Identification Applications Research Articles

Characterization of the complete mitogenome of Tiarella polyphylla, commonly known as Asian foamflower: insights into the multi-chromosomes structure and DNA transfers

BackgroundTiarella polyphylla D. Don has been traditionally used to cure asthma and skin eruptions. However, the sequence and the structure of the mitogenome of T. polyphylla remained elusive, limiting the genomic and evolution analysis based on the mitogenome.ResultsUsing a combination of Illumina and Nanopore sequencing reads, we de novo assembled the complete mitogenome of T. polyphylla. In addition to unveiling the major configuration of the T. polyphylla mitogenome was three circular chromosomes with lengths of 430,435 bp, 126,943 bp, and 55,269 bp, we revealed five (R01-R05) and one (R06) repetitive sequence could mediate the intra- and inter-chromosomal recombination, respectively. Furthermore, we identified 208 short and 25 long tandem segments, seven cp-derived mtDNAs, 106 segments of mtDNAs transferred to the nuclear genome, and 653 predicted RNA editing sites. Based on the sequence of the mitogenomes, we obtained the resolved phylogeny of the seven Saxifragales species.ConclusionsThese results presented the mitogenome features and expanded its potential applications in phylogenetics, species identification, and cytoplasmic male sterility (CMS) in the future.

High transmission efficiency long-wave infrared multispectral modulation array based on nanogap engineering

The long-wave infrared spectrum’s unique advantages in molecular fingerprinting and atmospheric transmission have led to its widespread application in detection, identification, medical diagnosis, and environmental monitoring. Consequently, there has been a strong focus on developing multispectral structure arrays with high transmission efficiency. In this study, we introduce a high-transmission-efficiency nanocoaxes field enhancement structures array based on complex nanogap engineering, exhibiting a transmission of 21.59 % within an open area of 3 % and accompanied by a 90-fold enhancement of the nanogap field. With the modulation of nanogaps, the absolute transmission can exceed 60 %. The experimental data and finite element simulation results of the spectrally tunable array confirm that the origin of resonance is attributed to the enhanced localized electromagnetic modes supported by nanogaps. Furthermore, we demonstrated the proposed nanocoaxes field-enhancement structures in practical applications such as molecular resonance absorption enhancement and material composition analysis. Our work not only provides a method for on-chip multispectral tuning in the long-wave infrared range but also contributes to further advancing the development of long-wave infrared nanophotonic structures.

Improved LMS Performance for System Identification Application

Improved LMS Performance for System Identification Application

Enhancing HVAC multivariable system performance through hybrid modeling and direct Nyquist Array control

This study explores a closed-loop multivariable control system for HVAC systems. A hybrid distributed parameter-lumped model was developed using MATLAB’s System Identification Application, resulting in a transfer function matrix. The Direct Nyquist Array (DNA) control strategy was applied, dividing the HVAC system into two SISO loops, each equipped with PID controllers. Stability analysis confirmed system stability according to the Nyquist criterion. Simulation of the system under DNA control showed improved performance over the open-loop setup, with faster response times of nearly 1 s and overshoots between 7% and 15%, maintaining thermal comfort and occupant satisfaction. The system effectively managed ambient heat transfer variations, maintaining desired airflow rate and air temperature. The closed-loop setup exhibited robustness against disturbances, quickly returning to steady-state values. Settling times ranged from 1 to 2 s with minimal impact on HVAC performance. Overall, the closed-loop multivariable DNA control with SISO PID controllers proved reliable for enhancing energy efficiency and thermal comfort in HVAC systems. Practical application This study provides a comprehensive framework for multivariable control in HVAC systems, significantly benefiting built environment professionals by enhancing energy efficiency and occupant comfort. By applying advanced control strategies, the research offers practical solutions for optimizing HVAC performance, reducing operational costs, and ensuring sustainability in building management. The detailed analysis and methodologies presented can be directly implemented to improve system reliability and responsiveness, thereby supporting professionals in creating smarter, more resilient, and environmentally friendly built environments.

