835 research outputs found
Effect of botanicals and synthetic insecticides on Pieris brassicae (L., 1758) (Lepidoptera: Pieridae)
No full textA Novel Approach to Evaluate, Highlight, and Conserve the Geologically Significant Geoheritage Sites from the Peshawar Basin, Khyber Pakhtunkhwa, Pakistan: Insights into Their Geoscientific, Educational, and Social Importance
No full textFactors affecting injury severity in motorcycle crashes: Different age groups analysis using Catboost and SHAP techniques
No full textObjective: Motorcycle crashes often result in severe injuries on roads that affect people's lives physically, financially, and psychologically. These injuries could be notably harmful to drivers of all age groups. The main objective of this study is to investigate the risk factors contributing to the severity of crash injuries in different age groups. Methods: This Objective is achieved by developing accurate machine learning (ML) based prediction models. This research examines the relationship between potential risk factors of motorcycle-associated crashes using (ML) and Shapley Additive explanations (SHAP) technique. The SHAP technique further helped interpreting ML methods for traffic injury severity prediction. It indicates the significant non-linear interactions between dependent and independent variables. The data for this study was collected from the Provincial Emergency Response Service RESCUE 1122 for the Rawalpindi region (Pakistan) over three years (from 2017 to 2020). The Synthetic Minority Oversampling Technique (SMOTE) is employed to balance injury severity classes in the pre-processing phase. Results: The results demonstrate that age, gender, posted speed limit, the number of lanes, and month of the year are positively associated with severe and fatal injuries. This research also assesses how the modeling framework varies between the ML and classical statistical methods. The predictive performance of proposed ML models was assessed using several evaluation metrics, and it is found that Catboost outperformed the XGBoost, Random Forest (RF) and Multinomial Logit (MNL) model. Conclusion: The findings of this study will assist road users, road safety authorities, stakeholders, policymakers, and decision-makers in obtaining substantial and essential guidance for reducing the severity of crash injuries in Pakistan and other countries with prevailing conditions
Optical-Interference Mitigation in Visible Light Communication for Intelligent Transport Systems Applications
No full textIntelligent Transport Systems (ITS) are anticipated to be one of the key technologies for the next decade and their deployment can benefit from the recent developments in the domain of Visible Light Communication (VLC). Light Emitting Diode (LED)-based low-cost VLC is considered in this work to provide a practical approach towards the implementation of an ITS by addressing the major issues of channel noise, free-space optical multipath reflections and interference from light sources. An analytical model is presented for the proposed Multiple-Input–Single-Output (MISO)-based VLC, and simulations are performed to analyze the performance of the system for various transmission distances. Results show that the proposed optimal receiver for 4 × 1 MISO can provide considerable improvement in the bit error rate for the forward error correction (FEC) threshold of 3.8 × 10−3 in the presence of optical interference, and is suitable to support an ITS with an inter-vehicle transmission approach. The comparison of achieved performance with existing solutions for VLC-based ITS depicts that the proposed framework provides much higher data rates, three times longer transmission distance and improved receiver sensitivity
A Non-Integer High-Order Sliding Mode Control of Induction Motor with Machine Learning-Based Speed Observer
No full textThe induction motor (IM) drives are prone to various uncertainties, disturbances, and non-linear dynamics. A high-performance control system is essential in the outer loop to guarantee the accurate convergence of speed and torque to the required value. Super-twisting sliding mode control (ST-SMC) and fractional-order calculus have been widely used to enhance the sliding mode control (SMC) performance for IM drives. This paper combines the ST-SMC and fractional-order calculus attributes to propose a novel super-twisting fractional-order sliding mode control (ST-FOSMC) for the outer loop speed control of the model predictive torque control (MPTC)-based IM drive system. The MPTC of the IM drive requires some additional sensors for speed control. This paper also presents a novel machine learning-based Gaussian Process Regression (GPR) framework to estimate the speed of IM. The GPR model is trained using the voltage and current dataset obtained from the simulation of a three-phase MPTC based IM drive system. The performance of the GPR-based ST-FOSMC MPTC drive system is evaluated using various test cases, namely (a) electric fault incorporation, (b) parameter perturbation, and (c) load torque variations in Matlab/Simulink environment. The stability of ST-FOSMC is validated using a fractional-order Lyapunov function. The proposed control and estimation strategy provides effective and improved performance with minimal error compared to the conventional proportional integral (PI) and SMC strategies
Bioactive Steroids and Saponins of the Genus Trillium
No full textThe species of the genus Trillium (Melanthiaceae alt. Trilliaceae) include perennial herbs with characteristic rhizomes mainly distributed in Asia and North America. Steroids and saponins are the main classes of phytochemicals present in these plants. This review summarizes and discusses the current knowledge on their chemistry, as well as the in vitro and in vivo studies carried out on the extracts, fractions and isolated pure compounds from the different species belonging to this genus, focusing on core biological properties, i.e., cytotoxic, antifungal and anti-inflammatory activities
Fintech applications in Islamic finance: AI, machine learning, and blockchain techniques
