Hybrid Deep Neural Network for Data Driven Missile Guidance with Maneuvering Target

Missile guidance, owing to highly complex and non-linear relative movement between the missile and its target, is a challenging problem. This is further aggravated in case of a maneuvering target which changes its own flight path while attempting to escape the incoming missile. In this study, to achieve computationally superior and accurate missile guidance, a deep learning is employed to propose a self-tuning technique for a fractional-order proportional integral derivative (FOPID) controller of a radar-guided missile chasing an intelligently maneuvering target. A multi-layer two-dimensional architecture is proposed for a deep neural network that combines the prediction feature of recurrent neural networks and estimation feature of feed-forward artificial neural networks. The proposed deep learning based missile guidance scheme is non-intrusive, data-based, and model-free wherein the parameters are optimized on-the-run while predicting the target’s maneuvering tactics to correct for processing time and loop delays of the system. Using deep learning for online optimization with minimal computational burden is the core feature of the proposed technique. Dual-core parallel simulations of missile-target dynamics and the control system were performed to demonstrate superiority of the proposed scheme in feasibility, adaptability, and the ability to effectively minimize the miss-distance in comparison with traditional and neural offline-tuned PID and FOPID based techniques. Compared to state-of-the-art offline-tuned neural control, the miss-distance was reduced by 68.42% for randomly maneuvering targets. Furthermore, a minimum miss-distance of 0.97 m was achieved for intelligently maneuvering targets for which the state-of-the-art method failed to hit the target. Overall, the proposed technique offers a novel approach for addressing the challenges of missile guidance in a computationally efficient and effective manner

RefreshNet: Learning Multiscale Dynamics through Hierarchical Refreshing

Forecasting complex system dynamics, particularly for long-term predictions, is persistently hindered by error accumulation and computational burdens. This study presents RefreshNet, a multiscale framework developed to overcome these challenges, delivering an unprecedented balance between computational efficiency and predictive accuracy. RefreshNet incorporates convolutional autoencoders to identify a reduced order latent space capturing essential features of the dynamics, and strategically employs multiple recurrent neural network (RNN) blocks operating at varying temporal resolutions within the latent space, thus allowing the capture of latent dynamics at multiple temporal scales. The unique "refreshing" mechanism in RefreshNet allows coarser blocks to reset inputs of finer blocks, effectively controlling and alleviating error accumulation. This design demonstrates superiority over existing techniques regarding computational efficiency and predictive accuracy, especially in long-term forecasting. The framework is validated using three benchmark applications: the FitzHugh-Nagumo system, the Reaction-Diffusion equation, and Kuramoto-Sivashinsky dynamics. RefreshNet significantly outperforms state-of-the-art methods in long-term forecasting accuracy and speed, marking a significant advancement in modeling complex systems and opening new avenues in understanding and predicting their behavior

Volatility Linkages between Equity Markets of Pakistan, India, Singapore and Hong Kong: A GARCH BEKK Approach

The purpose of current study is to explore the volatility linkages between four Asian equity markets, which arePakistan (Karachi Stock Exchange), India (Bombay Stock Exchange), Hong Kong (Hang Sang Index) and Singapore (Strait Time Index). We estimate Multivariate GARCH BEKK model using weekly returns from January 2000 to August 2011.Direct evidences of linkages are found among all markets with respect to conditional mean returns and volatility.Own volatility spillover is found greater than cross volatility spillover in all emerging and developed economies.The insinuation of this study is that overseas investors may take advantage from the decrease of uncertainty by accumulating the stocks in the emerging markets to their investment portfolio

Is COVID-19 impacting cancer screening in Pakistan? An observational study of cancer screening test requests during the pandemic

