Identification Success Research Articles

Absolute Train Localization Based on the Identification of Surrounding Structures Using 1D LiDAR Sensor

ABSTRACTTrain localization is an essential technology for effective train control. Currently, train localization primarily relies on using track circuits and balises, which are placed along the track to provide precise location information. However, balises have to be placed at intervals of a few kilometers. This increases maintenance costs and makes them vulnerable to being damaged by ice blocks falling from moving trains. Therefore, in this study, we propose a method for absolute train localization based on structure detection and identification using a 1D light detection and ranging (LiDAR) sensor to reduce the number of balises. Structure identification is achieved using scan matching. In the experiments using a car, the proposed method achieved an identification success rate of over 90%. We also considered the effect of raindrops by filtering the measurement data. By testing and analyzing the identification results, we successfully reduced all cases of misidentification.

Comprehensive Evaluation of Cryptic Juglans Genotypes: Insight from Molecular Markers and Phylogenetic Analysis

Background/Objectives: The current research work aimed to evaluate the cryptic walnut genotypes of the Hazara region in Pakistan by using DNA barcoding and phylogenetic analysis. Methods: Based on morphological traits such as nut size, nut shape, and the number of leaflets, five genotypes were chosen and samples were collected for the current study. For molecular analysis, gDNA was isolated from the fresh leaves, and the five most effective angiosperm-specific markers, ITS2, rbcLa, rbcLc, rpoC1, and UBE3, were utilized. Based on amplification, sequencing, and identification success rates, ITS2 and UBE3 were recorded as the most efficient markers followed by rbcLa, rbcLc, and rpoC1. Results: During phylogenetic analysis, the query genotype-1 based on ITS2 and genotype-2 based on UBE3 clustered with (KF454101.1-Juglans regia) and (KC870919.1-J. regia) with bootstraps of 56 and 100, respectively. Genotype-3 based on rbcla clustered in a major clade with J. regia L., cultivars (MN397935.1 J. regia ‘Vina’) and (MN397934.1-J. regia ‘Serr’), (MN397933.1 J. regia ‘Pedro’), (MN397932.1 J. regia ‘Lara’), (MN397931.1 J. regia ‘Howard’), and (MN397930.1 J. regia ‘Hartley’) with bootstrap of 100. Meanwhile, genotype-4 and genotype-5 based on rbclc and rpoC1 clustered with (MN397935.1 J. regia ‘Vina’) and (MN397934.1 J. regia ‘Serr’), across the database sequences. To clarify the taxonomic status of cryptic walnut genotypes, it is necessary to combine diverse DNA barcodes. The results of ITS2 and UBE3, followed by rbcL barcoding markers, are promising taxonomic tools for cryptic walnut genotypes in Pakistan. Conclusions: It has been determined that the genotypes of walnuts in the study area are both J. regia L. and its cultivars and that the accuracy of discrimination regarding the genus Juglans L. is greater than 90%. The reported DNA barcodes are recommended for the correct identification and genetic evaluation of Juglans taxa and its population.

Environmental niche models improve species identification in DNA barcoding

Abstract Recent advances in DNA barcoding have immeasurably advanced global biodiversity research in the last two decades. However, inherent limitations in barcode sequences, such as hybridization, introgression or incomplete lineage sorting can lead to misidentifications when relying solely on barcode sequences. Here, we propose a new Niche‐model‐Based Species Identification (NBSI) method based on the idea that species distribution information is a potential complement to DNA barcoding species identifications. NBSI performs species membership inference by incorporating niche modelling predictions and traditional DNA barcoding identifications. Systematic tests across diverse scenarios show significant improvements in species identification success rates under the newly proposed NBSI framework, where the largest increase is from 4.7% (95% CI: 3.51%–6.25%) to 94.8% (95% CI: 93.19%–96.06%). Additionally, obvious improvements were observed when using NBSI on potentially ambiguous sequences whose genetic nearest neighbours belongs to another species or more than two species, which occurs commonly with species represented by single or short DNA barcodes. These results support our assertion that environmental factors/variables are valuable complements to DNA sequence data for species identification by avoiding potential misidentifications inferred from genetic information alone. The NBSI framework is currently implemented as a new R package, ‘NicheBarcoding’, that is open source under GNU General Public Licence and freely available from https://CRAN.R‐project.org/package=NicheBarcoding.

