NUST Institutions Library Catalogue Search for 'an:"119573"'

Mental Workload on the Children in Multilingual Environment /

By Niazi, Anum . . 56p. 30cm.

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Enhancing the Functionality of Polyethersulfone Hemodialysis Membranes Using Water-Soluble Hydrophilic Additives /

By Manzoor, Saira . . 84p. , The kidneys play a critical part in maintaining internal equilibrium of the body. However, renal diseases present a significant public health concern, affecting millions of individuals worldwide. Polyethersulfone (PES) based hemodialysis membranes can provide a lifesustaining treatment procedure for patients suffering from renal disease. Nevertheless, the intrinsic hydrophobic nature of PES contributes to an inefficiency of uremic toxin clearance and a compromised hemocompatibility. This work evaluates the individual and combined effects of two water-soluble hydrophilic additives, polyethylene glycol (PEG) and polyvinylpyrrolidone (PVP), on the functionality of polyethersulfone (PES) membranes. The membranes were synthesized by the non-solvent phase inversion method by using NMP as the solvent. The fabricated membranes were characterized by using Scanning Electron Microscopy, ATR-FTIR, tensile testing, porosity, and contact angle analysis. The SEM images demonstrated the successful fabrication of the membranes. Each membrane possessed a thin skin layer and an asymmetric porous framework. As a result of the synergistic effect, the membrane with dual WSP—2.5% PVP and 2.5% PEG— performed better than membranes with a single water-soluble hydrophilic additive. The membranes comprising the two additives had excellent hydrophilicity, increased porosity, and a high-water retention capacity. Moreover, they showed a urea clearance of 77.3%, a pure water flux of 96 L/m²/h, and an outstanding BSA rejection of 99.10%. RSM modelling was employed to determine the urea clearance that verified the ideal conditions for urea removal were concentrations of 1200 mg/L and 0.6 MPa. The effectiveness of membrane containing dual WSP for hemodialysis was further demonstrated by hemocompatibility xv tests, which provided promising results (APTT 32s, PT 14s, plasma recalcification time 205.5s, and hemolysis rate 1.32%). 30cm.

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Effects of Broadband Noise on Sleep Quality /

By Zulfiqar, Zanib . . 78p. , Quality sleep is related to multiple valued performances in human life, including maintaining health and improving outcomes at work. Disturbance of sleep can increase the morbidity rate and can create multiple psychological and physiological issues. Broadband noises were hypothesized to mask disruptive noises and improve sleep quality. However, the efficacy of noise in improving sleep measures remained unclear due to the smaller sample size, uncontrolled noise environment, and sleep duration. The main aim of the study was to quantify sleep quality, sleep fragmentation, sleep latency, and relevant sleep factors by providing broadband noises and to assess whether white noise could be a non-pharmacological treatment for better sleep quality. For that purpose, sleep monitoring devices i.e., Fitbit Charge 4 and 5 as well as multiple questionnaires including PSQI, rMEQ, AASP, and St Mary questionnaire were used for data collection. Both hardware and questionnaire data were used to evaluate the effect of white noise on all the sleep factors i.e., sleep stages, sleep latency, total sleep duration, and overall sleep score. Also, environmental noise was measured by using decibel meters. Mostly healthy participants having the age of 25.07+/-4.66 for the questionnaire-based study and 24.25+/- 2.57 for hardware-based data were selected for data collection. Multiple statistical tests were performed on the collected data. ANOVA tests were performed on Fitbit’s data along with the post-hoc tests. Also, chi-square tests were conducted on questionnaire data. The p-value>0.0.5 in all tests suggested that no significant effect of white noise on sleep quality. The results concluded that white noise does not play a significant role in improving sleep quality. However, it can be used as a placebo effect for better sleep for specific persons. The results are based on both sleep monitoring devices and questionnaires with proper statistical analysis, having greater sample size and under controlled environmental noise. However, this work can be extended by changing the population of the experiment i.e., infants and ICU patients to check the effects of white noise. 30cm.

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Microfluidic Paper-Based Analytical Device for Milk Adulteration Analysis /

By Maryam, Ammara. , Hardcover 30cm..

