Frame Aggregation in 802.11ac

Inamullah, Muhammad; Raman, Bhaskaran

doi:10.1145/3264877.3264879

Cited by 6 publications

(6 citation statements)

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“…Humic acid was found to increase yield components (number of spikes and 100-grain weight), which may be attributed to the role of HA in increasing root vitality, improving nutrient uptake, increasing chlorophyll synthesis [64]. Moreover, increasing fertilizer retention and stimulating beneficial microbial activity thus increases NSM −2 [64]. Humic acid increased the 100-grain weight-explained by the role of HA in increasing crop growth rate-and it increased the accumulation of dry matter in the plant, which is transferred to the grains later by increasing the net assimilates stored in the stem and leaves and finally increased 100-grain weight in cereals crops [65,66].…”

Section: Discussionmentioning

confidence: 98%

“…Several research sources reported that increasing HA level increased plant height [62,63]. Humic acid was found to increase yield components (number of spikes and 100-grain weight), which may be attributed to the role of HA in increasing root vitality, improving nutrient uptake, increasing chlorophyll synthesis [64]. Moreover, increasing fertilizer retention and stimulating beneficial microbial activity thus increases NSM −2 [64].…”

Section: Discussionmentioning

confidence: 99%

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The Production of Dual-Purpose Triticale in Arid Regions: Application of Organic and Inorganic Treatments under Water Deficit Conditions

et al. 2022

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Most of the arid and semi-arid regions, particularly in the Mediterranean area, suffer from the lack of a sufficient quantity of high-quality feed, as well as a low amount of rainfall that is unevenly distributed, resulting in the region being highly vulnerable to drought. A field experiment was carried out at the experimental station of the Faculty of Desert and Environmental Agriculture, Fuka, Matrouh University, Egypt during the winter seasons of 2018/19 and 2019/20 to study the performance of triticale (X Triticosecale Wittmack), grown under water deficit conditions, in terms of productivity and quality. The study investigated the influence of five levels of potassium fertilization (PF; 0, 43.2, 86.4, 129.6, and 172.8 kg ha−1) and ascorbic acid (AA; 0, 25, 50, 75, and 100 mg L−1) that was applied to the triticale grains before sowing and humic acid (HA; 0, 2.4, 4.8, 7.2, and 9.6 kg ha−1) that was applied as powder to the soil 21 days after sowing followed by sprinkler irrigation on triticale forage and grain production when forage was removed at variable ages at cutting (AC), determined as days after sowing (AC; 40, 65, 90, 115, and 140 DAS) on forage yield and nutritive value, in addition to the final grain yield of triticale. The experimental design was a central composite design with one replicate. Results indicated that the PF*AC interaction was significant, and it gave values of 84.78 and 238.00 g kg−1 for crude protein (CP) and degraded neutral detergent fiber (DNDF). In addition, the interaction between AA and AC was significant for CP, acid detergent fiber (ADF), 100-grain weight (100 GW), number of spikes m−2 (NSM−2), and plant height (PH). Moreover, the AC*HA interaction was significant with values of 175.17 and 247.00 g kg−1 for CP and DNDF, respectively, and 0.55 t ha−1 for grain yield (GY). Age at cutting exerted the strongest effect on the studied characteristics. It was observed that the contents of neutral detergent fiber (NDF), ADF, acid detergent lignin (ADL), and non-fiber carbohydrates (NFC) in the triticale forage significantly increased when the crop was cut at an advanced age, unlike CP, DNDF, GY, NSM−2, 100 GW, and PH that decreased with advanced AC. The highest values of 271.00, 256.00, and 268.00 g kg−1 for DNDF were obtained with higher levels of either PF, AA, or HA, respectively. However, the highest value of GY (0.97 t ha−1) was obtained with higher levels of PF*HA averaged over the two seasons. The interaction between AA*HA resulted in 393.39, 311.00, 27.13 g kg−1, and 0.94 t ha−1, for NDF, DNDF, ADL, and GY, respectively. The highest significant NDF (413.11 g kg−1) and DNDF (307.50 g ka−1) values were obtained with the application of high levels of either AA or HA. In the dual-purpose production system, it is recommended to cut the triticale crop at 65 DAS to achieve the optimum balance between forage yield and quality on the one hand, and final grain yield on the other hand. In the arid regions, application of PF, AA, and HA could help in reducing the damage caused by water deficit.

