Synergistic Network Research Articles

Design and preparation of composite bipolar plates with synergistic conductive networks of graphite and metal foam

Graphite composite bipolar plate (BP) have limited conductivity, which makes them less than ideal for extensive application in proton exchange membrane fuel cell (PEMFC). In traditional graphite composite BP, the construction process of the conductive network is entirely random. To enhance conductivity, it is typically necessary to increase the content of conductive fillers or incorporate high aspect ratio carbon nanomaterials to optimize the density of the conductive network. In this work, we manufactured a highly conductive composite bipolar plate with synergistic conductive networks of graphite and metal foam (MF). With a synergistic conductive network, the ASR of CuG80-20ppi decreased to 7.7 mΩ cm2. Additionally, thermal conductivity increased to 24.76 W/(m·K), and in-plane conductivity reached 294.1 S/cm at 80 wt% mass fractions of conductive filler, using 20 ppi copper foam as the metallic conductive network. CuG80-20ppi exhibited a flexural strength of 52.2 MPa. G80 and CuG80-20ppi have the 80 wt% conductive filler and are molded into BP with parallel flow fields. CuG80-20ppi enhances the current density of PEMFC by 0.132 W/cm2 and reduces the ohmic impedance by 5.25%. Therefore, the findings of this study offer valuable insights for the enhancement of composite BP conductivity, while simultaneously ensuring the stability and continuity of the synergistic conductive network. A novel approach is presented to enhance the electrical conductivity of graphite composite bipolar plate (BP) for proton exchange membrane fuel cells (PEMFCs). By embedding metal foam as a supplementary conductive network within the graphite matrix, a synergistic conductive architecture is achieved. This innovation significantly reduces the area specific resistance (ASR) to 7.7 mΩ cm2 and boosts thermal conductivity to 24.76 W/(m·K). The resulting CuG80-20ppi BP, with 80 wt% conductive filler, exhibits improved mechanical strength and conductivity, contributing to a 0.132 W/cm2 increase in PEMFC current density and a 5.25% reduction in ohmic impedance. This work highlights the potential of metal foam integration to enhance BP conductivity and PEMFC performance.

Frabrication of carboxymethylchitin nanofibers and fish gelatin hybrid gels with robust gel performance

Compared to animal gelatin, the application of fish gelatin (FG) in the food industry is limited due to its lower gel hardness, gelation temperature, and melting temperature. To address these issues, we developed hybrid gels using carboxymethyl chitin nanofibers (CMC-NFs) with FG to enhance its gel properties. The chitin was carboxymethylated by monochloroacetic acid, increasing the negative potential from −6 mV to −35 mV. This modified chitin was then defibrillated into CMC-NFs through high-pressure homogenization to improve its water dispersibility. The addition of 0.1% CMC-NFs significantly increased the hardness of FG gels from 533 g to 989 g, which was notably higher than the ∼600 g hardness of porcine gelatin. Additionally, the gelation and melting temperatures of FG reached 17 °C and 32 °C, respectively, which are very close to the 20 °C and 35 °C of porcine gelatin. Mechanistic studies revealed that FG and CMC-NFs (0.1%) molecules formed a synergistic gel network, facilitated by hydrogen bonding and hydrophobic interactions. This interaction significantly increased the α-helix content in the secondary structure of the FG protein from 15.26% to 21.18%, thereby enhancing the FG-dominated three-dimensional network structure with CMC-NF molecules. Increasing the concentration of CMC-NFs further elevated the gel's melting temperature while reducing its hardness due to phase separation. This study not only presents a viable method for enhancing the gelling properties of modified FG but also offers a feasible solution for expanding FG's applications in the food industry.

