Study on the Predictive Algorithm of Plant Restoration under Heavy Metals

Heavy metal pollution of soil is becoming a more serious issue globally. Heavy metal contamination of the soil environment is inevitable as a result of the rapid and extensive growth of industry and agriculture, resulting in unfavorable environmental circumstances for both the flora and fauna. Traditional approaches for collecting field sampling with laboratory testing of soil heavy metals are restricted not only by their time and cost but also by their inability to gather sufficient information about the spatial distribution characteristics of heavy metals in soil over a vast area. The continuous development of the urban industrial processes leads to the degree of heavy metal pollution in urban gardens. For soil monitoring and cleanup, having quick and accurate access to heavy metal concentration data is very crucial and critical. In order to improve the restoration ability of garden heavy metal pollution, a new algorithm to predict plant restoration ability under the garden heavy metal pollution environment is put forward in this study. Firstly, we analyzed the composition of garden heavy metal pollution and the harm of garden heavy metal pollution. Secondly, we identified the restoration technology of garden heavy metal pollution to plants, determine the level of garden heavy metal pollution with the help of the land accumulation index method, and reflect the average pollution water level of garden heavy metal elements with the help of Numero comprehensive pollution heatstroke. On this basis, the plant repairability prediction model was constructed with the help of wavelet function, to predict the plant repairability under garden heavy metal pollution environment and to complete the prediction of plant repairability under garden heavy metal pollution environment. The experimental results show that the proposed method was better than the traditional approaches in terms of prediction accuracy and is also less time-consuming.

A Low Distortion Audio Self-Recovery Algorithm Robust to Discordant Size Content Replacement Attack

Although the development of watermarking techniques has enabled designers to tackle normal processing attacks (e.g., amplitude scaling, noise addition, re-compression), robustness against malicious attacks remains a challenge. The discordant size content replacement attack is an attack against watermarking schemes which performs content replacement that increases or reduces the number of samples in the signal. This attack modifies the content and length of the signal, as well as desynchronizes the position of the watermark and its removal. In this paper, a source-channel coding approach for protecting an audio signal against this attack was applied. Before applying the source-channel encoding, a decimation technique was performed to reduce by one-half the number of samples in the original signal. This technique allowed compressing at a bit rate of 64 kbps and obtaining a watermarked audio signal with an excellent quality scale. In the watermark restoration, an interpolation was applied after the source-channel decoding to recover the content and the length. The procedure of decimation–interpolation was taken because it is a linear and time-invariant operation and is useful in digital audio. A synchronization strategy was designed to detect the positions where the number of samples in the signal was increased or reduced. The restoration ability of the proposed scheme was tested with a mathematical model of the discordant size content replacement attack. The attack model confirmed that it is necessary to design a synchronizing strategy to correctly extract the watermark and to recover the tampered signal. Experimental results show that the scheme has better restoration ability than state-of-the-art schemes. The scheme was able to restore a tampered area of around 20% with very good quality, and up to 58.3% with acceptable quality. The robustness against the discordant size content replacement attack was achieved with a transparency threshold above −2.

CHEMICAL AND BIOLOGICAL INDICATORS FOR EVALUATION OF ARCTIC SOIL DEGRADATION AND ITS POTENTIAL TO REMEDIATION

In recent years, significant efforts have been made to accelerate the economic development of the Arctic zone, leading to intense environmental pollution of this region, accompanied by the significant impact of accumulated environmental damage in the region. The solution to these problems is difficult due to the remoteness of these areas and severe climatic conditions. Therefore, it is important to evaluate the potential for restoration of arctic soils. For this purpose, various indicators are used, including biological ones. In the analyzed arctic soil samples, high concentrations of petroleum hydrocarbons (up to 47,000 mg/kg) and chloride-ions (0.10–0.14 wt %) were established. Microbioassay demonstrated a presence of hydrocarbon-oxidizing microorganisms: Penicillium, Azotobacter chroococcum, Bacillus subtilis, Pseudomonas oleovorans. A low enzymatic activity and specific Arctic climate point out a low self-restoration ability of the soil, demonstrated the need for its remediation. The microbioassay with microbial strains identification and soil remediation methods suitable for the Arctic zone were recommended.

A Multistage Algorithm Design for Electrocardiogram Signal Denoising

This work describes a new scheme for denoising noisy electrocardiogram (ECG) signals. In the first step, the noise variance is estimated using the well-known DONOHO’s estimator followed by the wavelet-based baseline wander removing. In the second step, the estimated variance is employed in the adaptive 1D Wiener filter to reduce the additive noise. Next, a Low Pass filter, based on the FFT, is applied on the resulting denoised signal. Furthermore, a cascaded Savitzky–Golay (SG) smoother filter is applied to refine the restoration process. The final step consists in the recovering of the [Formula: see text]-peaks and the surrounding areas. It can be reported that the suggested algorithm is optimal for the additive Gaussian noise and is useful for other types of noises. Both qualitative and quantitative results, achieved from several experimental tests, establish high-quality restoration ability and the efficiency of the proposed method. Thus, when compared to some powerful techniques recently published, the designed algorithm demonstrates very competitive performances.