NUST Institutions Library Catalogue Search for 'kw,wrdl: (su-br:"Human Engineering.")'

The pentagon : a history /

By Vogel, Steve.. [S.l.] : Random House, 2007 . 656 p. ; 24 cm.. 1400063035 (hardcover) | 9781400063031 (hardcover)

Manufacturing Engineering and Technology /

By Kalpakjian, Serope,. Upper Saddle River, NJ : Prentice Hall, 2001 . xx, 1148 p. : 26 cm.. 0201361310

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Materials Handbook

By Vaccari, John A.. New York : | Blacklick : McGraw-Hill Professional Publishing | McGraw-Hill Companies, The [Distributor] 2002 9780071360760 | 007136076X (Trade Cloth)

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Unmanned Aerial Vehicles /

By Valavanis, Kimon P.. 2011 9789400711099

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Bio-Inspired Self-Organizing Robotic Systems /

By Meng, Yan.. 9783642207594

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Biologically Inspired Approaches for Locomotion, Anomaly Detection and Reconfiguration for Walking Robots

By Jakimovski, Bojan.. 9783642225055 | 9783642225048 (print)

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Modelling and Control for Intelligent Industrial Systems : Adaptive Algorithms in Robotics and Industrial Engineering /

By Rigatos, Gerasimos G.. 9783642178740

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Handbook of human factors and ergonomics /

By Salvendy Gavriel. Hoboken, NJ : Wiley, 2012 . xx, 1732 p. : , Includes index. http://www.amazon.com/Handbook-Factors-Ergonomics-Gavriel-Salvendy/dp/0470528389/ref=sr_1_1?s=books&ie=UTF8&qid=1405925902&sr=1-1&keywords=9780470528389 25 cm.. 9780470528389 (hardback)

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Handbook of space technology /

Chichester, U.K. : Wiley, 2009 . xxi, 882 p. : 26 cm..

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ARTIFICIAL INTELLIGENCE and SOFTWARE ENGINEERING(EBOOK)

By Partridge, Derek. New Delhi Glenlake Publishing 1998 . xi,274p 0814404413

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Biomedical Engineering (E-Book)

By Carlos Alexandre Barros de Mello. India In-Teh 2009 . x,658 P;

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Recent Advances in Biomedical Engineering (E-Book)

By Dr Ganesh R Naik. India In-Teh 2009 . xii,660 P;

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Cooperative Design, Visualization, and Engineering 10th International Conference, CDVE 2013, Alcudia, Mallorca, Spain, September 22-25, 2013. Proceedings /

By Luo, Yuhua.. . XII, 236 p. 89 illus. , E Book 9783642408403

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Mobile displays technology and applications E-Book

Chichester, England ; | Hoboken, NJ : J. Wiley & Sons, 2008 . xxiv, 625 p. : 26 cm.. 0470723742 (cloth) | 9780470723746 (cloth)

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The lake house : a novel /

By Patterson, James,. Boston : Little, Brown, 2003 . 401 p. ; , Sequel to: When the wind blows. 25 cm.. 9780755300280

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Chemistry for sustainable technologies : a foundation /

By Winterton, Neil.. Cambridge : RSC Pub., 2011 . xix, 480 p. : , H.B 25 cm.. 9781847558138 (hbk.) | 1847558135 (hbk.)

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Principles of ecotoxicology /

By Walker, C. H.. Boca Raton, FL : CRC Press, 2012 . xxv, 360 p. : , Rev. ed. of: Principles of ecotoxicology / C.H. Walker ... [et al.]. c2006. 26 cm.. 9781439862667 (pbk.)

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Dynamic manufacturing : creating the learning organization /

By Hayes, Robert H.. [S.l.] : Free Press, 1988 . 448 p. ; , Hardcover. 24 cm.. 0029142113 | 9780029142110

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Systems management for information technology and software engineering /

By Sage, Andrew P.. [S.l.] : Wiley-Interscience, 1995 . 624 p. ; , Hardcover. 25 cm.. 0471015830 | 9780471015833

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Building technology : mechanical and electrical systems /

By Stein, Benjamin.. New York : J. Wiley, 1997 . xix, 1048 p. : , Rev. ed. of: Building technology / William J. McGuinness, Benjamin Stein. c1977. 29 cm.. 0471593192 (cloth : alk. paper)

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Design like you give a damn : architectural responses to humanitarian crises /

New York, NY : Metropolis Books, 2006 . 333 p. : 22 cm.. 1933045256 (pbk.) | 9781933045252 (pbk.)

