30 research outputs found
Investigation of Sleep Neural Dynamics in Intracranial EEG Patients
No full textIntracranial electroencephalography (iEEG) provides superior diagnostic and research benefits over non-invasive EEG in terms of spatial resolution and the level of electrophysiological detail. Post-operative Computed Tomography (CT) scans provide the precision in electrode localization required for clinical purposes; however, to use this data for basic sleep research the challenge lies in identifying the precise locations of the implanted electrodes’ recording sites in terms of neuroanatomical regions as well as reliable scoring of their sleep data without the aid of facial electrodes. While existing methods can be combined to determine their exact locations in three-dimensional space, they fail to identify the functionally relevant gray matter areas that lie closest to them, especially if the points lie in the white matter. We introduce an iterative sphere inflation algorithm in conjunction with a unified pipeline to detect the exact as well as nearest regions of interest for these recording sites. Next, for sleep scoring purposes, we establish differences observed in alpha band activity between wakefulness and rapid eye movement (REM) sleep in frontal and temporal regions of iEEG patients. Lastly, we implement an automated sleep scoring method relying on the variations in alpha and delta bands power during sleep which can be applied to large sets of iEEG data recorded without accompanying electrooculogram (EOG) and electromyogram (EMG) electrodes available across labs for use in studies pertaining to neural dynamics during sleep.M.S.Patients with epilepsy (a neurological disorder characterized by seizures) who do not respond to medication often undergo invasive monitoring of their brains’ electrical activity using intracranial electroencephalography (iEEG). iEEG requires a surgery in which electrodes are inserted directly into the patient’s brain for better measurements. While they are monitored, these patients offer a unique opportunity for research studies that investigate the role of sleep in various learning, memory mechanisms and other health-related areas. This is because the direct contact of the electrodes with the brain tissue provides far superior quality and resolution of brain activity data in comparison to non-invasive cap-based EEG that healthy subjects wear over their scalp. However, in order to derive meaningful conclusions from these invasive recordings, we must first know the exact areas of the brain from which each site records the electrical data. We must then be able to identify which stage of sleep the patient is in at any given point in time, to be able to successfully correlate specific sleep stage-related activity with our research objectives; these patients often lack the facial electrodes used for standard sleep scoring procedures. To solve the first problem, we present an electrode localization method along with an algorithm to determine which neighboring regions contribute most to a given site’s recorded data. For the second problem, we first establish a difference in the behavior of alpha waves in the brain between wakefulness and rapid eye movement (REM) sleep. Lastly, we present an automated method to classify sleep data into different stages based on the variation in alpha waves and delta waves found during sleep
Modelling the Deflection of Flexible Pavement using Artificial Intelligence Techniques
No full textA flexible pavement is a structural system consisting of several layers made of different materials, with stiffer layer placed at top and weaker ones at the bottom. The major function of flexible pavement is to provide a better riding quality and to distribute the traffic load uniformly, in order to protect the subgrade from excessive stresses. The riding quality and safety of the pavement are affected due to various types of distress acting over the surface during the service life of pavement. The pavements deteriorate due to combined action of traffic loads, environmental factors like climate, construction quality, material and time. To predict the rate of deterioration, various pavement performance models are evaluated which are helpful in determining the need for rehabilitation and reconstruction of the damaged pavements.
The major characteristic governing the road performance study is the pavement deflection, which is often used to evaluate a pavement’s structural condition non- destructively. These deflection measurements can be made either by static equipment or by using impact load devices. The most widely used method of determining the pavement deflection is the Benkelman Beam Deflection (BBD) test which measures pavement responses to the static load applied by a standard truck. The rebound deflection is measured using the BBD test, which indicates the elastic response of the pavement. This deflection is corrected to various grounds to obtain the characteristic deflection which is useful in designing the overlay for the flexible pavement. Though, the use of BBD is widely accepted because of the low cost but it has various drawbacks. The use of this method for evaluating pavement performance is slow, time consuming and labor intensive.
