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Background: The use of smart phones inside hospitals especially in clinically sensitive areas is a subject of debate because it may improve the quality of healthcare but can also be a vehicle of hospital acquired infections. Aim: To determine dentist’s knowledge and behavior related to the use of smart phones in clinical environment and to determine the presence of microbial growth on these devices. Methods: This is a cross-sectional study in which validated survey tool was used to collect data about knowledge and behavior of 397 dental graduates from 8 dental colleges of Pakistan, regarding their usage of smart phones in clinical environment. Bacterial isolates were collected from the smart phones of 45 participants from Fatima Memorial Dental Hospital, Lahore. Results: The SPTC Scale was used to divide the participants into 3 categories; low, moderate and high users. The behavior related to smart phone usage in clinical environment was significantly different among the participants. Moderate users had significantly higher average behavior score of 3.7 (p-value = 0.034). The growth of pathogenic bacterial flora was greater on high users of smart phones (95%,) whereas those participants who were low users the percentage was 37%. Conclusion: Hospital-acquired infections (HAIs) are increasing significantly in number of patients and these can be prevented by adhering to proper hand hygiene practices and if hand hygiene is improved the amount of bacterial load will be less and disinfection of smart phone devices will not be required. Keywords: Smartphone; Dental Clinics; Smartphone time consumption scale (SPTC); cross infection; Hand washing

Background/Introduction: Professionalism is one critical cornerstone of a successful academic program and considered to be the mainstay of health care provision. It is of great importance in the life of dentists because it plays a significant factor of interpretation and prediction of human behavior in various contexts of social and clinical practice. The purpose of this study was to determine professionalism of dental students at various academic levels by PSCOM scale. Methods: This is a descriptive cross-sectional study, the participants consisted of undergraduate dental students at various academic levels. The study was conducted between 29th of December 2021 to 10th of June 2022 at various dental colleges of Pakistan. A survey questionnaire was submitted to all participants to collect data to assess their professionalism through a validated PSCOM Scale. Results: The total number of participants were 403 containing 129 (32%) of men and 236 (58.6%) of women. The minimum age of the respondents in the research sample was 18 and the maximum age was 28 years. The average value of professionalism is 2.934 for BDS1, 3.045 for BDS2, 2.959 for BDS3 and 3.013 for BDS4 respectively. The average value of sub components of professionalism is 3.044 for accountability, 2.945 for altruism, 3.038 for duty, 3.038 for excellence, 2.964 for honesty and integrity and 2.976 for respect. Conclusion: The results shows that professionalism has neither increased nor decreased significantly over the academic years which tend to overestimate the fact that there is a need to promote personal and professional development throughout the undergraduate dental education course. Keywords: Professionalism; academic levels; PSCOM scale; dental students; competency; dental care; curriculum

Background/Introduction: Social intelligence is of great importance in the life of health care professionals because it is a significant factor of prediction and interpretation of human behavior in various contexts of social and clinical practice. The purpose of this study was to determine social intelligence of dental students at various academic levels by Trosmo Social Intelligence scale. Methods: This is a descriptive cross-sectional study conducted at various dental colleges of Pakistan. Re-validated Trosmo social intelligence questionnaires were distributed to undergraduate dental students to assess their social intelligence at various academic levels. Likert scale was used to assess the opinions and attitudes of the respondents. Results: The study consisted of 403 participants, of which 129 (32%) were male and 236 (58.6%) were female. The minimum age of the respondents was 18 and 28 being the maximum. The average values of social intelligence at various academic levels are as follows: 1.944 for BDS 1, 1.980 for BDS 2, 1.87 for BDS 3, 2.053 for BDS 4, respectively. Nor growth in all three component of social intelligence was identified. Conclusion: There is insignificant change in social intelligence of students at various academic levels and no significant link is found between the three dimensions of social intelligence and the upgrading academic levels, which is concerning. Keywords: Social intelligence; academic levels; TROSMO scale; dental students, social skills, clinical practice, healthcare, human behavior, professionalism, curriculum

Recommender systems have been exhaustively implemented in the e-commerce industry for providing accurate, efficient, and effective personalized recommendations to candidate users. The variants of matrix factorization (MF) techniques incorporating the stochastic gradient descent (SGD) are exploited to improve the efficacy of recommender systems through effectively dealing the fuzzy behavior. The iterative update mechanism of MF-based SGD techniques involves current but limited information for providing related recommendations. The strength of sliding window and multi-innovation-based approximations with memory can improve the accuracy of the recommender systems through prior knowledge by utilizing the fuzziness among ratings. In this work, a moving-information-based computing paradigm is presented to effectively handle the fuzzy nature of preferences by recommender systems with the ability to capture the collective effect of ratings of previous update history obtained for a defined information length to provide fast and precise recommendations.

