Full Capability Research Articles

Equivalent-time-active-cavitation-imaging enables vascular-resolution blood-brain-barrier-opening-therapy planning

Objective. Linking cavitation and anatomy was found to be important for predictable outcomes in focused-ultrasound blood-brain-barrier-opening and requires high resolution cavitation mapping. However, cavitation mapping techniques for planning and monitoring of therapeutic procedures either (1) do not leverage the full resolution capabilities of ultrasound imaging or (2) place constraints on the length of the therapeutic pulse. This study aimed to develop a high-resolution technique that could resolve vascular anatomy in the cavitation map. Approach. Herein, we develop BandPass-sampled-equivalent-time-active-cavitation-imaging (BP-ETACI), derived from bandpass sampling and dual-frequency contrast imaging at 12.5 MHz to produce cavitation maps prior and during blood–brain barrier opening with long therapeutic bursts using a 1.5 MHz focused transducer in the brain of C57BL/6 mice. Main results. The BP-ETACI cavitation maps were found to correlate with the vascular anatomy in ultrasound localization microscopy vascular maps and in histological sections. Cavitation maps produced from non-blood-brain-barrier disrupting doses showed the same cavitation-bearing vasculature as maps produced over entire blood-brain-barrier opening procedures, allowing use for (1) monitoring focused-ultrasound blood-brain-barrier-opening (FUS-BBBO), but also for (2) therapy planning and target verification. Significance. BP-ETACI is versatile, created high resolution cavitation maps in the mouse brain and is easily translatable to existing FUS-BBBO experiments. As such, it provides a means to further study cavitation phenomena in FUS-BBBO.

Highly Sensitive Measurement of Horseradish Peroxidase Using Surface-Enhanced Raman Scattering of 2,3-Diaminophenazine.

The development of various enzyme-linked immunosorbent assays (ELISAs) coupled with surface-enhanced Raman scattering (SERS) detection is a growing area in analytical chemistry due to their potentially high sensitivity. A SERS-based ELISA with horseradish peroxidase (HRP) as an enzymatic label, an o-phenylenediamine (oPD) substrate, and a 2,3-diaminophenazine (DAP) enzymatic product was one of the first examples of such a system. However, the full capabilities of this long-known approach have yet to be revealed. The current study addresses a previously unrecognized problem of SERS detection stage performance. Using silver nanoparticles and model mixtures of oPD and DAP, the effects of the pH, the concentration of the aggregating agent, and the particle surface chloride stabilizer were extensively evaluated. At the optimal mildly acidic pH of 3, a 0.93 to 1 M citrate buffer, and AgNPs stabilized with 20 mM chloride, a two orders of magnitude advantage in the limits of detection (LODs) for SERS compared to colorimetry was demonstrated for both DAP and HRP. The resulting LOD for HRP of 0.067 pmol/L (1.3 amol per assay) underscores that the developed approach is a highly sensitive technique. We suppose that this improved detection system could become a useful tool for the development of SERS-based ELISA protocols.

BIKAGCN: Knowledge-Aware Recommendations Under Bi-layer Graph Convolutional Networks

Recommender systems are a popular solution for the problem of information overload, offering personalized recommendations to users. Recent years, research has aimed to enhance recommender systems by employing knowledge graphs in conjunction with Graph convolutional network (GCN) to extract user and item features. Although GCN possess a great potential, they are still far from reaching their full capability in recommender systems. This paper introduces a novel approach—knowledge-aware recommendations under bi-layer graph convolutional networks (BIKAGCN) that combines attention and bi-layer GCNs to improve performance. The first layer of the BIKAGCN model trains embedding representations of users and items based on user-item interaction graphs. The second layer introduces a novel knowledge-aware layer of attention and graph convolutional network (KAGCN) layer that leverages both the first layer’s user-item embeddings and item knowledge graph embeddings. Experimental results on three publicly available datasets (MovieLens-20M, Last-FM, and Book-Crossing) demonstrate that BIKAGCN leads to significant performance improvements in recall@20 metric (14.41%, 8.86%, and 20.90%, respectively) compared to currently available state-of-the-art approaches. Moreover, the model maintains satisfactory performance in cold-start cases.The research provides some guidance for the direction of subsequent research on recommender systems.

