High Performance Research Articles

Polymer Based on Asymmetrically Halogenated Benzotriazole Enables High Performance Organic Solar Cells Prepared in Nonhalogenated Solvent.

Halogenation on the A unit of the D-π-A-type polymer donor has been proven as an effective strategy to improve the performance of organic solar cells (OSCs). Compared with fluorination, chlorination usually increases the open-circuit voltage because of the downward shift of energy levels, but decreases the charge transport ability due to the large steric hindrance of the chlorine atom. We reported herein a method to balance the energy loss and charge transport through asymmetric halogenation on the benzotriazole (BTA) unit of the polymer. The designed PE3-FCl based on the BTA unit containing fluorine and chlorine atoms rendered the highest power conversion efficiency (PCE) of 17.83% when eC9-2Cl-γ and o-xylene were used as the electron acceptor and solvent, respectively. The performance is obviously higher than that of the polymer PE3 containing a difluorinated BTA unit (16.65%) and polymer PE3-2Cl with dichlorinated BTA (14.65%) due to the manipulated morphology by preaggregation, improved and more balanced charge carrier transport, and reduced recombination loss. Notably, this PCE is a breakthrough for the BTA-based polymers processed by nonhalogenated solvent. This work gives deep insight into the asymmetric halogenation of polymer donors for high-performance green solvent-processed OSCs.

A deep learning model for differentiating paediatric intracranial germ cell tumour subtypes and predicting survival with MRI: a multicentre prospective study

BackgroundThe pretherapeutic differentiation of subtypes of primary intracranial germ cell tumours (iGCTs), including germinomas (GEs) and nongerminomatous germ cell tumours (NGGCTs), is essential for clinical practice because of distinct treatment strategies and prognostic profiles of these diseases. This study aimed to develop a deep learning model, iGNet, to assist in the differentiation and prognostication of iGCT subtypes by employing pretherapeutic MR T2-weighted imaging.MethodsThe iGNet model, which is based on the nnUNet architecture, was developed using a retrospective dataset of 280 pathologically confirmed iGCT patients. The training dataset included 83 GEs and 117 NGGCTs, while the retrospective internal test dataset included 31 GEs and 49 NGGCTs. The model’s diagnostic performance was then assessed with the area under the receiver operating characteristic curve (AUC) in a prospective internal dataset (n = 22) and two external datasets (n = 22 and 20). Next, we compared the diagnostic performance of six neuroradiologists with or without the assistance of iGNet. Finally, the predictive ability of the output of iGNet for progression-free and overall survival was assessed and compared to that of the pathological diagnosis.ResultsiGNet achieved high diagnostic performance, with AUCs between 0.869 and 0.950 across the four test datasets. With the assistance of iGNet, the six neuroradiologists’ diagnostic AUCs (averages of the four test datasets) increased by 9.22% to 17.90%. There was no significant difference between the output of iGNet and the results of pathological diagnosis in predicting progression-free and overall survival (P = .889).ConclusionsBy leveraging pretherapeutic MR imaging data, iGNet accurately differentiates iGCT subtypes, facilitating prognostic evaluation and increasing the potential for tailored treatment.Graphical

In-situ bioprocessing of bacterial cellulose aerogel with covalent organic frameworks for enhanced uranium extraction

Developing efficient and cost-effective uranium adsorbents remain a significant challenge due to the complex composition of seawater. Herein, this study for the first time utilized an in-situ spray gel-assisted biosynthesis strategy to homogeneously incorporate the covalent organic frameworks (COF) into the three-dimensional pores of bacterial cellulose (BC) hydrogel during the culture of K. sucrofermentans. The BCCOF-SO3NH4 aerogel was fabricated through ammoniating modification of BC/COF (BCCOF-SO3H) hydrogel and freeze-drying processes. The BCCOF-SO3NH4 aerogels are characterized by the in-situ entanglement of COF within the three-dimensional network of BC, resulting in enhanced mechanical strength and groups interactions. The experimental maximum adsorption capacity of BCCOF-SO3NH4 aerogel reached 883.44 mg g−1, demonstrating exceptional uranium adsorption capacity compared with most of the reported adsorbents. The adsorption process follows the pseudo-second-order model and Langmuir isotherm. Throughout seven adsorption–desorption cycles, BCCOF-SO3NH4 maintained a stable adsorption capacity and desorption efficiency. The adsorption mechanism is verified by experiments and DFT calculations, which involves not only the ion exchange between uranyl and NH4+, but also the strong coordination interaction between the active groups (sulfonic acid group, hydroxyl group) and uranium. The in-situ biosynthesis strategy of composites shows a great application prospect in the preparation of high adsorption performance materials.