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Section: Discussionmentioning

confidence: 98%

Section: Discussionmentioning

confidence: 99%

The Production of Dual-Purpose Triticale in Arid Regions: Application of Organic and Inorganic Treatments under Water Deficit Conditions

et al. 2022

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“…In soybean, smaller stomata have been reported in response to water deficit [46][47][48], but changes in the stomatal density in response to water deficit are variable, as various reports show unchanged, increased, or decreased stomatal density [46][47][48][49][50]. Greater stomatal density was associated with a higher transpiration rate and increased water loss [17][18][19]51].…”

Section: Discussionmentioning

confidence: 99%

Assessment of Drought Responses of Wild Soybean Accessions at Different Growth Stages

Nguyen,

Jo,

Tran

et al. 2024

Agronomy

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Drought is a significant abiotic stress that limits crop production. Soybeans [Glycine max (L.) Merr.] are regarded as drought-sensitive. In the present study, the drought responses of wild soybean accessions were evaluated at different growth stages. Based on the leaf wilting index of 411 accessions at the vegetative stage, seven highly tolerant (HT) and 24 tolerant (T) accessions were identified, although most wild soybeans were classified as moderate (M), sensitive (S), and highly sensitive (HS) genotypes. In addition, with selected wild soybeans, stomatal density decreased in HT accessions but increased in HS accessions under drought conditions at the vegetative stage. However, for cultivated soybeans, the stomatal density of the drought-tolerant and drought-sensitive were not significantly different between the two conditions. The expression levels of drought-related transcriptional factors indicated that the HT genotype showed a higher expression level of drought-related genes than that of the HS genotype at the vegetative stage. At the reproductive stages, 12 wild soybeans randomly selected from HT, T, S, and HS based on assessment at the vegetative stage showed consistent drought responses with seed yield, root development, and water status. However, the extent of the detrimental effect of drought on the germination rates and root length of 165 wild soybeans at the germination stage varied depending on the genotype, indicating that there may not be a robust correlation between phenotypic measurements at the germination stage and drought-related assessments at the two growth stages. The information from this study can provide useful breeding materials for the development of drought-tolerant cultivars from wild soybeans.

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“…There is a growing interest among researchers and clinicians in applying CNN methods for segmentation, abnormality detection, disease classification and diagnosis [5][6][7][8]. Different variations of CNN methods use different approaches to improve their performance in wide ranges of image classification tasks [9,10]. The robustness and automatability of CNNs in addition to reports of CNN techniques outperforming human experts seem to be the driving forces for the enthusiastic surge of their use in medical image understanding [11][12][13].…”

Section: Importance Of Cnn For Medical Image Classificationmentioning

confidence: 99%

Medical Image Classifications Using Convolutional Neural Networks: A Survey of Current Methods and Statistical Modeling of the Literature

Mohammed,

Tune,

Assefa

et al. 2024

MAKE

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In this review, we compiled convolutional neural network (CNN) methods which have the potential to automate the manual, costly and error-prone processing of medical images. We attempted to provide a thorough survey of improved architectures, popular frameworks, activation functions, ensemble techniques, hyperparameter optimizations, performance metrics, relevant datasets and data preprocessing strategies that can be used to design robust CNN models. We also used machine learning algorithms for the statistical modeling of the current literature to uncover latent topics, method gaps, prevalent themes and potential future advancements. The statistical modeling results indicate a temporal shift in favor of improved CNN designs, such as a shift from the use of a CNN architecture to a CNN-transformer hybrid. The insights from statistical modeling point that the surge of CNN practitioners into the medical imaging field, partly driven by the COVID-19 challenge, catalyzed the use of CNN methods for detecting and diagnosing pathological conditions. This phenomenon likely contributed to the sharp increase in the number of publications on the use of CNNs for medical imaging, both during and after the pandemic. Overall, the existing literature has certain gaps in scope with respect to the design and optimization of CNN architectures and methods specifically for medical imaging. Additionally, there is a lack of post hoc explainability of CNN models and slow progress in adopting CNNs for low-resource medical imaging. This review ends with a list of open research questions that have been identified through statistical modeling and recommendations that can potentially help set up more robust, improved and reproducible CNN experiments for medical imaging.

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Frame Aggregation in 802.11ac

Cited by 6 publications

References 1 publication

The Production of Dual-Purpose Triticale in Arid Regions: Application of Organic and Inorganic Treatments under Water Deficit Conditions

The Production of Dual-Purpose Triticale in Arid Regions: Application of Organic and Inorganic Treatments under Water Deficit Conditions

Assessment of Drought Responses of Wild Soybean Accessions at Different Growth Stages

Medical Image Classifications Using Convolutional Neural Networks: A Survey of Current Methods and Statistical Modeling of the Literature

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