Synergistic Neural Network and Velocity Pausing Particle Swarm Optimization for Enhanced Residential Building Energy Efficiency: A Case Study in Kuwait

The global energy demand of buildings is on the rise, driven by factors such as rapid population growth, increasing comfort, technological advances, and ongoing developments in building construction. This escalating energy consumption in buildings is a major contributor to the energy crisis and climate change. Accurate prediction of building energy consumption is essential for gaining insight into energy utilization, reducing waste, and enhancing comfort conditions. This study aimed to introduce a reliable technique for predicting and optimizing the energy consumption of residential buildings, with a focus on a case study in Kuwait. A robust Artificial Neural Network (ANN) was developed, meticulously trained, and rigorously tested to provide accurate energy consumption predictions. Subsequently, an innovative variant of the Velocity Pausing Particle Swarm Optimization (VPPSO) algorithm was employed to identify optimal energy consumption solutions. This novel optimization technique can achieve significant reductions in building energy consumption, with potential savings of up to 43%. Additionally, a sensitivity analysis was performed using the Garson method to assess the impact of input parameters on energy utilization. The results reveal that the insulation and cooling setpoint exert the greatest influence on the objective function, followed by the outdoor airflow. The proposed model, which combines the power of ANN with VPPSO, can be applied to similar buildings, offering precise predictions and optimizing energy consumption. This approach holds promise in addressing the pressing challenges of energy efficiency in building environments.

A “rotating menu” of medical uncertainty for families affected by telomere biology disorders: A qualitative interview study

BackgroundMedical uncertainty may cause distress and challenge medical decision-making for patients with rare diseases and their caregivers. Few studies have examined the experience and management of medical uncertainty in rare disease and the dynamics of multiple medical uncertainty sources, issues, and management strategies. ObjectiveWe explored the experience and management of uncertainty in individuals with telomere biology disorders (TBDs), a set of rare cancer-prone bone marrow failure syndromes, and their caregivers. DesignParticipants (N = 32) in this qualitative-descriptive study were individuals with a TBD (n = 17) and/or their caregivers (n = 15). We thematically analyzed transcripts to describe the presence and dynamics of medical uncertainty in TBDs using categories from a previously published taxonomy. ResultsIndividuals with TBDs and caregivers described medical uncertainty as a chronic burden embodied amidst a range of interrelated sources and issues. Scientific uncertainty included diagnostic and prognostic ambiguity. Practical uncertainty focused on logistical challenges of building and maintaining medical care teams. Personal uncertainty included difficulty realigning self-identity, goals, and relationship expectations post-diagnosis. Scientific, practical, and personal uncertainty issues were entangled. The rarity of TBDs resulted in limited scientific knowledge, which gave rise to practical and personal uncertainties affecting medical decision-making and relationship formation (e.g., creating trusted care teams where patient knowledge of TBDs may exceed that of clinicians). Participants used multiple strategies for uncertainty management, particularly information-seeking and community-building. However, these management strategies could intensify, rather than resolve, participants’ medical uncertainty. ConclusionIn TBDs, medical uncertainty manifests as a network of multiple, interrelated, sources and issues, which require evolving management strategies. Researchers must be mindful that complex, synergistic uncertainty networks contribute to psychosocial challenges in TBDs. Additional research is warranted to address scientific uncertainty in TBDs, including clinical manifestations and underlying biology, and to develop psychosocial interventions that recognize and anticipate evolving uncertainty.

A coupled multi-factor synergistic graph network model for traffic prediction

Abstract Since only the spatio-temporal structure itself is considered in the current traffic prediction process, and there is a lack of multi-factor information about the scene, which leads to problems such as lack of universality and authenticity in the spatio-temporal scene, we proposed a coupled multi-factor synergistic graph network (CM-SGN). For this network, this paper firstly adopts a fusion mechanism to fuse traffic features with coupled scene features, constructs a complex feature correlation matrix, and enhances the multi-feature fusion effect of spatio-temporal scenes; secondly, it introduces a spatio-temporal feature network constrained by an attention mechanism to ensure the spatio-temporal stability of the fused features; and lastly, it reduces the sensitivity of the scene-traffic interdependent targets by using the regression layer combinations of the loss function to improve the prediction generalization capability. Experiments were conducted on two real datasets. Experiments were conducted on two real datasets, and the results show that the model proposed in this paper meets the complex spatio-temporal scenarios in traffic prediction, effectively captures the intrinsic spatio-temporal dependencies, and has good stability and generalization ability.