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Identifying Neurophysiological Correlates of Frontotemporal Dementia: Resting State EEG and Phase Synchronization Analysis /

By Ali, Salwa . . 123p. , The need to develop more efficient neuropsychological biomarkers is paramount in the identification of neurodegenerative diseases, tracking the efficiency of treatment and in an effort to avoid the huge financial cost required. While previous research utilizing neuroimaging techniques has pinpointed changes in functional connectivity (FC) as promising biomarkers for frontotemporal dementia (FTD), the constraints of cost and availability of neuroimaging equipment underscore the necessity for accessible alternatives. Electroencephalography (EEG) has emerged as a viable option due to its increasing robustness, wider usage, and affordability. To this end, the research focuses on a resting-state EEG data created from AD, FTD, and HC groups. Here ground data were obtained from nineteen leads using a clinical EEG device when the subjects were in a resting state and their eyes were closed. Another challenge was to follow strict standards for data quality and quality management for data quality to enhance consistency. It is a cross-sectional study, including data from MiniMental State Examination conducted on each participant, and tapes recorded from 20 AD patients, 20 FTD patients, and 20 HC. The Neuroimaging Data Structure (BIDS) format was utilized to present both preprocessed and raw EEG data. The foremost aim was to determine the Feasibility, Sensitivity, and Specificity of the preprocessed, feature extracted, time-efficient, and artifact reduced EEG-derived FC patterns as markers in FTD. Phase-lock values (PLVs) were computed among nineteen pairs of electrodes across five frequency bands using MATLAB and the Hilbert transform. Significant variations in brain connectivity were identified through statistical analyses. The study revealed significant differences in alpha and beta frequency patterns among the control, Alzheimer's, and FTD groups, particularly in frontal and temporal regions. These differences suggest alterations in neural activity associated with cognitive processing, potentially serving as biomarkers for distinguishing between the three groups. Alterations in beta frequency PLV were noted across various EEG pairs, indicating disruptions in neural communication and coordination. These alterations suggest xvi compensatory mechanisms or hyperactivity in frontal and prefrontal regions, alongside potential cognitive and motor deficits due to decreased PLV in central and temporal regions. While no statistically significant differences were observed in delta and theta frequency synchronization between groups, trends suggest potential regions of interest for further research, aligning with existing literature exploring neural oscillations in neurodegenerative diseases. Similarly, no significant differences were observed in gamma frequency synchronization between groups, indicating relatively preserved neural synchronization in this frequency range across control, Alzheimer's, and FTD patients. In summary, both Alzheimer's and FTD demonstrate significant reductions in alpha and beta frequency values, particularly in frontal and temporal regions, compared to healthy controls. These findings underscore the altered functional network topology in AD and FTD, offering valuable insights into the neural mechanisms underlying these conditions. The study's results contribute to the development of electrophysiological markers, potentially enhancing the clinical diagnosis and understanding of AD and FTD. The specificity and sensitivity of EEG-derived FC patterns highlight their potential as costeffective, accessible biomarkers for neurodegenerative disease. 30cm.

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Analysis of variations in brain states and impact of TES during behavioral task /

By Arshad, Sidra . . 88p. , This research targeted to investigate the effects of low-gamma High Definition transcranial alternating current (HD-tACS) at the left DLPFC and primary motor cortex in healthy individuals performing continuous attention task. We selected an openly accessible dataset from openneuro.org. Dataset includes within participant implementation of High-Definition tACS (HD-tACS), stimulating two cephalic regions (frontal & motor) with biphasic stimulation waveform (30 Hz) with a total 600 stimulation trials in 30 sessions. The physiological data i.e. EOG, ECG, along with EEG and behavioral data were being recorded over the course of two 70 and 70.5 minutes’ sessions. The demographic data were acquired before and after each session together with the wellness questionnaires. The participants were given two stimulation doses separately, with 20 stimulation trials per session. The within-subject results showed significant differences between the pre- and post-stimulation data (p-value <0.05) in the F30, in each frequency band. While in M30 session, there was a significant increase in alpha and beta oscillations (p-value < .05). The gamma oscillations were not altered by low-gamma tACS at M1, whereas the theta oscillations showed a significant decrease. The phase-locking values (PLV) of frontal channels decreased in theta, alpha, beta & gamma bands, suggesting a drop in the attention of participants with the onset of stimulation. Hence, the results indicate, that lowgamma HD-tACS over left DLPFC has the potential to inhibit attention and information processing. And the low-gamma HD-tACS can improve motor function over the left primary motor cortex. 30cm.

