Change Detection Research Articles

Synthesis, Characterization and DNA Binding Study of Silver N-Heterocyclic Carbene Complex [Bis (1,3-Dipentyl-1,3-Dihydro-2-Imidazole-2-Ylidene) Silver (I) hexafluorophosphate (V)] by Surface-Enhanced Raman Spectroscopy (SERS)

The silver organometallic compounds favorably target DNA in cancerous cells and stop their growth by inhibiting DNA replication. Specific properties of these compounds help in their action as drugs and different changes occur in the structure of DNA. In-depth study of the binding mechanism of drugs is of potential use for novel drug synthesis. Surface-enhanced Raman spectroscopy (SERS) coupled with multivariate data analysis was employed to probe the interaction of silver N-heterocyclic carbene complex (drug) with DNA. In this study, silver N-heterocyclic carbene complex was synthesized and various concentrations were exposed to known concentrations of DNA. Silver nanoparticles were used for enhancement of Raman signals, which enabled sensitive detection and characterization of molecular changes occurring during binding process. Principal component analysis (PCA) and partial least squares discriminant analysis (PLS-DA) were applied to extract meaningful information from the spectral data. Changes in intensity and position of significant SERS signals were noted, which gave an indication of the bonding of the drug with DNA. The results reveal the strong intercalative binding of drug with nitrogenous bases of DNA. PCA and PLS-DA provide evidence for successful DNA-drug interaction.

Detectability of gravity changes on the sea surface due to magma accumulation beneath submarine volcanoes

SUMMARY This study estimated the gravity change expected on the sea surface due to magma accumulation beneath submarine volcanoes. For calculation, a semi-analytical model describing the deformation of an infinite elastic cone was considered, in which a point spherical source was embedded on the axis. The expected gravity change exceeds 0.2 mGal in certain scenarios when $\delta V = 10^8\, \mathrm{m^3}$ of source volume change occurs 2 km below the summit, and the summit is shallower than 300 m below the sea surface. The steeper the slope of a submarine volcano, the greater the expected gravity change. 0.2 mGal of gravity variation can be detected with recent marine gravimeters such as cold atom interferometers. The computation method for the gravity change based on the integral transform (Mellin type) is elaborated in this paper.

A Comprehensive Review of In Situ Measurement Techniques for Evaluating the Electro-Chemo-Mechanical Behaviors of Battery Electrodes.

The global production landscape exhibits a substantial need for efficient and clean energy. Enhancing and advancing energy storage systems are a crucial avenue to optimize energy utilization and mitigate costs. Lithium batteries are the most effective and impressive energy utilization system at present, with good safety, high energy density, excellent cycle performance, and other advantages, occupying most of the market. However, due to the defects in the electrode material of the battery itself, the electrode will undergo the process of expansion, stress evolution, and electrode damage during electro-chemical cycling, which will degrade battery performance. Therefore, the detection of property changes in the electrode during electro-chemical cycling, such as the evolution of stress and the modulus change, are useful for preventing the degradation of lithium-ion batteries. This review presents a current overview of measurement systems applied to the performance detection of batteries' electrodes, including the multi-beam optical stress sensor (MOSS) measurement system, the digital image correlation (DIC) measurement system, and the bending curvature measurement system (BCMS), which aims to highlight the measurement principles and advantages of the different systems, summarizes a part of the research methods by using each system, and discusses an effective way to improve the battery performance.

Expert recommendations for magnetic resonance imaging of muscle disorders

Magnetic resonance (MRI) imaging of the skeletal muscles (muscle MRI for short) is increasingly being used in clinical routine for diagnosis and longitudinal assessment of muscle disorders. However, cross-centre standards for measurement protocol and radiological assessment are still lacking. The aim of this expert recommendation is to present standards for the application and interpretation of muscle MRI in hereditary and inflammatory muscle disorders. This work was developed in collaboration between neurologists, neuroradiologists, radiologists, neuropaediatricians, neuroscientists and MR physicists from different university hospitals in Germany. The recommendations are based on expert knowledge and afocused literature search. The indications for muscle MRI are explained, including the detection and monitoring of structural tissue changes and oedema in the muscle, as well as the identification of asuitable biopsy site. Recommendations for the examination procedure and selection of appropriate MRI sequences are given. Finally, steps for astructured radiological assessment are presented. The present work provides concrete recommendations for the indication, implementation and interpretation of muscle MRI in muscle disorders. Furthermore, it provides apossible basis for the standardisation of the measurement protocols at all clinical centres in Germany.

