Dual Modulation Research Articles

Novel high-valent copper species-involved photo-Fenton-like process boosted by nitrogen/oxygen dual defects modulation

High-valent metal species generated during peroxymonosulfate (PMS) activation have demonstrated significant potential in wastewater treatment. However, the rapid generation of high-valent metal species, especially high-valent copper (Cu(III)), has been challenging. Moreover, the mechanisms behind Cu(III) generation and its role in the PMS-based Fenton-like process have not been fully understood. Herein, a novel N, O dual defects co-modified 0D/2D hybrid of CuO nanodots (NDs)/g-C3N4 nanosheets was constructed as an integrated photocatalytic platform to efficiently activate PMS, which exhibited excellent catalytic activity and stability towards tetracycline degradation through consecutive generation of surface Cu(III) as the key active species. The N, O dual-defects and strong interfacial coupling of NDs/g-C3N4 markedly increased charge separation and induced electron-rich Cu sites. This led to a continuous and rapid Cu(II)/Cu(I) conversion, further triggering persistent Cu(III) generation through Cu(I)-oxidation with a two-electron transfer. The work provides new insights into exploring the properties of Cu(III) species during PMS activation and offers encouraging guidance for their application in pollutant removal.

Performance evaluation of attention-deep hashing based medical image retrieval in brain MRI datasets

BackgroundBrain MRI images pose significant challenges due to their complexity and voluminous data, which often hinder the accuracy of traditional image retrieval methods. In response, this research delves into a novel medical image retrieval approach grounded in attention mechanisms and deep hashing techniques. MethodologyThe study enhances the network's edge perception capability by incorporating a dual mixed attention module (CBAM-MA dual attention) into the convolutional neural network architecture. This addition optimally captures edge saliency and distributional features by introducing an extra attention layer atop CBAM. Furthermore, a novel triple loss function is introduced, amalgamating Euclidean and cosine distances. This fusion exploits the strengths of both distance metrics to comprehensively consider sample geometric attributes, thereby generating more robust and expressive feature representations crucial for preserving categorical details. ResultsExperimental evaluations showcase significant performance advancements in MRI image retrieval tasks using the proposed method. The average hit rate stands at 0.7783, average precision at 0.752246, and average inverse rank at 0.956721. These metrics collectively underscore the method's efficacy in enhancing the quality and expediting the diagnosis of brain diseases. ConclusionThe research underscores the critical role of attention and deep hashing techniques in augmenting the accuracy and efficiency of medical image retrieval, particularly in the context of brain MRI images. The proposed method's notable performance improvements pave the way for enhanced diagnostic capabilities, signaling a significant step forward in improving healthcare outcomes related to brain diseases.

Enhanced methanol electrooxidation catalysis via dual modulation of PtCu alloy and oxygen vacancies

Achieved by introducing defect sites, enhances both the catalytic activity and stability of catalysts. This is crucial for advancing key applications in energy conversion and environmental protection. Here, we introduce a novel Pt-Cu@CeO2/MWCNTs electrocatalyst prepared by dual modulation of the oxygen vacancy anchoring point and the center of the alloying d-band. This catalyst significantly boosts the rate of methanol oxidation. The Pt-Cu@CeO2/MWCNTs catalyst demonstrates a mass activity 3.7 times higher than that of commercial Pt/C, along with exceptional durability. The electron transfer between the PtCu alloy and the oxygen vacancies effectively modulates the d-band center of Pt, leading to a “volcano” relationship between the catalyst’s mass activity and the d-band center. This study advances the development of methanol electrooxidation catalysts by introducing a novel “double enhancement effect” model.

Incidence and time to onset of immunotherapy-related adrenal insufficiency in the I-SPY2 trial.

