Morphological Studies of the Predatory Ladybird Beetle Stethorus vegans (Blackburn) (Coleoptera: Coccinellidae)

Farman Ullah,Inamullah Khan,Hart, Robert-Spooner,Peter Bailey,Khalil, Said-Khan Korean Society of Applied Entomology 2002 Journal of Asia-Pacific Entomology Vol.5 No.1

Morphological features of the ladybird beetle Stethorus vegans (Blackburn) were studied at 25 $\pm$ $2^{\circ}$ with a photoperiod of 16L: 8D. All stages of S. vahans were examined under a stereo-zoom microscope. Newly laid eggs were translucent white, turning pale yellow after 4-5 hours. The mean egg dimension was 0.36 x 0.19 mm. Eggs laid by unmated females did not hatch or show any signs of development. Newly emerged larvae were white in color, but soon became pale creamy-white. There were four larval instars, which were differentiated from each other by the presence of exuviae and differences in head capsule size. The pre-pupa, not a distinct stage in the life cycle but a quiescent period at the end of the 4th larval instar, lasted for several hours. Pupae were oval, flattened and black-brown with (me hair like setae on their dorsal sides with a mean length and width of 1.06 x 0.74 mm. The adults were oval, convex and black with small yellow setae on their dorsal side.

Molecular Epidemiology of Methicillin Resistant Staphylococcus aureus Colonization in Burnt Patients

Farman Ullah Khan,Bashir Ahmad,Jawad Ahmed,Sun Young Hwang,Han Sang Yoo 대한수의학회 2013 대한수의학회 학술대회발표집 Vol.2013 No.-

Effect of extraction methods on structural, physiochemical and functional properties of dietary fiber from defatted walnut flour

Gul Mali Khan,Nasir Mehmood Khan,Zia Ullah Khan,Farman Ali,Abdul Khaliq Jan,Nawshad Muhammad,Rizwan Elahi 한국식품과학회 2018 Food Science and Biotechnology Vol.27 No.4

The effect of different extraction methods i.e. extraction with alkali (AEDF), enzyme (EEDF) and enzyme plus shear emulsifying hydrolysis (SEDF) on structure, physiochemical as well as the functional characteristics of dietary fiber (DF) from defatted walnut flour were studied. AEDF process showed significantly higher (P\0.05) amount of water retention capacity (WRC; 5.39 g/g), water swelling capacity (WSC; 3.16 g/mL), and particle size; while, shown lower value of oil adsorption capacity (OAC; 29 g/g) amongst all. Compared to AEDF, no major differences were observed in network except the matrix in EEDF and SEDF was more porous and honey comb like. DF extracted through AEDF, EEDF and SEDF showed good viscosity and emulsifying activity however, less stability indices. The results from this study suggest that AEDF and EEDF and SEDF had specific effects on the structure-functional properties of DF from defatted walnut flour, which has great potential in food applications.

Effects of Maillard reaction on physicochemical and functional properties of walnut protein isolate

Sahibzada Fahim Ullah,Nasir Mehmood Khan,Farman Ali,Shujaat Ahmad,Zia Ullah Khan,Noor Rehman,Abdul Khaliq Jan,Nawshad Muhammad 한국식품과학회 2019 Food Science and Biotechnology Vol.28 No.5

In this study, the Maillard reaction (MR) ofglucose was applied to improve the physicochemical andfunctional properties of walnut protein isolate (WNPI). TheMR products (MRPs) were prepared with glucose at 0 h(MRP0), 1 h (MRP1), 2 h (MRP2) and 3 h (MRP3) heatingat 95 C. The Infra-Red spectrum showed reduction ofamide and S–H functionalities in MRPs with completeintermixing of glucose in MRP3. Scanning electronmicroscopy indicated changes in the morphology of MRP3which also exhibited promising antioxidant effect. Significantdecrease (P\0.05) in hydrophobicity values (Ho)and increase (P\0.05) in emulsifying activity/emulsifyingstability indexes values were observed for MRPs. Uniform droplet distribution was observed in microscopyof emulsions while an increase in the interfacial proteinconcentration (U) was obtained for MRPs. These resultssuggest that MR is useful in improving the utilization ofthis protein in food product development.