On the nose: validating a novel, non-invasive method to identify individual koalas using unique nose patterns

Context Non-invasive identification of individual animals in wild populations can occur in species with unique coat patterns (e.g. zebras, giraffes, jaguars). However, identification in other species often relies on capture-mark–recapture techniques, involving physical handling of animals. Identification of individual koalas (Phascolarctos cinereus) is difficult and has so far relied mostly on invasive methods such as ear tagging, microchipping and/or collaring, which require capture. The validation of a non-invasive method to identify koalas could improve monitoring of individuals in the wild, allowing targeting of specific koalas in disease and survival studies, reducing the need to capture individuals. Aims This study describes a novel effective method to identify koalas from their nose markings, specifically using the unpigmented pattern of the nose to determine unique features of individuals. Methods Photographs of koalas from different populations in Victoria and New South Wales (NSW), Australia, were examined in the study. Nose patterns were traced from photographs and matched through visual assessment if they were thought to belong to the same individual. Differences in identification success between datasets from different populations and the effect of sex on match success were evaluated statistically. For the NSW koalas, the effect of lighting conditions and photographic angle were also assessed. Key results Overall identification success was 89.7% (range 87.1–91.8%) and was not affected by any of the variables tested, demonstrating that nose patterns can be used reliably to identify individual koalas. Conclusions The proposed non-invasive method is simple, yet accurate and stable over time, hence it offers a vital tool for monitoring endangered koalas whilst minimising human interference. Implications Pattern-based recognition of koalas is cost-effective, reduces stress on the animals, has the potential to improve data collection and allows involvement of citizen scientists in monitoring of populations or individuals.

Eyeing DNA barcoding for species identification of fish larvae.

Identification of fish larvae based on morphology is typically limited to higher taxonomic ranks (e.g., family or order), as larvae possess few morphological diagnostic characters for precise discrimination to species. When many samples are presented at any one time, the use of morphology to identify such specimens can be laborious and time-consuming. Using a reverse workflow for specimen sorting and identification leveraging high-throughput DNA sequencing, thousands of fish larvae can be DNA barcoded and sorted into molecular operational taxonomic units (mOTUs) in a single sequencing run with the nanopore sequencing technology (e.g., MinION). This process reduces the time and financial costs of morphology-based sorting and instead deploys experienced taxonomists for species taxonomic work where they are needed most. In this study, a total of 3022 fish larval specimens from plankton tows across four sites in Singapore were collected and sorted based on this workflow. Eye tissue from individual samples was used for DNA extraction and sequencing of cytochrome c oxidase subunit I. We generated a total of 2746 barcodes after quality filtering (90.9% barcoding success), identified 2067 DNA barcodes (75.3% identification success), and delimited 256 mOTUs (146 genera, 52 families). Our analyses identified specific challenges to species assignment, such as the potential misidentification of publicly available sequences used as reference barcodes. We highlighted how the conservative application and comparison of a local sequence database can help resolve identification conflicts. Overall, this proposed approach enables and expedites taxonomic identification of fish larvae, contributing to the enhancement of reference barcode databases and potentially better understanding of fish connectivity.

Deciphering the Plastome and Molecular Identities of Six Medicinal "Doukou" Species.

The genus Amomum includes over 111 species, 6 of which are widely utilized as medicinal plants and have already undergone taxonomic revision. Due to their morphological similarities, the presence of counterfeit and substandard products remains a challenge. Accurate plant identification is, therefore, essential to address these issues. This study utilized 11 newly sequenced samples and extensive NCBI data to perform molecular identification of the six medicinal "Doukou" species. The plastomes of these species exhibited a typical quadripartite structure with a conserved gene content. However, independent variation shifts of the SC/IR boundaries existed between and within species. The comprehensive set of genetic sequences, including ITS, ITS1, ITS2, complete plastomes, matK, rbcL, psbA-trnH, and ycf1, showed varying discrimination of the six "Doukou" species based on both distance and phylogenetic tree methods. Among these, the ITS, ITS1, and complete plastome sequences demonstrated the highest identification success rate (3/6), followed by ycf1 (2/6), and then ITS2, matK, and psbA-trnH (1/6). In contrast, rbcL failed to identify any species. This research established a basis for a reliable molecular identification method for medicinal "Doukou" plants to protect wild plant resources, promote the sustainable use of medicinal plants, and restrict the exploitation of these resources.