584 Background: I-SPY2 was the first randomized trial to demonstrate an improvement in pCR and EFS when Immune Checkpoint Inhibitors (ICI) were added to standard neoadjuvant chemotherapy. This finding was validated in the phase 3 KEYNOTE 522 trial for triple-negative breast cancer, leading to approval of pembrolizumab (pembro) in this setting. ICIs add the risk of immune-related adverse events (irAEs); while most irAEs are reversible, endocrinopathies appear to persist. Higher rates of adrenal insufficiency (AI), either from primary adrenalitis or hypophysitis, have been reported with ICI-based therapy (tx) in early breast cancer (EBC) compared to advanced disease. Here we describe the incidence and time of onset of AI with ICI-based tx in I-SPY2. Methods: I-SPY2 is a multicenter, phase 2 trial using response-adaptive randomization within biomarker subtypes defined by hormone receptor, HER2, and MammaPrint status to evaluate novel agents in the neoadjuvant setting in patients (pts) with high-risk EBC. Treatment included weekly paclitaxel (T) plus ICI-based tx x 4 cycles, followed by doxorubicin/cyclophosphamide (AC) every 2-3 weeks x 4. 6 completed investigational arms included ICIs: T+pembro x 4, T+pembro x 8 (+/- AC), T+pembro+SD101 (TLR9 agonist), T+durvalumab+olaparib, T+cemiplimab (cemi), and T+cemi+REGN3767 (anti-LAG-3). AI diagnoses were made by treating providers with review from two board certified endocrinologists. AI was defined as AM serum cortisol level < 3 mcg/dl in absence of recent glucocorticoid treatment. If ACTH was low or normal, pt was dx with hypophysitis; if ACTH was elevated, pt was dx with primary AI. Results: 442 pts in I-SPY2 received ICI-based tx; 11.8% (n=52) developed AI (primary+hypophysitis). Mean age of pts who developed AI was 47.3 vs 48.6 yrs in those who did not (p=0.44). Incidence of AI was highest in arms with dual immune modulation (18.1%) as compared to single ICI tx (8.5%). Median time to onset varied by arm, ranging from 92-204 days from first ICI dose. While some cases occurred during ICI tx (13.5%), the majority occurred between 12-24 wks after tx initiation, with 21.2% diagnosed after completion of all tx. The majority (94.2%) of pts with AI were symptomatic; the most common presenting symptoms included fatigue (59.6%), nausea (48.1%), and vomiting (34.6%). All pts with AI remain on glucocorticoid replacement tx. Conclusions: The incidence of AI in EBC is higher than has previously been reported in advanced disease, with higher rates observed in those tx with dual immune modulation. No correlation was observed with age; correlation of AI with response predictive subtype and other clinicopathologic features is ongoing and will be presented. Given the high incidence of AI in EBC, close monitoring in the peri/postoperative setting and education of pts and providers is critical, as risk persists even after completion of tx. Work to identify risk factors for AI is ongoing. Clinical trial information: NCT01042379 .

Enhanced Wind Field Spatial Downscaling Method Using UNET Architecture and Dual Cross-Attention Mechanism

Before 2008, China lacked high-coverage regional surface observation data, making it difficult for the China Meteorological Administration Land Data Assimilation System (CLDAS) to directly backtrack high-resolution, high-quality land assimilation products. To address this issue, this paper proposes a deep learning model named UNET_DCA, based on the UNET architecture, which incorporates a Dual Cross-Attention module (DCA) for multiscale feature fusion by introducing Channel Cross-Attention (CCA) and Spatial Cross-Attention (SCA) mechanisms. This model focuses on the near-surface 10-m wind field and achieves spatial downscaling from 6.25 km to 1 km. We conducted training and validation using data from 2020–2021, tested with data from 2019, and performed ablation experiments to validate the effectiveness of each module. We compared the results with traditional bilinear interpolation methods and the SNCA-CLDASSD model. The experimental results show that the UNET-based model outperforms SNCA-CLDASSD, indicating that the UNET-based model captures richer information in wind field downscaling compared to SNCA-CLDASSD, which relies on sequentially stacked CNN convolution modules. UNET_CCA and UNET_SCA, incorporating cross-attention mechanisms, outperform UNET without attention mechanisms. Furthermore, UNET_DCA, incorporating both Channel Cross-Attention and Spatial Cross-Attention mechanisms, outperforms UNET_CCA and UNET_SCA, which only incorporate one attention mechanism. UNET_DCA performs best on the RMSE, MAE, and COR metrics (0.40 m/s, 0.28 m/s, 0.93), while UNET_DCA_ars, incorporating more auxiliary information, performs best on the PSNR and SSIM metrics (29.006, 0.880). Evaluation across different methods indicates that the optimal model performs best in valleys, followed by mountains, and worst in plains; it performs worse during the day and better at night; and as wind speed levels increase, accuracy decreases. Overall, among various downscaling methods, UNET_DCA and UNET_DCA_ars effectively reconstruct the spatial details of wind fields, providing a deeper exploration for the inversion of high-resolution historical meteorological grid data.

