Farmland Fertility Research Articles

Red-tailed hawk algorithm for numerical optimization and real-world problems

This study suggests a new nature-inspired metaheuristic optimization algorithm called the red-tailed hawk algorithm (RTH). As a predator, the red-tailed hawk has a hunting strategy from detecting the prey until the swoop stage. There are three stages during the hunting process. In the high soaring stage, the red-tailed hawk explores the search space and determines the area with the prey location. In the low soaring stage, the red-tailed moves inside the selected area around the prey to choose the best position for the hunt. Then, the red-tailed swings and hits its target in the stooping and swooping stages. The proposed algorithm mimics the prey-hunting method of the red-tailed hawk for solving real-world optimization problems. The performance of the proposed RTH algorithm has been evaluated on three classes of problems. The first class includes three specific kinds of optimization problems: 22 standard benchmark functions, including unimodal, multimodal, and fixed-dimensional multimodal functions, IEEE Congress on Evolutionary Computation 2020 (CEC2020), and IEEE CEC2022. The proposed algorithm is compared with eight recent algorithms to confirm its contribution to solving these problems. The considered algorithms are Farmland Fertility Optimizer (FO), African Vultures Optimization Algorithm (AVOA), Mountain Gazelle Optimizer (MGO), Gorilla Troops Optimizer (GTO), COOT algorithm, Hunger Games Search (HGS), Aquila Optimizer (AO), and Harris Hawks optimization (HHO). The results are compared regarding the accuracy, robustness, and convergence speed. The second class includes seven real-world engineering problems that will be considered to investigate the RTH performance compared to other published results profoundly. Finally, the proton exchange membrane fuel cell (PEMFC) extraction parameters will be performed to evaluate the algorithm with a complex problem. The proposed algorithm will be compared with several published papers to approve its performance. The ultimate results for each class confirm the ability of the proposed RTH algorithm to provide higher performance for most cases. For the first class, the RTH mostly got the optimal solutions for most functions with faster convergence speed. The RTH provided better performance for the second and third classes when resolving the real word engineering problems or extracting the PEMFC parameters.

Determining the ecological compensation standards based on willingness to accept (WTA) for intensive agricultural production areas: A case in China

Ecological compensation is an effective way to encourage farmers to use minimal chemical fertilizers by paying for their losses when agricultural planting methods change. In this research, we conducted field surveys and created a conceptual framework to determine the compensation standard based on farmers' willingness to accept (WTA) and ecosystem service values in a rotational double cropping area of winter wheat–summer maize, Huantai County, China. The results showed that the farmers’ average WTA on the chemical fertilizer reduction application fluctuates within a range of 250.00–7740.00 CNY/hm2. The limiting compensation standards range from 362.74 to 4823.43 CNY/hm2 and the ceiling standard is from 367.29 to 4851.83 CNY/hm2 by reducing chemical fertilizer application from 5% to 95%. Huantai County has a potential of 15%–35% for chemical fertilizer reduction, and the compensation standard is the lowest (362.74 CNY/hm2) when 25% of the current chemical fertilizer usage is reduced. Our research provided a valid approach to determining ecological compensation standards toward the aims of efficiency and sustainability for protecting farmland ecology and highlighting the critical ecosystem service changes caused by the farmland fertilizer reduction program in China.

Straw Return Decomposition Characteristics and Effects on Soil Nutrients and Maize Yield

Straw return benefits soil nutrient circulation and avoids the environmental pollution caused by incineration. The straw return effect is determined by many factors, such as the returning mode and tillage method. To find the most suitable straw return mode in the hilly areas of south China, we conducted experiments with preceding maize straw in Nanchong (Sichuan Province, China) for three years. Five treatments were tested: (A) rotary tillage without straw return (RT), (B) crushed straw return with rotary tillage (CRT), (C) crushed straw return without rotary tillage (CSR), (D) whole straw return with rotary tillage (WRT), and (E) whole straw return without rotary tillage (WSR). The results indicated that CRT had the fastest decomposition rate, followed by CSR. Moreover, CRT had the fastest nutrient release rates for nitrogen, phosphorus, potassium, cellulose, hemicellulose, and lignin, as well as the highest maize yield (6.62% higher than RT). CRT increased the content of organic matter, total nitrogen, total phosphorus, and total potassium in the soil, as well as improved the soil pH. Furthermore, the numbers of bacteria, Actinomycetes, and fungi in the soil under CRT, CSR, and WSR treatments were higher than those under the other two treatments. This study has important reference value for exploring the most favourable straw return method for improving farmland fertility.