High efficiency in blue TADF OLED using favorable horizontal oriented host

An appropriate host featuring a wide band gap (Eg) and high triplet energy (T1) is crucial for facilitating highly efficient blue-light emitting devices. In this study, 4Ac26CzBz, a new host comprising acridan and carbazole moieties linked to a benzimidazole core, has been synthesized and characterized. Notedly, 4Ac26CzBz exhibits a wide optical gap (Eg) and high triplet energy (T1) of 3.3 and 3.0 eV, respectively, and has been proven a suitable host for blue thermally activated delayed fluorescence (TADF) OLED. By adopting 4TCzBN as a blue-light dopant, a remarkably high device external quantum efficiency of 35.8 % (59.8 cd/A and 62.8 lm/W) and a low turn-on voltage (<3 eV) have been demonstrated, with significant suppression of the efficiency roll-off, maintaining a high efficiency of 29.7 % as luminance at 1000 cd/m2. This excellent performance is deduced from the host’s ambipolar carrier transporting properties, which refer to its ability to transport both electrons and holes effectively, high photoluminescence quantum yield (PLQY) of 98.6 %, and highly horizontal orientation of transition dipole, as determined through diverse analytical measurements. The good material properties of 4Ac26CzBz and the high OLED performance reveal its potential for the next generation’s advanced display and lighting applications.

Growth Conditions and Interfacial Misfit Array in SnTe (111) Films Grown on InP (111)A Substrates by Molecular Beam Epitaxy.

Tin telluride (SnTe) is an IV-VI semiconductor with a topological crystalline insulator band structure, high thermoelectric performance, and in-plane ferroelectricity. Despite its many applications, there has been little work focused on understanding the growth mechanisms of SnTe thin films. In this manuscript, we investigate the molecular beam epitaxy synthesis of SnTe (111) thin films on InP (111)A substrates. We explore the effect of substrate temperature, Te/Sn flux ratio, and growth rate on the film quality. Using a substrate temperature of 340 °C, a Te/Sn flux ratio of 3, and a growth rate of 0.48 Å/s, fully coalesced and single crystalline SnTe (111) epitaxial layers with X-ray rocking curve full-width-at-half-maxima of 0.09° and root-mean-square surface roughness as low as 0.2 nm have been obtained. Despite the 7.5% lattice mismatch between the SnTe (111) film and the InP (111)A substrate, reciprocal space mapping indicates that the 15 nm SnTe layer is fully relaxed. We show that a periodic interfacial misfit (IMF) dislocation array forms at the SnTe/InP heterointerface, where each IMF dislocation is separated by 14 InP lattice sites/13 SnTe lattice sites, providing rapid strain relaxation and yielding the high quality SnTe layer. This is the first report of an IMF array forming in a rock-salt on zinc-blende material system and at an IV-VI on III-V heterointerface, and highlights the potential for SnTe as a buffer layer for epitaxial telluride film growth. This work represents an important milestone in enabling the heterointegration between IV-VI and III-V semiconductors to create multifunctional devices.

Insights into the dynamics of supported Au nanoparticles in the wet environments from first-principles and ReaxFF MD simulations

The interactions between metal and oxide supports have been widely studied, where the size effect of metal nanoparticles (NPs) and the gas environment play critical roles in catalytic performance. We employed density functional theory and ReaxFF reactive force field to investigate the size dependent properties of gold NPs supported on cerium dioxide, such as structural dynamics, adsorption, and electronic properties under wet environments. Distinct dynamic behaviors were observed in wet environments compared to vacuum conditions. In the wet environments, the hydroxide (OH) species preferentially adsorbs on the low coordination sites, such as edge sites, of NPs. This selective absorption induces a reconstruction effect on small NPs. Moderate-sized NPs exhibit significant surface reconstruction due to abundant OH adsorption at interface sites. However, this effect is negligible in larger NPs. A high interfacial population of OH significantly contributes the high catalytic performance of moderate-sized NPs. Moreover, OH adsorption modulates the charge distribution within the NPs, leading to their stabilization through OH-induced charge transfer. These findings provide insights into understanding and optimizing reactions in wet environments, offering a foundation for the development of more effective catalyst.