Enhancing Heart Disease Prediction using Advanced Feature Engineering and Ensemble Learning Techniques

This study introduces a holistic model for predicting heart disease ,integrating advanced algorithms with a focus on feature engineering. The dataset encompasses a diverse range of patient parameters, including demographics, lifestyle factors, and medical history. Feature engineering involves a meticulous process of selecting, transforming, and augmenting relevant features to enhance the model's ability to discern patterns and relationships within the data. This stage is essential for increasing the predicted accuracy of the model and gleaning insightful information from intricate datasets. The logistic regression algorithm is employed to establish a baseline predictive model, providing insights into the individual contribution of each feature. Subsequently, a neural network is implemented to capture intricate non-linear dependencies and interactions within the data, further refining the predictive capabilities. Results indicate that the incorporation of feature engineering significantly improves the model's performance compared to traditional approaches. Early experiments demonstrate promising accuracy rates in heart disease prediction, showcasing the potential for early detection and proactive healthcare interventions. This method not only enhances predictive modeling for heart disease but also emphasizes the significance of feature engineering in maximizing the full capabilities of machine learning algorithms for medical applications.

Parents' Perceptions of Eye-Gaze Technology Use by Children With Complex Communication Needs.

Some preschool students with complex communication needs explore eye-gaze computer technology (EGCT) and adopt computer-based augmentative and alternative communication (AAC). The objective of this study was to follow preschool explorers of EGCT who are now school aged to describe the children's use of technology and parents' perceptions of its utility for communication, participation, or leisure. Ten parents completed survey questions by Internet and phone and reported their perceptions of nine children's effectiveness in the use and acceptance of AAC and the support they received in implementing technology. The results are reported as a descriptive study. All children in this research continue to use AAC technology in school and most at home. Many children who tried and obtained EGCT while in preschool continue to use that technology. Most parents agreed that the children understood how to use the devices, which enhanced the children's communication, and that the parents received sufficient support. Most children were limited in their use of the devices for leisure and control of their environments. Computer-based AAC for school-aged children who trialed it when they were in preschool appears to be a powerful means for them to communicate and participate. However, the technology appears not to be used to its full capabilities to support the children's agency to control environments and to pursue leisure. Teams may want to consider how to support children in using their AAC devices to meet multiple needs. The study was limited by its small sample size and its descriptive nature. Additional research on this subject is needed.

Ordered magnetic fields around the 3C 84 central black hole

Context. 3C 84 is a nearby radio source with a complex total intensity structure, showing linear polarisation and spectral patterns. A detailed investigation of the central engine region necessitates the use of very-long-baseline interferometry (VLBI) above the hitherto available maximum frequency of 86 GHz. Aims. Using ultrahigh resolution VLBI observations at the currently highest available frequency of 228 GHz, we aim to perform a direct detection of compact structures and understand the physical conditions in the compact region of 3C 84. Methods. We used Event Horizon Telescope (EHT) 228 GHz observations and, given the limited (u, v)-coverage, applied geometric model fitting to the data. Furthermore, we employed quasi-simultaneously observed, ancillary multi-frequency VLBI data for the source in order to carry out a comprehensive analysis of the core structure. Results. We report the detection of a highly ordered, strong magnetic field around the central, supermassive black hole of 3C 84. The brightness temperature analysis suggests that the system is in equipartition. We also determined a turnover frequency of νm = (113 ± 4) GHz, a corresponding synchrotron self-absorbed magnetic field of BSSA = (2.9 ± 1.6) G, and an equipartition magnetic field of Beq = (5.2 ± 0.6) G. Three components are resolved with the highest fractional polarisation detected for this object (mnet = (17.0 ± 3.9)%). The positions of the components are compatible with those seen in low-frequency VLBI observations since 2017–2018. We report a steeply negative slope of the spectrum at 228 GHz. We used these findings to test existing models of jet formation, propagation, and Faraday rotation in 3C 84. Conclusions. The findings of our investigation into different flow geometries and black hole spins support an advection-dominated accretion flow in a magnetically arrested state around a rapidly rotating supermassive black hole as a model of the jet-launching system in the core of 3C 84. However, systematic uncertainties due to the limited (u, v)-coverage, however, cannot be ignored. Our upcoming work using new EHT data, which offer full imaging capabilities, will shed more light on the compact region of 3C 84.