Conservation tillage and moderate nitrogen application changed the composition, assembly pattern and interaction network of abundant and rare microbial community on straw surface

Microbial-driven straw decomposition is important for soil organic carbon accumulation, nutrient uptake, and crop utilization. However, the mechanism by which tillage and fertilization interactions form the abundant and rare microbial communities that mediate straw degradation in dry farmland remains unclear. Based on an interaction experiment between three tillage practices (zero-till, chisel-till, and conventional plow-till) and three levels of nitrogen fertilizer (180, 240, and 300 kg·N·ha−1), we aimed to understand the community assembly process of abundant and rare microbe in the decomposition of maize straw. We found that the abundant bacterial and fungal communities were governed by stochastic processes, whereas the rare bacterial communities were dominated by deterministic processes. Furthermore, the relative importance of deterministic assembly of all microbial communities was higher in conservation tillage; in addition, the deterministic assembly process of the abundant bacterial and fungal communities was significantly increased by the medium level of nitrogen fertilizer (240 kg·N·ha−1), but the rare microbial community was not significantly affected. Redundancy analysis (RDA) and threshold indicator taxa analysis (TITAN) indicated that soil temperature, pH, and NO3− are the key influencing factors for the abundant and rare microbial communities. Overall, conservation tillage and a medium level of nitrogen fertilizer (240 kg·ha−1) in dry farmland promoted straw decomposition by regulating the development and turnover of straw-decomposing communities, in which they regulated bacterial community succession rather than fungi within a short decomposition time. These findings highlight the distinct responses to conservation tillage and N fertilization in the community assembly of abundant and rare biospheres in maize straw decomposition, and provide additional evidence for the development of conservation tillage applications in drylands.

Application of Organic Fertilizers Optimizes Water Consumption Characteristics and Improves Seed Yield of Oilseed Flax in Semi-Arid Areas of the Loess Plateau

Organic fertilizers are an important source of nutrients for improving farmland fertility. To explore high-yield, efficient and green production technology for oilseed flax in dryland agricultural areas, a field split plot experiment was conducted in the semi-arid area of the Loess Plateau in northwest China from April to August in 2020 and 2021. The study compared and analyzed the effects of different nutrient sources and their application rates on water consumption characteristics, grain yield and water use efficiency of oilseed flax. The main plots were fertilizer types (sheep manure, chicken manure and chemical fertilizer), while the subplots were fertilizer application rates (sheep manure: S1-12,500 kg·hm−2 and S2-25,000 kg·hm−2; chicken manure: C1-5800 kg·hm−2 and C2-11,600 kg·hm−2; chemical fertilizer: F1-N 112.5 kg·hm−2, P 75 kg·hm−2, K 67.5 kg·hm−2 and F2-N 225 kg·hm−2, P2O5 150 kg·hm−2, K2O 135 kg·hm−2). The results showed that compared with chemical fertilizers, organic fertilizers significantly increased the soil water storage capacity of the 0–160 cm soil layer during the whole growth period of oilseed flax and significantly reduced water consumption. During two growing seasons, the application of 25,000 kg·hm−2 sheep manure significantly reduced water consumption during the seedling-bud period and green fruit period-maturity period of oilseed flax by 16.13% and 23.19% compared with CK, respectively. Thousand-grain weight, yield and water use efficiency were significantly increased by 14.70%, 48.32% and 61.29%, respectively. These results indicate that the application of 25,000 kg·hm−2 sheep manure can significantly increase soil water storage capacity of the 0–160 cm soil layer, reduce water consumption during the whole growth period of oilseed flax and thus improve grain yield and water use efficiency of oilseed flax. It is a suitable fertilization technology for the high-yield, efficient and green production of oilseed flax in the semi-arid areas of northwest Loess Plateau.

