Many biological processes rely on endogenous electric fields (EFs), including tissue regeneration, cell development, wound healing and cancer metastasis. Mimicking these biological EFs by applying external direct current stimulation (DCS) is therefore the key to many new therapeutic strategies. During DCS, the charge transfer from electrode to tissue relies on a combination of reversible and irreversible electrochemical processes, which may generate toxic or bio-altering substances, including metal ions and reactive oxygen species (ROS). Poly(3,4-ethylenedioxythophene) (PEDOT) based electrodes are emerging as suitable candidates for DCS to improve the biocompatibility compared to metals. This work addresses whether PEDOT electrodes can be tailored to favor reversible biocompatible charge injection. To this end, different PEDOT formulations and their respective back electrodes are studied using cyclic voltammetry, chronopotentiometry, and direct measurements of H2O2 and O2. This combination of electrochemical methods sheds light on the time dynamics of reversible and irreversible charge transfer and the relationship between capacitance and ROS generation. The results presented here show that although all electrode materials investigated generate ROS, the onset of ROS can be delayed by increasing the electrode’s capacitance, which has implications for future bioelectronic devices that allow longer reversibly driven pulse durations during DCS.

In Part I of this work, we presented a three-stage framework for solving stochastic non-convex optimal power flow problems using a parameterized deterministic look-ahead policy and minimizing the cost of scheduling flexibility reserves using blockchain and smart contracts. Here, in Part II, a case study with two balancing authority (BA) areas is presented to show the effectiveness of the proposed algorithm. The BA area is modeled by the collection of generation, transmission, and distribution networks. Next, the reserve sharing strategy between two or more BA areas is presented to collectively maintain and supply the flexibility reserves. The reserve sharing will help reduce the total flexibility reserve scheduling costs in the BA areas. And finally, the advantage of updating the flexibility reserve schedule at each time step using a smart contract, by tabulating the flexibility metrics “variation in generation capacity (VIGC)” and uncertainty quantification of epistemic and aleatory uncertainties in variable distributed energy resources (DERs) generation and loads, is also presented.

Motivated by the large number of wearables offering geolocation, human mobility mining has emerged as an novel research field  within AI. The study of mobility creates increasingly predictable models in which it is easy to find patterns of behaviour. However, this data is not publicly available and access to it is restricted to large telecommunications operators. In this context, this paper aims to solve one of the main problems of human mobility databases, i.e. the scarcity of data for the generation of human mobility models. For this purpose, Generative adversarial network (GANs) have been proposed to generate synthetic time-series mobility data. Moreover, several neural network models are proposed to assess the impact of synthetic data generation on the prediction of human mobility. Our results show that the use of synthetic data improves predictions of human mobility compared to models based on available measured data.

Efficient and ultrafast (aluminum)-gallium-nitride-based metal-semiconductor-metal (Alx)Ga1–xN ultraviolet photodiodes were implemented to investigate the spectral properties of GaN/AlGaN-based photodetectors. Al composition of the GaN templates were varied from 0-% to 30% to demonstrate the impact of Al mole fraction on the cutoff wavelength of these UV photodiodes. Asymmetric metal contact electrodes were fabricated to optimize the external quantum efficiency without compromising their ultrafast photoresponse. The best performing devices exhibited a peak response of 4 V and an oscilloscope limited temporal pulse width of 61 ps at a 20-V bias with a peak spectral responsivity of 36.5 mA/W at 280 nm.

Aiming for seamless satellite communications on the move (SOTM) between vehicles and new low-earth orbit satellite constellations, this paper presents a low-power beam-steering antenna system with a novel beam handover algorithm. The proposed antenna uses a 3D-printed Luneburg lens designed with a planar focal surface. The lens is illuminated with a hexagonal array of circularly polarized elliptical patch antennas operating at 11.7 to 12.7 GHz and fed with a novel cascaded beamformer network to achieve a gain of 21.5 (± 0.5) dBi with a total efficiency > 75% and side lobe levels below 15 dB. A highly efficient and novel handover algorithm has been developed to achieve a fast beam-steer of ±55⁰ in both azimuth and elevation planes. The proposed handover procedure has been demonstrated within an anechoic chamber. This paper has applications in ensuring a smooth beam-steer while maintaining uninterrupted connectivity between fast-moving satellites and vehicles on the road using the proposed antenna.

