Precision In Identification Research Articles

Fault Detection in Transmission Line

Abstract: This project focuses on the design and implementation of an advanced fault detection system for overhead transmission lines, utilizing key components such as the ESP 8266 microcontroller, current sensors, a GPS module, and IoT integration. The system is capable of detecting and classifying four major fault types: single line to ground (L-G), line to line (LL), double line to ground (L-L-G), and three-phase faults (L-L-LG). By placing current sensors at the transmission line's sending end, the system identifies faults and accurately determines their location through GPS coordinates .Simulations using protect us software were conducted to verify the system’s performance prior to building a hardware prototype. Upon fault detection, the system provides alerts via an LCD display and a buzzer, while real-time fault data is transmitted to an Android app and web server through the arduino IoT cloud. Testing confirmed the system’s precision in fault identification and location tracking, ensuring timely notifications for control room and remote device operators. This successful implementation highlights the system's potential for improving safety and efficiency in power transmission networks. the project lays the groundwork for further exploration into advanced fault detection and real-time monitoring solutions in electrical engineering.

Metaverse in surgery - origins and future potential.

The metaverse refers to a collective virtual space that combines physical and digital realities to create immersive, interactive environments. This space is powered by technologies such as augmented reality (AR), virtual reality (VR), artificial intelligence (AI) and blockchain. In healthcare, the metaverse can offer many applications. Specifically in surgery, potential uses of the metaverse include the possibility of conducting immersive surgical training in a VR or AR setting, and enhancing surgical planning through the adoption of three-dimensional virtual models and simulated procedures. At the intraoperative level, AR-guided surgery can assist the surgeon in real time to increase surgical precision in tumour identification and selective management of vessels. In post-operative care, potential uses of the metaverse include recovery monitoring and patient education. In urology, AR and VR have been widely explored in the past decade, mainly for surgical navigation in prostate and kidney cancer surgery, whereas only anecdotal metaverse experiences have been reported to date, specifically in partial nephrectomy. In the future, further integration of AI will improve the metaverse experience, potentially increasing the possibility of carrying out surgical navigation, data collection and virtual trials within the metaverse. However, challenges concerning data security and regulatory compliance must be addressed before the metaverse can be used to improve patient care.

PelviNet: A Collaborative Multi-agent Convolutional Network for Enhanced Pelvic Image Registration.

PelviNet introduces a groundbreaking multi-agent convolutional network architecture tailored for enhancing pelvic image registration. This innovative framework leverages shared convolutional layers, enabling synchronized learning among agents and ensuring an exhaustive analysis of intricate 3D pelvic structures. The architecture combines max pooling, parametric ReLU activations, and agent-specific layers to optimize both individual and collective decision-making processes. A communication mechanism efficiently aggregates outputs from these shared layers, enabling agents to make well-informed decisions by harnessing combined intelligence. PelviNet's evaluation centers on both quantitative accuracy metrics and visual representations to elucidate agents' performance in pinpointing optimal landmarks. Empirical results demonstrate PelviNet's superiority over traditional methods, achieving an average image-wise error of 2.8 mm, a subject-wise error of 3.2 mm, and a mean Euclidean distance error of 3.0 mm. These quantitative results highlight the model's efficiency and precision in landmark identification, crucial for medical contexts such as radiation therapy, where exact landmark identification significantly influences treatment outcomes. By reliably identifying critical structures, PelviNet advances pelvic image analysis and offers potential enhancements for broader medical imaging applications, marking a significant step forward in computational healthcare.

Scene understanding method utilizing global visual and spatial interaction features for safety production

Risk identification in power operations is crucial for both personal safety and power production. Existing risk identification methods mainly use target detection models to identify the common risks but the scene specificity of risk occurrence. For example, not wearing a safety harness, not wearing insulated gloves, etc. Since most methods for detecting safety gears make sense only under specific scene. But the power electric work is a complex object involving many elements such as personnel, equipment and safety tools. Therefore, this paper proposes a scene understanding method that integrates visual features and spatial relationship features among scene elements. This method constructs a scenean undirected scene graph to represent the interactive relationship among the elements, extracts the interactive features by using a graph encoder-decoder convolution module, and fuse perceived high-dimensional visual features and spatial topological features for scene recognition, in order to effectively solve addressing the power operation scene understanding problem under multi-element interaction. Finally, a power inspection operation scenario was chosen as the test case. The outcome of the evaluation indicates results indicate that the proposed approach suggested in this study exhibits superior precision in scene identification and shows ademonstrates strong generalization ability.

Prevalence, number of fractures, and hospital characteristics among the pediatric population with osteogenesis imperfecta: results from the nationwide registry of Türkiye.

