Pre-operative Anxiety Compounding Intra-operative Hypotension in Parturient women undergoing Cesarean Deliveries under Spinal Anesthesia

OBJECTIVE: To assess the relation of intra-operative hypotension with severity of pre-operative anxiety in patients undergoing caesarean section under spinal anesthesia. STUDY DESIGN: Prospective observational study. PLACE AND DURATION OF STUDY:  This study was carried out from June 2017 to May 2018 at anesthesia department of Combined Military Hospital Lahore. METHODOLOGY: One hundred and twenty patients belonging to America Society of Anesthesiology class I and II, undergoing caesarean section under spinal anesthesia were selected.  Verbal Analogue Scale for anxiety (VASA) and Straight Trait Anxiety Inventory (STAIs) questionnaire were used to measure pre-operative anxiety. Patients were divided into three groups as mild (VASA<3or STAIs<44), moderate (VASA 4-7 or STAIs 44-55) and severe (VASA 7-10 or STAIs>55) anxiety. Baseline mean arterial pressure was measured. Patients were placed in supine position immediately after induction of spinal anesthesia at L3-L4 or L4-L5 level with 12mg bupivacaine. Blood pressure was measured every two minutes until the delivery of baby. Hypotension was labeled when mean arterial pressure dropped by 20% below the baseline. The effect of level of anxiety on drop in MAP was assessed. RESULTS: Seventeen (14.17%) patients had mild pre-operative anxiety; out of which four (23.53%) developed hypotension. Seventy three (60.83%) patients had moderate anxiety; out of which twenty seven (36.99%) developed hypotension. Thirty (25%) patients had severe anxiety; out of which twenty two (73.33 %) developed hypotension. P-value (0.001) was quite significant. CONCLUSION:  It was concluded in our study that severity of pre-operative anxiety has significant effect on intra-operative spinal hypotension

Flow-Based Rules Generation for Intrusion Detection System using Machine Learning Approach

Rapid increase in internet users also brought new ways of privacy and security exploitation. Intrusion is one of such attacks in which an authorized user can access system resources and is major concern for cyber security community. Although AV and firewall companies work hard to cope with this kind of attacks and generate signatures for such exploits but still, they are lagging behind badly in this race. This research proposes an approach to ease the task of rules generationby making use of machine learning for this purpose. We used 17 network features to train a random forest classifier and this trained classifier is then translated into rules which can easily be integrated with most commonly used firewalls like snort and suricata etc. This work targets five kind of attacks: brute force, denial of service, HTTP DoS, infiltrate from inside and SSH brute force. Separate rules are generated for each kind of attack. As not every generated rule contributes toward detection that's why an evaluation mechanism is also used which selects the best rule on the basis of precision and f-measure values. Generated rules for some attacks have 100% precision with detection rate of more than 99% which represents effectiveness of this approach on traditional firewalls. As our proposed system translates trained classifier model into set of rules for firewalls so it is not only effective for rules generation but also give machine learning characteristics to traditional firewall to some extent.&nbsp

Seasonality in Presentation of Acute Appendicitis

Background:. To assess the trends in incidence of appendicitis and pattern of variation with age, sex, and seasons of the year. Methods: In this cross-sectional  prospective study patients who underwent appendectomy for acute appendicitis were included. The demographic features, length of hospital stay, seasonal variation and post-operative outcome were assessed . The diagnosis of acute appendicitis was  established by history, examination and investigations in term of leukocyte count, urinalysis and ultrasound exam in many of these cases. In North Punjab region, the year is divided into two well-marked seasons with short transitional periods between the long hot rainless summer (May to October) and comparatively short cool winter (December to February).SPSS version 16 was used for all the statistical assessments and analysis Results: Out of 972 patients, 53% patients were males. Age range was from 5-70 years. All the patients treated surgically by open and laparoscopic means. Forty patients were found to have perforated appendix, 12 patients presented with abdominal mass and 3 patients presented with appendicular abscess. A significant seasonal effect was observed, with the rate of acute appendicitis being higher in the summer months. Conclusion: A seasonal pattern of appendicitis with a mostly predominant peak is seen during the summer months could be due to increased gastrointestinal infections in summer. The males have higher incidence of acute appendicitis with 11-20 years of age being most common age grou