An efficient discrete rat swarm optimizer for global optimization and feature selection in chemoinformatics

Machine learning algorithms need feature selection (FS) as a significant step towards filtering unnecessary data. This paper proposes a wrapper FS approach that combines the rat swarm optimization (RSO) algorithm with genetic operators to avoid local optimal. In the proposed approach the transfer functions (TFs) are added to balance local and global search by converting a continuous search space into a discrete space. Eight variants of the bmRSO algorithm were applied for classification purposes using a support vector machine (SVM) to increase accuracy and decrease the number of features over several chemical datasets. The eight bmRSO proposed methods and the original RSO were evaluated using the CEC’20 test suite and twelve datasets (eight chemical and four toxicity effect datasets) to verify their performance in complex optimization problems and FS over real datasets, respectively. Moreover, the binary versions of other stable metaheuristic algorithms such as Harris Hawks Optimization (HHO), Grey Wolf Optimization (GWO), Farmland Fertility Algorithm (FFA), Artificial Gorilla Troops Optimizer (GTO), African Vultures Optimization Algorithm (AVOA), Runge Kutta Optimizer’s (RUN), and Slime Mould Algorithm (SMA) were used to compare the results obtained by the best variant of the bmRSO. Eventually, the experimental results have revealed that in most of the tests, the proposed bmRSO1 has achieved efficient search results with higher convergence speeds without increasing additional computational efforts. From the twelve datasets, the MAO dataset reached the highest results compared with other datasets, so the proposed method, bmRSO1-SVM, achieved an accuracy of 98.201% and a 20.001 number of selected features.

The energy management of islanded micro grid system using farmland fertility algorithm

ABSTRACT An energy management in micro grid portrays a significant role in coordinating the generation with the load demand and it is characterized as an optimization problem evaluated for economic dispatch (ED). In microgrid, energy management issues have thus far primarily been addressed with the purpose of lowering the operational costs. However, for a microgrid system to manage energy effectively, the environmental problems, or issues regarding emission must be addressed. In this research work, a novel metaheuristic approach, known as farmland fertility algorithm (FFA) is proposed for obtaining ED and integrated economic emission dispatch (IEED). The different test cases for ED and IEED are considered to minimize the fuel and the emission cost respectively. For the different cases contemplated for ED and IEED, the generation cost reduces after the inclusion of RES, and also in the case when wind energy source is included alone. These results depict that the ED and IEED perform using FFA achieves a cost optimization of 9.2% and 19.51% respectively. The ED and IEED with FFA offer more cost saving and provides reduced emission in comparison to reduced gradient method (RGM), ant colony optimization (ACO), cuckoo search algorithm (CSA), interior search algorithm (ISA), Bat algorithm (BA) and directional bat algorithm (dBA).

Identifying the critical areas and primary sources for agricultural non-point source pollution management of an emigrant town within the Three Gorges reservoir area

Agricultural non-point source pollution is threatening water environmental health of the Three Gorges reservoir. However, current studies for precision management of the agricultural non-point source pollution within this area are still limited. The objective of this study was identifying the critical areas and primary sources of agricultural non-point source pollution for precision management. Firstly, the inventory analysis approach was used to estimate the discharge amount of total nitrogen (TN), total phosphorus (TP), and chemical oxygen demand (COD) from farmland fertilizer, crop residues, livestock breeding, and daily activities. Afterwards, the deviation standardization method was applied to evaluate the emission intensity of TN, TP, and COD, as well as calculating the comprehensive pollution index (CPI) of each village, based on which the critical areas for agricultural non-point source pollution management could be distinguished. Moreover, the equivalence pollution load method was conducted to identify the primary pollution sources within each critical zone. The above methods were implemented to an emigrant town within the Three Gorges reservoir area named Gufu. Results showed that agricultural non-point source pollution in Gufu town has been alleviated to a certain extent since 2016. Nevertheless, in four areas of the town (i.e., Longzhu, Fuzi, Shendu, and Maicang), the agricultural non-point source pollution still deserved attention and improvement. For the mentioned critical areas, farmland fertilizer and livestock breeding were the primary sources causing agricultural non-point source pollution. The emission amount of TN and TP from farmland fertilizer accounted for 60% and 48% of the total, respectively. And those from livestock breeding were 29% and 46%. Our research could provide definite targets to relieve agricultural non-point source pollution, which had great significance to protect water environment while coordinating regional economic growth after emigrant resettlement.