Artificial lift systems (ALS) are among the most extensively used production techniques in global oil and gas operations. Wells that cannot produce liquids to the surface through their own pressure require lift technologies to enable the production. In this study, a poor-boy gas lifted well was replaced with a jet pumping system to enhance the production. The software analysis, jet pump (JP) installation, and operational results are presented to validate the utilization of technology. The candidate well was spud in October 1983 and produced an average of 3,200 BOPD and 1,000 BWPD at flowing wellhead pressure (FWHP) of 2,300 psi from two formations with a slotted-liner in an open hole. However, due to an increase in water production and excessive drop in the FWHP, oil production dropped to zero. The well was brought back to production with the help of a poor-boy gas lift system after making hole punches in the tubing string. But it flowed intermittently with marginal production. A jet pump was recommended as the best lift method based on the technical analysis for ALS. The JP can easily handle a wide range of production and doesn't require rig assistance for JP retrieval and overhauling in case of any changes in future wellbore conditions. Therefore, the well was reconstructed with a 5 inch liner and completed with 3-1/2 inch tubing with a bottomhole assembly (BHA) for JP. After the workover job, the operator tried to revive the well with poor-boy gas lift technology by injecting gas into the casing annulus which passes through the ports of the jet pump's BHA into the tubing. Nevertheless, it failed to lift the well because of excessive hydrostatic head. Thus, a jet pump was brought in to revitalize its life. Based on software analysis, a high-volume jet pump was designed with 12A Nozzle/Throat combination and run in hole with a free-style JP. In the beginning, surface injection pressure and rate were kept low to kick off the well. When production became stable and oil cut increased, pumping parameters were increased gradually while monitoring daily production. These techniques helped to optimize production by tuning injection parameters at the surface. The well was revived to a producing rate of 1,240-1,290 BFPD with 45-50 BOPD at 98% water cut and injection rate and pressure of 3,350 BPD and 3,700 psi, respectively. The main benefit of the free-style JP was that it deployed and retrieved hydraulically without requiring slickline intervention. In summary, jet pump was a proven well activation and lift solution for the candidate well. There are similar cases in the Middle East and North Africa (MENA) region and globally. In this case, JP technology was found to be an effective replacement for a poor-boy gas lifted well and helped the operator enhance production of the depleting brown oil field.

Visual object tracking is still considered a challenging task in computer vision research society. The object of interest undergoes significant appearance changes because of illumination variation, deformation, motion blur, background clutter, and occlusion. Kernelized correlation filter- (KCF) based tracking schemes have shown good performance in recent years. The accuracy and robustness of these trackers can be further enhanced by incorporating multiple cues from the response map. Response map computation is the complementary step in KCF-based tracking schemes, and it contains a bundle of information. The majority of the tracking methods based on KCF estimate the target location by fetching a single cue-like peak correlation value from the response map. This paper proposes to mine the response map in-depth to fetch multiple cues about the target model. Furthermore, a new criterion based on the hybridization of multiple cues i.e., average peak correlation energy (APCE) and confidence of squared response map (CSRM), is presented to enhance the tracking efficiency. We update the following tracking modules based on hybridized criterion: (i) occlusion detection, (ii) adaptive learning rate adjustment, (iii) drift handling using adaptive learning rate, (iv) handling, and (v) scale estimation. We integrate all these modules to propose a new tracking scheme. The proposed tracker is evaluated on challenging videos selected from three standard datasets, i.e., OTB-50, OTB-100, and TC-128. A comparison of the proposed tracking scheme with other state-of-the-art methods is also presented in this paper. Our method improved considerably by achieving a center location error of 16.06, distance precision of 0.889, and overlap success rate of 0.824.