Genome-centric metagenomic analysis unveils the influence of temperature on the microbiome in anaerobic digestion

Temperature plays a crucial role in shaping microbial ecosystems during anaerobic digestion. However, the specific microbial communities and their functions across a wide temperature range still remain elusive. This study employed a genome-centric metagenomic approach to explore microbial metabolic pathways and synergistic networks at temperatures of 35℃, 44℃, 53℃, 55℃, and 65℃. A total of 84 metagenome assembled genomes (MAGs) were assembled, with over 65% of these MAGs corresponding to novel bacterial and archaeal species (including Firmicutes, Chloroflexota, Bacteroidia and Methanobacteriota), greatly enhancing our current comprehension anaerobic digestion process. Notably, functional annotation identified that 44_bin.2 (Methanothrix_A sp.001602645) harbors enzymes associated with hydrogenotrophic metabolism. Additionally, this microorganism exhibited diverse metabolic capabilities at 44℃, a temperature commonly employed in industrial practice yet less explored in bench studies. Consequently, it implies a promising potential for conducting anaerobic digestion at a moderate thermophilic temperature, as opposed to the conventional mesophilic range. The microorganism exhibited a variety of metabolic capabilities at 44℃, a temperature frequently employed in industrial applications but underexplored in laboratory investigations. The findings suggest that anaerobic digestion carried out at moderate thermophilic temperatures may have a higher potential for methane production.

Wide-response-range and high-sensitivity piezoresistive sensors with triple periodic minimal surface (TPMS) structures for wearable human-computer interaction systems

High-performance pressure sensors are garnering interest in human-computer interaction technology, wearable devices, and bionic electronic skin development. However, highly sensitive sensors frequently have a limited response range. In this work, we developed composites with outstanding conductive network structures through the synergistic effect of transition metal carbides (MXene) and multi-walled carbon nanotubes (MWCNTs). Additionally, pressure sensors with various TPMS structures were prepared using innovative parametric design and Fused Deposition Molding (FDM) printing. Due to the stable synergistic conductive network and distinctive curved surface structure, the sensors exhibit exceptional sensing performance. This includes high sensitivity ranging from 4.67 MPa−1 to 7.03 MPa−1 (within the range of 0–0.1 MPa), a broad operating range (maximum 10 MPa), rapid response and recovery times (326 ms/193.4 ms), and long-term fatigue resistance (over 10,000 s cycles). By integrating mechanical properties, sensing properties, and finite element simulations, we analyzed the mechanism underlying the impact of various TPMS pore structures on the sensitivity and response range of the pressure sensor. In addition, the sensors were arrayed as 4 × 4 modules to successfully recognize a wide range of foot movements from different volunteers. These findings illuminate potential applications in human motion detection, healthcare rehabilitation, and artificial intelligence.

Underwater target detection network based on differential routing assistance and bilateral attention synergy

Underwater target detection technology holds significant importance in both military and civilian applications of ocean exploration. However, due to the complex underwater environment, most targets are small and often obscured, leading to low detection accuracy and missed detections in existing target detection algorithms. To address these issues, we propose an underwater target detection algorithm that balances accuracy and speed. Specifically, we first propose the Differentiable Routing Assistance Sampling Network named (DRASN), where differentiable routing participates in training the sampling network but not in the inference process. It replaces the down-sampling network composed of Maxpool and convolution fusion in the backbone network, reducing the feature loss of small and occluded targets. Secondly, we proposed the Bilateral Attention Synergistic Network (BASN), which establishes connections between the backbone and neck with fine-grained information from both channel and spatial perspectives, thereby further enhancing the detection capability of targets in complex backgrounds. Finally, considering the characteristics of real frames, we proposed a scale approximation auxiliary loss function named (Aux-Loss) and modify the allocation strategy of positive and negative samples to enable the network to selectively learn high-quality anchors, thereby improving the convergence capability of the network. Compared with mainstream algorithms, our detection network achieves 82.9% in mAP@0.5 on the URPC2021 dataset, which is 9.5%, 5.7%, and 2.8% higher than YOLOv8s, RT-DETR, and SDBB respectively. The speed reaches 75 FPS and meets the requirements for real-time performance.

Novel Combination Therapy for Heart Failure: Trimebutine-Methoxsalen Identified through Synergistic Network Virtual Screening and Experimental Validation.

