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Background: The goal of the study was to investigate the burden of transfusion- transmitted infections (TTIs) hepatitis B virus (HBV), hepatitis C virus (HCV), human immunodeficiency virus (HIV), syphilis, and malarial parasite (MP) in ABO Blood Groups and Rh Type System among voluntarily blood donors in Khyber Pakhtunkhwa (KPK), Pakistan. It is a retrospective single center cross sectional study. This study was conducted from June 2020 to September 2021 (16 months) at the frontier foundation thalassemia center Peshawar KPK. Donors were physically healthy and fit for donation. Donors with physical disabilities and/or having co-morbid conditions were excluded from the report. Methods: All the samples were screened for anti-HIV, anti-HCV, HBsAg, Syphilis, and Malarial Parasite via ELISA kit and Immune Chromatographic Technique (ICT), respectively. A total of 6311 blood donations were evaluated. The majority of the donations (92%) were from (VNRBD) voluntary non-remunerated blood donation, while only 8% came from replacement donors. Results: Amongst 6311 blood donations, 1.50 % (n = 95) were infected at least with one pathogen, HBV positive cases were 0.855 % (n = 54), HCV positive cases were 0.316% (n = 20), syphilis positive were 0.30% (n = 19) and MP positive cases were only 0.031% (n = 2). HBV, HCV, syphilis and malaria infections rates were found to be low as compared to the previous data published, while no case was reported for HIV. The study also revealed the distribution pattern of the aforementioned pathogens in blood groups and the Rh type system of the reactive samples. Conclusion: The lower reported in our study indicates the awareness among the people of Peshawar about TTIs and their precautions. The prevalence rate that we are reporting is less than previously published articles in the same domain.

Aim: A data structure visualisation system uses object-oriented thinking and COM technology to dynamically and interactively simulate and track data structure algorithms and realize the dynamic synchronisation and visualisation of abstract data structures and algorithms using the data modelling function and self-test function. Background: Teaching data structures and algorithms is difficult because of their abstraction and dynamics; the use of icons in classroom teaching can be partially abstracted into intuition, but analysing the instantaneous dynamic characteristics of the object and the dynamic execution process of the algorithm is difficult. Objective: A data structure visualisation system employs a data modelling function, visual data structures, a user-friendly and flexible interface, and multipath features for multilevel users. Such systems can be designed more effectively by using the ant colony algorithm. Methods: The more the ants pass by a certain path, the higher the concentration of residual pheromone, and the higher the probability of the subsequent ant selecting that path. Therefore, the individuals in an ant colony communicate messages and cooperate with each other for foraging. Results: The resulting speedup ratio indicates that the speedup is smaller when the number of nodes is 100 or more; the acceleration is higher when the node reaches a certain scale, and the speedup ratio does not change considerably. In this study, the traffic simulation software VISSIM was used to generate road network data; the generated traffic data were analysed and used to design a traffic network data structure. Conclusion: The traffic network data model oriented to the ant colony algorithm was established through abstraction. Accordingly, a parallel ant colony algorithm based on cloud computing was implemented. Finally, a Hadoop cloud computing platform was established and used to run and test the parallel ant colony algorithm program; several experiments were conducted, and the experimental results were analysed concurrently.

Continuous data stream analysis primarily focuses on the unanticipated changes in the transmission of data distribution over time. Conceptual change is defined as the signal distribution changes over the transmission of continuous data streams. A drift detection scenario is set forth to develop methods and strategies for detecting, interpreting, and adapting to conceptual changes over data streams. Machine learning approaches can produce poor learning outcomes in the conceptual change environment if the sudden change is not addressed. Furthermore, due to developments in concept drift, learning methodologies have been significantly systematic in recent years. The research introduces a novel approach using the fully connected committee machine (FCM) and different activation functions to address conceptual changes in continuous data streams. It explores scenarios of continual learning and investigates the effects of over-learning and weight decay on concept drift. The findings demonstrate the effectiveness of the FCM framework and provide insights into improving machine learning approaches for continuous data stream analysis. We used a layered neural network framework to experiment with different scenarios of continual learning on continuous data streams in the presence of change in the data distribution using a fully connected committee machine (FCM). In this research, we conduct experiments in various scenarios using a layered neural network framework, specifically the fully connected committee machine (FCM), to address conceptual changes in continuous data streams for continual learning under a conceptual change in the data distribution. Sigmoidal and ReLU (Rectified Linear Unit) activation functions are considered for learning regression in layered neural networks. When the layered framework is trained from the input data stream, the regression scheme changes consciously in all scenarios. A fully connected committee machine (FCM) is trained to perform the tasks described in continual learning with M hidden units on dynamically generated inputs. In this method, we run Monte Carlo simulations with the same number of units on both sides, K and M, to define the advancement of intersections between several hidden units and the calculation of generalization error. This is applied to over-learnability as a method of over-forgetting, integrating weight decay, and examining its effects when a concept drift is presented.

