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A Uniquely Finned Tube Heat Exchanger Design of a Condenser for Heavy-Duty Air Conditioning Systems
Ahmad M. Abubaker,Yousef S. H. Najjar,Adnan Darwish Ahmad 대한설비공학회 2020 International Journal Of Air-Conditioning and Refr Vol.28 No.1
This study introduces the design of a novel condenser for air conditioning systems. Enhancement of performance of the system was manifested by increasing the coefficient of performance (COP), decreasing the pressure drop and the power consumed by the refrigerant compressor and the cooling water pump. The design consists of an adiabatic double-pipe heat exchanger with longitudinal rectangular fins. This model can enhance heat transfer coefficient and expose more area per unit length. This novel design supersedes other conventional condenser designs by 4.7% higher COP, 8.2% lower water pressure drop, 4.68% lower compressor power. Two refrigerants have been examined in the study; R-134a which is used in commercial and industrial chillers and R-1234ze which has low global warming potential.
Adnan Malik,Faisal Inayat,Muhammad Hassan Naeem Goraya,Gul Nawaz,Ahmad Mehran,Atif Aziz,Saad Saleem 대한소화기내시경학회 2023 Clinical Endoscopy Vol.56 No.2
A plethora of paraneoplastic syndromes have been reported as remote effects of colorectal carcinoma (CRC). However, there is a dearth of data pertaining to the association of this cancer with demyelinating neuropathies. Herein, we describe the case of a young woman diagnosed with chronic inflammatory demyelinating polyneuropathy (CIDP). Treatment with intravenous immunoglobulins and prednisone did not improve her condition, and her neurological symptoms worsened. Subsequently, she was readmitted with exertional dyspnea, lightheadedness, malaise, and black stools. Colonoscopy revealed a necrotic mass in the ascending colon, which directly invaded the second part of the duodenum. Pathologic results confirmed the diagnosis of locally advanced CRC. Upon surgical resection of the cancer, her CIDP showed dramatic resolution without any additional therapy. Patients with CRC may develop CIDP as a type of paraneoplastic syndrome. Clinicians should remain cognizant of this potential association, as it is of paramount importance for the necessary holistic clinical management.
Ahmad, Naveed,Javed, Mohsin,Qamar, Muhammad A.,Kiran, Umbreen,Shahid, Sammia,Akbar, Muhammad B.,Sher, Mudassar,Amjad, Adnan Techno-Press 2022 Advances in materials research Vol.11 No.3
It includes the synthesis of pristine ZnO nanoparticles and a series of Ag-doped zinc oxide nanoparticles was carried out by reflux method by varying the amount of silver (1, 3, 5, 7 and 9% by mol.). The morphology of these nanoparticles was investigated by SEM, XRD and FT-IR techniques. These techniques show that synthesized particles are homogenous spherical nanoparticles having an average particle size of about 50-100 nm along with some agglomeration. The photocatalytic activity of the ZnO nanoparticles and Ag doped ZnO nanoparticles were investigated via photodegradation of methylene blue (MB) as a standard dye. The data from the photocatalytic activity of these nanoparticles show that 7% Ag-doped ZnO nanoparticles exhibit much enhanced photocatalytic activity as compared to pristine ZnO nanoparticles and other percentages of Ag-doped ZnO nanoparticles. Furthermore, 7% Ag-doped ZnO was made composites with sulfur-doped graphitic carbon nitride by physical mixing method and a series of nanocomposites were made (3.5, 7.5, 25, 50, 75% by weight). It was observed that the 25% composites exhibited better photocatalytic performance than pristine S-g-C 3 N 4 and pure 7% Ag-doped ZnO. Tauc's plot also supports the photodegradation results.
( Adnan Farooq ),( Ahmad Jalal ),( Shaharyar Kamal ) 한국인터넷정보학회 2015 KSII Transactions on Internet and Information Syst Vol.9 No.5
This paper addresses the issues of 3D human activity detection, tracking and recognition from RGB-D video sequences using a feature structured framework. During human tracking and activity recognition, initially, dense depth images are captured using depth camera. In order to track human silhouettes, we considered spatial/temporal continuity, constraints of human motion information and compute centroids of each activity based on chain coding mechanism and centroids point extraction. In body skin joints features, we estimate human body skin color to identify human body parts (i.e., head, hands, and feet) likely to extract joint points information. These joints points are further processed as feature extraction process including distance position features and centroid distance features. Lastly, self-organized maps are used to recognize different activities. Experimental results demonstrate that the proposed method is reliable and efficient in recognizing human poses at different realistic scenes. The proposed system should be applicable to different consumer application systems such as healthcare system, video surveillance system and indoor monitoring systems which track and recognize different activities of multiple users.