Combination therapy is increasingly favored by pharmaceutical companies and researchers as an effective way to quickly discover new drugs with excellent efficacy, especially in the treatment of complex diseases. Previously, we successfully developed a computational screening method to identify such combinations, although it fell short in elucidating their synergistic mechanisms. In this work, we have transitioned to a highest single agent (HSA) synergy model for network screening, which streamlines the discovery of promising combinations and facilitates the investigation of their synergistic effects. Through this refined approach, the trimebutine-methoxsalen combination emerged as a promising candidate for heart failure (HF) treatment, exhibiting significant in vitro cardioprotective effects and effectively mitigating isoproterenol (ISO)-induced structural remodeling in the mouse heart. Further mechanistic studies have demonstrated that trimebutine and methoxsalen could synergistically inhibit intracellular calcium overload in myocardial cells and reduce the production of ROS, thus exerting cardioprotective effects. Overall, this study introduces an advanced computational strategy that not only identifies a novel combination therapy against HF but also sheds light on its underlying synergistic mechanisms.

Biobased, degradable and directional porous carboxymethyl chitosan/lignosulfonate sodium aerogel-based piezoresistive pressure sensor with dual-conductive network for human motion detection

The rapid growth of wearable electronics, healthcare monitoring, and human–computer interaction has sparked growing interest in flexible piezoresistive pressure sensors, which in turn raised considerable concerns about the increasingly serious environmental pollution caused by electronic waste. Herein, a radial inward directional freezing-drying method was proposed to prepare biobased, degradable and directional porous carboxymethyl chitosan/lignosulfonate sodium aerogel with dual-conductive network constructed by carboxylated multiwalled carbon nanotubes (C-CNTs) and MXene for piezoresistive pressure sensor. Owing to the synergistic conductive network of C-CNTs and MXene as well as directional porous structure, the sensor exhibited high sensitivity (2.33 kPa−1 in 0–10 kPa, 1.04 kPa−1 in 10–31 kPa, 0.53 kPa−1 in 31–53 kPa), fast response (response/recovery time of 140/60 ms), and excellent repeatability (2,000 loading–unloading cycles). Moreover, the sensor was successfully applied for human motion detection, healthy monitoring, micro-expression identification, and speech recognition. In addition, the aerogel for sensor was able to be completely degraded within 70 h in 1 M hydrochloric acid solution or partially degraded in boiling water within 2.5 h for the recycling of conductive materials, which was beneficial for not only environment protection but also saving resource. Our findings conceivably stand out as a new strategy to prepare environmentally friendly and high-performance piezoresistive pressure sensor and will promote the further development and application of flexible electronics.

Atrial lead system for enhanced P-wave recording: A comparative study on optimal leads using gradient boosting and deep learning algorithms

Morphological characteristics of the P-wave in an Electrocardiogram (ECG) provide insights into interatrial and atrioventricular (AV) conduction pathologies. However, accurate diagnosis through visual inspection is hindered by the P-wave’s diminutive size and susceptibility to noise. Furthermore, conventional 12-lead systems often fail to capture these elusive P-waves. Consequently, our study describes a new Atrial Lead System (ALS) aimed at enhancing P-wave signal strength and ranking leads using advanced Gradient-Boosting (GB) and Deep Learning (DL) algorithms. ECG data from 75 healthy volunteers (mean age: 45 ± 17.2 years; 40 % women) were recorded using ALS and specially designed optimal bipolar leads to improve atrial activity. Employing Gradient Boosting Classifier (GBC), Extreme Gradient Boosting Classifier (XGBoost), Light Gradient Boosting Machine (LightGBM), and a synergistic One-dimensional Convolutional Neural Network (1D-CNN) with Bidirectional Long Short-Term Memory (Bi-LSTM) layers, we assessed and ranked optimal bipolar leads based on P-wave parameters, indices, and AV ratios. Notably, AL-I and AL-II exhibited significantly higher median amplitudes, RMS, and area for the recorded P-wave compared to other leads (p < 0.001). Evaluations using LightGBM, GBC, XGBoost, and 1D-CNN+Bi-LSTM models identified P-lead, AL-II, and AL-I as the top three leads, with accuracies of 0.96, 0.95, and 0.94; 0.96, 0.94, and 0.94; 0.96, 0.95, and 0.94; and 0.96, 0.94, and 0.93, respectively. These findings underscore the efficacy of ALS in elevating P-wave signal strength and AV ratios, suggesting its potential as a valuable tool for improved clinical screening and diagnosis of atrial arrhythmias.