Analysis of Impact of Well-Facilitated Farmland Construction—Engineering Measures on Farmland Quality

We studied the influence and correlation of soil improvement, farmland ecological protection, soil fertilization, and field infrastructure construction on the quality grade of well-fertilized farmland in the engineering measures of well-fertilized farmland construction. Taking Xiao County of the Anhui Province as the study area, based on the software platforms of SPSS, ArcGIS10.6, and the county farmland resource management information system, we investigated the farmland quality changes of well-facilitated farmland before and after construction using the fuzzy evaluation method and analytic hierarchy process. We used principal component analysis and the gray relational method to analyze the impact and correlation of various engineering measures on farmland quality. The farmland quality grade in the study area was improved by a 0.59 grade after the construction of the well-facilitated farmland. Well-facilitated farmland construction engineering measures mainly affected the farmland quality through 12 factors, such as the soil bulk density, tillage layer texture, irrigation and drainage guarantee rates, forest network density, and field road accessibility. There is a strong correlation between these factors and the characteristics of farmland quality; the degrees of correlation were 0.865–0.610, respectively. The highest correlation degree was 0.939 between the deep plowing and deep loosening soil improvement project and the improvement of the well-facilitated basic farmland quality; this was followed by soil fertilization with an increased application of organic fertilizer, farmland ecological protection, and the field infrastructure project with correlations of 0.936, 0.857, and 0.563, respectively. Represented by the improvement of farmland fertility, the soil improvement project had the strongest impact on well-facilitated farmland quality. The soil fertility project, farmland ecological protection project, and the field infrastructure project were the second most important, with very close degrees of correlation.

The tempo-spatial changes of soil fertility in farmland of China from the 1980s to the 2010s

The grain yield level is closely related to soil fertility. To clarify the status and variations of soil fertility in Chinese farmland, soil sample profile data from the Soil Species of China in the 1980s and the Soil Series of China in the 2010s were collected. And surface soil data of fertility indexes (pH, soil organic matter, total nitrogen, total phosphorus, and total potassium) were calculated. The tempo-spatial variations characteristics of both soil fertility indexes and soil comprehensive fertility of Chinese farmland were analyzed with descriptive statistics, comparative analysis of paired sample points, and Integrated Fertility Index method. The results showed that: 1) The farmland soils in southern China were acidic and those in northern were alkaline. From the 1980s to the 2010s, the pH mean decreased by 0.34, and the acidification of soils in the southern region was more significant; 2) SOM and TN contents showed similar spatial patterns, with higher contents in southern and northeastern regions. Compared with the 1980s, the SOM mean and TN mean in the 2010s increased by 2.72 g/kg and 0.06 g/kg, respectively, with a decreasing trend in some areas; 3) The spatial distribution of TP and TK contents showed significant variability, with change range of the mean values was 0.15 g/kg and −1.51 g/kg during the three decades, respectively; 4) In general, the soil fertility in Chinese farmland was poor, with the most sample points at Grade IV (Mid-low) in the 2010s (22.38 %). During the three decades, the IFI mean increased by 0.01. The number of sample points with increasing IFI in eastern China was more than that in western China. And the soil fertility in eastern China showed a trend of ameliorating. This study would have great scientific significance for guiding agricultural fertilizer application and improving fertility in farmland soils.

Energy aware farmland fertility optimization based clustering scheme for wireless sensor networks

Wireless Sensor Networks (WSNs) encompass an enormous set of spatially dispersed sensor nodes connected via wireless links for monitoring and recording the physical data in the target region. The nodes in WSN operate via an inbuilt battery which depletes the energy after a certain period. The partitioning of networks into clusters or groups is one of the efficient solutions to lengthen the lifespan of the WSN. The clustering process can be treated as an NP-hard optimization problem and the utilization of metaheuristics has drastically raised the ability to resolve continuous optimization problems. This study designs an energy-aware farmland fertility optimization-based clustering scheme (EAFFOCS) for WSN. An objective of the EAFFOCS technique is for determining the optimal nodes as CHs and enhancing the overall network efficiency. The proposed EAFFOCS technique is based on the concepts of farmland fertility. Besides, the EAFFOCS technique derives a fitness function involving three input variables like distance to neighbours, distance to BS, and energy. Using the fitness function values, the EAFFOCS technique chooses the node with maximum fitness as CHs. Then, the nodes placed closer to the CHs are joined together to form a cluster. To demonstrate the improved outcomes of the EAFFOCS technique, a wide range of simulations was carried out and the outcomes are inspected under several aspects. The comparative outcomes reported the better performance of the EAFFOCS technique with the maximum residual energy of 11% under the presence of 1000 sensor nodes whereas the HABC-MBOA, FFCGWO, FFOCR, and HASPSO systems have resulted in least RE of 10%, 5%, 2%, and 1% correspondingly.