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Ground and surface water hydrology /

By Mays, Larry W.. Hoboken, N.J. : Wiley, 2012 . xxii, 617 p. : 26 cm.. 9780470169872 (hardback) | 0470169877 (hardback)

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Modern hydrology and sustainable water development /

By Gupta, Sushil K.. Chichester, West Sussex, UK ; | Hoboken, NJ : Wiley-Blackwell, 2011 . xx, 437 p., [12] p. of plates : 26 cm.. 9781405171243 (cloth) | 1405171243 (cloth)

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Water resources : an integrated approach /

. pages cm 9781138329218 | 9781138329225

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New Approaches to Building Pathology and Durability /

. 1 online resource (VIII, 207 pages) 9789811006487

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Development Of An Fnirs Equipment For Monitoring Human Brain Activity In The Pre-Frontal Region

By Ehtisham Ul Hasan. Islamabad : SMME - NUST, 2016 . 38 p. ; 30cm..

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Hacking h(app)iness : why your personal data counts and how tracking it can change the world /

By Havens, John C.. . xxxvi, 268 pages ; 24 cm. 9780399165313 (hardback)

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How innovation works : and why it flourishes in freedom

By Ridley, Matt,. New York, NY : Harper, an imprint of HarperCollinsPublishers, 2020 . 406 pages ; , "Originally published as How innovation works: serendipity, energy and the saving of time in Great Britain in 2020 by 4th Estate, an imprint of HarperCollinsPublishers"--Title page verso 24 cm. 9780062916594 | 0062916599

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His Very Best : jimmy carter, a life

By Alter, Jonathan,. Waterville : Thorndike Press, 2020 . 782p 9781432884529

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Sustainable approaches to urban transport /

. pages cm 9781138544239 (hardback)

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Age-Specific Study of Airflow Distribution, Aerosol Transport and Deposition in an Unhealthy Human Lung using CFD-DPM Approach /

By Riaz, Hafiz Hamza . . 58p. 30cm.

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Comparison of Quantitative Proxemics to measure trust in HRI /

By Ahmad, Fatima . . 45p. ; 30cm..

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Perception of Emotion in Human-Robot Interaction /

By Zia, Muhammad Faisal. . 59p. , Perception of emotion is an intuitive replication of a person’s internal state without the need for verbal communication. Visual emotion recognition has been broadly studied and several end-toend deep neural networks (DNNs)-based and Machine learning-based models have been proposed but they lack the ability to be implemented in low-specification devices like robots, and vehicles. The drawbacks of conventional handcrafted feature-based Facial Emotion Recognition (FER) methods are eliminated by DNNs-based FER approaches. In spite of that, Deep Neural Network based FER techniques suffer from high processing costs and exorbitant memory requirements, their application is constrained in fields like Human-Robot Interaction (HRI) and HumanComputer Interaction (HCI) and relies on hardware requirements. In aforementioned study, we presented a computationally inexpensive and robust FER system for the perception of six basic emotions (i.e., disgust, surprise, fear, anger, happy, and sad) that is capable of running on embedded devices with constrained specifications. In the first step after pre-processing input images, geometric features are extracted from detected facial landmarks, considering the facial spatial position among influential landmarks. The extracted features are given as input to trainthe SVM classifier. Our proposed FER system was trained and evaluated experimentally using two databases, Karolinska Directed Emotional Faces (KDEF) and Extended Cohn-Kanade (CK+) database. Fusion of KDEF and CK+ datasets at the training level were also employed in order to generalize the FER system’s response to the variations of ethnicity, race, national and provincial backgrounds. The results show that our proposed FER system is optimized for real-time embedded applications with constrained specifications and yields an accuracy of 96.8%, 86.7% and 86.4% for CK+, KDEF and fusion of CK+ and KDEF databases respectively. As a part of our future research objectives, the developed system will make a robotic agent capable of perceiving emotion and interacting naturally without the need for additional hardware during HRI. 30cm.

