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"Abbasi, Ali"
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Autonomous airborne wireless networks
\"Airborne networks have led to the development of a range of applications including surveillance and monitoring, military and rescue operations. Whilst the conventional focus on airborne networks revolves around control, trajectory optimization and navigation, its application for providing communications has recently emerged and is developing at a very fast pace. With contributions from international experts, this book explores recent advances in the theory and practice of airborne wireless networks for the next generation of wireless networks to support various applications including emergency communications, coverage and capacity expansion, Internet of Things, information dissemination, future healthcare, pop-up networks, etc.\"-- Provided by publisher.
Book
Transformer windings defects identification using frequency response analysis and advanced data visualization techniques
Transformers, as critical components of power networks, are subjected to various mechanical and electrical stresses under different loading conditions. Their windings may experience minor, recurring faults that are difficult to detect in the early stages before they become apparent. Therefore, early prediction and diagnosis of these faults are of utmost importance. In the power industry, Frequency Response Analysis (FRA) is widely used for transformer fault diagnosis. However, one of the main challenges of this method is the complex interpretation of its results. This paper addresses this challenge by presenting advanced data visualization techniques for interpreting FRA results and diagnosing transformer winding faults. To achieve this, three independent methods—Factor Analysis, Fuzzy Clustering Analysis, and Principal Component Analysis—are employed. Each method demonstrates outstanding performance due to its unique characteristics: Factor Analysis identifies complex faults by uncovering hidden factors; Fuzzy Clustering Analysis detects combined fault conditions by handling uncertainty; and Principal Component Analysis enhances interpretability by reducing data dimensionality. Based on these techniques, a two-stage identification model is developed. In the first stage, the distinction between healthy and faulty conditions is made, and in the second stage, fault classification under faulty conditions is performed. Experimental results show that the proposed techniques can effectively extract various frequency response features and identify faults with high accuracy. After implementing the proposed techniques on a transformer, the need for expertise
in
fault diagnosis and classification is significantly reduced. This approach helps engineers and operators interpret the results more simply and efficiently.
Journal Article
Prediction of dynamic strength index (DSI) using one low-cost 2D video camera: a machine learning approach
This study examined whether a low-cost, two-dimensional (2D) video camera combined with supervised machine learning (ML) models could accurately predict the Dynamic Strength Index (DSI), a key indicator of neuromuscular performance. A total of 263 healthy participants performed countermovement jumps (CMJ) and isometric mid-thigh pulls (IMTP). Unlike the conventional method where both ballistic and isometric peak forces are measured using a force plate, in this study the isometric force was assessed using a back and leg dynamometer, while ballistic force during CMJ was estimated from 2D video data. Six spatiotemporal features extracted from the video, together with height and weight, were used to train multiple supervised regression models. Ground truth validation of the video-based ballistic force estimation was performed using force plate data. Gaussian Process Regression and Neural Networks achieved the highest prediction accuracy (R² > 0.92, RMSE < 0.06), demonstrating excellent agreement with laboratory-derived values. These findings highlight the potential of combining simple video systems with ML algorithms to estimate DSI accurately and affordably. The proposed method provides a scalable and non-invasive alternative to lab-grade equipment, enabling broader access to neuromuscular diagnostics and performance monitoring, particularly in youth, community sports, and field-based environments.
Journal Article
An investigation of mixed convection flow on a vertical flat plate of a saturated nanofluid in a porous medium near the stagnation point
In the present paper, a non-similar solution of a steady saturated nanofluid flow, heat, and mass transfer is investigated. The nanofluid flow is under dual effects of stagnation flow and natural convection heat transfer on a vertical flat plate in a porous medium. Effects of variations in thermophoresis, Brownian motion, and buoyancy force have been studied. The partial differential equations are transformed into six ordinary differential equations with appropriate non-similarity variables, which also consider the longitudinal coordinate of the x-axis. In order to solve, we have formed a set of fourteen ordinary differential equations of the first order. A complicated double method finds six unknown initial values in the boundary value problem. Variations of longitudinal velocity, shear stress, temperature, and nanoparticle volume fraction are considered as functions of transverse and longitudinal coordinates. As a result, the minimum accuracy of the first-order non-similar solution in regions very close to the stagnation point is 96%. Also, the velocity profile is observed to vary along the longitudinal x-axis near the stagnation point, which is an improvement over the existing knowledge which largely assumes a constant velocity profile throughout the stagnant flow region.
Journal Article
Determinants of SME’s Social Media Marketing Adoption: Competitive Industry as a Moderator
In light of the growing role of social media marketing in the success of businesses and its low adoption rate among small and medium enterprises (SMEs), this study aims to identify determinants of SMEs’ social media marketing adoption by considering the competitive industry as a moderator. Data were collected from 214 SMEs in Malaysia. Unlike extant literature, this study proposed a dual-stage analysis involving partial least squares (PLS) technique and artificial intelligence named deep artificial neural network (ANN). The application of deep ANN architecture is used to predict 91% of accuracy for the proposed model. The results showed that perceived relative advantage, perceived cost, top management support, perceived competitor pressure, and perceived vendor pressure have a significant impact on social media marketing adoption. Furthermore, the competitive industry moderates the effects of competitive pressure and customer pressure on social media marketing adoption. The results of the study extend the literature on social media marketing by illustrating the influence of technological, organizational, and environmental (TOE) factors on social media marketing adoption among SMEs concerning the extent of industry competition. The results of the study enable policymakers and managers of SMEs to understand the factors that influence social media marketing adoption in both competitive and non-competitive industries and invest effectively in digital marketing.
