Search

Since the outbreak of coronavirus (COVID-19) in December 2019, understanding regarding coronavirus symptoms has been evolving. Medical issues in pregnancy usually resolve after delivery. Similarly, pregnant ladies with COVID-19 pneumonia had improvement in their condition after giving birth. However, in this case, the patient tested positive for "SARS-Cov-19 antigen PCR" and intended for vaginal birth, which was unsuccessful and resulted in a caesarean surgery. After the healthy baby was delivered, she experienced a life-threatening cytokine storm that necessitated elective intubation and mechanical breathing, as well as anti-viral medication. She was extubated after two weeks and then discharged on recovery. Previously, the Middle Eastern Respiratory Syndrome Coronavirus (MERS-SARS) and the Influenza virus (H1N1) were the most well-known viruses for generating potentially fatal results in pregnancy. This is first reported instance in Pakistan of a pregnant lady developing severe COVID-19 pneumonia. This research fills a vacuum in the medical literature and alerts obstetricians and clinicians to the likelihood of life-threatening postpartum, COVID-19 pneumonia complications.

We present a rare case of a child with penetrating cardiac injury (PCI) with a knife and its successful repair. A 10-year-old boy was brought to the emergency department with penetrating injury under the nipple on the left side of the chest, with entrance sized 1x0.5 cm, in a hemodynamically unstable condition. Physical examination showed findings suggestive of cardiac tamponade and shock. Focused Assessment with Sonography in Trauma (FAST) and echocardiography visualized pericardial effusion, and chest X-ray showed left pleural effusion. Needle thoracentesis was performed, followed by urgent sternotomy. Right ventricular tear of size 1x1cm visualized and repaired. The left pleura was drained blood up to 800 milliliters. The pericardium was closed with sutures. We then placed retrosternal and pericardial drains and sealed the chest with wire sutures at the sternum. During the induction of anesthesia, the patient underwent cardiac arrest, immediate cardiopulmonary resuscitation revived the patient and surgery was performed. Here, expertise lies in performing the procedure on a pumping heart without cardiac bypass. In addition, the most common cause of PCI is an intentional gunshot injury in adults. In contrast, our case presents the accidental nature of injury in children by knife, which makes this interesting. Therefore, we documented our experience of this rare finding. In addition, we present a literature review about penetrating injury to the heart, focused on the pediatric population and survival, morbidity, and mortality of the condition with and without access to the hospital.

Background Software process improvement (SPI) is an indispensable phenomenon in the evolution of a software development company that adopts global software development (GSD) or in-house development. Several software development companies do not only adhere to in-house development but also go for the GSD paradigm. Both development approaches are of paramount significance because of their respective advantages. Many studies have been conducted to find the SPI success factors in the case of companies that opt for in-house development. Still, less attention has been paid to the SPI success factors in the case of the GSD environment for large-scale software companies. Factors that contribute to the SPI success of small as well as medium-sized companies have been identified, but large-scale companies have still been overlooked. The research aims to identify the success factors of SPI for both development approaches (GSD and in-house) in the case of large-scale software companies. Methods Two systematic literature reviews have been performed. An industrial survey has been conducted to detect additional SPI success factors for both development environments. In the subsequent step, a comparison has been made to find similar SPI success factors in both development environments. Lastly, another industrial survey is conducted to compare the common SPI success factors of GSD and in-house software development, in the case of large-scale companies, to divulge which SPI success factor carries more value in which development environment. For this reason, parametric (Pearson correlation) and non-parametric (Kendall’s Tau correlation and the Spearman correlation) tests have been performed. Results The 17 common SPI factors have been identified. The pinpointed common success factors expedite and contribute to SPI in both environments in the case of large-scale companies.

