Identification Technology Research Articles

Mechanism model for effect of eccentricity on capacitive control rod position indicators

The continuous identification technology for position signals based on the capacitive method has a good application potential in the field of the control rods position measurement in nuclear reactors. The main source for error of the Capacitive Control Rod Position Indicators (CCRPIs) comes from the effect of eccentricity. The conformal mapping technique was used in this paper, and the geometry of the CCRPIs could be transformed. A model for revealing the mechanism of the effect of eccentricity was established, and it was tested and verified by the method of numerical calculation. The characteristics of the distribution of the sensitive field was clarified by the model. The results indicate that the maximum error of the model is smaller than 13 %, and the eccentric effect of the indicator can be described by the model.

CO-TSM: A Flexible Model for Secure Embedded Device Ownership and Management

The Consumer-Oriented Trusted Service Manager (CO-TSM) model has been recognised as a significant advancement in managing applications on Near Field Communication (NFC)-enabled mobile devices and multi-application smart cards. Traditional Trusted Service Manager (TSM) models, while useful, often result in market fragmentation and limit widespread adoption due to their centralised control mechanisms. The CO-TSM model addresses these issues by decentralising management and offering greater flexibility and scalability, making it more adaptable to the evolving needs of embedded systems, particularly in the context of the Internet of Things (IoT) and Radio Frequency Identification (RFID) technologies. This paper provides a comprehensive analysis of the CO-TSM model, highlighting its application in various technological domains such as smart cards, HCE-based NFC mobile phones, TEE-enabled smart home IoT devices, and RFID-based smart supply chains. By evaluating the CO-TSM model’s architecture, implementation challenges, and practical deployment scenarios, this paper demonstrates how CO-TSM can overcome the limitations of traditional TSM approaches. The case studies presented offer practical insights into the model’s adaptability and effectiveness in real-world scenarios. Through this examination, the paper aims to underscore the CO-TSM model’s role in enhancing scalability, flexibility, and user autonomy in secure embedded device management, while also identifying areas for future research and development.

Enabling Technologies and Techniques for Floor Identification

Location information has initiated a multitude of applications such as location-based services, health care, emergency response and rescue operations, and assets tracking. A plethora of techniques and technologies have been presented to ensure enhanced location accuracy, both horizontal and vertical. Despite many surveys covering horizontal localization technologies, the literature lacks a comprehensive survey incorporating up-to-date vertical localization approaches. This article provides a detailed survey of different vertical localization techniques such as path loss models, time of arrival, received signal strength, reference signal received power, fingerprinting utilized by WiFi, radio-frequency identification (RFID), global system for mobile communications (GSM), long-term evolution (LTE), barometer, inertial measurement unit (IMU) sensors, and geomagnetic field. The article primarily aims at human localization in indoor environments using smartphones in essence. Besides the localization accuracy, the presented approaches are evaluated in terms of cost, infrastructure dependence, deployment complexity, and sensitivity. We highlight the pros and cons of these approaches and outline future research directions to enhance the accuracy to meet the future needs of floor identification standards set by the Federal Communications Commission.

Drone-based mobile full-duplex optical communication

Wireless optical communication plays a vital role in mobile communication networks, and both drones and vehicles are essential mobile communication nodes. Here, image identification technology, gimbal, and full-duplex optical communication systems are integrated to manage the challenging issues of light alignment and target tracking for mobile full-duplex optical communication under the transmission control protocol/internet protocol (TCP/IP) scheme. Both drones and vehicles are equipped with the proposed setup to realize full-duplex wireless optical data transmission at a communication rate of 2 Mbps in both air-to-ground and air-to-air scenarios. By further integrating wireless-fidelity (Wi-Fi) modules, drone-based mobile optical communication systems (MOCSs) can be interconnected with the network via the TCP/IP scheme, and real-time video transmission can be demonstrated. This work provides a feasible route toward a mobile optical communication network (MOCN) for diverse applications.