Background: The purpose of this study is to assess how the COVID-19 pandemic affected cancer screening at a large tertiary care setting in the city of Karachi, the third largest city in the world, and to identify if there has been any decrease in cancer screening during the ongoing pandemic.Methods: A retrospective observational study was conducted at the clinical chemistry laboratory at the Department of Pathology & Laboratory Medicine, Aga Khan University Hospital (AKUH), Karachi Pakistan. Data for test volumes was extracted from the Integrated Laboratory Management System (ILMS) for the following tumor markers: CA19 Carbohydrate Antigen 19-9 (CA 19-9), Calcitonin, Prostate Specific Antigen (PSA), from 2017 to 2020. Data from January 1st, 2017 till December 31st, 2019 was recorded and compared with the test volume data from January 1st, 2020 till December 31st, 2020. Number of tests performed in the prior 3 years were compared with tests performed in 2020, specifically looking at changes during the lockdown period in 2020 (1st March - 9 th April) and compared with the same period in preceding years.Results: During the four-year period, a total of 6,530 tests were performed for CA19-9, 893 for Calcitonin, and 54,769 for PSA. Year 2019 recorded the highest volume for all 3 tests with test volumes increasing continuously from 2017 to 2019. Number of tests performed decreased throughout the year 2020 for Calcitonin and PSA, whereas volume of tests for CA19-9 only reduced during the lockdown period while increased in the non-lockdown period as compared to previous years. Highest percent decline during the 2020 lockdown period was seen for Calcitonin (-62.5%), followed by PSA (-51.8%) and CA19-9 (-19%).Conclusion: In conclusion, the amount of CA19-9, Calcitonin, and PSA tests performed in Karachi, Pakistan has drastically reduced due to the lockdown that was mandated due to the COVID-19 outbreak. It is crucial that despite an imposed lockdown, regular cancer screening must continue

Knowledge about osteoporosis among healthy women attending a tertiary care hospital

INTRODUCTION: To determinate the knowledge on osteoporosis-risk factors and disease in three age groups of Pakistani women.METHODS: In this exploratory cross-sectional study, an osteoporosis knowledge assessment questionnaire (OKAT) was used to collect data and it was delivered through a face-to-face interview. Questions were asked about symptoms of osteoporosis, knowledge of risk factors, preventive factors and treatment. A convenience sample (n =320) comprising of three groups of healthy women aged 25-35 years, 36-45 years, and over 45 years was taken. The scoring range was 0 to 20. Among-group comparisions of means were analyzed by two-way ANOVA. To determine the overall influence of osteoporosis-risk factors, the multivariate analysis was used.RESULTS: The knowledge on osteoporosis in younger women was very poor compared to relatively older females. However, women belonging to higher socioeconomic status and better education had slightly more knowledge about osteoporosis compared to those with a low education level, regardless of age.CONCLUSION: The majority of women had modest knowledge on osteoporosis. Younger women were at increased risk for low bone mass and premature osteoporosis

Impacto del COVID-19 en los medios de vida y en la seguridad alimentaria

French version available in IDRC Digital Library: L’impact de la COVID-19 sur les moyens de subsistance et la sécurité alimentaireEnglish version available in IDRC Digital Library: Impact of COVID-19 on livelihoods and food securit

Inventory Management and Its Effects on Customer Satisfaction

This study examines how inventory management puts positive impact on customer satisfaction and how easily we can check the performance. It also helps retailers to put their inventories in proper order which tells them about demand and supply of their inventories. Proper inventory management system reduces the risk of short of inventories which reduce the cost of lost customers. The objective of the study is to minimize the risk of dissatisfaction of customers and found how to sustain customer satisfaction with the help of proper inventories system. This paper also outlines significant relationship between Customer needs, Quality with variable of prime interest. Poor association has been found between performance and customer satisfaction

Impact of COVID-19 on livelihoods and food security

French version available in IDRC Digital Library: L’impact de la COVID-19 sur les moyens de subsistance et la sécurité alimentaireSpanish version available in IDRC Digital Library: Impacto del COVID-19 en los medios de vida y en la seguridad alimentari