DNA barcodes are ineffective for species identification of Acropora corals from the aquarium trade.

Species identification of stony corals (Scleractinia), which are regulated under the Convention on International Trade in Endangered Species of Wild Fauna and Flora, is critical for effective control of harvest quotas, enforcement of trade regulations and species conservation in general. DNA barcoding has the potential to enhance species identification success, depending on the specific taxon concerned and genetic markers used. For Acropora, DNA barcoding, based on the mitochondrial putative control region (mtCR) and the nuclear PaxC intron (PaxC), has been commonly used for species identification and delimitation, but the reliability and robustness of these loci remain contentious. Therefore, we sought to verify the applicability of this approach. In this study, we obtained 127 Acropora colonies from the aquarium trade to test the effectiveness of barcoding mtCR and PaxC for species identification. We were able to recover sequences for both loci in over half of the samples (n = 68), while gene amplification and sequencing of mtCR (n = 125) outperformed PaxC (n = 70). Amongst the 68 samples with both loci recovered, just a single sample could be unambiguously identified to species. Preliminary identities, based on only one gene, were assigned for 40 and 65 samples with mtCR and PaxC, respectively. Further analyses of 110 complete mitochondrial genomes obtained from GenBank showed that, despite the full length of the sequences, only eight species were delimited, of which only three species were correspondingly monophyletic. Therefore, we conclude that the commonly used DNA barcoding markers for Acropora are ineffective for accurate species assignments due to limited variability in both markers and even across the entire mitochondrial genome. Therefore, we propose that barcoding markers should generally not be the only means for identifying corals.

Comparison of Culture-Dependent and Culture-Independent Methods for Routine Identification of Airborne Microorganisms in Speleotherapeutic Caves.

The effective identification of bacterial and fungal isolates is essential for microbiological monitoring in environments like speleotherapeutic caves. This study compares MALDI-TOF MS and the OmniLog ID System, two high-throughput culture-based identification methods. MALDI-TOF MS identified 80.0% of bacterial isolates to the species level, while the OmniLog ID System identified 92.9%. However, species-level matches between the methods were only 48.8%, revealing considerable discrepancies. For discrepant results, MALDI-TOF MS matched molecular identification at the genus level in 90.5% of cases, while the OmniLog ID System matched only in 28.6%, demonstrating MALDI-TOF MS's superiority. The OmniLog ID System had difficulties identifying genera from the order Micrococcales. Fungal identification success with MALDI-TOF MS was 30.6% at the species level, potentially improvable with a customised spectral library, compared to the OmniLog ID System's 16.7%. Metagenomic approaches detected around 100 times more microbial taxa than culture-based methods, highlighting human-associated microorganisms, especially Staphylococcus spp. In addition to Staphylococcus spp. and Micrococcus spp. as indicators of cave anthropisation, metagenomics revealed another indicator, Cutibacterium acnes. This study advocates a multi-method approach combining MALDI-TOF MS, the OmniLog ID System, culture-based, and metagenomic analyses for comprehensive microbial identification. Metagenomic sampling on nitrocellulose filters provided superior read quality and microbial representation over liquid sampling, making it preferable for cave air sample collection.

Identification success rates in the post-Spanish Civil War mass graves located in the cemetery of Paterna, Spain: Meta-research on 15 mass graves with 933 subjects

Spain suffered a Civil War between 1936 and 1939 that ended with the victory of the National Forces led by General Franco. Once the Spanish Civil War ended, 2238 subjects were executed and buried in several mass graves in the Cemetery of Paterna, one of Spain's largest mass grave sites. Efforts to locate and identify all the victims of the mass graves of the Paterna cemetery are ongoing, but the actual data of the percentage of DNA identifications remains uncertain. Following this, we conducted a meta-research study including 15 mass graves and 933 subjects to determine the DNA identification rates in the mass graves of the Paterna cemetery. We found that the total proportion of identified subjects in the mass graves was 15.9 % (95 % CI: 10.0–22.9). Moreover, we found that the model between the identification success rate (ISR) and the number of relatives that donated DNA (NRTDD) in the mass graves of the cemetery of Paterna was ISR = NRTDD−0.424. Results obtained about the proportion of identified subjects and the model between the ISR and the NRTDD imply the need for a scientific reflection between all the research groups involved in the identification tasks to modify deficiencies and update identification protocols to obtain better future results.