Non-destructive detection of male and female information in ducklings based on near-infrared spectral wavelength selection and deep learning

The technology for sex identification in ducklings can contribute to increased revenue and cost savings in modern duck farming. However, traditional manual identification techniques require high skill levels and involve significant labor intensity. In this study, a non-destructive, user-friendly, and efficient duckling sex identification technique was proposed using near-infrared spectroscopy and deep learning algorithms. Spectral data from 600 groups of newly hatched ducklings were collected. These data were divided into training, testing, and validation sets in a ratio of 7:2:1. The raw spectral data was preprocessed using the Savitzky-Golay convolution derivative method, which was employed for subsequent spectral feature wavelength extraction and modeling. The characteristic wavelengths were extracted using competitive adaptive reweighted sampling (CARS), successive projections algorithm (SPA), and uninformative variable elimination combined with SPA (UVE-SPA). Conventional machine learning methods − support vector machine (SVM) and different deep learning models, including multilayer perceptron (MLP), mobile network version 2 (MobileNetV2), and residual neural network (ResNet), were studied and compared. The experiment showed that deep learning algorithms outperform traditional spectral analysis models in terms of classification performance. Furthermore, conducting feature wavelength extraction before constructing the classification model could reduce the model’s testing time and even improve its classification performance. Finally, the four models with better classification performance were validated using a validation set, and the combination of MobileNetV2 model UVE-SPA was selected as the optimized model for ducklings’ gender determination, with a classification accuracy of 98.3 % and an average validation time of 1.1 ms. In summary, the detection model established using near-infrared spectroscopy and MobileNetV2 can achieve non-destructive identification of the gender of ducklings. The findings can provide a preliminary research foundation and technical support for the subsequent design of related online intelligent detection systems.

A-273 Rapid, Portable Invasive Fungal Infection Diagnostic and Candida Antimicrobial Susceptibility Testing

Abstract Background Traumatic wounds are associated with a higher risk of invasive fungal infections (IFI), which lead to a significant increase in amputation or death. Actionable treatment decisions are hampered because true infections can be difficult to differentiate from external wound contaminates, and tissue biopsy and quantification of fungal load is required to determine if treatment is necessary, which currently takes several days. GeneCapture is developing the CAPTURE 60-minute, portable infection identification platform to detect infectious levels of common fungi including the genera Aspergillus, Fusarium, and Scedosporium, the family Herpotrichiellaceae, and the order Mucorales. The company is also pursuing rapid identification and antimicrobial susceptibility testing for Candida species from multiple infection types. Methods This study seeks to determine the Limit of Detection (LoD), sensitivity and specificity of the CAPTURE ID assay for fungal pathogens. Since tissue biopsy samples must be used to assess the presence of IFI in wounds, GeneCapture has developed a manual process for the crude homogenization of muscle tissue to allow this matrix to be used as the starting sample for the ID assay. Mock samples were tested for inclusivity and exclusivity on a series of IFI ID panels against multiple strains of the included fungi, against fungi not yet specifically detected on the panel, and against bacteria that commonly infect wounds. While the LoD for yeasts may be determined in CFU/mL, there is no similar, easy-to-define metric for molds. GeneCapture has developed a quantitative PCR method to detect the single copy beta-tubulin gene to roughly correlate fungal dry mass and nuclei number to assay signal for molds cultured in Potato Dextrose Broth. Determining beta-tubulin copy numbers for extracts from mock samples then allowed detection of the progression of IFI over the course of infection. Lastly, antimicrobial susceptibility testing (AST) was performed on two species of Candida to validate utilizing GeneCapture’s rapid phenotypic AST method for fungal infections. Results To date, the CAPTURE ID panel has been tested against 27 included or excluded strains of fungi or bacteria with 97% sensitivity and 100% specificity. The current LoD for mold organisms varies from approximately 1,000 nuclei for Aspergillus species, to 100,000 nuclei for Fusarium species. Additional work to understand this range is underway. Lastly, we have correctly determined the susceptibility of Candida species using our novel approach within 4-6 hours. Conclusions GeneCapture’s ID and AST technology offer a unique solution to address the increasing prevalence of fungal infections. Our platform can identify the most common fungal components in a wound within 60 minutes of receiving a tissue biopsy sample. Candida infections occur across multiple matrices and rapidly identifying the species of Candida and then determining its phenotypic susceptibility profile will have a large impact on treating these difficult pathogens.

Application of machine learning based genome sequence analysis in pathogen identification.