The IRIDE constellation is an ambitious Italian space program that, comprising a series of small satellite subconstellations exploiting a wide range of remote sensing technologies, will support the national authorities in their analysis and monitoring activities, with a focus on the Italian territory mapping. This paper analyses the NIMBUS X-Band Synthetic Aperture Radar (SAR) IRIDE sub-constellation, exploring potential orbital configurations beyond the more conventional and widespread Dawn-Dusk Sun-Synchronous Orbit (SSO) one. In particular, starting from the mission target, we show that a 49° Mid Inclination Orbit (MIO) in a right-looking StripMap acquisition mode represents a highly effective choice for NIMBUS, as it enhances the systematic coverage of the Italian territory with 6 days of interferometric revisit time and high spatial resolution, thereby facilitating detailed observations of both natural phenomena and anthropic activities. In addition, in terms of Differential Interferometric SAR (DInSAR) performance, we prove that MIO, besides showing no significant limitations for what attains the critical baseline and geometric distortions, allows for the recovery of the North-South deformation component that, conversely, cannot be precisely measured with DInSAR systems operating in SSO. This may lead to future advances in creating 3D deformation maps when considering the synergy between the MIO NIMBUS IRIDE satellites and SSO configurations operating with similar resolutions and wavelengths, as for the COSMO-SkyMed constellation.

This study proposes a new approach to periodic multilayer mirrors (PMMs) characterizations based on measured X-ray reflectivity (XRR) data. Here, XRR data are used to reconstruct the internal structure of PMMs using grazing incidence X-ray reflectivity (GIXR). A mathematical model of electromagnetic wave reflection by PMMs is employed to implement forward prediction, which will then be used iteratively in a global optimization framework in order to reconstruct the PMM unknown structure parameters. A typical simulation of PMM often includes tens to thousands of unknown structure parameters, rendering standard curve fitting methods cumbersome and impractical. To make the PMM characterization method computationally feasible, this study combines implementation of the Levy flight particle swarm optimization (LFPSO) algorithm with a parallelized version of the electromagnetic solver X-Ray Calc in order to simplify the model parameter reconstruction process. Levy flight, a random walk wherein the Levy distribution is used to determine step size, is a more efficient search strategy for global optimization because of the long jumps made by the particles.  It is demonstrated that a PMM model with up to thousands of structure parameters can be reconstructed within several seconds on a regular workstation. The algorithm is tested with both measured and theoretical XRR data using in-house fabricated PMMs with known structures. Excellent agreement with the actual structures is observed, which is attained in short computation time. The new approach avoids manual curve fitting and simplified GIXR analysis and is observed to scale linearly with the size of the PMM structure, making it attractive for X-ray optics systems involving large-and-complex reflecting mirrors.

This paper presents a novel approach to video super-resolution (VSR) by focusing on the selection of input frames, a process critical to VSR. VSR methods typically rely on deep learning techniques, those that are able to learn features from a large dataset of low-resolution (LR) and corresponding high-resolution (HR) videos and generate high-quality HR frames from any new LR input frames using the learned features. However, these methods often use as input the immediate neighbouring frames to a given target frame without considering the importance and dynamics of the frames across the temporal dimension of a video. This work aims to address the limitations of the conventional sliding-window mechanisms by developing input frame selection algorithms. By dynamically selecting the most representative neighbouring frames based on content-aware selection measures, our proposed algorithms enable VSR models to extract more informative and accurate features that are better aligned with the target frame, leading to improved performance and higher-quality HR frames. Through an empirical study, we demonstrate that the proposed dynamic content-aware selection mechanism improves super-resolution results without any additional architectural overhead, offering a counter-intuitive yet effective alternative to the long-established trend of increasing architectural complexity to improve VSR results.