Impact of simulation fidelity on identifying swing-over hazards in virtual environments for novice crane operators

Crane-related accidents often occur due to failure in timely and accurate hazard identification. Virtual Reality (VR) technology facilitates immersive environments for improving hazard identification without real-world consequences. However, the impact of simulation fidelity on hazard identification performance, particularly for novice crane operators, remains underexplored. This paper examines how simulation fidelity and task complexity affect hazard identification performance among novice operators through a VR experiment with two fidelity and two complexity levels. Findings demonstrate high-fidelity (HF) scenarios significantly improve hazard identification success rate and speed for swing-over hazards but extend task completion times. Task complexity modulates simulation fidelity's impact on task completion, with significant differences between fidelity levels observed only under low complexity. These insights inform the design and calibration of simulation fidelity in VR platforms, potentially improving training efficacy and crane lifting safety for novice operators.

Effects of Speleotherapy on Aerobiota: A Case Study from the Sežana Hospital Cave, Slovenia

Speleotherapy is one of the non-pharmacological methods for the treatment and rehabilitation of patients with chronic respiratory diseases, especially those with chronic obstructive pulmonary disease (COPD) and asthma. On the one hand, one of the alleged main advantages of speleotherapeutic caves is the low microbial load in the air and the absence of other aeroallergens, but on the other hand, due to the lack of comprehensive air monitoring, there is little information on the pristine and human-influenced aerobiota in such environments. The aim of this study was to assess the anthropogenic effects of speleotherapy on the air microbiota and to investigate its potential impact on human health in Sežana Hospital Cave (Slovenia). From May 2020 to January 2023, air samples were collected in the cave before and after speleotherapeutic activities using two different volumetric air sampling methods—impaction and impingement—to isolate airborne microbiota. Along with sampling, environmental data were measured (CO2, humidity, wind, and temperature) to explore the anthropogenic effects on the aerobiota. While the presence of patients increased microbial concentrations by at least 83.3%, other parameters exhibited a lower impact or were attributed to seasonal changes. The structure and dynamics of the airborne microbiota are similar to those in show caves, indicating anthropization of the cave. Locally, concentrations of culturable microorganisms above 1000 CFU/m3 were detected, which could have negative or unpredictable effects on the autochthonous microbiota and possibly on human health. A mixture of bacteria and fungi typically associated with human microbiota was found in the air and identified by MALDI-TOF MS with a 90.9% identification success rate. Micrococcus luteus, Kocuria rosea, Staphylococcus hominis, and Staphylococcus capitis were identified as reliable indicators of cave anthropization.

Rock Layer Classification and Identification in Ground-Penetrating Radar via Machine Learning

Ground-penetrating radar (GPR) faces complex challenges in identifying underground rock formations and lithological structures. The diversity, intricate shapes, and electromagnetic properties of subsurface rock formations make their accurate detection difficult. Additionally, the heterogeneity of subsurface media, signal scattering, and non-linear propagation effects contribute to the complexity of signal interpretation. To address these challenges, this study fully considers the unique advantages of convolutional neural networks (CNNs) in accurately identifying underground rock formations and lithological structures, particularly their powerful feature extraction capabilities. Deep learning models possess the ability to automatically extract complex signal features from radar data, while also demonstrating excellent generalization performance, enabling them to handle data from various geological conditions. Moreover, deep learning can efficiently process large-scale data, thereby improving the accuracy and efficiency of identification. In our research, we utilized deep neural networks to process GPR signals, using radar images as inputs and generating structure-related information associated with rock formations and lithological structures as outputs. Through training and learning, we successfully established an effective mapping relationship between radar images and lithological label signals. The results from synthetic data indicate a rock block identification success rate exceeding 88%, with a satisfactory continuity identification of lithological structures. Transferring the network to measured data, the trained model exhibits excellent performance in predicting data collected from the field, further enhancing the geological interpretation and analysis. Therefore, through the results obtained from synthetic and measured data, we can demonstrate the effectiveness and feasibility of this research method.

