Cloud based Smart Irrigation for Agricultural Area of Pakistan

A beneficial product of Smart Irrigation for Agricultural Area of Pakistan has been presented in this paper. Pakistan stands in need of a participatory solution that is efficiently workable, sustainable, and profitable, to develop the way for the agricultural sector by improving crop productivity with minimum water loss. The goal of this project is to introduce Cloud support to the Smart Irrigation System for Agricultural Area of Pakistan. To achieve this objective Wireless Sensor Network (WSN) is used to determine how much water to apply and when to irrigate. The system is divided into four main modules, i.e. Sensor node, Coordinator node, Server Module and Web Application. On the basis of acquired parameters from the WSN, the software application is programmed to take intelligent decisions increase the efficiency of the agricultural system

Retrospective cost-utility and budget impact assessments of Hypericum perforatum in contrast with Fluoxetine treatment for depression in Karachi, Pakistan

In this study we have compared two different types of therapies i.e. herbal and allopathic system of therapies for Depression and studied them from the social perspectives. The Hypericum perforatum is compared with Fluoxetine [HCL] in terms of cost-utility and financial savings thereby evaluating its influence on annual expenditure of depressive patients that were randomly selected from 178 union councils of the city of Karachi, Pakistan. For both system of therapies a total of 356 patients were selected by stratified random sampling. Taking frequency of depression as ‘1’ annually with discount rate at 3% for calculating the burden-of-illness in terms of disability-adjusted-life-years. The cost-utility and the budget-impact assessments were carried out to assess incremental-cost-effectiveness-ratio, and the budget-impact-per-onset as well as budget-impact-per-year values. In comparison with the Fluoxetine therapy, the Hypericum perforatum was found to relieve symptoms in 21.47% less cost; owing 29.23% less disability-adjusted-life-years and 21.45% less budget-impact-per-onset as well as budget-impact-peryear. The annual mean incremental-cost-effectiveness-ratio was found to be at 36.95±270.74 (less than GDP per capita threshold of Rs. 38,173.02). Hypericum perforatum provide the optimal utility with less impact on budget of a patient in comparison with the treatment of symptoms of depression with Fluoxetine

Oxidative Stress in Human Pathology and Aging: Molecular Mechanisms and Perspectives

Reactive oxygen and nitrogen species (RONS) are generated through various endogenous and exogenous processes; however, they are neutralized by enzymatic and non-enzymatic antioxidants. An imbalance between the generation and neutralization of oxidants results in the progression to oxidative stress (OS), which in turn gives rise to various diseases, disorders and aging. The characteristics of aging include the progressive loss of function in tissues and organs. The theory of aging explains that age-related functional losses are due to accumulation of reactive oxygen species (ROS), their subsequent damages and tissue deformities. Moreover, the diseases and disorders caused by OS include cardiovascular diseases [CVDs], chronic obstructive pulmonary disease, chronic kidney disease, neurodegenerative diseases and cancer. OS, induced by ROS, is neutralized by different enzymatic and non-enzymatic antioxidants and prevents cells, tissues and organs from damage. However, prolonged OS decreases the content of antioxidant status of cells by reducing the activities of reductants and antioxidative enzymes and gives rise to different pathological conditions. Therefore, the aim of the present review is to discuss the mechanism of ROS-induced OS signaling and their age-associated complications mediated through their toxic manifestations in order to devise effective preventive and curative natural therapeutic remedies

A Comparative Analysis of Metaheuristic Techniques for High Availability Systems

In the ever-evolving technological landscape, ensuring high system availability has become a paramount concern. This research paper focuses on cloud computing, a domain witnessing exponential growth and emerging as a critical use case for high-availability systems. To fulfil the criteria, many services in cloud infrastructures should be combined, relying on the user’s demands. Central to this study is load balancing, an integral element in harnessing the full potential of heterogeneous computing systems. In cloud environments, dynamic management of load balancing is crucial. This study explores how virtual machines can effectively remap resources in response to fluctuating loads dynamically, optimizing overall network performance. The core of this research involves an in-depth analysis of several metaheuristic algorithms applied to load balancing in cloud computing. These include Genetic Algorithm, Particle Swarm Optimization, Ant Colony Optimization, Artificial Bee Colony, and Grey Wolf Optimization. Utilizing CloudAnalyst, the study conducts a comparative analysis of these techniques, focusing on key performance metrics such as Total Response Time (TRT) and Data Center Processing Time (DCPT). The findings of this research offer insights into the varying behaviors of these algorithms under different cloud configurations and user retention levels. The ultimate aim is to pave the way for developing innovative load-balancing strategies in cloud computing. By providing a comprehensive evaluation of existing metaheuristic methods, this paper contributes to advancing high-availability systems, underscoring the importance of tailored solutions in the dynamic realm of cloud technology

