Escaping the Echo: Understanding the Impact of Social Media on Overconcentration of Emerging Technology Security Threats

Social media platforms prioritize sensational or trending content, often overshadowing less popular but important topics and hindering discourse diversification. They evolve into echo chambers, where users predominantly encounter views aligned with their own. Security threat awareness for emerging technologies remains restricted, primarily because of the overconcentration of discussions influenced by both human and algorithmic factors. We seek to identify security threats related to emerging technology that are overshadowed and underrepresented due to the overconcentration of others. Next, we study uncertainty reduction approaches and emotional appraisal dimensions to understand how they contribute to the amplification or overconcentration of specific security threats. By combining computational NLP techniques to detect overconcentrated topics with scenario-based factorial surveys, this study proposes to provide a thorough examination of threat amplification in the realm of social media

A Novel Approach to Autonomous Farming Robot

Now-a-days everyone has lost interest from farming as it has become a very difficult and tedious job. Although hi-tech vehicles and equipment have overcome older version of vehicles and equipment and also they made farming quite easy. But it still requires a plenty of human effort. Today automation has been introduced in almost every form of industry and a prominent reason to reducing human effort. Our Objective is to reduce human efforts in farming as we planned to develop an autonomous guidance system for farm vehicles. Our system will be based on Global Positioning System (GPS) [1]. To develop complete autonomous system, other than GPS, systems like machine vision, laser-based sensors, inertial sensors would be needed to be employed for avoiding obstacles in the path and overcoming other challenges. However making such systems would require more time and monetary resources then available, hence developing such complete autonomous system is out of scope of the current task at hand. Our aim is to develop a Mixture of such complete autonomous system which will fulfill one of the Basic needs of a complete autonomous guidance system. DOI: 10.17762/ijritcc2321-8169.160412

Use of Hydralazine‐Isosorbide Dinitrate Combination in African American and Other Race/Ethnic Group Patients With Heart Failure and Reduced Left Ventricular Ejection Fraction

Background: Hydralazine‐isosorbide dinitrate (H‐ISDN) therapy is recommended for African American patients with moderate to severe heart failure with reduced ejection fraction (<40%) (HFrEF), but use, temporal trends, and clinical characteristics associated with H‐ISDN therapy in clinical practice are unknown. Methods and Results: An observational analysis of 54 622 patients admitted with HFrEF and discharged home from 207 hospitals participating in the Get With The Guidelines–Heart Failure registry from April 2008 to March 2012 was conducted to assess prescription, trends, and predictors of use of H‐ISDN among eligible patients. Among 11 185 African American patients eligible for H‐ISDN therapy, only 2500 (22.4%) received H‐ISDN therapy at discharge. In the overall eligible population, 5115 of 43 498 (12.6%) received H‐ISDN at discharge. Treatment rates increased over the study period from 16% to 24% among African Americans and from 10% to 13% among the entire HFrEF population. In a multivariable model, factors associated with H‐ISDN use among the entire cohort included younger age; male sex; African American/Hispanic ethnicity; and history of diabetes, hypertension, anemia, renal insufficiency, higher systolic blood pressure, and lower heart rate. In African American patients, these factors were similar; in addition, being uninsured was associated with lower use. Conclusions: Overall, few potentially eligible patients with HFrEF are treated with H‐ISDN, and among African‐Americans fewer than one‐fourth of eligible patients received guideline‐recommended H‐ISDN therapy. Improved ways to facilitate use of H‐ISDN therapy in African American patients with HFrEF are needed

Cerebral salt wasting following traumatic brain injury

Hyponatraemia is the most commonly encountered electrolyte disturbance in neurological high dependency and intensive care units. Cerebral salt wasting (CSW) is the most elusive and challenging of the causes of hyponatraemia, and it is vital to distinguish it from the more familiar syndrome of inappropriate antidiuretic hormone (SIADH). Managing CSW requires correction of the intravascular volume depletion and hyponatraemia, as well as mitigation of on-going substantial sodium losses. Herein we describe a challenging case of CSW requiring large doses of hypertonic saline and the subsequent substantial benefit with the addition of fludrocortisone