Change detection on multi-sensor imagery using mixed interleaved group convolutional network

The difference of the spatio-spectral features of multi-sensor image causes big difficulty in change detection because of the difficulties of the feature extraction. Unlike the traditional approaches that mainly relying on manually feature design, the advances of deep learning-based methods in deep feature extraction provide new alternatives for multi-sensor imagery change detection. Specifically, the incorporation of multi-scale information from remote sensing images holds paramount importance in change detection, consistently applied in the design of various deep learning models. This study investigated a change detection approach utilizing a mixed interleaved group convolutional network (MIGCNet) on multi-sensor remote sensing imagery, with a specific focus on fine-grained kernel space and multi-scale feature analysis within convolution operations. The proposed MIGCNet, with parallel branches as the fundamental architecture, can distinguish the change information effectively by the proposed mixed interleaved group convolution (MIGC) module, which combined mixed convolution with interleaved group convolution. Meanwhile, multi-loss supervision is utilized to promote the performance of the proposed MIGCNet. Experimental results demonstrate the outperformance of the MIGCNet to handle change detection with multi-sensor images on urban area. Considering different datasets, the Overall Accuracy and Kappa Coefficient are reaching 0.97 and 80.67%, respectively, and the miss detection rate and the false alarm rate are as low as 0.17 and 0.18, respectively.

Negative Pressure Wound Therapy-A Vacuum-Mediated Positive Pressure Wound Therapy and a Closer Look at the Role of the Laser Doppler.

Background: Negative pressure wound therapy (NPWT) is an intensely investigated topic, but its mechanism of action accounts for one of the least understood ones in the area of wound healing. Apart from a misleading nomenclature, by far the most used diagnostic tool to investigate NPWT, the laser Doppler, also has its weaknesses regarding the detection of changes in blood flow and velocity. The aim of the present study is to explain laser Doppler readings within the context of NPWT influence. Methods: The cutaneous microcirculation beneath an NPWT system of 10 healthy volunteers was assessed using two different laser Dopplers (O2C/Rad-97®). This was combined with an in vitro experiment simulating the compressing and displacing forces of NPWT on the arterial and venous system. Results: Using the O2C, a baseline value of 194 and 70 arbitrary units was measured for the flow and relative hemoglobin, respectively. There was an increase in flow to 230 arbitrary units (p = 0.09) when the NPWT device was switched on. No change was seen in the relative hemoglobin (p = 0.77). With the Rad-97®, a baseline of 92.91% and 0.17% was measured for the saturation and perfusion index, respectively. No significant change in saturation was noted during the NPWT treatment phase, but the perfusion index increased to 0.32% (p = 0.04). Applying NPWT compared to the arteriovenous-vessel model resulted in a 28 mm and 10 mm increase in the venous and arterial water column, respectively. Conclusions: We suspect the vacuum-mediated positive pressure of the NPWT results in a differential displacement of the venous and arterial blood column, with stronger displacement of the venous side. This ratio may explain the increased perfusion index of the laser Doppler. Our in vitro setup supports this finding as compressive forces on the bottom of two water columns within a manometer with different resistances results in unequal displacement.

Stimulus-dependent differences in cortical versus subcortical contributions to visual detection in mice

The primary visual cortex (V1) and the superior colliculus (SC) both occupy stations early in the processing of visual information. They have long been thought to perform distinct functions, with the V1 supporting the perception of visual features and the SC regulating orienting to visual inputs. However, growing evidence suggests that the SC supports the perception of many of the same visual features traditionally associated with the V1. To distinguish V1 and SC contributions to visual processing, it is critical to determine whether both areas causally contribute to the detection of specific visual stimuli. Here, mice reported changes in visual contrast or luminance near their perceptual threshold while white noise patterns of optogenetic stimulation were delivered to V1 or SC inhibitory neurons. We then performed a reverse correlation analysis on the optogenetic stimuli to estimate a neuronal-behavioral kernel (NBK), a moment-to-moment estimate of the impact of V1 or SC inhibition on stimulus detection. We show that the earliest moments of stimulus-evoked activity in the SC are critical for the detection of both luminance and contrast changes. Strikingly, there was a robust stimulus-aligned modulation in the V1 contrast-detection NBK but no sign of a comparable modulation for luminance detection. The data suggest that behavioral detection of visual contrast depends on both V1 and SC spiking, whereas mice preferentially use SC activity to detect changes in luminance. Electrophysiological recordings showed that neurons in both the SC and V1 responded strongly to both visual stimulus types, while the reverse correlation analysis reveals when these neuronal signals actually contribute to visually guided behaviors.

