Pakistan’s Counter-Terrorism Narrative and Non-Traditional (Holistic) Security Paradigm with Civic Engagement

The woes of terrorism and extremism pose a threat to the stability, economic development and human growth of nations. It, therefore, remains a serious concern for the state to rethink its strategies towards bringing stability. This paper, on deploying content analysis technique, explores the traditional security paradigm as a state-centric approach under the diverse concept of security. The non-traditional approaches; Buzan’s holistic perspective of a national security complex, Mehbub-ul-Haq’s human security notion and Mohammed Ayoob’s concept of weak state, are scrutinized to study the actual and potential role of civic engagement towards constructing an effective counter-terrorism narrative of Pakistan. It is established that due civic engagement has the potential to counter the extreme dogmas through collaborative efforts at home. It can also address the wrong perception about Pakistan’s inefficient counter-terror measures at the regional and global level

Portable UWB RADAR Sensing System for Transforming Subtle Chest Movement into Actionable Micro-Doppler Signatures to Extract Respiratory Rate Exploiting ResNet Algorithm

Contactless or non-invasive technology for the monitoring of anomalies in an inconspicuous and distant environment has immense significance in health-related applications, in particular COVID-19 symptoms detection, diagnosis, and monitoring. Contactless methods are crucial specifically during the COVID-19 epidemic as they require the least amount of involvement from infected individuals as well as healthcare personnel. According to recent medical research studies regarding coronavirus, individuals infected with novel COVID-19-Delta variant undergo elevated respiratory rates due to extensive infection in the lungs. This appalling situation demands constant real-time monitoring of respiratory patterns, which can help in avoiding any pernicious circumstances. In this paper, an Ultra-Wideband RADAR sensor “XeThru X4M200” is exploited to capture vital respiratory patterns. In the low and high frequency band, X4M200 operates within the 6.0-8.5 GHz and 7.25-10.20 GHz band, respectively. The experimentation is conducted on six distinct individuals to replicate a realistic scenario of irregular respiratory rates. The data is obtained in the form of spectrograms by carrying out normal (eupnea) and abnormal (tachypnea) respiratory. The collected spectrogram data is trained, validated, and tested using a cutting-edge deep learning technique called Residual Neural Network or ResNet. The trained ResNet model’s performance is assessed using the confusion matrix, precision, recall, F1-score, and classification accuracy. The unordinary skip connection process of the deep ResNet algorithm significantly reduces the underfitting and overfitting problem, resulting in a classification accuracy rate of up to 90%

AI-driven lightweight real-time SDR sensing system for anomalous respiration identification using ensemble learning

In less than three years, more than six million fatalities have been reported worldwide due to the coronavirus pandemic. COVID-19 has been contained within a broad range due to restrictions and effective vaccinations. However, there is a greater risk of pandemics in the future, which can cause similar circumstances as the coronavirus. One of the most serious symptoms of coronavirus is rapid respiration decline that can lead to mortality in a short period. This situation, along with other respiratory conditions such as asthma and pneumonia, can be fatal. Such a condition requires a reliable, intelligent, and secure system that is not only contactless but also lightweight to be executed in real-time. Wireless sensing technology is the ultimate solution for modern healthcare systems as it eliminates close interactions with infected individuals. In this paper, a lightweight real-time solution for anomalous respiration identification is provided using the radio-frequency sensing device USRP and the ensemble learning approach extra-trees. A wireless software-defined radio platform is used to acquire human respiration data based on the change in the channel state information. To improve the performance of the trained models, the respiration data is utilised to produce large simulated data sets using the curve fitting technique. The final data set consists of eight distinct types of respiration: eupnea, bradypnea, tachypnea, sighing, biot, Cheyne-stokes, Kussmaul, and central sleep apnea. The ensemble learning approach: extra-trees are trained, validated, and tested. The results showed that the proposed platform is lightweight and highly accurate in identifying several respirations in a static setting

FORMULATION AND EVALUATION OF METFORMIN HCL RELEASE FROM TOPICAL PREPARATION USING TWO DIFFERENT TYPES OF MEMBRANE

Objective: Present study was carried to formulate and evaluate the transdermal ointment containing the metformin HCl active ingredient and to assess their Physicochemical studies. Methods: Metformin HCl ointment was prepared with various thymol oil concentrations. Ointments were assessed with different characterizations; Physical appearance, viscosity, pH, drug content, Consistency, homogeneity, consistency. Differential scanning calorimetry analysis, XRD studies. It was used in vitro via using Franz cells along with the use of two membranes i.e. Nylon and cellulose membrane. Results: SEM and XRD studies showed that there were no physical and chemical interactions between excipients and drug. All the formulations showed good physicochemical characteristics. The formulation showed different releases. It was observed that nylon had better release properties as compared to cellulose. Conclusion: In the study conducted here, it was observed that Nylon membrane showed better discriminating power to compare among the formulation. This indicates that it has gotten prime importance to watch the effect of the membrane upon the release pattern of the various formulations. In order to improve the formulation, we can use in vitro diffusion cell experiments of transdermal drug delivery

RELATIVE COMPARISON OF STABILITY AND DEGRADATION OF METHYLCOBALAMIN TABLETS OF DIFFERENT BRANDS AT DIFFERENT STORAGE SETTINGS