Diversity pattern of Symplocos tree species in China under climate change scenarios: Toward conservation planning

Symplocos, a genus rich in endemic and keystone species in China's subtropical forests, plays a vital role in maintaining biodiversity. Climate change impacts on Symplocos trees affect ecosystem composition, dynamics, and functions. This study delves into the potential ramifications of climate change on the distribution and richness of Symplocos species across China under different climatic conditions. A comprehensive analysis was conducted using occurrence records of 29 species of Symplocos, focusing on habitat suitability modeling and spatial analysis, to demonstrate the possible effects of climate change on the distribution and diversity patterns of Symplocos species in China. Environmental data, including bioclimatic variables, were collected, and the future climate scenarios were projected using the combined average of the two global climate models. Four modeling algorithms were applied in an ensemble species distribution models to assess the distribution of Symplocos species and decipher the pattern of species richness and endemism. The results indicate high accuracy and performance of the models with a mean AUC of 0.94 and TSS of 0.77, particularly the random forest. The analysis identified key bioclimatic variables influencing Symplocos species distribution, with precipitation patterns (precipitation of driest month and precipitation of warmest quarter) and temperature variability (temperature mean diurnal range) playing significant roles. Predictions suggest a reduction in Symplocos species richness over the next six decades, particularly in southern and southwestern China, with potential gains in other regions. Overall, this study highlights the vulnerability of Symplocos species to climate change and underscores the importance of proactive conservation efforts and forest plantations to mitigate future loss in Symplocos species diversity.

Constructing multiple capture domains in molecularly imprinted nanocomposite membranes by flower-like covalent organic framework for separation of acteoside

The selective separation of bioactive components by molecularly imprinted nanocomposite membranes (MINMs) has attracted widespread attention, but the traditional MINMs often were confronted with low rebinding capacity. Herein, the flower-like covalent organic framework (FL-COF) based-molecularly imprinted nanocomposite membranes (FMINMs) with multiple capture domains were developed for separation of acteoside (ACT). The multiple capture domains of FMINMs were constructed by the following three different domains: Firstly, the imprinted layer of FMINMs constructed the imprinted capture domain for specific capture of ACT, improving separation performance of FMINMs for ACT. Secondly, the amino groups on the surface of petals of FL-COF formed affinity capture domain to enrich ACT during the adsorption process, enhancing rebinding capacity and permselectivity of FMINMs for ACT. Thirdly, the hollow structure of FL-COF provided a large spatial capture domain for accommodating ACT, thus enhancing the rebinding capacity of FMINMs. Consequently, the FMINMs with multiple capture domains exhibited high separation performance with rebinding capacity (141.47 mg/g) and permselectivity (βECH/ACT=8.25) of ACT. It demonstrated that designed MINMs with multiple capture domains has great potential for efficient separation of bioactive components.

Comprehensive evaluation of interfacial interactions optimization for CF reinforced high-performance thermoplastic composites by electrochemical deposition of conjugated polymers

The mechanical properties of carbon fiber (CF) reinforced high-performance thermoplastic composites are significantly compromised due to inadequate interface bonding between CF and matrix. In this study, electrochemical polymerization techniques were employed to modify CFs with polypyrrole (PPy), poly-aniline (PANI), and poly-o-phenylenediamine (PoPd), to systematically investigate the impact of conjugated polymers on the interfacial properties of CF/copoly (phthalazinone ether sulfone ketone)s (PPESK) composites through a combination of experimental and molecular dynamics (MD) approaches. The findings revealed distinct differences among these conjugated polymers in terms of their effects on both IFSS and ILSS for CF/PPESK composites. Among all tested laminates, PoPd@CF-60s/PPESK exhibited the highest IFSS, ILSS and flexural strength values of 39.8 MPa, 75.6 MPa, and 1494 MPa, respectively, 94.1 %, 20.6 %, and 47.6 % higher than those of CF-desized/PPESK, without compromising CFs strength and thermal resistance of CF/PPESK composites. MD simulations combined with fracture morphology analysis confirmed that compatibility between conjugated polymers and matrix played a pivotal role in enhancing interface properties for CF/PPESK composites. Furthermore, the AFM outcomes demonstrated that the PoPd layer could serve as the modulus transition layer between the CF phase and the PPESK phase, which effectively enhancing the load transfer efficiency between the two phases under external forces. To summarize, this work presents a straightforward yet non-destructive approach that effectively enhances the interface strength within advanced CF reinforced high performance thermoplastic polymer composites (CFRHPTPs).

