Restoration of degraded lands by adoption of recommended conservation management practices can rehabilitate watersheds and lead to improving soil and water quality. The objective was to evaluate the effects of grass buffers (GBs), biomass crops (BCs), grass waterways (GWWs), agroforestry buffers (ABs), landscape positions, and distance from tree base for AB treatment on soil quality compared with row crop (RC) (corn [Zea mays L.]–soybean [Glycine max (L.) Merr.] rotation) on claypan soils. Soil samples were taken from 10‐cm‐depth increments from the soil surface to 30 cm for GB, BC, GWW, and RC with three replicates. Soil samples were collected from summit, backslope, and footslope landscape positions. Samples were taken at 50‐ and 150‐cm distances from the tree base. β‐Glucosidase, β‐glucosaminidase, dehydrogenase, fluorescein diacetate hydrolase (FDA), soil organic carbon (SOC), total nitrogen (TN), active carbon (AC), and water‐stable aggregates (WSA) were measured. Results showed that β‐glucosidase, β‐glucosaminidase, dehydrogenase, FDA, AC, WSA, and TN values were significantly greater (P < 0.01) for the GB, BC, GWW, and AB treatments than for the RC treatment. The first depth (0–10 cm) revealed the highest values for all soil quality parameters relative to second and third depths. The footslope landscape had the highest parameter values compared with summit and backslope positions. The 50‐cm distance of AB treatment had higher values than the 150‐cm distance for all measured parameters. Results showed that perennial vegetation practices enhanced soil quality by improving soil microbial activity and SOC.
Core Ideas
Permanent vegetative management (trees and grasses) enhanced soil quality.
Perennial practices improved microbial activity and increased soil organic carbon.
Perennial vegetative practices have agricultural and environmental significance.
Establishing perennial practices is an effective approach to enhance soil quality.
New two experiments of the three factors, in this study were constructed to investigate the effects, of the fixed variations to the box plot on subjects' judgments of the box lengths. These two experiments were constructed as an extension to the group B experiments, the ratio experiments the experiments with two variables carried out previously by Hussin, M.M. (1989, 2006, 2007). The first experiment box notch experiment, and the second experiment outlier values experiment. Subjects were asked to judge what percentage the shorter represented of the longer length in pairs of box lengths and give an estimate of percentage, one being a standard plot and the other being of a different box lengths and
Calcium carbonate is predominantly present in aqueous systems, which is commonly used in industrial processes. It has inverse solubility characteristics resulting in the deposition of scale on heat transfer surface. This paper focuses on developing methods for inhibition of calcium carbonate scale formation in cooling tower and air cooler system where scaling can cause serious problems, ZnCl 2 and ZnI 2 has been investigated as scale inhibitor on AISI 316 and 304. ZnCl 2 were more effective than ZnI 2 in both systems, and AISI 316 show more receptivity to the chlorides salt compared to AISI 304. The inhibitors were more effective in cooling tower than air cooler system. AISI 316 show more constant inhibition effic
The COVID-19 pandemic has profoundly affected the healthcare sector and the productivity of medical staff and doctors. This study employs machine learning to analyze the post-COVID-19 impact on the productivity of medical staff and doctors across various specialties. A cross-sectional study was conducted on 960 participants from different specialties between June 1, 2022, and April 5, 2023. The study collected demographic data, including age, gender, and socioeconomic status, as well as information on participants' sleeping habits and any COVID-19 complications they experienced. The findings indicate a significant decline in the productivity of medical staff and doctors, with an average reduction of 23% during the post-COVID-19 period. T
Because of the quick growth of electrical instruments used in noxious gas detection, the importance of gas sensors has increased. X-ray diffraction (XRD) can be used to examine the crystal phase structure of sensing materials, which affects the properties of gas sensing. This contributes to the study of the effect of electrochemical synthesis of titanium dioxide (TiO2) materials with various crystal phase shapes, such as rutile TiO2 (R-TiO2NTs) and anatase TiO2 (A-TiO2NTs). In this work, we have studied the effect of voltage on preparing TiO2 nanotube arrays via the anodization technique for gas sensor applications. The results acquired from XRD, energy dispersion spectro
Because of the quick growth of electrical instruments used in noxious gas detection, the importance of gas sensors has increased. X-ray diffraction (XRD) can be used to examine the crystal phase structure of sensing materials, which affects the properties of gas sensing. This contributes to the study of the effect of electrochemical synthesis of titanium dioxide (TiO2) materials with various crystal phase shapes, such as rutile TiO2 (R-TiO2NTs) and anatase TiO2 (A-TiO2NTs). In this work, we have studied the effect of voltage on preparing TiO2 nanotube arrays via the anodization technique for gas sensor applications. The results acquired from XRD, energy dispersion spectroscopy (EDX), and field emission scanning electron microscopy
Protecting information sent through insecure internet channels is a significant challenge facing researchers. In this paper, we present a novel method for image data encryption that combines chaotic maps with linear feedback shift registers in two stages. In the first stage, the image is divided into two parts. Then, the locations of the pixels of each part are redistributed through the random numbers key, which is generated using linear feedback shift registers. The second stage includes segmenting the image into the three primary colors red, green, and blue (RGB); then, the data for each color is encrypted through one of three keys that are generated using three-dimensional chaotic maps. Many statistical tests (entropy, peak signa
This study looks into the many methods that are used in the risk assessment procedure that is used in the construction industry nowadays. As a result of the slow adoption of novel assessment methods, professionals frequently resort to strategies that have previously been validated as being successful. When it comes to risk assessment, having a precise analytical tool that uses the cost of risk as a measurement and draws on the knowledge of professionals could potentially assist bridge the gap between theory and practice. This step will examine relevant literature, sort articles according to their published year, and identify domains and qualities. Consequently, the most significant findings have been presented in a manne
Disease diagnosis with computer-aided methods has been extensively studied and applied in diagnosing and monitoring of several chronic diseases. Early detection and risk assessment of breast diseases based on clinical data is helpful for doctors to make early diagnosis and monitor the disease progression. The purpose of this study is to exploit the Convolutional Neural Network (CNN) in discriminating breast MRI scans into pathological and healthy. In this study, a fully automated and efficient deep features extraction algorithm that exploits the spatial information obtained from both T2W-TSE and STIR MRI sequences to discriminate between pathological and healthy breast MRI scans. The breast MRI scans are preprocessed prior to the feature
Wireless Body Area Sensor Networks (WBASNs) have garnered significant attention due to the implementation of self-automaton and modern technologies. Within the healthcare WBASN, certain sensed data hold greater significance than others in light of their critical aspect. Such vital data must be given within a specified time frame. Data loss and delay could not be tolerated in such types of systems. Intelligent algorithms are distinguished by their superior ability to interact with various data systems. Machine learning methods can analyze the gathered data and uncover previously unknown patterns and information. These approaches can also diagnose and notify critical conditions in patients under monitoring. This study implements two s