Plants Disease Phenotyping using Quinary Patterns as Texture Descriptor
( Wakeel Ahmad ),( S. M. Adnan Shah ),( Aun Irtaza ) 한국인터넷정보학회 2020 KSII Transactions on Internet and Information Syst Vol.14 No.8
Plant diseases are a significant yield and quality constraint for farmers around the world due to their severe impact on agricultural productivity. Such losses can have a substantial impact on the economy which causes a reduction in farmer's income and higher prices for consumers. Further, it may also result in a severe shortage of food ensuing violent hunger and starvation, especially, in less-developed countries where access to disease prevention methods is limited. This research presents an investigation of Directional Local Quinary Patterns (DLQP) as a feature descriptor for plants leaf disease detection and Support Vector Machine (SVM) as a classifier. The DLQP as a feature descriptor is specifically the first time being used for disease detection in horticulture. DLQP provides directional edge information attending the reference pixel with its neighboring pixel value by involving computation of their grey-level difference based on quinary value (-2, -1, 0, 1, 2) in 0o, 45o, 90o, and 135o directions of selected window of plant leaf image. To assess the robustness of DLQP as a texture descriptor we used a research-oriented Plant Village dataset of Tomato plant (3,900 leaf images) comprising of 6 diseased classes, Potato plant (1,526 leaf images) and Apple plant (2,600 leaf images) comprising of 3 diseased classes. The accuracies of 95.6%, 96.2% and 97.8% for the above-mentioned crops, respectively, were achieved which are higher in comparison with classification on the same dataset using other standard feature descriptors like Local Binary Pattern (LBP) and Local Ternary Patterns (LTP). Further, the effectiveness of the proposed method is proven by comparing it with existing algorithms for plant disease phenotyping.
Scour modeling using deep neural networks based on hyperparameter optimization
Mohammed Asim,Adnan Rashid,Tanvir Ahmad 한국통신학회 2022 ICT Express Vol.8 No.3
Design of bridge piers and abutments is significantly impacted by hydrodynamic processes that cause scouring of the foundation. Although, many empirical formulae are available in the literature to estimate the depth of scouring, but they suffer from several limitations. A major limitation of empirical formulae is that they are largely applicable to the hydraulic conditions for which they have been derived. In this research, a deep neural network (DNN) has been developed and applied to predict the depth of scour around bridge piers and abutments. The practicality of the proposed model has been demonstrated using the experimental data sets consisting of 211 data points. The novelty of the DNN model applied herein lies in the use of Adam Optimizer for optimizing the parameters of the DNN model. The performance of the DNN model was evaluated for each parameter set using statistical indicators such as the coefficient of determination, root mean square error, and mean absolute error. A regression equation based upon the available data set has also been proposed. Based upon the values of the statistical parameters, the DNN model has been found to be significantly better than the regression model. The model proposed herein performs better than the regression model. A distinct practical advantage of the model proposed herein is that it eliminates the need of hit and trial procedure to determine the optimal parameter set for the model.
Protecting Privacy of User Data in Intelligent Transportation Systems
Yazed Alsaawy,Ahmad Alkhodre,Adnan Abi Sen International Journal of Computer ScienceNetwork S 2023 International journal of computer science and netw Vol.23 No.5
The intelligent transportation system has made a huge leap in the level of human services, which has had a positive impact on the quality of life of users. On the other hand, these services are becoming a new source of risk due to the use of data collected from vehicles, on which intelligent systems rely to create automatic contextual adaptation. Most of the popular privacy protection methods, such as Dummy and obfuscation, cannot be used with many services because of their impact on the accuracy of the service provided itself, they depend on changing the number of vehicles or their physical locations. This research presents a new approach based on the shuffling Nicknames of vehicles. It fully maintains the quality of the service and prevents tracking users permanently, penetrating their privacy, revealing their whereabouts, or discovering additional details about the nature of their behavior and movements. Our approach is based on creating a central Nicknames Pool in the cloud as well as distributed subpools in fog nodes to avoid intelligent delays and overloading of the central architecture. Finally, we will prove by simulation and discussion by examples the superiority of the proposed approach and its ability to adapt to new services and provide an effective level of protection. In the comparison, we will rely on the wellknown privacy criteria: Entropy, Ubiquity, and Performance.