Flood Forecasting by Using Machine Learning: A Study Leveraging Historic Climatic Records of Bangladesh

Forecasting rainfall is crucial to the well-being of individuals and is significant everywhere in the world. It contributes to reducing the disastrous effects of floods on agriculture, human life, and socioeconomic systems. This study discusses the challenges of effectively forecasting rainfall and floods and the necessity of combining data with flood channel mathematical modelling to forecast floodwater levels and velocities. This research focuses on leveraging historical meteorological data to find trends using machine learning and deep learning approaches to estimate rainfall. The Bangladesh Meteorological Department provided the data for the study, which also uses eight machine learning algorithms. The performance of the machine learning models is examined using evaluation measures like the R2 score, root mean squared error and validation loss. According to this research’s findings, polynomial regression, random forest regression, and long short-term memory (LSTM) had the highest performance levels. Random forest and polynomial regression have an R2 value of 0.76, while LSTM has a loss value of 0.09, respectively

Current Status and Distribution of Reptiles of Sindh

In Sindh province, total 103 species of reptiles comprising of 42 Lizards, 45 Snakes, 14 Turtles, 1 species each of Tortoise, Crocodile and Gavial has been recorded. 13 key areas for the reptiles have been identified in Sindh along with three important sites viz. Karachi coast, Deh Akro Wildlife Sanctuary and Nara Wetland Complex. Some reptiles are under threat due to large scale exploitation for skin, medicinal use and as food item. Marine Turtles are threatened mainly due to habitat degradation. The common species include Indian Fringe-toad Lizard, Indian Sand Swimmer, Indian Garden Lizard, Sindh Sand Gecko, Desert Monitor and Saw-scaled Viper. Eight species are threatened and 16 species are of special conservation interest and need to be conserved

Current Status and Distribution of Reptiles of Sindh

In Sindh province, total 103 species of reptiles comprising of 42 Lizards, 45 Snakes, 14 Turtles, 1 species each of Tortoise, Crocodile and Gavial has been recorded. 13 key areas for the reptiles have been identified in Sindh along with three important sites viz. Karachi coast, Deh Akro Wildlife Sanctuary and Nara Wetland Complex. Some reptiles are under threat due to large scale exploitation for skin, medicinal use and as food item. Marine Turtles are threatened mainly due to habitat degradation. The common species include Indian Fringe-toad Lizard, Indian Sand Swimmer, Indian Garden Lizard, Sindh Sand Gecko, Desert Monitor and Saw-scaled Viper. Eight species are threatened and 16 species are of special conservation interest and need to be conserved

Respiratory support in patients with severe COVID-19 in the International Severe Acute Respiratory and Emerging Infection (ISARIC) COVID-19 study: a prospective, multinational, observational study

