Electrical transport and optical studies of ferromagnetic Cobalt doped ZnO nanoparticles exhibiting a metal-insulator transition

The observed correlation of oxygen vacancies and room temperature ferromagnetic ordering in Co doped ZnO1-o nanoparticles reported earlier (Naeem et al Nanotechnology 17, 2675-2680) has been further explored by transport and optical measurements. In these particles room temperature ferromagnetic ordering had been observed to occur only after annealing in forming gas. In the current work the optical properties have been studied by diffuse reflection spectroscopy in the UV-Vis region and the band gap of the Co doped compositions has been found to decrease with Co addition. Reflections minima are observed at the energies characteristic of Co+2 d-d (tethrahedral symmetry) crystal field transitions, further establishing the presence of Co in substitutional sites. Electrical transport measurements on palletized samples of the nanoparticles show that the effect of a forming gas is to strongly decrease the resistivity with increasing Co concentration. For the air annealed and non-ferromagnetic samples the variation in the resistivity as a function of Co content are opposite to those observed in the particles prepared in forming gas. The ferromagnetic samples exhibit an apparent change from insulator to metal with increasing temperatures for T>380K and this change becomes more pronounced with increasing Co content. The magnetic and resistive behaviors are correlated by considering the model by Calderon et al [M. J. Calderon and S. D. Sarma, Annals of Physics 2007 (Accepted doi: 10.1016/j.aop.2007.01.010] where the ferromagnetism changes from being mediated by polarons in the low temperature insulating region to being mediated by the carriers released from the weakly bound states in the higher temperature metallic region.Comment: 7 pages, 6 figure

The impact of different weed management systems on weed flora and dry biomass production of barley grown under various barley-based cropping systems

Weeds are among the major issues responsible for reduction in yield and profit in any crop production system. Herbicides are the easiest and quickest solution of weeds; however, their frequent use exert negative consequences on environment, human health, and results in the evolution of herbicide-resistant weed species. Due to these reasons, alternative weed management methods that are less harmful to environment and human health are needed. This two-year study evaluated the impact of different weed management options, i.e., false seedbed (FS), allelopathic water extracts (AWE), chemical control (CC), weed-free (WF) weedy-check (WC) on weed spectrum in various barley-based cropping systems, i.e., fallow-barley (FB), maize-barley (MB), cotton-barley (CB), mungbean-barley (M*B), and sorghum-barley (SB). Data relating to density, diversity, and biomass production of weed species prevailing in the studied cropping systems were recorded. Interactive effect of weed management methods and barley-based cropping systems significantly altered weed diversity, and densities of individual, broadleaved, and grassy weeds. A total 13 weed species (ten broadleaved and three grass) were recorded during both years of study. The highest dry biomass, diversity, and density of individual, broadleaved, and grassy weeds were noted in WC treatment, whereas WF treatment resulted in the lowest values of these traits. Chemical control resulted in the highest suppression of weed flora and improved dry biomass production of barley followed by AWE. The SB cropping system with CC or AWE resulted in the least weed flora. The M*B cropping system with CC or AWE produced the highest dry biomass of barley. It is concluded that including sorghum crop in rotation and applying AWE could suppress weeds comparable to herbicides. Similarly, including mungbean in rotation and applying AWE could increase dry biomass production of barley. In conclusion, herbicides can be replaced with an eco-friendly approach, i.e., allelopathy and inclusion of sorghum crop could be helpful in suppressing weed flora

Semi-leptonic Ds+D_s^+(1968) decays as a scalar meson probe

The unusual multiplet structures associated with the light spin zero mesons have recently attracted a good deal of theoretical attention. Here we discuss some aspects associated with the possibility of getting new experimental information on this topic from semi-leptonic decays of heavy charged mesons into an isosinglet scalar or pseudoscalar plus leptons.Comment: 11 pages, 4 figure

Cyber-threat detection system using a hybrid approach of transfer learning and multi-model image representation

