent with the measurement spectra, except for environmental interferences stemming from interactions with the mechanical components below 10 keV which varied slightly according to sample type. To quantitatively evaluate the effect of these environmental interferences on the whole spectrum, the coefficient of determination (R2) was used. In the case of graphite, the effect of the environmental interferences was evaluated to be about 20% on the conformity of the measured and simulated results, while those for copper, SiO2, and UO2 were about 1%, 3%, and less than 1%, respectively. These results indicate that samples having elements with higher rates of photoelectric absorption followed by fluorescence compared to scattering tend to decrease the effect of the environmental interferences over the entire spectrum. The origin of the environmental interferences was estimated to be interference with the detector shield and/or X-ray tube collimator, which are particular design features of the device used. Their effect on contributing to the environmental interferences was evaluated by experiment for the detector shield and simulation for the X-ray tube collimator. As the detector shield was found to only contribute to a decrease in overall spectrum intensity, the major contributor to the environmental interferences was determined to be the collimator. It is believed that the results of this study will help to confirm that Monte Carlo simulation can properly provide calibration data for using HH-XRF on nuclear materials for which reference materials are hard to obtain.
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•HH-XRF spectra and Monte Carlo simulation on various solid samples was compared.•MCNP6 code was selected for the Monte Carlo simulation of HH-XRF spectra.•The results were entirely consistent well except for the range of 0 to 10 keV.•R2 was used to evaluate conformity and effect of environmental interferences.•This study will lead to validate that MCNP6 can properly provide calibration data.
In this paper, we demonstrated a miniaturized bacteria-based biosensing platform for sensitive, reliable, and practical online monitoring of water quality. Two biosensors were integrated into a ...dual-channel microfluidic device which operated as a detection and a reference sensor, respectively. By providing a reference-compensated sensing response, the device was capable of minimizing environmental interferences, such as temperature and flow rate, ultimately leading to high sensitivity and reliability in water quality monitoring. We used a microbial fuel cell (MFC) technology as a biosensor for the detection of toxic substances in water. Toxic components inhibit bacterial metabolic activity, generating a distinct change in the current output of the MFC-based biosensor. Each biosensor incorporated a single-chambered MFC with an air cathode, which substantially decreased the device complexity and enhanced the practicability as a real-world application. The miniaturization of the MFC biosensor having a 90-μL microfluidic chamber provided rapid and sensitive sensing responses with a concentration range from 0.003% to 0.075% of formaldehyde in media. By controlling the introduction time of the toxic sample with sequential injection of the fresh media into the detection channel, the biosensor is completely reusable and is potentially applicable to long-term in situ monitoring of water quality.
Predicting the capacity of lithium-ion battery (LIB) plays a crucial role in ensuring the safe operation of LIBs and prolonging their lifespan. However, LIBs are easily affected by environmental ...interference, which may impact the precision of predictions. Furthermore, interpretability in the process of predicting LIB capacity is also important for users to understand the model, identify issues, and make decisions. In this study, an interpretable method considering environmental interference (IM-EI) for predicting LIB capacity is introduced. Spearman correlation coefficients, interpretability principles, belief rule base (BRB), and interpretability constraints are used to improve the prediction precision and interpretability of IM-EI. Dynamic attribute reliability is introduced to minimize the effect of environmental interference. The experimental results show that IM-EI model has good interpretability and high precision compared to the other models. Under interference conditions, the model still has good precision and robustness.
The rapid development of society and the expansion of human activities have resulted in interference with the natural environment. Assessing the environmental interference (EI) caused by human ...activities is highly important for socio-economic sustainable development. In this study, the spatial distance model (SDM) and resource endowment index (REI)-human activity index (HAI) ratio model were developed to calculate the environmental interference index (EII) in northern China (NC). The current spatial distribution and patterns of EII in NC were analyzed based on geographic information system (GIS) technology. In addition, the factors that influence the level of EI were examined through a geographical detector method. The results showed that the EII value in the eastern region was significantly higher than that in the western region and that differences in EI were spatial heterogeneity. The spatial distribution of EI was analyzed at the provincial, municipal and county scales, respectively. It was found that its distribution was closely related to urban development. The spatial distribution of EI displayed longitudinal zonality. East of 104.987°E, there were many large cities, such as Beijing, Tianjin, Qingdao and Zhengzhou, with high population densities and developed economies. Thus, these areas had high EI values. To the west of 104.987°E, such as in the Qinghai, Gansu, Xinjiang and Inner Mongolia regions, the EI values were generally low, with low environmental quality and fewer human activities. The level of EI in the Huang-Huai-Hai Plain region was higher than that in other areas, displaying obvious spatial dependence. Moreover, the distribution of EI exhibited high-high and low-low aggregation patterns, which accounted for 24.06% and 27.35% of the total study area, respectively. Specifically, in NC, the EI caused by human activities displayed obvious regional characteristics. In addition, the factors that influence EI were determined through a geographical detector model. The land use intensity was the direct factor related to changes in and the levels of EI, and the cover and growth of vegetation were the most important factors associated with mitigating human interference. The assessment results can provide a reference for the formulation of environmental governance and related policies.
