Terrorism is a global threat in which perpetrators aim to maximize fear in society through devastating attacks. India’s peninsular geography and transnational borders create strategic terrorism challenges. We analyzed multiple hypotheses about the proximity of high-impact attacks (HIAs) from international and inter-state boundaries of Indian states, categorized into states with maritime and transnational geographies. The K-means suggested four classifications of attacks of interests’ (AOI) distances from international boundaries. The KS test on HIA’s shortest border distances demonstrated that underlying distributions vary between maritime and land-bordered states. We identified that HIA’s lethality is inversely proportional to their distance from borders. Most HIAs occurred within 68 kilometres of interstate borders. Maritime-bordered states reported a three times longer time between AOIs than land-bordered states. These insights regarding HIAs can help agencies to formulate better border security measures.

Terrorists aim for widespread attention and fear from the target society through every attack they perpetrate. They achieve this goal by increasing the ferocity of the attack to maximize damages, which they envision while planning an attack. The choice of weapon by the perpetrator for a particular terror attack is paramount in achieving this goal. This research investigates the most prominent weapon types terrorists utilize to maximize their ferocity using the Global Terrorism Database (GTD). Next, we implemented the innovative caterpillar diagram introduced by the authors in their earlier works to quantify the impact and its associated variations on the usage of a particular weapon type. The color schema based caterpillar diagram effectively captures this variation and facilitates comparative analysis across multiple countries. Further, we demonstrate that the state transition between consecutive cohorts of the caterpillar diagram imitates a Markovian process. The Markovian property helps to develop a predictive model for future variations in the usage of the concerned weapon type.

The success of an infrastructure project is often judged by its timely completion within budget and quality standards. Most construction projects experience delays due to poor project planning, budget conflicts, project complexity, weather and natural hazards, injuries and safety issues, and management issues. While the construction industry primarily uses network-based scheduling approaches that rely on the existence of a conventional critical path estimated using deterministic activity times. These approaches do not account for uncertainties and variabilities associated with activities and ignore the possibility of alternate paths becoming critical due to changes incurred in activity times. This research uses a simulation-based approach to demonstrate the impact of the stochastic nature of activity times and to quantify the impact of variation in activity times on project completion. The study presents a novel data-driven decision framework to determine a robust project planning horizon that effectively accommodates uncertainties and variabilities in construction activities. This refined approach was found to be effective for initial project scheduling and for estimating the time extensions required if the project incurs any delay during execution. The results clearly show that minor changes in activity times can lead to the emergence of alternate critical paths and can reduce the probability of timely completion of construction projects. The study recommends the use of average completion times (Cmax) estimated using Cmax of all possible critical paths in the network, which can provide an accurate estimate of project completion time. In addition, a path with the highest variance can be different from the critical path estimated using deterministic activity times. Hence if any delays incur during execution, additional resources need to be invested on paths with higher variance to bring project completion time back to acceptable levels. The study also proposes a graphical approach to estimate realistic completion times, enhancing the probability of achieving timely project delivery amidst uncertainties related to risk, reliability, and natural hazards.

Accurate numerical values of aerodynamic parameters are important in aircraft design. The knowledge of stability and control aerodynamic parameters is essential to postulate high-fidelity control laws. The aerodynamic forces and moments are strong functions of the angle of attack (AOA), Reynolds number, and control surface deflections. Typically, conventional estimation techniques such as maximum likelihood (MLE) and least-squares (LS) principles facilitate the determination of these parameters. Unsteady aerodynamics may complicate the estimation of aerodynamic parameters at high AOA. Data-driven techniques employing neural networks provide an alternative for modeling the system behavior based on its observed state and control input variables. Nonlinearity increases because of flow separation at high AOA, which is close to stall. This paper explores the feasibility of employing a machine learning approach using neural networks to predict aircraft dynamics in a limited sense to identify aerodynamic characteristics. Integrating a neural network with the artificial bee colony (ABC) method facilitated the optimization of unknowns of the proposed aerodynamic model (AM). The proposed neural artificial bee colony (NABC) optimization approach estimated the longitudinal dynamics and stall properties for two experimental aircraft. Comparison of the estimates provided by the NABC approach with those of the standard MLE and neural Gauss-Newton (NGN) techniques established its efficacy. Furthermore, robust statistical analysis indicated that the proposed method provides a viable alternative for parameter estimation in nonlinear applications.