Transferable and data efficient metamodeling of storm water system nodal depths using auto-regressive graph neural networks

Storm water systems (SWSs) are essential infrastructure providing multiple services including environmental protection and flood prevention. Typically, utility companies rely on computer simulators to properly design, operate, and manage SWSs. However, multiple applications in SWSs are highly time-consuming. Researchers have resorted to cheaper-to-run models, i.e. metamodels, as alternatives of computationally expensive models. With the recent surge in artificial intelligence applications, machine learning has become a key approach for metamodelling urban water networks. Specifically, deep learning methods, such as feed-forward neural networks, have gained importance in this context. However, these methods require generating a sufficiently large database of examples and training their internal parameters. Both processes defeat the purpose of using a metamodel, i.e., saving time. To overcome this issue, this research focuses on the application of inductive biases and transfer learning for creating SWS metamodels which require less data and retain high performance when used elsewhere. In particular, this study proposes an auto-regressive graph neural network metamodel of the Storm Water Management Model (SWMM) from the Environmental Protection Agency (EPA) for estimating hydraulic heads. The results indicate that the proposed metamodel requires a smaller number of examples to reach high accuracy and speed-up, in comparison to fully connected neural networks. Furthermore, the metamodel shows transferability as it can be used to predict hydraulic heads with high accuracy on unseen parts of the network. This work presents a novel approach that benefits both urban drainage practitioners and water network modeling researchers. The proposed metamodel can help practitioners on the planning, operation, and maintenance of their systems by offering an efficient metamodel of SWMM for computationally intensive tasks like optimization and Monte Carlo analyses. Researchers can leverage the current metamodel’s structure for developing new surrogate model architectures tailored to their specific needs or start paving the way for more general foundation metamodels of urban drainage systems.

Online Supercritical Fluid Chromatography Hyphenated to Fourier Transform Ion Cyclotron Resonance Mass Spectrometry with Quadrupole Detection for Microalgae Bio-Oil Characterization.

Microalgae are an attractive feedstock for biofuel production thanks to their renewable nature, high growth rate, and ability to use anthropogenic CO2. The conversion of microalgae by hydrothermal liquefaction (HTL) leads to a solid residue, a gaseous phase, and a biocrude. However, the bio-oil is rich in heteroatoms and requires upgrading processes to be used as biofuels. For these treatments to be effective, detailed knowledge of the sample is crucial. The bio-oil was characterized by direct introduction into a Fourier transform ion cyclotron resonance mass spectrometer (DI-FTICR MS) with an electrospray ionization source (ESI). Thousands of molecular formulas were assigned with a high level of confidence, mainly compounds with nitrogen and oxygen atoms. Additionally, the bio-oil was analyzed by coupling supercritical fluid chromatography (SFC) and FTICR to combine the separation power of SFC to the high performances of a 12 T FTICR. Quadrupole detection (2ω) was used in FTICR to have a high resolving power with a lower transient time. The coupling allowed the separation of many isomers along the chromatogram, showing the isomeric complexity of microalgae bio-oils. Moreover, classes of compounds were separated according to their heteroatom class thanks to the SFC separation. In this work, the advantages of DI-FTICR MS and SFC-FTICR MS proved complementary, and DI was useful to study the bio-oil at the molecular scale thanks to the high performances, while SFC proved useful for the characterization at the isomeric scale. This demonstrated the significant potential of this new online hyphenated technique for the characterization of complex matrices.

Molecular age prediction using skull bone samples from individuals with and without signs of decomposition: a multivariate approach combining analysis of posttranslational protein modifications and DNA methylation.