A robust multi-scale feature extraction framework with dual memory module for multivariate time series anomaly detection

Although existing reconstruction-based multivariate time series anomaly detection (MTSAD) methods have shown advanced performance, most assume the training data is clean. When faced with noise or contamination in training data, they can also reconstruct the anomaly well, weakening the distinction between normal and anomaly. Some probabilistic generation-based methods have been used to address this issue because of their implicit robust structure to noise, but the training process and suppression of anomalous generalization are not stable. The recently proposed explicit method based on the memory module would also sacrifice the reconstruction effect of normal patterns, resulting in limited performance improvement. Moreover, most existing MTSAD methods use a single fixed-length window for input, which weakens their ability to extract long-term dependency. This paper proposes a robust multi-scale feature extraction framework with the dual memory module to comprehensively extract features fusing different levels of semantic information and lengths of temporal dependency. First, this paper designs consecutive neighboring windows as inputs to allow the model to extract local and long-term dependency information. Secondly, a dual memory-augmented encoder is proposed to extract global typical patterns and local common features. It ensures the reconstruction ability of normal data while suppressing the generalization of the anomaly. Finally, this paper proposes a multi-scale fusion module to fuse latent variables representing different levels of semantic information and uses the reconstructed latent variables to reconstruct samples for anomaly detection. Experimental results on five datasets from diverse domains show that the proposed method outperforms 16 typical baseline methods.

An Efficient Shunt Modulated AC Green Plug–Switched Filter Compensation Scheme for Nonlinear Loads

Nonlinear loads, crucial components of power system grids, pose a challenge due to harmonics injection. This work tackles this issue with a novel modified green plug–switched filter compensation scheme using fuzzy logic controllers. This innovative scheme presented in this paper utilizes dual action pulse width modulation to ensure switching functions from harmonics reduction and capacitive compensation for inrush nonlinear-type AC loads. The scheme’s multi-loop regulations and online switching effectively handle dynamic-type slow-acting inrush, motorized- and other rectifier-type nonlinear loads, enhancing the power factor, power quality at source and load buses, and reducing the total harmonics distortion at the key source and sensitive nonlinear load buses. A simulation model in the MATLAB/SIMULINK-2023b software environment demonstrates the efficiency of the proposed FACTS technique. The modulated dual mode switched filter-capacitive compensation scheme controlled by a fuzzy logic controller ensures less harmonics distortion and improved voltage stabilization. The results show that voltage, current, active power, reactive power, power factor regulation, and effective energy utilization are achievable with the designed Flexible AC Transmission System-Modulated Filter Capacitor Compensation–Switched Filter Compensator (FACTS-MFCC-SFC). The switched modulated AC green plug filter significantly improves power quality and enhances power factor in cases of inrush and nonlinear loads.

Membrane protein guided and ATP-gated dual module DNA nanodevice for amplified fluorescence labeling of cancer cells

The occurrence and progression of tumors are accompanied by changes in both the microenvironment and cancer cells. Therefore, the detection of extracellular alert molecule accumulation, as well as the identification of abnormal cancer biomarkers both contribute to the accurate localization of individual cancer cells. Here, we have designed a cancer cell fluorescence labeling nanodevice with a membrane protein targeting domain and extracellular ATP-fueled amplification switch. The AND logic-based nanodevice light-up the cancer cell membrane only in the coexistence of target membrane proteins and abnormal ATP concentration. We demonstrated the ability of the nanodevice to selectively label target cells in a cell mixture supplemented with ATP at concentrations within the range in the tumor microenvironment (1–5×10−4 M). Additionally, the modular design of DNA nanodevices enables broad application to different cancer cells by replacing them with corresponding membrane protein aptamers. Our fluorescence labeling nanodevice with signal amplification shows promise in serving as an imaging tool for visualizing the impact of ATP regulation on cancer evolution.