Minimization of the interdepartmental flow within a given facility is often treated as a quadratic assignment problem (QAP). It is an NP-hard, combinatorial optimization problem. Multi-objective quadratic assignment problem (mQAP) is considered when there exists more than one type of flow within the same facility. Metaheuristic algorithms are commonly utilized to estimate the Pareto optimal sets of these problems. The significant challenge in developing and applying an appropriate metaheuristic algorithm for solving multi-objective optimization problems within considerably less time is the selection of an apposite neighbourhood search approach along with proper settings of the algorithm-specific parameters. This paper narrates development of a Pareto communicating multi-objective artificial bee colony (pMOABC) algorithm for efficiently solving the mQAPs. Its optimization performance is thereafter compared with that of seven other state-of-the-art multi-objective optimization algorithms to prove its efficacy in solving bi-objective quadratic assignment problems (bi-QAPs). The values of different tuning parameters of pMOABC are later estimated based on sensitivity analysis studies. The comparative results based on statistical analysis show that the performance of pMOABC is robust over various runs and it can better estimate the Pareto optimal sets for various benchmarked bi-QAPs compared to other considered state-of-the-art algorithms with at least 99.998

Fixed-wing hybrid vertical take-off and landing (VTOL) unmanned aerial vehicles (UAV) are popular due to their interoperability in the military and civilian domains, primarily where significant terrain difficulties exist for humans. Additionally, they can operate without requiring any runway infrastructure and have extended air endurance and efficiency. Since the hybrid UAV operates in distinct flight modes, viz., (a) VTOL and (b) fixed-wing cruise, carrying different payloads, the airframe structure requires careful design and manufacturing to realize sufficient strength. This experimental study aimed to identify the best combinations of various composite materials for manufacturing a lightweight, low-altitude long endurance (LALE) hybrid VTOL UAV. Primary materials include carbon fiber, Kevlar, fiber-reinforced plastic (FRP), resins, etc. Different rectangular test specimens of 120 × 5 mm size were made from ten different grades of carbon fiber, FRP, and resins using vacuum bagging. After properly curing these test specimens, we quantified their dynamic mechanical characteristics using various bending load experiments on a universal testing machine (UTM). An analysis of the experimental data facilitated the identification of the best composite combinations that provide maximum strength while reducing overall weight. Thus, we could understand the dynamic interplay between peak stress and test specimen weight. We also manufactured a UAV prototype using the identified combination and instrumented and flight-tested it to substantiate the experimental findings.

Aerodynamic parameter estimation involves modeling both force and moment coefficients along with the computation of stability and control derivatives from recorded flight data. Classical methods like output, filter, and equation errors apply extensively to this problem. Machine learning approaches like artificial neural networks (ANNs) provide an alternative to model-based methods. This work presents a novel aerodynamic parameters estimation technique involving the fusion of two of the most popular machine learning methods. The process uses biologically inspired optimization techniques, the artificial bee colony (ABC) optimization with the widely used ANN for simulated data contaminated with the noise of varying intensity (5%, 10%) and for real aircraft while considering system and measurement uncertainty. Combining ABC and ANN results in a novel and promising method that can address the sensor noise challenges of system identification and parameter estimation. Comparing the proposed approach's results with other benchmark estimation techniques like Least Square and Filter Error Methods established its efficacy. Furthermore, the feasibility of the proposed hybrid method in extracting the stability and control variables from the stable aircraft kinematics is shown even with insufficient information in its data history.

This research develops an innovative terror threat advisory system capable of visually communicating variations in the terrorism levels to policymakers or the public and forecasts future levels. Earlier attempts to create similar advisory systems by policymakers were either discontinued or lost their relevance due to a trust deficit in the system. We propose a novel approach for creating a color scheme and utilize it to develop an intuitive caterpillar diagram summarizing various stages of terrorism. It incorporates Global Terrorism Impact Scores for a nation or region using the Global Terrorism Database (GTD). Further, color transitions in the caterpillar diagram between consecutive periods mimic a Markovian process, thereby enabling us to develop the forecasting model. We successfully demonstrated the effectiveness of the proposed caterpillar diagram and forecasting model for India, Iraq, and their respective regions. The forecasting model suggests that the aggressive terrorism stage depicted by red color and transient stages of ascent (yellow) and descent (cyan) are the most probable in these nations and their regions. The proposed caterpillar diagram is an innovative visualization approach to identify terrorism patterns, from which a Markovian forecasting model is developed to aid policymakers. Our approach applies to any event-based database like GTD. Finally, the caterpillar diagram is a domain-independent framework that can visualize variations in any univariate data series, thereby assisting in system monitoring.