Infectious diseases caused by pathogenic microorganisms pose a serious threat to human health. Despite advances in molecular biology, genetics, computation, and medicinal chemistry, infectious diseases remain a significant public health concern. Addressing the challenges posed by pathogen outbreaks, pandemics, and antimicrobial resistance requires concerted interdisciplinary efforts. With the development of computer technology and the continuous exploration of artificial intelligence(AI)applications in the biomedical field, the automatic morphological recognition and image processing of microbial images under microscopes have advanced rapidly. The research team of Institute of Microbiology, Chinese Academy of Sciences has developed a single cell microbial identification technology combining Raman spectroscopy and artificial intelligence. Through laser Raman acquisition system and convolutional neural network analysis, the average accuracy rate of 95.64% has been achieved, and the identification can be completed in only 5 min. These technologies have shown substantial advantages in the visible morphological detection of pathogenic microorganisms, expanding anti-infective drug discovery, enhancing our understanding of infection biology, and accelerating the development of diagnostics. In this review, we discuss the application of AI-based machine learning in image analysis, genome sequencing data analysis, and natural language processing (NLP) for pathogen identification, highlighting the significant role of artificial intelligence in pathogen diagnosis. AI can improve the accuracy and efficiency of diagnosis, promote early detection and personalized treatment, and enhance public health safety.

Identification and Disinfection Treatment Technology of a New Guangdong Choy Sum Disease

Seed-borne diseases cause significant economic losses in modern agriculture. The use of large amounts of chemical pesticides to control seed-borne diseases also results in negative consequences such as environmental pollution and pesticide residue in agricultural products. This has generated great interest among scholars in detecting and controlling seed-borne diseases. This study screened and isolated a new pathogenic fungus, Penicillium decumbens, from three Guangdong choy sum varieties. The virulence test showed that it inhibited seed germination rate by 40% and germination vigor by 50%. The conventional seed disinfectants (Thiram WP) did not completely eliminate the pathogenic bacteria. A comparative study of nine seed disinfectants revealed that 50% Carbendazim WP, 80% Thiram WP, 15% Carbendazim+15% Thiram WP, and 45% Prochloraz EW exhibited significant fungicidal effects against Penicillium decumbents infection in Guangdong choy sum seeds. Based on comprehensive indicators including germination rate, germination vigor, and root length after disinfection treatment, the most effective disinfectant was 45% Prochloraz EW with a sterilization effect of 100%, an increase in germination vigor by 37.5%, and an increase in germination rate by 27.9%.

Novel fungicide and neonicotinoid insecticide impair flight behavior in pollen foraging honey bees, Apis mellifera

Bees are often exposed to pesticides affecting physiological functions and molecular mechanisms. Studies showed a potential link between altered expression of energy metabolism related transcripts and increased homing flight time of foragers exposed to pesticides. In this study, we investigated the effects of thiamethoxam and pyraclostrobin on longevity, flight behavior, and expression of transcripts involved in endocrine regulation (hbg-3, buffy, vitellogenin) and energy metabolism (cox5a, cox5b, cox17) using radio frequency identification (RFID) technology and quantitative polymerase chain reaction. Parallel, a laboratory study was conducted investigating whether pesticide exposure alone without the influence of flight activity caused similar expression patterns as in the RFID experiment. No significant effect on survival, homing flight duration, or return rate of exposed bees was detected. The overall time foragers spent outside the hive was significantly reduced post-exposure. Irrespective of the treatment group, a correlation was observed between cox5a, cox5b, cox17 and hbg-3 expression and prolonged homing flight duration. Our results suggest that flight behavior can impact gene expression and exposure to pesticides adversely affects the expression of genes that are important for maintaining optimal flight capacity. Our laboratory-based experiment showed significantly altered expression levels of cox5a, cox6c, and cox17. However, further work is needed to identify transcriptional profiles responsible for prolonged homing flight duration.

Production efficiency analysis based on the RFID-collected manufacturing big data

Radio Frequency Identification (RFID) technology is widely used for production data collection in manufacturing shop-floors. The RFID-collected manufacturing big data reflects detailed statuses of production processes and manufactured products, which, in turn, can be used to support a comprehensive production analysis. This paper introduces an analytical approach to conduct production efficiency analysis based on the RFID-collected manufacturing big data. The proposed method involves four key steps: data cleansing, data processing, key performance indicator (KPI) estimation, and data analytics. Specifically, speed and quality aspects of shopfloor manufacturing are investigated collectively as production efficiency to support the comprehensive production analysis. The findings highlight the influence of various factors, such as operation date, working hour, and machine failure, on production efficiency.

