Factors Responsible for Resistance in Okra against Aphid, Aphis Gossiypii Glover (Homoptera: Aphididae)

Aphids are herbivores that feed on plant’s sap and are widespread throughout the globe. To assess the factors affecting the infestation of Aphis gossypii (Glover) and to use antixenosis a trial was conducted using 5 okra genotypes (Sabz Pari, Advanta, Durga, Kaveri, and Shandar) during spring, 2017 at “Agriculture Research Institute” (ARI) Tarnab, under Random Complete Block Design (RCBD) in field and Completely Randomized Design (CRD) in lab with 3 and 8 replications, respectively. Weekly data gathering for mean percent infestation of A. gossypii on each genotype to note variation among genotypes. The aphid infestations (2.5 Aphid leaf-1) recorded on Shandar was higher than others and lowest (2.0 Aphids leaf-1) was recorded on Durga. Initially the infestation was lesser (0.5) but with time it reaches to peak (3.62) on 1st May and then gradually declined to least (2.0 aphid leaf-1) in the 10th week. A statistically significant negative relationship existed between aphid abundance and crop yield. In the antixenosis trial, the Durga variety showed significant antixenosis resistance towards aphids after 12, 24, and 48 hours. Furthermore, the maximum yield of Durga variety (8.3 Tons (t)/ha) and the least yield (5.2 tons/ha) Shandar was obtained. Relating to aphid infestation and yield, the Durga variety performed exceptionally well. It is concluded from the results that the varieties showing antixenosis resistance towards insects must be recommended to not only reduce insect attacks but also to enhance yield

Sentiment analysis of tweets through Altmetrics: A machine learning approach

The purpose of the study is to (a) contribute to annotating an Altmetrics dataset across five disciplines, (b) undertake sentiment analysis using various machine learning and natural language processing–based algorithms, (c) identify the best-performing model and (d) provide a Python library for sentiment analysis of an Altmetrics dataset. First, the researchers gave a set of guidelines to two human annotators familiar with the task of related tweet annotation of scientific literature. They duly labelled the sentiments, achieving an inter-annotator agreement (IAA) of 0.80 (Cohen’s Kappa). Then, the same experiments were run on two versions of the dataset: one with tweets in English and the other with tweets in 23 languages, including English. Using 6388 tweets about 300 papers indexed in Web of Science, the effectiveness of employed machine learning and natural language processing models was measured by comparing with well-known sentiment analysis models, that is, SentiStrength and Sentiment140, as the baseline. It was proved that Support Vector Machine with uni-gram outperformed all the other classifiers and baseline methods employed, with an accuracy of over 85%, followed by Logistic Regression at 83% accuracy and Naïve Bayes at 80%. The precision, recall and F1 scores for Support Vector Machine, Logistic Regression and Naïve Bayes were (0.89, 0.86, 0.86), (0.86, 0.83, 0.80) and (0.85, 0.81, 0.76), respectively

A comprehensive perspective of traditional Arabic or Islamic medicinal plants as an adjuvant therapy against COVID-19

COVID-19 is a pulmonary disease caused by SARS-CoV-2. More than 200 million individuals are infected by this globally. Pyrexia, coughing, shortness of breath, headaches, diarrhoea, sore throats, and body aches are among the typical symptoms of COVID-19. The virus enters into the host body by interacting with the ACE2 receptor. Despite many SARS-CoV-2 vaccines manufactured by distinct strategies but any evidence-based particular medication to combat COVID-19 is not available yet. However, further research is required to determine the safety and effectiveness profile of the present therapeutic approaches. In this study, we provide a summary of Traditional Arabic or Islamic medicinal (TAIM) plants’ historical use and their present role as adjuvant therapy for COVID-19. Herein, six medicinal plants Aloe barbadensis Miller, Olea europaea, Trigonella foenum-graecum, Nigella sativa, Cassia angustifolia, and Ficus carica have been studied based upon their pharmacological activities against viral infections. These plants include phytochemicals that have antiviral, immunomodulatory, antiasthmatic, antipyretic, and antitussive properties. These bioactive substances could be employed to control symptoms and enhance the development of a possible COVID-19 medicinal synthesis. To determine whether or if these TAIMs may be used as adjuvant therapy and are appropriate, a detailed evaluation is advised

Conceptual Review of DoS Attacks in Software Defined Networks

Software-defined networking (SDN) is a new developing technology that has been widely adopted by businesses because of its low cost and greater innovation in networks specially Scaling or manually configuring physical infrastructure. This makes it easier to design networks and manage huge networks. Scaling or manually configuring physical infrastructure is a problem for older networks, which cannot keep up with today's rapid technological advancements. Another important issue is the lack of information and the absence of a skill set or experienced experts required with technological expertise, which is very much concerned about the current situation. For this reason, a software-defined network architecture has been developed that allows for a more flexible network that can be reconfigured to meet changing needs. However, there are also other security concerns to consider especially the controller’s security. In this paper, we examine the impact of Denial of Service (DoS) attacks on the SDN controller and different mitigation techniques to overcome these attacks. The research effort also points out possible future research on this topic, as well as its limitations