Blockchain: The Economic and Financial Institution for Autonomous AI?

This paper examines how the combination of artificial intelligence (AI) and blockchain technology can enable autonomous AI agents to engage and execute economic and financial transactions. We critically examine the constraints on AI agents in achieving predefined objectives independently, especially due to their limited access to economic and financial institutions. We argue that AI’s access to these institutions is vital in enhancing its capabilities to augment human productivity. Drawing on the theory of institutional economics, we propose that blockchain provides a solution for creating digital economic and financial institutions, permitting AI to engage with these institutions through the management of private keys. This extends AI’s capabilities to form and execute contracts, participate in marketplaces, and utilize financial services autonomously. The paper encourages further research on AI as a general-purpose technology and blockchain as an institutional technology that can unlock the full capabilities of autonomous AI agents.

Treatment of along defect fracture of the right lower leg using medullary nailing, cage interposition and autologous spongiosa graft (MaCIS) in aUkrainian soldier

This article presents the MaCIS method for treatment of long defect fractures of long bones using the example of aUkrainian soldier. This method includes locking medullary nailing of the affected long bone by an individually tailored cage interposed through the defect and filling of the cage with autologous spongiosa. In comparison to bone segment transport this method results in immediate full load-bearing capability of the affected extremity with substantially higher patient comfort.

Research progress of oxygen redox in sodium‐layered oxides

AbstractSodium‐ion batteries are emerging as promising alternative energy sources compared to lithium‐ion batteries, due to the abundant sodium resources in Earth's crust and their low cost. Nevertheless, the larger ionic radius of sodium ions leads to minor energy density in sodium‐layered oxide cathodes. To address this, anionic redox has attracted significant attention as it provides additional capacity beyond cationic redox. In this comprehensive review, the history and fundamental mechanisms of anionic redox are systematically summarized, and the recent advancements in sodium‐layered oxides with anionic redox is categorized and discussed according to deficient sodium‐layered oxides, stoichiometric sodium‐layered oxides, and sodium‐rich layered oxides. Finally, several prospects and challenges for anionic redox‐layered oxide cathodes have also been proposed. This review sheds light on the potential trajectory of sodium‐ion battery technology and highlights the pathways to harness the full capabilities of anionic redox for energy storage applications.

Residual wave vision U-Net for flood mapping using dual polarization Sentinel-1 SAR imagery

The increasing severity, duration, and frequency of destructive floods can be attributed to shifts in climate, infrastructure, land use, and population demographics. Obtaining precise and timely data about the extent of floodwaters is crucial for effective emergency preparedness and mitigation efforts. Deep convolutional neural networks (CNNs) have shown astonishing effectiveness in various remote sensing applications, including flood mapping. One of the key limitations of CNNs is that they can only predict whether a desired feature will appear in an image, not where it can be recognized. To address this limitation, the incorporation of self-attention mechanisms deployed in vision transformers (ViTs) can be particularly effective. However, the self-attention modules in the ViTs are complex and computationally expensive, and they require a wealth of ground data to attain their full capability in image classification/segmentation. Thus, in this paper, we develop the Residual Wave Vision U-Net (WVResU-Net), a deep learning segmentation architecture that utilizes advanced Vision Multi-Layer Perceptrons (MLPs) and ResU-Net for accurate and reliable flood mapping using Sentinel-1 SAR’s dual polarization data. Results showed the significant superiority of the developed WVResU-Net algorithms over several well-known CNN and ViT deep learning models, including Swin U-Net, U-Net+++, Attention U-Net, R2U-Net, ResU-Net, TransU-Net and TransU-Net++. For example, the segmentation accuracy of TransU-Net++, SwinU-Net, ResU-Net, R2U-Net, Attention U-Net, TransU-Net, and U-Net+++, was significantly improved by approximately 5, 12, 13, 13, 16, 19, and 23 percentage points, respectively in terms of recall obtained by the WVResU-Net with a recall value of about 69.67%. The code will be made publicly available at https://github.com/aj1365/RWVUNet.