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Auditory spatial attention through transcranial direct current stimulation in bilinguals

By Javed ,Saima . . 63p. ; 30cm..

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Effect of Tactile Stimulation on Visual Memory Performance /

By Latif, Asma . . 80p. , The human eye continuously perceives information about surroundings to be processed and stored in memory so that it can be retrieved. Environmental factors may have positive and negative effects on memory performance and human cognitive processing. Many studies have addressed the effect of auditory circumstances on spatial tasks and visual memory performance. However, only a few studies highlighted the cross-modal interaction between vision for visual cue and touch for training of same visual pattern in tactile pattern. In addition, very little research has been conducted on the effect of tactile stimulation towards memorizing visual tasks. The main objective of this study is to investigate the effect of visuo-tactile stimulation on adult memory. Sixty-two subjects participated in this behavioral study having normal and corrected to normal vision. Participants are divided into two groups and each subject goes thorough Mini mental state examination and Edinburg handedness inventory. The visual assessment task consists of different shapes along with three-digit numbers. During the memorization period, visual assessment task was displayed on computer screen and tactile stimulation was delivered on index finger of the dominant hand of the participant. The participant was provided with an evaluation sheet containing shapes only. If the shape is paired with its corresponding number, then it was be considered correct. The p-value < 0.05 in visual assessment test showed a significant effect of tactile stimulation on visual memory performance. The findings of this study concluded that participants memorized the object number pair task better in the presence of tactile stimulation as compared to control/no stimulation. One of the conclusions of our work is that combing vision and touch sense may improve cognitive ability and may be provided to people during learning and remembering visual tasks. For future recommendations, heterogenous sample along with brain response can be studied. 30cm.

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Deep Learning Based Approach for Epilepsy Detection Using EEG Data /

By Khalid, Mina . . 68p. , Epilepsy is one of the most common neurological disorders characterized by recurrent seizures. Electroencephalography (EEG) serves as a crucial diagnostic tool for epilepsy, yet traditional diagnosis relies heavily on manual interpretation, which is time-intensive, subjective, and prone to errors. This study addresses the need for automated, reliable, and efficient diagnostic methods by exploring the classification of healthy and epileptic individuals using raw EEG data analyzed through a one-dimensional Convolutional Neural Network (1D CNN). The proposed model was trained and evaluated on a dataset comprising 148 scalp EEG recordings (72 epileptic patients and 78 healthy individuals) obtained from a local hospital. The CNN model automatically extracted features from the EEG signals and achieved an accuracy of 97.73%, sensitivity of 98%, and specificity of 98%. Channel-specific analyses were conducted to evaluate the contribution of individual EEG channels, and the model's performance was further examined by progressively reducing the number of channels. These findings underscore the potential of integrating EEG data with deep learning for accurate, automated, and non-invasive epilepsy diagnosis. Additionally, the study highlights the significance of channel reduction in simplifying data while preserving diagnostic precision, facilitating more efficient clinical applications and real-time seizure detection systems. 30cm.

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Effect of Binaural Beats Combined with Continuous Rhythmic Noise on Sleep Quality /

By Latif, Rabia . . 71p. , Getting enough sleep is linked to several important aspects of human existence, such as preserving health and achieving better results at work. Sleep disturbance can result in several physiological and psychological problems. There are many factors affecting sleep quality including sleep habits, exercise, stress and anxiety, sleep disorders, light intensity, temperature and environmental noise. The primary goal of the study was to determine if continuous rhythmic white noise in combination with 3Hz binaural beats might be used as a non-pharmacological therapy for improved sleep quality, sleep fragmentation, latency, and relevant sleep parameters. The impact of continuous rhythmic white noise on sleep parameters was assessed using both hardware and questionnaire methods. Data from thirty subjects (16 females, mean age, 24.9±1.08 years), was collected using a variety of questionnaires, including the Pittsburgh Sleep Quality Index, reduced Morningness-Eveningness Questionnaire, Adolescent/Adult Sensory Profile, St. Mary Hospital Sleep Questionnaire, and a wearable sleep tracking device, Fitbit Charge 4. The questionnaire-based data was evaluated using chi mean square and the hardware-based data was analyzed using ANOVA and post hoc analysis where applicable. The results show that continuous rhythmic white noise combined with binaural beats has a significant impact on sleep quality, sleep score, total sleep duration, deep sleep duration and REM duration (p-value<0.05). The continuous rhythmic white noise in combination with binaural beats can be used to enhance specific aspects of sleep, particularly REM sleep, which is critical for cognitive and emotional well-being. In conclusion, this study demonstrated the potential of continuous rhythmic white noise in combination with 3 Hz binaural beats for improving the sleep quality of the individuals in noisy settings. 30cm.

