ZOLPIDEM IS AN EFFECTIVE OPTION WITH A REDUCED RISK FOR DEPENDENCE IN THE TREATMENT OF INSOMNIA

Insomnia is a highly prevalent sleep disorder that frequently occurs in its acute form and occurs at a rate of approximately 10 per cent in its chronic form in many countries. There is a high prevalence of insomnia in a variety of medical and psychiatric conditions for which insomnia often serves as a risk factor. There are various types of insomnia which are categorized in terms of how it affects sleep it has been shown to negatively affect many physiological, cognitive, and behavioural measures within the body. Recent years have observed that there is sudden increase of various diseases like hypertension, Heart attack, Obesity, Diabetes etc which occurs as a result of insomnia. Hence its impact on financial, social and psychological status of patients and their caregivers cannot be ignored. Thus finding a novel way to tackle these health problems is the need of present times. The most commonly prescribed medications for insomnia are the benzodiazepines (BZP) such as temazepam and diazepam. Although these medications are efficacious, they are associated with tolerance, dependence, residual daytime sedative effects, cognitive and psychomotor impairment, and discontinuation syndromes including rebound insomnia and withdrawal symptoms. For this reason, BZD use should be judicious and is replaced by Zolpidem, a novel non-benzodiazepine hypnotics of Imidazopyridine class that has various advantages over benzodiazepines. Chronic administration of zolpidem produces neither tolerance to its sedative effects nor signs of withdrawal when the drug is discontinued. Also it has little effect on the stages of sleep in normal human subjects. The drug is as effective as benzodiazepines in shortening sleep latency and prolonging total sleep time in patients with insomnia. Tolerance and physical dependence develop only rarely and under unusual circumstances. Keywords: Insomnia, sleep disorder, benzodiazepines, tolerance, dependence, zolpidem, ImidazopyridineÂ

Navigating the Ocean with DRL: Path following for marine vessels

Human error is a substantial factor in marine accidents, accounting for 85% of all reported incidents. By reducing the need for human intervention in vessel navigation, AI-based methods can potentially reduce the risk of accidents. AI techniques, such as Deep Reinforcement Learning (DRL), have the potential to improve vessel navigation in challenging conditions, such as in restricted waterways and in the presence of obstacles. This is because DRL algorithms can optimize multiple objectives, such as path following and collision avoidance, while being more efficient to implement compared to traditional methods. In this study, a DRL agent is trained using the Deep Deterministic Policy Gradient (DDPG) algorithm for path following and waypoint tracking. Furthermore, the trained agent is evaluated against a traditional PD controller with an Integral Line of Sight (ILOS) guidance system for the same. This study uses the Kriso Container Ship (KCS) as a test case for evaluating the performance of different controllers. The ship's dynamics are modeled using the maneuvering Modelling Group (MMG) model. This mathematical simulation is used to train a DRL-based controller and to tune the gains of a traditional PD controller. The simulation environment is also used to assess the controller's effectiveness in the presence of wind.Comment: Proceedings of the Sixth International Conference in Ocean Engineering (ICOE2023

A CROSS-SECTIONAL STUDY OF STRESS AMONG UNDERGRADUATE MEDICAL STUDENTS IN A TERTIARY CARE TEACHING INSTITUTE, JHARKHAND.

