The agricultural sector is one of the most important sources of CO2 emissions. Thus, the current study predicted CO2 emissions based on data from the agricultural sectors of 25 provinces in Iran. The gross domestic product (GDP), the square of the GDP (GDP2), energy use, and income inequality (Gini index) were used as the inputs. The study used support vector machine (SVM) models to predict CO2 emissions. Multiobjective algorithms (MOAs), such as the seagull optimization algorithm (MOSOA), salp swarm algorithm (MOSSA), bat algorithm (MOBA), and particle swarm optimization (MOPSO) algorithm, were used to perform three important tasks for improving the SVM models. Additionally, an inclusive multiple model (IMM) used the outputs of the MOSOA, MOSSA, MOBA, and MOPSO algorithms as the inputs for predicting CO2 emissions. It was observed that the best kernel function based on the SVM-MOSOA was the radial function. Additionally, the best input combination used all the gross domestic product (GDP), squared GDP (GDP2), energy use, and income inequality (Gini index) inputs. The results indicated that the quality of the obtained Pareto front based on the MOSOA was better than those of the other algorithms. Regarding the obtained results, the IMM model decreased the mean absolute errors of the SVM-MOSOA, SVM-MOSSA, SVM-MOBA, and SVM-PSO models by 24, 31, 69, and 76%, respectively, during the training stage. The current study showed that the IMM model was the best model for predicting CO2 emissions.
A high-voltage generator (HVG) is an essential part of a radio frequency identification electrically erasable programmable read-only memory (RFID-EEPROM). An HVG circuit is used to generate a regulated output voltage that is higher than the power supply voltage. However, the performance of the HVG is affected owing to the high-power dissipation, high-ripple voltage and low-pumping efficiency. Therefore, a regulator circuit consists of a voltage divider, comparator and a voltage reference, which are respectively required to reduce the ripple voltage, increase pumping efficiency and decrease the power dissipation of the HVG. Conversely, a clock driving circuit consists of the current-starved ring oscillator (CSRO), and the non- overlapping clock generator is required to drive the clock signals of the HVG circuit. In this study, the Mentor Graphics EldoSpice software package is used to design and simulate the HVG circuitry. The results showed that the designed CSRO dissipated only 4.9 μW at 10.2 MHz and that the phase noise was only -119.38 dBc/Hz at 1 MHz. Moreover, the proposed charge pump circuit was able to generate a maximum VPP of 13.53 V and it dissipated a power of only 31.01 μW for an input voltage VDD of 1.8 V. After integrating all the HVG modules, the results showed that the regulated HVG circuit was also able to generate a higher VPP of 14.59 V, while the total power dissipated was only 0.12 mW with a chip area of 0.044 mm2. Moreover, the HVG circuit produced a pumping efficiency of 90% and reduced the ripple voltage to <4 mV. Therefore, the integration of all the proposed modules in HVG ensured low-ripple programming voltages, higher pumping efficiency, and EEPROMs with lower power dissipation, and can be extensively used in low-power applications, such as in non-volatile memory, radiofrequency identification transponders, on-chip direct current DC-DC converters.
Organic pollution continues to be an important worldwide obstacle for tackling health and environmental concerns that require ongoing and prompt response. To identify the LAB content levels as molecular indicators for sewage pollution, surface sediments had obtained from the South region of Malaysia. The origins of the LABs were identified using gas chromatography-mass spectrometry (GC-MS). ANOVA and a Pearson correlation coefficient at p
This work reports silver nanoparticles (AgNPs) supported on biopolymer carboxymethyl cellulose beads (Ag-CMC) serves as an efficient catalyst in the reduction process of p-nitrophenol (p-NP) and methyl orange (MO). For Ag-CMC synthesis, first CMC beads were prepared by crosslinking the CMC solution in aluminium nitrate solution and then the CMC beads were introduced into AgNO3 solution to adsorb Ag ions. Field emission scanning electron microscopy (FE-SEM) analysis suggests the uniform distribution of Ag nanoparticles on the CMC beads. The X-ray photoelectron spectroscopy (XPS) and X-ray diffraction (XRD) analysis revealed the metallic and fcc planes of AgNPs, respectively, in the Ag-CMC catalyst. The Ag-CMC catalyst exhibits remarkable reduction activity for the p-NP and MO dyes with the highest rate constant (kapp) of a chemical reaction is 0.519 and 0.697 min-1, respectively. Comparative reduction studies of Ag-CMC with CMC, Fe-CMC and Co-CMC disclosed that Ag-CMC containing AgNPs is an important factore in reducing the organic pollutants like p-NP and MO dyes. During the recyclability tests, the Ag-CMC also maintained high reduction activity, which suggests that CMC protects the AgNPs from leaching during dye reduction reactions.