Despite the simple and extensively used program evaluation and review technique (PERT), a project management tool for estimating project duration poses many limitations. PERT only considers the overly estimated critical path as the lifeline of the project and neglects the variance of other paths, which later affect the project’s timely completion. This paper uses a simulation-based approach to illustrate that a project network contains multiple critical paths that depend upon the network’s variability. A real construction project scenario augmented with five different cases. The outcome shows many instances of other critical paths along with the dominant critical path in simulation.

Aircraft system identification aims to estimate the aerodynamic force and moment coefficients utilizing intelligent modeling and parametric identification methodologies. Classical methods like output, filter, and equation error methods apply extensively as parametric approaches. In contrast, machine learning approaches like Artificial Neural Networks (ANN), Adaptive Neuro-Fuzzy Inference Systems (ANFIS), etc., are alternatives to model-based methods. This work presents a novel aerodynamic parameters estimation technique that fuses two biologically inspired optimization techniques, (i) the Artificial Bee Colony (ABC) optimization and (ii) ANN for an actual aircraft while incorporating system and measurement uncertainty. The fusion of ABC and ANN imparts the ability to address sensor noise challenges associated with system identification and parameter estimation. Comparison of the proposed method's results with the benchmark techniques like Least Square, Filter Error, and Neural Gauss Methods using recorded flight data of the ATTAS (DLR German Aerospace Centre) and HANSA-3 (IIT Kanpur) aircrafts established its adequacy and efficacy. Furthermore, the capability of the proposed hybrid method to extract stability and control variables from the stable aircraft kinematics is shown even with insufficient information in its data history.

This study proposes a framework for developing a realistic model for throttle and servo control algorithms for a powered parafoil unmanned aerial vehicle (PPUAV) using artificial neural networks (ANNs). Two servo motors on an L-shaped platform, control and steer the PPUAV. Six degrees of freedom mathematical model of a dynamic parafoil system is built to test the technique's efficacy using a simulation in which disturbances mimic actual flights. A guiding law is then established, including the cross-track error and the line-of-sight approach. Furthermore, a path-following controller is constructed using the proportional-integral derivative, and a simulation platform was created to evaluate numerical data illustrating the route's validity following the technique. PPUAV was developed, built, and instrumented to collect real-time flight data to test the controller. These dynamic characteristics were sent into the ANN for training. A diverging-converging design was identified to obtain the best consistency between predicted and observed throttle and servo control values. For a comparable flight route, the control signal of the simulated model is compared with those of the actual and ANN-predicted models. The comparative findings show that the ANN-predicted and actual control inputs were almost identical, with an 80%–99% match. However, the simulated response showed deviation from the actual control input, with an accuracy of 50%–80%.

Terrorism perpetrated in any country by either internal or external actors jeopardizes the country’s security, economic growth, societal peace, and harmony. Hence, accurate modelling of terrorism has become a necessary component of the national security mission of most nations. This research extracted and analyzed high impact attacks (HIAs) perpetrated by terrorists in India and its neighboring countries since 1970 using the Global Terrorism Database (GTD). We evaluated the extraction efficacy of the Global Terrorism Index Impact Score (GTI-IS) against the GTD measure “nkill” using the iterative outlier analysis (IOA) heuristic. The heuristic identified 6117 common HIAs using nkill or GTI-IS attributes. GTI-IS extracted 1718 exclusive HIAs that nkill missed, while nkill extracted 2233 exclusive HIAs. We further classified the extracted HIAs into lethal and non-lethal attacks. Next, we conducted a rigorous spatiotemporal exploratory analysis of countries that reported the most HIAs. Though Afghanistan, India, and Sri Lanka exhibited global spatial autocorrelation, Pakistan did not. Ripley’s G function suggested the recurrence of lethal attacks near other similar events. This analysis showed that lethal and non-lethal attacks in those countries follow different statistical distributions, which can aid in focused counterterrorism tactics.

