PRIMARY GASTROINTESTINAL STROMAL TUMOUR OF THE PROSTATE: A CASE REPORT OF A RARE TUMOUR

A 70-year-old gentleman underwent prostatectomy for bladder outlet obstruction due to enlarged prostate and was found to have primary extragastrointestinal stromal tumour (EGIST). He has been started on imatinib therapy and is presently on follow-up. Prostatic EGIST should be one of the differential diagnoses in patients with enlarged prostate with normal prostate-specific antigen levels.Key words: Prostate, gastrointestinal stromal tumour, PSA 

Intrusion detection based on bidirectional long short-term memory with attention mechanism

With the recent developments in the Internet of Things (IoT), the amount of data collected has expanded tremendously, resulting in a higher demand for data storage, computational capacity, and real-time processing capabilities. Cloud computing has traditionally played an important role in establishing IoT. However, fog computing has recently emerged as a new field complementing cloud computing due to its enhanced mobility, location awareness, heterogeneity, scalability, low latency, and geographic distribution. However, IoT networks are vulnerable to unwanted assaults because of their open and shared nature. As a result, various fog computing-based security models that protect IoT networks have been developed. A distributed architecture based on an intrusion detection system (IDS) ensures that a dynamic, scalable IoT environment with the ability to disperse centralized tasks to local fog nodes and which successfully detects advanced malicious threats is available. In this study, we examined the time-related aspects of network traffic data. We presented an intrusion detection model based on a two-layered bidirectional long short-term memory (Bi-LSTM) with an attention mechanism for traffic data classification verified on the UNSW-NB15 benchmark dataset. We showed that the suggested model outperformed numerous leading-edge Network IDS that used machine learning models in terms of accuracy, precision, recall and F1 score

Asymmetric Impact of Institutional Quality on Tourism Inflows Among Selected Asian Pacific Countries

From an idealistic viewpoint, the existence of the tourism industry in a country/region is a blessing because of its anticipated sustainable economic benefits. To turn this idealistic state into a realistic one, institutions need to play a pivotal role in optimizing the desired incentives. The present study examines the asymmetric role of institutional quality in stimulating tourism inflows (receipts and arrivals) in selected Asia Pacific countries involved in tourism. The previous literature has established that improving institutional quality attracts tourism inflows to a destination. However, the literature fails to identify the specific point (threshold level) above (below) which the relationship turns positive (negative). To the best of our knowledge, this is the first study that estimates the asymmetries in the nexus of institutions and tourism inflows, using robust nonlinear autoregressive distributed lag approach. Our results show that the tourism inflow in Asian Pacific countries responds asymmetrically to any changes in institutional quality, and there is a single threshold of 7.52 points, where the impact of institutional quality reverses. We conclude that our findings are robust to the alternative measures of tourism inflows. The study offers useful policy inputs for devising short and long-run policies for the betterment of the institutional framework in the region by understanding the asymmetric impact of institutional quality on tourism inflow

Domestic animals’ identification using PCR-RFLP analysis of cytochrome b gene

Background: Species identification is an important process to identify the origin of meat, adulteration and for  cooked and processed meat. The present study was conducted to identify cattle (Bos taurus) and buffalo (Bubalus bubalis) by using mitochondrial cytochrome-b (Cyt-b) gene. Size of the gene is 1140 bp, but we amplified 359 bp that is cleaved by specific restriction endonucleases. The aim of this study was species identification through Cyt-b gene by using PCR-RFLP analysis.Methods: For this study, 55 blood samples were collected from different species of domestic animals. The DNA was extracted from the whole blood through blood extraction kit. The DNA of these samples were amplified through PCR using universal Cyt-b primers. The amplified product was treated with restriction enzymes Alu I. The resultant fragments were viewed on 3.0 % agarose gel.Results: Cyt-b gene was amplified of all included animals. Different bands were observed as compared with 50 bp DNA ladder. Animals were identified on the base RFLP mediated by Alu1 restriction enzyme.Conclusion: We identified domestic animals on the basis of Mitochondrial Cyt-b gene by the process of PCR-RFLP. To identify specific animals through RFLP, a larger sample size and confirmation by gene sequence analysis may be helpful.Keywords: Domestic Animal Identification; Cytochrome b gene; AluI restriction enzyme; PCR-RFLP Analysi

A Novel Image Retrieval Based on a Combination of Local and Global Histograms of Visual Words

Content-based image retrieval (CBIR) provides a sustainable solution to retrieve similar images from an image archive. In the last few years, the Bag-of-Visual-Words (BoVW) model gained attention and significantly improved the performance of image retrieval. In the standard BoVW model, an image is represented as an orderless global histogram of visual words by ignoring the spatial layout. The spatial layout of an image carries significant information that can enhance the performance of CBIR. In this paper, we are presenting a novel image representation that is based on a combination of local and global histograms of visual words. The global histogram of visual words is constructed over the whole image, while the local histogram of visual words is constructed over the local rectangular region of the image. The local histogram contains the spatial information about the salient objects. Extensive experiments and comparisons conducted on Corel-A, Caltech-256, and Ground Truth image datasets demonstrate that the proposed image representation increases the performance of image retrieval