The primary objective of this study is to develop an advanced, automated system for the early detection and classification of leaf diseases in potato plants, which are among the most cultivated vegetable crops worldwide. These diseases, notably early and late blight caused by Alternaria solani and Phytophthora infestans, significantly impact the quantity and quality of global potato production. We hypothesize that the integration of Vision Transformer (ViT) and ResNet-50 architectures in a new model, named EfficientRMT-Net, can effectively and accurately identify various potato leaf diseases. This approach aims to overcome the limitations of traditional methods, which are often labor-intensive, time-consuming, and prone to inaccuracies due to the unpredictability of disease presentation. EfficientRMT-Net leverages the CNN model for distinct feature extraction and employs depth-wise convolution (DWC) to reduce computational demands. A stage block structure is also incorporated to improve scalability and sensitive area detection, enhancing transferability across different datasets. The classification tasks are performed using a global average pooling layer and a fully connected layer. The model was trained, validated, and tested on custom datasets specifically curated for potato leaf disease detection. EfficientRMT-Net’s performance was compared with other deep learning and transfer learning techniques to establish its efficacy. Preliminary results show that EfficientRMT-Net achieves an accuracy of 97.65% on a general image dataset and 99.12% on a specialized Potato leaf image dataset, outperforming existing methods. The model demonstrates a high level of proficiency in correctly classifying and identifying potato leaf diseases, even in cases of distorted samples. The EfficientRMT-Net model provides an efficient and accurate solution for classifying potato plant leaf diseases, potentially enabling farmers to enhance crop yield while optimizing resource utilization. This study confirms our hypothesis, showcasing the effectiveness of combining ViT and ResNet-50 architectures in addressing complex agricultural challenges.

Coronary artery aneurysms (CAAs) occur when an artery dilates 1.5 times the reference vessel. They occur most commonly because of atherosclerosis. CAAs are a rare phenomenon, and it is even rarer to find a giant CAA, which is roughly defined as a size 400% above the reference vessel. Giant CAAs are commonly found in the right coronary artery. The sinoatrial nodal artery (SNA) is among the least common sites for CAA involvement. Sometimes, communication exists between the aneurysm and a chamber of the heart or a great vessel. The consequences of the fistula depend on its size. Because of the rarity of the condition, guidelines are not well developed. However, small CAAs can be managed conservatively, whereas giant CAAs require resection, ligation, and bypass grafting. CAAs have a predilection for males and the elderly. We describe a 40-year-old South Asian woman presenting with mild dyspnea on exertion of 1 year’s duration. Echocardiography showed a 60×60 mm cystic sac, subsequently confirmed by computerized tomography, which showed 3 large aneurysms (70×61 mm) and 3 small aneurysms in the SNA. Coronary angiography illustrated that the SNA branched off the left main coronary artery, and the aneurysm communicated with the right coronary artery. The aneurysm was partially resected and plicated.

The lungs are critical components of the respiratory system because they allow for the exchange of oxygen and carbon dioxide within our bodies. However, a variety of conditions can affect the lungs, resulting in serious health consequences. Lung disease treatment aims to control its severity, which is usually irrevocable. The fundamental objective of this endeavor is to build a consistent and automated approach for establishing the intensity of lung illness. This paper describes MixNet-LD, a unique automated approach aimed at identifying and categorizing the severity of lung illnesses using an upgraded pre-trained MixNet model. One of the first steps in developing the MixNet-LD system was to build a pre-processing strategy that uses Grad-Cam to decrease noise, highlight irregularities, and eventually improve the classification performance of lung illnesses. Data augmentation strategies were used to rectify the dataset’s unbalanced distribution of classes and prevent overfitting. Furthermore, dense blocks were used to improve classification outcomes across the four severity categories of lung disorders. In practice, the MixNet-LD model achieves cutting-edge performance while maintaining model size and manageable complexity. The proposed approach was tested using a variety of datasets gathered from credible internet sources as well as a novel private dataset known as Pak-Lungs. A pre-trained model was used on the dataset to obtain important characteristics from lung disease images. The pictures were then categorized into categories such as normal, COVID-19, pneumonia, tuberculosis, and lung cancer using a linear layer of the SVM classifier with a linear activation function. The MixNet-LD system underwent testing in four distinct tests and achieved a remarkable accuracy of 98.5% on the difficult lung disease dataset. The acquired findings and comparisons demonstrate the MixNet-LD system’s improved performance and learning capabilities. These findings show that the proposed approach may effectively increase the accuracy of classification models in medicinal image investigations. This research helps to develop new strategies for effective medical image processing in clinical settings.