Decoding the scientific creative-ability of subjects using dual attention induced graph convolutional-capsule network

There is an increasing demand of creative individuals in scientific research, innovation sectors of software industries and industrial research/development sectors. On the other hand, there are requirements of analytical minded people in investigation departments, academia and management sectors. Unfortunately, classification of individuals into creative and analytical categories based on their behavioral response is not easy. This paper makes an attempt to classify people into 4 categories: Analytical, High Creative, Medium Creative and Low Creative from their brain response during their participation in a creativity-test based on convergent problems. The proposed classification problem involves two main phases. In the first phase, a brain connectivity map is constructed from the electroencephalogram (EEG) response of the brain using Pearson’s correlation technique. In the second phase, a set of three centrality features, namely degree, closeness and betweenness are extracted from the connectivity map and fed to a classifier model for categorizing the afore-said class labels. The classifier model here synergistically combines one Graph Convolution Network (to abstract the brain connectivity-based centrality features) and one Capsule Network (to undertake the classification task) to develop the proposed Dual Attention Induced Graph Convolutional-Capsule network (DAIGC-CapsNet). The novelty of the proposed classifier model lies in the dual attention module and a new routing algorithm. The dual attention modules includes a) a Mish Induced Attention Module (MI-AM) to guide the graph convolution layers to focus on the most significant node attributes, and b) a Fused Attention Module (F-AM) to ensure the transmission of the most relevant predictions from the primary capsule to the class capsule layers. The latter attention module combines the effects of two sub-modules (channel and spatial) that concentrate on determining "what" and "where" to prioritize within the channel and spatial dimensions of the primary capsules. Lastly, the coupling between the primary and class capsule layers is strengthened by a Sparsemax based routing algorithm. Experiments conducted yield fruitful and definitive outcomes that substantiate the effectiveness of the proposed framework with respect to its conventional counterparts. Moreover, statistical validation of the proposed classifier using Friedman’s test also proves its efficacy compared to its competitors.

Cobalt-doping-induced energy level and oxygen vacancy dual modulation in tin dioxide for efficient triethylamine detection

Highly sensitive triethylamine (TEA) gas sensors have undergone extensive research, yet slow response/recovery speed, poor moisture resistance, and high detection limitation limited their further application. Herein, pure SnO2 and Co-doped SnO2 nanofibers have been synthesized by a simple electrostatic spinning technique, and their sensing performance has been systematically analyzed. As a result, the sensors based on the 2 mol% Co-doped SnO2 nanofibers show a good response (Ra/Rg = 50.2) to 100 ppm TEA at 200 °C. In addition, the 2 mol% Co-doped SnO2 based sensors display a rapid response and recovery speed, low limit of detection, superior anti-humidity property, and excellent reproducibility for TEA. The improved sensing performance owing to enhanced chemisorbed oxygen as a consequence of the Fermi level regulation and the abundant oxygen vacancies. All in all, 2 mol% Co-doped SnO2 nanofibers have the potential for efficient detection of low concentrations of TEA.

Leveraging a rationally designed veliparib-based anilide eliciting anti-leukemic effects for the design of pH-responsive polymer nanoformulation

Careful recruitment of the components of the HDAC inhibitory template culminated in veliparib-based anilide 8 that elicited remarkable cell growth inhibitory effects against HL-60 cell lines mediated via dual modulation of PARP [(IC50 (PARP1) = 0.02 nM) and IC50 (PARP2) = 1 nM)] and HDACs (IC50 value = 0.05, 0.147 and 0.393 μM (HDAC1, 2 and 3). Compound 8 downregulated the expression levels of signatory biomarkers of PARP and HDAC inhibition. Also, compound 8 arrested the cell cycle at the G0/G1 phase and induced autophagy. Polymer nanoformulation (mPEG-PCl copolymeric micelles loaded with compound 8) was prepared by the nanoprecipitation technique. The mPEG-PCL diblock copolymer was prepared by ring-opening polymerization method using stannous octoate as a catalyst. The morphology of the compound 8@mPEG-PCL was examined using TEM and the substance was determined to be monodispersed, spherical in form, and had an average diameter of 138 nm. The polymer nanoformulation manifested pH-sensitive behaviour as a greater release of compound 8 was observed at 6.2 pH as compared to 7.4 pH mimicking physiological settings. The aforementioned findings indicate that the acidic pH of the tumour microenvironment might stimulate the nanomedicine release which in turn can attenuate the off-target effects precedentially claimed to be associated with HDAC inhibitors.