Highly efficient Dy3+ activated Sr9Al6O18 nanophosphors for W-LEDs, optical thermometry and deep learning-based intelligent system for personal identification applications

A novel single-phase Dy3+ activated Sr9Al6O18 nanophosphors (NPs) was synthesized using a solution combustion method with urea as the fuel. When phosphors are excited at 350 nm, their photoluminescence (PL) spectra exhibit strong emissions at 484 and 574 nm, which are associated with the 4F9/2→6H15/2 and 4F9/2→6H13/2 transitions. These emissions are attributed to magnetic and electric dipole allowed transitions, correspondingly. It is found that dipole–dipole interactions are the main source of concentration quenching and energy transfer among the activator ions. The prepared NPs show thermal stability up to 480 K having an activation energy (Ea) of 0.2437 eV, as determined by temperature-dependent photoluminescence (TDPL). With a remarkable internal quantum efficiency (IQE) of 66.14 %, the optimized phosphor achieves notable relative sensitivity (Sr) and absolute sensitivity (Sa), estimated to be 2.65 % K−1 and 1.38×10−3 K−1 at 300 K, respectively. Thus, Sr9Al6O18:Dy3+ is a promising material for non-contact optical thermometry applications. Latent fingerprints (LFPs) and latent lip prints (LLPs) can be efficiently produced on a variety of surfaces by using the optimized NPs with the simple powder dusting technique. The prints are made visible under UV light with a 365 nm wavelength. The You Only Look Once version 8 (YOLOv8x) approach analyzes the overall morphological correlations of different minutiae to identify essential minutiae that indicate the identities of people from FPs. Splitting and matching multi-scale fingerprints (FPs) features improves the validity of the authenticating findings. The results show that the recommended method, which improves the entire FP authentication process, offers exceptional accuracy, resilience and speed. The findings provide strong support for the implementation of the optimized NPs in photonic devices, optical thermometers, and advanced forensic applications.

GaitAE: A Cognitive Model-Based Autoencoding Technique for Gait Recognition

Gait recognition is a long-distance biometric technique with significant potential for applications in crime prevention, forensic identification, and criminal investigations. Existing gait recognition methods typically introduce specific feature refinement modules on designated models, leading to increased parameter volume and computational complexity while lacking flexibility. In response to this challenge, we propose a novel framework called GaitAE. GaitAE efficiently learns gait representations from large datasets and reconstructs gait sequences through an autoencoder mechanism, thereby enhancing recognition accuracy and robustness. In addition, we introduce a horizontal occlusion restriction (HOR) strategy, which introduces horizontal blocks to the original input sequences at random positions during training to minimize the impact of confounding factors on recognition performance. The experimental results demonstrate that our method achieves high accuracy and is effective when applied to existing gait recognition techniques.

Path-wise continuous-time transmission with applications in source identification from partial observations

Path-wise continuous-time transmission with applications in source identification from partial observations

Expression of Steroidogenic Factor (SF1) and Application in Cytopathologic Identification of Intrapancreatic Ectopic Splenic Tissue

Expression of Steroidogenic Factor (SF1) and Application in Cytopathologic Identification of Intrapancreatic Ectopic Splenic Tissue

Application of a molecular networking approach using LC-HRMS combined with the MetWork webserver for clinical and forensic toxicology

Backgrounds and aimsIn toxicology, LC-HRMS for untargeted screening yields a great deal of high quality spectral data. However, there we lack tools to visualize/organize the MS data. We applied molecular networking (MN) to untargeted screening interpretation. Our aims were to compare theoretical MS libraries obtained in silico with our experimental dataset in patients to broaden its application, and to use the MetWork web application for metabolite identification. MethodsSamples were analyzed using an LC-HRMS system. For MN, data was generated using MZmine, and analyzed and visualized using MetGem. MetWork annotations were filtered and this file was used for annotation of the previously obtained MN. Results155 compounds including drugs found in patients were recorded. Using this dataset, we confirmed in 60 patients intake of tramadol, amitriptyline bromazepam, and cocaine. The results obtained by the reference methods were confirmed by MN approaches. Eighty percent of the compounds were common to both conventional and MN approaches. Using MetWork, metabolites and parent drugs such as amitriptyline, its metabolite nortriptyline and amitriptyline glucuronide phase 2 metabolites were anticipated and proposed as putative annotations. ConclusionThe workflow increases confidence in toxicological screening by highlighting putative structures in biological matrices in combination with CFM-ID (Competitive Fragmentation Modeling for Metabolite Identification) and MetWork to extend the annotation of potential drugs even without a reference standard.

