Developing and Building Ontologies in Cyber Security

Cyber Security is one of the most arising disciplines in our modern society. We work on Cybersecurity domain and in this the topic we chose is Cyber Security Ontologies. In this we gather all latest and previous ontologies and compare them on the basis of different analyzing factors to get best of them. Reason to select this topic is to assemble different ontologies from different era of time. Because, researches that included in this SLR is mostly studied single ontology. If any researcher wants to study ontologies, he has to study every single ontology and select which one is best for his research. So, we assemble different types of ontology and compare them against each other to get best of them. A total 24 papers between years 2010-2020 are carefully selected through systematic process and classified accordingly. Lastly, this SLR have been presented to provide the researchers promising future directions in the domain of cybersecurity ontologies.Comment: 8 pages, 2 figure

Jaundice: a basic review

Jaundice is a complex disease. Jaundice is actually the high bilirubin level in the body. Yellowing of skin, mucous membranes and skin are common presentations of jaundice. Jaundice has various variants including pre-hepatic jaundice (due to hemolysis of red blood cells), hepatic jaundice (due to defect in capture, conjugation and excretion of bilirubin by liver) and post hepatic jaundice (due to the obstruction of extra hepatobiliary system). The causes of various variants of Jaundice is either acquired or congenital. High plasma bilirubin level can cause various manifestations involving satiety, gastrointestinal bleeding, diarrhea, anemia, edema, weight-loss and can be fatal because it can cause psychosis, lethargy, seizures, coma or even death. High bilirubin level can help in the diagnosis of Jaundice. Differential diagnosis of various variants of Jaundice can be carried out on the basis of bilirubin level (conjugated and unconjugated), ultrasonography and other radiological techniques. The proper management of Jaundice is high water intake and low fat diet. The primary effective treatment for pre-hepatic jaundice and neonatal physiological jaundice is phototherapy. Infusion of immunoglobulins is also used for treatment of pre-hepatic jaundice. Proper nutrition, steroids and immunosuppressant are used for treatment of hepatic jaundice. The treatment for post hepatic jaundice is decompression and surgery

Misuse of Antibiotics in Poultry Threatens Pakistan Communitys Health

A survey was conducted from February 2022 to May 2022 on the usage of antibiotics at a poultry farm in different areas of Multan, Punjab Pakistan. A well-organized questionnaire was used for the collection of data. Sixty poultry farms were surveyed randomly in the Multan district. All of these Farms were using antibiotics. Antibiotics are commonly used for the treatment of diseases. Some are used as preventive medicine and a few are used as growth promotors. neomycin, erythromycin, oxytetracycline, streptomycin, and colistin are the broad-spectrum antibiotics that are being used commercially. Enrofloxacin and Furazolidone are the common antibiotics that are being used in Studies these days. The class of Fluoroquinolones is commonly used in poultry farms. Thirty-three patterns of antibiotic usage were observed at poultry farms. multi-drug practices were also observed on various farms. In this study, 25% of antibiotics are prescribed by the veterans while more than 90 % were acquired from the veterinary store. This study provides information about the antibiotics which are commonly being used in the study location district Multan. It is expected that the finding of this survey will be helpful in the development of new strategies against the misuse of antibiotics on farms

Relationship between Marketing Strategies and Firms’ Financial Performance in Food Producers Sector of Pakistan

Study reflects the effect of marketing strategies on the firm’s financial performance. Marketing strategy is not just evaluating the external and internal factors, but it also needs to be financed efficiently to develop an attractive product and distribution channel, and to hire an effective sales team to generate business support for the firm. The study incorporates secondary data of 14 firms of Food Producers Sector for the period of five year from 2009 to 2013. The study compared low marketing cost firms and high marketing cost firms in the terms of their sales revenue and financial performance. The findings of this research paper contribute to marketing theories, by using the marketing expense as a variable to know the influence on financial performance of a firm. Overall descriptive and econometric results suggest that firms can achieve financial performance through appropriate marketing strategy. The study is a contribution in the field of marketing research and provides managers useful insight in their own strategic decisions. Keywords: Financial Performance, Low and High Marketing Costs Firms, Sales Revenue, Selling and Marketing Expenses. JEL Classification: M31, G3

Phytoremediation of heavy metals from industrially contaminated soil using sunflower (Helianthus annus L.) by inoculation of two indigenous bacteria

The phytoremediation technique is gaining excessive consideration as a promising method to remediate industrially contaminated soils with heavy metals. In this study, a pot experiment was performed in which the ornamental plant Helianthus annus L. was grown in the pots with three concentrations i.e., 0, 5, and 10 % of contaminated soil amended with compost (2 %) in all pots of the experiment following three treatments of bacteria i.e., Co, Stutzerimonas stutzeri and Pseudomonas sundara. After sixty days of the experiment, the plants were harvested and morphological, physiological, antioxidants and pollution parameters were investigated. The plant height and biomass of the sunflower were increased by the inoculation of S. stutzeri and P. sundara. Pigments e.g., chlorophyll a, b, carotenoids and proteins of the plants were enhanced. There was an increase in antioxidants e.g., catalase, peroxidase, ascorbate peroxidase and proline content and a decrease in hydrogen peroxide content of plants by inoculation of S. stutzeri and P. sundara. The bacteria boosted the uptake of heavy metals (cadmium, chromium and lead) in parts of plants. Post-harvested soil analysis indicated decreased electronic conductivity, total dissolved solids, bicarbonates and heavy metals in the soil. In the future, the combination of H. annus and bacteria could be a better technique to remediate the heavy metals in industrially polluted soils