A Metaheuristic-Based Modularity Optimization Algorithm Driven by Edge Directionality for Directed Networks

Community detection of directed networks has been demonstrated to be able to reveal network function and dynamic characteristics, due to the special information brought by asymmetric edge directionality, but the critical issue of improving the quality of detected communities has not been fully addressed. In this study, we propose a metaheuristic-based modularity optimization method for directed network community detection (MMDCD), which comprehensively leverages nature-inspired metaheuristics and edge directionality information. Specifically, a nature-inspired metaheuristic algorithm, termed diversified farmland fertility (DFF), is improved to provide an efficient optimization strategy for directed modularity maximization. More importantly, the information induced by edge directionality, including edge density, edge pattern, and node importance, are fully used to improve the quality of optimized partitions. Experimental results on synthetic and real-life networks prove the effectiveness of MMDCD, which is superior to state-of-the-art community detection algorithms in terms of quality and accuracy.

Computer Vision and Deep Learning-enabled Weed Detection Model for Precision Agriculture

Presently, precision agriculture processes like plant disease, crop yield prediction, species recognition, weed detection, and irrigation can be accomplished by the use of computer vision (CV) approaches. Weed plays a vital role in influencing crop productivity. The wastage and pollution of farmland's natural atmosphere instigated by full coverage chemical herbicide spraying are increased. Since the proper identification of weeds from crops helps to reduce the usage of herbicide and improve productivity, this study presents a novel computer vision and deep learning based weed detection and classification (CVDL-WDC) model for precision agriculture. The proposed CVDL-WDC technique intends to properly discriminate the plants as well as weeds. The proposed CVDL-WDC technique involves two processes namely multiscale Faster RCNN based object detection and optimal extreme learning machine (ELM) based weed classification. The parameters of the ELM model are optimally adjusted by the use of farmland fertility optimization (FFO) algorithm. A comprehensive simulation analysis of the CVDL-WDC technique against benchmark dataset reported the enhanced outcomes over its recent approaches interms of several measures.

An Improved Farmland Fertility Algorithm with Hyper-Heuristic Approach for Solving Travelling Salesman Problem

Travelling Salesman Problem (TSP) is a discrete hybrid optimization problem considered NP-hard. TSP aims to discover the shortest Hamilton route that visits each city precisely once and then returns to the starting point, making it the shortest route feasible. This paper employed a Farmland Fertility Algorithm (FFA) inspired by agricultural land fertility and a hyper-heuristic technique based on the Modified Choice Function (MCF). The neighborhood search operator can use this strategy to automatically select the best heuristic method for making the best decision. Lin-Kernighan (LK) local search has been incorporated to increase the efficiency and performance of this suggested approach. 71 TSPLIB datasets have been compared with different algorithms to prove the proposed algorithm’s performance and efficiency. Simulation results indicated that the proposed algorithm outperforms comparable methods of average mean computation time, average percentage deviation (PDav), and tour length.

Screening of phosphate-solubilizing bacteria and their abilities of phosphorus solubilization and wheat growth promotion.

Phosphate-solubilizing bacteria (PSB) can enhance plant growth and phosphorus (P) solubilization, it also has been reported to reduce the negative effects of overused agricultural fertilizer in farmland and protect the soil environment. However, the mechanism behind this interaction has not been fully elucidated. In this study, we screened out Pseudomonas moraviensis, Bacillus safensis, and Falsibacillus pallidus which can both solubilize P efficiently and produce indole-3-acetic acid (IAA) from sandy fluvo-aquic soils. The yield of wheat (Triticum aestivum) under PSB inoculation significantly increased up to 14.42% (P < 0.05) compared with the control treatment in phosphate fertilizer-used farmland. Besides promoting wheat growth, we found the labile P fraction in soil was significantly increased by over 122.04% (P < 0.05) under PSB inoculation compared with it in soils without, in parallel, the stable P fraction was significantly reduced by over 46.89% (P < 0.05). Furthermore, PSB inoculation increased the soil microbial biomass and activity, indicating that PSB screened out in this work performed a remarkable ability to colonize the soils in the wheat field. PSB from sandy fluvo-aquic soil improve wheat growth and crop productivity by increasing the labile P fraction and IAA content in the greenhouse and wheat field. Our work provides an environment and economy-friendly bacterial resource that potentially promotes sustainable agricultural development in the long term.