The prediction of the chronological age of a deceased individual at time of death can provide important information in case of unidentified bodies. The methodological possibilities in these cases depend on the availability of tissues, whereby bones are preserved for a long time due to their mineralization under normal environmental conditions. Age-dependent changes in DNA methylation (DNAm) as well as the accumulation of pentosidine (Pen) and D-aspartic acid (D-Asp) could be useful molecular markers for age prediction. A combination of such molecular clocks into one age prediction model seems favorable to minimize inter- and intra-individual variation. We therefore developed (I) age prediction models based on the three molecular clocks, (II) examined the improvement of age prediction by combination, and (III) investigated if samples with signs of decomposition can also be examined using these three molecular clocks. Skull bone from deceased individuals was collected to obtain a training dataset (n = 86), and two independent test sets (without signs of decomposition: n = 44, with signs of decomposition: n = 48). DNAm of 6 CpG sites in ELOVL2, KLF14, PDE4C, RPA2, TRIM59 and ZYG11A was analyzed using massive parallel sequencing (MPS). The D-Asp and Pen contents were analyzed by high performance liquid chromatography (HPLC). Age prediction models based on ridge regression were developed resulting in mean absolute errors (MAEs)/root mean square errors (RMSE) of 5.5years /6.6 years (DNAm), 7.7 years /9.3 years (Pen) and 11.7 years /14.6 years (D-Asp) in the test set. Unsurprisingly, a general lower accuracy for the DNAm, D-Asp, and Pen models was observed in samples from decomposed bodies (MAE: 7.4-11.8 years, RMSE: 10.4-15.4 years). This reduced accuracy could be caused by multiple factors with different impact on each molecular clock. To acknowledge general changes due to decomposition, a pilot model for a possible age prediction based on the decomposed samples as training set improved the accuracy evaluated by leave-one-out-cross validation (MAE: 6.6-12 years, RMSE: 8.1-15.9 years). The combination of all three molecular age clocks did reveal comparable MAE and RMSE results to the pure analysis of the DNA methylation for the test set without signs of decomposition. However, an improvement by the combination of all three clocks was possible for the decomposed samples, reducing especially the deviation in case of outliers in samples with very high decomposition and low DNA content. The results demonstrate the general potential in a combined analysis of different molecular clocks in specific cases.

High performance flexible phase change hydrogel applied to thermal management of new cigarette device

To address the thermal management challenges of new heat-not-burn (HNB) cigarette devices, this study aims to enhance overall energy conversion efficiency while maintaining unchanged battery capacity. The goal is to minimize heat dissipation and reduce the temperature of the device's housing that contacts the user. In this research, CaCl2·6H2O (CCH) was selected as the phase change material (PCM), and a polyacrylamide (PAM) phase change hydrogel encapsulating CCH was prepared via in-situ polymerization. Additionally, konjac glucomannan (KGM) was incorporated into the three-dimensional network structure of PAM through hydrogen bonding to promote the formation of a porous gel structure. CNTs-CuO composites, featuring in-situ generated CuO nanoparticles on carbon nanotubes (CNTs), were synthesized using a microwave hydrothermal method and employed to enhance the thermal conductivity of the phase change hydrogel.This study demonstrates that the three-dimensional porous network structure of the hydrogel not only provides effective encapsulation protection for CCH and maintains high latent heat (144.9 J/g), but also imparts excellent flexibility and stability to the composites. The incorporation of CNTs-CuO composites introduces efficient heat transfer channels, significantly increasing the thermal conductivity of the hydrogels by approximately 37 % (0.687 W·m−1·K−1). Moreover, the hydrogel composites exhibit exceptional temperature regulation properties.The innovative design and preparation of these hydrated salt-based flexible hydrogel composites offer a novel approach in the fields of thermal management and energy storage. This material demonstrates practical applications not only in HNB devices but also in various thermal management systems, such as wearable electronics, battery thermal management, and electronic device cooling. Furthermore, its application in energy storage systems, including thermal energy storage units and smart textiles, underscores its broad applicability and significance.

