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2022 ◽  
Vol 22 (2) ◽  
pp. 1-27
Author(s):  
Tingmin Wu ◽  
Wanlun Ma ◽  
Sheng Wen ◽  
Xin Xia ◽  
Cecile Paris ◽  
...  

Computer users are generally faced with difficulties in making correct security decisions. While an increasingly fewer number of people are trying or willing to take formal security training, online sources including news, security blogs, and websites are continuously making security knowledge more accessible. Analysis of cybersecurity texts from this grey literature can provide insights into the trending topics and identify current security issues as well as how cyber attacks evolve over time. These in turn can support researchers and practitioners in predicting and preparing for these attacks. Comparing different sources may facilitate the learning process for normal users by creating the patterns of the security knowledge gained from different sources. Prior studies neither systematically analysed the wide range of digital sources nor provided any standardisation in analysing the trending topics from recent security texts. Moreover, existing topic modelling methods are not capable of identifying the cybersecurity concepts completely and the generated topics considerably overlap. To address this issue, we propose a semi-automated classification method to generate comprehensive security categories to analyse trending topics. We further compare the identified 16 security categories across different sources based on their popularity and impact. We have revealed several surprising findings as follows: (1) The impact reflected from cybersecurity texts strongly correlates with the monetary loss caused by cybercrimes, (2) security blogs have produced the context of cybersecurity most intensively, and (3) websites deliver security information without caring about timeliness much.


2024 ◽  
Vol 84 ◽  
Author(s):  
F. Awan ◽  
M. M. Ali ◽  
I. Q. Afridi ◽  
S. Kalsoom ◽  
S. Firyal ◽  
...  

Abstract The present study involves the chemical and bacteriological analysis of water from different sources i.e., bore, wells, bottle, and tap, from Peshawar, Mardan, Swat and Kohat districts of Khyber Pakhtunkhwa (KP) province, Pakistan. From each district, 50 water samples (10 samples from each source), regardless of urban and rural status, were collected from these sources and analysed for sulphates, nitrates, nitrites, chlorides, total soluble solids and coliforms (E. coli). Results indicated that majority of the water sources had unacceptable E. coli count i.e.> 34 CFU/100mL. E. coli positive samples were high in Mardan District, followed by Kohat, Swat and Peshawar district. Besides this, the some water sources were also chemically contaminated by different inorganic fertilizers (nitrates/nitrites of sodium, potassium) but under safe levels whereas agricultural and industrial wastes (chloride and sulphate compounds) were in unsafe range. Among all districts, the water quality was found comparatively more deteriorated in Kohat and Mardan districts than Peshawar and Swat districts. Such chemically and bacteriologically unfit water sources for drinking and can cause human health problems.


Handwritten documents in an Enterprise Resource Planning (ERP) system can come from different sources and usually have different designs, sizes, and subjects (i.e. bills, checks, invoices, etc.). Given these documents were filled manually, they have to be inspected to detect various kinds of issues (missing signature or stamp, missing name, etc.) before being saved in the ERP system or processed by an OCR engine. In this paper, the authors present a transfer learning approach to detect issues in scanned handwritten documents, using an award-winning deep convolutional neural network (InceptionV3) and different machine learning algorithms such as Logistic Regression (LR), Support Vector Machine (SVM) and Naive Bayes (NB). The experiment shows that the combination of InceptionV3 and LR got an accuracy of 91.8% for missing stamp detection. This can allow using this approach in an ERP system as an automatic verification procedure in a document processing flow.


Author(s):  
Jadli Aissam ◽  
Mustapha Hain ◽  
Adil Chergui

Handwritten documents in an Enterprise Resource Planning (ERP) system can come from different sources and usually have different designs, sizes, and subjects (i.e. bills, checks, invoices, etc.). Given these documents were filled manually, they have to be inspected to detect various kinds of issues (missing signature or stamp, missing name, etc.) before being saved in the ERP system or processed by an OCR engine. In this paper, the authors present a transfer learning approach to detect issues in scanned handwritten documents, using an award-winning deep convolutional neural network (InceptionV3) and different machine learning algorithms such as Logistic Regression (LR), Support Vector Machine (SVM) and Naive Bayes (NB). The experiment shows that the combination of InceptionV3 and LR got an accuracy of 91.8% for missing stamp detection. This can allow using this approach in an ERP system as an automatic verification procedure in a document processing flow.