A Reinforcement Learning controller optimizing costs and battery State of Health in smart grids

Smart Grids are the evolution of traditional electric grids and allow two-way flows of electricity and information between different actors. At the edge of this network, customers can both produce and consume energy. Due to the intermittent nature of renewable energy sources, customers are characterized by moments of energy surplus and deficit. To solve this problem, customers are connected to the power grid, and, usually, they are also provided with Lithium-Ion battery packs positioned near the energy source used to store energy in excess for later use, reducing expensive energy exchanges with the grid. On the one hand, using the battery at its full capabilities produces significant economic savings. On the other hand, massive use of the battery leads to degradation and consequently to a more frequent substitution of the battery. Therefore, depending on the cost of energy and batteries, one should carefully choose when it is favorable to use it. To avoid inefficiencies, it is common to design controllers that regulate the energy flow within the battery packs, deciding whether to exchange energy with the network or store it in the battery. In this work, a Reinforcement Learning controller optimizing energy flow is developed. The controller’s goal is to balance the costs of exchanges with the power grid and those derived from the degradation due to battery usage. A synthetic experimental campaign conducted using real-world data demonstrates that the policy learned shows an improvement in the worst-case of 3% w.r.t. state-of-the-art baselines.

Path planning algorithms in the autonomous driving system: A comprehensive review

This comprehensive review focuses on the Autonomous Driving System (ADS), which aims to reduce human errors that are the reason for about 95% of car accidents. The ADS consists of six stages: sensors, perception, localization, assessment, path planning, and control. We explain the main state-of-the-art techniques used in each stage, analyzing 275 papers, with 162 specifically on path planning due to its complexity, NP-hard optimization nature, and pivotal role in ADS. This paper categorizes path planning techniques into three primary groups: traditional (graph-based, sampling-based, gradient-based, optimization-based, interpolation curve algorithms), machine and deep learning, and meta-heuristic optimization, detailing their advantages and drawbacks. Findings show that meta-heuristic optimization methods, representing 23% of our study, are preferred for being general problem solvers capable of handling complex problems. In addition, they have faster convergence and reduced risk of local minima. Machine and deep learning techniques, accounting for 25%, are favored for their learning capabilities and fast responses to known scenarios. The trend towards hybrid algorithms (27%) combines various methods, merging each algorithm’s benefits and overcoming the other’s drawbacks. Moreover, adaptive parameter tuning is crucial to enhance efficiency, applicability, and balancing the search capability. This review sheds light on the future of path planning in autonomous driving systems, helping to tackle current challenges and unlock the full capabilities of autonomous vehicles.

Absolute Calibration of a UAV-Mounted Ultra-Wideband Software-Defined Radar Using an External Target in the Near-Field

We describe a method to calibrate a Software-Defined Radar (SDRadar) system mounted on an uncrewed aerial vehicle (UAV) with an ultra-wideband (UWB) waveform operated in the near-field region. Radar calibration is a prerequisite for using the full capabilities of the radar system to retrieve geophysical parameters accurately. We introduce a framework and process to calibrate the SDRadar with the UWB waveform in the 675 MHz–3 GHz range in the near-field region. Furthermore, we present the framework for computing the near-field radar cross section (RCS) of an external passive calibration target, a trihedral corner reflector (CR), using HFSS software and with consideration for specific antennas. The calibration performance was evaluated with various distances between the calibration target and radar antennas. The necessity for the knowledge of the near-field RCS to calibrate SDRadar was demonstrated, which sets this work apart from the standard method of using a trihedral CR for backscatter radar calibration. We were able to achieve approximately 0.5 dB accuracy when calibrating the SDRadar in the anechoic chamber using a trihedral CR. In outdoor field conditions, where the ground rough surface scattering effects are present, the calibration performance was lower, approximately 1.5 dB. A solution is proposed to overcome the ground effect by elevating the CR above the ground level, which enables applying time-gating around the CR echo, excluding the reflection from the ground.