In this paper, we propose a neural network with an encoder-decoder architecture that incorporates atrous spatial pyramid pooling (ASPP) and convolutional block attention module (CBAM).  We evaluated our approach by using the ISIC 2018 dataset. The results demonstrate that the proposed framework significantly enhances segmentation performance.

With the advancement of technology, the development of telecommunication industries, the construction of new electrical devices, and the increasing use of electromagnetic field generators in modern industrial societies, the study of the biological effects of these waves on the growth and development of living organisms are of interest to many societies. The scientific world is placed. Today, one of the ecological problems is electromagnetic pollution in the environment. The use of mobile phones has increased in the world in recent years, which in turn adds to this pollution. Some bee species worldwide are becoming extinct, called the CCD phenomenon. Our goal in this article is to investigate the effect of electromagnetic waves and their relationship with the new phenomenon of CCD and to offer a proposal to guide bees to return to the hive by fluorescent blue light.

Optimal network reconfiguration helps with the reduction of total real power losses and the improvement of bus voltages. Several optimal network reconfiguration approaches are available in literature. However, as shown by a very recent study on the IEEE 33-bus radial distribution system, these approaches fail to attain the least total real power losses. Despite that, the recent study did not consider multiple test systems and load variations. Consequently, this article addresses those unexplored areas by introducing an optimal network reconfiguration approach that minimizes the total real power losses in the standard IEEE 33- and 69-bus radial distribution systems under nominal (100%) and heavy (160%) load conditions. The developed approach, which is based on Sparrow Search Algorithm, intelligently searches for the optimum tie and sectionalizing branches that have to be switched on and off, respectively, in order to minimize the systems’ total real power losses. The search for the optimum tie and sectionalizing branches is performed in the permutations of loops created by the radial distribution systems’ tie and sectionalizing branches. For the considered two test systems,  the developed approach gave the least total real power losses and the best bus voltage than the referenced approaches.

Voltage unbalance is a growing issue that, among other things, can impact three-phase motor and drive loads, result in nuisance tripping of generation units and capacitor banks, and prevent optimization of conservative voltage regulation strategies. This difference between the three phases of voltage delivered to customers can damage the equipment of these customers as well as negatively impact the power system itself. This work presents an approach for predicting voltage unbalance using machine learning. Historical megawatt and megavar data–obtained through a Supervisory Control And Data Acquisition (SCADA) system–are used to train an Artificial Neural Network model as a binary classifier with a portion of the data serving to validate the trained model. Voltage unbalance is predicted at an accuracy above 95% for eight substations within the power utility’s Extra-High Voltage transmission network and over 91% for all forty-two substations. The trained model is tested in a manner in which it would be used through the use of using simulated data generated by state estimation software. Using this simulated data validates the model’s capacity to predict the substation buses that would experience voltage unbalance.

As blockchain technology gains prominence in the financial sector, the architectural frameworks guiding its integration play a crucial role in determining operational efficacy. This research delves into the architectural juxtaposition of monolithic versus microservices approaches, specifically within the context of blockchain applications tailored for financial enterprises. By analyzing the distinct attributes of these architectures, the study sheds light on their practical advantages and challenges, especially concerning scalability, security, transactional integrity, and seamless integration with existing financial systems. Drawing from real-world blockchain implementations in banking, trading, and insurance, the research provides actionable insights into the architectural intricacies that dictate system robustness, adaptability, and long-term viability. The ultimate goal of this exploration is to furnish financial institutions with pragmatic guidance, facilitating architectural decisions that resonate with their immediate operational needs and strategic aspirations.

Multimode interference (MMI) coupler for multimode system is proposed based on Si waveguide platform. Each mode is separated and injected into the mNxmN MMI coupler with m being the number of modes and N is the multiplication number of input image.