Using Natural Language Processing (GPT-4) for Computed Tomography Image Analysis of Cerebral Hemorrhages in Radiology: Retrospective Analysis.

Cerebral hemorrhage is a critical medical condition that necessitates a rapid and precise diagnosis for timely medical intervention, including emergency operation. Computed tomography (CT) is essential for identifying cerebral hemorrhage, but its effectiveness is limited by the availability of experienced radiologists, especially in resource-constrained regions or when shorthanded during holidays or at night. Despite advancements in artificial intelligence-driven diagnostic tools, most require technical expertise. This poses a challenge for widespread adoption in radiological imaging. The introduction of advanced natural language processing (NLP) models such as GPT-4, which can annotate and analyze images without extensive algorithmic training, offers a potential solution. This study investigates GPT-4's capability to identify and annotate cerebral hemorrhages in cranial CT scans. It represents a novel application of NLP models in radiological imaging. In this retrospective analysis, we collected 208 CT scans with 6 types of cerebral hemorrhages at Ren Ji Hospital, Shanghai Jiao Tong University School of Medicine, between January and September 2023. All CT images were mixed together and sequentially numbered, so each CT image had its own corresponding number. A random sequence from 1 to 208 was generated, and all CT images were inputted into GPT-4 for analysis in the order of the random sequence. The outputs were subsequently examined using Photoshop and evaluated by experienced radiologists on a 4-point scale to assess identification completeness, accuracy, and success. The overall identification completeness percentage for the 6 types of cerebral hemorrhages was 72.6% (SD 18.6%). Specifically, GPT-4 achieved higher identification completeness in epidural and intraparenchymal hemorrhages (89.0%, SD 19.1% and 86.9%, SD 17.7%, respectively), yet its identification completeness percentage in chronic subdural hemorrhages was very low (37.3%, SD 37.5%). The misidentification percentages for complex hemorrhages (54.0%, SD 28.0%), epidural hemorrhages (50.2%, SD 22.7%), and subarachnoid hemorrhages (50.5%, SD 29.2%) were relatively high, whereas they were relatively low for acute subdural hemorrhages (32.6%, SD 26.3%), chronic subdural hemorrhages (40.3%, SD 27.2%), and intraparenchymal hemorrhages (26.2%, SD 23.8%). The identification completeness percentages in both massive and minor bleeding showed no significant difference (P=.06). However, the misidentification percentage in recognizing massive bleeding was significantly lower than that for minor bleeding (P=.04). The identification completeness percentages and misidentification percentages for cerebral hemorrhages at different locations showed no significant differences (all P>.05). Lastly, radiologists showed relative acceptance regarding identification completeness (3.60, SD 0.54), accuracy (3.30, SD 0.65), and success (3.38, SD 0.64). GPT-4, a standout among NLP models, exhibits both promising capabilities and certain limitations in the realm of radiological imaging, particularly when it comes to identifying cerebral hemorrhages in CT scans. This opens up new directions and insights for the future development of NLP models in radiology. ClinicalTrials.gov NCT06230419; https://clinicaltrials.gov/study/NCT06230419.

High-precision sensor for glucose solution using active multidimensional feature THz spectroscopy.

Terahertz waves are known for their bio-safety and spectral fingerprinting features, and terahertz spectroscopy technology holds great potential for both qualitative and quantitative identification in the biomedical field. There has been a substantial amount of research utilizing this technology in conjunction with machine learning algorithms for substance identification. However, due to the strong absorption of water for terahertz waves, the single-dimensional features of the sample become indistinct, thereby diminishing the efficiency of the algorithmic recognition. Building upon this, we propose a method that employs terahertz time-domain spectroscopy (THz-TDS) in conjunction with multidimensional feature spectrum identification for the detection of blood sugar and glucose mixtures. Our research indicates that combining THz-TDS with multidimensional feature spectrum and linear discriminant analysis (LDA) algorithms can effectively identify glucose concentrations and detect adulteration. By integrating the multidimensional feature spectrum, the identification success rate increased from 68.9% to 96.0%. This method offers an economical, rapid, and safe alternative to traditional methods and can be applied in blood sugar monitoring, sweetness assessment, and food safety.