Dual conductive network enables mechanically robust polymer composites with highly electrical and thermal conductivities

Thermally and electrically conductive polymer composites (CPCs) have been widely used in smart and flexible electronics; however, huge challenges remain in simultaneously improving the electrical and thermal conductivity of CPCs while maintaining the satisfactory mechanical properties including sufficient strength and toughness. In this study, we propose an effective interfacial regulation approach to fabricate CPCs with a dual conductive network through decoration of Ag nanoparticles (AgNPs) onto the polyurethane (PU) nanofibers containing graphite nanoplatelets (GNPs), followed by hot-pressing. Rigid GNPs expand the PU nanofiber network, facilitating Ag precursor adsorption, while subsequent hot-pressing eliminates the big channels and pores inside nanofiber composite membranes and hence greatly enhance their mechanical properties. After hot-pressing, the synergistic dual conductive network with greatly reduced thermal and electrical contact resistance leads to significant improvements in both thermal and electrical conductivity (up to 27.71 W (m K)−1 and 1.87 × 106 S/m, respectively). Moreover, the mechanically robust CPCs with exceptional durability possess superior Joule heating performance at low voltages and heat dissipation capability. This study provides inspiration for designing highly thermally and electrically conductive CPCs with potential applications as flexible thermal interface materials.

Poly(p-terphenyl-piperidine-bromoacetophenone) anion exchange membranes with pendant N-spirocyclic cations: Cations synergistic build efficient ion transport networks

The establishment of well-developed ionic conduction channels and the preparation of excellent chemical stability are urgent needs for anion exchange membranes. In this study, we successfully prepared poly (p-terphenyl-piperidine-bromoacetophenone) polymers using superacid catalysis. Then a series of QPTPP-N-ASU-x AEMs were prepared by introducing N-spirocyclic side chain cations (N-ASU) with different grafting rates. The introduction of N-spirocyclic cations with better alkali resistance increases multiple OH− transport sites, contributes to the microphase separation structure, and is synergistic with piperidine cations to rapidly construct ion transport networks, giving the membranes good conductivity and alkali resistance stability. The AFM showed that QPTPP-N-ASU-5% AEM had a remarkable microstructure and formed large ionic domains, the conductivity of the membrane was 121.2 mS cm−1, corresponding to a peak power density of 477.25 mW cm−2. The conductivity residual of QPTPP-N-ASU-5% AEM was 94.15 % and 87.71 % after immersion in 2 M and 4 M NaOH at 80 °C for 1440 h. It shows that membrane has good potential for application.

Sensitive strain sensors with wide-range detection and high stability for intelligent human motion monitoring

Miniaturized wearable electronic devices with strain sensors as core sensing elements hold significant applications in health monitoring and human-machine interaction. However, it remains a challenge to develop strain sensors with optimal response linearity, a wide detection range, and stability. In this study, to satisfy the above requirements, we construct high-performance wearable strain sensors (rGO-CNTOH@PSE) by a strategy of hybridizing conductive fillers with stretchable substrates. In detail, a synergistic conductive network is developed by adopting OH-containing carbon nanotube (CNTOH) and reduced graphene oxide (rGO) fillers with an appropriate mass ratio. After the network is integrated into a porous silicone elastomer substrate formed by the salt templating method, the developed strain sensor exhibits distinct resolution, consistent repeatability, and superior response linearity across a wide strain detection range of 0.3–303.03 %. Furthermore, the sensor also maintains good response stability over 15000 cycles of strain. We also demonstrate that this strain sensor showing outstanding exceptional detection performance has the capability for full-range human motion detection. A wireless smart electronic glove is developed based on the sensor, which can accurately recognize gesture movements and control a bionic robotic hand to replicate human hand actions, offering the potential for replacing manual tasks (such as, grasping in hazardous environments) in the future.