A well-known eye disorder called diabetic retinopathy (DR) is linked to elevated blood glucose levels. Cotton wool spots, confined veins in the cranial nerve, AV nicking, and hemorrhages in the optic disc are some of its symptoms, which often appear later. Serious side effects of DR might include vision loss, damage to the visual nerves, and obstruction of the retinal arteries. Researchers have devised an automated method utilizing AI and deep learning models to enable the early diagnosis of this illness. This research gathered digital fundus images from renowned Pakistani eye hospitals to generate a new “DR-Insight” dataset and known online sources. A novel methodology named the residual-dense system (RDS-DR) was then devised to assess diabetic retinopathy. To develop this model, we have integrated residual and dense blocks, along with a transition layer, into a deep neural network. The RDS-DR system is trained on the collected dataset of 9860 fundus images. The RDS-DR categorization method demonstrated an impressive accuracy of 97.5% on this dataset. These findings show that the model produces beneficial outcomes and may be used by healthcare practitioners as a diagnostic tool. It is important to emphasize that the system’s goal is to augment optometrists’ expertise rather than replace it. In terms of accuracy, the RDS-DR technique fared better than the cutting-edge models VGG19, VGG16, Inception V-3, and Xception. This emphasizes how successful the suggested method is for classifying diabetic retinopathy (DR).

Accidental penetrating injuries are common among children, either with a sharp or a blunt object. The screwdriver is an uncommon weapon; therefore, injuries caused by it represent an even rarer subset. Inadvertent injuries in the chest with a screwdriver as a stabbing weapon are extremely unusual. Penetrating chest injuries can be fatal if they cause wounds in the cardiac chambers or major thoracic vessels. We describe a 9-year-old child with an unintentional penetrating thoracic injury caused by a screwdriver. An explorative left anterior thoracotomy showed that the tip of the implanted screwdriver was lying near the left subclavian vessels and the apex of the lung, but it did not perforate any of those. The screwdriver was dislodged, and the wound was closed. The patient had an event-free 1-week hospital stay.

Congenital anomalous coronary arteries (CACAs) comprise an important variant of the coronary vasculature. They are benign in the vast majority of cases, whereas a small minority may be affected by serious consequences such as myocardial infarction, arrhythmia, cardiac arrest, and even death. We herein describe a 62-year-old man with sudden and severe substernal chest pain; Q waves in electrocardiographic leads II, III, and aVF; and positive serum troponin I enzyme. Left heart cardiac catheterization revealed triple coronary vessel disease with a 60% to 70% occlusion in the left main coronary artery (LMCA). The left anterior descending (LAD) and the left circumflex artery arose from the ostium of the right coronary artery. Additionally, a rudimentary type IV dual LAD originated from the LMCA. A coronary artery bypass graft surgery was performed using a left internal mammary artery graft for the LAD and a saphenous vein graft for the diagonal branches (I & II) of the LAD and the posterior descending artery. The patient was discharged after an uneventful 1-week hospital course.

This contribution attempts to discuss the current scenario of rare material available in the libraries of Lahore city. Lahore is capital of Punjab province of Pakistan. The study seeks to explore the various aspects such as acquisition, organization, access, preservation & conservation of rare material of libraries of Lahore, Pakistan. The study is qualitative in nature; based on multiple case study design. A multiple case study helps the researchers in finding the differences among cases. Semi-structured interview guide was used as data collection tool. The observation and document analysis of log registers of the cases (libraries) were also done wherever facilitated. It was found that rare collection is underutilized in the libraries of Lahore. Findings also established that separate funds are not allocated to the public libraries of Lahore for acquisition and management of the rare material. This paper identifies the current status and issues in acquisition, management, access and use of rare material at public libraries of Lahore, Pakistan.