ASSOCIATION OF VITAMIN D SERUM LEVELS AND OVARIAN CANCER : A SYSTEMATIC REVIEW

Background: Knowing the modifiable risk factors for prevention and improved prognosis is crucial, as the prognosis of ovarian cancer is poor and there are few population-level interventions for early identification and long-term therapeutic success. Research on cancer has shown a great deal of scientific interest in vitamin D since it may be one of these factors. Aims : This systematic review is to review the association of vitamin D serum levels in patients with ovarian cancer Methods: By comparing itself to the standards set by the Preferred Reporting Items for Systematic Review and Meta-Analysis (PRISMA) 2020, this study was able to show that it met all of the requirements. So, the experts were able to make sure that the study was as up-to-date as it was possible to be. For this search approach, publications that came out between 2014 and 2024 were taken into account. Several different online reference sources, like Pubmed and SCIENCE DIRECT, were used to do this. It was decided not to take into account review pieces, works that had already been published, or works that were only half done. Result: In the PubMed database, the results of our search brought up 329 articles, whereas the results of our search on SCIENCE DIRECT brought up 298 articles. The results of the search conducted for the last year of 2014 yielded a total 8 articles for PubMed and 12 articles for SCIENCE DIRECT. In the end, we compiled a total of 4 papers, 3 of which came from PubMed and 1 of which came from SCIENCE DIRECT. We included four research that met the criteria. Conclusion: In summary, some of the studies showed vitamin D supplementation is not necessary to increase the survival rate of ovarian cancer in the absence of a vitamin D shortage. There are limited studies about the association of vitamin D and ovarian cancer.

A cost-effective joint multi-parameter optical performance monitoring scheme for high baud rate mode division multiplexing system

Inter-symbol interference (ISI), mode coupling (MC) and other impairments present in high baud rate mode division multiplexing (MDM) systems significantly degrade the reliability and accuracy of optical signal transmission. Therefore, performing joint multi-parameter optical performance monitoring (OPM) is crucial to ensure the efficient operation of the system. We propose a scheme that combines asynchronous single channel sampling (ASCS) images with a multi-task residual neural network (MT-ResNet) to enable cost-effective joint OPM at intermediate nodes in a 100 GBaud MDM system. The scheme requires only one photodetector to receive the signal. Based on the distinct characteristics exhibited by ASCS images for different parameters in the MDM system, the MT-ResNet is improved to capture correlations among parameters and learn residual information between inputs and outputs. This enables high-precision and rapid simultaneous implementation of modulation format identification (MFI), MC estimation, optical signal-to-noise ratio (OSNR) estimation, and chromatic dispersion identification (CDI) in the intermediate nodes of a 100 GBaud MDM system. The effectiveness of the proposed scheme is verified using a 100 GBaud QPSK/8QAM/16QAM/32QAM/64QAM MDM simulation transmission system with five spatial modes (LP01/LP11a/LP11b/LP21a/LP21b). The results show that under the influence of different linewidth (LW) of laser and differential mode group delay (DMGD), a 100 % identification success rate can be achieved for five commonly used MFs and eight different cumulative CD values in high baud rate MDM systems, across a wide range of OSNR. Additionally, the mean absolute error (MAE) of OSNR estimation corresponding to the five MFs is 0.22 dB, 0.26 dB, 0.30 dB, 0.32 dB, and 0.36 dB, respectively. The MAE of the eight different MC intensities fluctuate between 0.015 and 0.030. These results fully demonstrate the superior performance of the proposed scheme. Furthermore, the scheme exhibits favorable tolerance to large LWs and fiber nonlinear effects. Moreover, the complexity of the scheme is accurately analyzed through floating-point operations (FLOPs) and parameters (Params), which are much less than existing typical schemes, with specific values of 14.8849 M and 1.3798 M.