Automatic interpretation of geometric information of discontinuities and its influence on the stability of highly-jointed rock slopes

The discrete fracture system of a rock mass plays a crucial role in controlling the stability of rock slopes. To fully account for the geometric shape and distribution characteristics of jointed rock masses, terrestrial laser scanning (TLS) was employed to acquire high-resolution point-cloud data, and a developed automatic discontinuity-identification technology was coupled to automatically interpret and characterize geometric information such as orientation, trace length, spacing, and set number of the discontinuities. The discrete element method (DEM) was applied to study the influence of the geometric morphology and distribution characteristics of discontinuities on slope stability by generating a discrete fracture network (DFN) with the same statistical characteristics as the actual discontinuities. Based on slope data from the Yebatan Hydropower Station, a simulation was conducted to verify the applicability of the automatic discontinuity identification technology and the discrete fracture network-discrete element method (DFN-DEM). Various geological parameters, including trace length, persistence, and density, were examined to investigate the morphological evolution and response characteristics of rock slope excavation under different joint combination conditions through simulation. The simulation results indicate that joint parameters affect slope stability, with density having the most significant impact. The impact of joint parameters on stability is relatively small within a reasonable range but becomes significant beyond a certain threshold, further validating that the accuracy of field geological surveys is critical for simulation. This study provides a scientific basis for the construction of complex rock slope models, engineering assessments, and disaster prevention and mitigation, which is of great value in both theory and engineering applications.

Sports Player Action Recognition based on Deep Learning

A sports auxiliary evaluation system suitable for China’s national conditions was established using big data and sports identification technology. First of all, this paper extends the data of normative behavior and constructs a normative library of scores and comparisons. The acquisition of 3D data is emphasized. The method based on Fourier descriptors is used to locate the motion accurately. In this way, better gait recognition results can be obtained. The Fourier characteristics before and after wavelet transform are compared with the actual object characteristics, and the results show that the proposed algorithm can extract the features with high precision. This scheme can obtain a more accurate identification effect. The system proposed in this paper provides a powerful means for judges to score.

RAPiD: a rapid and accurate plant pathogen identification pipeline for on-site nanopore sequencing.

Nanopore sequencing technology has enabled the rapid, on-site taxonomic identification of samples from anything and anywhere. However, sequencing errors, inadequate databases, as well as the need for bioinformatic expertise and powerful computing resources, have hampered the widespread use of the technology for pathogen identification in the agricultural sector. Here we present RAPiD, a lightweight and accurate real-time taxonomic profiling pipeline. Compared to other metagenomic profilers, RAPiD had a higher classification precision achieved through the use of a curated, non-redundant database of common agricultural pathogens and extensive quality filtering of alignments. On a fungal, bacterial and mixed mock community RAPiD was the only pipeline to detect all members of the communities. We also present a protocol for in-field sample processing enabling pathogen identification from plant sample to sequence within 3 h using low-cost equipment. With sequencing costs continuing to decrease and more high-quality reference genomes becoming available, nanopore sequencing provides a viable method for rapid and accurate pathogen identification in the field. A web implementation of the RAPiD pipeline for real-time analysis is available at https://agrifuture.senckenberg.de.

Damage imaging identification of plate structures based on linear array near-field MUSIC algorithm

ABSTRACT The paper proposes a damage imaging identification method for plate structures based on piezoelectric Lamb waves and the MUSIC algorithm, focusing on enhancing the accuracy of damage identification by using the sensing signal mode separation technique, and on the MUSIC-algorithm-based damage level evaluation. The mode separation of damage scattering signals is realised by using pairwise geometric symmetry to simplify the processing of sensing signals and improving the accuracy of damage imaging. A damage factor constructed by using the maximum eigenvalue of the sensing signal matrix is proposed, and it is combined with imaging technology to realise the precise locating and preliminary evaluation of damage. A combination of finite element simulation and experimental validation is used to verify the effectiveness of the proposed method. The results show that accurate damage localisation imaging can be obtained by using a mode-separated damage scattering signal; the damage level has an approximately linear relationship with the damage factor, which can be used to initially recognise the damage level. Within a certain level of damage, the proposed method has high accuracy and stability, enhancing the effectiveness of the damage identification technology for plate structures based on the MUSIC algorithm.