Automatic Aircraft Identification with High Precision from SAR Images Considering Multiscale Problems and Channel Information Enhancement

The SAR system possesses the ability to carry out all-day and all-weather imaging, which is highly valuable in the application of aircraft identification. However, aircraft identification from SAR images still faces great challenges due to speckle noise interference, multiscale problems, and complex background interference. To solve these problems, an efficient bidirectional path multiscale fusion and attention network (EBMA-Net) is proposed in this paper. It employs bidirectional connectivity to fuse the features of aircraft with different scales to perform the accurate detection of aircraft even when the background is highly complex. In the presented EBMA-Net, a module called efficient multiscale channel attention fusion (EMCA) and three parallel squeeze efficient channel attention (SECA) modules are proposed. In the EMCA module, the bidirectional paths are created by stacking upper and lower fusion modules, which effectively integrate shallow detailed features and deep semantic information. So, the detection performance of aircraft at different scales is improved. In the SECA module, the dependency relationship between feature channels is explicitly modeled, which can automatically learn the importance of different channels, prioritize key features, so as to improve the precision and robustness of aircraft identification. In the experiment, the public dataset of aircraft identification (i.e., SAR-AIRcraft-1.0, which is generated from the GF-3 satellite) from high-resolution SAR systems is used, and several other excellent target-detection networks are used for performance comparison, namely, YOLOv5s, YOLOv7, MGCAN, and EBPA2N. According to the results, the average aircraft detection accuracy of EBMA-Net is 91.31%, which is 4.5% higher than YOLOv7; and the false alarm rate is decreased by 5%. Its accuracy in the identification of aircraft can reach 95.6%, which is about 3.7% higher than YOLOv7. Therefore, the EBMA-Net obviously outperforms the other networks for aircraft detection and identification. The proposed EBMA-Net, which can capture the detailed information and better restrain the background interference, could also be used to perform the detection and identification of dense targets with different scales and background from SAR images.

Identification Methods of Tumor Tissue Origin Based on Different STR Typing Kits.

To establish the identification method of tumor tissue origin based on commonly used STR typing kits. ForenSeqTM DNA Signature Prep kit was used to detect the typing of 27 autosomal STR loci in 55 paired tumor tissue samples (tumor tissue paired with normal tissue of the same individual) and 75 unrelated individual whole blood samples. The genotyping data of full sibling pairs and parent-child pairs of 55 tumor tissues were simulated. The number of total identical alleles (An) and identity by state (IBS) scores were calculated within the paired carcinoma (PC), the tumor-unrelated individual (UI), the tumor-simulated full sibling (FS) and the tumor-simulated parent-offspring (PO) groups. The tumor tissue origin identification and prediction models of 8 commonly used STR typing kits were established based on the statistical results obtained above, and an attempt was made to establish a specific model for tumor tissue origin identification. The accuracy, sensitivity and specificity were verified and evaluated using the additional 23 paired tumor tissue samples. (1) In any kit, there was no statistically significant difference in the number of loci shared with 0 identical allele (A0) between the PC and PO groups. The number of loci shared with 1 identical allele (A1), 2 identical alleles (A2), and IBS scores were statistically significant different between the PC group and the UI, FS and PO groups. (2) The An and IBS scores of different STRs varied in different groups. The A2 levels of 13 STRs (CSF1PO, D12S391, D19S433, D20S482, D2S1338, D3S1358, D4S2408, D7S820, D8S1179, FGA, TH01, TPOX, vWA) in PC group were higher than those of other STR loci. The A2 levels of two STRs (D6S1043, Penta E) in UI group were significantly lower than those of other STR loci. (3) The tumor tissue origin identification and prediction models of 8 commonly used STR typing kits and the identification model of tumor tissue origin with 15 STR loci (15-STRs) were successfully established, with sensitivity of 100%, specificity of 97.56%-99.88%, and accuracy of 97.59%-99.89%. Among them, the 15-STRs model had 100% sensitivity, 99.88% specificity, and 99.89% accuracy, which were higher than those of commonly used commercial kits. This study successfully establishes the tumor tissue origin identification methods with 8 commonly used STR typing kits, which expands the application of tumor tissue origin identification. In addition, the differences of different loci in the identification of tumor tissue origin were compared, and 15 STR loci which were particularly suitable for the identification of tumor tissue origin were selected, providing the data basis for the establishment of tumor origin tracing kits in future.