Vertical cavity surface emitting lasers (VCSEL) are of great interest for photonic and telecom applications, however challenges in fabrication of efficient VSCEL GaN devices are yet to be resolved. In this work we present a study of micro-photoluminescence (PL) emission from a novel InGaN quantum well (QW) emitting structure with integrated micro-cavity, which can be used for VCSEL applications. The micro-cavity exhibiting Tamm plasmon optical states is formed by porous GaN distributed Bragg reflector (DBR) bottom mirror and top plasmonic silver metal mirror. Results of PL, Fourier imaging spectroscopy and finite-difference time-domain simulations are presented and discussed. An estimated 8.5 times enhancement of QW PL intensity at around 480-500 nm and a red-shift of QW peak emission is attributed to the Tamm plasmon resonance in the cavity at around 514 nm.

 Assisted and automated driving (AAD) systems heavily rely on data collected from perception sensors, such as cameras. While prior research has explored the quality of camera data via traditional and well-established image quality assessment (IQA) metrics (e.g. PSNR, SSIM, BRISQUE) or have considered when noisy/degraded data affects perception algorithms (e.g. deep neural network (DNN) based object detection), there are no works that approach the holistic relationship between IQA and DNN performance. This work proposes that traditional IQA metrics, designed to evaluate digital image quality according to human visual perception, can help to predict the sensor data degradation level that perception algorithms can tolerate before performance deterioration occurs. Consequently, a correlation analysis was conducted between 17 selected IQA metrics (with and without reference) and DNN average precision. The evaluated data was increasingly compressed to generate degradation and artefacts. Notably, the experimental results show that several IQA metrics had a strong positive correlation (exceeding correlation scores of 0.7) with average precision, with IW-SSIM and DSS having very high correlation (> 0.9). Interestingly, the results show that re-training BRISQUE on compressed data causes an exceptionally high positive correlation (> 0.97), making it very suitable for predicting the performance of DNN object detectors. By effectively relating traditional image quality metrics to DNN performance, this research offers a series of significant tools to understand and predict perception degradation based on the quality of data, thus resulting in a significant impact on the development of automated driving systems.Â

Modern distribution networks are undergoing several technical challenges, such as voltage fluctuations, because of high penetration of distributed energy resources (DERs). This paper proposes a deep reinforcement learning (DRL)-based Volt- VAR co-optimization technique for reducing voltage fluctuations as well as power loss under high penetration of DERs. In addition, the proposed approach minimizes the operational cost of the grid. A stochastic policy optimization based soft actor critic (SAC) agent is proposed to configure the optimal set-points of the reactive power outputs of the inverters. The performance of the proposed model is verified on the modified IEEE 34- and 123-bus systems and compared with a base case scenario with no reactive supply by inverters, and a local droop control approach. The results demonstrate that the proposed framework outperforms the conventional droop control method in improving the voltage profile, minimizing the network power loss, and reducing grid operational cost.

This paper explores the intersection of Artificial Intelligence (AI) and digital storytelling in marketing, focusing on how AI-driven techniques can enhance emotional attachment and influence consumer behavior. With the rapid advancement of AI, its integration into marketing strategies has become crucial, particularly for personalizing consumer experiences and enhancing brand narratives. This study investigates AI's role in creating emotionally engaging narratives, a largely unexplored area in marketing and advertising. The research is motivated by the need to understand the dynamics between AI-driven techniques and emotional attachment in digital marketing. The paper hypothesizes a significant relationship between consumers' emotional attachment to brands, influenced by AI-driven storytelling, and their purchasing behavior. A mixed-methods research approach is employed to test this hypothesis, combining a survey with detailed interviews. The study assesses how emotional attachment, influenced by AI and storytelling, impacts consumer purchasing decisions and brand loyalty in online shopping. It also evaluates the effectiveness of AI-driven storytelling techniques in digital marketing campaigns from the perspective of online consumers. Preliminary findings suggest that while emotional attachment significantly influences consumer purchasing behavior, other factors also play a crucial role. The study reveals that AI's role in marketing is valued, but the essence of storytelling should remain grounded in human experiences. The paper concludes that the future of digital marketing lies in a harmonious blend of AI and traditional storytelling, where AI's data-driven insights complement the authenticity and emotional resonance of narratives. This research contributes to the understanding of AI's potential in enhancing emotional attachment-driven branding and online selling performance, offering insights for future strategies in digital marketing.