Enhancing medical students` confidence and performance in integrated structured clinical examinations (ISCE) through a novel near-peer, mixed model approach during the COVID-19 pandemic

Background: Near-peer medical education serves as an important method of delivering education to junior students by senior students. Due to the reduced clinical exposure because of the COVID-19 pandemic, we developed a mentorship scheme to help medical students with their Integrated Structured Clinical Examinations (ISCEs) by providing a combination of near-peer mentorship together with lecture-based teaching on a weekly basis for a 12-week period. Students attended a specialty-focused lecture every Tuesday followed by a small group teaching session organised by their tutor. Methods: A longitudinal evaluative interventional study was undertaken by the international student led medical education organisation, OSCEazy. The teaching programme was organised and conducted by third year medical students to a recruited cohort of second year medical students. Students’ perceptions of ISCEs (confidence, anxiety, and overall performance) were evaluated using 5-point Likert scales while their knowledge of the specialty was assessed using 10 single best answer questions which were distributed via Google® forms at the start and end of each week. In addition, we assessed tutor perceptions of their teaching and learning experience. Results: Seventy-two tutees were enrolled in the programme (mean age: 24.4, female: 77.8%). 88.9% of the participants had not attended any online ISCE teaching prior to this. They preferred in-person ISCE teaching as compared to virtual sessions [median 4.5 (IQR 4–5) vs 3 (IQR 3–4), p < 0.0001), respectively]. There was a significant overall increase in knowledge when comparing pre-session and post-session performance [mean 53.7% vs 70.7%, p < 0.0001)]. There was a significant increase in student confidence [Confidence: median 3 (IQR:3–4) vs 4 (IQR 3–4), p < 0.0001] while no change was seen in the anxiety and perception of their overall performance in an ISCE. [Anxiety: median 3 (IQR 2–4) vs 3 (IQR 3–4), p = 0.37, Performance: median 3 (IQR 3–4) vs median 3 (IQR 3–4), p < 0.0001]. The tutors reported an increase in their confidence in teaching ISCEs online [median 3 (IQR 2–3.25) vs median 4 (IQR 4–5), p < 0.0001)]. Conclusion: Online near-peer teaching increases the confidence of both tutees and tutors involved while enhancing the tutees’ knowledge of the specialty. Thus, medical schools should incorporate near-peer teaching in their curriculum to enhance the student learning experience

A Convolutional Neural Network ensemble model for Pneumonia Detection using chest X-ray images

Pneumonia is a respiratory infection caused by microbes and other environmental factors. It infects the lungs causing a buildup of fluid and difficulty in breathing and is the leading cause for death in children under the age of 5 years. Timely detection proves essential in preventing adverse consequences including death. However, most areas in underdeveloped and developing nations do not have access to conventional diagnostic measures, preventive measures and adequate expert treatment. Computer-aided systems based on machine learning techniques can aid this task. However, most smart diagnostic systems may have the drawback of requiring extensive hardware and heavy computation power. The objective of this experiment is to develop a lightweight, deployable and accurate model to aid in the detection of Pneumonia. A Convolutional Neural Network architecture utilizing three different models of varying kernel sizes was developed. The outputs of these models were combined using a novel weighted ensemble approach which proposes an adjustable threshold value to change the model’s diagnostic capabilities as required. The flexible threshold value provides a means to adjust the weightage given to each model’s output and hence change the classification result depending on the actual case on hand. The model was evaluated on metrics including accuracy, recall, precision and f1-score and was able to achieve a high recall value of 99.23% with an f1-score of 88.56% which are critically high values for the given domain resulting in almost no chances of a Pneumonia positive case being misclassified. The absence of transfer learning or deep neural networks makes the model lightweight and hence, a plausibly deployable diagnostic-aid solution. Further studies were carried out to find methods such as – larger dataset, better preprocessing and more – to improve the model performance

Healthcare-Associated nontuberculous mycobacterial endocarditis following coronary artery angiography

Infective endocarditis in a patient with structural heart disease following coronary artery angiography is a rare complication. We report a rare case of Mycobacterium chelonae infective endocarditis following coronary artery angiography in a young male with congenital heart disease. This case illustrates the diagnostic as well as therapeutic challenges we faced when managing this rare infectious entity