Intra-Ethnic Fragmentation and the Politics of Ethnically Decentralising Constitutional Change in Pakistan: A Comparative Study

Within the literature on the politics of ethnic conflict resolution via constitutional reforms, ethnic ‘majorities’ are often presumed to oppose constitutional forms of power-sharing (ethnic decentralisation). In this thesis, I challenge this assumption to show how institutionalised forms of divisions within the dominant ethnic groups in the ethnic majoritarian states of Pakistan, Indonesia and Fiji drove the political process of ethnic decentralisation, and how the absence of these divisions caused the political process of ethnic decentralisation to fail in Sri Lanka. Building upon fieldwork in Pakistan and Sri Lanka and, owing to the COVID-19 related restrictions, relying on secondary literature on Indonesia and Fiji, I argue that the politics of constitutional forms of ethnic decentralisation is driven by institutionalised forms of intra-ethnic divisions and when these divisions combine with or manifest as: (a) civil-military institutional tensions involving the political and military elites from within the dominant ethnic group as mutually competing ethnic factions, (b) social movements emerging from within the dominant ethnic group and pushing for, in alliance with the relevant elites from the civil-military equation, ethnic decentralisation, and (c) a politics of cross-ethnic, multi-party consensus involving the relevant political elites from within the dominant and non-dominant ethnic groups. Employing process tracing as my primary method of investigation, I show how these variables help produce, both individually and collectively, constitutional forms of ethnic decentralisation. When these factors temporally coexist and causally reinforce each other vis-à-vis constitutional forms of ethnic decentralisation, they drive the political process of ethnic decentralisation as a causal mechanism. When these factors do not coexist as a contingently linked causal mechanism, ethnic decentralisation, as the Sri Lankan case shows, fails to happen. I conclude: the persistence of an ethnic majoritarian system is tied, not to ‘majoritarian intransigence’ but to the absence of the identified causal mechanism of ethnic decentralisation

Impact of different moisture regimes and nitrogen rates on yield and yield attributes of maize (Zea mays L.)

Nitrogen and irrigation, both are essential to determine the yield and quality of maize (Zea mays L.). A field study was accomplished to determine the upshots of different levels of irrigation and varying nitrogen rates on yield, yield contributing attributes and radiation use efficiency (RUE) of maize hybrid on sandy clay loam soil. Different nitrogen rates and moisture regime treatments comprised of N0 = 0, N1 = 100 and N2 = 200kg N ha-1, I1 (25 mm water deficit), I2 (50 mm water deficit), I3 (three irrigations during vegetative development + one irrigation at tasseling stage) and I4 (three irrigations during vegetative development + one irrigation at tasseling stage + one irrigation at silking stage + one irrigation at grain filling stage), respectively. Results showed that maximum grain yield (7.04 t ha-1) was recorded when six irrigations were applied (three irrigations during vegetative development + one irrigation at tasseling stage + one irrigation at silking stage + one irrigation at grain filling stage) coupled with 200 kg N ha-1 (N2 × I4). The lowest grain yield (2.08 t ha-1) was obtained in response to 25 mm water deficits. Overall, N2 × I2 also gave a positive response in terms of yield attributes but highest plant height (160.80 cm), cob length (29.00 cm), number of grains per cob (308.33), 1000-grain weight (294.33 g) and biological yield (25.67 t ha-1) with maximum coefficient of correlation (R2) values (0.9399; 0.8851; 0.9161; 0.8743 and 0.9126), respectively, was attained with N2 × I4 treatment combinations. The superior (RUE) radiation use efficiency (5.33 g MJ-1) with higher R2 value (0.8821) was significantly affected by nitrogen rates and irrigation levels as obtained from N2 × I4 treatment. However, in all treatment combinations, N2 × I4 was superior by producing the highest maize grain yield.Keywords: Moisture regimes, nitrogen rates, deficit irrigation, Zea mays L., radiation use efficiency, maize yiel

Evaluation of seed priming on germination of Gladiolus alatus

Seed priming improves seed performance under environmental conditions. The study was designed to evaluate the effect of different priming treatments on germination behavior of Gladiolus alatus. The experiment was conducted under complete randomized design (CRD) with four replications. Seed priming was done with different concentration of potassium nitrate (KNO3) and hydropriming. All the treatments had significant effect on germination percentage, germination test in growth room, time for 50% germination and mean germination time. Results show that maximum invigoration was observed in seeds osmoprimed at lower concentrations of KNO3 and with hydropriming while minimum invigoration was observed at higher concentration of KNO3-. It was concluded that germination percentage can be increased by using lower concentrations of KNO3 and with hydropriming.Key words: Priming, hydropriming, gladiolus, germination

Machine Vision Approach for Identification of Four Variant Pakistani Rice Using Multi-Features Dataset