Barriers to implementation of rapid identification and antimicrobial susceptibility testing technologies in the clinical microbiology laboratory: an American perspective.

Clinical microbiology laboratories are responsible for confirming the aetiology of infectious diseases and providing antimicrobial susceptibility testing results. Traditional culture-based testing can be augmented by more rapid testing modalities to provide clinically actionable information as quickly as possible. Despite improvements in patient outcomes, many clinical microbiology laboratories are facing challenges to in-sourcing these technologies. Depending on a multitude of factors, including size, location and patient population served, these barriers may affect some laboratories and hospital systems to greater or lesser extents than others. It will be up to each individual facility to ascertain its ability to overcome barriers. To aid in this self-assessment, we present for thoughtful consideration a discussion of the barriers to implementation of rapid identification and antimicrobial susceptibility testing technologies, with specific attention to matters of financial cost, staff expertise, operational issues and stakeholder buy-in.

Application of small animal ultrasound imaging technology for identification of polycystic ovary syndrome in a mouse model

Background and AimsPolycystic ovary syndrome (PCOS) is a hormonal disorder common among women of reproductive age, characterized by irregular menstrual periods, elevated levels of androgens, and polycystic ovaries, leading to various symptoms and complications such as infertility, metabolic issues, and increased risk of diabetes and heart disease. This study aimed to compare traditional histological methods and ultrasound imaging for consistency in identifying PCOS in a mouse model. The shortest time to construct the PCOS model using letrozole was determined. MethodsFemale C57/BL mice were randomly divided into three groups: Group A received normal saline and a regular diet; Group B received 1 mg/kg/day of letrozole with a regular diet; and Group C received 1 mg/kg/day of letrozole with a high-fat diet. All mice were administered letrozole by intragastric gavage daily for five weeks. The traditional identification method included measuring body weight, examining vaginal smears, monitoring the estrous cycle, measuring serum androgen levels, and performing H&E staining of ovarian tissues. The PCOS model was evaluated using ultrasound imaging to identify and monitor follicles. The significance of the difference between the traditional identification method and the ultrasonic method was calculated using the nonparametric McNemar test, and consistency between the two methods was assessed with the kappa-coefficient test. On this basis, the ultrasound imaging technology was used to monitor the model-making process for 2, 3 and 4 weeks, and to monitor the parameters of the ovary and follicles to judge the shortest time that gavage letrozole caused the appearance of vesicular follicles in the mouse ovary. ResultsThe traditional identification method showed no PCOS phenotype in group A mice, while groups B and C showed multiple ovarian cystic follicles, indicating successful model induction. The ultrasound imaging results were consistent with the traditional method, showing no PCOS in group A and multiple cystic follicles in groups B and C. The McNemar test revealed no significant difference between the traditional and ultrasonic identification methods. The kappa-coefficient test assessed consistency, yielding a value of 0.903, indicating strong agreement between the methods. The ovarian area, diameter, and the number and diameter of cystic follicles were not significantly changed at two weeks in the letrozole group compared with the control group. At three weeks, there were significant increases in the number and in the diameter of vesicular follicles compared with control cells. At four weeks, the number and diameter, the maximum cross-sectional area and diameter of the ovary were significantly increased compared with the control group. ConclusionsThe ultrasound and traditional methods provide consistent results for identifying PCOS in a mouse model. Construction of the PCOS model by letrozole gavage takes at least three weeks.

ARCHITECTURE DESIGN OF HEALTH ASSET DETECTION SYSTEM IN HOSPITAL

Efficient management of hospital assets is essential to ensure that operations can run optimally and the quality of health services is good. However, the recording and management of assets in hospitals carried out manually often causes data errors, information mismatches, and also assets are only known by the manager without being explicitly recorded. In overcoming this problem, the researcher aims to develop a hospital asset detection system architecture using an iterative and incremental methodology approach. The stages of this system development include identification of needs and conceptual models, logical architecture design, conceptual architecture design, logical architecture, physical architecture, technology selection, and evaluation. This system utilizes YOLO model reading technology for asset detection and identification, storing detection results into a local database using SQLite3, sending data to a central server via API, and post-processing data by selecting the highest confidence score stored in a MySQL database and then using the data to manage asset management and asset visualization. The implementation of this system successfully reduces manual recording time, improves asset visibility, and optimizes resource usage, thus contributing to the improvement of efficiency and quality of health services.

