Diagnosis Health Effects of Pollution Produced by Coal Power Plants Using Mamdani Fuzzy Inference System

Coal Power Plants emit harmful products which pose potentially large risk to human health and the environment. Coal-fired power plant are source of nitrogen oxide, airborne fly ash and Sulphur. In substantially less attentions has been paid to detect the effect of these toxic elements present in coal pollution on the human health. In this Article, Fuzzy System (FS) is proposed to detect the Health effects of pollution produced by coal power plants. The proposed DHEPCPP-FS Smart System can categorize the different Health effects of pollution produced by coal power plants. The Proposed DHEPCPP-MFIS Expert System has six input variables at layer. The input variables are Carbon monoxide (CO). Black carbon, Nitrogen dioxide (NO ), Sulfur dioxide (SO ), Heavy metals and Black smoke which produce output condition of health like Lungs problem, Heart problem or Cancer. This paper also present an scrutiny of the results achieved by using Proposed DHEPCPP-MFIS Expert System ascertain effects of pollution on health produced by coal power plants processes with the medical expert opinion and researches conducted in the past

Classification of Water using Mamdani Fuzzy Inference System

The basic reason to conduct the research and state the problems regarding the contaminated water is to highlight the basic elements that should be available in water and some that are not, to a ratio that the water is declared safe for drinking. To do so we will keep account of the basic elements in water, odor, taste, hardness, pH level and color of water by following the guidelines given by World Health Organization (WHO) and researches conducted in past on the safe drinking water in Pakistan. Our basic objective is to conclude these mention factors are either in the range of safety parameters or not and also set some of the parameter on which the previous researches in Pakistan are lacking. In past researches the methods used to calculate the values of water were manual but for our research we will use significant software tool for calculations because we want zero tolerance in the output values of elements in the water up to the safety level . The method proposed for this type of research is Fuzzification to calculate the values and measurements of the proposed problem. In this research we areusing our computer science expertise combined with mathematical knowledge to provide the solution and these things that set our project apart

Exploring the Effects of Training and Development Practices on Organization Performance: A Case Study of Pakistan Telecommunication Authority

Training and development (T&D) is important for survival of every organization. It plays a strategic role to achieve the current as well as future goals of the organization. The core objective of the research was to explore the impact of training and development on organizational effectiveness. The research was based on a case study of Pakistan Telecommunication Authority. The study consists of the data gathered through a structured questionnaire from the employees and management of Pakistan Telecommunication Authority in Islamabad and Rawalpindi region. The results have disclosed the importance of training and development practices and its impact on individuals as well as on organization. Furthermore, it is suggested that T&D programs should be carefully assessed, designed, implemented and evaluated in order to fill the gap between existing and required skills, abilities and knowledge of the employees; in addition to enhance the organizational effectiveness

Complications In The Management Of High-Energy Closed Fractures Of Proximal Tibial Plateau. A Retrospective Study

Objective: To analyze the management of high-energy Schatzker type V and VI tibial plateau fractures which are associated with infectious and noninfectious complications. Methods: This study was carried out in the Department of Orthopedic Surgery, Rawalpindi Medical University from July 1, 2018, to June 30, 2021. This is a retrospective study which is done in three years. Patients had to be between the ages of 18 and 60, have no history of arthritis, have a closed fracture of the proximal tibia (Schatzker type V and VI), or have AO type 41-C1, C2 or C3 involvement of the lower limb. Each patient received treatment using techniques such as internal fixation with locking plates and open reduction which are minimally invasive. Results: This study involved a total of 132 patients.Mean age was 35.15±10.59.115(87%) were men and 17(13%) were women out of 132. A total of 39 out of 132 patients experienced complications (29.54%). Infectious complications (18.93%) were found in (25/132) patients 16 out of 25 patients had superficial infections. Routine dressing changes and antibiotic treatment were carried out in patients who had superficial infections.9 out of 25 patients who had faced a deep-seated infection underwent repeated implant removal, debridements, amputation, and flap covering depending on the reaction of the host. Noninfectious complications had been reported in 14 patients(10.6%). Six patients had hardware-related issues and four of them required a secondary treatment.08 individuals had malalignment, with five of them having it in their immediate postoperative radiographs and three others having it in their late postoperative radiographs. Conclusion: In closed wounds, substantial soft tissue destruction is linked to the fractures of the proximal tibial plateau, particularly Shatzker type V and VI. By selecting the right patients and minimising soft tissue dissection, the problems related to the management of these fractures can be reduced.

Patterns of Head Injuries in Pediatric Patients Treated in Emergency Department of Children Hospital and Institute of Child Health Lahore

Objective:  To analyze the pattern of head injuries along with characteristics and outcomes among pediatric age group presenting in The Children hospital Lahore, Pakistan. Material and Methods:  A cross-sectional study was conducted and a total of 384 children of both genders aged up to 12 years presenting with head injuries were included. After initial review and resuscitation by the trauma unit or neurosurgery unit, children were evaluated clinically and radiologically and the plan was decided for further treatment. Gender, age, place of injury occurrence, etiology of injury, Glasgow coma score (GCS) at the time of enrollment, the interval between injury and admission, management, outcome, and total duration of hospital stay were recorded on a predesigned proforma. Results:  In a total of 384 children, 249 (64.8%) were boys. Overall, the mean age was 5.8 ± 3.3 years. Falls were the commonest etiology in 210 (54.7%) children while motor vehicle accidents were the cause of head trauma among 78 (20.3%) children. The mean interval between injury and presentation was noted to be 3.2 ± 2.1 hours. Mortality was reported in 56 (14.6%) children and it was observed that a significant association was noted between outcome and GCS at the time of presentation (p < 0.0001). Conclusion:  The majority of the pediatric head injury cases were male and aged above 5 years. The most common etiology of head injuries was falls followed by motor vehicle accidents. GCS ? 8 at the time of presentation was significantly linked with poor outcomes

