STOCK RETURNS PREDICTION BY USING ARTIFICIAL NEURAL NETWORK MODEL FOR PAKISTAN STOCK EXCHANGE

Artificial neural networks are extensively used to predict the financial time series. This study implements the neural network model for predicting the daily returns of the Pakistan Stock Exchange (PSE). Such an application for PSE is very rare. A multi-layer perception network is used for the model used in this study, while the network is trained using the Error Back Propagation algorithm. The results showed that the predictive power of the network was performed by the return of the previous day rather than the input of the first three days. Therefore, this study showed satisfactory results for PSE. In short, artificial intelligence can be used to give a better picture of stock market operators and can be used as an alternative or additional to predict financial variables

Measuring Efficiency of Hospitals by DEA: An Empirical Evidence from Pakistan

ABSTRACT There has been increasing focus on efficiency measurement in health sector around the world. This empirical study aims at measuring efficiency level of non-profit private organization by using Data Envelopment Analysis (DEA) in the health sector of Pakistan. DEA is non-parametric linear programming based approach for homogeneous decision making units. Layton Rehmatullah Benevolent Trust (non-profit private organization) will be subject matter for investigational analysis constituting over 16 sub-units spreading across the country. Secondary data of number of patient beds, specialists and nurses in all the 16 branches of LRBT hospitals will be used applying quantitative specification tool, both scale and technical efficiency levels in an environment where multiple of inputs and outputs are in place, will be used for final evaluation. The outcomes so expected will help policy makers to formulate effective plans and programs in order to enhance the efficiency of health measures conducted by non- profit private organizations.

STOCK RETURNS PREDICTION BY USING ARTIFICIAL NEURAL NETWORK MODEL FOR PAKISTAN STOCK EXCHANGE

Artificial neural networks are extensively used to predict the financial time series. This study implements the neural network model for predicting the daily returns of the Pakistan Stock Exchange (PSE). Such an application for PSE is very rare. A multi-layer perception network is used for the model used in this study, while the network is trained using the Error Back Propagation algorithm. The results showed that the predictive power of the network was performed by the return of the previous day rather than the input of the first three days. Therefore, this study showed satisfactory results for PSE. In short, artificial intelligence can be used to give a better picture of stock market operators and can be used as an alternative or additional to predict financial variables

Synthesis, spectral analysis and pharmacological study of N'- substituted-2-(5-((2,4-dimethylphenoxy)methyl)-1,3,4-oxadiazol-2-ylthio)acetohydrazides

A series of molecules bearing multiple functional groups were synthesized to study their antibiotic effect against Gram-positive and Gram-negative bacteria and lipoxygenase activity as well. 2,4-Dimethylcarbolic acid (1) was refluxed with ethyl 2-bromoacetate to synthesize ethyl 2-(2,4-dimethylphenoxy)acetate (2). Compound 2 was converted to the corresponding hydrazide 3, again on refluxing with hydrazine. The compound 5-((2,4-dimethylphenoxy)methyl)-1,3,4-oxadiazol-2-thiol (4) was synthesized by the reaction of 3 and CS2 in the presence of KOH. Compound 4 was further converted to the corresponding ester 5 and then 2-(5-((2,4-dimethylphenoxy)methyl)-1,3,4-oxadiazol-2-ylthio)acetohydrazide (6). The final molecules N'-substituted-2-(5-((2,4-dimethylphenoxy)methyl)-1,3,4-oxadiazol-2-ylthio)acetohydrazide, 8a-m, bearing ether, 1,3,4-oxadiazole, thioether, hydrazone and azomethine functional groups were synthesized by stirring the aryl carboxaldehydes 7a-m with 6 in methanol at room temperature. The depicted structures of all synthesized molecules were corroborated by IR, 1H-NMR and EIMS spectral data analysis. 8m and 8i showed substantial antibacterial activity and lipoxygenase inhibitory activity, respectively

Synthesis, Characterization, Antibacterial, α-Glucosidase Inhibition and Hemolytic Studies on Some New N-(2,3- Dimethylphenyl)benzenesulfonamide Derivatives