Schottky Junction Enhanced Photosynthesis of Hydrogen Peroxide by Ultrathin Porous Carbon Nitride Supported Ni Nanoparticles.

Artificial photosynthesis for high-value hydrogen peroxide (H2O2) through a two-electron reduction reaction is a green and sustainable strategy. However, the development of highly active H2O2 photocatalysts is impeded by severe carrier recombination, ineffective active sites, and low surface reaction efficiency. We developed a dual optimization strategy to load dense Ni nanoparticles onto ultrathin porous graphitic carbon nitride (Ni-UPGCN). In the absence and presence of sacrificial agents, Ni-UPGCN achieved H2O2 production rates of 169 and 4116 μmol g-1 h-1 with AQY (apparent quantum efficiency) at 420 nm of 3.14% and 17.71%. Forming a Schottky junction, the surface-modified Ni nanoparticles broaden the light absorption boundary and facilitate charge separation, which act as active sites, promoting O2 adsorption and reducing the formation energy of *OOH (reaction intermediate). This results in a substantial improvement in both H2O2 generation activity and selectivity. The Schottky junction of dual modulation strategy provides novel insights into the advancement of highly effective photocatalytic agents for the photosynthesis of H2O2.

Graphene oxide membranes using MOF@Chitosan core-shell nanoparticles as dual modulators for dye separation

Graphene oxide membranes using MOF@Chitosan core-shell nanoparticles as dual modulators for dye separation

SmallMap: Low-cost Community Road Map Sensing with Uncertain Delivery Behavior

Accurate road networks play a crucial role in modern mobile applications such as navigation and last-mile delivery. Most existing studies primarily focus on generating road networks in open areas like main roads and avenues, but little attention has been given to the generation of community road networks in closed areas such as residential areas, which becomes more and more significant due to the growing demand for door-to-door services such as food delivery. This lack of research is primarily attributed to challenges related to sensing data availability and quality. In this paper, we design a novel framework called SmallMap that leverages ubiquitous multi-modal sensing data from last-mile delivery to automatically generate community road networks with low costs. Our SmallMap consists of two key modules: (1) a Trajectory of Interest Detection module enhanced by exploiting multi-modal sensing data collected from the delivery process; and (2) a Dual Spatio-temporal Generative Adversarial Network module that incorporates Trajectory of Interest by unsupervised road network adaptation to generate road networks automatically. To evaluate the effectiveness of SmallMap, we utilize a two-month dataset from one of the largest logistics companies in China. The extensive evaluation results demonstrate that our framework significantly outperforms state-of-the-art baselines, achieving a precision of 90.5%, a recall of 87.5%, and an F1-score of 88.9%, respectively. Moreover, we conduct three case studies in Beijing City for courier workload estimation, Estimated Time of Arrival (ETA) in last-mile delivery, and fine-grained order assignment.

A novel non-pretrained deep supervision network for polyp segmentation

In this paper, we propose a non-pretrained deep supervision network (NPD-Net) for polyp segmentation. Unlike previous deep supervision networks that rely on ground truth (GT) or pre-training with GT to supervise deep features(the prediction maps from decoder), we propose a novel deep supervision strategy that directly utilizes the GT encoder (that encodes GT to get its maps) after initialization to mitigate overfitting and enhance generalization ability without pre-training, in other words, a non-pretrained. This strategy makes up the gap of directly using GT for deep supervision while mitigates the risk of overfitting due to leverage the well-train pre-trained weights on a small polyp datasets. In addition, we introduce a simple and efficient parallel dual attention module (PDA) to enhance the global modeling ability. PDA executes spatial and channel attention in parallel, and adopts implicit positional encoding and transpose operation to reduce computational complexity. Finally, NPD-Net is able to effectively supervise deep features, expand the range of context information acquisition and improve segmentation performance, particularly in terms of generalization ability. Our experimental results on five benchmark datasets demonstrate that NPD-Net outperforms other state-of-the-art methods. The code will be available at https://github.com/guobaoxiao/NPD-Net.