2022 ◽  
Vol 12 ◽  
Author(s):  
Faez Iqbal Khan ◽  
Fakhrul Hassan ◽  
Dakun Lai

Various metabolites identified with therapeutic mushrooms have been found from different sources and are known to have antibacterial, antiviral, and anticancer properties. Over thousands soil growth-based mushroom metabolites have been discovered, and utilized worldwide to combat malignancy. In this study, psilocybin-mushroom that contains the psychedelic compounds such as psilacetin, psilocin, and psilocybine were screened and found to be inhibitors of SARS-CoV-2 Mprotease. It has been found that psilacetin, psilocin, and psilocybine bind to Mprotease with −6.0, −5.4, and −5.8 kcal/mol, respectively. Additionally, the psilacetin was found to inhibit human interleukin-6 receptors to reduce cytokine storm. The binding of psilacetin to Mprotease of SARS-CoV-2 and human interleukin-6 receptors changes the structural dynamics and Gibbs free energy patterns of proteins. These results suggested that psilocybin-mushroom could be utilized as viable potential chemotherapeutic agents for SARS-CoV-2.


2022 ◽  
Vol 12 ◽  
Author(s):  
Xitong Liu ◽  
Stephen E. Strelkov ◽  
Rifei Sun ◽  
Sheau-Fang Hwang ◽  
Rudolph Fredua-Agyeman ◽  
...  

Clubroot is a serious soil-borne disease of crucifers caused by the obligate parasite Plasmodiophora brassicae. The genetic basis and histopathology of clubroot resistance in two Chinese cabbage (Brassica rapa ssp. pekinensis) inbred lines Bap055 and Bap246, challenged with pathotype 4 of P. brassicae, was evaluated. The Chinese cabbage cultivar “Juxin” served as a susceptible check. The resistance in Bap055 was found to be controlled by the CRa gene, while resistance in Bap246 fit a model of control by unknown recessive gene. Infection of the roots by P. brassicae was examined by inverted microscopy. Despite their resistance, primary and secondary infection were observed to occur in Bap055 and Bap246. Primary infection was detected at 2 days post-inoculation (DPI) in “Juxin,” at 4 DPI in Bap055, and at 6 DPI in Bap246. Infection occurred most quickly on “Juxin,” with 60% of the root hairs infected at 10 DPI, followed by Bap055 (31% of the root hairs infected at 12 DPI) and Bap246 (20% of the root hairs infected at 14 DPI). Secondary infection of “Juxin” was first observed at 8 DPI, while in Bap055 and Bap246, secondary infection was first observed at 10 DPI. At 14 DPI, the percentage of cortical infection in “Juxin,” Bap055 and Bap246 was 93.3, 20.0, and 11.1%, respectively. Although cortical infection was more widespread in Bap055 than in Bap246, secondary infection in both of these hosts was restricted relative to the susceptible check, and the vascular system remained intact. A large number of binucleate secondary plasmodia were observed in “Juxin” and the vascular system was disrupted at 16 DPI; in Bap055 and Bap246, only a few secondary plasmodia were visible, with no binucleate secondary plasmodia. The defense mechanisms and expression of resistance appears to differ between Chinese cabbage cultivars carrying different sources of resistance.


Author(s):  
Prof. (Dr.) S. M. Safdar Ashraf ◽  

Background: The environmental pollution is a growing world problem specifically in developed & developing countries. In these areas G.I.T disorders & diarrheal diseases have replaced by airborne environmental disorders.Methods: Literatures was reviewed on the subject to find out the knowledge regarding Environmental threats of air pollution & its effects on the health of human body. Data and details have been located, selected, extracted and synthesized from different national & international Journals, websites, Proceedings, books, google scholar etc.Result & Conclusion: Changes are taking place in air regularly. Different pollutants are being created from different sources. Indoor air pollution is common among underdeveloped & developing nations. To improve health situation different professional, have to play their roles. Effects of air pollution are sometimes general in nature otherwise may be immediate or delayed. Leading causes of death is cardiovascular diseases like IHD are now being declared as airborne. Emergent airborne diseases are more than 30 like COVID 19 only result in 43.6 lakhs death so far. Toxic & hazardous chemicals are present in air in the form of allergens, neurotoxin, mutagen, carcinogen etc. Air pollutants are carbon monoxide, nitrogen oxides, Benzene, Ozone, Lead, sulphates, SPM etc. Meteorological effects on health are also related with air including season, atmospheric pressure, heat, cold etc. Indoor air pollutants have also specific health effects.