Cross-level and multiscale CNN-Transformer network for automatic building extraction from remote sensing imagery

ABSTRACT Automatic building detection from remote sensing images holds significant applications in various domains, including cartography, land-use change detection, urban planning, and 3D intelligent city construction. Driven by the innovation of deep learning technology, convolutional neural networks (CNNs) have emerged as a powerful framework for automatic interpretation, exhibiting hierarchical feature generation and robust representation for building extraction. However, the inherent computational limitations of convolutional operators hinder the acquisition of an effective global receptive field. Recent advancements in deep learning have witnessed the adoption of the multi-head self-attention (MHSA) mechanism by Transformer, presenting the superiority of global context construction. Nonetheless, this network structure encounters difficulties in effectively modelling two-dimensional spatial structure features with scale variation in semantic segmentation and often has huge parameters with high algorithm complexity. To address the above problems, this paper proposed a hybrid deep neural network synergistically harnessing the superiority of CNN and Transformer to extract buildings from remote sensing images. Meanwhile, an improved MHSA mechanism is developed to facilitate the construction of multiscale global representations, concurrently reducing computational complexity. Furthermore, the cross-level MHSA modules the semantic correlation between the deep and shallow-level features to refine spatial fine-grained information. The proposed method was comprehensively evaluated on two building datasets via quantitative analysis and visualization. The experimental results confirmed that the developed network and modules outperform other state-of-the-art methods and can effectively improve the accuracy (93.4% F1-score on the WHU dataset, 89.38% F1-score on the CAMB dataset) with high efficiency for building extraction.

A lightweight building change detection network with coordinate attention and multiscale fusion

A lightweight building change detection network with coordinate attention and multiscale fusion

A self-powered and supercapacitive microneedle continuous glucose monitoring system with a wide range of glucose detection capabilities

Continuous detection of sudden changes in blood glucose is essential for individuals with diabetes who have difficulty in maintaining optimal control of their blood glucose levels. Hypoglycemic shock or a hyperglycemic crisis are likely to occurs in patients with diabetes and poses a significant threat to their lives. Currently, commercial continuous glucose monitoring (CGM) has limits in the glucose concentration detection range, which is 40–500 mg/dL, making it difficult to prevent the risk of hyperglycemic shock. In addition, current CGMs are invasive, cause pain and irritation during usage, and expensive. In this research, we overcome these limitations by introducing a novel mechanism to detect glucose concentration using supercapacitors. The developed CGM, which is self-powered and minimally invasive due to the use of microneedles, can detect a wider range of glucose concentrations than commercial sensors. In addition, efficacy and stability were proven through in vitro and in vivo experiments. Thus, this self-powered, microneedle and supercapacitive-type CGM can potentially prevent both hypoglycemic and complications of hyperglycemia without pain and with less power consumption than current commercial sensors.

Modes of Variability in E3SM and CESM Large Ensembles

Abstract An adequate characterization of internal modes of climate variability (MoV) is a prerequisite for both accurate seasonal predictions and climate change detection and attribution. Assessing the fidelity of climate models in simulating MoV is therefore essential; however, doing so is complicated by the large intrinsic variations in MoV and the limited span of the observational record. Large ensembles (LEs) provide a unique opportunity to assess model fidelity in simulating MoV and quantify intermodel contrasts. In this work, these goals are pursued in four recently produced LEs: the Energy Exascale Earth System Model (E3SM) versions 1 and 2 LEs, and the Community Earth System Model (CESM) versions 1 and 2 LEs. In general, the representation of global coupled modes is found to improve across successive E3SM and CESM versions in conjunction with the fidelity of the base state climate while the patterns of extratropical modes are well simulated across the ensembles. Various persistent shortcomings for all MoV are however identified and discussed. The results both demonstrate the successes of these recent model versions and suggest the potential for continued improvement in the representation of MoV with advances in model physics. Significance Statement Modes of variability play a critical role in prediction of seasonal to decadal climate variability and detection of forced climate change, but historically many modes have been poorly simulated by coupled climate models. Using recently produced large ensembles, this work demonstrates the improved simulation of a broad range of internal modes in successive versions of the E3SM and CESM and discusses opportunities for further advances.

