Corrigendum to "Improved STNNet, A benchmark for detection, tracking, and counting crowds using Drones" [MethodsX 13 (2024) 1-7/102820].

[This corrects the article DOI: 10.1016/j.mex.2024.102820.].

Improved STNNet, A benchmark for detection, tracking, and counting crowds using Drones.

In computer vision, navigating multi-object tracking in crowded scenes poses a fundamental challenge with broad applications ranging from surveillance systems to autonomous vehicles. Traditional tracking methods encounter difficulties associating noisy object detections and maintaining consistent labels across frames, particularly in scenarios like video surveillance for crowd control and public safety. This paper introduces 'Improved Space-Time Neighbor-Aware Network (STNNet),' an advanced framework for online Multi-Object Tracking (MOT) designed to address these challenges. Expanding upon the foundational STNNet architecture, our enhanced model incorporates deep reinforcement learning techniques to refine decision-making. By framing the online MOT problem as a Markov Decision Process (MDP), Improved STNNet learns a sophisticated policy for data association, adeptly handling complexities such as object birth/death and appearance/disappearance as state transitions within the MDP. Through extensive experimentation on benchmark datasets, including the MOT Challenge, our proposed Improved STNNet demonstrates superior performance, surpassing existing methods in demanding, crowded scenarios. This study showcases the effectiveness of our approach and lays the groundwork for advancing real-time video analysis applications, particularly in dynamic, crowded environments. Additionally, we utilize the dataset provided by STNNET for density map estimation, forming the basis for our research.•Develop an advanced framework for online Multi-Object Tracking (MOT) to address crowded scene challenges, particularly improving object association and label consistency across frames.•Explore integrating Deep Reinforcement learning techniques into the MOT framework, framing the problem as an MDP to refine decision-making and handle complexities such as object birth or death and appearance or disappearance transitions.

Improved method for stress detection using bio-sensor technology and machine learning algorithms.

Maintaining an optimal stress level is vital in our lives, yet many individuals struggle to identify the sources of their stress. As emotional stability and mental awareness become increasingly important, wearable medical technology has gained popularity in recent years. This technology enables real-time monitoring, providing medical professionals with crucial physiological data to enhance patient care. Current stress-detection methods, such as ECG, BVP, and body movement analysis, are limited by their rigidity and susceptibility to noise interference. To overcome these limitations, we introduce STRESS-CARE, a versatile stress detection sensor employing a hybrid approach. This innovative system utilizes a sweat sensor, cutting-edge context identification methods, and machine learning algorithms. STRESS-CARE processes sensor data and models environmental fluctuations using an XG Boost classifier. By combining these advanced techniques, we aim to revolutionize stress detection, offering a more adaptive and robust solution for improved stress management and overall well-being.•In the proposed method, we introduce a state-of-the-art stress detection device with Galvanic Skin Response (GSR) sweat sensors, outperforming traditional Electrocardiogram (ECG) methods while remaining non-invasive•Integrating machine learning, particularly XG-Boost algorithms, enhances detection accuracy and reliability.•This study sheds light on noise context comprehension for various wearable devices, offering crucial guidance for optimizing stress detection in multiple contexts and applications.

A Comparative Study between Mean Platelet Volume and Hba1c Levels in Patients of Type 2 Diabetes Mellitus.

Diabetes mellitus is a major global health problem, increasingly affecting the population across the world. Diabetic patients have an increased risk of developing micro and macro vascular diseases, and platelets may be involved as a causative agent with respect to altered platelet morphology and function. There are studies evaluating the association between Mean Platelet Volume (MPV) and HbA1c and its role in predicting glycaemic control with conflicting results. Thus the present study was conducted to assess the relationship between HbA1c levels and platelet activity (MPV), determine the association among MPV, glycemic control, and diabetic vascular complications and to evaluate the influence of improved glycemic control on MPV in type 2 diabetic patients. MATERIAL: This was a hospital based observational comparative study on 100 cases of diabetes mellitus divided in 2 groups i.e Group A (HbA1c <7) and Group B (HbA1c >7) and 50 healthy controls in Group C in hospital wards and OPD of SMS Medical College, Jaipur. INCLUSION CRITERIA: Age more than 18 years, and newly diagnosed or old cases of diabetes mellitus using the definition given by American Diabetes Association. EXCLUSION CRITERIA: Abnormal platelet count (<100 and >450×103/µL), Acute febrile illness, Use of drugs affecting platelet function, Male patients with Hb<12.5mg/dl and females with Hb<11.5 mg/dl and Pregnant females. OBSERVATION: It was observed that mean MPV(fl) was maximum in Group B (13.35±1.27), followed by Group A (10.77±.77) and Group C (9.09±.85) and a significant (p-value<0.05) relation was found statistically. We also observed that mean HbA1c (%) was maximum in Group B (8.82±1.41), followed by Group A (6.66±.004) and Group C (5.67±.45) and a significant (p-value<0.05) relation was found statistically in these 3 groups. In group B, at baseline MPV(fl) levels were more (13.35±1.26) than at follow up after glycemic control of 3mths (12.13±1.20) and this was found to be statistically significant. CONCLUSION: We found that Mean platelet volume in diabetic mellitus type 2 patients was significantly higher than non-diabetic group. We also found that the mean platelet volume in uncontrolled diabetic group (HbA1c more than 7 percent) was significantly higher than controlled diabetic group (HbA1c less than 7 percent). Our study showed that in diabetes mellitus, platelets become more reactive and aggregable and their mean volume (MPV) is increased. We also found that increase in HbA1c concentration was directly proportional to increased MPV.

Effect of mifepristone on steroid receptor expression and biotransformation of oestrogen and progesterone in rat uterus and deciduoma.

BACKGROUND: [corrected] Mifepristone is a synthetic antiprogestin which terminates early pregnancy. Since it interferes with the progesterone maintained decidua, we compared the effect of mifepristone on oestrogen and progesterone receptors, and on the biotransformation of these hormones in normal and deciduous uterus. METHODS: Ovariectomized rats were treated with an oestrogen-progesterone hormone regimen and deciduoma was induced by trauma in one horn of the rat uterus while the other served as a control under an identical hormonal milieu. Hormone receptor and biotransformation studies were done using radiolabelled oestradiol and progesterone with high specific activity. RESULTS: The artificially formed decidual tissue was comparable with that of early pregnancy. Mifepristone replenished oestrogen and progesterone receptors which were suppressed by progesterone in both the normal and decidualized uterine horns. Inhibition of oestrogen receptors by progesterone correlated with decreased oestradiol levels at the site of action. Metabolism of progesterone to less potent compounds was promoted by mifepristone. The enzymatic activities of 17beta-hydroxysteroid dehydrogenase (which metabolizes oestradiol), and 20alpha-hydroxysteroid dehydrogenase and 5alpha-reductase (which metabolize progesterone) were altered by mifepristone. CONCLUSION: The effect of mifepristone in varying the hormone receptor population and the availability of different levels of active metabolites of ovarian hormones have an Important role in the antiprogestin action of mifepristone.