UAV-enabled healthcare architecture: Issues and challenges

Ullah, Sana,Kim, Ki-Il,Kim, Kyong Hoon,Imran, Muhammad,Khan, Pervez,Tovar, Eduardo,Ali, Farman North-Holland 2019 Future generations computer systems Vol.97 No.-

<P><B>Abstract</B></P> <P>Unmanned Aerial Vehicles (UAVs) have great potential to revolutionize the future of automotive, energy, and healthcare sectors by working as wireless relays to improve connectivity with ground networks. They are able to collect and process real-time information by connecting existing network infrastructures including Internet of Medical Things (e.g., Body Area Networks (BANs)) and Internet of Vehicles with clouds or remote servers. In this article, we advocate and promote the notion of employing UAVs as data collectors. To demonstrate practicality of the idea, we propose a UAV-based architecture to communicate with BANs in a reliable and power-efficient manner. The proposed architecture adopts the concept of wakeup-radio based communication between a UAV and multiple BANs. We analyze the performance of the proposed protocol in terms of throughput and delay by allocating different priorities to the hubs or gateways. The proposed architecture may be useful in remote or disaster areas, where BANs have poor or no access to conventional wireless communication infrastructure, and may even assist vehicular networks by monitoring driver’s physiological conditions through BANs. We further highlight open research issues and challenges that are important for developing efficient protocols for UAV-based data collection in smart healthcare systems.</P>

Smart Real-Time Video Surveillance Platform for Drowsiness Detection Based on Eyelid Closure

Tayab Khan, Muhammad,Anwar, Hafeez,Ullah, Farman,Ur Rehman, Ata,Ullah, Rehmat,Iqbal, Asif,Lee, Bok-Hee,Kwak, Kyung Sup WILEY INTERSCIENCE 2019 WIRELESS COMMUNICATIONS AND MOBILE COMPUTING Vol.2019 No.-

<P>We propose drowsiness detection in real-time surveillance videos by determining if a person’s eyes are open or closed. As a first step, the face of the subject is detected in the image. In the detected face, the eyes are localized and filtered with an extended Sobel operator to detect the curvature of the eyelids. Once the curves are detected, concavity is used to tell whether the eyelids are closed or open. Consequently, a concave upward curve means the eyelid is closed whereas a concave downwards curve means the eye is open. The proposed method is also implemented on hardware in order to be used in real-time scenarios, such as driver drowsiness detection. The evaluation of the proposed method used three image datasets, where images in the first dataset have a uniform background. The proposed method achieved classification accuracy of up to 95% on this dataset. Another benchmark dataset used has significant variations based on face deformations. With this dataset, our method achieved classification accuracy of 70%. A real-time video dataset of people driving the car was also used, where the proposed method achieved 95% accuracy, thus showing its feasibility for use in real-time scenarios.</P>

Type-2 fuzzy ontology–aided recommendation systems for IoT–based healthcare

Ali, Farman,Islam, S.M. Riazul,Kwak, Daehan,Khan, Pervez,Ullah, Niamat,Yoo, Sang-jo,Kwak, K.S. Elsevier 2018 Journal of Computer Communications Vol.119 No.-

<P><B>Abstract</B></P> <P>The number of people with a chronic disease is rapidly increasing, giving the healthcare industry more challenging problems. To date, there exist several ontology and IoT-based healthcare systems to intelligently supervise the chronic patients for long-term care. The central purposes of these systems are to reduce the volume of manual work in recommendation systems. However, due to the increase of risk and uncertain factors of the diabetes patients, these healthcare systems cannot be utilized to extract precise physiological information about patient. Further, the existing ontology-based approaches cannot extract optimal membership value of risk factors; thus, it provides poor results. In this regards, this paper presents a type-2 fuzzy ontology–aided recommendation systems for IoT-based healthcare to efficiently monitor the patient's body while recommending diets with specific foods and drugs. The proposed system extracts the values of patient risk factors, determines the patient's health condition via wearable sensors, and then recommends diabetes-specific prescriptions for a smart medicine box and food for a smart refrigerator. The combination of type-2 Fuzzy Logic (T2FL) and the fuzzy ontology significantly increases the prediction accuracy of a patient's condition and the precision rate for drug and food recommendations. Information about the patient's disease history, foods consumed, and drugs prescribed is designed in the ontology to deliver decision-making knowledge using Protégé Web Ontology Language (OWL)-2 tools. Semantic Web Rule Language (SWRL) rules and fuzzy logic are employed to automate the recommendation process. Moreover, Description Logic (DL) and Simple Protocol and RDF Query Language (SPARQL) queries are used to evaluate the ontology. The experimental results show that the proposed system is efficient for patient risk factors extraction and diabetes prescriptions.</P> <P><B>Highlights</B></P> <P> <UL> <LI> The available healthcare systems are imperfect to extract precise physiological information of patients. </LI> <LI> The classical ontologies are unable to recommend diets without knowing the current condition of a patient. </LI> <LI> Wearable sensors with type-2 fuzzy logic efficiently monitor the patient's body. </LI> <LI> Fuzzy ontology-based knowledge precisely suggests diabetes-specific prescriptions. </LI> <LI> Type-2 fuzzy ontology significantly increases the prediction accuracy of a patient's condition. </LI> </UL> </P>

Aberrant Promoter Methylation at CpG Cytosines Induce the Upregulation of the E2F5 Gene in Breast Cancer