Purpose: To synthesize a series of new N-(2,3-dimethylphenyl)benzenesulfonamide derivatives with pharmacological analysis.Methods: N-(2,3-Dimethylphenyl)benzenesulfonamide (3) was synthesized by the reaction between 2,3-dimethylaniline (1) and benzenesulfonyl chloride (2) in aqueous basic medium. Compound 3 was further treated with various alkyl/aralakyl halides (4a-m) to yield new compounds, 5a-m, in a weak basic aprotic polar organic medium. The proposed structures of synthesized compounds were confirmed using proton-nuclear magnetic resonance (1H-NMR), infra red spectroscopy (IR) and electron impact mass spectrometry (EIMS). The synthesized compounds were screened for in vitro antibacterial, antienzymatic and hemolytic activities using standard procedures.Results: All the synthesized compounds showed moderate to high activity against Gram-positive and Gram-negative bacterial strains. The molecules 5g and 5j exhibited good inhibition of α-glucosidase enzyme with half-maximal inhibitory concentration (IC50) of 59.53 ± 0.01 and 55.31 ± 0.01 μmoles/L, respectively, relative to acarbose with IC50 of 38.25 ± 0.12 μmoles/L. All the compounds exhibited cytotoxicity levels ranging from 27.20 ± 0.24 to 5.20 ± 0.41 %, relative to Triton X-100.Conclusion: Compound 5f is the most potent antibacterial while 5j is the best α-glucosidase inhibitor; 5e showed the least cytotoxicity.Keywords: 2,3-Dimethylaniline, Antibacterial activity, Anti-enzymatic activity, α-Glucosidase inhibitor, Hemolytic activity, Sulfonamide

Biological activity of synthesized 5-{1-[(4-chlorophenyl)sulfonyl]piperidin-4- yl}-2-mercapto-1,3,4-oxadiazole derivatives demonstrated by in silico and BSA binding studies

We synthesized a series of compounds bearing pharmacologically important 1,3,4-oxadiazole and piperidine moieties. Spectral data analysis by 1 H-NMR, 13C-NMR, IR and EI-MS was used to elucidate the structures of the synthesized molecules. Docking studies explained the different types of interaction of the compounds with amino acids, while bovine serum albumin (BSA) binding interactions showed their pharmacological effectiveness. Antibacterial screening of these compounds demonstrated moderate to strong activity against Salmonella typhi and Bacillus subtilis but only weak to moderate activity against the other three bacterial strains tested. Seven compounds were the most active members as acetyl cholinesterase inhibitors. All the compounds presented displayed strong inhibitory activity against urease. Compounds 7l, 7m, 7n, 7o, 7p, 7r, 7u, 7v, 7x and 7v were highly active, with respective IC50 values of 2.14±0.003, 0.63±0.001, 2.17±0.006, 1.13±0.003, 1.21±0.005, 6.28±0.003, 2.39±0.005, 2.15±0.002, 2.26±0.003 and 2.14±0.002 µM, compared to thiourea, used as the reference standard (IC50 = 21.25±0.15 µM). These new urease inhibitors could replace existing drugs after their evaluation in comprehensive in vivo studies

The global burden of adolescent and young adult cancer in 2019 : a systematic analysis for the Global Burden of Disease Study 2019

