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In recent times cardiovascular diseases (CVDs) are the leading cause of mortality universally, caused more or less 17.7 million casualties with 45% of all illnesses (except communicable ones) in 2015 as per World Health Organization (WHO). According to American National Center for Health Statistics, cardiac disorders are costliest. Moreover, health care expenditures related to cardiac disorders are anticipated to exceed than diabetes and Alzheimer’s. Straining of reactive oxygen species with diminished neutralization & inflammation critically adds to atherosclerosis and also proceed to other cardiovascular diseases such as cardiac remodeling and myocardial infarction (MI). In the past few years, researchers revealed multiple drug targets from animal studies and evaluated them in the therapeutics of cardiac disorders, which offered exciting clues for novel therapeutic strategies. Although, only few newer agents approved clinically and actual approaches for treatment are lagging behind. Several novel drugs found effective for the treatment of hypertension, congestive heart failure, cardiac arrhythmia and angina pectoris. Detailed mechanism of action, basic and clinical pharmacology of all novel drugs has been discussed in this review.

Pakistan is ranked among the most climate change adversely affected countries of the world due to facing higher frequency of extreme natural hazards events as floods, drought, and earthquakes. Inadequate mitigation measures regarding severity and consecutive events of floods have badly affected millions of households’ livelihoods. Pakistan has experienced five consecutive floods in the current decade from 2010 to 2015. These floods severely affected the mostly districts of Punjab province among these three most flood-affected districts Rahim Yar Khan, Muzaffargarh, and Rajanpur were selected for this research work. This study used the cross-sectional data of 840 flood-affected households from these three districts. Empirical estimates indicated the study area households major adaptation measures were foundation strengthen, elevated ground floor, precautionary saving, and construction of the house with reinforced material. Probit model estimates signified age, gender, location, and family size; monthly income, physical disability, education, and house ownership were major factors influencing mitigation strategies of households’ level. Limited resources, inadequate planning for land use, lack of advanced and early warning system, and inadequate sound financial status were major obstacles regarding households’ level adaptation of mitigation strategies. In addressing household level obstacles regarding advanced adaptation measures, development requires for local disaster policies such as ample spatial planning, codes of house building, practices of building infrastructure, propagation and forecasting updated flood information, and advanced and updated early warning system.

Rural communities inhabited on riverbank areas are frequently facing the ever-increasing psychological, social and economic distress due to negative effects of riverbank erosion. This study focused to investigate the impact of climate-based hazards particularly riverbank erosion on human displacement, food security and livelihood of rural riverine households and how vulnerable households act in response. The survey data of 398 households of erosion-prone riverbank area were collected, and group discussions connecting household heads from this area were also used for this study. In human displacement scenario of the last ten years due to riverbank erosion, almost 60% households lost their homestead once while 38% more than three times and forced to displaced. Empirical estimates of households’ food security status indicated the value of Food Security Index 2.11, highlighting households face issue of food security all over the year. Food security issue of vulnerable households is highly related with migration because these households have insufficient employment chances, and coupled with limited or no farming land, they are highly prone to migration. In conclusion, this study estimated that riverbank erosion risk is a co-exist reason of population displacement, increasing rural environmental vulnerability and obstacles to psychological, cultural and socioeconomic development. Implications of local-based proper policy interventions such as developing advance research regarding infusion of agro-based technology packages for emerging Bait areas for developing resilience, human capital development, credit access and institution service are necessary for improving livelihood and food security of these riverbank erosion households. State-based institutions and local community mutually need to focus increasing forestry specifically in riverbank areas to save fertile land from riverbank erosion and reducing environmental pollution. Convalescing livelihood and food security for erosion riverbank households, more employment opportunity needs to provided, investing more in training and education programmes to promoting income-generating activities that subsequently will develop livelihood and food security of households.

