Recent publications
The recovery of furfural from hemicellulosic biowastes is important for developing sustainable and renewable energy alternatives to fossil fuels. However, current methods are inefficient and environmentally questionable. To address this issue, this study introduces neoteric hydrophobic solvents, specifically deep eutectic solvents (DESs) and ionic liquids (ILs). Of the 32 solvents tested, thymol:decanoic acid 1:1 (Thy:DecA) DES and trihexyltetradecyl phosphonium bis(trifluoro methylsulfonyl) imide [P14,6,6,6][NTf2] IL were the most effective, with extraction efficiencies of 94.1% and 97.1%, respectively. These solvents outperformed the reference solvent toluene, with an efficiency of 81.2%, while also showing favorable characteristics in multiple investigated criterions. For the first time, excellent performance stability was demonstrated under various operational conditions and reusability over multiple extraction and regeneration cycles. Furthermore, to provide insights into the molecular mechanisms of extraction, computational quantum chemistry modeling was employed, which showed a strong agreement with the experimental results. The development of these new neoteric solvents for furfural recovery from biowaste offers a highly effective, sustainable, and eco-friendly alternative to traditional solvents, representing a significant breakthrough in the field of renewable bioenergy production and sustainable materials recovery.
This letter deals with a single-phase, nonisolated on-board charger (OBC) for electric vehicles (EVs) based on a four-switch three-port (FSTP) converter topology with a power decoupling function. However, in the published literature, the size of the decoupling cell (inductor and capacitor) is generally large, making promotion and application challenging. To minimize the decoupling cell size, an improved power decoupling control method is proposed in this letter. Specifically, the decoupling cell inductor current reference value is generated by using the output of a proportional integral (PI) plus proportional-resonant (PR) controller so that the decoupling cell can absorb double-line frequency power to the maximum extent. The proposed control method is implemented in a 500Wlaboratory prototype. Compared with the existing control method, under the same operating condition, the proposed approach reduces the output direct current (dc) voltage ripple by 71.2%, the decoupling capacitance by 40%, and the decoupling inductance by 66.6%.
To provide sustainable energy support while meeting the demands for serving a rapidly increasing number of devices, in this paper, we propose a new Reconfigurable intelligent surface (RIS) Assisted simultaneous wireless information and Power transfer (SWIPT) and over-the-aIr computation (AirComp) feDerated learning system which is termed RAPID. Specifically, at each training iteration, an access point (AP) first simultaneously broadcasts global model and transfers wireless energy to the selected devices via the RIS-assisted SWIPT. These devices then use the harvested power to compute and upload their local gradients to the AP for aggregation via the RIS-assisted AirComp. To identify the performance improvement to federated learning, we first analyze and derive the expected convergence rate for the proposed RAPID system taking into account factors of device selection and wireless communication. We then formulate a joint learning-communication optimization problem in terms of device selection, transmit beamforming, power splitting, receive beamforming, and RIS coefficients design. To solve the formulated non-convex problem, we propose a new two-stage algorithm by successively solving the downlink SWIPT and the uplink AirComp sub-problems based on alternating optimization and successive convex approximation techniques. Simulation results are presented to demonstrate that our proposed RAIPD system can significantly improve the convergence and accuracy of federated learning compared with benchmark schemes.
The pursuit of seamless formation and robust control of inverters in power electronic-dominated grids face challenges arising from uncertainties in grid impedance, dynamic load variations, and transitional phase jump scenarios, leading to elevated instability during mode transitions. These challenges necessitate a nuanced approach to voltage regulation and stability analysis to manage the stochastic nonlinearities and inherent dynamics effectively. This paper introduces a novel Unified Koopman-based Model Predictive Control (K-MPC) method that synergizes a modified MPC framework with an ensemble approach and a saturation-like automatic adaptation function for seamless inverter transitions. It facilitates precise power-sharing among inverters in isolated microgrids by adjusting output impedances without the need for communication lines, thereby addressing the limitations in dynamic response, sensor requirements, filter fluctuations, and controller complexity. The K-MPC method enhances system performance and fidelity by incorporating online adaptation norms for external disturbance rejection, aligning closely with a predefined reference model. Quantitative validation, conducted through frequency response analysis and hardware-in-the-loop (HIL) experiments on an IEEE 123-node test system, underscores the method’s effectiveness. The K-MPC approach notably reduces total harmonic distortion (THD) by up to 30% relative to conventional control strategies and improves power-sharing precision among inverters by 25% under dynamic loading conditions. Furthermore, it exhibits a 40% faster response in adapting to external disturbances, ensuring voltage and frequency remain within target thresholds.