Terrorist attacks aim to maximize human fatalities and related damages to instill fear within the community. Such attacks are considered High Impact Attacks (HIAs) and orchestrating them requires considerable organizational setup and resources. This study extends the implementation of the Iterative Outlier Analysis (IOA) heuristics developed earlier to each region reported in the Global Terrorism Database (GTD). It reinforces and generalizes the finding that the "nkill" attribute resulted in richer sets of HIAs in regions where terrorism is prolific compared to the composite measure Global Terrorism Index-Impact Score (GTI-IS). HIA dataset of each region facilitates the identification and ranking of the Most Active Organizations (MAOs). Moreover, this study proposes a consistency and intensity code (CIC) to classify terrorist organizations capable of HIAs using four color categories. K-Means validate the number of clusters. The frequency of MAOs rank follows distinct probability distribution in each CIC category. Finally, this research identified the most virulent consistent and intense terrorist organizations (CITO) capable of perpetrating attacks in multiple regions. Regional counterterrorism policymakers can use such a classification method. A non-parametric hypothesis test confirmed that the contribution of ideologies varies significantly by region.

Fixed-wing unmanned aerial vehicles (UAVs) offer the best aerodynamic efficiency required for long-distance or high-endurance applications, albeit their runway requirement for take-off and landing in comparison with quadcopters, helicopters, and flapping-wing UAVs that can perform vertical take-off and landing (VTOL). Integrating a multirotor system with a fixed-wing UAV imparts VTOL capabilities without significantly compromising fixed-wing aerodynamic efficiency, endurance, payload capacity or range. Documented system design approaches to address various challenges of such fusion processes are sparse. This research proposes a holistic approach for designing, prototyping, and testing an electric-powered fixed-wing hybrid VTOL UAV. The proposed system design approach augments the standard aircraft design process with additional steps to integrate VTOL capabilities. Separate fixed-wing and multirotor designs were derived from the frozen mission requirements, which were then fused. The process used simulation for modeling and evaluating alternatives for the hybrid UAV created using standard aircraft design equations. We prototyped and instrumented the final design to validate operational capabilities through test flights. Multiple flight trials identified the ideal combination of Lithium-Polymer (Li-Po) batteries for VTOL (8000mAh) and fixed-wing (14000mAh) modes to meet the endurance and range requirements. The redundant power supplies also increased the survivability chances of the hybrid UAV during failures.

Aerodynamic parameter estimation entails modelling force and moment coefficients as well as computing stability and control derivatives from flight data. This topic has been thoroughly researched utilizing traditional procedures such as output, filter, and equation error methods. Machine learning, such as artificial neural networks, provides an alternate way to these model-based methodologies. This paper proposes a novel estimation technique for aerodynamic parameters of a real aircraft in the presence of system and measurement uncertainty. A fusion between biologically inspired optimization i.e., Artificial Bee Colony (ABC) optimization and widely used Artificial Neural Network (ANN), which mimics the functional unit of the brain, the neuron, has been demonstrated to be novel and a promising method to the challenges of system identification and parameter estimation (sensor noise). The obtained results were compared to Least Square, and Maximum Likelihood Method (MLE), benchmark estimation techniques.

System identification methods have extensive application in the aerospace industry’s experimental stability and control studies. Accurate aerodynamic modeling and system identification are necessary because they enable performance evaluation, flight simulation, control system design, fault detection, and model aircraft’s complex non-linear behavior. Various estimation methods yield different levels of accuracies with varying complexity and computational time requirements. The primary motivation of such studies is the accurate quantification of process noise. This research evaluates the performance of two recursive parameter estimation methods, viz.; First is the Fourier Transform Regression (FTR). The second approach describes the Extended version of Recursive Least Square (EFRLS), where E.F. refers to the Extended Forgetting factor. Also, the computational viability of these methods was analyzed for real-time application in aerodynamic parameter estimation for both linear and non-linear systems. While the first method utilizes the frequency domain to evaluate aerodynamic parameters, the second method works when noise covariances are unknown. The performance of both methods was assessed by benchmarking against parameter estimates from established methods like Extended Kalman Filter (EKF), Unscented Kalman Filter (UNKF), and Output Error Method (OEM).