Ultrasensitive electrospinning fibrous strain sensor with synergistic conductive network for human motion monitoring and human-computer interaction

With the rapid development of wearable electronic skin technology, flexible strain sensors have shown great application prospects in the fields of human motion and physiological signal detection, medical diagnostics, and human-computer interaction owing to their outstanding sensing performance. This paper reports a strain sensor with synergistic conductive network, consisting of stable carbon nanotube dispersion (CNT) layer and brittle MXene layer by dip-coating and electrostatic self-assembly method, and breathable three-dimensional (3D) flexible substrate of thermoplastic polyurethane (TPU) fibrous membrane prepared through electrospinning technology. The MXene/CNT@PDA-TPU (MC@p-TPU) flexible strain sensor had excellent air permeability, wide operating range (0–450 %), high sensitivity (Gauge Factor, GFmax = 8089.7), ultra-low detection limit (0.05 %), rapid response and recovery times (40 ms/60 ms), and excellent cycle stability and durability (10,000 cycles). Given its superior strain sensing capabilities, this sensor can be applied in physiological signals detection, human motion pattern recognition, and driving exoskeleton robots. In addition, MC@p-TPU fibrous membrane also exhibited excellent photothermal conversion performance and can be used as a wearable photo-heater, which has far-reaching application potential in the photothermal therapy of human joint diseases.

Evaluating Virtual Contrast-Enhanced Magnetic Resonance Imaging in Nasopharyngeal Carcinoma Radiation Therapy: A Retrospective Analysis for Primary Gross Tumor Delineation

PurposeTo investigate the potential of virtual contrast-enhanced MRI (VCE-MRI) for gross-tumor-volume (GTV) delineation of nasopharyngeal carcinoma (NPC) using multi-institutional data. Methods and MaterialsThis study retrospectively retrieved T1-weighted (T1w), T2-weighted (T2w) MRI, gadolinium-based contrast-enhanced MRI (CE-MRI) and planning CT of 348 biopsy-proven NPC patients from three oncology centers. A multimodality-guided synergistic neural network (MMgSN-Net) was trained using 288 patients to leverage complementary features in T1w and T2w MRI for VCE-MRI synthesis, which was independently evaluated using 60 patients. Three board-certified radiation oncologists and two medical physicists participated in clinical evaluations in three aspects: image quality assessment of the synthetic VCE-MRI, VCE-MRI in assisting target volume delineation, and effectiveness of VCE-MRI-based contours in treatment planning. The image quality assessment includes distinguishability between VCE-MRI and CE-MRI, clarity of tumor-to-normal tissue interface and veracity of contrast enhancement in tumor invasion risk areas. Primary tumor delineation and treatment planning were manually performed by radiation oncologists and medical physicists, respectively. ResultsThe mean accuracy to distinguish VCE-MRI from CE-MRI was 31.67%; no significant difference was observed in the clarity of tumor-to-normal tissue interface between VCE-MRI and CE-MRI; for the veracity of contrast enhancement in tumor invasion risk areas, an accuracy of 85.8% was obtained. The image quality assessment results suggest that the image quality of VCE-MRI is highly similar to real CE-MRI. The mean dosimetric difference of planning target volumes were less than 1Gy. ConclusionsThe VCE-MRI is highly promising to replace the use of gadolinium-based CE-MRI in tumor delineation of NPC patients.

Effect of nano-filler/matrix interfacial friction on temperature-dependent fatigue property of flexible pressure sensor

Flexible pressure sensors based on polymeric nanocomposites have attracted widespread attention owing to exceptional low-temperature fatigue tolerance, yet the damage evolution of the nano-filler/matrix interface is still unclear. Herein, we prepare Cu/MXene/multi-wall carbon nanotubes (MWCNTs)/thermoplastic polyurethane (TPU)/Cu and Cu/MXene/MWCNTs/carbon black (CB)/TPU/Cu pressure sensors using solution blending-casting method, and systematically investigate the influence of the nano-fillers/matrix interfacial friction damage on low-temperature bending fatigue of the sensor under cyclic bending. It is found that the sensing performance of the device with binary [MXene/MWCNTs] synergistic network decreases by 65 % with decreasing temperature from 20 down to − 40 °C under 20,000 bending cycles, while that of the sensor with ternary [MXene/MWCNTs/CB] synergistic network drops by only 14.9 %. For the ternary composites, owing to the introduction of CB particles, the synergistic lubrication effect among the nano-fillers and the nano-mechanical interlocking interface between the nano-fillers and the polymer can cooperatively enhance the anchorage of the nano-fillers to the TPU chains, and hence alleviating friction energy accumulation at the nano-fillers/polymer interface. Our work shows that the synergistic reinforcing strategy may be valuable for the development of ultra-flexible sensors with low-temperature fatigue resistant.