Carbon Sequestration by Native Tree Species around the Industrial Areas of Southern Punjab, Pakistan

Industries have been a major culprit in increasing carbonaceous emissions and major contributors to global warming over the past decades. Factories in the urban periphery tend to warm cities more as compared with rural surroundings. Recently, nature-based solutions have been promoted to provide solutions related to climate adaptations and mitigation issues and challenges. Among these solutions, urban trees have proven to be an effective solution to remove air pollutants and mitigate air pollution specifically caused by carbon emissions. This work was designed to assess the role of tree species in mitigating air emissions of carbon around the vicinity of various industrial sites. For this purpose, three different industrial sites (weaving, brick kiln, and cosmetic) were selected to collect data. Selected industrial sites were divided into two areas, i.e., (a) area inside the industry and (b) area outside the industry. The samples were collected from 100 square meters inside the industries and 100 square meters outside the industries. Five different trees species comprised of four replications were selected for sampling. About twenty trees species from inside and outside of the industries were measured, making it 120 trees from all three selected industries for estimating aboveground and belowground biomass, showing their carbon estimation. The results showed that Moringa oleifera depicted overall higher total biomass from both inside (2.58, 0.56, and 4.57 Mg ha−1) and outside sites from all three selected industries. In terms of total carbon stock and carbon sequestration inside the industry sites, Syzygium cumini had the most dominant values in the weaving industry (2.82 and 10.32 Mg ha−1) and brick kiln (3.78 and 13.5 Mg ha−1), while in the cosmetic industry sites, Eucalyptus camaldulensis depicted higher carbon, stock, and sequestration values (7.83 and 28.70 Mg ha−1). In comparison, the sites outside the industries’ vicinity depicted overall lower carbon, stock, and sequestration values. The most dominant tree inside came out to be Dalbergia sisso (0.97 and 3.54 Mg ha−1) in the weaving industry sites, having higher values of carbon stock and carbon sequestration. Moringa oliefra (1.26 and 4.63) depicted dominant values in brick kiln sites, while in the cosmetic industry, Vachellia nilotica (2.51 and 9.19 Mg ha−1) displayed maximum values as compared with other species. The findings regarding belowground biomass and carbon storage indicate that the amount of soil carbon decreased with the increase in depth; higher soil carbon stock values were depicted at a 0–20 cm depth inside and outside the industries. The study concludes that forest tree species present inside and outside the vicinity of various industries have strong potential in mitigating air emissions

Carbon Sequestration by Native Tree Species around the Industrial Areas of Southern Punjab, Pakistan

Industries have been a major culprit in increasing carbonaceous emissions and major contributors to global warming over the past decades. Factories in the urban periphery tend to warm cities more as compared with rural surroundings. Recently, nature-based solutions have been promoted to provide solutions related to climate adaptations and mitigation issues and challenges. Among these solutions, urban trees have proven to be an effective solution to remove air pollutants and mitigate air pollution specifically caused by carbon emissions. This work was designed to assess the role of tree species in mitigating air emissions of carbon around the vicinity of various industrial sites. For this purpose, three different industrial sites (weaving, brick kiln, and cosmetic) were selected to collect data. Selected industrial sites were divided into two areas, i.e., (a) area inside the industry and (b) area outside the industry. The samples were collected from 100 square meters inside the industries and 100 square meters outside the industries. Five different trees species comprised of four replications were selected for sampling. About twenty trees species from inside and outside of the industries were measured, making it 120 trees from all three selected industries for estimating aboveground and belowground biomass, showing their carbon estimation. The results showed that Moringa oleifera depicted overall higher total biomass from both inside (2.58, 0.56, and 4.57 Mg ha−1) and outside sites from all three selected industries. In terms of total carbon stock and carbon sequestration inside the industry sites, Syzygium cumini had the most dominant values in the weaving industry (2.82 and 10.32 Mg ha−1) and brick kiln (3.78 and 13.5 Mg ha−1), while in the cosmetic industry sites, Eucalyptus camaldulensis depicted higher carbon, stock, and sequestration values (7.83 and 28.70 Mg ha−1). In comparison, the sites outside the industries’ vicinity depicted overall lower carbon, stock, and sequestration values. The most dominant tree inside came out to be Dalbergia sisso (0.97 and 3.54 Mg ha−1) in the weaving industry sites, having higher values of carbon stock and carbon sequestration. Moringa oliefra (1.26 and 4.63) depicted dominant values in brick kiln sites, while in the cosmetic industry, Vachellia nilotica (2.51 and 9.19 Mg ha−1) displayed maximum values as compared with other species. The findings regarding belowground biomass and carbon storage indicate that the amount of soil carbon decreased with the increase in depth; higher soil carbon stock values were depicted at a 0–20 cm depth inside and outside the industries. The study concludes that forest tree species present inside and outside the vicinity of various industries have strong potential in mitigating air emissions