For this reason, a prediction modelling is done to estimate the characteristic deflection at the particular pavement section without conducting the Benkelman beam test. Hence, data driven modeling is done for deflection at the stretch of Durg bypass – Chhattisgarh / Maharashtra border of NH - 06 under NHDP phase IIIA (chainage 322.000km to 480.000km) using heuristic approaches for predicting the characteristic deflection using various input variables measured from the same road section. The study presents two branches of artificial intelligence (AI) techniques, namely linear genetic programming (GP) variant, multi expression programming (MEP) and multivariate adaptive regression splines (MARS) for the evaluation of deflection measured on the surface of flexible pavement using Benkelman beam. The experimental data validation is done for the model predicted by AI techniques to compute the error and propose a prediction model for characteristic deflection of flexible pavement. The input variables considered are the moisture content, plasticity index of subgrade soil and pavement surface temperature. The predicted deflection values are compared with the observed values from field study for both MEP and MARS to get the best fit model with least error and high correlation coefficient by comparing various statistical parameters and efficiency coefficients
Towards A Comprehensive Evaluation of Driving Impairment and Assessment Technologies
No full textImpaired driving is a persistent threat to traffic safety, with alcohol and cannabis frequently involved in motor vehicle crashes. This dissertation examines how alcohol, cannabis, and their combination influence driving behavior and performance in real-world settings, and for alcohol, in a controlled driving environment. These investigations focus on vehicle control, behavioral adaptations, and, in the case of alcohol, the potential value of physiological signals as early indicators of impairment.
The first study analyzed over two years of naturalistic driving data from 41 participants. Trip-level substance use was self-reported and, when available, confirmed using breath or oral fluid testing. Alcohol-positive trips mostly occurred between 6:00 PM and 1:00 AM and on the weekends and showed a statistically significant reduction in highway mileage compared to baseline (-13%, p = 0.0475). Cannabis-positive trips followed a temporal distribution similar to baseline, apart from elevated activity on Fridays, and showed reduced highway mileage and a modest 3.2% reduction in speeding (F = 4.81, p = 0.0371) along with slight degradation in lateral control. Polysubstance trips occurred predominantly on weekends and exhibited the lower overall speeding proportions. Kinematic event rates increased at lower severity thresholds during impaired trips, particularly at higher speeds, suggesting either subtle destabilization or compensatory behavior. While high-severity safety-critical event rates were comparable between cannabis-positive and baseline trips, these findings challenge assumptions that cannabis-positive drivers may engage in safer driving behavior. In general, this dataset provides detailed insight into how alcohol- and cannabis-related impairment may manifest in routine driving behavior.
The second study evaluated alcohol's effects on physiology and driving performance, and the feasibility of using wearable sensors to monitor impaired driving. Five participants completed standardized drives under both sober and alcohol-impaired conditions (BrAC = 0.08%) while instrumented with ECG, respiration, and EEG sensors. Alcohol consumption produced consistent changes in heart rate (+13.5 bpm, p = 0.0267), heart rate variability (−150.3 ms, p = 0.0165), respiration patterns (reduced RVT, increased variability), and EEG signals (increased frontal alpha and theta power, reduced peak alpha frequency). ECG and respiration sensors performed reliably, while EEG data quality varied and required extensive processing. Behavioral changes on the road were consistent but subtle, with only lab-based reaction time tests reaching statistical significance (+22 ms, p = 0.0132). Participants showed poor accuracy in estimating their own intoxication (20% error) and expressed ambivalence toward driving under hypothetical impaired scenarios, consistent with longstanding evidence that drivers often lack accurate insight into their own impairment and risk. Overall, this study demonstrated that wearable physiological sensors can reliably capture alcohol-induced changes in heart, respiratory, and brain activity during real-world driving, even when observable effects on driving performance are subtle or inconsistent.
This dissertation advances impaired driving research by integrating large-scale naturalistic observation with controlled experimental testing. It clarifies how alcohol and cannabis influence real-world driving behavior, demonstrates that physiological signals can reveal alcohol impairment even when driving effects are subtle, and underscores the limitations of driver self-assessment. These findings support the development of intelligent monitoring systems that use objective, physiological data to improve impaired driving detection and prevention.Doctor of PhilosophyImpaired driving is a major public safety concern, with alcohol and cannabis frequently involved in crashes. Understanding their effects on driving and physiology is key to improving detection and safety interventions. This dissertation examines how alcohol, cannabis, and their combination influence driver behavior and performance in real-world and, for alcohol, controlled driving settings. These examinations are made in the context of vehicle control, driver behavioral patterns, and the usefulness of physiological signals as potential leading indicators of driver impairment.
The first study analyzes over two years of naturalistic driving data from 41 participants using sensor-equipped personal vehicles. In that study, self-reported substance use, occasionally verified using breathalyzer and oral fluid tests, was related to driving exposure and performance. The analysis found that both alcohol- and cannabis-positive trips showed less highway driving than substance-negative trips, suggesting possible driver compensatory strategies to reduce impaired driving risks. Cannabis-positive trips also showed slightly reduced speeding and modest lane-keeping deterioration. Similar rates of safety-relevant events were observed between substance-positive and substance-negative trips, weakening arguments of safer driving occurring when cannabis is consumed. In general, this dataset provides detailed insight into how alcohol- and cannabis-related impairment may manifest in routine driving behavior.