Transcranial ultrasound stimulation effect in the redundant and synergistic networks consistent across macaques

Abstract Low-intensity transcranial ultrasound stimulation (TUS) is a non-invasive technique that safely alters neural activity, reaching deep brain areas with good spatial accuracy. We investigated the effects of TUS in macaques using a recent metric, the synergy minus redundancy rank gradient, that quantifies different kinds of neural information processing. We analyzed this high-order quantity on the fMRI data after TUS in two targets: the supplementary motor area (SMA-TUS) and the frontal polar cortex (FPC-TUS). The TUS produced specific changes at the limbic network at FPC-TUS and the motor network at SMA-TUS and altered the sensorimotor, temporal, and frontal networks in both targets, mostly consistent across macaques. Moreover, there was a reduction in the structural and functional coupling after both stimulations. Finally, the TUS changed the intrinsic high-order network topology, decreasing the modular organization of the redundancy at SMA-TUS and increasing the synergistic integration at FPC-TUS.

Synergistic fusion network for landslide segmentation: the vital complementarity of geological information to remote sensing imagery

In the contemporary technological environment, access to a multitude of data sources, such as digital elevation models (DEM), slope data, and other geological information, has become essential for augmenting remote sensing imagery in landslide analysis. These datasets enhance landslide identification capabilities, but existing methods often fail to fully leverage their unique characteristics due to oversimplified fusion strategies. To overcome this challenge, we introduce the Synergistic Fusion Network (SFNet), which is specifically designed to utilize the complementary nature of geological data to significantly improve the accuracy and efficiency of landslide segmentation. SFNet incorporates Multi-Source Fusion Modules (MSFM) and Reverse Feature Refinement Modules(RFRM), through an innovative network architecture, enhancing remote sensing imagery with integrated geological information. In our experiments, SFNet outperforms existing mainstream networks, demonstrating superior F1 scores, thereby showcasing its ability to effectively segment landslide regions with high precision. This advancement highlights the importance of considering the unique contributions of each data source, especially the complementary effects of geological information.

Tunable porous fiber-shaped strain sensor with synergistic conductive network for human motion recognition and tactile sensing

With the rapid development of portable electronic products, smart wearable devices show great application potential in personalized motion monitoring, health monitoring and even in human-machine interaction. In particular, high-performance flexible fiber-shaped strain sensors are receiving more and more attention because they can be well integrated with clothes or human skin for real-time human activities monitoring. However, achieving improved sensitivity, wide detection range, diverse applications and easy operation of them remains a challenge. Here, we adopt a simple wet-spinning technique to prepare a porous fiber-shaped carbon nanotubes (CNTs)/silver nanoparticles (AgNPs)/thermoplastic polyurethane (TPU) fiber (CATF) for high-performance strain sensor, of which the synergistic conductive network of CNTs and AgNPs enables a wide detection range (∼248 %), high sensitivity (GF > 4295), fast response/recovery time (120 ms/140 ms) and excellent stability. Interestingly, strain sensing range and sensitivity of the sensor can be tuned by changing the AgNPs loading to make it suitable for different application scenarios. All the merits of the sensor make it capable for diverse human movements monitoring, pronunciation recognition, and gesture recognition. More importantly, our prepared CATF strain sensor can be easily weaved into wearable smart textiles, showing great potential for tactile sensing to realize multi-touch and pressure tracking. Finally, the CATF also displays efficient photothermal conversion capability even under different tensile strains up to 150 %, showing great potential in human body thermal management to improve the wearing comfort of wearable electronic fabric.