As the demand for VANETs data transmission continues to increase, the defined cellular band becomes a bottleneck to meet the demands for all vehicle-to-everything (V2X) users. To deal with this problem, an efficient cluster based resource management scheme and its performance analysis for V2X networks is suggested to discover exceptional needs for various types of VANETs connections, namely vehicle-to-vehicle (V2V) and vehicle-to-infrastructure (V2I) connections, and to enhance the efficiency of cellular user with respect to sum ratio, packet received ratio and average throughput for V2I connections whereas maintaining constancy for each V2V link. To deal with the fast channel deviations because of high mobility, we developed an efficient cluster based resource management technique to attain spectrum sharing and power control that relies on large scale fading. In addition, we have also examined the resource management problem of VANETs and V2X users to minimize data communication effects. Primarily, the total cellular sum ratio of every V2I connections is employed as an analysis target to enhance the throughput and to minimize end-to-end latency of the whole V2I link. Moreover, efficient resource management and cluster head selection algorithms are developed which grant the optimum resource distribution. According to our results, the proposed scheme with efficient resource management improves cellular user sum rate, average packet received ratio and throughput in comparison with existing schemes.

Financial Technology (FinTech) has attracted a wide range of attention and is rapidly proliferating. As a result of its consistent growth new terms have been introduced in this domain. The term `FinTech' is one such terminology. This term is used for describing various operations that are being frequently employed in the financial technology sector. These operations are usually practiced in enterprises or organizations and provide requested services by using Information Technology based applications. The term does take into account various other sensitive issues, like, security, privacy, threats, cyber-attacks, etc. This is important to note that the development of FinTech is indebted to the mutual integration of different state of the art technologies, for example, technologies related to a mobile embedded system, mobile networks, mobile cloud computing, big data, data analytics techniques, and cloud computing etc. However, this technology is facing several security and privacy issues that are much needed to be addressed in order to improve the acceptability of this new technology among its users. In an effort to secure FinTech, this article provides a comprehensive survey of FinTech by reviewing the most recent as well as anticipated financial industry privacy and security issues. It provides a comprehensive analysis of current security issues, detection mechanisms and security solutions proposed for FinTech. Finally, it discusses future challenges to ensure the security and privacy of financial technology applications.

Efficient data dissemination is a big challenge for both IEEE 802.11p (MAC contentions problem) and cellular vehicle-to-everything communications (limited bandwidth in the high dense network). In this paper, a two-level clustering scheme is proposed for efficient data dissemination in 5G V2X communications. In the proposed protocol, level-1 cluster heads (L1CHs) are selected by a fuzzy logic algorithm using three factors, i.e., relative velocity factor, ${k}$ -connectivity factor, and link reliability factor. The slide link vehicle-to-vehicle (V2V) or the Third Generation Partnership Project V2V overcomes the MAC contention problem in L1CHs selections. Next, the level-2 cluster heads are selected by an improved Q-learning aiming to reduce the number of iterations in the gateway selection to LTE base station. The proposed scheme is evaluated under different network conditions showing that our protocol achieves good results compared to the existing schemes.

Abstract In this paper, the existing ad hoc on demand multipath distance vector (AOMDV) routing scheme for vehicular ad hoc networks (VANETs) is extended to a reliable low-latency multipath routing (RLLMR) scheme based on multipath link reliability, capable of determining the reliable routes preemptively. Here, the link reliability refers to the probability that a direct transmission link among any two vehicles remains constantly available for a specific period of time, which can be computed based on the position, route, and velocity of the vehicles to facilitate the reliable routing process in VANETs. In the proposed RLLMR scheme, the mathematical distribution of vehicles movements and link breakages is also considered to increase the reliability of the vehicular networks. The simulation results show that proposed RLLMR scheme performs better compared to existing schemes in terms of latency, reliability, throughput, and energy consumption at the cost of marginally increased routing overhead.

Bureaucratic organizational structure (OS) is perceived as an impediment to employees’ positive behavior including their knowledge-sharing behavior. This study investigates the role of formal, centralized and hierarchal OS in shaping the knowledge sharing behavior of public employees. It investigates the mediation role of social dilemma, i.e., a clash between self and collective interests. Cross-sectional data are collected from 309 executive employees of five federal ministries in Pakistan. The results confirm that formal and centralized OS receive significant positive association, whereas hierarchal OS receives a significant negative association with employees’ knowledge-sharing behavior. Partial negative mediation role of the social dilemma is also evident from the results. It implies that employees prefer to pursue self-interest when they find a clash between self and collective interests. Therefore, the study suggests concrete measures in human resource policies and practices that may improve the ethical environment of public sector institutions.