Crops are the most important and beneficial food source in Pakistan. The demand for food has been an increase in Pakistan due to population growth. Pakistan produced 7,410 million tons of rice according to the financial year survey 2020 (FYS-2020). Pakistani rice has been cultivated in 3,304 hectares of the agricultural land zone, and it is also export around the world. Rice is also increased by 0.6% Gross Domestic Product (GDP) of Pakistan (FYS-2020). The old and manual process of rice classification is more expensive and time-consuming. In this study, we describe a machine vision approach for rice identification. We use four different varieties of rice for the experimental process such as Pakei_Kaynat, Kaynat_Kauchei, and Kauchei_Super_Banaspati and Tootaa_Kauchei (P1, P2, P3, and P4). The 100 images dataset have been used for practical work and total calculated of 400 (4 x 100) image of rice. The different process has been deploying on available datasets such as introduction, preprocessing methodology, and result discussion. A quality enhancement technique has been implementing for clarifying between rice color and shape sampling, and it is also converted color image in gray scale level. Every image has been employing six different non-overlapping regions of interest (ROI’s) and calculated a total of 2400 (6 x 400) ROI’s. Binary (B), Histogram (H) and Texture (T) features have been implemented and extract 43 features on each ROI’s and total calculated 103,200 (2400 x 43) machine learning (ML) features. Best First Search (BFS) Algorithm was used for feature optimization. Different ML classifiers are implementing for experimental process namely; Function Multi-Layer-Perception, Function SMO, Random Tree, J48 Tree, Meta Classifier via Regression and Meta Bagging. The Function Multi-Layer-Perception overall accuracy (OA) has describe better accuracy result is 99.8333%

In vitro and in vivo evaluation of different measures to control Ascochyta blight in chickpea

Ascochyta blight, an infection caused by Ascochyta rabiei is a destructive disease in many chickpea growing regions and it caused significant yield losses. To minimize the impact of Ascochyta blight, 5 fungicides viz., Aliette, Cabrio Top, Thiovit Jet, Cymoxanil and Difenoconazole, 5 plants extracts namely Azadirachta indica, Azadirachta azedarach, Datura stramonium, Chenopodium album and Allium sativum L. and two strains T-22 and E58 of bio-control agents (BCAs) Trichoderma viride and Aspergillus flavus were evaluated on the growth of A. rabiei under in vitro conditions by using the food poison technique. The colony growth of Ascochyta rabiei was inhibited at all concentrations of fungicides @ 0.07, 0.15, 0.21%, plants extracts @ 4, 6, 9% and bio-control agents @ 105, 106 and 107 conidia ml-1 respectively. Among all applied treatments, maximum inhibition colony growth of pathogen was recorded in the case of Aliette (83.4%), followed by Cabrio Top (74.3%), Azadirachta indica (50.3%) and Trichoderma viride (60.3%) at their high concentrations. Field trials showed that Aliette and Cabario Top significantly reduced the disease severity to 10 % and 24% respectively, followed by Azadirachta indica and Allium sativum which reduced the disease severity to 40% and 50% respectively. Bio-control agent Trichoderma viride proved less effective in controlling Ascochyta bight severity under field conditions. The present study showed that systemic and sulphur containing fungicides, plant extracts and bio-control agents (BCAs) have the potential to control Ascochyta blight in both in vitro and in vivo conditions

Big Data Management in Drug–Drug Interaction: A Modern Deep Learning Approach for Smart Healthcare

The detection and classification of drug–drug interactions (DDI) from existing data are of high importance because recent reports show that DDIs are among the major causes of hospital-acquired conditions and readmissions and are also necessary for smart healthcare. Therefore, to avoid adverse drug interactions, it is necessary to have an up-to-date knowledge of DDIs. This knowledge could be extracted by applying text-processing techniques to the medical literature published in the form of ‘Big Data’ because, whenever a drug interaction is investigated, it is typically reported and published in healthcare and clinical pharmacology journals. However, it is crucial to automate the extraction of the interactions taking place between drugs because the medical literature is being published in immense volumes, and it is impossible for healthcare professionals to read and collect all of the investigated DDI reports from these Big Data. To avoid this time-consuming procedure, the Information Extraction (IE) and Relationship Extraction (RE) techniques that have been studied in depth in Natural Language Processing (NLP) could be very promising. Since 2011, a lot of research has been reported in this particular area, and there are many approaches that have been implemented that can also be applied to biomedical texts to extract DDI-related information. A benchmark corpus is also publicly available for the advancement of DDI extraction tasks. The current state-of-the-art implementations for extracting DDIs from biomedical texts has employed Support Vector Machines (SVM) or other machine learning methods that work on manually defined features and that might be the cause of the low precision and recall that have been achieved in this domain so far. Modern deep learning techniques have also been applied for the automatic extraction of DDIs from the scientific literature and have proven to be very promising for the advancement of DDI extraction tasks. As such, it is pertinent to investigate deep learning techniques for the extraction and classification of DDIs in order for them to be used in the smart healthcare domain. We proposed a deep neural network-based method (SEV-DDI: Severity-Drug–Drug Interaction) with some further-integrated units/layers to achieve higher precision and accuracy. After successfully outperforming other methods in the DDI classification task, we moved a step further and utilized the methods in a sentiment analysis task to investigate the severity of an interaction. The ability to determine the severity of a DDI will be very helpful for clinical decision support systems in making more accurate and informed decisions, ensuring the safety of the patients