Nanoimprint lithography guiding templates for advanced magnetic media fabrication

Nanoimprint lithography presents unique opportunities for advanced magnetic storage media with ordered bit arrangements such as bit patterned media or heated dot magnetic recording. Providing sub-10 nm resolution and full disk imprinting capability, UV-nanoimprint lithography based on rigid quartz templates bears the entitlement for patterned recording media manufacturing with high throughput at low cost. However, a key challenge is the fabrication of the high-resolution template that can transfer the desired pattern onto the disk with high fidelity and low line edge roughness. In this article, we present fabrication routes and overcome challenges to the fabrication of quartz templates suitable for self-alignment and guiding purposes to be used for template replication toward full disk imprints. Guiding patterns down to 40 nm pitch are prepared using a rotary electron beam lithography tool. We compare three different process approaches to fabricate an etching mask for patterning the quartz. Two methods target chromium patterning, one with traditional lift-off and another by dry etching, both using an e-beam resist mask. The third approach is based on the development of a carbon-based Tri-layer hard mask. The template pattern profile is optimized for imprint-suitable sidewall angles using dry etching in a CF4/O2 chemistry. The templates were characterized using scanning electron microscopy and atomic force microscopy to evaluate the quality of the transferred pattern as well as line edge roughness. Our results show that the Tri-layer process using carbon resulted in the lowest line edge roughness of ≈0.65 nm at the imprinted disk level. In addition, we show that Tri-layer masking allowed for the use of conventional ZEP e-beam resist and fast writing speeds, while gaining high selectivity during quartz patterning.

The BINGO Project

Context. The Baryon Acoustic Oscillations (BAO) from Integrated Neutral Gas Observations (BINGO) radio telescope will use the neutral hydrogen emission line to map the Universe in the redshift range 0.127 ≤ z ≤ 0.449, with the main goal of probing BAO. In addition, the instrument’s optical design and hardware configuration support the search for fast radio bursts (FRBs). Aims. In this work, we propose the use of a BINGO Interferometry System (BIS) including new auxiliary, smaller radio telescopes (hereafter outriggers). The interferometric approach makes it possible to pinpoint the FRB sources in the sky. We present the results of several BIS configurations combining BINGO horns with and without mirrors (4 m, 5 m, and 6 m) and five, seven, nine, or ten for single horns. Methods. We developed a new Python package, the <mono>FRBlip</mono>, which generates mock catalogs of synthetic FRB and computes, based on a telescope model, the observed signal-to-noise ratio, which we use to numerically compute the detection rates of the telescopes and how many interferometry pairs of telescopes (baselines) can observe an FRB. The FRBs observed by more than one baseline are the ones whose location can be determined. We thus evaluated the performance of BIS regarding FRB localization. Results. We found that BIS would be able to localize 23 FRBs yearly with single horn outriggers in the best configuration (using ten outriggers of 6-m mirrors), with redshift z ≤ 0.96. The full localization capability depends on the number and type of the outriggers. Wider beams are best for pinpointing FRB sources because potential candidates will be observed by more baselines, while narrow beams search deep in redshift. Conclusions. The BIS can be a powerful extension of the BINGO telescope, dedicated to observe hundreds of FRBs during Phase 1. Many of FRBs will be well localized with a single horn and a 6-m dish as outriggers.

0.5-V 281-nW Versatile Mixed-Mode Filter Using Multiple-Input/Output Differential Difference Transconductance Amplifiers.

This paper presents a new low-voltage versatile mixed-mode filter which uses a multiple-input/output differential difference transconductance amplifier (MIMO-DDTA). The multiple-input of the DDTA is realized using a multiple-input bulk-driven MOS transistor (MI-BD-MOST) technique to maintain a single differential pair, thereby achieving simple structure with minimal power consumption. In a single topology, the proposed filter can provide five standard filtering functions (low-pass, high-pass, band-pass, band-stop, and all-pass) in four modes: voltage (VM), current (CM), transadmittance (TAM), and transimpedance (TIM). This provides the full capability of a mixed-mode filter (i.e., twenty filter functions). Moreover, the VM filter offers high-input and low-output impedances and the CM filter offers high-output impedance; therefore, no buffer circuit is needed. The natural frequency of all filtering functions can be electronically controlled by a setting current. The voltage supply is 0.5 V and for a 4 nA setting current, the power consumption of the filter was 281 nW. The filter is suitable for low-frequency biomedical and sensor applications that require extremely low supply voltages and nano-watt power consumption. For the VM low-pass filter, the dynamic range was 58.23 dB @ 1% total harmonic distortion. The proposed filter was designed and simulated in the Cadence Virtuoso System Design Platform using the 0.18 µm TSMC CMOS technology.