In-line rate encrypted links using pre-shared post-quantum keys and DPUs

The development of quantum computers poses a risk to the cryptographic algorithms utilized by current public key infrastructure (PKI). High performance computing (HPC), inter- and intra-datacenter communications, and governmental communications are particularly vulnerable to security attacks by quantum computers. In response, there are on-going efforts on implementing post-quantum cryptography (PQC) despite the considerable overhead involved in processing PQC encrypted data packets in comparison with their classical counterparts. This work aims to help with the transition to quantum resistant cryptographic schemes by means of providing a thorough experimental performance comparison of the four algorithms selected by the National Institute of Standards and Technology (NIST) for standardization when implemented in a data processing unit (DPU). The experiments are conducted at line-rate, demonstrating for the first time operation at almost 100 Gbit/s and hence suitability for high-capacity data center environments.

Support-Free Printing Origami Based 3D Negative Poisson Ratio Structured Piezoresistive Sensor for Motion Monitoring

A convenient strategy for preparing meta-structures with a negative Poisson's ratio (NPR), which enhance mechanoelectrical responsiveness and are typically based on inner concave hollow cells, is 3D printing without support structures. However, this process requires the material to have high melt strength, often linked to high viscosity and low printability. In this work, we introduced 0D carbon black (CB) into a thermoplastic polyurethane (TPU)/1D carbon nanotubes (CNT) composite to create a dimension-hybrid filler system, enhancing strength with minimal viscosity increase. The results show that the bending modulus and Young's modulus of CB/CNT(1:3)/TPU composites are increased by 1.6 times and 1.8 times that of TPU, while its viscosity (2697.98 Pa·s) is lower than the allowed value of printer. Then, the 3D-printed suspension bridge of TPU is 42.7% less saggy, significantly benefiting to preparing meta-structures. Based on a support-free 3D printable enclosed hollow structure, the TPU/CNT/CB composite material achieves an adjustable NPR between -0.59 and -0.20. Compared to unfolded structure, this NPR characteristic further improves the piezoelectric output voltage by 9.00 times, enhances the compressive modulus by 3.15 times, and improves the piezoresistive sensitivity by 122.0%. Sensors based on this 3D-printed material showcase high performance stability and reproducibility, demonstrating their potential in wearable equipment.

Enhancing Efficiency and Stability of Inverted Perovskite Solar Cells through Solution-Processed and Structurally Ordered Fullerene.

The electron transporting layer (ETL) used in high performance inverted perovskite solar cells (PSCs) is typically composed of C60, which requires time-consuming and costly thermal evaporation deposition, posing a significant challenge for large-scale production. To address this challenge, herein, we present a novel design of solution-processible electron transporting material (ETM) by grafting a non-fullerene acceptor fragment onto C60. The synthesized BTPC60 exhibits an exceptional solution processability and well-organized molecular stacking pattern, enabling the formation of uniform and structurally ordered film with high electron mobility. When applied as ETL in inverted PSCs, BTPC60 not only exhibits excellent interfacial contact with the perovskite layer, resulting in enhanced electron extraction and transfer efficiency, but also effectively passivates the interfacial defects to suppress non-radiative recombination. Resultant BTPC60-based inverted PSCs deliver an impressive power conversion efficiency (PCE) of 25.3% and retain almost 90% of the initial values after aging at 85°C for 1500 hours in N2. More encouragingly, the solution-processed BTPC60 ETL demonstrates remarkable film thickness tolerance, and enables a high PCE up to 24.8% with the ETL thickness of 200 nm. Our results highlight BTPC60 as a promising solution-processed fullerene-based ETM, opening an avenue for improving the scalability of efficient and stable inverted PSCs.

Mgikit: Demultiplexing toolkit for MGI fastq files.

MGI sequencing is reported to be an inexpensive solution to obtain genomics information. There is a growing need for software and tools to analyse MGI's outputs efficiently. mgikit is a tool collection to demultiplex MGI fastq data, reformat it effectively and produce visual quality reports. mgikit overcomes several limitations of the standard MGI demultiplexer. It is highly customisable to suit different kinds of datasets and is designed to achieve high performance and optimal memory utilisation. The tool and its documentation are available at: https://sagc-bioinformatics.github.io/mgikit/. Supplementary data are available at Bioinformatics.