An epidemiological, strategic and response analysis of the COVID-19 pandemic in South Asia: A population-based observational study

Introduction: South Asia has had a dynamic response to the ongoing COVID-19 pandemic. The overall burden and response have remained comparable across highly-burdened countries within the South Asian Region. Methodology: Using a population-based observational design, all eight South Asian countries were analyzed using a step-wise approach. Data were obtained from government websites and publicly-available repositories for population dynamics and key variables. Results: South Asian countries have a younger average age of their population. Inequitable distribution of resources centered in urban metropolitan cities within South Asia is present. Certain densely populated regions in these countries have better testing and healthcare facilities that correlate with lower COVID-19 incidence per million populations. Trends of urban-rural disparities are unclear given the lack of clear reporting of the gaps within these regions. COVID-19 vaccination lag has become apparent in South Asian countries, with the expected time to complete the campaign being unfeasible as the COVID-19 pandemic progresses. Conclusion: With a redesigning of governance policies on preventing the rise of COVID-19 promptly, the relief on the healthcare system and healthcare workers will allow for adequate time to roll out vaccination campaigns with equitable distribution. Capacity expansion of public health within the Region is required to ensure a robust healthcare response to the ongoing pandemic and future infectious disease outbreak

Understanding of final year medical-, pharmacy- and nursing students in Pakistan towards antibiotic use, antimicrobial resistance and stewardship : findings and implications

Antimicrobial resistance (AMR) is a leading public health threat, which is exacerbated by high and inappropriate use of antibiotics. Consequently, there is a need to evaluate knowledge regarding antibiotic use, AMR and their readiness to implement antimicrobial stewardship programmes (ASPs) among final year medical, pharmacy and nursing students in Pakistan. This reflects high and increasing rates of AMR in the country, and students being the future healthcare professionals (HCPs). A cross-sectional study was conducted among 1251 final year students from 23 public and private educational institutions in Punjab. The majority of the surveyed participants possessed good knowledge of antibiotic use, AMR and the potential causes of AMR. The most common sources of the information on antibiotics were physicians (69.9%), peers (35.9%) and medical journals (30.6%). However, most surveyed participants were not fully prepared to participate in ASPs. They knew though how to reduce AMR by educating HCPs about appropriate prescribing, implementing ASPs and improving laboratory facilities. There was a significant association between antibiotic knowledge and causes of AMR with sex, family income, and student type (p < 0.05). Being a student at a public sector university (OR= 4.809; CI= 3.261- 7.094; p<0.001), and age (OR=0.524, CI=0.327-0.842; p<0.008) were among key factors impacting students training on ASPs. Educational curricula must be improved to include more information about appropriate antibiotic use and ASPs along with sufficient training, workshops and clinical rotations in the final year to fully equip students on graduation

Biochemical testing for the diagnosis of Wilson\u27s disease: A systematic review

Background: Wilson\u27s disease (WD) is a rare inherited disorder that leads to copper accumulation in the liver, brain, and other organs. WD is prevalent worldwide, with an occurrence of 1 per 30,000 live births. Currently, there is no gold standard diagnostic test for WD. The objective of this systematic review is to determine the diagnostic accuracy for WD of three biochemical tests, namely hepatic copper, 24-hour urinary copper, and ceruloplasmin using the Leipzig criteria.Methods: Adhering to PRISMA guidelines, databases including PubMed/MEDLINE, CINAHL Plus, Web of Science, and Cochrane were searched. Studies that comprised of confirmed or suspected WD along with normal populations were included with adult and pediatric group. The sensitivity, specificity, negative predictive value and positive predictive value were computed using RevMan 5.4.Results: Nine studies were included. The best practice evidence for 24-hour urinary copper test ranged from a cutoff value of 0.64-1.6 μmol/24 h (N = 268; sensitivity = 75.6%, specificity = 98.3%). Hepatic copper test was optimally cutoff based on the ROC curve analysis at 1.2 μmol/g yielding a power of 96.4% sensitivity and 95.4% specificity (N = 1,150); however, the tried and tested 4 μmol/g cutoff, with 99.4% sensitivity and 96.1% specificity, is more widely accepted. The ceruloplasmin test cutoff value was found to be ranging from 0.14 to 0.2 g/L (N = 4,281; sensitivity = 77.1%-99%, specificity = 55.9%-82.8%).Conclusion: This paper provides a large-scale analysis of current evidence pertaining to the biochemical diagnosis of WD employing the Leipzig criteria. The laboratory values are typically based on specific subgroups based on age, ethnicity, and clinical subgroups. The findings of this systematic review must be used with caution, given the over- or under-estimation of the index tests

2-Benzenesulfonamidobenzoic acid

In the title compound, C13H11NO4S, the dihedral angle between the planes of the benzene ring and the carboxyl group is 13.7 (1)°. The mol­ecular structure contains intra­molecular N—H⋯O and C—H⋯O hydrogen-bonding inter­actions, while the crystal packing is stabilized by C—H⋯O and O—H⋯O hydrogen bonds and C—H⋯π inter­actions. The O—H⋯O hydrogen bonds form a cyclic dimer, with graph-set motif R 2 2(8), about a centre of symmetry