Silent uterine rupture of scarred uterus--an unusual presentation as amniocele

Obstetricians should be aware of the possibility of silent rupture of scarred uterus. Ultrasound has an important role in the diagnosis of silent uterine rupture. A case of silent uterine rupture with foetal demise, that remained undiagnosed for many weeks, is described

Spontaneous bilateral tubal pregnancy

With the increase in incidence of ectopic pregnancy over the decades, bilateral ectopic pregnancy is also increasing. It is usually associated with assisted reproductive techniques (ART) but in recent years few cases of spontaneous bilateral ectopic pregnancy have been reported. Gynaecologists should be aware of this and that ultrasonography has limitations in diagnosis. In cases of ectopic pregnancy where contralateral adnexa is not clearly identified on ultrasound and fertility needs to be conserved, patient should be managed by experts in well equipped centres. A case of spontaneous bilateral tubal pregnancy that remained undiagnosed till laparotomy, is described

Detecting Unknown Attacks in IoT Environments: An Open Set Classifier for Enhanced Network Intrusion Detection

The widespread integration of Internet of Things (IoT) devices across all facets of life has ushered in an era of interconnectedness, creating new avenues for cybersecurity challenges and underscoring the need for robust intrusion detection systems. However, traditional security systems are designed with a closed-world perspective and often face challenges in dealing with the ever-evolving threat landscape, where new and unfamiliar attacks are constantly emerging. In this paper, we introduce a framework aimed at mitigating the open set recognition (OSR) problem in the realm of Network Intrusion Detection Systems (NIDS) tailored for IoT environments. Our framework capitalizes on image-based representations of packet-level data, extracting spatial and temporal patterns from network traffic. Additionally, we integrate stacking and sub-clustering techniques, enabling the identification of unknown attacks by effectively modeling the complex and diverse nature of benign behavior. The empirical results prominently underscore the framework's efficacy, boasting an impressive 88\% detection rate for previously unseen attacks when compared against existing approaches and recent advancements. Future work will perform extensive experimentation across various openness levels and attack scenarios, further strengthening the adaptability and performance of our proposed solution in safeguarding IoT environments.Comment: 6 Pages, 5 figure

Payload-Byte: A Tool for Extracting and Labeling Packet Capture Files of Modern Network Intrusion Detection Datasets

Adapting modern approaches for network intrusion detection is becoming critical, given the rapid technological advancement and adversarial attack rates. Therefore, packet-based methods utilizing payload data are gaining much popularity due to their effectiveness in detecting certain attacks. However, packet-based approaches suffer from a lack of standardization, resulting in incomparability and reproducibility issues. Unlike flow-based datasets, no standard labeled dataset exists, forcing researchers to follow bespoke labeling pipelines for individual approaches. Without a standardized baseline, proposed approaches cannot be compared and evaluated with each other. One cannot gauge whether the proposed approach is a methodological advancement or is just being benefited from the proprietary interpretation of the dataset. Addressing comparability and reproducibility issues, we introduce Payload-Byte, an open-source tool for extracting and labeling network packets in this work. Payload-Byte utilizes metadata information and labels raw traffic captures of modern intrusion detection datasets in a generalized manner. Moreover, we transformed the labeled data into a byte-wise feature vector that can be utilized for training machine learning models. The whole cycle of processing and labeling is explicitly stated in this work. Furthermore, source code and processed data are made publicly available so that it may act as a standardized baseline for future research work. Lastly, we present a brief comparative analysis of machine learning models trained on packet-based and flow-based data

Predictors of slow clinical response and extended treatment in patients with extra-pulmonary tuberculosis in Pakistan, A hospital-based prospective study

The optimal duration of treatment in different forms of extrapulmonary tuberculosis (EPTB) is not clearly defined. This study aimed to identify predictors of slow clinical response and extended anti-TB treatment in EPTB patients. Socio-demographic, clinical, and microbiological characteristics of EPTB patients registered for anti-TB treatment at a tertiary care hospital, were analysed for identification of predictors of extended treatment. A total of 251 patients (137 lymphadenitis, and 114 pleuritis) were included in the analysis. Treatment was extended to more than 6 months in 58/251 (23%) patients. In the multivariate regression analysis, culture-positive EPTB (p = 0.007) [OR (95% CI) = 3.81 (1.43, 10.11)], history of diabetes (p = 0.014) [OR (95% CI) = 25.18 (1.94, 325.83)], smokeless tobacco use (p = 0.002) [OR (95% CI) = 17.69 (2.80, 111.72)], and slow regression of local signs and symptoms after 2 months of treatment (p < 0.001) [OR (95% CI) = 17.09 [(5.79, 50.39)] were seen to be significantly associated with treatment extension. Identification of predictors of extended treatment can help clinical decisions regarding optimal duration of treatment. Further studies are needed to identify subgroups of EPTB patients who can benefit from a shorter or longer treatment regimen.publishedVersio