In scientific research, assessing the impact and influence of authors is crucial for evaluating their scholarly contributions. Whereas in literature, multitudinous parameters have been developed to quantify the productivity and significance of researchers, including the publication count, citation count, well-known h index and its extensions and variations. However, with a plethora of available assessment metrics, it is vital to identify and prioritize the most effective metrics. To address the complexity of this task, we employ a powerful deep learning technique known as the Multi-Layer Perceptron (MLP) classifier for the classification and the ranking purposes. By leveraging the MLP’s capacity to discern patterns within datasets, we assign importance scores to each parameter using the proposed modified recursive elimination technique. Based on the importance scores, we ranked these parameters. Furthermore, in this study, we put forth a comprehensive statistical analysis of the top-ranked author assessment parameters, encompassing a vast array of 64 distinct metrics. This analysis gives us treasured insights in between these parameters, shedding light on the potential correlations and dependencies that may affect assessment outcomes. In the statistical analysis, we combined these parameters by using seven well-known statistical methods, such as arithmetic means, harmonic means, geometric means etc. After combining the parameters, we sorted the list of each pair of parameters and analyzed the top 10, 50, and 100 records. During this analysis, we counted the occurrence of the award winners. For experimental proposes, data collection was done from the field of Mathematics. This dataset consists of 525 individuals who are yet to receive their awards along with 525 individuals who have been recognized as potential award winners by certain well known and prestigious scientific societies belonging to the fields’ of mathematics in the last three decades. The results of this study revealed that, in ranking of the author assessment parameters, the normalized h index achieved the highest importance score as compared to the remaining sixty-three parameters. Furthermore, the statistical analysis results revealed that the Trigonometric Mean (TM) outperformed the other six statistical models. Moreover, based on the analysis of the parameters, specifically the M Quotient and FG index, it is evident that combining these parameters with any other parameter using various statistical models consistently produces excellent results in terms of the percentage score for returning awardees.

Higher demands of food led to higher nitrogen application to promote cropping intensification and produce more which may have negative effects on the environment and lead to pollution. While sustainable wheat production is under threat due to low soil fertility and organic matter due to nutrient degradation at high temperatures in the region. The current research explores the effects of different types of coated urea fertilizers and their rates on wheat crop under arid climatic conditions of Pakistan. Enhancing nitrogen use efficiency by using eco-friendly coated urea products could benefit growers and reduce environmental negative effects. A trial treatment included N rates (130, 117, 104, and 94 kg ha -1 ) and coated urea sources (neem coated, sulfur coated, bioactive sulfur coated) applied with equal quantity following split application method at sowing, 20 and 60 days after sowing (DAS). The research was arranged in a split-plot design with randomized complete block design had three replicates. Data revealed that bioactive sulfur coated urea with the application of 130 kg N ha -1 increased chlorophyll contents 55.0 (unit value), net leaf photosynthetic rate (12.51 μmol CO 2 m -2 s -1 ), and leaf area index (5.67) significantly. Furthermore, research elucidates that bioactive sulfur urea with the same N increased partial factor productivity (43.85 Kg grain Kg -1 N supplied), nitrogen harvest index (NHI) 64.70%, and partial nutrient balance (1.41 Kg grain N content Kg -1 N supplied). The neem-coated and sulfur-coated fertilizers also showed better results than monotypic urea. The wheat growth and phenology significantly improved by using coated fertilizers. The crop reached maturity earlier with the application of bioactive sulfur-coated urea than others. Maximum total dry matter 14402 (kg ha -1 ) recorded with 130 kg N ha -1 application. Higher 1000-grain weight (33.66 g), more number of grains per spike (53.67), grain yield (4457 kg ha -1 ), and harvest index (34.29%) were obtained with optimum N application 130 kg ha -1 (recommended). There is a significant correlation observed for growth, yield, and physiological parameters with N in the soil while nitrogen-related indices are also positively correlated. The major problem of groundwater contamination with nitrate leaching is also reduced by using coated fertilizers. Minimum nitrate concentration (7.37 and 8.77 kg ha -1 ) was observed with the application of bioactive sulfur-coated and sulfur-coated urea with lower N (94 kg ha -1 ), respectively. The bioactive sulfur-coated urea with the application of 130 kg N ha -1 showed maximum phosphorus 5.45 mg kg -1 and potassium 100.67 mg kg -1 in the soil. Maximum nitrogen uptake (88.20 kg ha -1 ) is showed by bioactive sulfur coated urea with 130 kg N ha -1 application. The total available NPK concentrations in soil showed a significant correlation with physiological attributes; grain yield; harvest index; and nitrogen use efficiency components, i.e., partial factor productivity, partial nutrient balance, and nitrogen harvest index. This research reveals that coating urea with secondary nutrients, neem oil, and microbes are highly effective techniques for enhancing fertilizer use efficiency and wheat production in calcareous soils and reduced N losses under arid environments.