MDANet: A High-Resolution City Change Detection Network Based on Difference and Attention Mechanisms under Multi-Scale Feature Fusion

In the domains of geographic information systems and remote sensing image analysis, change detection is vital for examining surface variations in high-resolution remote sensing pictures. However, the intricate texture characteristics and rich details found in high-resolution remote sensing photos are difficult for conventional change detection systems to deal with. Target misdetection, missed detections, and edge blurring are further problems with current deep learning-based methods. This research proposes a high-resolution city change detection network based on difference and attention mechanisms under multi-scale feature fusion (MDANet) to address these issues and improve the accuracy of change detection. First, to extract features from dual-temporal remote sensing pictures, we use the Siamese architecture as the encoder network. The Difference Feature Module (DFM) is employed to learn the difference information between the dual-temporal remote sensing images. Second, the extracted difference features are optimized with the Attention Refinement Module (ARM). The Cross-Scale Fusion Module (CSFM) combines and enhances the optimized attention features, effectively capturing subtle differences in remote sensing images and learning the finer details of change targets. Finally, thorough tests on the BTCDD dataset, LEVIR-CD dataset, and CDD dataset show that the MDANet algorithm performs at a cutting-edge level.

T-UNet: triplet UNet for change detection in high-resolution remote sensing images

ABSTRACT Remote sensing image change detection aims to identify differences between images acquired at different times in the same area, crucial for land management, environmental monitoring, and disaster assessment. Current change detection methods mainly use Siamese or early fusion structures. Siamese networks focus on object features at different times but lack attention to change information, leading to false alarms and missed detections. Early fusion structures focus on fused features but neglect temporal object features, hindering accurate change detection. To address these issues, we propose a novel network, Triplet UNet (T-UNet), employing a triplet encoder to simultaneously extract object features and change features between the pre- and post-time-phase images. To effectively interact features extracted from the three branches of triplet encoder, we propose a multi-branch spatial-spectral cross-attention module. In the decoder, we employ channel attention and spatial attention mechanisms to fully mine and integrate detailed texture and semantic localization information. The proposed T-UNet surpasses seven other state-of-the-art methods on three publicly available datasets. Extensive experiments verify the effectiveness of the proposed structure and modules as well as the superiority of the proposed T-UNet. The source code for the proposed T-UNet is accessible at https://github.com/Pl-2000/T-UNet.

HSAA-CD: A Hierarchical Semantic Aggregation Mechanism and Attention Module for Non-Agricultural Change Detection in Cultivated Land

Cultivated land plays a fundamental role in the sustainable development of the world. Monitoring the non-agricultural changes is important for the development of land-use policies. A bitemporal image transformer (BIT) can achieve high accuracy for change detection (CD) tasks and also become a key scientific tool to support decision-making. Because of the diversity of high-resolution RSIs in series, the complexity of agricultural types, and the irregularity of hierarchical semantics in different types of changes, the accuracy of non-agricultural CD is far below the need for the management of the land and for resource planning. In this paper, we proposed a novel non-agricultural CD method to improve the accuracy of machine processing. First, multi-resource surveying data are collected to produce a well-tagged dataset with cultivated land and non-agricultural changes. Secondly, a hierarchical semantic aggregation mechanism and attention module (HSAA) bitemporal image transformer method named HSAA-CD is performed for non-agricultural CD in cultivated land. The proposed HSAA-CD added a hierarchical semantic aggregation mechanism for clustering the input data for U-Net as the backbone network and an attention module to improve the feature edge. Experiments were performed on the open-source LEVIR-CD and WHU Building-CD datasets as well as on the self-built RSI dataset. The F1-score, intersection over union (IoU), and overall accuracy (OA) of these three datasets were 88.56%, 84.29%, and 68.50%; 79.84%, 73.41%, and 59.29%; and 98.83%, 98.39%, and 93.56%, respectively. The results indicated that the proposed HSAA-CD method outperformed the BIT and some other state-of-the-art methods and proved to be suitable accuracy for non-agricultural CD in cultivated land.

