Difference between Traditional and Non-traditional Learning Methods in Virtual and Real-world Environment

The advancement of information and communication technology in 21st century is introducing new way of learning. The physical classroom learning method is very effective and a prevailing type learning. The main objective of the study was to explore the impact of non-traditional method of education in undergraduates. The main objective was to study the general impact of traditional learning method among students. The researcher had used the social survey method of research for data collection and researcher had used simple random sampling. The researcher had selected 60 respondents from Bahauddin Zakariya University Multan and 40 respondents from Virtual university Multan campus. Online learning saves time 88.3% real class students said that yes it saves time and 92.5% virtual learners agree that it saves time. The online learning reduced the dependency of place 70% traditional students were agreed this and 80% virtual students said that it reduced the dependency of place. Virtual learning is beneficial for both learning and job together 80% traditional students said yes, it is beneficial for both and 90% virtual students had also same views.Recently, use of information communication technology has been increased in our educational institutions. Due to this advancement in technology has made physical and virtual learning more effective for learners

Toxicity screening of different modified biochars on the germination and early seedling growth

Applying biochar as soil amendment improves soil physicochemical properties, carbon sequestration and plant growth. However, prior to use as amendment, BC must be investigated for both its potential positive and negative effects on soil and plants. Seed germination and early seedling growth are considered to be very sensitive to various external factors and are therefore frequently used for initial screening of different soil amendments. In this study we assessed the impact of different biochar modifications on seed germination, i.e., (germination rate and seedling growth). Ten different types of biochar representing different biochar modifications, such as physical and chemical activation, mineral (ash) enhanced biochar (Buss et.al.,2019) phosphorus-loaded biochar, and potassium-loaded biochar (Mašek et.al., 2019) were screened for their toxicity using sand with a uniform biochar application rate of 0.5% in petri dishes. The room temperature was (CRD maintained approx. 25 °C during the whole experiment period. The experiment was conducted under Complete Randomized Design). It is known that the size of a seed affects the fitness of the plant growing from it; larger seeds often have higher fitness (Kering and Zhang 2015; Giles 1990) and are therefore initially less affected by external conditions. Most past studies involving study of phytotoxic effects of biochar on seed germination have focused on a single crop and did not account for the effect of the seed size. Based on a relevant literature review and preliminary experiments, we selected seeds of different plants based on their size, such as, spring barley, white clover and cress seed. The result obtained to date show that biochar none of the biochar exhibited any significant detrimental effects on the germination of the barley seeds, however there are differences observed, depending on the type of biochar modification used and also the size of seeds selected for the tests figure 1. Please click Additional Files below to see the full abstract

Deep reinforcement learning based Evasion Generative Adversarial Network for botnet detection

Botnet detectors based on machine learning are potential targets for adversarial evasion attacks. Several research works employ adversarial training with samples generated from generative adversarial nets (GANs) to make the botnet detectors adept at recognising adversarial evasions. However, the synthetic evasions may not follow the original semantics of the input samples. This paper proposes a novel GAN model leveraged with deep reinforcement learning (DRL) to explore semantic aware samples and simultaneously harden its detection. A DRL agent is used to attack the discriminator of the GAN that acts as a botnet detector. The agent trains the discriminator on the crafted perturbations during the GAN training, which helps the GAN generator converge earlier than the case without DRL. We name this model RELEVAGAN, i.e. [“relieve a GAN” or deep REinforcement Learning-based Evasion Generative Adversarial Network] because, with the help of DRL, it minimises the GAN's job by letting its generator explore the evasion samples within the semantic limits. During the GAN training, the attacks are conducted to adjust the discriminator weights for learning crafted perturbations by the agent. RELEVAGAN does not require adversarial training for the ML classifiers since it can act as an adversarial semantic-aware botnet detection model. The code will be available at https://github.com/rhr407/RELEVAGAN

Inter-Linkage between FDI, Imports and Exchange Rate: An Empirical Evidence from Pakistan

Foreign Direct Investment (FDI) is a very important phenomenon of the millennium. It is considered a substitute and complementary for trade. Numerous researches regarding FDI and imports have been conducted, and contradictory results on complex relationship between FDI and Imports are found. The vector error correction model (VECM) and linear hypothesis testing have been applied by considering exchange rate as supplement for better and accurate modelling. The results of the study indicated short run as well as highly significant long run relationship among all variables under study. For imports causality runs from FDI to imports indicating FDI to be complementary variable for imports. Govt needs to implement policies which must boost up exports but curtail imports burden. In order to generate employment and reduce the balance of payments problems, it is therefore, suggested that government should opt and encourage FDI policies relevant to export oriented industries like manufacturing and production sectors along with the exploration of natural resources. Policies should aim to encourage FDI in industrial sector where the surge in import bill is compensated with export performance of the firms. Keywords: Foreign Direct Investment, Imports, Exchange rate and VECM

Essential Oils Based Nano Formulations against Postharvest Fungal Rots

Postharvest phytopathogenic rot fungi affect the quality and quantity of perishable fruits and vegetables. About 30–40% peaches deteriorate annually after harvest in world whereas 40–50% losses are reported from Pakistan. Our research envisages importance of an eco-friendly plant essential oils based nano formulations as a management strategy against postharvest deteriorating fungal rots by enhancing their shelf-life and to attenuate reliance on synthetic fungicides. Plant essential oils mode of action against fungal postharvest rots is responsible of rupturing plasma membrane of fungal cell wall. The natural ripening process of perishable commodities does not get affected by the presence of antifungal packaging in the form of plant essential oil nano formulations as no significant alteration in weight loss of produce was recorded. Challenges in applying EOs for microbial suppression in postharvest systems include optimizing their positioning in commercial fruit storage containers. Several innovative approaches are analyzed in terms of work environment and implementation regarding disease management along with future perspectives in concerning field

Large expert-curated database for benchmarking document similarity detection in biomedical literature search

Document recommendation systems for locating relevant literature have mostly relied on methods developed a decade ago. This is largely due to the lack of a large offline gold-standard benchmark of relevant documents that cover a variety of research fields such that newly developed literature search techniques can be compared, improved and translated into practice. To overcome this bottleneck, we have established the RElevant LIterature SearcH consortium consisting of more than 1500 scientists from 84 countries, who have collectively annotated the relevance of over 180 000 PubMed-listed articles with regard to their respective seed (input) article/s. The majority of annotations were contributed by highly experienced, original authors of the seed articles. The collected data cover 76% of all unique PubMed Medical Subject Headings descriptors. No systematic biases were observed across different experience levels, research fields or time spent on annotations. More importantly, annotations of the same document pairs contributed by different scientists were highly concordant. We further show that the three representative baseline methods used to generate recommended articles for evaluation (Okapi Best Matching 25, Term Frequency-Inverse Document Frequency and PubMed Related Articles) had similar overall performances. Additionally, we found that these methods each tend to produce distinct collections of recommended articles, suggesting that a hybrid method may be required to completely capture all relevant articles. The established database server located at https://relishdb.ict.griffith.edu.au is freely available for the downloading of annotation data and the blind testing of new methods. We expect that this benchmark will be useful for stimulating the development of new powerful techniques for title and title/abstract-based search engines for relevant articles in biomedical research.Peer reviewe