DOES PATHOLOGICAL T3A UPSTAGING OF CLINICAL T1 STAGE HAS ANY DIFFERENCE ON LONG-TERM SURVIVAL WHEN COMPARED TO PATHOLOGICAL AND CLINICAL T1 STAGE RENAL CELL CARCINOMA

Background: A high number of clinical T1 (cT1) stage renal cell carcinoma (RCC) is upstaged to pathological T3a (pT3a) stage on histopathological findings. Several study results show that there is no survival difference among those cT1 stage who are upstaged on histopathological findings to those who remain pT1 stage RCC.Objectives: The objectives of this study were to assess any survival difference for cT1 stage renal cell carcinoma (RCC) which is upstaged to pT3a stage as compared to those which remain pT1 stage RCC on histopathological findings.Materials and Methods: It was a retrospective cohort study looking at patient aged ≥18 years with cT1 RCC who underwent nephrectomy between January 2006 and December 2016. Patients were divided into two groups based on histopathological findings (pT1 vs. pT3a). Survival was analysed for the  two groups using Kaplan–Meier method, and the difference in survival was calculated using log-rank model.Results: The study included 187 patients. The mean age at presentation was 52.56 years, with 58.3% of the patients being male while 41.7% were female. The most common presentation was incidental diagnosis (50.3%). Overall5-year survival for cT1a and pT1a RCC was 68% while that for cT1a and pT3a RCC was 100%. There was no significant survival difference among the two groups (P = 0.316). The overall 5-year survival for cT1b and pT1b RCC was 81% while that for cT1b and pT3a was 65%. There was no significant survival difference among the two groups (P = 0.136).Conclusion: We found no survival difference in cT1 RCC who were upstaged to pT3a on histopathology as compared to cT1 RCC-staged pT1 on histopathology.Key words: Clinical T1 stage, pathological T3a stage, radical nephrectomy, renal cell carcinoma, surviva

Sharia Screening Process: A Comparison of Pakistan and Malaysia

This paper aims to examine the Sharia screening methodologies used by Securities Commission of Malaysia and KSE Meezan Index (KMI-30 of Pakistan). The two set of screens used by both Islamic indices are business screens and financial screens. The existence of certain similarities and differences in screening methodology is evident. The findings also implicate that there is a dire need for standardisation of said process which will be beneficial in many ways and will surely aid in the development of ICM worldwide.

Towards On-Device AI and Blockchain for 6G enabled Agricultural Supply-chain Management

6G envisions artificial intelligence (AI) powered solutions for enhancing the quality-of-service (QoS) in the network and to ensure optimal utilization of resources. In this work, we propose an architecture based on the combination of unmanned aerial vehicles (UAVs), AI and blockchain for agricultural supply-chain management with the purpose of ensuring traceability, transparency, tracking inventories and contracts. We propose a solution to facilitate on-device AI by generating a roadmap of models with various resource-accuracy trade-offs. A fully convolutional neural network (FCN) model is used for biomass estimation through images captured by the UAV. Instead of a single compressed FCN model for deployment on UAV, we motivate the idea of iterative pruning to provide multiple task-specific models with various complexities and accuracy. To alleviate the impact of flight failure in a 6G enabled dynamic UAV network, the proposed model selection strategy will assist UAVs to update the model based on the runtime resource requirements.Comment: 8 pages, 5 figures, 1 table. Accepted to IEEE Internet of Things Magazin

An Examination of Challenges and Prospects of Microfinance Sector of Pakistan

Abstract The aim of this study refers to highlighting the key challenges and prospects for the microfinance sector of Pakistan. This study has been carried out using the following four steps: present scenario of microfinance in Pakistan, identification of internal and external challenges, prospects in microfinance, and policy recommendations to boost microfinance sector in Pakistan. In this research, microfinance framework has been used to better understand the process of building a sustainable microfinance institution. The findings have been obtained through Primary data, which has been collected through questionnaires. The participants are banks and other financial institutions. This research contributes in three ways: First, microfinance institutions will be facilitated in highlighting the challenges and prospects that are being neglected in the process of analyzing the problems and 147 European Journal of Economics, Finance and Administrative Sciences -Issue 31 (2011) opportunities faced by this sector. Second, other financial institutions and banks, realizing the significance of this business, will get encouragement to enter this sector with more innovative products and better standard practices. Thirdly, it will help the country in coming up with new strategies for micro financing. Moreover, microfinance sector may start conducting more training sessions to enhance the skills of the clients, which will affect positively this sector

Toward On-Device AI and Blockchain for 6G-Enabled Agricultural Supply Chain Management

6G envisions artificial intelligence (AI) powered solutions for enhancing the quality of service (QoS) in the network and to ensure optimal utilization of resources. In this work, we propose an architecture based on the combination of unmanned aerial vehicles (UAVs), AI, and blockchain for agricultural supply chain management with the purpose of ensuring traceability and transparency, tracking inventories, and contracts. We propose a solution to facilitate on-device AI by generating a roadmap of models with various resource-accuracy trade-offs. A fully convolutional neural network (FCN) model is used for biomass estimation through images captured by the UAV. Instead of a single compressed FCN model for deployment on UAVs, we motivate the idea of iterative pruning to provide multiple task-specific models with various complexities and accuracy. To alleviate the impact of flight failure in a 6G-enabled dynamic UAV network, the proposed model selection strategy will assist UAVs to update the model based on the runtime resource requirements