Chattering in Sliding Mode (SM) control is known as fast self-excited periodic motions (oscillations) that occur due to the combination of two factors: nonlinearities that are not Lipschitz continuous and additional dynamics of actuators and sensors (the dynamics not accounted for in SM control system design). It is found in the present research that under certain combinations of parameters of the discontinuous homogeneous sliding mode controller and actuator, the chattering is manifested not as periodic but as chaotic oscillations. This work investigates the phenomenon of chaotic chattering through the Locus of Perturbed Relay Systems (LPRS) method and the formulated conditions for the periodic solutions found through the LPRS to manifest themselves as periodic motions. Adherence to or violation of these conditions is correlated with the occurrence of periodic or chaotic chattering. The particular controller investigated is a Homogeneous Sliding Mode Controller. Bifurcation points in terms of the time constant of the actuator and the relay amplitude are determined.
While hybrid microgrids (HMGs) offer several advantages, protection schemes for HMGs face several challenges, such as the inconsistent and low fault current contributions of the inverter-dominated distributed generations as well as the lack of a unified protection technique for both AC and DC sub-grids. To address these challenges, this paper develops a unified differential protection technique (UDPT) for islanded HMGs. The proposed UDPT utilizes the line parameters and the current measurements at both line terminals to calculate the voltage drop (VD) along the line. The VD is calculated twice by considering two fault locations (close end and far end). Under normal operation and external fault scenarios, the VD values calculated for the two locations have the same direction, but on the contrary, have opposite directions under internal faults. Consequently, a unified fault detection index (UFDI) is proposed to discriminate between internal faults and other scenarios. The effectiveness of the proposed UDPT is evaluated through PSCAD/EMTDC simulation on the modified IEEE 33-bus islanded HMG, involving load switching, generator switching, capacitor switching, topology changes, external faults, and internal faults. The proposed UDPT shows remarkable performance compared with previous schemes in the literature, even with inaccuracies in measurements and line parameters as well as high-resistance faults.
Software Defined Radio Frequency (SDRF) sensing technology has revolutionized healthcare by enabling real-time monitoring and early diagnosis of patient health status with higher reliability, diagnostic accuracy, and enhanced healthcare services in a non-contact and non-invasive manner. However, RF sensing for breathing disorder diagnosis and monitoring is still an open research challenge. Further research is necessary to determine RF sensing accuracy and reliability for breathing disorders in different environments and applications. RF sensing is sensitive to environmental changes and shows non-linear responses. Existing studies have explored RF sensing for breathing monitoring using fixed RF parameters to evaluate the system’s performance. However, several key parameters in RF sensing, such as operating frequency, sampling rate, bandwidth, gain, power, the height of antennas, and distance between transmitter and receiver, affect the system’s performance practicality. In this paper, we used a re-configurable SDRF sensing system to evaluate the RF parameters for monitoring breathing in order to understand their effects and enhance the performance of the sensing system. The correlation between RF sensing characteristics and wearable breathing sensors is evaluated using the correlation coefficient (CC) and mean square error (MSE). The findings reveal that a higher operating frequency of 4.8 GHz, a sampling rate of 300 samples/s, antennas on the line of sight and distance up to 2 feet show the best performance, with an MSE of less than 0.1111 and a CC of 0.9943, indicating a significant correlation. The experimental study concludes that breathing monitoring performance using RF sensing heavily depends on RF parameters.
Matrix-valued (multivariate) correlation functions are increasingly used within both the statistics and machine learning communities, but their properties have been studied to a limited extent. The motivation of this paper comes from the fact that the celebrated local stationarity construction for scalar-valued correlations has not been considered for the matrix-valued case. The main reason is a lack of theoretical support for such a construction. We explore the problem of extending a matrix-valued correlation from a d-dimensional ball with arbitrary radius into the d-dimensional Euclidean space. We also consider such a problem over product spaces involving the d-dimensional ball with arbitrary radius. We then provide a useful architecture to matrix-valued local stationarity by defining the class of p-exponentially convex matrix-valued functions, and characterize such a class as scale mixtures of the d-Schoenberg kernels against certain families of measures. We exhibit bijections from such a class into the class of positive semidefinite matrix-valued functions and we extend exponentially convex matrix-valued functions from d-dimensional balls into the d-dimensional Euclidean space. We finally provide similar results for the case of function-valued correlations defined over certain Hilbert spaces.
An AMP-derived short 15-mer peptide and its cyclic derivatives have low micromolar broad spectrum antibacterial activity, with rapid onset of bactericidal effect and a membrane-targeting mode of action.