Background: Up to 30% of hospitalised patients with COVID-19 require advanced respiratory support, including high-flow nasal cannulas (HFNC), non-invasive mechanical ventilation (NIV), or invasive mechanical ventilation (IMV). We aimed to describe the clinical characteristics, outcomes and risk factors for failing non-invasive respiratory support in patients treated with severe COVID-19 during the first two years of the pandemic in high-income countries (HICs) and low middle-income countries (LMICs). Methods: This is a multinational, multicentre, prospective cohort study embedded in the ISARIC-WHO COVID-19 Clinical Characterisation Protocol. Patients with laboratory-confirmed SARS-CoV-2 infection who required hospital admission were recruited prospectively. Patients treated with HFNC, NIV, or IMV within the first 24 h of hospital admission were included in this study. Descriptive statistics, random forest, and logistic regression analyses were used to describe clinical characteristics and compare clinical outcomes among patients treated with the different types of advanced respiratory support. Results: A total of 66,565 patients were included in this study. Overall, 82.6% of patients were treated in HIC, and 40.6% were admitted to the hospital during the first pandemic wave. During the first 24 h after hospital admission, patients in HICs were more frequently treated with HFNC (48.0%), followed by NIV (38.6%) and IMV (13.4%). In contrast, patients admitted in lower- and middle-income countries (LMICs) were less frequently treated with HFNC (16.1%) and the majority received IMV (59.1%). The failure rate of non-invasive respiratory support (i.e. HFNC or NIV) was 15.5%, of which 71.2% were from HIC and 28.8% from LMIC. The variables most strongly associated with non-invasive ventilation failure, defined as progression to IMV, were high leukocyte counts at hospital admission (OR [95%CI]; 5.86 [4.83–7.10]), treatment in an LMIC (OR [95%CI]; 2.04 [1.97–2.11]), and tachypnoea at hospital admission (OR [95%CI]; 1.16 [1.14–1.18]). Patients who failed HFNC/NIV had a higher 28-day fatality ratio (OR [95%CI]; 1.27 [1.25–1.30]). Conclusions: In the present international cohort, the most frequently used advanced respiratory support was the HFNC. However, IMV was used more often in LMIC. Higher leucocyte count, tachypnoea, and treatment in LMIC were risk factors for HFNC/NIV failure. HFNC/NIV failure was related to worse clinical outcomes, such as 28-day mortality. Trial registration This is a prospective observational study; therefore, no health care interventions were applied to participants, and trial registration is not applicable

Respiratory support in patients with severe COVID-19 in the International Severe Acute Respiratory and Emerging Infection (ISARIC) COVID-19 study: a prospective, multinational, observational study

Background: Up to 30% of hospitalised patients with COVID-19 require advanced respiratory support, including high-flow nasal cannulas (HFNC), non-invasive mechanical ventilation (NIV), or invasive mechanical ventilation (IMV). We aimed to describe the clinical characteristics, outcomes and risk factors for failing non-invasive respiratory support in patients treated with severe COVID-19 during the first two years of the pandemic in high-income countries (HICs) and low middle-income countries (LMICs). Methods: This is a multinational, multicentre, prospective cohort study embedded in the ISARIC-WHO COVID-19 Clinical Characterisation Protocol. Patients with laboratory-confirmed SARS-CoV-2 infection who required hospital admission were recruited prospectively. Patients treated with HFNC, NIV, or IMV within the first 24 h of hospital admission were included in this study. Descriptive statistics, random forest, and logistic regression analyses were used to describe clinical characteristics and compare clinical outcomes among patients treated with the different types of advanced respiratory support. Results: A total of 66,565 patients were included in this study. Overall, 82.6% of patients were treated in HIC, and 40.6% were admitted to the hospital during the first pandemic wave. During the first 24 h after hospital admission, patients in HICs were more frequently treated with HFNC (48.0%), followed by NIV (38.6%) and IMV (13.4%). In contrast, patients admitted in lower- and middle-income countries (LMICs) were less frequently treated with HFNC (16.1%) and the majority received IMV (59.1%). The failure rate of non-invasive respiratory support (i.e. HFNC or NIV) was 15.5%, of which 71.2% were from HIC and 28.8% from LMIC. The variables most strongly associated with non-invasive ventilation failure, defined as progression to IMV, were high leukocyte counts at hospital admission (OR [95%CI]; 5.86 [4.83-7.10]), treatment in an LMIC (OR [95%CI]; 2.04 [1.97-2.11]), and tachypnoea at hospital admission (OR [95%CI]; 1.16 [1.14-1.18]). Patients who failed HFNC/NIV had a higher 28-day fatality ratio (OR [95%CI]; 1.27 [1.25-1.30]). Conclusions: In the present international cohort, the most frequently used advanced respiratory support was the HFNC. However, IMV was used more often in LMIC. Higher leucocyte count, tachypnoea, and treatment in LMIC were risk factors for HFNC/NIV failure. HFNC/NIV failure was related to worse clinical outcomes, such as 28-day mortality. Trial registration This is a prospective observational study; therefore, no health care interventions were applied to participants, and trial registration is not applicable