Water Chemical Characteristics and Safety Assessment of Irrigation Water in the Northern Part of Hulunbeier City, Grassland Area in Eastern China

Hulun Buir Grassland is a world-famous natural pasture. The Chenbalhu Banner coalfield, the hinterland of the grassland, is located on the west slope of the Great Khingan Mountains and on the north bank of the Hailar River in China. The proven geological reserves of coal are 17 billion tons. Hulun Buir Grassland plays a role in the ecological barrier, regional coal industry, power transmission from west to east and power transmission from north to south. The proportion of local groundwater in irrigation, domestic and industrial production water sources is about 86%. The large-scale exploitation of coal resources and the continuous emergence of large unit and coal-fired power plants have consumed a large amount of local water resources, resulting in the decrease of the local groundwater level and changing the natural flow field of groundwater. This paper studies the background hydrochemical values and evaluates the irrigatibility of the whole Chenbaerhu Banner coalfield, and studies the impact of coal industry chains such as mining areas and coal chemical plants on the hydrochemistry characteristics of groundwater. The above two studies provide important guiding values for guiding local economic structure planning, groundwater resources exploitation and ecological governance. The study found that Na+ and HCO3− in the groundwater in the study area occupy a dominant position. Referring to the comparison of the lowest values of three types of water standards in the Quality Standards for Groundwater (GB/T14848-2017), the amount of NH4+, Na+ and NO2− exceeding the standard is close to more than 30%. The main chemical types of river water in the study area are HCO3− Na and HCO3− Ca·Na, the main chemical types of surface water are HCO3− Na and HCO3− Na·Ca, and the main chemical type of confined water is HCO3−Na. The formation of hydrochemical types is mainly affected by the dissolution, filtration and evaporation of rocks, specifically the dissolution and filtration of sodium and calcium salts. The chemical correlation analysis of groundwater shows that there are abnormal values at many points in the study area. Further combining with the horizontal comparison of surface human activities in the study area, it shows that the influence scope of coal mine production and coal chemical plants on groundwater is extremely limited. The local groundwater is mainly polluted by a large quantity of local cattle and sheep manure, industrial and domestic sewage pollution and farmland fertilizer.

Grey Wolf Optimization algorithm based on Cauchy-Gaussian mutation and improved search strategy

The traditional Grey Wolf Optimization algorithm (GWO) has received widespread attention due to features of strong convergence performance, few parameters, and easy implementation. However, in actual optimization projects, there are problems of slow convergence speed and easy to fall into local optimal solution. The paper proposed a Grey Wolf Optimization algorithm based on Cauchy-Gaussian mutation and improved search strategy (CG-GWO) in response to the above problems. The Cauchy-Gaussian mutation operator is introduced to increase the population diversity of the leader wolves and improve the global search ability of the algorithm. This work retains outstanding grey wolf individuals through the greedy selection mechanism to ensure the convergence speed of the algorithm. An improved search strategy was proposed to expand the optimization space of the algorithm and improve the convergence accuracy. Experiments are performed with 16 benchmark functions covering unimodal functions, multimodal functions, and fixed-dimension multimodal functions to verify the effectiveness of the algorithm. Experimental results show that compared with four classic optimization algorithms, particle swarm optimization algorithm (PSO), whale optimization algorithm (WOA), sparrow optimization algorithm (SSA), and farmland fertility algorithm (FFA), the CG-GWO algorithm shows better convergence accuracy, convergence speed, and global search ability. The proposed algorithm shows the same better performance compared with a series of improved algorithms such as the improved grey wolf algorithm (IGWO), modified Grey Wolf Optimization algorithm (mGWO), and the Grey Wolf Optimization algorithm inspired by enhanced leadership (GLF-GWO).

Optimal Deep Learning-Based Vocal Fold Disorder Detection and Classification Model on High-Speed Video Endoscopy.