Background In estimating the global burden of cancer, adolescents and young adults with cancer are often overlooked, despite being a distinct subgroup with unique epidemiology, clinical care needs, and societal impact. Comprehensive estimates of the global cancer burden in adolescents and young adults (aged 15-39 years) are lacking. To address this gap, we analysed results from the Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) 2019, with a focus on the outcome of disability-adjusted life-years (DALYs), to inform global cancer control measures in adolescents and young adults. Methods Using the GBD 2019 methodology, international mortality data were collected from vital registration systems, verbal autopsies, and population-based cancer registry inputs modelled with mortality-to-incidence ratios (MIRs). Incidence was computed with mortality estimates and corresponding MIRs. Prevalence estimates were calculated using modelled survival and multiplied by disability weights to obtain years lived with disability (YLDs). Years of life lost (YLLs) were calculated as age-specific cancer deaths multiplied by the standard life expectancy at the age of death. The main outcome was DALYs (the sum of YLLs and YLDs). Estimates were presented globally and by Socio-demographic Index (SDI) quintiles (countries ranked and divided into five equal SDI groups), and all estimates were presented with corresponding 95% uncertainty intervals (UIs). For this analysis, we used the age range of 15-39 years to define adolescents and young adults. Findings There were 1.19 million (95% UI 1.11-1.28) incident cancer cases and 396 000 (370 000-425 000) deaths due to cancer among people aged 15-39 years worldwide in 2019. The highest age-standardised incidence rates occurred in high SDI (59.6 [54.5-65.7] per 100 000 person-years) and high-middle SDI countries (53.2 [48.8-57.9] per 100 000 person-years), while the highest age-standardised mortality rates were in low-middle SDI (14.2 [12.9-15.6] per 100 000 person-years) and middle SDI (13.6 [12.6-14.8] per 100 000 person-years) countries. In 2019, adolescent and young adult cancers contributed 23.5 million (21.9-25.2) DALYs to the global burden of disease, of which 2.7% (1.9-3.6) came from YLDs and 97.3% (96.4-98.1) from YLLs. Cancer was the fourth leading cause of death and tenth leading cause of DALYs in adolescents and young adults globally. Interpretation Adolescent and young adult cancers contributed substantially to the overall adolescent and young adult disease burden globally in 2019. These results provide new insights into the distribution and magnitude of the adolescent and young adult cancer burden around the world. With notable differences observed across SDI settings, these estimates can inform global and country-level cancer control efforts. Copyright (C) 2021 The Author(s). Published by Elsevier Ltd.Peer reviewe

Global, regional, and national burden of disorders affecting the nervous system, 1990–2021: a systematic analysis for the Global Burden of Disease Study 2021

BackgroundDisorders affecting the nervous system are diverse and include neurodevelopmental disorders, late-life neurodegeneration, and newly emergent conditions, such as cognitive impairment following COVID-19. Previous publications from the Global Burden of Disease, Injuries, and Risk Factor Study estimated the burden of 15 neurological conditions in 2015 and 2016, but these analyses did not include neurodevelopmental disorders, as defined by the International Classification of Diseases (ICD)-11, or a subset of cases of congenital, neonatal, and infectious conditions that cause neurological damage. Here, we estimate nervous system health loss caused by 37 unique conditions and their associated risk factors globally, regionally, and nationally from 1990 to 2021.MethodsWe estimated mortality, prevalence, years lived with disability (YLDs), years of life lost (YLLs), and disability-adjusted life-years (DALYs), with corresponding 95% uncertainty intervals (UIs), by age and sex in 204 countries and territories, from 1990 to 2021. We included morbidity and deaths due to neurological conditions, for which health loss is directly due to damage to the CNS or peripheral nervous system. We also isolated neurological health loss from conditions for which nervous system morbidity is a consequence, but not the primary feature, including a subset of congenital conditions (ie, chromosomal anomalies and congenital birth defects), neonatal conditions (ie, jaundice, preterm birth, and sepsis), infectious diseases (ie, COVID-19, cystic echinococcosis, malaria, syphilis, and Zika virus disease), and diabetic neuropathy. By conducting a sequela-level analysis of the health outcomes for these conditions, only cases where nervous system damage occurred were included, and YLDs were recalculated to isolate the non-fatal burden directly attributable to nervous system health loss. A comorbidity correction was used to calculate total prevalence of all conditions that affect the nervous system combined.FindingsGlobally, the 37 conditions affecting the nervous system were collectively ranked as the leading group cause of DALYs in 2021 (443 million, 95% UI 378–521), affecting 3·40 billion (3·20–3·62) individuals (43·1%, 40·5–45·9 of the global population); global DALY counts attributed to these conditions increased by 18·2% (8·7–26·7) between 1990 and 2021. Age-standardised rates of deaths per 100 000 people attributed to these conditions decreased from 1990 to 2021 by 33·6% (27·6–38·8), and age-standardised rates of DALYs attributed to these conditions decreased by 27·0% (21·5–32·4). Age-standardised prevalence was almost stable, with a change of 1·5% (0·7–2·4). The ten conditions with the highest age-standardised DALYs in 2021 were stroke, neonatal encephalopathy, migraine, Alzheimer's disease and other dementias, diabetic neuropathy, meningitis, epilepsy, neurological complications due to preterm birth, autism spectrum disorder, and nervous system cancer.InterpretationAs the leading cause of overall disease burden in the world, with increasing global DALY counts, effective prevention, treatment, and rehabilitation strategies for disorders affecting the nervous system are needed