Arshad Ali,Farman Ullah,Irum Sabir Ali,Ahmad Faraz,Mumtaz Khan,Syed Tahir Ali Shah,Nawab Ali,Muhammad Saeed 한국유방암학회 2016 Journal of breast cancer Vol.19 No.2

Purpose: The promoter methylation status of cell cycle regulatory genes plays a crucial role in the regulation of the eukaryotic cell cycle. CpG cytosines are actively subjected to methylation during tumorigenesis, resulting in gain/loss of function. E2F5 gene has growth repressive activities; various studies suggest its involvement in tumorigenesis. This study aims to investigate the epigenetic regulation of E2F5 in breast cancer to better understand tumor biology. Methods: The promoter methylation status of 50 breast tumor tissues and adjacent normal control tissues was analyzed. mRNA expression was determined using SYBR® green quantitative polymerase chain reaction (PCR), and methylation- specific PCR was performed for bisulfite-modified genomic DNA using E2F5-specific primers to assess promoter methylation. Data was statistically analyzed. Results: Significant (p<0.001) upregulation was observed in E2F5 expression among tumor tissues, relative to the control group. These samples were hypo-methylated at the E2F5 promoter region in the tumor tissues, compared to the control. Change in the methylation status (Δmeth) was significantly lower (p=0.022) in the tumor samples, indicating possible involvement in tumorigenesis. Patients at the postmenopausal stage showed higher methylation (75%) than those at the premenopausal stage (23.1%). Interestingly, methylation levels gradually increased from the early to the advanced stages of the disease (p<0.001), which suggests a putative role of E2F5 methylation in disease progression that can significantly modulate tumor biology at more advanced stage and at postmenopausal age (Pearson’s r=0.99 and 0.86, respectively). Among tissues with different histological status, methylation frequency was higher in invasive lobular carcinoma (80.0%), followed by invasive ductal carcinoma (46.7%) and ductal carcinoma in situ (20.0%). Conclusion: Methylation is an important epigenetic factor that might be involved in the upregulation of E2F5 gene in tumor tissues, which can be used as a prognostic marker for breast cancer.

The Impact of Ethical Leadership on Employees Turnover Intention: An Empirical Study of the Banking Sector in Malaysia

Tajneen Affnaan SALEH,Wajid MEHMOOD,Jehanzeb KHAN,Farman Ullah JAN 한국유통과학회 2022 The Journal of Asian Finance, Economics and Busine Vol.9 No.2

The purpose of this paper is to investigate the influence of ethical leadership in determining the organizations’ individual-type ethical climate (self-interest, friendship, and personal morality ethical climate) in reducing employee turnover intention. It seeks to identify the role of individual-type climate in mediating the association between ethical leadership and employee turnover intention. Moreover, the moderation effect of emotional exhaustion among employees on the relationship between ethical leadership and turnover intention has been researched to establish the ethical degree of leadership. Using a sample of 260 questionnaires from employees working full-time in the banking sector, the results were analyzed in PLS-SEM. The results of the social exchange theory indicated that ethical leadership is vital in shaping the workplace’s individual-type ethical climate and reducing employees’ turnover intention. The findings demonstrate that the relationship between ethical leadership and turnover intention is mediated by an individual-type ethical climate, which means that employees in a positive ethical climate do not wish to leave immediately. Furthermore, emotional exhaustion was found to moderate the association between ethical leadership and employees’ turnover intention under high emotional exhaustion, where low ethical leadership is experienced, reporting higher levels of turnover intention.

Estimation of Solar Panel Output based on Weather Parameters using Machine Learning Algorithms

Raheel Siddiqui,Sulaiman Umer,Asif Iqbal,Farman Ullah,Ajmal Khan,Kyung Sup Kwak 한국통신학회 2020 한국통신학회 학술대회논문집 Vol.2020 No.8

Solar energy is one of the most extensively used renewable energy sources. However, it is highly variable and needs accurate estimation for its wide range of integration into the electricity grid. Solar voltage and current are estimated in areas where only sunlight is considered as a primary solar parameter, and information about their weather conditions are unknown. Weather plays a vital role in the prediction of solar panel output. In this paper, we propose solar panel output prediction considering the solar panel and weather parameters using machine learning algorithms. We estimate the solar panel voltage and current consider the weather parameters such as temperature, humidity, rain rate, wind speed, and wind direction. For estimating the output voltage and current, Linear Regression (LR) and Artificial Neural Network (ANN) are applied on weather and solar data. The datasets are extracted from Bancroft close 49KW substation, which is placed in the UK, for three months. The performance of the given model is evaluated using two matrices Root Mean Square Error (RMSE) and Absolute Error (AE). The Neural Network shows better accuracy compared to the linear regression.