The rise of digitalization in constructions: State-of-the-art in the use of sensing technology for advanced building-assistance systems

The construction sector is traditionally affected by on-site errors that significantly impact both budget and schedule. To minimize these errors, researchers have long hypothesized the development of AR-enriched 4D models that can guide workers on components deployment, assembly procedures, and work progress. Such systems have recently been referred to as Advanced Building-Assistance Systems (ABAS). However, despite the clear need to reduce the on-site errors, an ABAS has not been implemented and tested yet. This is partially due to a limited comprehension of the current wealth of available sensing technologies in the construction industry. To bridge the current knowledge gap, this paper evaluates the capabilities of current use of sensing technologies for objects identification, location, and orientation. This study employs and illustrates a systematic methodology to select according to eight criteria and analyzed in three level the literature on the field to ensure comprehensive coverage of the topic. The findings highlight both the capabilities and constraints of current sensing technologies, while also providing insight into potential future opportunities for integrating advanced tracking and identification systems in the built environment.

Multi-step HPLC fractionation enabled in-depth and unbiased characterization of histone PTMs

Histone post-translational modifications (PTMs) are critical epigenetic regulatory factors. Histone PTMs are highly dynamic and complicated, encompassing over 30 structurally diverse modifications across nearly 180 amino acid residues, which generated extensive information regarding histone marks. In proteomics-based characterization of histone PTMs, chemical derivatization and antibody-based affinity enrichment were frequently utilized to improve the identification depth. However, chemical derivatization suffered from the occurrence of side reactions, and antibody-based affinity enrichment focused on specific PTM types of interest. In this research, we developed a multi-step fractionation strategy for comprehensively unbiased detection of histone PTM sites. By combining protein-level fractionation with peptide-level alkaline and acid phase fractionation, we developed the Multidimensional Fractionation based Histone Mark Identification Technology (MudFIT) and increased PTM identification to a total of 264 histone PTM sites. To the best of our knowledge, this strategy achieved the most comprehensive characterization of histone PTM sites in a single proteomics study. Using the same starting amount of sample, MudFIT identified more Kac sites and Kac peptides than those in antibody-based acetylated peptide enrichment. Moreover, in addition to well-studied histone marks, we discovered 36 potential new histone PTM sites including H2BK116bu, H4R45me2, H1K63pr, and uncovered unknown histone PTM types like aminoadipic on lysine and nitrosylation on tyrosine. Our data provided a method and resource for in-depth characterization of histone PTM sites, facilitating further biological understanding of histone marks.

Comparative Analysis of FaceNet, VGGFace, and GhostFaceNets Face Recognition Algorithms For Potential Criminal Suspect Identification

The escalating concerns surrounding criminal activities underscore the imperative for bolstered security measures to safeguard public welfare. Despite concerted efforts, the identification of suspects remains fraught with limitations, hindering the attainment of comprehensive individual profiles. Leveraging advancements in facial detection and identification technologies, this study assesses the efficacy of three prominent deep learning models—FaceNet, VGGFace, and GhostFaceNets—in the domain of facial recognition for suspect identification. Drawing upon data collected in 2023, the investigation scrutinizes FaceNet's intricate methodologies, including triplet loss optimization and Euclidean space mappings, yielding exceptional accuracy rates of 97.05% during validation and 97.4% during testing. Conversely, VGGFace, while displaying commendable accuracies, registers marginally lower accuracy metrics, standing at 97.05% and 96.1% during validation and testing, respectively. GhostFaceNets, integrating novel architectural components, exhibit diminished accuracy rates, signaling avenues for refinement. These empirical insights underscore FaceNet's prowess in furnishing robust and reliable facial recognition outcomes, while delineating the imperative for iterative enhancements in GhostFaceNets to foster their pragmatic applicability in security domains.