The rise of infections associated with indwelling medical devices is a growing concern, often complicated by biofilm formation leading to persistent infections. This study investigates a novel approach to prevent Candida albicans attachment on the surface by altering surface topography. The research focuses on two distinct surface topographies: symmetry (squares) and non-symmetry (lines), created through a direct laser photolithography process on a Cyclic olefin copolymer (COC) surface. The wettability of these patterned surfaces was then examined immediately after fabrication and plasma treatment to mimic the sterilization process of indwelling devices through UV plasma. The results reveal directional wettability in the line pattern and size-dependent wettability in both square and line patterns. Candida albicans were cultured on these surfaces to assess the efficacy of the topography in preventing biofilm formation. The study demonstrates that symmetry and non-symmetry pattern topography inhibit biofilm formation, providing a promising strategy for mitigating Candida-associated infections on medical devices. The research sheds light on the potential of surface modification techniques to enhance the biocompatibility of medical devices and reduce the risk of biofilm-related infections.
The present study puts forward a novel approach for seismic design of bridges, wherein the optimum joint gap size is one of the design parameters of the bridge; the methodology integrates the optimization of the joint gap with a ‘mainstream’ seismic design for energy dissipation in the piers. Another contribution of this study is the assessment of the effect of bridge configuration on the selection of the optimum joint gap sizes, focusing on the effect of pier height for bridges with ductile piers. It is found that designing bridges for optimal gap sizes in both directions leads to a notable reduction in pier reinforcement requirements when the aim is to satisfy the Code criteria while, at the same time, the safety margin against exceeding the specified performance criteria (limit states) remains practically unaffected. On the other hand, the required design effort inevitably increases. Regarding the effect of pier height, an interesting finding is that as piers increase in height, leading to increased flexibility and, hence, larger displacements, other components of the bridge, such as the abutment-backfill system, tend to become the critical ones in identifying the optimum gaps.
Computer-assisted diagnosis (CAD) plays a key role in cancer diagnosis or screening. Whereas, current CAD performs poorly on whole slide image (WSI) analysis, and thus fails to generalize well. This research aims to develop an automatic classification system to distinguish between different types of carcinomas. Obtaining rich deep features in multi-class classification while achieving high accuracy is still a challenging problem. The detection and classification of cancerous cells in WSI are quite challenging due to the misclassification of normal lumps and cancerous cells. This is due to cluttering, occlusion, and irregular cell distribution. Researchers in the past mostly obtained the hand-crafted features while neglecting the above-mentioned challenges which led to a reduction of the classification accuracy. To mitigate this problem we proposed an efficient dual attention-based network (CytoNet). The proposed network is composed of two main modules (i) Efficient-Net and (ii) Dual Attention Module (DAM). Efficient-Net is capable of obtaining higher accuracy and enhancing efficiency as compared to existing Convolutional Neural Networks (CNNs). It is also useful to obtain the most generic features as it has been trained on ImageNet. Whereas DAM is very robust in obtaining attention and targeted features while negating the background. In this way, the combination of an efficient and attention module is useful to obtain the robust, and intrinsic features to obtain comparable performance. Further, we evaluated the proposed network on two well-known datasets (i) Our generated thyroid dataset (ii) Mendeley Cervical dataset (Hussain in Data Brief, 2019) with enhanced performance compared to their counterparts. CytoNet demonstrated a 99% accuracy rate on the thyroid dataset in comparison to its counterpart. The precision, recall, and F1-score values achieved on the Mendeley Cervical dataset are 0.992, 0.985, and 0.977, respectively. The code implementation is available on GitHub. https://github.com/naveedilyas/CytoNet-An-Efficient-Dual-Attention-based-Automatic-Prediction-of-Cancer-Sub-types-in-Cytol.
Septins are a family of cytokinesis-related proteins involved in regulating cytoskeletal design, cell morphology, and tissue morphogenesis. Apart from cytokinesis, as a fourth component of cytoskeleton, septins aid in forming scaffolds, vesicle sorting and membrane stability. They are also known to be involved in the regulation of intracellular calcium (Ca²⁺) via the STIM/Orai complex. Infertility affects ~ 15% of couples globally, while male infertility affects ~ 7% of men. Global pregnancy and live birth rates following fertility treatment remain relatively low, while there has been an observable decline in male fertility parameters over the past 60 years. Low fertility treatment success can be attributed to poor embryonic development, poor sperm parameters and fertilisation defects. While studies from the past few years have provided evidence for the role of septins in fertility related processes, the functional role of septins and its related complexes in cellular processes such as oocyte activation, fertilization, and sperm maturation are not completely understood. This review summarizes the available knowledge on the role of septins in spermatogenesis and oocyte activation via Ca²⁺ regulation, and cytoskeletal dynamics throughout pre-implantation embryonic development. We aim to identify the currently less known mechanisms by which septins regulate these immensely important mechanisms with a view of identifying areas of investigation that would benefit our understanding of cell and reproductive biology, but also provide potential avenues to improve current methods of fertility treatment.
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Abu Dhabi, United Arab Emirates
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Dr. Arif Sultan Al Hammadi
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