BMC Medicine ◽  
2022 ◽  
Vol 20 (1) ◽  
Author(s):  
Chun Zhou ◽  
Chengzhang Liu ◽  
Zhuxian Zhang ◽  
Mengyi Liu ◽  
Yuanyuan Zhang ◽  
...  

Abstract Background The relation of the variety and quantity of different sources of dietary proteins intake and diabetes remains uncertain. We aimed to investigate the associations between the variety and quantity of proteins intake from eight major food sources and new-onset diabetes, using data from the China Health and Nutrition Survey (CHNS). Methods 16,260 participants without diabetes at baseline from CHNS were included. Dietary intake was measured by three consecutive 24-h dietary recalls combined with a household food inventory. The variety score of protein sources was defined as the number of protein sources consumed at the appropriate level, accounting for both types and quantity of proteins. New-onset diabetes was defined as self-reported physician-diagnosed diabetes or fasting glucose ≥7.0mmol/L or glycated hemoglobin ≥6.5% during the follow-up. Results During a median follow-up of 9.0 years, 1100 (6.8%) subjects developed diabetes. Overall, there were U-shaped associations of percentages energy from total protein, whole grain-derived and poultry-derived proteins with new-onset diabetes; J-shaped associations of unprocessed or processed red meat-derived proteins with new-onset diabetes; a reverse J-shaped association of the fish-derived protein with new-onset diabetes; L-shaped associations of egg-derived and legume-derived proteins with new-onset diabetes; and a reverse L-shaped association of the refined grain-derived protein with new-onset diabetes (all P values for nonlinearity<0.001). Moreover, a significantly lower risk of new-onset diabetes was found in those with a higher variety score of protein sources (per score increment; HR, 0.69; 95%CI, 0.65–0.72). Conclusions There was an inverse association between the variety of proteins with appropriate quantity from different food sources and new-onset diabetes.


2022 ◽  
pp. 0272989X2110699
Author(s):  
Thomas Allen ◽  
Dorte Gyrd-Hansen ◽  
Søren Rud Kristensen ◽  
Anne Sophie Oxholm ◽  
Line Bjørnskov Pedersen ◽  
...  

Background Many physicians are experiencing increasing demands from both their patients and society. Evidence is scarce on the consequences of the pressure on physicians’ decision making. We present a theoretical framework and predict that increasing pressure may make physicians disregard societal welfare when treating patients. Setting We test our prediction on general practitioners’ antibiotic-prescribing choices. Because prescribing broad-spectrum antibiotics does not require microbiological testing, it can be performed more quickly than prescribing for narrow-spectrum antibiotics and is therefore often preferred by the patient. In contrast, from a societal perspective, inappropriate prescribing of broad-spectrum antibiotics should be minimized as it may contribute to antimicrobial resistance in the general population. Methods We combine longitudinal survey data and administrative data from 2010 to 2017 to create a balanced panel of up to 1072 English general practitioners (GPs). Using a series of linear models with GP fixed effects, we estimate the importance of different sources of pressure for GPs’ prescribing. Results We find that the percentage of broad-spectrum antibiotics prescribed increases by 6.4% as pressure increases on English GPs. The link between pressure and prescribing holds for different sources of pressure. Conclusions Our findings suggest that there may be societal costs of physicians working under pressure. Policy makers need to take these costs into account when evaluating existing policies as well as when introducing new policies affecting physicians’ work pressure. An important avenue for further research is also to determine the underlying mechanisms related to the different sources of pressure.JEL-code: I11, J28, J45 Highlights Many physicians are working under increasing pressure. We test the importance of pressure on physicians’ prescribing of antibiotics. The prescribed rate of broad-spectrum antibiotics increases with pressure. Policy makers should be aware of the societal costs of pressured physicians. [Formula: see text]


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