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•The spatial distance model (SDM) was proposed to evaluate the natural environmental factors at large scale.•Human interference factors were quantified and calculated the environmental interference index as input variable.•The spatial visual expression is combined with GIS to comprehensively express the environmental interference.
The mechanical equipment often faces complex working environments in practical operating conditions, and the external environmental interference generated by operating conditions, environmental ...factors and other components causes the vibration signals to exhibit characteristics with frequency distortion and multi-modality. The existing fault diagnosis methods rarely consider the issue of external environmental interference. Aiming at the background of fault diagnosis under external environment interference, a fault diagnosis method based on Markov transfer field (MTF) with enhanced properties and multi-scale convolutional neural network with attention mechanism (AM-MSCNN) is proposed. The fault features embedded in vibration signals under external environmental interference can be extracted, and an important contribution to the fault diagnosis method under external environmental interference can be made. Firstly, an interference mode selection model based on symplectic geometry modal decomposition is constructed to address the issues of distortion and multi-modality caused by external environmental interference. Next, a two-dimensional feature extraction method based on the MTF with enhanced properties is established. The challenge of extracting temporal correlation features from one-dimensional vibration signals affected by external environmental interference is addressed by Markov transition probability. The impact of external environmental interference can be mitigated, and that has strong anti-interference capability and robustness. Finally, an attention mechanism that can adaptively assign weights is designed, and the AM-MSCNN model is designed to effectively extract global features by incorporating attention mechanisms in the parallel layers of MSCNN and the attention mechanism helps to suppress external environmental interference and improve the diagnostic results. An experimental platform for simulating the typical faults under external environmental interference is constructed, and the experimental results demonstrate that the proposed method exhibits superior generalization performance under varying degrees of different interference environments. The overall average accuracy reaches 92.2%, and the highest accuracy reaches 94.0% for external interference working conditions.
The accuracy of underwater target recognition by autonomous underwater vehicle (AUV) is a powerful guarantee for underwater detection, rescue, and security. Recently, deep learning has made ...significant improvements in digital image processing for target recognition and classification, which makes the underwater target recognition study becoming a hot research field. This article systematically describes the application of deep learning in underwater image analysis in the past few years and briefly expounds the basic principles of various underwater target recognition methods. Meanwhile, the applicable conditions, pros and cons of various methods are pointed out. The technical problems of AUV underwater dangerous target recognition methods are analyzed, and corresponding solutions are given. At the same time, we prospect the future development trend of AUV underwater target recognition.
Conducting evaluations of socio-economic activities in a large area is a difficult and complex process that is affected by many factors. EI represents the actual environmental interference caused by ...socio-economic development, and it is a quantitative expression in this paper. Understanding EI is of great significance for promoting the scientific management and sustainable development of social economies. This study selected 11 indexes that included the following eight aspects: terrain, climate, hydrology, soil, vegetation, land, population and economy. The SDM and RM were used to calculate the EII, and the EI of mainland China from 1980 to 2015 was comprehensively evaluated from a spatiotemporal perspective. The results indicated that the NOI, NEI and LII categories of EI accounted for more than 70% of the total area, while regions with HII, STI and HEI presented a gradually increasing trend over 35 years. The distribution of EI had obvious scale effects at different administrative units. In general, eastern China and central China had the highest EI, which presented high-high clusters. Western China had the lowest EI, and the low-low clusters were generally distributed in unoccupied areas. In addition, the areas did not change significantly. The change of EI in mainland China was the most drastic from 2000 to 2010 but stable after 2010, especially in 2015. The evaluation results provide a reference for the formulation of related policies for the coordination between the natural environment and socio-economic development.
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