Objective: To assess relative comparison of stability and degradation of Methylcobalamin tablets of different brands at various storage circumstances. Methods: The comparative in vitro study of Methycobal (innovator brand) with its other 5 different brands Cobalamin, Neuromet, Incobal, Qbal and Mecobal was organized for evaluation of physicochemical features of hardness, thickness, friability, weight variation, disintegration time and accelerated stability at 3 temperatures, 25 °C, 30 °C±65 % and 40 °C±75 % respectively for 3 mo. Later all brands were passed through HPLC for checking the extent of degradation of drug products. Results: All tablet brands were within the weight variation specified limits except Mecobal with a relative standard deviation of 6.83%. The weight variation values of Methycobal, Cobalamin, Neuromet, Incobal, Qbal and Mecobal were 0.29%, 0.11%, 0.09%, 0.13%, 0.09% and 0.14% after friability test respectively as per standard limits. The average thickness of Cobalamin, Incobal and Mecobal were not within specified limits. The average hardness of all trades was within limits except Cobalamin and Mecobal exceeding 6kp. The disintegration time of all companies was as per specifications. Conclusion: Qbal was found economical and cost-effective. However, study facts unveiled no noteworthy variety in the Q. C assessments of Methylcobalamin brands

AI-driven lightweight real-time SDR sensing system for anomalous respiration identification using ensemble learning

In less than three years, more than six million fatalities have been reported worldwide due to the coronavirus pandemic. COVID-19 has been contained within a broad range due to restrictions and effective vaccinations. However, there is a greater risk of pandemics in the future, which can cause similar circumstances as the coronavirus. One of the most serious symptoms of coronavirus is rapid respiration decline that can lead to mortality in a short period. This situation, along with other respiratory conditions such as asthma and pneumonia, can be fatal. Such a condition requires a reliable, intelligent, and secure system that is not only contactless but also lightweight to be executed in real-time. Wireless sensing technology is the ultimate solution for modern healthcare systems as it eliminates close interactions with infected individuals. In this paper, a lightweight real-time solution for anomalous respiration identification is provided using the radio-frequency sensing device USRP and the ensemble learning approach extra-trees. A wireless software-defined radio platform is used to acquire human respiration data based on the change in the channel state information. To improve the performance of the trained models, the respiration data is utilised to produce large simulated data sets using the curve fitting technique. The final data set consists of eight distinct types of respiration: eupnea, bradypnea, tachypnea, sighing, biot, Cheyne-stokes, Kussmaul, and central sleep apnea. The ensemble learning approach: extra-trees are trained, validated, and tested. The results showed that the proposed platform is lightweight and highly accurate in identifying several respirations in a static setting

British Sign Language detection using ultra-wideband radar sensing and residual neural network

This study represents a significant advancement in sign language detection (SLD), a crucial tool for enhancing communication and fostering inclusivity among the hearing-impaired community. It innovatively combines radar technology with deep learning techniques to develop a sophisticated, noninvasive SLD system. Traditional SLD methods often rely on cumbersome wearable devices or struggle with environmental inconsistencies. In contrast, this system uses the distinctive ability of radar to function effectively across various lighting conditions. The core of this research lies in its application to British Sign Language (BSL) detection, using advanced neural network architectures for real-time interpretation. A key highlight is the impressive 92% accuracy rate achieved in BSL recognition, using the residual neural network (ResNet) model. This success is attributed to a comprehensive dataset and the strategic adaptation of ResNet for processing radar data. The fusion of radar technology with deep learning in this context not only marks a novel approach in the field but also establishes this research as a foundational contribution to the realm of SLD. Its implications extend beyond technical achievement, offering a more accessible and inclusive communication alternative for the hearing-impaired

Discrete Human Activity Recognition and Fall Detection by Combining FMCW RADAR Data of Heterogeneous Environments for Independent Assistive Living

Human activity monitoring is essential for a variety of applications in many fields, particularly healthcare. The goal of this research work is to develop a system that can effectively detect fall/collapse and classify other discrete daily living activities such as sitting, standing, walking, drinking, and bending. For this paper, a publicly accessible dataset is employed, which is captured at various geographical locations using a 5.8 GHz Frequency-Modulated Continuous-Wave (FMCW) RADAR. A total of ninety-nine participants, including young and elderly individuals, took part in the experimental campaign. During data acquisition, each aforementioned activity was recorded for 5–10 s. Through the obtained data, we generated the micro-doppler signatures using short-time Fourier transform by exploiting MATLAB tools. Subsequently, the micro-doppler signatures are validated, trained, and tested using a state-of-the-art deep learning algorithm called Residual Neural Network or ResNet. The ResNet classifier is developed in Python, which is utilised to classify six distinct human activities in this study. Furthermore, the metrics used to analyse the trained model’s performance are precision, recall, F1-score, classification accuracy, and confusion matrix. To test the resilience of the proposed method, two separate experiments are carried out. The trained ResNet models are put to the test by subject-independent scenarios and unseen data of the above-mentioned human activities at diverse geographical spaces. The experimental results showed that ResNet detected the falling and rest of the daily living human activities with decent accuracy