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Design and Development of an Air-Driven Posture Correction Device /

By Haris, Muhammad. . 129p. , The prevalence of postural kyphosis continues to rise across all demographics because of inactive lifestyles and extended screen time usage which has established itself as a major musculoskeletal issue. The current solutions including static braces and feedback-only wearables, fail to provide adaptability and comfort with active correction features. This restricts their ability to achieve longterm success and user adherence. The research introduces a new wearable air-driven posture correction device which combines real-time sensing with pneumatic actuation to treat flexible thoracic kyphosis. The proposed wearable tech device focuses on posture correction by integrating multiple features into one compact device. The system includes an MPU-6050 inertial measurement unit which tracks the user’s trunk in real time in six degrees of freedom. A microcontroller processes this data and determines if changes to posture exceed a calibrated angular threshold. If so, the system triggers a pneumatic actuation module within an orthopedic vest that has been altered for this purpose. This module includes butyl rubber chambers which are constrained but designed to inflate and mechanically stress the upper back by simulating the action of scapular retractor muscles. To measure the corrective force, force-sensitive resistors (FSRs) are placed where the actuators and the body interface. A closed-loop control system dynamically adaptive and responsive to real time conditions with sensor fusion guarantees timely action and feedback. The prototype was tested by means of both objective and subjective methods, with a full-scale experimental protocol involving 24 healthy participants. Data collected in this study included realtime pitch angle vs actuator force, and subjective user feedback through standardized ergonomic surveys such as the Borg CR10 scale, Corlett & Bishop discomfort map, and the System Usability Scale (SUS). Results indicate that the system successfully minimized thoracic pitch deviation while maintaining safe tactile force levels, achieving average corrective pressures of 7–12 kPa, resulting in notable postural enhancement. The system reliably attained enhancements in posture, surpassing 85% in 22 out of 24 participants. This work contributes a fully automated, textile-integrated pneumatic solution for posture correction, combining real-time sensing, adaptive actuation, and ergonomic design. The proposed xix system offers a replicable framework for intelligent musculoskeletal rehabilitation wearables and lays the groundwork for future closed-loop personalization strategies in postural health technologies. 30cm..

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Effects of anodal tDCS on Motor Cortex to improve fine motor Skills /

By Ali, Ahmad Maaz . . 102p. , This investigation examined the efficacy of anodal transcranial direct current stimulation (tDCS) targeting the motor cortex for enhancing fine motor skill acquisition. Twenty healthy participants were randomly divided into two equivalent groups: an Active tDCS experimental cohort (n=10) and a Sham control cohort (n=10). The study implemented a comprehensive six-day experimental protocol to systematically assess tDCS effects on both behavioral motor performance and neurophysiological activity patterns. The experimental framework commenced with Day 1 baseline evaluations, during which all subjects completed initial electroencephalographic (EEG) recordings while performing a standardized training regimen. This regimen incorporated two validated assessments from the Fundamentals of Laparoscopic Surgery (FLS) evaluation suite: a bimanual peg transfer exercise and a unimanual bead placement task. These particular tasks were chosen based on their proven reliability for measuring fine motor coordination capabilities and manual precision. Throughout the intervention period (Days 2-4), both cohorts completed three daily training sessions, with the experimental group receiving active tDCS while the control group received sham stimulation. The stimulation protocol was administered concurrently with task performance to investigate potential motor learning facilitation effects. Post-intervention evaluations were conducted on Day 5, incorporating EEG recordings during task execution to capture immediate neuroplastic adaptations and assess short-term motor skill enhancements. To evaluate the persistence of observed improvements, a retention assessment was performed on Day 12, occurring one week following the final stimulation session. This follow-up evaluation required participants to repeat the identical FLS tasks while undergoing EEG monitoring to quantify sustained motor skill improvements. The results demonstrated statistically significant fine motor skill enhancement in the Active tDCS group compared to the Sham control group. The bimanual peg transfer task showed significant improvement (p = 0.0006), while the unimanual beads placement task demonstrated highly significant enhancement (p < 0.00000001). Motor skill retention remained statistically significant at the one-week follow-up assessment (p = 0.0021). XX Neurophysiological analysis revealed significant changes in EEG frequency bands, particularly within the beta frequency range, which showed a significant group × day interaction (p = 0.0444), indicating that tDCS produced greater changes in motor-related neural activity compared to sham stimulation. Alpha and gamma frequency bands exhibited trends toward statistical significance, though these did not reach conventional significance thresholds. Event-related desynchronization/synchronization (ERD/ERS) analysis demonstrated that the Active group exhibited increased ERD following intervention, reflecting greater cortical engagement, and elevated ERS at follow-up assessment, suggesting enhanced neural efficiency. Motor cortex electrodes (C3 and C4) in the Active group displayed greater ERD, indicating increased engagement of motor cortical regions during task performance. This investigation provides robust statistical evidence supporting tDCS as an effective intervention for enhancing motor cortex excitability and facilitating fine motor skill development. The findings demonstrate significant clinical relevance for applications in neurorehabilitation protocols, surgical skill training programs, and cognitive-motor therapeutic interventions. Future research directions may investigate varying stimulation parameters and diverse task configurations to further optimize tDCS efficacy for motor skill enhancement applications. 30cm.