The second study used a closed-course test track to assess both driving performance and physiological responses under sober and alcohol-impaired conditions. Participants completed standardized driving tasks while wearing sensors that recorded heart activity, breathing patterns, and brain signals. This controlled design allowed researchers to safely evaluate whether physiological monitoring could detect alcohol-related impairment, and the results confirmed that such impairment produced consistent, measurable changes in heart, breathing, and brain activity.
Together, these studies build upon existing literature by combining large-scale naturalistic observation with structured experimental validation. They contribute to a better understanding of how substance use alters driving behavior and physiology, while highlighting the limitations of driver impairment self-assessment and the challenges of reliable real-time detection. The findings support the continued development of intelligent driver monitoring systems that can eliminate impaired driving from our roads
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Remembering the New International Economic Order: On the Loss of the Global Socialist and Anticolonial Revolutionary Project
Get PDFThe end of the twentieth century saw the collapse of the global socialist and anticolonial revolutionary project. In its wake, citizens across the world, especially on the Left, found themselves without a future to look forward to, stranded in the present, amidst the ruins of the past. As the failings of the post-Cold War neoliberal economic order have become increasingly clear, there has been growing interest in the idea of reviving a now forgotten policy from almost half a century ago: the New International Economic Order (NIEO). While this essay begins by discussing the prospect of reviving the NIEO, it is this tension between the Bandung era and its romantic yearning for total revolution and the neoliberal era and the accompanying loss of futures that is of particular interest. This essay argues that the project of reviving the NIEO relies on a set of concepts, terms, and temporal arrangements that are no longer available in the postcolonial neoliberal present. Thus, the central motivating question of this paper regards the type of relationship we can have with a past that no longer speaks to our present. While this essay argues that revival is no longer an option, it also argues against forgetting projects like the NIEO and the tradition of global socialist and anticolonial revolution that they drew from. Instead, this essay makes an argument for remembrance, which exists within a space between reviving and forgetting that allows us to maintain a connection with the past while freeing us to develop new ways of dealing with the challenges of the postcolonial neoliberal present
On-Road Evaluation of an Unobtrusive In-Vehicle Pressure-Based Driver Respiration Monitoring System
No full textIn-vehicle physiological sensing is emerging as a vital approach to enhancing driver monitoring and overall automotive safety. This pilot study explores the feasibility of a pressure-based system, repurposing commonplace occupant classification electronics to capture respiration signals during real-world driving. Data were collected from a driver-seat-embedded, fluid-filled pressure bladder sensor during normal on-road driving. The sensor output was processed using simple filtering techniques to isolate low-amplitude respiratory signals from substantial background noise and motion artifacts. The experimental results indicate that the system reliably detects the respiration rate despite the dynamic environment, achieving a mean absolute error of 1.5 breaths per minute with a standard deviation of 1.87 breaths per minute (9.2% of the mean true respiration rate), thereby bridging the gap between controlled laboratory tests and real-world automotive deployment. These findings support the potential integration of unobtrusive physiological monitoring into driver state monitoring systems, which can aid in the early detection of fatigue and impairment, enhance post-crash triage through timely vital sign transmission, and extend to monitoring other vehicle occupants. This study contributes to the development of robust and cost-effective in-cabin sensor systems that have the potential to improve road safety and health monitoring in automotive settings
EFFECTS OF CINNAMOMUM ZEYLANICUM BARK EXTRACT ON NOCICEPTION AND ANXIETY LIKE BEHAVIOR IN MICE
Get PDFObjectives: The aim of the study was to assess the effect of the extract of Cinnamomum zeylanicum (CZ) bark in the experimental models of pain and anxiety-like behavior in mice.
Methods: The extract of CZ bark was administered at the doses of 100, 200, and 400 mg/kg, per orally (p.o) and morphine used as a positive control for pain models, was administered at the dose of 5 mg/kg, intraperitoneally (i.p.). Antinociceptive activity was evaluated using three experimental animal models of pain, namely, tail flick, hot plate, and formalin test. Elevated plus maze test was used to assess the effect on anxiety-like behavior. Rotarod apparatus and actophotometer were used to test muscle coordination and locomotor activity, respectively.
Results: Administration of CZ bark extract in the dose of 200 and 400 mg/kg showed significantly increased in the tail-flick latency and latency to reaction time in hot plate test as compared to the control group. In the first phase (0–5 min) of the formalin test, a significant reduction in the pain response was found in CZ (200 and 400 mg/kg) and morphine-treated groups, however during the second phase (30–35 min) significant reduction in formalin-induced pain response was observed in 100, 200, and 400 mg/kg CZ extract-treated group when compared to control group. CZ extract administration at 200 and 400 mg/kg dose caused a significant increase in the percentage of time spent in open arms in the elevated plus maze as compared to the control group.