Deep Learning for early Identification and Prediction of Knee Osteoarthritis

Evaluation of persons with knee osteoarthritis (OA) whose structural progression is anticipated is essential to the development of medications that treat diseases in different ways. Deep learning approaches have rapidly gained popularity as the preferred tool for analyzing medical image segmentation. This study explores many contributions in the field of Deep Learning in medicine, covers the most common difficulties in recent years, and also analyzes the principles of Deep Learning ideas relevant to medical image categorization. We have used Deep Learning to make predictions from X-ray images. Deep Learning research can be applied to image classification, object identification, segmentation, registration, and other applications. Medical image analysis has a number of problems, such as poor picture enhancement and low image analysis dependability, that deep learning has been developed to define and solve. We propose research to address these current problems and advance the development of medical image segmentation. This feasibility research shows the interest in deep learning for osteoarthritis and could help even experienced radiologists with the difficult challenge of determining people who are at risk for illness development.

Beyond SIRD models: a novel dynamic model for epidemics, relating infected with entries to health care units and application for identification and restraining policy.

Epidemic models of susceptibles, exposed, infected, recovered and deceased (SΕIRD) presume homogeneity, constant rates and fixed, bilinear structure. They produce short-range, single-peak responses, hardly attained under restrictive measures. Tuned via uncertain I,R,D data, they cannot faithfully represent long-range evolution. A robust epidemic model is presented that relates infected with the entry rate to health care units (HCUs) via population averages. Model uncertainty is circumvented by not presuming any specific model structure, or constant rates. The model is tuned via data of low uncertainty, by direct monitoring: (a) of entries to HCUs (accurately known, in contrast to delayed and non-reliable I,R,D data) and (b) of scaled model parameters, representing population averages. The model encompasses random propagation of infections, delayed, randomly distributed entries to HCUs and varying exodus of non-hospitalized, as disease severity subdues. It closely follows multi-pattern growth of epidemics with possible recurrency, viral strains and mutations, varying environmental conditions, immunity levels, control measures and efficacy thereof, including vaccination. The results enable real-time identification of infected and infection rate. They allow design of resilient, cost-effective policy in real time, targeting directly the key variable to be controlled (entries to HCUs) below current HCU capacity. As demonstrated in ex post case studies, the policy can lead to lower overall cost of epidemics, by balancing the trade-off between the social cost of infected and the economic contraction associated with social distancing and mobility restriction measures.

Single-Probe-Based Sensor Array for Fingerprint Recognition of Trivalent Metal Ions and Application in Water Identification.

Abnormal concentration levels of trivalent metal ions (M3+) might hinder their natural biological activities in physiological processes and cause severe health hazards. Herein, a dual-chromophore probe (RhB-TPE) composed of rhodamine and tetraphenylethene (TPE) units was synthesized and explored for discriminating M3+ ions. It exhibited special aggregation and AIE properties in aqueous media. Its ensemble with anionic surfactant SDBS assemblies (RhB-TPE/SDBS) could be utilized as fluorescent sensors for selective and sensitive detection of M3+ ions such as Fe3+, Al3+, and Cr3+ by illustrating quenched TPE emission and switched-on rhodamine emission. Moreover, the use of SDBS assemblies at two concentrations could provide a single-probe-based sensor array and realize four-signal pattern recognition of different concentrations of the three M3+ ions and identify M3+ mixtures or unknown samples. The cross-reactive fluorescence variation was attributed to the M3+ influence on the FRET process from TPE to open-ring form rhodamine in the two ensemble sensors. With the coexistence of Al3+, the optimized RhB-TPE/SDBS ensemble sensor array was successfully applied to differentiate commercially available brand mineral water and purified water, as well as tap water. The present work provides a novel strategy to generate a single-probe-based sensor array and realizes fingerprint recognition of three trivalent metal ions and efficient discrimination of different types of water. The modulation FRET process of a dual chromophore in different surfactant ensembles inspires the future construction of novel and effective sensing platforms.