Effects of a high-dose 24-h infusion of tranexamic acid on death and thromboembolic events in patients with acute gastrointestinal bleeding (HALT-IT): an international randomised, double-blind, placebo-controlled trial

Background: Tranexamic acid reduces surgical bleeding and reduces death due to bleeding in patients with trauma. Meta-analyses of small trials show that tranexamic acid might decrease deaths from gastrointestinal bleeding. We aimed to assess the effects of tranexamic acid in patients with gastrointestinal bleeding. Methods: We did an international, multicentre, randomised, placebo-controlled trial in 164 hospitals in 15 countries. Patients were enrolled if the responsible clinician was uncertain whether to use tranexamic acid, were aged above the minimum age considered an adult in their country (either aged 16 years and older or aged 18 years and older), and had significant (defined as at risk of bleeding to death) upper or lower gastrointestinal bleeding. Patients were randomly assigned by selection of a numbered treatment pack from a box containing eight packs that were identical apart from the pack number. Patients received either a loading dose of 1 g tranexamic acid, which was added to 100 mL infusion bag of 0·9% sodium chloride and infused by slow intravenous injection over 10 min, followed by a maintenance dose of 3 g tranexamic acid added to 1 L of any isotonic intravenous solution and infused at 125 mg/h for 24 h, or placebo (sodium chloride 0·9%). Patients, caregivers, and those assessing outcomes were masked to allocation. The primary outcome was death due to bleeding within 5 days of randomisation; analysis excluded patients who received neither dose of the allocated treatment and those for whom outcome data on death were unavailable. This trial was registered with Current Controlled Trials, ISRCTN11225767, and ClinicalTrials.gov, NCT01658124. Findings: Between July 4, 2013, and June 21, 2019, we randomly allocated 12 009 patients to receive tranexamic acid (5994, 49·9%) or matching placebo (6015, 50·1%), of whom 11 952 (99·5%) received the first dose of the allocated treatment. Death due to bleeding within 5 days of randomisation occurred in 222 (4%) of 5956 patients in the tranexamic acid group and in 226 (4%) of 5981 patients in the placebo group (risk ratio [RR] 0·99, 95% CI 0·82–1·18). Arterial thromboembolic events (myocardial infarction or stroke) were similar in the tranexamic acid group and placebo group (42 [0·7%] of 5952 vs 46 [0·8%] of 5977; 0·92; 0·60 to 1·39). Venous thromboembolic events (deep vein thrombosis or pulmonary embolism) were higher in tranexamic acid group than in the placebo group (48 [0·8%] of 5952 vs 26 [0·4%] of 5977; RR 1·85; 95% CI 1·15 to 2·98). Interpretation: We found that tranexamic acid did not reduce death from gastrointestinal bleeding. On the basis of our results, tranexamic acid should not be used for the treatment of gastrointestinal bleeding outside the context of a randomised trial

Financial integration and portfolio diversification: Evidence from CIVETS stock markets

This paper investigates the extent of financial integration among a new group of six frontier markets called CIVETS by utilizing the multivariate GARCH framework of Engle and Kroner [1]. These countries are expected to show sustainable growth in productivity and domestic consumption over the next decade and are considered as potential corridor for the international investor from portfolio diversification point of view. We utilize weekly stock market return series of all the CIVIETS nations, and results exhibit significant return and volatility spillovers among all the markets under investigation. Our results reveal that there are significant linkages among CIVETS stock markets during the time of our analysis. However, the direction of relationship is asymmetric depending on the countries in the model. We believe, CIVIETS stock markets have full potential of being the future investment targets worldwide