A fusion-based machine learning approach for the prediction of the onset of diabetes

A growing portfolio of research has been reported on the use of machine learning-based architectures and models in the domain of healthcare. The development of data-driven applications and services for the diagnosis and classification of key illness conditions is challenging owing to issues of low volume, low-quality contextual data for the training, and validation of algorithms, which, in turn, compromises the accuracy of the resultant models. Here, a fusion machine learning approach is presented reporting an improvement in the accuracy of the identification of diabetes and the prediction of the onset of critical events for patients with diabetes (PwD). Globally, the cost of treating diabetes, a prevalent chronic illness condition characterized by high levels of sugar in the bloodstream over long periods, is placing severe demands on health providers and the proposed solution has the potential to support an increase in the rates of survival of PwD through informing on the optimum treatment on an individual patient basis. At the core of the proposed architecture is a fusion of machine learning classifiers (Support Vector Machine and Artificial Neural Network). Results indicate a classification accuracy of 94.67%, exceeding the performance of reported machine learning models for diabetes by ~1.8% over the best reported to date

Deep learning for diabetic retinopathy analysis : a review, research challenges, and future directions

Deep learning (DL) enables the creation of computational models comprising multiple processing layers that learn data representations at multiple levels of abstraction. In the recent past, the use of deep learning has been proliferating, yielding promising results in applications across a growing number of fields, most notably in image processing, medical image analysis, data analysis, and bioinformatics. DL algorithms have also had a significant positive impact through yielding improvements in screening, recognition, segmentation, prediction, and classification applications across different domains of healthcare, such as those concerning the abdomen, cardiac, pathology, and retina. Given the extensive body of recent scientific contributions in this discipline, a comprehensive review of deep learning developments in the domain of diabetic retinopathy (DR) analysis, viz., screening, segmentation, prediction, classification, and validation, is presented here. A critical analysis of the relevant reported techniques is carried out, and the associated advantages and limitations highlighted, culminating in the identification of research gaps and future challenges that help to inform the research community to develop more efficient, robust, and accurate DL models for the various challenges in the monitoring and diagnosis of DR

Exploring the effect of zinc and boron application on oil contents, protein contents, growth and yield of sunflower

Sunflower is sensitive to boron (B) and zinc (Zn) deficiency when grown on deficient soil, A field experiment was conducted to determine the main and interactive effects of soil applied Zn and B on total production of sunflower at Agronomic Research Area, University of Agriculture, Faisalabad. Experiment was laid out in randomized complete block design (RCBI) with factorial arrangement using three replications with net plot size of 6 m x 4.5 m. The soil application of variable levels of Zn (0, 10, 20 and 30 kg ha-1) and B (0, 1, 2 and 3 kg ha-1) in the form of zinc sulphate and boric acid, respectively were applied at time of sowing. All other agronomic and plant protection practices were kept uniform. The data regarding growth, yield and quality parameters were noted by using standard procedures. Results showed that Zn @ 20 kg ha-1 and B @ 3 kg ha-1 significantly increased the number of plants per plot at harvest, stem diameter, head diameter, number of achenes per head, 1000-achene weight, biological yield and days to maturity, achene yield kg per, harvest index, leaf concentrations or Zn at heading stage, leaf concentrations of B at heading stage (ppm), achene oil content (%), achene protein contents as compared to control. This study concluded that higher growth and yield of sunflower can be achieved by application of Zn at 20 kg ha-1 and B at 3 kg ha-1 under Faisalabad conditions

Novel VPS13B Mutations in Three Large Pakistani Cohen Syndrome Families Suggests a Baloch Variant with Autistic-Like Features.

BackgroundCohen Syndrome (COH1) is a rare autosomal recessive disorder, principally identified by ocular, neural and muscular deficits. We identified three large consanguineous Pakistani families with intellectual disability and in some cases with autistic traits.MethodsClinical assessments were performed in order to allow comparison of clinical features with other VPS13B mutations. Homozygosity mapping followed by whole exome sequencing and Sanger sequencing strategies were used to identify disease-related mutations.ResultsWe identified two novel homozygous deletion mutations in VPS13B, firstly a 1 bp deletion, NM_017890.4:c.6879delT; p.Phe2293Leufs*24, and secondly a deletion of exons 37-40, which co-segregate with affected status. In addition to COH1-related traits, autistic features were reported in a number of family members, contrasting with the "friendly" demeanour often associated with COH1. The c.6879delT mutation is present in two families from different regions of the country, but both from the Baloch sub-ethnic group, and with a shared haplotype, indicating a founder effect among the Baloch population.ConclusionWe suspect that the c.6879delT mutation may be a common cause of COH1 and similar phenotypes among the Baloch population. Additionally, most of the individuals with the c.6879delT mutation in these two families also present with autistic like traits, and suggests that this variant may lead to a distinct autistic-like COH1 subgroup