Swarm of UAVs for Network Management in 6G: A Technical Review

Fifth-generation (5G) cellular networks have led to the implementation of beyond 5G (B5G) networks, which are capable of incorporating autonomous services to swarm of unmanned aerial vehicles (UAVs). They provide capacity expansion strategies to address massive connectivity issues and guarantee ultra-high throughput and low latency, especially in extreme or emergency situations where network density, bandwidth, and traffic patterns fluctuate. On the one hand, 6G technology integrates AI/ML, IoT, and blockchain to establish ultra-reliable, intelligent, secure, and ubiquitous UAV networks. 6G networks, on the other hand, rely on new enabling technologies such as air interface and transmission technologies, as well as a unique network design, posing new challenges for the swarm of UAVs. Keeping these challenges in mind, this article focuses on the security and privacy, intelligence, and energy-efficiency issues faced by swarms of UAVs operating in 6G mobile networks. In this state-of-the-art review, we integrated blockchain and AI/ML with UAV networks utilizing the 6G ecosystem. The key findings are then presented, and potential research challenges are identified. We conclude the review by shedding light on future research in this emerging field of research.Comment: 19,

ISSR markers for analysis of molecular diversity and genetic structure of Indian teak (Tectona grandis L.f.) populations

Inter simple sequence repeats (ISSR) constitute a powerful dominantDNA molecular marker system used for diversity analysis, which isindispensable for making estimates of genetic base and demarcation of populations for undertaking conservation and improvement program of forest tree species. Twenty nine populations of teak (Tectona grandis L.f.) were collected from central and peninsular India for analysis of genetic diversity and structure. Genomic DNA from ten randomly selected individuals of each population was extracted and amplified using five ISSR primers(UBC-801, 834, 880, 899 and 900). The primers showed 100% polymorphism. UBC-900 recorded the highest Nei’s genetic diversity (0.32 to 0.40)and UBC-899 had the highest Shannon’s Information Index (0.49 to 0.59). AMOVA revealed a very high intra-population genetic diversity (91%), in comparison to inter-population genetic diversity among states (6.17%) and within states (2.77%) which were also indirectly confirmed by large standard deviations associated with genetic diversity estimates for individual population, as well as poor bootstrapping values for most of the cluster nodes. However, UPGMA dendrogram revealed several clusters, with populationsfrom central India being present almost in each cluster, makinggroups with populations of adjoining states and distant states. Nevertheless,the cluster analysis distinguished the drier teak populations of central India from the moist teak populations of south India, which was also confirmed by Principle Coordinate Analysis. The findings advocates the need not only for enhancing selection intensity for large number of plus trees, but also for laying out more number of in situ conservation plots within natural populations of each cluster for germplasm conservation of teak aimed at improving the teak productivity and quality in future

A preliminary investigation on AFLP marker-wood density trait association in teak (Tectona grandis L. f.)

Association between 276 AFLP loci and wood density of 46 teak (Tectona grandis L. f.) genotypes was evaluated, confirming the genetic structure among the genotypes and significant (p &lt; 0.01) linkage disequilibrium between 9.4% loci-pair. AFLP markers with Bayesian correction for inbreeding coefficient detected a low genetic structure vis-à-vis high genetic diversity (0.23) and high polymorphism (57.41 ± 9.62%). AMOVA allocated 26.34% variation among the populations and 73.65% variation among the genotypes with FST = 0.16. The wood density with 8.71% variation displayed significant normal distribution. The careful control of statistical estimates incorporating Q and K to avoid the false discovery resulted in four AFLP loci significantly associated with the wood density trait. This is the first report dealing with marker-trait association in teak against the scarcity of background genomic information in this species. The AFLP markers associated with the wood density trait may be developed into STSmarkers for marker-assisted selection and breeding for genetic improvement of the species.</p

Energy Efficient UAV Flight Path Model for Cluster Head Selection in Next‐Generation Wireless Sensor Networks

Abstract: Please refer to full text to view abstrac

Trusted autonomous vehicles: an interactive exhibit

Recent surveys about autonomous vehicles show that the public is concerned about the safety consequences of system or equipment failures and the vehicles' reactions to unexpected situations. We believe that informing about the technology and quality, e.g., safety and reliability, of autonomous vehicles is paramount to improving public expectations, perception and acceptance. In this paper, we report on the design of an interactive exhibit to illustrate (1) basic technologies employed in autonomous vehicles, i.e., sensors and object classification; and (2) basic principles for ensuring their quality, i.e., employing software testing and simulations. We subsequently report on a public engagement event involving this exhibit at the Royal Society Summer Science Exhibition 2019 in the exhibit titled "Trusted Autonomous Vehicles". We describe the process of designing and developing the artefacts used in our exhibit, the theoretical background associated to them, the design of our stand, and the lessons learned. The activities and findings of this study can be used by other educators and researchers interested in promoting trust in autonomous vehicles among the general public