Search

Cereal crops have been the backbone of national agriculture for decades, as they are major staple food crops. Crop nutrients major sources are organic, inorganic and bio-fertilizers. Supplementation of beneficial microbes with organic and inorganic fertilizers is a feasible technology to improve wheat productivity. A field experiment was conducted to evaluate the impact of organic sources (OS) and inorganic N fertilizer (Urea) along with the application of bio-aab (beneficial microbes) on the wheat crop at the Agronomy Research Farm, The University of Agriculture Peshawar Pakistan, during 2020-2021. Two separate experiments were conducted (with and without bio-aab) in Randomized Complete Block Design each consisted of 10 treatments. Bio-aab treatments are T1: Poultry manure (PM) + 80 kg N + bioabb, T2: PM + 120 kg N + bio-aab, T3: PM + 160 kg N + bio-aab, T4: Farm yard manure (FYM) + 80 kg N + bioabb, T5: FYM + 120 kg N + bio-aab, T6: FYM + 160 kg N + bioaab, T7: Maize residues (MR) + 80 kg N + bioabb, T8: MR + 120 kg N + bio-aab, T9: MR + 160 kg N + bio-aab, T10: Control), same treatments followed for without bio-aab. OS have applied accordingly to the plots one month before sowing, bio-aab solution (60 litres ha-1) was applied with first irrigation to one experiment before sowing to encourage microbial action necessary for manure decomposition, while half N was applied at sowing time and half after seven days of sowing with first irrigation. Statistical analysis was done combined over bio-aab. The principle beneficial microorganisms (BM) in bio-aab contain photosynthetic bacteria, lactic acid bacteria and yeast. These BM produced useful substances such as amino acids, nucleic acids, lactic acid, bioactive substances, hormones, enzymes and sugars. All these substances promote active cell and root division, plant growth, productivity, suppress harmful microorganisms, and enhance the decomposition of organic matter. Results showed that integrated use of PM and N (120 or 160 kg ha-1) along with the application of bio-aab significantly (P=0.05) improved tillers m-2 (12%), grains spike-1 (15%), biological yield (31%) and grain yield (39%) as compared to control plots. Results of the bio-aab treated plots were found (8%) superior yield than without bio-aab. [ABSTRACT FROM AUTHOR]

Traffic density is growing day by day due to the increasing population and affordable prices of cars. It created a void for traffic management systems to cope with traffic congestion and prioritize ambulances. The consequences can be a terrible situation. Emergency vehicles are the most affected in these situations, and inadequate traffic control can put many lives at stake. Ambulances on the road are detected using an acoustic-based Artificial Intelligence system in this article. Emergency vehicle siren and road noise datasets have been developed for ambulance acoustic monitoring. The dataset is developed along with a deep learning (MLP-based) model and trained to use audio monitoring to predict the ambulance presence on the roads. This model achieved 90% accuracy when trained and validated against a developed dataset of only 300 files. With this validated algorithm, researchers can develop a real-time hardware-based model to detect emergency vehicles and make them arrive at the hospital as soon as possible. [ABSTRACT FROM AUTHOR]

Existing classification studies use two non-parametric classifiers- k-nearest neighbours (kNN) and decision trees, and one parametric classifier-logistic regression, generating high accuracies. Previous research work has compared the results of these classifiers with training patterns of different sizes to study alcohol tests. In this paper, the Improved Version of the kNN (IVkNN) algorithm is presented which overcomes the limitation of the conventional kNN algorithm to classify wine quality. The proposed method typically identifies the same number of nearest neighbours for each test example. Results indicate a higher Overall Accuracy (OA) that oscillates between 67% and 76%. Among the three classifiers, the least sensitive to the training sample size was the kNN and produced the unrivalled OA, followed by sequential decision trees and logistic regression. Based on the sample size, the proposed IVkNN model presented 80% accuracy and 0.375 root mean square error (RMSE). [ABSTRACT FROM AUTHOR]