Currently, Android apps are easily targeted by malicious network traffic because of their constant network access. These threats have the potential to steal vital information and disrupt the commerce, social system, and banking markets. In this paper, we present a malware detection system based on word2vec-based transfer learning and multi-model image representation. The proposed method combines the textual and texture features of network traffic to leverage the advantages of both types. Initially, the transfer learning method is used to extract trained vocab from network traffic. Then, the malware-to-image algorithm visualizes network bytes for visual analysis of data traffic. Next, the texture features are extracted from malware images using a combination of scale-invariant feature transforms (SIFTs) and oriented fast and rotated brief transforms (ORBs). Moreover, a convolutional neural network (CNN) is designed to extract deep features from a set of trained vocab and texture features. Finally, an ensemble model is designed to classify and detect malware based on the combination of textual and texture features. The proposed method is tested using two standard datasets, CIC-AAGM2017 and CICMalDroid 2020, which comprise a total of 10.2K malware and 3.2K benign samples. Furthermore, an explainable AI experiment is performed to interpret the proposed approach

The Effect of Fish Size and Condition on the Contents of Twelve Essential and Non Essential Elements in Aristichthys nobilis

The correlation coefficients between fish size (body weight and total length) and metal contents (Na, K, Ca, Mg, Mn, Fe, Cu, Zn, Cr, Co, Cd and Pb) in whole fish (Aristichthys nobilis) were determined. A total of 71 fish samples were collected from hatcheries and fish reservoirs located in Islamabad and Fatehjung. Highly significant (P<0.001) relationship between metal concentrations and fish size was found. Most of the metals (Na, K, Ca, Mg, Cu, Zn, Cr, Cd and Pb) showed an isometric increase, while Mn, Fe and Co showed an allometric increase in with increasing body weight. All metals showed isometric increase, while, Na, Mn, Fe, Cu, and Co showed positive allometric growth in relation to total length. The correlation coefficient (r) between different variables and wet body weight, condition factor was found highly significant (P<0.001) in examined fish except for Na, Ca, Cu, Zn, Cd and Pb while for total length the same results found except Ca, Cd, Zn and Pb. Variance inflation factor values of regression coefficients in multiple regression analysis for each variable were lesser than 10. The metal levels of the examined fish were lower than the recommended values in fish and fishery products set by FAO

Acute-on-chronic Liver Failure: MELD Score 30-day Mortality Predictability and Etiology in a Pakistani Population

Background: Cirrhosis is a pathological condition that ultimately leads to liver failure. Acute on chronic liver failure (ACLF) has a high short term mortality rate. Viral hepatitis is the most common cause of liver failure in our local population. We carried out this study to identity the 30-day mortality and etiology of patients presenting with ACLF using Model for End-Stage Liver Disease (MELD) score predictability. Methodology: This was a descriptive case series, conducted at Sheikh Zayed Hospital, Lahore, Pakistan from January 31, 2018 to July 30, 2018. One hundred and eighty five patients who met the inclusion criteria were enrolled using 95% confidence level and 4% margin of error. Data was entered and analyzed with SPSS version 23.0. Numerical variables including age was presented by Mean ± S.D. Categorical variables i.e. gender, etiology of acute-on-chronic liver failure and 30-day mortality were presented by frequency and percentage. Data was stratified for age, gender, duration of chronic liver disease and MELD grade to address the effect modifiers. Post-stratification chi-square test was calculated using 95% significance (p≤0.05). Results: Majority of the enrolled patients were male (74.6%) while only 25.4% of the patients were female. One hundred and thirty patients (70.3%) had underlying viral hepatitis while twelve patients (6.5%) and forty three patients (23.2%) presented with alcoholic liver disease and drug-induced ACLF, respectively. Eighty patients (43.2%) died within 30 days of admission.The 30-day mortality with respect to MELD grade was statistically significant (p&lt;0.001) with the highest mortality noted in grade-IV and thirty five patients (43.8%) dying within 30 days of admission (p&lt;0.001). Grade-II and III MELD scores also contributed to the 30-day mortality with twenty three patients (28.8%) and nineteen patients (23.8%) dying within 30 days of admission (p&lt;0.001). Conclusion: MELD scores are able to accurately predict the short-term mortality in patients with ACLF and viral hepatitis was the most common etiology in our population. Early detection and use of appropriate prognostic models may alleviate mortality and morbidity in paitents with ACLF