Double-pipe counter-flow heat exchangers are considered more suitable for heat recovery in the heat transfer industry. Numerous studies havebeen conducted to develop static tools for optimizing operating parameters of heat exchangers. Using this study, an improved heat exchanger system will be developed. This is frequently used to solve optimization problems and find optimal solutions. The Taguchi method determines the critical factor affecting a specific performance parameter of the heat exchanger by identifying the significant level of the factor affecting that parameter. Gray relational analysis was adopted to determine the gray relational grade to represent the multi-factor optimization model, and the heat exchanger gray relation coefficient target values that were predicted have been achieved using ANN with a back propagation model with the Levenberg–Marquardt drive algorithm. The genetic algorithm improved the accuracy of the gray relational grade by assigning gray relational coefficient values as input to the developed effective parameter. This study also demonstrated significant differences between experimental and estimated values. According to the results, selecting the parameters yielded optimal heat exchanger performance. Using a genetic algorithm to solve a double-pipe heat exchanger with counterflow can produce the most efficient heat exchanger.

Recommender system (RS) is a unique type of information clarification system that anticipates the user's evaluation of items from a large pool based on the expectations of a single stakeholder. The proposed system is highly useful for getting expected meaning suggestions and guidance for choosing the proper product using artificial intelligence and IoT (Internet of Things) such as chatbot. The current proposed technique makes it easier for stakeholders to make context-based decisions that are optimal rather than reactive, such as which product to buy, news classification based on high filtering views, highly recommended wanted music to choose, and desired product to choose. Recommendation systems are a critical tool for obtaining verified information and making accurate decisions. As a result, operational efficiency would skyrocket, and the risk to the company that uses a recommender system would plummet. This proposed solution can be used in a variety of applications such as commercial hotels OYO and other hotels, hospitals (GYAN), public administrative applications banks HDFC, and ICICI to address potential questions on the spot using intelligence computing as a recommendation system. The existing RS is considering a few factors such as buying records, classification or clustering items, and user's geographic location. Collaborative filtering algorithms (CFAs) are much more common approaches for cooperating to mesh the respective documents they retrieved from the historical data. CFAs are distinguished in plenty of features that are uncommon from other algorithms. In this existing system classification, precision and efficiency and error rate are statistical measurements that need to be enhanced according to the current need to fit for global requirements. The proposed work deals with enhancing accuracy levels of text reviews with the recommender system while interacting by the numerous users for their domains. The authors implemented the recommender system using a user-based CF method and presented the significance of collaborative filtering on the movie domain with a recommender system. This whole experiment has been implanted using the RapidMiner Java-based tool. Results have been compared with existing algorithms to differentiate the efficiency of the current proposed approach.

Nowadays, information is critical in every organization's decision-making process, especially in the business world. We are currently living in an information-processing civilization. Any organization or corporation relies on information about its most essential source of revenue. Every day, a large amount of sensitive information is generated by various corporate organizations such as banking, health care, and other similar organizations. This type of information is disseminated among numerous sources via various communication channels. As a result, safeguarding such sensitive information is critical in any data mining application. The data anonymization using the opposition fruitfly technique is described in detail in this article, including an upgraded privacy protection technique. For clustering, the suggested method uses an updated Fuzzy C-Means algorithm. The oppositional fruitfly algorithm, the proposed approach's privacy, has been validated for effectiveness. Comparing the suggested article with two existing methodologies demonstrates that the submitted article increased accuracy and privacy level. The suggested privacy preservation employing data anonymization methodology outperforms the existing method by 94.17% while the existing method outperforms it by 82.17%.