Object tracking is still an intriguing task as the target undergoes significant appearance changes due to illumination, fast motion, occlusion and shape deformation. Background clutter and numerous other environmental factors are other major constraints which remain a riveting challenge to develop a robust and effective tracking algorithm. In the present study, an adaptive Spatio-temporal context (STC)-based algorithm for online tracking is proposed by combining the context-aware formulation, Kalman filter, and adaptive model learning rate. For the enhancement of seminal STC-based tracking performance, different contributions were made in the proposed study. Firstly, a context-aware formulation was incorporated in the STC framework to make it computationally less expensive while achieving better performance. Afterwards, accurate tracking was made by employing the Kalman filter when the target undergoes occlusion. Finally, an adaptive update scheme was incorporated in the model to make it more robust by coping with the changes of the environment. The state of an object in the tracking process depends on the maximum value of the response map between consecutive frames. Then, Kalman filter prediction can be updated as an object position in the next frame. The average difference between consecutive frames is used to update the target model adaptively. Experimental results on image sequences taken from Template Color (TC)-128, OTB2013, and OTB2015 datasets indicate that the proposed algorithm performs better than various algorithms, both qualitatively and quantitatively.

Magnetic Resonance Imaging (MRI) is an important yet slow medical imaging modality. Compressed sensing (CS) theory has enabled to accelerate the MRI acquisition process using some nonlinear reconstruction techniques from even 10% of the Nyquist samples. In recent years, interpolated compressed sensing (iCS) has further reduced the scan time, as compared to CS, by exploiting the strong interslice correlation of multislice MRI. In this paper, an improved efficient interpolated compressed sensing (EiCS) technique is proposed using radial undersampling schemes. The proposed efficient interpolation technique uses three consecutive slices to estimate the missing samples of the central target slice from its two neighboring slices. Seven different evaluation metrics are used to analyze the performance of the proposed technique such as structural similarity index measurement (SSIM), feature similarity index measurement (FSIM), mean square error (MSE), peak signal to noise ratio (PSNR), correlation (CORR), sharpness index (SI), and perceptual image quality evaluator (PIQE) and compared with the latest interpolation techniques. The simulation results show that the proposed EiCS technique has improved image quality and performance using both golden angle and uniform angle radial sampling patterns, with an even lower sampling ratio and maximum information content and using a more practical sampling scheme.

In order to maintain the efficiency of any power system, there is a strict need for its continuous monitoring. The supply and the demand gap is corrected as well as the outages rectified within the specific timeframe. In order to accomplish this task, SCADA (Supervisory Control and Data Acquisition) systems are implemented. This study is conducted for capital territory of Pakistan. The capital city Islamabad is divided into different regions and their output results are estimated. The covers the impact of the SCADA systems and their implementation for Islamabad. Simulation results are conducted for the AC voltage and the AC current signs in the power systems and corresponding conclusions are devised.

Artificial lift systems are among the most widely used production technologies in global oil and gas operations. Wells that cannot produce liquids to the surface under their own pressure require lift technologies to enable production. Some liquid wells need lift assistance from the beginning and almost all require it sooner or later. In this paper, a poor-boy gas lifted well was replaced with a jet pumping system to enhance production. Well –2A was spud in July, 1984 and drilled to 14808 ft TD. After performing Drill Stem Test (DST), the well was completed with dual strings of 3-1/2 inche. It produced from Chorgali/Sakessar formations with an average 3,500 BOPD, 9.0 MMSCFD at 2,500 psi of WHP since 1985. However, the production declined to zero between 1991–1995, the well was revived on production, yet halted again. It was then brought back on production in 2002 and 2004, with the help of poor-boy gas lift, by tubing punches, but the water cut reached 100% during the lift and the well was shut in. Well –2A was revitalized again on production by poor-boy gas lift, in which gas was injected through the ports of a jet pump Bottom Hole Assembly (BHA), after performing a work-over, where it was completed with a single string. Yet, because of repeated failure of the gas compressors, unavailability of high-pressure compressors and marginal production, the well was put on jet pump. The jet pump was run in hole with 12A Nozzle/Throat combination in free-style after ensuring sufficient liquid level within the wellbore. Based on the results, a marginal well was optimized with the cumulative production of 820 BPD i.e., an average of 120 BOPD and 700 BWPD, which was higher than the targeted production rates evaluated for this well. A free-style jet pump deployment achieved the expected performance and reliability. Moreover, the jet pump can easily be re-optimized by reversing it out to change the nozzle and/or throat without requiring sick-line job.

Face detection is a very hot research topic in the fields of pattern recognition and computer vision. Its applications are widely used in artificial intelligence, surveillance video, identity authentication and human machine interaction. Face detection is based on identifying and locating a human face in the image, regardless of position, size, and condition. Various algorithms are proposed to detect faces in an image. This implementation is based on adaptive boosting algorithm and uses haar features which is based on statistical methods to detect face. Algorithm is implemented in MATLAB and synthesized by using Verilog on XILINX.