The use of high-speed video-endoscopy (HSV) in the study of phonatory processes linked to speech needs the precise identification of vocal fold boundaries at the time of vibration. The HSV is a unique laryngeal imaging technology that captures intracycle vocal fold vibrations at a higher frame rate without the need for auditory inputs. The HSV is also effective in identifying the vibrational characteristics of the vocal folds with an increased temporal resolution during retained phonation and flowing speech. Clinically significant vocal fold vibratory characteristics in running speech can be retrieved by creating automated algorithms for extracting HSV-based vocal fold vibration data. The best deep learning-based diagnosis and categorization of vocal fold abnormalities is due to the usage of HSV (ODL-VFDDC). The suggested ODL-VFDDC technique starts with temporal segmentation and motion correction to identify vocalized regions from the HSV recording and gathers the position of movable vocal folds across frames. The attributes gathered are fed into the deep belief network (DBN) model. Furthermore, the agricultural fertility algorithm (AFA) is used to optimize the hyperparameter tuning of the DBN model, which improves classification results. In terms of vocal fold disorder classification, the testing results demonstrated that the ODL-VFDDC technique beats the other existing methodologies. The farmland fertility algorithm (FFA) is then used to accurately determine the glottal limits of vibrating vocal folds. The suggested method has successfully tracked the speech fold boundaries across frames with minimum processing cost and high resilience to picture noise. This method gives a way to look at how the vocal folds move during a connected speech that is completely done by itself.

Preparation and application of a surfactant‐modified Halloysite nanotubes for the adsorption of nitrates from aqueous solutions: Kinetic and equilibrium studies

AbstractThe current research proposes the hexadecyltrimethylammonium bromide‐modified Algerian halloysite nanotubes (HNTs‐CTAB) as adsorbent for nitrate ions removal from aqueous solution via electrostatic interactions. X‐ray diffraction (XRD), scanning electron microscope (SEM), transmission electron microscope (TEM), Fourier‐transform infrared spectroscopy (FT‐IR), and nitrogen adsorption–desorption analysis techniques have been used to characterize the prepared adsorbants. The different operating condition that may affect the adsorption of nitrate was carried out. HNTs and HNTs‐CTAB adsorption capacities are considerably pH dependent. The optimum conditions for maximum removal of nitrate from aqueous solution were as follows: adsorbent masse was 40 mg, initial pH was 4, temperature was 25°C, initial nitrate concentration of 100 mg/L and equilibrium time was 3 h. Under optimal value of process parameters, maximum NO3− adsorption capacity of 47.4 mg/g was obtained. Kinetic investigation confirmed that the nitrate adsorption on modified HNTs followed a pseudo‐second‐order with correlation coefficients greater than 0.98 (R2 = 0.9870) and that empirical Freundlich isotherm was best fitted nitrate equilibrium adsorption. The thermodynamic studies indicated that the adsorption of nitrate onto HNTs‐CTAB is endothermic and spontaneous. HNTs‐CTAB displayed high regeneration ability by retaining a capacity above 40 mg/g after four reuse cycles. HNTs‐CTAB containing plentiful nitrate could be returned to the soil directly as an economic slow release fertilizers or a soil amendment due to its environmental‐friendly performance. Therefore, HNTs‐CTAB used for remediation of wastewater might be employed easily as farmland fertilizer.

Overview of the current situation of consumer demand for linseed oil in Kazakhstan

The growth in flaxseed oil consumption is due to the popularity of a healthy lifestyle, the ease of its use in nutrition, and the increased demand of food manufacturers. Flax has gained great popularity as an ingredient for various flour products, jelly, cereals, etc. The purpose of the study is to study the flax oil market in Kazakhstan, the problems and prospects for its development. Methods – industry information from open and closed professional databases, reporting of enterprises, information about their foreign economic activity, publications in scientific literature and the media, consumer and expert surveys, monitoring of wholesale and retail prices, aggregated into a single information array was analyzed. Results – a detailed systematic analysis of market offers of oil of this agricultural crop in the republic in 2017-2021 was carried out, growth rates of its main indicators, trade balance in physical and value terms, production of this type of food in the regions of the Republic of Kazakhstan are presented. External factors affecting consumption and distribution channels are considered. The impact of quarantine restrictions due to the coronovirus pandemic, which disrupted established economic ties along the entire chain from growing oilseeds to their processing, was noted. The main directions for the sale of raw materials and commercial products were identified. Conclusions – the Kazakhstani linen sub-complex in a short period entered the top three world leaders. In the medium term, domestic exporters have the opportunity to strengthen their positions by market structuring and branding their products. It is necessary to carry out indu stry research, including development of the latest formulations of oil and fat products with the involvement of processing facilities; increasing the area under flax cultivation, applying the best farming practices that ensure preservation and increase in the fertility of farmland.