Assessing the current farm-level efforts of climate change adaptation is essential to distinguish their usefulness and implying policy level advance measures for future. The present study investigated cotton farmers’ climate change adaptation and its impact on increasing cotton productivity and net cotton income in Punjab province of Pakistan. A pretested and well-structured questionnaire was used for data collection of 480 cotton farmers from three major cotton-producing divisions of cotton-wheat zone of Punjab, Pakistan. Logistic regression analysis approach was used in this study to find out the factors of adaptation and propensity score matching method employed to identify connecting adaptation impact on cotton productivity and cotton income. Empirical estimates of this study indicated as owing to some external and internal constraint farmers were limited focused on adaptation while conscious about adverse effects of climate change. Usage of required and recommended types of fertilizer, variation in planting dates, and changing varieties of crop were main adaptation strategies implemented by cotton farmers. Cotton farmers’ adaptation decision was significantly influenced by some major factors as weather forecasting, market information, easy access to agricultural extension services, farming experience, and education of cotton farmer. Farm-level increase in cotton productivity and net cotton crop income was direct while overall increases in national output and improving rural area farmer well-being were indirect and significant outcomes of implementing climate change adaptation of cotton farmers. Cotton farmers were using various combinations of adaptation strategies and achieving more benefits regarding their crop productivity and net returns. Findings of the study suggest need for larger investment in farm-level extension services, farmers’ schooling, and develop climate change institutional setup for enhancing farmers’ adaptation capability to increasing cotton productivity, improving well-being of farming community, and securing agriculture from future climatic uncertainties. Future policies must deal with farm-level limitations of advanced adaptation measures like making available information and sustaining sponsoring soil conservation practices, launching climate smart varieties and advanced adaptation measures based on various agro-ecological zones.

Many correlations are available in the literature to predict the higher heating value (HHV) of raw biomass using the proximate and ultimate analyses. Studies on biomass torrefaction are growing tremendously, which suggest that the fuel characteristics, such as HHV, proximate analysis and ultimate analysis, have changed significantly after torrefaction. Such changes may cause high estimation errors if the existing HHV correlations were to be used in predicting the HHV of torrefied biomass. No study has been carried out so far to verify this. Therefore, this study seeks answers to the question: “Can the existing correlations be used to determine the HHV of the torrefied biomass”? To answer this, the existing HHV predicting correlations were tested using torrefied biomass data points. Estimation errors were found to be significantly high for the existing HHV correlations, and thus, they are not suitable for predicting the HHV of the torrefied biomass. New correlations were then developed using data points of torrefied biomass. The ranges of reported data for HHV, volatile matter (VM), fixed carbon (FC), ash (ASH), carbon (C), hydrogen (H) and oxygen (O) contents were 14.90 MJ/kg–33.30 MJ/kg, 13.30%–88.57%, 11.25%–82.74%, 0.08%–47.62%, 35.08%–86.28%, 0.53%–7.46% and 4.31%–44.70%, respectively. Correlations with the minimum mean absolute errors and having all components of proximate and ultimate analyses were selected for future use. The selected new correlations have a good accuracy of prediction when they are validated using another set of data (26 samples). Thus, these new and more accurate correlations can be useful in modeling different thermochemical processes, including combustion, pyrolysis and gasification processes of torrefied biomass.

This study introduces a method for analyzing the propagation of electromagnetic waves in cylindrical structures with central chambers facilitating beam-plasma interactions, particularly relevant for slow-wave structures in backward wave oscillators. The boundary value problem, governed by the Helmholtz equation, is resolved using the mode-matching technique, yielding an exact solution. The analysis elucidates key phenomena, including reflection, transmission, orthogonality relations, and power flux variations with frequency and material properties. By examining the effects of plasma frequency and beam radius on phase velocity, group velocity, and interaction efficiency, the study provides insights into optimizing wave propagation and energy transfer. The results demonstrate that higher plasma frequencies and reduced beam radii enhance scattering characteristics, offering practical guidance for designing efficient electromagnetic devices.