Application of artificial intelligence sensor and visual image technology in the analysis of hydrophilic space landscape characteristics

With the continuous development of intelligent sensor technology and artificial intelligence, the application of artificial intelligence feature recognition at the level of intelligent sensor information acquisition to urban landscape analysis has become a current research trend and hot spot, but also brings us new research directions and challenges. In this paper, the multi-sensor collaborative filtering algorithm is optimized, and a method based on evidence theory is proposed to deal with the fuzzy and unclear information generated in the process of multi-sensor collaboration. Then, the analysis and recognition of multi-sensor fusion information using AI technology are analyzed in detail. This paper discusses the problems in multi-sensor information fusion and related solutions. Combined with the key nodes of 3D object AI feature recognition, multi-sensor collaborative Dempster Shafer evidence theory and 3D convolutional neural network waterfront space landscape feature recognition sub-model are constructed, and the waterfront space landscape recognition analysis model is tested and analyzed. The results show that the multi-sensor information collection Kalman filtering fusion algorithm effectively realizes the recognition of landscape feature information and the filtering of irrelevant interference information combined with an artificial intelligence algorithm to build a landscape feature recognition model. Among all kinds of landscape recognition, the recognition effect of beach inflow is the best, and the recognition accuracy, recall, and F1 value are all above 95. However, the identification effect of hydrophilic plank roads and underwater breakwater is relatively poor. The recognition accuracy of the hydrophilic platform is the lowest at 83.16 % among the eight landscape types, and the recall rate of underwater breakwater is the lowest at 79.96 % among the eight landscape types. In general, the multi-sensor Kalman filtering algorithm information fusion model designed in this paper has higher recognition accuracy and better classification performance for the collection and classification of the entire landscape dataset. The work of this paper provides a new idea and direction for the design and application of waterfront landscape identification and analysis systems based on intelligent sensors and artificial intelligence technology and lays a certain foundation for the intelligent design of urban waterfront landscapes.

Navigating the immunosuppressive brain tumor microenvironment using spatial biology

With the application of spatial biology, the detection and identification of the diverse cell types present in the tumor microenvironment, including specific immune subsets, is possible at single cell resolution. Since spatial biology analysis of tumor tissue allows multiple biological parameters to be measured, including cell type, cell number, cell state, as well as the precise location and the spatial relationship of every cell to other cells and histopathological hallmarks, a vast amount of data is generated. The power of this is realized when correlating the spatial biology data with clinical data for each patient, from which the tissue was collected during biopsy or surgery, conducted as part of the patient's diagnosis and treatment. Aside from the enormous leap in chemistry and molecular biology technology required to develop the analytical tools for spatial biology, collection, analysis of cells in the tumor microenvironment has been possible only with the development of computational tools capable of deciphering tumor tissue complexity to predict tumor evolution and response to treatment and the role of immune cells in regulating tumor biology. Here we describe how spatial biology analysis, combined with computational analysis have been used to deconstruct the complexity of the brain tumor microenvironment and shed light on why brain tumors exhibit extreme immunosuppression. We also discuss how the understanding gained using spatial biology has shed light on how tumor immunosuppression can be overcome.

The Application of GPS-Based Friend/Foe Localization and Identification to Enhance Security in Restricted Areas.

This paper is devoted to the application of object localization and identification with information combined from a radar system and a dedicated portable/mobile electronic device equipped with a global positioning system (GPS) receiver. This device is able to provide object's (staff member, and staff vehicle) rough location and identification. Such systems are required in very restrictive security areas like airports (e.g., open-air area and apron). Currently, the outdoor area of the airport is typically protected by a surveillance system operated by security guards. Surveillance systems are composed of different sensors, video and infrared cameras, and microwave radars. The sheer number of events generated via the system can lead to fatigue among staff, potentially resulting in the omission of critical events. To address this issue, we propose an electronic system equipped with a wireless module and a GPS module. This approach enables automatic identification of objects through the fusion of data from two independent systems (GPS and radar). The radar system is capable of precisely localizing and tracking objects, while the described system is able to identify registered objects. This paper contains a description of the subsystems of a portable/mobile electronic device. The fusion of information from the proposed system (rough location and identification) with the precise location obtained from short-range radar is intended to reduce the number of false alerts in the surveillance system.