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The politics of parametricism : digital technologies in architecture /

. xiv, 258 pages : 24 cm. 9781472581662 (hardback) | 9781472581655 (paperback)

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Simulation of Structural Integrity of Rubber Brick Wall for Constructional Purposes /

By Masood, M Faizan Iqbal . . 172p. , An earthquake is a natural phenomenon that causes extreme destruction and death. Earthquakes are caused by sudden releases of energy from the earth's interior that cause the earth's crust to shake. A tectonic plate slips past another during this event. A natural disaster such as an earthquake may be rarer than other types, but it can create more devastation and death than any other natural hazard, making it one of the most destructive on the planet. In Newbury, UK, the International Serological Centre (ISC) and the World Data Centre A in Boulder, Colorado, USA, hold comprehensive lists of seismic events. As part of the efforts to improve earthquake predictability, much research has also been conducted on earthquakeprone areas worldwide. To counteract this destruction, humankind has been evolving and dealing with different methods to reduce the number of lives lost. The objective was to simulate on Abaqus a 4x4 wall of bricks made from polyurethane joined by epoxy to test on what magnitude does wall break. The wall was tested and almost sustained 7.1 magnitude. There were cracks in the wall but the bricks did not break instead the bonding agent gave up and the wall fractured. 30cm.

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Assessment of Stress Biomarkers in the Saliva of Smokers and Nonsmokers via UV Photospectrometry and POMS /

By Fahim, Maria . . 99p. , Smoking is one of the major health catastrophes. Smoking is believed to be the major cause of chronic diseases like Cardiovascular complications, stroke, pregnancy issues, respiratory failure, etc. There are three important transdiagnostic emotional factors that make the population vulnerable to initiation of smoking i.e. anhedonia anxiety sensitivity, distress tolerance. Research studies for the past five decades have proven the adverse effect of stress on brain physiology and functioning. The human body responds to trauma (physical or non-physical stress) in a definite manner. This response of the body can be qualitatively and quantitatively monitored through several chemicals in the bloodstream, saliva, or urine; responding to the stress, called stress biomarkers i.e. Brain-derived neurotrophic factor, cortisol, cytokines etc. Saliva delivers an efficient specimen for various diagnostic procedures due to the presence of different biological products and secondary metabolites of xenobiotics and helps in determining the disease progressions as well as therapy outcomes depending on the variations in the markers/triggers. The nature of mindset and mood states are evaluated by a scales designed to rate the behavior of an individual towards the environmental stimuli that may be physical or psychological in nature. This psychological rating scale is known as the profile of mood state (POMS). This scale was initially originated by McNair, Droppleman, and Lorr in 1971. This scale is presented in the form a questionnaire including different questions regarding the mode and feelings of a subject.This research work aims to further elucidate the utilization of UV Photospectrometry for quantitatively assessing POMS and its relation to the stress biomarker. The samples were collected form the vicinity of the university campus H-12 Islamabad. The samples were processed and stored at the biomedical laboratory of School of Mechanical and Manufacturing Engineering (SMME), NUST. A total of twenty-four (24) male subjects were analyzed. A total of two groups were considered. Group 1 included the non-smoking participants, while group 2 included smoking participants. Simple spitting technique was used for the collection of unstimulated saliva. About 4 ml unstimulated saliva was collected in the sterile falcon tube. Saliva was temporarily stored in cool boxes at 4°C and immediately II shifted to the facility. Centrifugation of the salivary sample was done at 4°C for 5 minutes and 10,000 rpm. Saliva sample was frozen at -80°C until sample collection span was completed. The mood state of the participants was also evaluated using the profile of mood state technique used initially by McNair, Lorr, and Droppleman in 1971. The total mood disturbance (TMD) score was calculated that ranges from -32 to 200. The questionnaire was accessed from ―Mackenzie, B. (2001) Profile of Mood States (POMS) [WWW] Available from: https://www.brianmac.co.uk/poms.htm [Accessed 26/6/2022]‖. Simulated neural networking (SNN) was applied to the collected data from smokers and non-smokers for accuracy scoring. The required statistical analysis was performed and the data was statistically analyzed through a software ―GraphPad Prism 8.0‖ and the respective graphs were plotted. UV spectrophotometry studies provided peak plasma concentration peaks at the lower UV range of 190 to 210 nm, but with no significant difference, representing the presence of biological stress markers. The profile of mood state evaluation studies concluded that the smoking participants were presented with a significantly higher level of individual mood profile scores i.e. anger (****, P<0.0001), confusion (**, P<0.0014), fatigue (*, P<0.0354), tension (*, P<0.0422) and stress as compared to nonsmoking participants. The vigorous score was significantly high in the nonsmoking individuals (****, P<0.0001). Similarly, total mood disturbance score was also significantly high in the smoking participants. The application of artificial neural networking through artificial machine learning scored the accuracy of the results 84% which is a reliable outcome. The current research work concludes that different stress stimuli including physiological stress and psychological stress tends to initiate/increase the smoking behavior among the community. Likewise, it is also concluded that smoking initiation may not be always triggered in response to stress. Numerous factors i.e. lack of education, negative inspiration, or behavior to impress are also involved. Furthermore, the adaptation of smoking behavior as a result of stressful stimuli is not a valid approach to reduce the noxious/stressful stimuli. The stress may further be exaggerated by smoking. 30cm.