Journal Article
The Promise of Real-World Data for Research — What Are We Missing?
Real-world data could help advance disease prevention and treatment, enhance quality of care, and empower patients to improve their health. But various challenges continue to impede their utilization.
Journal Article
Menstrual cycle does not change sagittal plane segments coordination variability during deadlift, a nonlinear dynamical analysis approach
The menstrual cycle is a key biological rhythm in women that influences multiple physiological systems, including neuromuscular control. However, its impact on inter-segmental coordination during strength-based tasks remains unclear. This study investigated the effect of menstrual cycle phase on sagittal-plane coordination variability during deadlifts under different loading conditions in healthy, recreationally active women. Ten eumenorrheic females (20–27 years) performed conventional deadlifts during three self-reported menstrual phases: early follicular (days 1–3), late follicular (days 11–13), and luteal (days 21–23). Kinematic data were captured with a 3D inertial motion system (200 Hz), and inter-segmental coordination between trunk, pelvis, thigh, shank, and foot was assessed using modified vector coding. Coordination patterns (in-phase and anti-phase, proximal/distal dominance) and coordination variability (CV) were quantified across repetitions. Segmental range of motion (ROM) was also calculated. Segmental ROM tended to increase with external loading (50% bodyweight vs. bodyweight) and was slightly higher in the luteal phase compared with the follicular phases, but no statistically significant differences were found across phases or loads (
p
> 0.05). Similarly, no significant differences were detected in coordination pattern frequencies or CV waveforms across phases or loading conditions (
p
> 0.05). Contrary to the hypothesis, menstrual cycle phase did not significantly affect sagittal-plane coordination or its variability during deadlifts. These findings suggest that the deadlift, as a bilateral and strength-dominant movement, is biomechanically stable across the menstrual cycle. Coaches and clinicians may not need to adjust deadlift training or rehabilitation protocols based on menstrual timing. However, further studies with larger cohorts, direct hormonal assays, and multi-planar analyses are warranted to clarify potential cycle-related effects in more dynamic or unstable tasks.
Journal Article
Lower-extremity inter-joint coordination variability in active individuals with transtibial amputation and healthy males during gait
This study was aimed to compare the variability of inter-joint coordination in the lower-extremities during gait between active individuals with transtibial amputation (TTAs) and healthy individuals (HIs). Fifteen active male TTAs (age: 40.6 ± 16.24 years, height: 1.74 ± 0.09 m, and mass: 71.2 ± 8.87 kg) and HIs (age: 37.25 ± 13.11 years, height: 1.75 ± 0.06 m, and mass: 74 ± 8.75 kg) without gait disabilities voluntarily participated in the study. Participants walked along a level walkway covered with Vicon motion capture system, and their lower-extremity kinematics data were recorded during gait. The spatiotemporal gait parameters, lower-extremity joint range of motion (ROM), and their coordination and variability were calculated and averaged to report a single value for each parameter based on biomechanical symmetry assumption in the lower limbs of HIs. Additionally, these parameters were separately calculated and reported for the intact limb (IL) and the prosthesis limb (PL) in TTAs individuals. Finally, a comparison was made between the averaged values in HIs and those in the IL and PL of TTAs subjects. The results showed that the IL had a significantly lower stride length than that of the PL and averaged value in HIs, and the IL had a significantly lower knee ROM and greater stance-phase duration than that of HIs. Moreover, TTAs showed different coordination patterns in pelvis-to-hip, hip-to-knee, and hip-to-ankle couplings in some parts of the gait cycle. It concludes that the active TTAs with PLs walked with more flexion of the knee and hip, which may indicate a progressive walking strategy and the differences in coordination patterns suggest active TTA individuals used different neuromuscular control strategies to adapt to their amputation. Researchers can extend this work by investigating variations in these parameters across diverse patient populations, including different amputation etiologies and prosthetic designs. Moreover, Clinicians can use the findings to tailor rehabilitation programs for TTAs, emphasizing joint flexibility and coordination.
Journal Article
Efficient sorption and secure immobilization of strontium ions onto nanoporous alumino-borosilicate as a new matrix
The objective behind developing the nanoporous alumino-borosilicate (AlBS) was to remove strontium ion (Sr
2+
) from liquid waste and subsequently stabilize it. The sorption capacity of the nanoporous AlBS was assessed in relation to various experimental factors, including contact time, temperature, initial pH solution, and initial concentration of Sr ions. According to the obtained results, nanoporous AlBS shows a maximum Sr
2+
sorption capacity of 163.08 mg/g. In order to achieve stable immobilization of the sorbed Sr ions, heat treatments at different temperatures were applied to the Sr-containing nanoporous AlBS. Various eluents were used in the leach tests to examine the Sr ions leaching from heat-treated materials. Only 3.43% of the Sr ions initially adsorbed in the nanoporous AlBS matrix was washed out with 1 M sodium chloride eluent, showing that heating the sample to around 1100 °C successfully trapped Sr ions in the nanoporous AlBS matrix.
Journal Article