Intelligent mechanical fault diagnosis using multiscale residual network and multisensor fusion

Abstract Mechanical faults in manufacturing systems need to be diagnosed accurately to ensure safety and cost savings. With the development of sensor technologies, data from multiple sensors is frequently utilized to assess the health of intricate industrial systems. In such cases, it is necessary to study the multisensor data based intelligent mechanical fault diagnosis method. First, the multisensor data is converted into grey images and then fused into a three-channel red-green-blue (RGB) image. Then, a multiscale with residual convolution module is proposed, which can extract multiscale deep features of the complex raw signal. Additionally, an attention module for channel and spatial attention is introduced to adaptively adjust the feature response values of each scale. Two datasets and a specific engineering application are used to validate the superiority of the network. The results show that the multisensor multiscale residual network outperforms other fault diagnosis networks in terms of fault identification accuracy, diagnostic efficiency, and applicability.

Advancements in and Research on Coplanar Capacitive Sensing Techniques for Non-Destructive Testing and Evaluation: A State-of-the-Art Review.

In contrast to conventional non-destructive testing (NDT) and non-destructive evaluation (NDE) methodologies, including radiography, ultrasound, and eddy current analysis, coplanar capacitive sensing technique emerges as a novel and promising avenue within the field. This paper endeavors to elucidate the efficacy of coplanar capacitive sensing, also referred to as capacitive imaging (CI), within the realm of NDT. Leveraging extant scholarly discourse, this review offers a comprehensive and methodical examination of the coplanar capacitive technique, encompassing its fundamental principles, factors influencing sensor efficacy, and diverse applications for defect identification across various NDT domains. Furthermore, this review deliberates on extant challenges and anticipates future trajectories for the technique. The manifold advantages inherent to coplanar capacitive sensing vis-à-vis traditional NDT methodologies not only afford its versatility in application but also underscore its potential for pioneering advancements in forthcoming applications.

Artificial intelligence and machine learning applications in urinary tract infections identification and prediction: a systematic review and meta-analysis.

Urinary tract infections (UTIs) have been one of the most common bacterial infections in clinical practice worldwide. Artificial intelligence (AI) and machine learning (ML) based algorithms have been increasingly applied in UTI case identification and prediction. However, the overall performance of AI/ML algorithms in identifying and predicting UTI has not been evaluated. The purpose of this paper is to quantitatively evaluate the application value of AI/ML in identifying and predicting UTI cases. MEDLINE, EMBASE, Web of Science, and PubMed databases were systematically searched for articles published up to December 31, 2023. Quality Assessment of Diagnostic Accuracy Studies tool (QUADAS-2) and Prediction Model Risk of Bias Assessment Tool (PROBAST) were used to assess the risk of bias. Study characteristics and detailed algorithm information were extracted. Pooled sensitivity, specificity, and area under the receiver operating characteristic curve (AUC) were synthesized using a bivariate mix-effects model. Meta-regression and subgroup analysis were conducted to test the source of heterogeneity. In total, 11 studies with 14 AI/ML models were included in the final meta-analysis. The overall pooled AUC was 0.89 (95%CI 0.86-0.92). Additionally, the pooled Sen, Spe, PLR, NLR, and DOR were 0.78 (95%CI 0.71-0.84), 0.89 (95%CI 0.83-0.93), 6.99 (95%CI 4.38-11.14), 0.25 (95%CI 0.18-0.34) and 28.07 (95%CI 14.27-55.20), respectively. The results of meta-regression suggested that reference standard definitions might be the source of heterogeneity. AI/ML algorithms appear to be promising to help clinicians detect and identify patients at high risk of UTIs. However, further studies are demanded to evaluate the application value of AI/ML more thoroughly.