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Adaptive Hemodynamic Signal Estimation Using Kalman Estimator /

By Quddusi, Basiq Warrad . . 80p. , Functional Near Infrared Spectroscopy (fNIRS) is a technology that measures changes in the oxygenation level of blood present in brain whenever an activity is performed by human being. It is a non-invasive technique and uses near infrared light to detect changes in the concentration of two chromophores i.e., oxygenated and deoxygenated haemoglobin. During the recording, information related to neural activity in fNIRS signals gets compromised. This is due to the interference of noises from the environment outside as well as inside the human body. External noises can be light in the room and powerline noise. Internal noises are physiological noises such as cardiac, respiratory and mayer waves. Therefore, during analysis, it is required to remove these noises first and then extract main activity signal i.e., hemodynamic signal. Many techniques and methods have been proposed and practiced up to this date. Among them the most popular technique is General Linear Modelling (GLM). GLM models the signal by breaking it down into sum of all components present in the signal along with an error term. Previous studies and research that have used GLM for the reconstruction of activity signal used single frequency value for each noise but in reality, the frequency for each noise varies with the level of activity performed by the subject. This can lead to less accurate reconstruction of activity signal. In this study, this problem is kept under consideration and a method is developed to keep account for all the values of frequency that can corrupt fNIRS signal. Ranges of frequencies are considered instead of single values. These frequency ranges are first extracted using Continuous Wavelet Transform (CWT) and their possible magnitudes are estimated using Kalman filter. Similarly, activity signal is extracted from fNIRS signal using Discrete Wavelet Transform (DWT) and then its magnitude is estimated using Kalman filter. Output of these two steps is fed to GLM for reconstruction of possible hemodynamic signal. Results from this method are compared with the results of conventional GLM and significant improvement is observed both visually and statistically. 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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Fixed-Value MBLL based Cognitive Hemodynamic response assessment using P-fNIRS system: Applications to Deep Learning Brain Machine Interface (BMI) /