In this project, we are going to measure the force applied by a headphone on a human ear. For this, we are using FSR (Force Sensitive Resistor) to measure the force. The FSR is interfaced with Arduino Uno for the required purpose along with a 10 km ohm resistor (pull down resistor). The resistance offered is a sum of two resistances means FSR resistance + pull down resistor's resistance which fluctuates between 100 Kohm to 10 Kohm . Also a LCD display connected with a variable 10 Kohm resistor is used to display the force value in Newton. We have taken 2 readings of each different headset one is the maximum value observed and second is the minimum value observed. A mean is taken of these two values and an overall mean of all values of different headsets is taken. Hence the value got, is an average suitable value that a headphone should apply on human ears so that they can be worn for a longer period of time. (approx 4 to 5 hours) Keywords: Force Resistive Sensors, Arduino Nano, Force Measurement.

Innumerable plants have been used widely as integral medicinal sources since the start of human civilization. The demand for herbal medicines is constantly increasing with time overtime. Licorice ( Glycyrrhiza glabra family Leguminosae) is one of the most used herbal plants in foods, in medicinal forms, and substantially researched on a worldwide scale. It was used as traditional and complementary medicine against innumerable ailments including allergies, liver toxicity, gastric ulcer, lung diseases, skin disorders, oral health problems including tooth decay, and inflammation. The constituents of licorice include various essential oils, sugars, inorganic salts, resins, amino acids, and nucleic acids. Biological activity has been observed to be portrayed by active compounds of licorice including triterpene, flavonoids, and saponins. In recent years, licorice has been widely researched to discover its benefits, constituents, and its mechanism of action. The review summarizes the therapeutic and pharmacological benefits of licorice and its uses in different health-related conditions along with its toxicity and maximum levels of licorice consumption.

One of the most frequent antecedents of mortality in world is coronavirus that proceeds multitude metabolic disorders and is consequence of damaged lungs that is ultimately caused by contagious infection and virus called coronavirus. It effects people of every age mostly dominating in child, aged, people who have already poor immune system and lungs related disorders. It has tremendous effects on body affecting physical, emotional, cognitive and intellectual abilities as well. It leads to various clinical moderate to severe complications including acute respiratory distress syndrome (ARDS), pneumonia, coughing up blood, persistent chest pain, fever, SOB (shortness of breath), coughing up sputum, chills, sore throat, respiratory sputum production (phlegm), loss of sense and smell, headache, muscle aches, rashes and diarrhea etc. One of the most imprudent factors examined in these patients is acute respiratory distress syndrome (ARDS) as well which refers to respiratory failure, a mechanism in which body is unable to respond normally sequentially results by high level of widespread inflammation in lungs. Penurious immune system which contributes to higher rate of mortality worldwide present strong association with COVID-19. Metabolic disturbances contribute to the development and progression of respiratory distress, as well as they have serious consequences in next stages of life that can have harmful effects on health. In this review we will discuss the pathophysiology as well as the signs and symptoms of COVID-19, the morbidities related to this disease and the treatment required controlling this disorder. Keywords: Covid-19, Diet, Health Benefits.

In today's era, supply chain functions play a significant role in every business model, whether service-based or product based. Effective and efficient supply chain practices help the business grow, provide value to its buyers, and help make rational decisions that encompass the nation's economic, social, and environmental benefits. These supply chain functions can only be implemented and provide sustainable benefits if no direct or indirect disruption is inferred due to internal or external environmental factors. Our nation's textile manufacturers and exporters are the primary sources that propel the nation's economy. In short, the textile industries drive ninety per cent of the economy. Eventually, their supply chain practices are surrounded by many factors, which lead them to run or halt their operations. If we enlighten the past, a major global environmental factor, significant supply chain disruptions occur and especially strikes our country's primary economy-driven sector. It is a cross-sectional study. The present study was conducted in the urban areas of Karachi, Pakistan. Data collection will be through questionnaires and surveys. It is designed for information collection so it can use to determine the impact of this factor in significant supply chain functions, including procurement, warehousing, logistics, and import & export.

