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Agricultural production is greatly influenced by environmental parameters such as temperature, rainfall, humidity, and wind speed. The accurate information about environmental parameters plays a vital and useful role when making policies for the agriculture sector as well as for other sectors. Pakistan meteorological department observed these environmental parameters at more than 90 stations. The allocation of these monitoring stations is not made systematically correct. This leads to inaccurate predictions for unobserved locations. The study aims to propose a monitoring network by which these prediction errors of the environmental parameters can be minimized. The well-known prediction techniques named, model-based ordinary kriging and model-based universal kriging (UK) with the known Matheron variogram model are used for prediction purposes. We investigate the monitoring network of Pakistan for rainfall and focus on both the optimal deletion/addition of monitoring stations from/to this network. The two stochastic search algorithms, spatial simulated annealing, and genetic algorithm are used for optimization purposes. Furthermore, the minimization of the Average Kriging Variance (AKV) is taken as the interpolation accuracy measure. The spatial simulated annealing exhibits a lower AKV as compared to the Genetic algorithm when adding/removing the optimal/redundant locations from the monitoring network.

Agricultural production is greatly influenced by environmental parameters such as temperature, rainfall, humidity, and wind speed. The accurate information about environmental parameters plays a vital and useful role when making policies for the agriculture sector as well as for other sectors. Pakistan meteorological department observed these environmental parameters at more than 90 stations. The allocation of these monitoring stations is not made systematically correct. This leads to inaccurate predictions for unobserved locations. The study aims to propose a monitoring network by which these prediction errors of the environmental parameters can be minimized. The well-known prediction techniques named, model-based ordinary kriging and model-based universal kriging (UK) with the known Matheron variogram model are used for prediction purposes. We investigate the monitoring network of Pakistan for rainfall and focus on both the optimal deletion/addition of monitoring stations from/to this network. The two stochastic search algorithms, spatial simulated annealing, and genetic algorithm are used for optimization purposes. Furthermore, the minimization of the Average Kriging Variance (AKV) is taken as the interpolation accuracy measure. The spatial simulated annealing exhibits a lower AKV as compared to the Genetic algorithm when adding/removing the optimal/redundant locations from the monitoring network.

Malrotation of the gut can present at any age but the majority of patients present in neonatal period or early infancy. Most children present with a history of recurrent colicky abdominal pain, vomiting which can be bilious and failure to thrive. Delay in diagnosis leads to increase in mortality and morbidity. Here we present a series of three patients, one girl aged 6 years, one 10 months old boy and one 10 years old boy, who got admitted at our Pediatric Department in a one month period. Patients had a history of recurrent abdominal pain and occasional vomiting, seen and managed by multiple paediatricians and GP doctors with extensive diagnostic workup but never the accurate diagnosis was made. Thorough history was taken and examination done, planned work up done for diagnosis. High index of suspicion is needed in children presenting with recurrent pain in the abdomen. Targeted work-up should be done and patients be kept on follow-up for diagnosis of late presentation of congenital malrotation of the gut.

Malrotation of the gut can present at any age but the majority of patients present in neonatal period or early infancy. Most children present with a history of recurrent colicky abdominal pain, vomiting which can be bilious and failure to thrive. Delay in diagnosis leads to increase in mortality and morbidity. Here we present a series of three patients, one girl aged 6 years, one 10 months old boy and one 10 years old boy, who got admitted at our Pediatric Department in a one month period. Patients had a history of recurrent abdominal pain and occasional vomiting, seen and managed by multiple paediatricians and GP doctors with extensive diagnostic workup but never the accurate diagnosis was made. Thorough history was taken and examination done, planned work up done for diagnosis. High index of suspicion is needed in children presenting with recurrent pain in the abdomen. Targeted work-up should be done and patients be kept on follow-up for diagnosis of late presentation of congenital malrotation of the gut.

Yousaf Latif Khan, 1, 2 Sohail Khursheed Lodhi, 1, 2 Shahzad Bhatti, 2–4 Waqas Ali 2 1Department of Gynecology and Obstetrics, Hameed Latif Hospital, Lahore 54800, Pakistan; 2Department of Medical Education, Rashid Latif Medical College, Lahore, Pakistan; 3Lahore Institute of Fertility and Endocrinology, Hameed Latif Hospital, Lahore 54800, Pakistan; 4Department of Human Genetics and Molecular Biology, University of Health Sciences, Lahore 54600, PakistanCorrespondence: Shahzad BhattiDepartment of Medical Education, Rashid Latif Medical College, 14 – Abu Bakar Block, New Garden Town, Lahore 54800, PakistanTel +923214529974Email drshahzadbhatti@yahoo.com Firstly, we would like to thank you for the opportunity to respond to the points raised by Rishikesh Gandhewari and Kalyan Vemulapalli and their effort to clarify aspects of our findings concerning their  suggestions.We also thank the Gandhewari and colleagues for their interest in our study of “Does Absenteeism Affect Academic Performance Among Undergraduate Medical Students? Evidence from “Rashid Latif Medical College (RLMC).” Although to the best of our knowledge, we could not find their published research finding regarding that subject.  This is in response to the Letter to the Editor View the original paper by Khan and colleagues