Process controlled microstructure of polyvinylidene fluoride/functionalized single walled carbon nanotube composites

AbstractPolyvinylidene fluoride (PVDF)‐functionalized single walled carbon nanotube (FSWCNT) film composites were prepared by two different process conditions—air drying and vacuum drying. Though reports on –PVDF‐FSWCNT composites are available, we provide a new outlook here, which is based on optimized functionalization and novel processing conditions. The microstructural and phase changes occurring in PVDF having nanotubes functionalized for different extent of time were investigated and confirmed that those functionalized for 4 h (FSWCNAT‐4) create polar γ phase in PVDF with improved mechanical and electrical properties. X‐ray photoelectron spectroscopy studies showed that PVDF/FSWCNT‐4 composites have the maximum percentage of functional groups. It is also found that PVDF/FSWCNT‐4 composite films prepared through air drying were rough and had a cloudy appearance suitable for applications demanding open porous structures, whereas those prepared through vacuum drying process were smooth and homogeneous with high optical transparency, suitable for microelectronic applications. Thus, by suitably controlling the processing conditions and by using optimally functionalized nanotubes, PVDF films with multifunctional properties could be achieved. In order to extract the full capabilities of PVDF/SWCNT films, functionalization of the nanotube together with processing conditions plays a significant role.

Anti-frosting characteristics of superhydrophobic-hydrophilic wettability switchable surfaces

Combining hydrophobicity with hydrophilicity on hybrid surfaces generates unique phenomena in anti-frosting. However, limited attention has been paid to investigate the anti-frosting effects of wettability switchable surfaces in response to temperature changes. Herein, frosting and defrosting characteristics of thermo-responsive, wettability switchable surfaces are investigated. Specifically, the wettability switchable surfaces are successfully fabricated by coating a poly(ε-caprolactone) (PCL) layer onto the microstructured aluminum substrates, resulting in the wettability transition from superhydrophobicity with an apparent contact angle of water droplet as 152° in the surface temperatures under 60 °C to hydrophilicity with an apparent contact angle of water droplet as 63° in the surface temperatures over 60 °C. The wettability transition capabilities of the surfaces not only enable excellent frost retardation comparable to superhydrophobic surfaces in frosting process, but also realize fast full drying capabilities slightly shorter than superhydrophilic surfaces in defrosting process. The unique feature of wettability switchable surfaces represents an effort to combine the distinct advantages of superhydrophobic and superhydrophilic surfaces to realize anti-frosting functionalities.

Sustainable Harnessing of SiO2 Nanoparticles from Rice Husks: A Review of the Best Synthesis and Applications

The extraction of silica particles from rice husks has been extensively studied. This review aims to present the most efficient approach to harnessing rice husk biomass and converting silica into high-value-added materials for direct applications to address current challenges like water purification. Rice husks, as a residue from agriculture, had been largely used as a source of power through direct incineration in major rice-producing countries. However, rice husks present an intriguing opportunity as a renewable source of SiO2, offering a low-cost adsorbent with a high surface area and ease of functionalization that can be transformed into diverse mesoporous silica structures or composites, enabling applications in catalysis, drug delivery, water treatment, etc. This dual potential of rice husks can be harnessed by combining bio-oil and syngas production through pyrolysis with the efficient extraction of SiO2, ensuring the comprehensive utilization of the biomass. This review not only highlights the immense potential of silica nanoparticles but also serves as a roadmap for future investigations, with the ultimate aim of harnessing the full capabilities of this renewable and sustainable resource, contributing to the circular economy by yielding valuable by-products.

Series Arc Fault Identification in DC Distribution Based on Random Forest Predicted Probability

Series dc arc fault generates arcing noise cross-talk to adjacent electrical loads, making it challenging to identify the arc fault location, which creates a fire hazard that endangers the system’s safety. This study proposes a series arc fault identification method in a dc zonal electrical distribution using Random Forest based local detectors to monitor constant power loads, and output predicted nominal and arc fault probabilities. The predicted probabilities are then sent to a centralized master detector to obtain the final decision. With full communication capability among all local detectors, the master detector makes the final decision using Random Forest algorithms. If there is any disconnection in the communication links, the local detector can operate independently by using the predicted arc fault probability to output the flag signal autonomously, while the master detector continues to operate with other local detectors. The proposed fault identification method is experimentally verified with a zonal electrical distribution testbed that comprises three constant power loads.