Background Medical students are more exposed to stressful situations due to their academic pressure, difficult learning environment, and challenging competency-based medical education design that does not provide enough time for their personal life events. So, chronic stress among medical students results in depression, substance abuse, and even suicide. This study aims to determine the prevalence of stress among undergraduate medical students of RIMS, Ranchi, Jharkhand, India. Methodology  This was a cross-sectional study conducted among 258 undergraduate medical students of RIMS, Ranchi from January 2022 to December 2022. Perceived Stress Scale-10 was used to evaluate the degree of stress among undergraduate medical students. Data obtained was analyzed using MS Excel and SSS based on SPSS and Minitab (2018).  Results A total of 258 undergraduate medical students participated in the study of which 41.4% were male and 58.52% were female. Although a moderate stress rate of 68.9% was registered in most participants, 22.48% were affected by high stress. Participants in the 4th professional MBBS are more likely to experience high stress (45.06%) as compared to students in the 2nd professional MBBS, 1st professional MBBS, and 3rd professional MBBS respectively. The difference in stress severity was statistically significant at p <0.05. Conclusion Most undergraduate medical students (68.99%) have moderate stress. Female (29.8%) are more likely to have high stress. The final professional MBBS students (44.06%) have more high stress.  Recommendation Counseling services to medical college students are strongly recommended to address the stress

AI on the Water: Applying DRL to Autonomous Vessel Navigation

Human decision-making errors cause a majority of globally reported marine accidents. As a result, automation in the marine industry has been gaining more attention in recent years. Obstacle avoidance becomes very challenging for an autonomous surface vehicle in an unknown environment. We explore the feasibility of using Deep Q-Learning (DQN), a deep reinforcement learning approach, for controlling an underactuated autonomous surface vehicle to follow a known path while avoiding collisions with static and dynamic obstacles. The ship's motion is described using a three-degree-of-freedom (3-DOF) dynamic model. The KRISO container ship (KCS) is chosen for this study because it is a benchmark hull used in several studies, and its hydrodynamic coefficients are readily available for numerical modelling. This study shows that Deep Reinforcement Learning (DRL) can achieve path following and collision avoidance successfully and can be a potential candidate that may be investigated further to achieve human-level or even better decision-making for autonomous marine vehicles.Comment: Proceedings of the Sixth International Conference in Ocean Engineering (ICOE2023

Comparison of path following in ships using modern and traditional controllers

Vessel navigation is difficult in restricted waterways and in the presence of static and dynamic obstacles. This difficulty can be attributed to the high-level decisions taken by humans during these maneuvers, which is evident from the fact that 85% of the reported marine accidents are traced back to human errors. Artificial intelligence-based methods offer us a way to eliminate human intervention in vessel navigation. Newer methods like Deep Reinforcement Learning (DRL) can optimize multiple objectives like path following and collision avoidance at the same time while being computationally cheaper to implement in comparison to traditional approaches. Before addressing the challenge of collision avoidance along with path following, the performance of DRL-based controllers on the path following task alone must be established. Therefore, this study trains a DRL agent using Proximal Policy Optimization (PPO) algorithm and tests it against a traditional PD controller guided by an Integral Line of Sight (ILOS) guidance system. The Krisco Container Ship (KCS) is chosen to test the different controllers. The ship dynamics are mathematically simulated using the Maneuvering Modelling Group (MMG) model developed by the Japanese. The simulation environment is used to train the deep reinforcement learning-based controller and is also used to tune the gains of the traditional PD controller. The effectiveness of the controllers in the presence of wind is also investigated.Comment: Proceedings of the Sixth International Conference in Ocean Engineering (ICOE2023

Dielectric, electrical and microstructural properties of unfilled and MWCNTs filled polystyrene nanocomposite prepared by in-situ polymerization technique using ultrasonic irradiation

The unfilled and ­multi-walled carbon nanotubes (MWCNTs) filled polystyrene (PS) nanocomposites are prepared by in-situ polymerization technique using ultrasonic irradiation. The effect of different volume fraction () of MWCNTs in PS has been studied with respect to their dielectric and electrical characterizations as a function of frequency and temperature. The dielectric constant of different volume fraction () of MWCNTs shows higher value at low frequency region and it shows frequency independent behaviour at higher frequency. The dc conductivity is found to increase with increasing volume fraction () of MWCNTs whereas ac conductivity increases with increasing frequency. The SEM image reveals uniform dispersion of MWCNTs in polymer matri

Improved Analgesic and Anti-Inflammatory Effect of Diclofenac Sodium by Topical Nanoemulgel: Formulation Development—In Vitro and In Vivo Studies