Symmetrical precoding and algorithms play a vital role in the field of wireless communications and cellular networks. This paper proposed a low-complexity hybrid precoding algorithm for mmWave massive multiple-input multiple-output (MIMO) systems. The traditional orthogonal matching pursuit (OMP) has a large complexity, as it requires matrix inversion and known candidate matrices. Therefore, we propose a bird swarm algorithm (BSA) based matrix-inversion bypass (MIB) OMP (BSAMIBOMP) algorithm which has the feature to quickly search the BSA global optimum value. It only directly finds the array response vector multiplied by the residual inner product, so it does not require the candidate’s matrices. Moreover, it deploys the Banachiewicz–Schur generalized inverse of the partitioned matrix to decompose the high-dimensional matrix into low-dimensional in order to avoid the need for a matrix inversion operation. The simulation results show that the proposed algorithm effectively improves the bit error rate (BER), spectral efficiency (SE), complexity, and energy efficiency of the mmWave massive MIMO system as compared with the existing OMP hybrid and SDRAltMin algorithm without any matrix inversion and known candidate matrix information requirement. Keywords: beamforming; 5G; massive MIMO; mmWave communications; spectral efficiency

Symmetrical precoding and algorithms play a vital role in the field of wireless communications and cellular networks. This paper proposed a low-complexity hybrid precoding algorithm for mmWave massive multiple-input multiple-output (MIMO) systems. The traditional orthogonal matching pursuit (OMP) has a large complexity, as it requires matrix inversion and known candidate matrices. Therefore, we propose a bird swarm algorithm (BSA) based matrix-inversion bypass (MIB) OMP (BSAMIBOMP) algorithm which has the feature to quickly search the BSA global optimum value. It only directly finds the array response vector multiplied by the residual inner product, so it does not require the candidate’s matrices. Moreover, it deploys the Banachiewicz–Schur generalized inverse of the partitioned matrix to decompose the high-dimensional matrix into low-dimensional in order to avoid the need for a matrix inversion operation. The simulation results show that the proposed algorithm effectively improves the bit error rate (BER), spectral efficiency (SE), complexity, and energy efficiency of the mmWave massive MIMO system as compared with the existing OMP hybrid and SDRAltMin algorithm without any matrix inversion and known candidate matrix information requirement.

Symmetrical precoding and algorithms play a vital role in the field of wireless communications and cellular networks. This paper proposed a low-complexity hybrid precoding algorithm for mmWave massive multiple-input multiple-output (MIMO) systems. The traditional orthogonal matching pursuit (OMP) has a large complexity, as it requires matrix inversion and known candidate matrices. Therefore, we propose a bird swarm algorithm (BSA) based matrix-inversion bypass (MIB) OMP (BSAMIBOMP) algorithm which has the feature to quickly search the BSA global optimum value. It only directly finds the array response vector multiplied by the residual inner product, so it does not require the candidate’s matrices. Moreover, it deploys the Banachiewicz–Schur generalized inverse of the partitioned matrix to decompose the high-dimensional matrix into low-dimensional in order to avoid the need for a matrix inversion operation. The simulation results show that the proposed algorithm effectively improves the bit error rate (BER), spectral efficiency (SE), complexity, and energy efficiency of the mmWave massive MIMO system as compared with the existing OMP hybrid and SDRAltMin algorithm without any matrix inversion and known candidate matrix information requirement.

Yousaf Latif Khan, 1, 2 Sohail Khursheed Lodhi, 1, 2 Shahzad Bhatti, 2– 4 Waqas Ali 2 1Department of Gynecology and Obstetrics, Hameed Latif Hospital, Lahore 54800, Pakistan; 2Department of Medical Education, Rashid Latif Medical College, Lahore, Pakistan; 3Department of Embryology, Lahore Institute of Fertility and Endocrinology, Hameed Latif Hospital, Lahore 54800, Pakistan; 4Department of Human Genetics and Molecular Biology, University of Health Sciences, Lahore 54600, PakistanCorrespondence: Shahzad BhattiDepartment of Medical Education, Rashid Latif Medical College, Lahore, PakistanTel +923214529974Email drshahzadbhatti@yahoo.comBackground: Despite the strictness in attendance policies, absenteeism is an important current issue among medical and health sciences that affects the performance of undergraduate students worldwide. Attending lectures is the key to reaping the rewards of academic achievements in undergraduate medical students. As attendance pattern and performance go hand in hand– the physical engagement of students in the classroom and practical teaching approach have a synergistic impact on the output of lower-performing students to do their best.Methods: A total of 404 full-time undergraduate MBBS male and female students of Rashid Latif Medical College (RLMC) of age 18 or above were included in this study. The principal outcome variable, such as class grades or performance, is the dependent variable, while absenteeism is a unique independent variable.Results: Our results capture the positive and significant impact of attendance coefficient in all proxy variables of regression models but exhibit significant decline from OLS3 to OLS4 when proxy variables for ability, effort, and motivation were included. However, we found the effect disappears, when we incorporate panel data fixed effect estimators to minimize the time-invariant student-specific unobservable traits on performance.Conclusion: Attendance of the student is only the