Impact of silver substitution on the structural, magnetic, optical, and antibacterial properties of cobalt ferrite

Abstract Silver-doped Cobalt Ferrite nanoparticles AgxCo1−xFe2O4 with concentrations (x = 0, 0.05, 0.1, 0.15) have been prepared using a hydrothermal technique. The XRD pattern confirms the formation of the spinel phase of CoFe2O4 and the presence of Ag ions in the spinel structure. The spinel phase AgxCo1−xFe2O4 nanoparticles are confirmed by FTIR analysis by the major bands formed at 874 and 651 cm−1, which represent the tetrahedral and octahedral sites. The analysis of optical properties reveals an increase in band gap energy with increasing concentration of the dopant. The energy band gap values depicted for prepared nanoparticles with concentrations x = 0, 0.05, 0.1, 0.15 are 3.58 eV, 3.08 eV, 2.93 eV, and 2.84 eV respectively. Replacement of the Co2+ ion with the nonmagnetic Ag2+ ion causes a change in saturation magnetization, with Ms values of 48.36, 29.06, 40.69, and 45.85 emu/g being recorded. The CoFe2O4 and Ag2+ CoFe2O4 nanoparticles were found to be effective against the Acinetobacter Lwoffii and Moraxella species, with a high inhibition zone value of x = 0.15 and 8 × 8 cm against bacteria. It is suggested that, by the above results, the synthesized material is suitable for memory storage devices and antibacterial activity

An Efficient Deep Learning-Based Skin Cancer Classifier for an Imbalanced Dataset

Efficient skin cancer detection using images is a challenging task in the healthcare domain. In today’s medical practices, skin cancer detection is a time-consuming procedure that may lead to a patient’s death in later stages. The diagnosis of skin cancer at an earlier stage is crucial for the success rate of complete cure. The efficient detection of skin cancer is a challenging task. Therefore, the numbers of skilful dermatologists around the globe are not enough to deal with today’s healthcare. The huge difference between data from various healthcare sector classes leads to data imbalance problems. Due to data imbalance issues, deep learning models are often trained on one class more than others. This study proposes a novel deep learning-based skin cancer detector using an imbalanced dataset. Data augmentation was used to balance various skin cancer classes to overcome the data imbalance. The Skin Cancer MNIST: HAM10000 dataset was employed, which consists of seven classes of skin lesions. Deep learning models are widely used in disease diagnosis through images. Deep learning-based models (AlexNet, InceptionV3, and RegNetY-320) were employed to classify skin cancer. The proposed framework was also tuned with various combinations of hyperparameters. The results show that RegNetY-320 outperformed InceptionV3 and AlexNet in terms of the accuracy, F1-score, and receiver operating characteristic (ROC) curve both on the imbalanced and balanced datasets. The performance of the proposed framework was better than that of conventional methods. The accuracy, F1-score, and ROC curve value obtained with the proposed framework were 91%, 88.1%, and 0.95, which were significantly better than those of the state-of-the-art method, which achieved 85%, 69.3%, and 0.90, respectively. Our proposed framework may assist in disease identification, which could save lives, reduce unnecessary biopsies, and reduce costs for patients, dermatologists, and healthcare professionals

SARS-CoV-2 vaccination modelling for safe surgery to save lives: data from an international prospective cohort study

Background: Preoperative SARS-CoV-2 vaccination could support safer elective surgery. Vaccine numbers are limited so this study aimed to inform their prioritization by modelling. Methods: The primary outcome was the number needed to vaccinate (NNV) to prevent one COVID-19-related death in 1 year. NNVs were based on postoperative SARS-CoV-2 rates and mortality in an international cohort study (surgical patients), and community SARS-CoV-2 incidence and case fatality data (general population). NNV estimates were stratified by age (18-49, 50-69, 70 or more years) and type of surgery. Best- and worst-case scenarios were used to describe uncertainty. Results: NNVs were more favourable in surgical patients than the general population. The most favourable NNVs were in patients aged 70 years or more needing cancer surgery (351; best case 196, worst case 816) or non-cancer surgery (733; best case 407, worst case 1664). Both exceeded the NNV in the general population (1840; best case 1196, worst case 3066). NNVs for surgical patients remained favourable at a range of SARS-CoV-2 incidence rates in sensitivity analysis modelling. Globally, prioritizing preoperative vaccination of patients needing elective surgery ahead of the general population could prevent an additional 58 687 (best case 115 007, worst case 20 177) COVID-19-related deaths in 1 year. Conclusion: As global roll out of SARS-CoV-2 vaccination proceeds, patients needing elective surgery should be prioritized ahead of the general population