The present study aimed to develop diclofenac sodium nanoemulgel for managing pain and inflammation using the low-energy emulsification technique. Nanoemulsion of diclofenac was formulated using clove oil with adequate amount of surfactants and cosurfactants, and it was converted to hydrogel form using Carbopol 980 as the gelling agent. The droplet size of the oil globules in the nanoemulsion was found to be 64.07 ± 2.65 nm with a low polydispersity index (0.238 ± 0.02) along with high negative zeta potential (−39.06 mV). The developed nanoemulgel exhibited non-Newtonian and pseudoplastic behavior. The in vitro release profile of the developed nanoemulgel was higher as compared to marketed and conventional gel. The carrageenan-induced paw edema test was performed in rats to evaluate the anti-inflammatory activity of developed nanoemulgel. The developed nanoemulgel showed significantly higher (p<0.01) effect in reducing pain and inflammation symptoms as compared to marketed as well as conventional gel of diclofenac. The overall findings of the study suggest that the developed nanoemulgel formulation of diclofenac can be used as a potential approach for the management of pain and inflammation

Experimental and numerical investigations on the effect of a novel internal surface micro grooving towards improving convective heat transfer performance of tube heat exchangers

In the present work, the use of the passive heat transfer enhancement technique through surface alteration was explored. The enhancement was achieved through internal conduit surface microgrooving using a new apparatus that was developed by modifying a Magnetic Abrasive Finishing technique. A new surface profile was created and later verified using optical and laser profiler measurement. The new profile was investigated numerically to compare the heat transfer and hydrodynamic performance against other profiles that have been studied in the literature. It was found from the results that the new profile shows much higher heat transfer improvement and comparable pressure loss to the previously investigated profiles (i.e. square, rectangular, trapezoidal and circular). Overall, the new groove geometry provides the highest performance followed by the circular, triangular, curvy, square and rectangular grooves. Also, the results indicate that designs with a smooth profile performs better than those with sharp edges, owing to the elimination of stationary fluid spots within the grooves. The effectiveness between the profiles was compared based on the level of heat transfer enhancement against the flow penalty. Experimental validation was further conducted for the experimentally generated groove. The results revealed that a relatively small surface temperature drop was obtained, corresponding to a slight improvement in heat transfer. This confirms the results generated by the simulation that groove size plays a major role in attaining significant improvement in heat transfe

Development, optimization, and evaluation of luliconazole nanoemulgel for the treatment of fungal infection

The present study aimed to optimize luliconazole nanoemulsion using Box–Behnken statistical design, which was further incorporated into the polymeric gel of Carbopol 934. The formulation was characterized for its size, entrapment efficiency, ex vivo permeation, and mechanism of release. The size of the dispersed globules of the optimized drug-loaded nanoemulsion was found to be 17 ± 3.67 nm with a polydispersity index (PDI) less than 0.5. Although the surface charge was recorded at –9.53 ± 0.251, the stability was maintained by the polymeric matrix that prevented aggregation and coalescence of the dispersed globules. The luliconazole-nanoemulgel (LUL-NEG) was characterized for drug content analysis, viscosity, pH, and refractive index, where the results were found to be 99.06 ± 0.59%, 9.26 ± 0.08 Pa.s, 5.65 ± 0.17, and 1.31 ± 0.08, respectively. The permeation across the rat skin was found to be significantly higher with LUL-NEG when compared with LUL gel. Furthermore, the skin irritation test performed in experimental animals revealed that the blank NEG, as well as the LUL-NEG, did not produce any signs of erythema following 48 h exposure. In addition, the histopathological findings of the experimental skins reported no abnormal signs at the formulation application site. Finally, the NEG formulation was found to create a statistically significant zone of inhibition (P < 0.05) when compared to all other test groups. Overall, it could be summarized that the nanoemulgel approach of delivering luliconazole across the skin to treat skin fungal infections could be a promising strategy