Acute myeloid leukemia (AML) is a disease characterized by hematopoietic and progenitor stem cells disorder, resulting in their proliferation and accumulation in bone marrow which leads to hematopoietic failure. It is an aggressive form of cancer that typically demands quick decision-making. In case of AML, the addition of drug efflux inhibitors to the chemotherapeutic regimen may improve outcomes in patients. With the advancement in treatment strategies, patients can now receive chemotherapy, radiation therapy or additional stem cell transplants. Specific genetic mutations in leukemic cells provide the direction for treatment and determine the overall survival rate of patients. Older patients have adverse treatment consequences as age is an important factor in AML prognosis. We critically reviewed multiple existing therapies for AML. Optimization of traditional therapies remain major concern of scientists. However, little bit advancement has been made in current years. There is immense need of novel and targeted therapies to treat AML and reduce its relapse chances.

Acute myeloid leukaemia (AML) is a heterogeneous, most common type of acute leukaemia that involves mutation in haematopoietic and progenitors stem cells (HPSCs) leading to uncontrolled division, self-renewal and differentiation. Though it was untreatable about a half century ago, AML is now considered to be treatable in up to 40% of adults and those who are at or under 60 years of age. For the post-induction treatment, the mutation testing and cytogenetics are still an important prognostic tool. The AML treatment remains unchanged for almost three decades, although the field is advanced with the discovery of new drugs and deep understanding of the disease biology. Still, many people are relapsing and are dying eventually from the disease. This review discusses the broader aspects of AML, reflecting some of the most important and productive areas of research on the subject and was conducted using thoroughly searching databases, including Health Research and Development Information network Plus (HERDIN Plus), google scholar and PubMed to critically analyses the recent advances, available treatments and future prospectives of AML.

Single nucleotide polymorphisms (SNPs) in CEBPA gene have been found to be associated with cancer especially Acute Myeloid Leukemia (AML). Therefore, the identification of functional and structural polymorphisms in CEBPA is important to study and discover therapeutics targets and potential malfunctioning. For this purpose, several bioinformatics tools were used for the identification of disease-associated nsSNPs, which might be vital for the structure and function of CEBPA, making them extremely important. In silico tools used in this study included SIFT, PROVEAN, PolyPhen2, SNP&GO and PhD-SNP, followed by ConSurf and I-Mutant. Protein 3D modelling was carried out using I-TASSER and MODELLER v9.22, while GeneMANIA and string were used for the prediction of gene-gene interaction in this regard. From our study, we found that the L345P, R333C, R339Q, V328G, R327W, L317Q, N292S, E284A, R156W, Y108N and F82L mutations were the most crucial SNPs. Additionally, the gene-gene interaction showed the genes having correlation with CEBPA’s co-expressions and importance in several pathways. In future, these 11 mutations should be investigated while studying diseases related to CEBPA, especially for AML. Being the first of its kind, future perspectives are proposed in this study, which will help in precision medicine. Animal models are of great significance in finding out CEBPA effects in disease.

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Roads undergo rapid deterioration due to various economic, social and natural reasons. The uncertainty and unavailability of road safety information has become a major issue in reliably transporting goods, movement of heavy machinery, transport of people and materials to/from disaster areas and in even giving plain guarantees for safe traversability of a road that is good on paper. In this paper, we propose a novel approach towards reducing this uncertainty. Instead of asking qualitative questions about whether a road network is generally traversable for a general class of vehicles, we propose a solution that gives precise quantitative answers as a distance modulated roadmap and confirms whether a particular segment of the road is traversable for a particular type of vehicle. In this paper, we extend on our previous work Khan et al. (2016) of traversability analysis by empirically proving that our framework is robust to shape and size of the roadmap graph. We show that obstacle size, count, and location can have varying effects on traversability of different vehicles. We also propose a new Road Safety Index (RSI) which is an extension of our previously proposed index, Road Traversability Index (RTI). RSI takes into account multiple disjoint narrow passages that belong to the same connected component of the graph. Lastly, we present application scenarios where our roadmap based traversability can be used to assess the safety of the road for a particular vehicle.

Miscible CO2-based enhanced oil recovery (CO2-EOR) flooding has been used, on many occasions, to maximize the recovery by mobilizing the residual oil. The success of this method, however, depends highly on the minimum miscibility pressure (MMP), the compatibility of CO2 with the reservoir rock and fluids. Although there have been several studies on the application of CO2-EOR, the impact of oil compositions associated with MMP on the feasibility of this technique has not been addressed elsewhere. In this study, a simulation study was carried out to investigate the effect of three different oil compositions on the miscibility and ultimate oil recovery of miscible CO2-EOR method. The results obtained indicated the oil type does not pose a significant impact on the success of miscible CO2-EOR. It was also found that the ultimate recovery of oil increases in a short period of time by injecting CO2 at the miscible conditions at the early stage of natural production. Comparatively, the reservoir with the fluid sample S2 seems to be a suitable choice for the miscible CO2 flooding once the recovery factor was considered together with the total amount of the fluid injected and produced.