By Asgher,Umer . . 172p. , Humans in the modern systems not only interact with other humans but also have to interact with intelligent machines, robots in form of cyber physical systems to collaborate in order to carry out different tasks in real working environment. The modern Industrial system comprises of humans, machines, and cyber systems with a collective aim of optimized industrial manufacturing objectives, human factors, and ergonomics goals. Different macrohuman factors are considered while designing and formulating human work safety of such systems and one of the important neuroergonomic factors in is the Cognitive and Mental Workload (C-MWL). The mental workload (MWL) in the human’s brain is measured with difference non-invasive neuroimaging techniques. Most of the cognitive load measuring methods either require massive system protocols like fMRI (functional magnetic resonance imagining), positron-emission tomography (PET) or strict human anatomical movements restrictions like electroencephalogram (EEG) and magnetoencephalography (MEG). To address these limitations, fNIRS (functional Near infrared Spectroscopy) is used in this research to measure the hemodynamic changes in the human brain’s tissues as a measure of the brain activity. The brain’s hemodynamic signals are measured using a light weight portable fNIRS system (P-fNIRSSyst) that is designed to measure relative change in concentration of chromophores (oxy and deoxy hemoglobin) in brain tissues. In this study a novel variant of MBLL (Modified Beer-lambert Law) is designed by keeping the previous intensity value as a reference by taking the average from initial four seconds activity stimuli in optical density. The four second stimuli average in novel and important in calculation the changes in concentration of chromophores. This novel variant of MBLL is Fixed Value - Modified Beer-lambert Law (FV-MBLL). In this research, MWL is measured and classified in different real time working environments. The two-state cognitive load is measured with fNIRS system and classified using FV-MBLL using machine learning techniques like k-nearest neighbors (k-NN), support vector machines (SVM), and artificial neural networks (ANN). The classification accuracies of FV-MBLL are better than MBLL. The research further explores the classification capabilities of deep neural networks (DNN) such as convolutional neural network s (CNN) and Long short-term memory (LSTM) for the first time in assessment and classification of four- iv state MWL. The classification accuracies of LSTM outperform not only ML algorithms (SVM, KNN and ANN) but CNN as well in classification of multi-state MWL. The research experimental validation is performed using the accuracies with MWL that are further utilized in neurorehabilitation as neurofeedback to operate bionic systems using Brain Machine Interface (BMI). 30cm.

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Multi-Robot Interactive Therapy for Children with Autism Spectrum Disorder /

By Ali ,Sara . . 139p. , Robot-Assisted Therapies (RAT) is an emerging field and has shown promising results. Recently, one of the prominent applications of robots is assistive therapy is for Autism Spectrum Disorder (ASD). Autism Spectrum Disorder is a set of neurodevelopment disorder affecting 7.5 million people around the world. Children with ASD lack social and communication skills which affects their ability in schools as well as in community. Recently, humanoid robots are used for the treatment of children with autism spectrum disorder to improve the development of communicational, behavioral, motor movements, joint attention, and physical behaviors. These interactive interventions that use robots for children with ASD, is one of the favorable tools for improving the behavior of children. In particular, the area of robotics is helping a lot in the treatment of ASD as the robot acts as a mediator as well as measures the response of an autistic child. However, the research aiming that the treatment of children with autism is limited, these therapies introduced by robots are successful in establishing basic communication skills. This research has proposed a novel mathematical model for an adaptive therapy of children with Autism Spectrum Disorder called Multi-robot-mediated Intervention System (MRIS). Three different therapies related to improvement in joint attention and imitation skills, effective human-human interaction and comparison of effective stimulus are introduced under this mathematical model. This research aims to introduce multi-sensory data that provides the quantitative support for improvement in social skills of children with autism, replacing the current techniques of measuring the improvement from physically observing the ASD child and with video analysis. Besides ensuring the accuracy in results, this method also introduces consistency as robots are immune to fatigue, unlike humans. The effectiveness of the model has been validated using cognitive brain state of the children with Electroencephalogram (EEG) neuroheadsets. Moreover, the effectiveness of the results has been validated using statistical analysis and the Childhood Autism Rating Scale (CARS). 30cm.

