Risk assessment of heavy metal pollution in agricultural soil surrounding a typical pharmaceutical manufacturing complex

The identification and effective control of pollution sources is essential because heavy metal pollution in agricultural soil is associated with food safety and public health. Industrial wastewater, waste gas, and residues generated from pharmaceutical manufacturing are important sources of heavy metal pollutants in soil, but the research of their risk for surrounding agricultural soil is inadequate. In this study, the typical pharmaceutical manufacturing complex and its surrounding farmland in Hubei Province, China was employed to systematically and comprehensively assess its environmental risk and source apportionment. The results revealed the potential risk of cadmium (Cd), lead (Pb), arsenic (As), and mercury (Hg) from pharmaceutical production for farmland soil around, and among these heavy metals, As and Cd were observed to have the higher pollution level. The accumulated Cd and As had contribution to a series of risks, including comprehensive pollution risk, geo-accumulation risk, potential ecological risk, and the carcinogenic and non-carcinogenic risk. Positive matrix factorization (PMF) source analysis combining with the geographic distribution of heavy metal surrounding pharmaceutical manufacturing confirmed that there were three main heavy metal pollution sources, including pharmaceutical wastewater, traffic, and agricultural chemicals, which had the 52.37%, 16.49%, and 31.14% contributions to the surrounding agricultural soil. The present study provided systematic strategies of environment risk assessment and source apportionment, and can be referred for casual analysis and prevention strategies for farmland soil surrounding pharmaceutical manufacturing complex

Short-term prediction of PV output based on weather classification and SSA-ELM

In this paper, according to the power output characteristics of distributed photovoltaic users, the SSA-ELM (Sparrow Search Algorithm - Extreme Learning Machine) model based on weather type division is proposed for photovoltaic power day ahead prediction. Because the solar panel power generation sequence of photovoltaic users contains high frequency fluctuations, in this paper we use the power sequence convergence effect to make cluster prediction on all photovoltaic panels to reduce the randomness of distributed photovoltaic. The prediction accuracy is further improved by dividing weather types. The historical data of distributed PV users in a region of Gansu province is used for modeling verification, and the results show that the prediction error of the proposed method is lower. In bad weather, the root mean square error is at least 0.02 less than the comparison model, and the average annual accuracy rate is 93.2%, which proves the applicability of the proposed method in different output types

multiobjectivedynamicdetectionofseedsbasedonmachinevision

An approach to inspecting massive numbers of moving seeds was studied based on the techniques of dynamic inspection and machine vision. A progressive scanning CCD camera with external trigger function was used for real-time capture of dynamic images of seeds. The methods based on R channel of RGB (Red, Green and Blue) and region-dependent segmentation were adopted to reduce the data size of image processing and improve the efficiency of seeds inspection. All the seeds were sorted into four grades according to their morphological characteristics, such as surface area, perimeter, major axis, minor axis, circularity and eccentricity. The detection experiments indicated that the eligible ratio of the classifications was about 81.90% by this real-time inspection system

Discrete-Time Adaptive Decentralized Control for Interconnected Multi-Machine Power Systems with Input Quantization

This study contrives a discrete-time adaptive decentralized control algorithm with input quantization for interconnected multi-machine power systems with SVC. First, a dynamic surface scheme is applied to the excitation controller design, in which first-order digital low-pass filters are used to predict the next virtual control law, which overcomes the model conversion problem in backstepping. Therefore, the controller design and structure are simplified. Further, an improved hysteresis quantizer is utilized for amplitude quantization of control input signals; along with the discretization of time, this achieves digital decentralized control. Finally, semi-global uniformly ultimately boundedness (SGUUB) of the whole control system is demonstrated based on the Lyapunov stability theory, and the effectiveness of the proposed control algorithm is verified on the ModelingTech real-time simulation experimental platform for power electronics

Discrete-Time Adaptive Decentralized Control for Interconnected Multi-Machine Power Systems with Input Quantization

This study contrives a discrete-time adaptive decentralized control algorithm with input quantization for interconnected multi-machine power systems with SVC. First, a dynamic surface scheme is applied to the excitation controller design, in which first-order digital low-pass filters are used to predict the next virtual control law, which overcomes the model conversion problem in backstepping. Therefore, the controller design and structure are simplified. Further, an improved hysteresis quantizer is utilized for amplitude quantization of control input signals; along with the discretization of time, this achieves digital decentralized control. Finally, semi-global uniformly ultimately boundedness (SGUUB) of the whole control system is demonstrated based on the Lyapunov stability theory, and the effectiveness of the proposed control algorithm is verified on the ModelingTech real-time simulation experimental platform for power electronics

Type-printable photodetector arrays for multichannel meta-infrared imaging

Multichannel meta-imaging, inspired by the parallel-processing capability of neuromorphic computing, offers significant advancements in resolution enhancement and edge discrimination in imaging systems, extending even into the mid- to far-infrared spectrum. Currently typical multichannel infrared imaging systems consist of separating optical gratings or merging multi-cameras, which require complex circuit design and heavy power consumption, hindering the implementation of advanced human-eye-like imagers. Here, we present a novel approach for printable graphene plasmonic photodetector arrays driven by a ferroelectric superdomain for multichannel meta-infrared imaging with enhanced edge discrimination. The fabricated photodetectors exhibited multiple spectral responses with zero-bias operation by directly rescaling the ferroelectric superdomain instead of reconstructing the separated gratings. We also demonstrated enhanced and faster shape classification (98.1%) and edge detection (98.2%) using our multichannel infrared images compared with single-channel detectors. Our proof-of-concept photodetector arrays simplify multichannel infrared imaging systems and hold great potential for applications in efficient edge detection in human-brain-type machine vision.Comment: Some specific concerns should be addressed or updated. e.g. Fig. 3