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Predicting Healthy and Pathological EEG Patterns with Machine Learning Algorithms /

By Abbas, Ghulam . . 85p. , Neurological disorders pose major global health challenge, affecting an estimated one billion individuals worldwide. According to the World Health Organization (WHO), these neurological disorders contribute to approximately six million deaths annually, representing a significant burden. Early and accurate identification of brain pathological features in electroencephalogram (EEG) recordings is important for the diagnosis and management of these disorders. However, manual interpretation of EEG recordings is not only time-consuming but also requires expertise. This problem is compounded by the scarcity of trained neurologists in the healthcare sector, especially in low- and middleincome countries. These limitations emphasize the necessity for automated diagnostic processes. With the advancement of machine learning algorithms, have sparked significant interest in automating the process of early diagnoses using EEGs. Therefore, this paper presents a novel deep learning model consisting of distinct path, Hybrid-CNNTransformer, for the automatic detection of abnormal raw EEG data. Through multiple ablation studies, we demonstrated the effectiveness of all parts of proposed model. The performance of our proposed model was evaluated using NMT Scalp EEG Dataset and achieved a high classification accuracy of 87.77%, which outperforms the original baseline model and other research studies. Moreover, we demonstrated the generalization of our proposed model by evaluating it on another independent dataset, TUH abnormal EEG Corpus V.2.0.0. (TUAB), without any hyperparameter tuning or adjustment. Furthermore, a Explainable AI (XAI) analysis confirmed that the model's decision-making process is not only transparent but also neurologically plausible. 30cm.

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Design and development of a Wearable Assistive device for Individuals with Vision Loss /

By Mashal, Abdul Hameed . . 75p. , This thesis presents the design and implementation of an intelligent, multi-functional visual aid on a low-cost embedded platform to help visually impaired people become more aware of their surroundings and more independent. A lot of the time, traditional assistance devices are too expensive, don't do enough, or take up too much computing power. To overcome these constraints, this study creates a portable system utilizing a Raspberry Pi 5 and a conventional camera module, proficient in executing four essential functions in real-time: object detection, facial recognition, monocular depth-based obstacle avoidance, and currency identification. The core innovation of this work is a computationally efficient, two-stage processing pipeline. The lightweight primary object detector YOLOv8 initially looks at the scene to give it some general context. Based on this first analysis, the system smartly sends out specialised secondary models only when they are needed. For example, it might turn on a facial recognition module when it sees a "person" or a MiDaS-based depth estimate model when a huge, nearby object could be an obstacle. This context-aware method cuts down on the amount of processing power needed by a lot, making it possible for edge hardware to work in real time. The system gives the user clear audio announcements through a text-to-speech engine that are easy to understand and act on. The final implementation shows that a modular, intelligently layered AI architecture can provide a flexible, high-performance, and lowcost assistive solution that effectively connects complex computer vision capabilities with practical, real-world use for people who are blind or have low vision. 30cm.

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