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Brain-Computer Interface for Mental State Detection of Drivers /

By Arif, Saad . . 169p. , Background: Each year millions of vehicles suffer crashes on the roads globally due to deteriorated mental state of the drivers during driving tasks which result in higher casualties. Drowsy driving is the leading cause of high fatality rate which is instigated due to sleep deprivation, fatigue, and anxiety, etc. Vehicular, and driver’s behavioral data-dependent systems detect the drowsiness after its onset when an accident is more likely, and they are also subject to false identification. The drowsy mental state of drivers must be detected earlier for in-time warning to avoid fatal losses, and also the system must be less intrusive, and adaptable for normal driving tasks. Detection systems using physiological signals from human organs are comparatively underexplored in which bodily states of the subject can be identified earlier and with more reliability. It is postulated that all the bodily states are primarily originated from the human brain which could be a promising region to detect drowsiness at an earlier stage. Among many physiological signals, electroencephalography (EEG), and functional near-infrared spectroscopy (fNIRS) are chosen for this study because they are proved to be more portable, easy to use, non-invasive, and reliable brain modalities for human mental state detection. Aim and Objectives: This study aimed to design a passive brain-computer interface (pBCI) scheme for the earlier detection of driver drowsiness with minimum intrusion into the driving task. Design objectives for the pBCI scheme were to select the channel of interest (COI), online detection with a shorter time window, minimum recalibration and setup time, and development of a widely applicable standard as an inter-subject transfer framework (iSTF) to cater to the inter-subject variability. All these objectives lead to such a pBCI system that is readily available for any subject, anytime, easy to use with minimal design, and yet detecting the drowsiness correctly at an earlier stage to avoid life losses. Methodology: Multichannel EEG and fNIRS brain signals from anterior, posterior, and lateral brain regions of sleep-deprived drowsy subjects were acquired during the simulated driving task for post hoc analysis. Initial pBCIschemes used labeled EEG data acquired from prefrontal (PFC), frontal, and occipital cortices for extracting the eight spectral, and eight temporal xvi features of EEG signals. Seven supervised machine learning classifiers were used to do the cross-validated binary classification of drowsy, and alert brain states. Initial design only achieved a few objectives and generated the need to use different modalities to meet all the requirements. The final pBCI scheme used labeled fNIRS data acquired from PFC and dorsolateral prefrontal cortices (DLPFC) for extracting the six cerebral oxygen regulation (CORE) and three hemodynamic signal features. CORE states of wakefulness and non-rapid eye movement (NREM) sleep stages were used to design a novel standard framework for wide applicability, and sleep stage classification using the vector phase analysis (VPA) approach. VPA was used for classifying microsleep/lapse, and drowsiness detection was done using the proposed brain hemodynamic patterns. Results: 𝛿, 𝜃, 𝛼, 𝛽 band powers as EEG spectral features achieved 82% accuracy in 10 s detection window. Signal skewness, variance, mean, peak as EEG temporal features achieved 87.2% accuracy in 1 s detection window. Ensemble classifier declared F8 as COI for earlier drowsiness detection using both the EEG pBCI schemes. Only the objectives of the short detection window and COI were achieved with the initial designs. In the final design with fNIRS, the novel VPA features: CORE vector gradients, achieved 94.1% accuracy in 5 s detection window for NREM sleep stage classification using ensemble classifier with the least computation time of 44 ms. Precise spatial localization of fNIRS declared AF8 position in right DLPFC as COI. The novel sleep stages-based threshold criteria along with VPA were crossvalidated as a standard iSTF for online microsleep detection with the least recalibration and setup time. Feature selection and achieved results were validated with various statistical significance tests. All the design objectives were attained with the fNIRS-based pBCI scheme. Conclusion: The aim to detect the driver's drowsiness earlier with minimum intrusion into the driving task, is accomplished. The presented fNIRS-based adaptive pBCI scheme is readily available for any subject, anytime, easy to wear with minimal ergonomic design, capable of real-time, correct, and early detection of the driver drowsiness to lessen the life losses in vehicular driving scenarios. The recommended research directions will surely justify, improve, and broaden the application horizons of the presented design 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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Comparison of Different Machine Learning Models for Quality Control in Biscuit Manufacturing Industry of Pakistan /