Conclusion: Results suggest that CZ bark extract possesses the antinociceptive activity and modulates anxiety-like behavior
The Indian prisons and the search for equality: The problems faced by transgender inmates
No full textThe social stigma around the transgender community is changing and taking a turn for the better. But this change may be slower than expected if we take into consideration the discrimination the transgender community faces not just in a particular aspect but in all walks of life. They are denied education and employment opportunities, discriminated against in their homes, and looked down upon by society. Even more so, they are ridiculed and made fun of and act as a source of amusement which people get by humiliating such people. The authors, through this article, try to explore the discrimination and humiliation faced by transgender people in an Indian prison and emphasise upon the advisory given by the ministry of home affairs regarding the provision of separate housing cells for people belonging to these marginalised communities. The authors try to explore the societal gap which exists acting as an imminent factor in the unequal treatment and harassment of transgender inmates. The paper touches upon the various problems faced by transgender inmates in Indian Prisons and offers solutions which could help provide such inmates with a habitable and reputable environment to be detained in. The paper includes a descriptive analysis of the issue at hand with the solutions and the change in perspective that society needs to leave behind to prevent the stigmatisation of the transgender community
Fault Diagnosis of Neural Network Modelled Mechanical Systems using a Sparse Bayesian Learning Framework
No full textThe increasing complexity of mechanical systems has resulted in an increased usage and dependence on data driven modelling techniques in order to obtain simple yet accurate models of these systems. Neural networks have emerged as a popular modelling choice due to their proven ability to learn complex nonlinear relationships between inputs and outputs of any given system. Moreover, they are capable of generalizing on data that they have not been trained on. The downside of modelling with neural networks is that they do not provide any insight into the dynamics of the system they model. This limits the application of neural networks in carrying out fault diagnosis of mechanical systems to just the fault detection and isolation (FDI) tasks. While in some applications this may be sufficient, sometimes alongside FDI, it is also desirable to carry out a fault identification task in order to determine the necessary adjustments to bring the faulty system back to its normal operating condition. This thesis explores the possibility of carrying out a fault identification task alongside an FDI task for a mechanical system that has been modelled by a neural network. Traditionally, the weights of a trained neural network represent the strength of a connection between the two neurons they connect. The possibility of an existing correlation between the weights of a neural network and the properties of the mechanical system being modelled is a concept that has not been fully explored yet. This study considers that such a relation exists, implying that the change in certain properties of the mechanical system due to the occurrence of a fault can be related to a change in the corresponding weights of the neural network modelling the system. Consequently, the change in weights of the neural network could give an insight into the fault occurrence in a mechanical system. Taking this idea forward, two fault diagnosis algorithms have been proposed in this study - a fault detection algorithm using adaptive threshold, and a fault isolation & identification algorithm based on sparse Bayesian learning framework. The proposed algorithms were tested on (hypothetical) faulty linear and non-linear systems. The results show that the adaptive threshold based fault detection algorithm was successful in detecting the occurrence of faults in the linear system. For the non-linear system, although a simplistic neural network was used to model the system, the fault detection algorithm was still successful while returning few and sparse false positives and negatives. The fault isolation & identification algorithm was also successful in isolating and identifying all the changed weights in the neural networks modelling the system for both linear and non-linear cases. Although the algorithms proposed show promising results for the experiments conducted, further research is needed to establish the suitability of using them in real world applications.Mechanical Engineerin
Empennage Wake Filling using Steady Chord-wise Blowing for Propulsive Fuselage Concepts
No full textIt is the need of the hour to focus research and development towards curbing emissions due to the growing aviation industry. Employing boundary layer Ingesting(BLI) propellers for development of propulsive fuselage concept(PFC) aircraft while utilizing hydrogen as an alternative energy source, is the idea behind project APPU(Advanced Propulsion and Power Unit). However, the empennage wake is detrimental to the propeller. This study uses chord-wise wake blowing technique to fill the empennage wake in order to mitigate the detrimental effects of the wake on the inflow of the aft mounted BLI propeller. A near complete wake filling was observed for the blown configuration, with a 99.7% uniformity in the velocity profiles for the filled empennage wake. The addition to the overall aircraft drag due to the wake blowing system was less than 2 drag counts and a mass flow rate of 0.91-1.23Kg/s was found to be required in the blown cases.APPU ProjectAerospace Engineering | Flight Performance and Propulsio