No full textIn the realm of Islamic finance, a pivotal challenge looms-the escalating complexity of investment decisions, macroeconomic analyses, and credit evaluations. In response, we present a groundbreaking solution that resonates with the rapidly evolving fintech era. Fintech Applications in Islamic Finance: AI, Machine Learning, and Blockchain Techniques offers a compelling repository of knowledge, meticulously curated by renowned editors Mohammad Irfan, Seifedine Kadry, Muhammad Sharif, and Habib Ullah Khan. At the heart of this challenge lies a convergence of technologies-artificial intelligence (AI), machine learning (ML), and blockchain-that have revolutionized financial services. Our book unravels the symbiotic relationship between these innovations and the intricate world of Islamic finance. As institutions strive to navigate this landscape, our solution emerges as a guiding light. By delving into AI's predictive power and ML's capacity to analyze vast datasets, the book empowers financial institutions to automate processes, enhance efficiency, and make informed decisions. As the global Muslim population grows, the demand for Islamic financial services intensifies, presenting a unique growth opportunity. However, with growth comes the challenge of managing resources, regulations, and strategies. This book serves as an invaluable guide for governments, academic institutions, and industry players, offering insights into AI/ML's transformative potential within Islamic fintech. From financial monitoring to blockchain applications, this compendium addresses diverse topics, equipping researchers, academicians, industrialists, and investors with the knowledge to navigate the intricacies of modern Islamic finance. Fintech Applications in Islamic Finance: AI, Machine Learning, and Blockchain Techniques is a call to action, an exploration of innovation, and a guide for both academia and industry. In an era where AI, ML, and blockchain reshape finance, this book stands as a beacon of knowledge, ushering Islamic finance into a realm of unprecedented efficiency and insight. As we invite readers to embark on this transformative journey, we illuminate the path to a future where technology and tradition converge harmoniously
Pyramiding of Four Broad Spectrum Bacterial Blight Resistance Genes in Cross Breeds of Basmati Rice
No full textPyramiding of major resistance (R) genes through marker-assisted selection (MAS) is a useful way to attain durable and broad-spectrum resistance against Xanthomonas oryzae pv. oryzae pathogen, the causal agent of bacterial blight (BB) disease in rice (Oryza sativa L.). The present study was designed to pyramid four broad spectrum BB-R genes (Xa4, xa5, xa13 and Xa21) in the background of Basmati-385, an indica rice cultivar with much sought-after qualitative and quantitative grain traits. The cultivar, however, is susceptible to BB and was therefore, crossed with IRBB59 which possesses R genes xa5, xa13 and Xa21, to attain broad and durable resistance. A total of 19 F1 plants were obtained, some of which were backcrossed with Basmati-385 and large number of BC1F1 plants were obtained. In BC1F2 generation, 31 phenotypically superior genotypes having morphological features of Basmati-385, were selected and advanced up to BC1F6 population. Sequence-tagged site (STS)-based MAS was carried out and phenotypic selection was made in each successive generation. In BC1F6 population, potentially homozygous recombinant inbred lines (RILs) from each line were selected and evaluated on the bases of STS evaluation and resistance to local Xanthomonas oryzae pv. oryzae (Xoo) isolates. Line 23 was found pyramided with all four BB-R genes i.e., Xa4, xa5, xa13 and Xa21. Five genotypes including line 8, line 16, line 21, line 27 and line 28 were identified as pyramided with three R genes, Xa4, xa5 and xa13. Pathological study showed that rice lines pyramided with quadruplet or triplet R genes showed the highest level of resistance compared to doublet or singlet R genes. Thus, line 23 with quadruplet, and lines 8, 16, 21, 27, and 28 with triplet R genes, are recommended for replicated yield and resistance trials before release as new rice varieties. Further, traditional breeding coupled with MAS, is a solid way to attain highly effective BB-resistant rice lines with no yield cost
Influence of Bacterial Secondary Symbionts in Sitobion avenae on Its Survival Fitness against Entomopathogenic Fungi, Beauveria bassiana and Metarhizium brunneum
No full textThe research was focused on the ability of wheat aphids Sitobion avenae, harboring bacterial secondary symbionts (BSS) Hamiltonella defensa or Regiella insecticola, to withstand exposure to fungal isolates of Beauveria bassiana and Metarhizium brunneum. In comparison to aphids lacking bacterial secondary symbionts, BSS considerably increased the lifespan of wheat aphids exposed to B. bassiana strains (Bb1022, EABb04/01-Tip) and M. brunneum strains (ART 2825 and BIPESCO 5) and also reduced the aphids’ mortality. The wheat aphid clones lacking bacterial secondary symbionts were shown to be particularly vulnerable to M. brunneum strain BIPESCO 5. As opposed to wheat aphids carrying bacterial symbionts, fungal pathogens infected the wheat aphids lacking H. defensa and R. insecticola more quickly. When treated with fungal pathogens, bacterial endosymbionts had a favorable effect on the fecundity of their host aphids compared to the aphids lacking these symbionts, but there was no change in fungal sporulation on the deceased aphids. By defending their insect hosts against natural enemies, BSS increase the population of their host society and may have a significant impact on the development of their hosts
Assessment of toxicological health risk of trace metals in vegetables mostly consumed in Punjab, Pakistan
No full textIn the present study, levels of trace metals in two commonly consumed vegetables (Spinacia oleracea and Brassica campestris) were assessed. Both vegetables are cultivated in a semi-arid area which receives effluents from various sources. Leafy parts and tender stems are used for human consumption as well as for farm animals as a source of nourishment. However, the aim of the present study was to appraise the concentrations of trace metals in different tissues of both vegetables at the time of harvesting when they become accessible to the humans. The analysis showed that mean trace metal levels in the stem of S. oleracea showed the highest bioconcentration of Zn (25.43 mg kg−1), while in the stem (28.56 mg kg−1) and roots (22.40 mg kg−1), Fe level was the highest. In case of B. campestris, Fe level was highest (18.45 mg kg−1) in the stems. Copper (Cu) has the highest bioconcentration factor values (BF = 1.030) in the stem of B. campestris, whereas Mn (BF = 0.010) was the least accumulated element in the leaves and roots of B. campestris. Collectively, bioconcentration of trace metals in plant tissues exceeded the standard values set by the World Health Organization (WHO) and the US Environmental Protection Agency. So, the vegetables cultivated in effluent-impacted areas may stance a potential public health risk for end-consumers
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