By Awais, Muhammad . . 113p. , Ensuring consistent product quality is a critical challenge in the biscuit manufacturing industry, particularly in developing economies like Pakistan, where manual inspection remains the norm. This approach, however, is prone to human error, fatigue, and inconsistency, leading to variable quality control outcomes. This research proposes an intelligent, machine learning–based automated quality inspection system tailored for real-world biscuit production lines. A comprehensive dataset of 40,000 high-resolution images was curated, representing four popular biscuit varieties Rio, Sooper, Candy, and Marie captured under actual factory lighting conditions. The dataset includes both defective and non-defective samples, covering a wide range of defect types such as breakage, charring, deformation, and missing pieces. To ensure high-quality annotations with scalable efficiency, a semi-automated iterative annotation framework was developed, combining initial manual labeling with model-assisted annotation and human-in-the-loop refinement across multiple cycles. Multiple state-ofthe-art machine learning models were implemented, fine-tuned, and rigorously evaluated, including YOLOv8, YOLOv11, YOLOv12, Faster R-CNN (via Detectron2), and Vision Transformer (ViT-B/16). Models were assessed using key performance metrics such as mAP@0.5:0.95, precision, recall, F1-score, inference speed (FPS), and computational efficiency. The results demonstrate that modern deep learning models, particularly YOLOv12 and Vision Transformers, achieve high detection accuracy (mAP > 90%) while maintaining feasibility for real-time deployment when optimized. This study provides a comparative analysis of accuracy-speed trade-offs, offering actionable insights for manufacturers seeking cost-effective, scalable solutions. The research concludes with practical recommendations for integrating AI-driven inspection systems into existing production infrastructure in resource-constrained environments, balancing performance, hardware requirements, and long-term maintainability. 30cm.

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Microplastic Behavior in Idealistic Exhalation Dynamics: Exploring Shape, Density, and Diameter Effects Using CFD /

By Siddiqui, Hadia Alam . . 60p. , Microplastics are now ubiquitous contaminants in the atmosphere which have raised substantial public health issues. While extensive research has focused on particle deposition during inhalation, the exhalation phase, a critical component of the complete respiratory cycle, remains comparatively less explored. The study aims at addressing this research gap by conducting a comprehensive computational investigation into the deposition dynamics of microplastic particles during exhalation within an idealized human tracheobronchial airway model from generations G3–G6. A three-dimensional airway geometry was constructed via Weibel's morphometric data, adjusted for a 50-year-old adult. Computational Fluid Dynamics (CFD) simulations are performed using the Reynolds-Averaged Navier-Stokes (RANS) approach with the (SST) 𝑘-𝜔 turbulence model. The Discrete Phase Model (DPM) was employed to track trajectories of spherical microplastic particles 2-22 µm under four exhalation flow rates 125, 300, 500, and 1000 ml/s, representing varying breathing intensities from resting to heavy exercise. The results demonstrate an inverse relationship between exhalation flow rate and overall deposition efficiency (DE). Lower flow rates resulted in the highest DE, as particles had greater residence time for gravitational sedimentation and were less influenced by turbulent dispersion. In contrast, higher flow rates generated significant turbulent kinetic energy, which enhanced particle mixing and reduced net deposition. Furthermore, deposition was strongly governed by inertial impaction, as evidenced by a positive correlation with the Stokes number. Spatial analysis revealed that while high flow rates created intense deposition hotspots at major bifurcations, the cumulative particle deposition isXIV significantly lower than at gentler flow rates, where deposition is more widespread. This study concludes that gentle exhalation poses a greater risk for microplastic retention in the lower bronchial airways. The findings challenge the assumption that higher airflow invariably leads to increased deposition and provide crucial insights into the mechanisms of particle exposure during the exhalation phase. 30cm.

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Automated Karyotyping: Segmentation and Classification /