The challenges of developing a textile-based device for recognizing swallowing movements during different swallowing tasks

Swallowing difficulty, also known as dysphagia, is a fairly common symptom that may lead to malnutrition, dehydration, pulmonary complications, and a reduced quality of life. Currently, a swallowing evaluation requires a hospital environment, skilled personnel, and expensive devices. Thus, it is important to develop a successful method for evaluating swallowing in an everyday environment. This study developed a textile-based solution that resembles a neck gaiter. Wireless accelerometer and gyroscope sensors embedded inside the gaiter were used to detect swallowing movements. The recognition of swallows was achieved through cervical auscultation and video footage, and this data was subsequently compared with the information obtained from wireless accelerometer and gyroscope sensors. Based on the measurement results for 17 adults without dysphagia, the developed neck gaiter can identify individual swallows of 5 sips of water (with a 97 % identification success rate) and consecutive swallows of 100 ml of water (98 % identification success rate). However, we were not able to recognize swallowing movements from speaking, chewing and head movements. Future research is now needed to develop a prototype to differentiate swallowing movements from other movements, such as chewing and speaking.

Identification of Counterfeit Tobacco using Attenuated Total Reflectance Fourier Transform Infrared, Raman, and Near-Infrared Spectroscopy

Counterfeit and illicit tobacco may contain potentially toxic organic impurities that result in adverse health effects for the consumer. Carcinogenic substances have been found in elevated concentrations in counterfeit tobacco. The current study explored swift and cost-effective methods, such as Fourier-transform infrared, Raman, and near-infrared spectroscopy to identify counterfeit tobacco products. Moreover, it also assessed the spectral quality based on range, number of peaks, maximum peak intensity, and signal-to-noise ratio. In spectral analysis, infrared exhibited superior quality over Raman and near-infrared with a higher signal-to-noise ratio. Near-infrared, although with a limited wavelength range, showed good quality. Raman, however, displayed poor spectral quality due to noise interference. Identification success varied with chemo-metric methods, with infrared correctly identifying four out of five counterfeit tobacco products and near-infrared identifying three out of five. Raman failed in identification, while infrared showed success with some mismatches using principal component analysis. Near-infrared and Raman successfully identified counterfeit tobacco, with near-infrared providing the most distinct separation. Quantification results varied across spectroscopy techniques and chemo-metric methods.

Novel tactile bottle neck adaptor facilitates eye drop adherence in visually impaired patients

PurposeTo test the use of Ring-IT, a novel 3D tactile bottle neck adaptor in topical eye drop adherence in visually impaired patients.MethodsBottle neck ring adaptors with either one, two or...

Intelligent joint multi-parameter optical performance monitoring scheme based on HT images and MT-ResNet for elastic optical network

In elastic optical network (EON), joint multi-parameter optical performance monitoring (OPM) can effectively manage, diagnose, and reduce operational costs for transmission optical links. In this paper, we propose a novel joint monitoring scheme utilizing Hough transform (HT) images combined with multi-task residual neural network (MT-ResNet) for EON. The scheme can realize baud rate identification (BRI), modulation format identification (MFI), residual chromatic dispersion identification (CDI), optical signal-to-noise ratio (OSNR) and residual differential group delay (DGD) estimation at the same time. The HT image is obtained by preprocessing original constellation diagram, which is a key feature of parameter monitoring for EON signals and presents obvious differentiation in parameter space. By optimizing the skip connections in MT-ResNet, we effectively resolve the issue of details information loss or incompleteness caused by the transmission of impaired optical signals in the neural network. The simulation results demonstrate that the identification success rate can reach 100 % for two common BRs, five mainstream MFs, and seven residual CD values with different impairment degrees. The mean absolute errors (MAEs) of OSNR and residual DGD estimates are 0.42 dB and 0.014 times symbol period respectively. The scheme has excellent tolerance for fiber nonlinear effects. In experimental verification, the accuracies of BRI and MFI are 100 %, and the MAEs of corresponding OSNR estimation for PDM-QPSK/16QAM/32QAM are 0.25 dB, 0.36 dB, and 0.40 dB, respectively. Compared with the existing typical schemes, our scheme significantly improves performance and reduces complexity while simultaneously monitoring a large number of parameters.