From psychophysiology to brain imaging: forty-five years MMN history of investigating acoustic change sensitivity.

Forty-five years have passed since the first publication of the mismatch negativity (MMN) event-related brain potential (ERP) component. The first 10years of research hardly gained any particular attention of the scientific community interested in acoustic perception. Debates on the nature of sensation versus perception were going on, and the technical possibilities to record ERPs, called in general evoked potentials, were very limited. Subtle changes in pure tone frequency or intensity giving rise to the MMN component were first investigated in humans. The background of the theoretical model developed by Risto Näätänen was the orientation reaction model of E.N. Sokolov published in 1963 so that the MMN was seen first as an electrophysiological correlate of auditory change detection. This fundamental ability of the auditory system seen as crucial for survival led to the development of the first animal model of the MMN (Csépe et al. in Clin Neurophysiol 66: 571-578, 1987). Indeed, it was confirmed that the MMN was the brain correlate of subtle changes detected that might alert to potential threats in the environment and direct the behavioral orientation. The investigations performed after 2000 introduced complex models and more sophisticated methods, both in animal and human studies, so that the MMN method was on the way to become a tool on the first place and not the main goal of research. This approach was further strengthened by the increasing number of studies on different clinical populations aiming at future applications. The aim of our review is to describe and redefine what the MMN may reflect in auditory perception and to show why and how this brain correlate of changes in the auditory scene can be used as a valuable tool in cognitive neuroscience research. We refer to publications selectedto underly theargument the MMN cannot be classified anymore as a sign of simple change detection and not all the indicators used to confirm howgenuine theMMN elicited byvariations of tones are valid for those tospeechcontrasts. We provide a fresh view on the broadly used MMN models, provided by some influential publications as well as on the unwritten history of MMN research aiming to give revised picture on what the MMN may truly reflect. We show how the focus and terminology of the MMN research have changed and what kind of misunderstandings and seemingly contradictive results prevent the MMN community to accept a generally usable cognitive model.

Unsupervised Change Detection in HR Remote Sensing Imagery Based on Local Histogram Similarity and Progressive Otsu

Unsupervised change detection of land cover in multispectral satellite remote sensing images with a spatial resolution of 2–5 m has always been a challenging task. This paper presents a method of detecting land cover changes in high-spatial-resolution remote sensing imagery. This method has three characteristics: (1) Extended center-symmetric local binary pattern (XCS-LBP) is used to extract image features to emphasize spatial context information in initial change detection. Then, spectral information is combined to improve the accuracy of change detection. (2) The local histogram distance of XCS-LBP features is used as the change vector to improve the expression of change information. (3) A progressive Otsu method is developed for threshold segmentation of the change vector to reduce the false detection rate. Four datasets with different landscape complexities and seven state-of-the-art unsupervised change detection methods were used to test the performance of the proposed method. Quantitative results showed that the proposed method reduced the false detection rate and improved the accuracy of the detection of land cover changes. The F1 score achieved by the proposed method reached 0.8688, 0.8867, 0.7725, and 0.6634, respectively, which are higher than the highest corresponding F1 score achieved by the benchmark methods (0.8533, 0.8549, 0.6545, and 0.5895, respectively).

Functionalized gold nanoparticle enhanced nanorod hyperbolic metamaterial biosensor for highly sensitive detection of carcinoembryonic antigen