Extreme return-volume relationship in cryptocurrencies: Tail dependence analysis

2020 The Author(s). This open access article is distributed under a Creative Commons Attribution (CC-BY) 4.0 license. We explore extreme return-volumes dependence among different cryptocurrencies such as Bitcoin, Ethereum, Ripple, and Litecoin by using the Copula approach. We use Student-t, Frank, Clayton, Survival Clayton, Gumbel, and SJC copulas. We filter out margins by using the EGARCH model for return series and GARCH model for volume series. Evidence of significant symmetric dependence between return-volume is not found due to insignificance of student-t and Frank copula parameters. In a return-volume relationship, coefficients of lower tail dependence are significant for Bitcoin, Ripple, and Litecoin which means that low returns are followed by low volumes. Lower tail dependence for the return-volume relationship is stronger than the upper tail dependence for Bitcoin, Ripple, and Litecoin. Moreover, for negative return-volume, left tail dependence coefficients are significant for Ripple and Litecoin, which means that high returns are followed by low volumes for Ripple and Litecoin. Our investigation shows that investors (buyer or seller) are very careful in extreme market conditions for both Ripple and Litecoin. Extreme upper tail and lower tail dependence coefficients are insignificant for Ethereum

Learning Representations of Network Traffic Using Deep Neural Networks for Network Anomaly Detection: A Perspective towards Oil and Gas IT Infrastructures

Oil and Gas organizations are dependent on their IT infrastructure, which is a small part of their industrial automation infrastructure, to function effectively. The oil and gas (O&G) organizations industrial automation infrastructure landscape is complex. To perform focused and effective studies, Industrial systems infrastructure is divided into functional levels by The Instrumentation, Systems and Automation Society (ISA) Standard ANSI/ISA-95:2005. This research focuses on the ISA-95:2005 level-4 IT infrastructure to address network anomaly detection problem for ensuring the security and reliability of Oil and Gas resource planning, process planning and operations management. Anomaly detectors try to recognize patterns of anomalous behaviors from network traffic and their performance is heavily dependent on extraction time and quality of network traffic features or representations used to train the detector. Creating efficient representations from large volumes of network traffic to develop anomaly detection models is a time and resource intensive task. In this study we propose, implement and evaluate use of Deep learning to learn effective Network data representations from raw network traffic to develop data driven anomaly detection systems. Proposed methodology provides an automated and cost effective replacement of feature extraction which is otherwise a time and resource intensive task for developing data driven anomaly detectors. The ISCX-2012 dataset is used to represent ISA-95 level-4 network traffic because the O&G network traffic at this level is not much different than normal internet traffic. We trained four representation learning models using popular deep neural network architectures to extract deep representations from ISCX 2012 traffic flows. A total of sixty anomaly detectors were trained by authors using twelve conventional Machine Learning algorithms to compare the performance of aforementioned deep representations with that of a human-engineered handcrafted network data representation. The comparisons were performed using well known model evaluation parameters. Results showed that deep representations are a promising feature in engineering replacement to develop anomaly detection models for IT infrastructure security. In our future research, we intend to investigate the effectiveness of deep representations, extracted using ISA-95:2005 Level 2-3 traffic comprising of SCADA systems, for anomaly detection in critical O&G systems

HyDra: Hybrid Task Mapping Application Framework for NOC-Based MPSoCs

Multiprocessor System-On-Chip (MPSoCs) with Networks-on-Chip (NoCs) has been proposed to address the communication challenges in modern dynamic applications. One of the key aspects of design exploration in NoC-based MPSoC is application mapping, which is critical for the parallel execution of multiple applications. However, mapping for dynamic workloads becomes challenging due to the unpredictable arrival times of applications and the availability of resources. In this work, we propose a hybrid task mapping approach, HyDra, that combines design-time mapping and efficient runtime remapping to reduce communication and energy costs. The proposed approach generates multiple application mappings during the design phase by minimizing latency, energy, and communication costs. The diverse mapping possibilities produced at design time consider multiple performance metrics. However, we cannot predict the arrival time of applications and the availability of resources at design time. To further optimize the MPSoC performance, our dynamic mapping phase re-configures the design time mappings based on the runtime availability of resources and applications. The simulation results show that HyDra reduces communication costs by 14% while using 15% less energy for small and large NoCs compared to state-of-the-art task mapping techniques. Furthermore, our approach provides an average of 19% reduction in end-to-end latency for applications. Our hybrid task allocation and scheduling approach effectively addresses communication issues in NoC-based MPSoCs for dynamic workloads. HyDra achieves improved performance by combining design-time and runtime mapping, providing a promising solution for future MPSoC design