The seasonal variation of petroleum pollution including n-alkanes in surface sediments of the Selangor River in Malaysia during all four climatic seasons was investigated using GC-MS. The concentrations of n-alkanes in the sediment samples did not significantly correlate with TOC (r = 0.34, p > 0.05). The concentrations of the 29 n-alkanes in the Selangor River ranged from 967 to 3711 µg g-1 dw, with higher concentrations detected during the dry season. The overall mean per cent of grain-sized particles in the Selangor River was 85.9 ± 2.85% sand, 13.5 ± 2.8% clay, and 0.59 ± 0.34% gravel, respectively. n-alkanes are derived from a variety of sources, including fresh oil, terrestrial plants, and heavy/degraded oil in estuaries. The results of this study highlight concerns and serve as a warning that hydrocarbon contamination is affecting human health. As a result, constant monitoring and assessment of aliphatic hydrocarbons in coastal and riverine environments are needed.
This research was established to accurately forecast daily scale air quality index (AQI) which is an essential environmental index for decision-making. Researchers have projected different types of models and methodologies for AQI forecasting, such as statistical techniques, machine learning (ML), and most recently deep learning (DL) models. The modelling development was adopted for Delhi city, India which is a major city with air pollution issues simialir to entire urban cities of India especially during winter seasons. This research was predicted AQI using different versions of DL models including Long-Short Term Memory (LSTM), Bidirectional LSTM (Bi-LSTM) and Bidirectional Recurrent Neural Networks (Bi-RNN) in addition to Kernel Ridge Regression (KRR). Results indicated that Bi-RNN model consistently outperformed the other models in both training and testing phases, while the KRR model consistently displayed the weakest performance. The outstanding performance of the models development displayed the requirement of adequate data to train the models. The outcomes of the models showed that LSTM, BI-LSTM, KRR had lower performance compared with Bi-RNN models. Statistically, Bi-RNN model attained maximum cofficient of determination (R2 = 0.954) and minimum root mean square error (RMSE = 25.755). The proposed model in this research revealed the robust predictable to provide a valuable base for decision-making in the expansion of combined air pollution anticipation and control policies targeted at addressing composite air pollution problems in the Delhi city.
To maintain human health and purity of drinking water, it is crucial to eliminate harmful chemicals such as nitrophenols and azo dyes, considering their natural presence in the surroundings. In this particular research study, the application of machine learning techniques was employed in order to make an estimation of the performance of reduction catalysis in the context of ecologically detrimental nitrophenols and azo dyes contaminants. The catalyst utilized in the experiment was Ag@CMC, which proved to be highly effective in eliminating various contaminants found in water, like 4-nitrophenol (4-NP). The experiments were carefully conducted at various time intervals, and the machine learning procedures used in this study were all employed to forecast catalytic performance. The evaluation of the performance of such algorithms were done by means of Mean Absolute Error. The noteworthy findings of this research indicated that the ADAM and LSTM algorithm exhibited the most favourable performance in the case of toxic compounds i.e. 4-NP. Moreover, the Ag@CMC catalyst demonstrated an impressive reduction efficiency of 98 % against nitrophenol in just 8 min. Thus, based on these compelling results, it can be concluded that Ag@CMC works as a highly effective catalyst for practical applications in real-world scenarios.