A novel augmented deep transfer learning for classification of COVID-19 and other thoracic diseases from X-rays

Deep learning has provided numerous breakthroughs in natural imaging tasks. However, its successful application to medical images is severely handicapped with the limited amount of annotated training data. Transfer learning is commonly adopted for the medical imaging tasks. However, a large covariant shift between the source domain of natural images and target domain of medical images results in poor transfer learning. Moreover, scarcity of annotated data for the medical imaging tasks causes further problems for effective transfer learning. To address these problems, we develop an augmented ensemble transfer learning technique that leads to significant performance gain over the conventional transfer learning. Our technique uses an ensemble of deep learning models, where the architecture of each network is modified with extra layers to account for dimensionality change between the images of source and target data domains. Moreover, the model is hierarchically tuned to the target domain with augmented training data. Along with the network ensemble, we also utilize an ensemble of dictionaries that are based on features extracted from the augmented models. The dictionary ensemble provides an additional performance boost to our method. We first establish the effectiveness of our technique with the challenging ChestXray-14 radiography data set. Our experimental results show more than 50% reduction in the error rate with our method as compared to the baseline transfer learning technique. We then apply our technique to a recent COVID-19 data set for binary and multi-class classification tasks. Our technique achieves 99.49% accuracy for the binary classification, and 99.24% for multi-class classification

Barley-Based cropping systems and weed control strategies influence weed infestation, soil properties and barley productivity

Barley-based cropping systems (BCS) alter barley production by influencing weed infestation rates and soil nutrient dynamics. This two-year field study evaluated the interactive effects of five BCS and five weed control strategies (WCS) on soil properties and the growth and yield of barley. Barley was planted in five different cropping systems, i.e., fallow-barley (FB), maize-barley (MaB), cotton-barley (CB), mungbean-barley (MuB) and sorghum-barley (SB). Similarly, five different WCS, weed-free (control, WF), weedy-check (control, WC), false seedbeds (FS), chemical control (CC) and use of allelopathic water extracts (AWE), were included in the study. The SB system had the highest soil bulk density (1.48 and 1.47 g cm−3 during the period 2017–2018 and 2018–2019, respectively) and lowest total soil porosity (41.40 and 41.07% during the period 2017–2018 and 2018–2019, respectively). However, WCS remained non-significant for bulk density and total soil porosity during both years of the study. Barley with WF had a higher leaf area index (5.28 and 4.75) and specific leaf area (65.5 and 64.9 cm−2 g−1) compared with barley grown under WC. The MuB system under WC had the highest values of extractable NH4-N (5.42 and 5.58 mg kg−1), NO3-N (5.79 and 5.93 mg kg−1), P (19.9 and 19.5 mg kg−1), and K (195.6 and 194.3 mg kg−1) with statistically similar NO3-N in the MaB system under WC and extractable K in the MuB system under FS. Grain yield ranged between 2.8–3.2 and 2.9–3.3 t ha−1 during the period 2017–2018 and 2018–2019, respectively, among different WCS. Similarly, grain yield ranged between 2.9–3.2 and 3.0–3.2 t ha−1 during the period 2017–2018 and 2018–2019, respectively, within different BCS. Among WCS, the highest grain yield (3.29 and 3.32 t ha−1) along with yield-related traits of barley were in WF as compared to WC. Overall, MuB system recorded better yield and yield-related traits, whereas the lowest values of these traits were recorded for FB systems. In conclusion, the MuB system with WF improved soil characteristics and barley yield over other cropping systems. The AWE significantly suppressed weeds and was equally effective as the chemical control. Therefore, MuB and AWE could be used to improve barley productivity and suppress weeds infestation