A deep learning GNSS spoofing and jamming detection method with dual-frequency C/N0 heatmap

Abstract The Global Navigation Satellite System (GNSS) is vulnerable to interference due to the open signal structure and low signal strength, posing a significant threat to the billions of terminals worldwide that rely on GNSS receivers for precise Positioning, Navigation, and Timing (PNT) services. In this paper, we propose a cloud-edge framwork for GNSS spoofing and jamming monitoring, comprising the data acquisition module, GNSS monitoring module, detecting and reporting module. In this framwork, we design a Deep Learning (DL) method for detecting GNSS interference through Dual-frequency Carrier-to-Noise density ratio (C/N0) heatmaps (DD-C/N0). This method involves extracting and correlating features from C/N0 heatmaps of visible navigation satellites operating in the GPS L1 and L2 frequency bands, allowing the identification of anomalous patterns. A U-BLOX receiver was utilized to capture the GNSS satellite signals, while commercial jammers and Software-Defined Radio (SDR) HackRF One kits were employed to simulate the interference sources. Experimental results demonstrate that the proposed method achieves significantly higher performance, with an accuracy of 99% and 98% on the public dataset and real-time testing data, compared to unsupervised, semi-supervised, and supervised detectors that rely solely on single-channel data (L1 frequency band). Integrated with the DD-C/N0 method, the online GNSS monitoring system will be improved and deployed to automate spoofing and jamming detection tasks in the next step.

Enhancing complex upper-limb motor imagery discrimination through an incremental training strategy

Motor Imagery (MI)-based Brain–Computer Interface (BCI) systems are a great technological advance for the recovery of lost movements in people with severe motor impairments. Different Artificial Intelligence (AI) techniques with supervised methods have been explored for MI task discrimination, especially static movements from left and right hands. Due to different factors affecting MI-based Electroencephalography (EEG) signals related to physical and cognitive conditions, the success rate of BCIs is still low. Currently, there is a need to explore the MI of complex movements associated with Activities of Daily Living (ADLs), which brings challenges to the scientific community. In this work, an incremental training methodology for Artificial Neural Networks (ANNs) is proposed for discrimination of complex MI tasks. MI-Rest and MI-Task related to the imagination of manipulating a drinking cup were discriminated by implementing an Action Observation (AO)-based protocol in a first-person 2D virtual reality. Thirty healthy individuals were recruited to evaluate our complex MI classification approach. The incremental training proposed achieves significantly higher performance (p<0.05) compared to the non-incremental training. Additionally, the proposed method is significantly superior compared to widely applied methods for MI task discrimination: Power Spectrum (PS), Common Spatial Patterns (CSP), Filter Bank Common Spatial Patterns (FBCSP), each using four baseline Machine Learning (ML) methods for classification. The results show an improvement between 5 and 20% according to both Accuracy (ACC) and False Positive Rate (FPR). The proposed methodology obtained promising results, useful for AI-based BCI training, which would allow the development of more robust systems for neurorehabilitation purposes.

Effect of process parameters on the mechanical performance of fusion-joined additively manufactured segments

Purpose Herein, the authors report the effects of printing parameters, joining method, and annealing conditions on the structural performance of fusion-joined short-beam sections produced by additive manufacturing. Design/methodology/approach The authors first identified appropriate printing parameters for joining segmented short beams and then used those parameters to print and fusion-join segments with different configurations of stiffeners to form a longer section of a wing or small wind turbine blade structure. Findings It was found that the beams with three lateral and three base stiffening ribs give the highest flexural strength among the three beams investigated. Results on joined beams annealed at different conditions showed that annealing at 70 °C for 0.5 h yields higher performance than annealing at the same temperature for longer times. It is also found that in the case of the hot-plate-welded three-dimensional (3D)-printed structures, no annealing is needed for reaching a high strength-to-weight ratio, but annealing is helpful for maximizing the modulus-to-weight ratio. Both thermal buckling and edge wrapping were observed under annealing at 70°C for 0.5 h for 3D-printed beams comprising two lateral and four base stiffening plates. Originality/value Fusion-joining of additively manufactured segments is needed owing to the constraint in building volume of a typical commercial 3D-printer. However, study of the effect of process parameters is needed to quantify their effect on mechanical performance. This investigation has therefore identified key printing parameters and annealing conditions for fusion-joining short segments to form larger structures, from multiple 3D-printed sections, such as wind blade structures.