By Umbreen, Neelam. . 130p. , Karyotyping continues to be the bedrock of cytogenetic diagnosis, providing key information on chromosomal abnormalities causative of a broad range of genetic disorders, developmental abnormalities, and cancers. But standard karyotyping is time-consuming, requires extensive specialist interpretation, and is vulnerable to human mistake and inefficiency, especially in high-volume clinical settings. Despite advances in medical image analysis, the automation of karyotyping faces persistent challenges, including the lack of large-scale annotated datasets, difficulties in segmenting overlapping chromosomes, and variability in chromosome morphology and staining. These challenges define a significant research gap in developing scalable, accurate, and clinically deployable deep learning models for automated chromosome analysis. In order to overcome this gap, we first present a large-scale, clinically annotated cytogenetic database, built from 1,311 patients and consisting of 10,057 karyograms with 514,949 manually annotated chromosome singlets. Furthermore, 3,935 metaphase images are annotated at the instance level in COCO format. This data set reflects true-world diversity, ranging from normal and abnormal karyotypes to different Giemsa (G-banding) staining intensities, structural abnormalities, and overlapping difficult cases, and thus presents a solid basis for the creation and testing of deep learning models within automated cytogenetics. Based on this work, we create two primary methodologies aimed at the fundamental tasks of karyotyping. For segmentation of chromosomes, we introduce a variant Mask R-CNN model involving an Attention-based Feature Pyramid Network (AttFPN), spatial attention, and a LastLevelMaxPool component to improve multi-scale feature representation and contextual perception. It enhances performance in difficult situations, including overlapping chromosomes and weak banding patterns, and gains considerable improvements in mean Average Precision (mAP) compared to standard baselines. For chromosome classification, we present the Dual Attention Multiscale Pyramid Network (DAMP), a specifically designed model that combines channel and spatial attention mechanisms to concentrate on discriminative features, as well as a multiscale pyramid architecture to cope with size, orientation, and quality variation in chromosomes. DAMP's highest classification accuracy is 96.76% on both public and commercial datasets, performingxv better than state-of-the-art models like ResNet-50, Vision Transformers, and Siamese Networks. Overall, this thesis provides interpretable and scalable deep learning models for automating chromosome classification and segmentation. Through the closure of key gaps in dataset quality, model resilience, and clinical utility, the work facilitates the insertion of clever decision-support systems into cytogenetic pipelines, ultimately leading to improved diagnostic reliability and efficiency in the face of chromosomal disorders. 30cm.

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Omics technologies and bio-engineering : towards improving quality of life /

. xvii, 625 pages : 25 cm. 9780128046593 | 0128046597

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Omics technologies and bio-engineering : towards improving quality of life /

. xvii, 625 pages : 25 cm. 9780128046593 | 0128046597

Place Hold on Omics technologies and bio-engineering :

Legume Nitrogen Fixation in Soils with Low Phosphorus Availability : Adaptation and Regulatory Implication /

. 1 online resource (XIII, 287 pages 41 illustrations, 38 illustrations in color.) 9783319557298

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Development of New Content Based Image Retrieval Techniques /

By Sukhia, Komal Nain . Rawalpindi, MCS (NUST), 2020 . xv, 118 p

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Social Media Analytics for Mental Health Assessment /

By Liaquat, Umna . . 94p. , Social media has emerged as a tool for exploring the human psyche, offering exceptional access to real-time behavioral signals that are transforming the landscape of computational psychiatry. Among these, bipolar disorder is difficult to detect because of its volatility, requiring robust modeling of emotion, behavior, and timing. Prior studies have largely focused on text-based sentiment analysis and linguistic features to classify mental health conditions; however, these approaches often neglect non-verbal markers such as circadian rhythms and affective variability. Existing models primarily depend on static textual cues, limiting their ability to capture the dynamic, multimodal nature of psychiatric expression. This study addresses these limitations by integrating temporal rhythms, emotional dynamics, and behavioral signals extracted from Reddit user histories to develop predictive models of bipolar disorder and high-risk psychological states. We propose a series of interpretable multimodal architectures employing classical machine learning (Logistic regression, Random Forest, and XGBoost), deep sequence models (LSTM, GRU), and transformer-based frameworks (Roberta, GPT). Our approach incorporates temporal posting features, emotional entropy, and community-level interaction structures. Compared to benchmark studies, our models demonstrate significant improvements in both classification (F1 > 0.99) and regression (R² > 0.89), highlighting the predictive power of fused behavioral signals. This work advances the field by providing a scalable, languageindependent framework for the early detection of psychiatric risk. It also holds broader implications for public health by offering a foundation for real-time, ethically deployable digital mental health tools. 30cm.

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