Terrorism adversely impacts the investment decisions of multinational enterprises. This research proposes the iterative outlier analysis heuristic capable of utilizing a univariate attribute to extract high impact attacks (HIA) from a comprehensive and actively maintained global terrorism database (GTD). The two different univariate attributes used for HIA extraction were nkill (pure GTD univariate attribute) and global terrorism impact score (GTI-IS) (a derived composite attribute). Calendar year extraction of HIAs returns local point outliers, whereas taking whole dataset at once provides global point outliers. The nkill-based extraction resulted in 5 and 14 times more exclusive HIAs than GTI-IS in local and global point analysis, thereby establishing its superiority. Further, correspondence analysis using extracted HIAs demonstrated the affinity towards explosives in the Middle East & North Africa region, with Military and Business as preferred targets. Also, HIAs facilitated the geospatial visualization of terrorism hotbeds. Eliciting location-specific relationships from HIAs on weapon type and target type can assist in formulating better counterterrorism strategies. This study scrutinized the averaging out evaluation methodology of GTI ranking based on GTI-IS score. By equalizing terrorist attacks of distinct outcomes using an average score can induce bias among business decision-makers interested in a particular nation.

This paper provides a novel estimating approach for longitudinal aerodynamic parameters in the presence of system and measurement uncertainty. The proposed method dubbed as NABC is a hybrid technique that employs biologically inspired optimization, namely Artificial Bee Colony (ABC) in fusion with widely used Artificial Neural Network. For parameter estimation, the motion variables and control inputs are initially used to train the neural network, and the corresponding predicted output of aerodynamic coefficients is obtained. The method is first validated on the simulated dataset intentionally contaminated with 5% and 10% of random noise, resembling the actual flight data. Then, the algorithm's efficacy is tested on the real flight data of ATTAS aircraft. The aerodynamic parameter estimations obtained using the proposed method are compared to the benchmark estimate methodologies, like Least Squares and Maximum Likelihood Method (MLE). The confidence in the estimates generated using the suggested technique has been strengthened by statistical analysis of the parameter estimations.

This research aimed to identify objects from a real-time video (30 Hz) stream transmitted from a low-altitude long-endurance (LALE) fixed-wing hybrid vertical takeoff and landing (VTOL) unmanned aerial vehicle (UAV) designed for asset monitoring. Illumination, rotation, and scale variations complicate the video frames created by UAVs’ optical payload, requiring preprocessing. Detection, identification, and classification of different objects (or targets) from a real-time video feed require fast and reliable detection algorithms, models, and computation. The weight restrictions and the onboard power supply only allowed for a Raspberry Pi onboard the UAV, thereby seriously limiting computational power. Image frames were created from the video data after enhancing its quality using image processing algorithms. Then, anchor-based object detection algorithms were applied to the processed and enhanced image frames. The Raspberry Pi at airspeeds of 16–18 m/s requires fast object detection algorithms having minimal computational overhead. A supervised learning technique using a supporting library further augmented the real-time detection at the ground control station (GCS). Four scenarios trained the candidate algorithms, namely: 1) helipad; 2) green area; 3) burned area; and 4) under construction area. The single-shot multibox detector (SSD) aided the supervised learning process during trials utilizing the labeled image datasets. The comparison of detection and identification performance of the SSD with ResNet-50 and ResNet-101 backbones with other methods, such as Faster-RCNN with four backbone architectures, namely: 1) ResNet-101; 2) ResNet-50; 3) deformable ResNet; and 4) FPN in four detection scenarios indicated better precision and reduced detection delays.

Presently, unmanned aerial vehicles (UAVs) have a broad spectrum of applications, in which regular monitoring of critical infrastructure assets is an important risk mitigation usage. This paper presents a novel approach for modeling leakage from natural gas pipelines using machine learning algorithms operating on the gas leakage detection (Methane) data collected using an UAV having a gas sensor and a light detecting and ranging (LiDAR) payload. The detection system has a lightweight design along with a small form factor to ensure compatibility with most autonomous mobile platforms like UAVs or wheeled robots. Two experiments were conducted to collect the gas leakage detection data at various loitering heights in real atmospheric conditions before applying the estimation algorithms. The first experiment measured natural gas leakage data in varying loitering radiuses and altitudes for leakage pressures around one pound per square inch (PSI), while the second experiment did the same for leakage pressures around two PSI. Real atmospheric conditions were incorporated along with additional aspects of propeller down-wash and environmental air movements that can influence leakage detection. Two estimation algorithms, viz., reduced support vector machine (RSVM) and artificial neural network (ANN) were applied to the leakage data collected from the UAV experiments. It was found that the ANN approach resulted in more faster and accurate detection than RSVM that heavily depended on the kernel function for its performance. The efficacy of leakage detection of natural gas using a UAV payload was demonstrated, which is a faster and cost-effective alternative to the manual inspection process.