An advanced Incremental processing technique is planned for data examination in knowledge to have the clustering results inform. Data is continuously arriving by different data generating factors like social network, online shopping, sensors, e-commerce etc. [1]. On account of this Big Data the consequences of data mining applications getting stale and neglected after some time. Cloud knowledge applications regularly perform iterative calculations (e.g., PageRank) on continuously converting datasets. Though going before trainings grow Map-Reduce aimed at productive iterative calculations, it's miles also pricey to carry out a whole new big-ruler Map-Reduce iterative task near well-timed quarter new adjustments to fundamental records sets. Our usage of MapReduce keeps running [4] scheduled a big cluster of product technologies and is incredibly walkable: an ordinary Map-Reduce computation procedure several terabytes of records arranged heaps of technologies. Processor operator locates the machine clean to apply: masses of MapReduce applications, we look at that during many instances, The differences result separate a totally little part of the data set, and the recently iteratively merged nation is very near the recently met state. I2MapReduce clustering adventures this commentary to keep re-calculated by way of beginning after the before affected national [2], and by using acting incremental up-dates on the converging information. The approach facilitates in enhancing the process successively period and decreases the jogging period of stimulating the consequences of big data.

Land use land cover (LULC) change has become a major concern for biodiversity, ecosystem alteration, and modifying the climatic pattern especially land surface temperature (LST). The present study assessed past and predicted future LULC and LST change in the Swabi District of Pakistan. LULC maps were generated from satellite data for years 1987, 2002, and 2017 using supervised classification. Mean LST and its areal change were estimated for different LULC classes from thermal bands of satellite images. LULC and LST were projected for the year 2047 using the integrated weighted evidence-cellular automata (WE-CA) model and a regression equation developed in this study, respectively. LULC change revealed an increase of 5% in the built-up while a decrease in the agricultural area by ~ 9%. There was an increase of ~ 63% area in the LST class ≥ 27 °C which may create urban heat island (UHI). Simulation results indicated that the built-up area will further be increased by ~ 3% until 2047. Area associated with LST class 30 °C indicated a further increase of ~ 38% till 2047 with reference to year 2017. Findings of this study suggested proper utilization of LULC in order to mitigate the creation of UHIs associated with urbanization and built-up areas.

Background: Quercus serrata Murray leaves are traditionally used for the treatment of diabetes, dysmenorrhoea, inflammation and urinary tract infection. So, far no study has reported on the toxicological profile and antioxidant properties of the plant. Objective: The present study aimed to investigate the in-vivo toxicological profile and in-vitro antioxidant activities of the methanolic extract of standardized Quercus serrata leaves. Methods: Per-oral sub-acute toxicity study was performed in rats using three dose levels (200, 400 and 800 mg/kg b.w.) of the extract for 28 days. The control group received gum acacia suspended in water. Bodyweight was measured weekly. Biochemical parameters were analysed using the serum; the blood-cell count was performed using the whole blood. Pathological changes were also checked in highly perfused tissues. Further, in-vitro reducing power assay, nitric oxide scavenging assay, and DPPH free-radical scavenging assay were performed to evaluate the antioxidant activity of the extract. Results: There were no significant alterations found in the blood-cell count and biochemical parameters analysed in the treatment group when compared with the normal control. Histopathology study of liver, kidney, pancreas, heart and brain revealed normal cellular architecture in the treatment groups alike the control group animals. Quercus serrata also showed a significant reduction of DPPH with an IC50 value of 4.48±0.254 μg/mL, in-vitro reducing power activity with an IC50 value of 121.65±0.320 μg/mL and nitric oxide scavenging activity with an IC50 value of 106.43±0.338 μg/mL. Conclusion: The study showed that standardized methanolic extract of Quercus serrata leaves was safe after sub-acute oral administration in rats and possesses good antioxidant potential.