Design of equally-spaced flat comb generator based on dual cascade mach-zehnder modulators

Design of equally-spaced flat comb generator based on dual cascade mach-zehnder modulators

MUNet++: Multilevel wavelet nested UNet++ demoiréing residual network

When mobile phones and digital cameras are used to capture information on a screen or store scenes with rich textures, an unpleasant moiré phenomenon occurs, which seriously degrades the quality of the image and affects subsequent image processing tasks. Therefore, to remove the moiré patterns, we propose a multilevel wavelet nested UNet++ demoiréing residual network called MUNet++. Unlike these methods based on the RGB domain, our method is performed in the frequency domain. The multilevel discrete wavelet transform (DWT) and the inverse discrete wavelet transform (IDWT) are elegantly embedded into the UNet++ encoder-decoder structure and transformed to the multilevel frequency domain, which captures more moiré features and preserves details of the image. In addition, we design a dual branch efficient pixel fusion attention module (DBEPFAM) that combines multiscale information, channel attention and the proposed pixel attention fusion module (PAFM) to improve the representation of features. In the network layer, a simple nonlinear unit (NU) is designed and embedded in the middle of the network layer to improve the representation capability of the network. Experimental results on two public datasets show that MUNet++ can effectively remove the moiré patterns and outperform existing state-of-the-art techniques.

Design and Study of a Rotating Piezoelectric Energy Harvester with Dual Excitation Modules

Design and Study of a Rotating Piezoelectric Energy Harvester with Dual Excitation Modules

Detecting Line Segments in Motion-Blurred Images With Events.

Making line segment detectors more reliable under motion blurs is one of the most important challenges for practical applications, such as visual SLAM and 3D line mapping. Existing line segment detection methods face severe performance degradation for accurately detecting and locating line segments when motion blur occurs. While event data shows strong complementary characteristics to images for minimal blur and edge awareness at high-temporal resolution, potentially beneficial for reliable line segment recognition. To robustly detect line segments over motion blurs, we propose to leverage the complementary information of images and events. Specifically, we first design a general frame-event feature fusion network to extract and fuse the detailed image textures and low-latency event edges, which consists of a channel-attention-based shallow fusion module and a self-attention-based dual hourglass module. We then utilize the state-of-the-art wireframe parsing networks to detect line segments on the fused feature map. Moreover, due to the lack of line segment detection datasets with pairwise motion-blurred images and events, we contribute two datasets, i.e., synthetic FE-Wireframe and realistic FE-Blurframe, for network training and evaluation. Extensive analyses on the component configurations demonstrate the design effectiveness of our fusion network. When compared to the state-of-the-arts, the proposed approach achieves the highest detection accuracy while maintaining comparable real-time performance. In addition to being robust to motion blur, our method also exhibits superior performance for line detection under high dynamic range scenes.

Peak-power and width tunable noise-like pulses from a figure-9 cavity with two separate gain segments

A novel feature of noise-like pulses (NLPs) from a dual pump module based figure-9 cavity has been presented in this manuscript. The laser is mode-locked by taking advantage of a non-linear amplifying loop mirror (NALM) and the cavity contains a LOOP and an ARM, each section comprises an Yb-doped fiber pumped by an independently controllable pump module. In stable conditions, the laser delivers nanosecond rectangular pulses at a fundamental repetition rate of 1.96 MHz. The NLPs are recognized from their double-scaled auto-correlation trace. Akin to typical NLPs, the pulse duration increases with the pump power of the pump modules placed at the LOOP, whereas, it is noticed that the pulse width tends to reduce with the power of the pump module placed outside the LOOP, which is an unconventional behavior for NLPs. Alongside the pulse duration, the peak power also behaves oppositely with the pump power of individual modules. In the best operating condition, the maximum average power is 766 mW corresponding to 136 W peak power and pulse energy of 390 nJ. This study offers a possible direction to explore the dynamics of NLPs in laser cavities with two independent pump modules.