Hyperbolic metamaterial (HMM) biosensors based on metals have superior performance in comparison with conventional plasmonic biosensors in the detection of low concentrations of molecules. In this study, a nanorod HMM (NHMM) biosensor based on refractive index changes for carcinoembryonic antigen (CEA) detection is developed using secondary antibody modified gold nanoparticle (AuNP-Ab2) nanocomposites as signal amplification element for the first time. Numerical analysis based on finite element method is conducted to simulate the perturbation of the electric field of bulk plasmon polariton (BPP) supported by a NHMM in the presence of a AuNP. The simulation reveals an enhancement of the localized electric field, which arises from the resonant coupling of BPP to the localized surface plasmon resonance supported by AuNPs and is beneficial for the detection of changes of the refractive index. Furthermore, the AuNP-Ab2 nanocomposites-based NHMM (AuNP/Ab2-NHMM) biosensor enables CEA detection in the visible and near-infrared regions simultaneously. The highly sensitive detection of CEA with a wide linear range of 1–500 ng/mL is achieved in the near-infrared region. The detectable concentration of the AuNP/Ab2-NHMM biosensor has a 50-fold decrease in comparison with a NHMM biosensor. A low detection limit of 0.25 ng/mL (1.25 pM) is estimated when considering a noise level of 0.05 nm as the minimum detectable wavelength shift. The proposed method achieves high sensitivity and good reproducibility for CEA detection, which makes it a novel and viable approach for biomedical research and early clinical diagnostics.

A Training-Free Estimation Method for the State of Charge and State of Health of Series Battery Packs under Various Load Profiles

To ensure the accuracy of state of charge (SOC) and state of health (SOH) estimation for battery packs while minimizing the amount of pre-experiments required for aging modeling and the scales of computation for online management, a decisive-cell-based estimation method with training-free characteristic parameters and a dynamic-weighted estimation strategy is proposed in this paper. Firstly, to reduce the computational complexity, the state estimation of battery packs is summed up to that of two decisive cells, and a new selection approach for the decisive cells is adopted based on the detection of steep voltage changes. Secondly, two novel ideas are implemented for the state estimation of the selected cells. On the one hand, a set of characteristic parameters that only exhibit local curve shrinkage with aging is chosen, which keeps the corresponding estimation approaches away from training. On the other hand, multiple basic estimation approaches are effectively combined by their respective dynamic weights, which ensures the estimation can maintain a good estimation accuracy under various load profiles. Finally, the experimental results show that the new method can quickly correct the initial setting deviations and have a high estimation accuracy for both the SOC and SOH within 2% for a series battery pack consisting of cells with obvious inconsistency.

A new online learning algorithm for streaming data and decision support with a Bayesian approach

ABSTRACT With the new revolution in data technology, many types of streaming data are automatically generated in our living environment. The vast amount of information carried by this streaming data demands decision support from new online learning algorithms. In this paper, we propose a new online learning algorithm to monitor changes in stream data/system status and provide automatic decision support using streaming data. The new online learning algorithm we propose consists of two components. Firstly, we adapt an existing Bayesian approach into an online learning algorithm for change detection, which serves as Component A. Then, we integrate this algorithm with a modified online learning method from repeated games (Play against Random Past), which forms Component B. Theoretical support for Component B is provided with a mathematical proof at the end. We demonstrate the algorithm's performance in this paper through simulations using both artificial data from a random process and data from the 2009 H1N1 pandemic.

Increasing seasonal variation in the extent of rivers and lakes from 1984 to 2022

Abstract. Knowledge of the spatial and temporal distribution of surface water is important for water resource management, flood risk assessment, monitoring ecosystem health, constraining estimates of biogeochemical cycles and understanding our climate. While global-scale spatiotemporal change detection of surface water has significantly improved in recent years due to planetary-scale remote sensing and computing, it has remained challenging to distinguish the changing characteristics of rivers and lakes. Here we analyze the spatial extent of permanent and seasonal rivers and lakes globally over the past 38 years based on new data of river system extents and surface water trends. Results show that while the total permanent surface area of both rivers and lakes has remained relatively constant, the areas with intermittent seasonal coverage have increased by 12 % and 27 % for rivers and lakes, respectively. The increase is statistically significant in over 84 % of global water catchments based on Spearman's rank correlations (rho) above 0.05 and p values less than 0.05. The seasonal river extent is nearly 32 % larger than the previously observed annual mean river extent, suggesting large seasonal variations that impact not only ecosystem health but also estimations of terrestrial biogeochemical cycles of carbon. The outcomes of our analysis are shared as the Surface Area of Rivers and Lakes (SARL) database, serving as a valuable resource for monitoring and research of hydrological cycles, ecosystem accounting, and water management.