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

Fuzzy Logic and the Semantic Web

By Sanchez, Elie. San Diego : Elsevier Science [Imprint] | Elsevier Science & Technology Books 2006 9780444519481 | 0444519483 (Trade Cloth)

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Cambrian Intelligence : The Early History of the New AI /

By Brooks, Rodney A.. Cambridge : MIT Press 1999 0262024683 (Trade Cloth)

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Manufacturing Engineering and Technology /

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

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Handbook of ambient intelligence and smart environments \

By Nakashima, Hideyuki.. New York : Springer, 2010 9780387938073 (hbk.)

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Handbook of conflict analysis and resolution /

London ; | New York : Routledge, 2009 . xxiv, 543 : 26 cm.. 9780415577359(Paperback) | 9780203893166 (ebook) | 0203893166 (ebook)

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The process of parenting /

By Brooks, Jane B.. [S.l.] : McGraw-Hill Humanities/Social Sciences/Languages, 2003 . 478 p. ; , Paperback. 24 cm.. 007282669X | 9780072826692

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Doing ebusiness : thriving in an electronic marketplace /

By Taylor, David.. [S.l.] : Wiley, 2000 . 292 p. ; , Hardcover. 24 cm.. 0471380652 | 9780471380658

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Fundamentals of organizational behavior /

By DuBrin, Andrew J.. [S.l.] : South-Western College Pub, 2004 . 352 p. ; , Paperback. 25 cm.. 0324259921 | 9780324259926

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Managing organizations and people : cases in management, organizational behavior and human resource management /

By Buller, Paul F.. [S.l.] : South-Western College Pub, 2003 . 469 p. ; 28 cm.. 032411687X (paperback) | 9780324116878 (paperback)

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The workforce scorecard : managing human capital to execute strategy /

By Huselid, Mark A.. [S.l.] : Harvard Business Press, 2005 . 278 p. ; 24 cm.. 1591392454 (hardcover) | 9781591392453 (hardcover)

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Reinventing organization development /

By Bradford, David L.. [S.l.] : Pfeiffer, 2005 . 256 p. ; 24 cm.. 0787981184 (hardcover) | 9780787981181 (hardcover)

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The hr scorecard : linking people, strategy, and performance /

By Ulrich, Dave.. [S.l.] : Harvard Business Press, 2001 . 235 p. ; 24 cm.. 1578511364 (hardcover) | 9781578511365 (hardcover)

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The golden ratio : the story of phi, the world's most astonishing number /

By Livio, Mario.. New York : Broadway, 2002 . 294 p. : 21 cm.. 0767908163 (paperback) | 9780767908160 (paperback)

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Gimme, the human nature of successful marketing

By Hallward, John. USA JOHN WILEY & SONS 2007 . XX,216P 9780470-100295

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Handbook of conflict analysis and resolution /

London ; | New York : Routledge, 2009 . xxiv, 543p 26 cm.. 9780415433952 (hardback) | 0415433959 (hardback) | 9780203893166 (ebook) | 0203893166 (ebook)

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How to be an inclusive leader : your role in creating cultures of belonging where everyone can thrive /

By Brown, Jennifer,. Oakland, CA : Berrett-Koehler Publishers, Inc. 2019 . xiii, 148 pages : 24 cm. 9781523085170 (hardback) | 9781523093144 (paperback)

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Caste : the origins of our discontents

By Wilkerson, Isabel,. New York : Random House, 2020 . 1 online resource 9780593230268

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A world without email : reimagining work in an age of communication overload

By Newport, Cal,. New York Portfolio / Penguin, 2021 . xxii, 296 pages ; 22 cm. 9780525536550 | 0525536558 | 9780593332603 | 9780525536574 | 9780241341414 | 0241341418

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The age of surveillance capitalism : the fight for a human future at the new frontier of power

By Zuboff, Shoshana,. New York : PublicAffairs, 2019 . x, 691 pages ; 25 cm.. 9781610395694 (hardcover)

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Identification and Characterization of Hormonal Response in Depression Induced Mice Models /

By Ilyas, Noor-Ul-Ain. . 55p. , Background: Early life stress is correlated with escalated peril for anxiety, mood, substance, impulse control and depressive disorders. Such disorders might spring from chemical imbalance in brain. A well-grounded mouse prototype of childhood adversity contributing to the everlasting behavioral changes in an individual would help understand the mechanism underlying these adverse effects. Maternal deprivation is frequently used paradigm of early neglect. Natriuretic peptides especially Atrial Natriuretic Peptide (ANP) released from Atrial myocytes have significant anxiolytic role corroborated by certain animal and human trails. The concentration of ANP has been found to be elevated by Levothyroxine (LT4) which treats the adverse symptoms of anxiety and depression. Method and Results: In an attempt to contrive the paradigm of childhood adversity in mouse models with everlasting impacts on behavior of balb/c mice, maternal separation model followed by early weaning model were developed and behavioral tests were performed 60 days following maternal separation followed by early weaning (MSEW) paradigm for the validation of model. The experimental and control groups were further divided into 3 groups: MSEW group with drug administration, MSEW group with no drug administration and control group with no MSEW and no drug administration. Following a 7 days administration of LT4 at the concentration of 15 micrograms per mice to MSEW group and one of the control groups, decline in anxiety level in mice subjected to MSEW was observed in comparison to the MSEW group that was not administered LT4. These findings were validated by performing anxiety related paradigms after drug injection and the difference in the behavior was observed accordingly which suggested decrease in behavioral despair and anxiety related symptoms in mice. Conclusion: Our findings elucidate that maternal separation model followed by early weaning contributes as a substantial paradigm to scrutinize the intricate behavioral anomalies in organisms with early life adversity history and by increasing the concentration of ANP by injection of LT4 cures the anxiety-related symptoms and provides a n apprehension for a futuristic therapeutic plan of actions. 30cm.

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Conducting Research in Developmental Psychology: A Topical Guide for Research Methods Utilized Across the Lifespan /

New York: Routledge, 2020 . xiii, 213 p. 9780367340223(pbk)

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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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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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Nelson essentials of pediatrics /

. xv, 829 pages; 29 cm.. 9780323775632 | 9780323775625

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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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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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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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