Osteogenesis imperfecta is a rare connective tissue disorder with an estimated number of 4-20 cases per 100 000 inhabitants. Although the prevalence differs among regions, there are only a few number of national registry studies published previously. The aim of this study was to determine the prevalence of osteogenesis imperfecta among the pediatric age group in Türkiye, together with the patient and hospital characteristics. Via the e-health database of the Turkish Ministry of Health, we collected and retrospectively evaluated the medical records of the patients who were under 18 years of age with the diagnosis of osteogenesis imperfecta between 2016 and 2022. Total number of fractures, treatment modalities, and the hospital characteristics were also recorded. Two thousand seven hundred forty patients were extracted with a mean age of 9.77 ± 4.81 years. The prevalence of osteogenesis imperfecta in Türkiye among the pediatric population was calculated as 11.6 per 100 000 individuals. The median annual incidence was 31.5 per 100 000 live births between 2016 and 2022. There were 17.4 hospital admissions per patient per year. The mean age at the time of in-hospital mortality was 4.08 ± 5.03 years. The fracture rate per patient per year was 0.56 and conservative treatment was the most commonly preferred modality for all ages. This is the first registry-based nationwide study of osteogenesis imperfecta patients in Türkiye, providing important characteristics of the disease. Together with the help of the ongoing development of national health database systems, precision in patient identification would yield substantial benefits in terms of management of osteogenesis imperfecta.

Improving Sex Trafficking Victim Identification: Indicators of Trafficking in Online Escort Ads

ABSTRACT Although law enforcement and researchers have shared certain escort ad characteristics to detect sex trafficking, such as looking “young” or specific language cues, these indicators do not necessarily predict trafficking when tested against the counterfactual. We examined over 250 indicators in 1,263 ads from four states for which case outcomes were known (positive and negative for trafficking) and held focus groups and individual interviews with trafficking survivors, investigators, and non-trafficked sex workers on indicator meanings and ad posting practices. Language signaling a provider is trustworthy, use of an obscured phone number, language suggesting youth, and specification of the provider’s ethnicity were statistically significant predictors of trafficking. Additionally, and importantly for practice, photos in which the subject “appears young” or that displayed visible tattoos were associated more often with ads that did not belong to a trafficking case than ads related to trafficking. Results could be readily applied in practice to increase precision in victim identification, allowing shifts in resource allocation to cases most likely associated with trafficking. Policy recommendations to improve laws governing advertising websites are also made.

Advancing metagenome-assembled genome-based pathogen identification: unraveling the power of long-read assembly algorithms in Oxford Nanopore sequencing.

Oxford Nanopore sequencing is one of the high-throughput sequencing technologies that facilitates the reconstruction of metagenome-assembled genomes (MAGs). This study aimed to assess the potential of long-read assembly algorithms in Oxford Nanopore sequencing to enhance the MAG-based identification of bacterial pathogens using both simulated and mock communities. Simulated communities were generated to mimic those on fresh spinach and in surface water. Long reads were produced using R9.4.1+SQK-LSK109 and R10.4 + SQK-LSK112, with 0.5, 1, and 2 million reads. The simulated bacterial communities included multidrug-resistant Salmonella enterica serotypes Heidelberg, Montevideo, and Typhimurium in the fresh spinach community individually or in combination, as well as multidrug-resistant Pseudomonas aeruginosa in the surface water community. Real data sets of the ZymoBIOMICS HMW DNA Standard were also studied. A bioinformatic pipeline (MAGenie, freely available at https://github.com/jackchen129/MAGenie) that combines metagenome assembly, taxonomic classification, and sequence extraction was developed to reconstruct draft MAGs from metagenome assemblies. Five assemblers were evaluated based on a series of genomic analyses. Overall, Flye outperformed the other assemblers, followed by Shasta, Raven, and Unicycler, while Canu performed least effectively. In some instances, the extracted sequences resulted in draft MAGs and provided the locations and structures of antimicrobial resistance genes and mobile genetic elements. Our study showcases the viability of utilizing the extracted sequences for precise phylogenetic inference, as demonstrated by the consistent alignment of phylogenetic topology between the reference genome and the extracted sequences. R9.4.1+SQK-LSK109 was more effective in most cases than R10.4+SQK-LSK112, and greater sequencing depths generally led to more accurate results.IMPORTANCEBy examining diverse bacterial communities, particularly those housing multiple Salmonella enterica serotypes, this study holds significance in uncovering the potential of long-read assembly algorithms to improve metagenome-assembled genome (MAG)-based pathogen identification through Oxford Nanopore sequencing. Our research demonstrates that long-read assembly stands out as a promising avenue for boosting precision in MAG-based pathogen identification, thus advancing the development of more robust surveillance measures. The findings also support ongoing endeavors to fine-tune a bioinformatic pipeline for accurate pathogen identification within complex metagenomic samples.

A Systematic Study on the Application of Computer Big Data Analysis to Improve the Accuracy of Referee Decision in Sports Events

Due to the complex working environment, long communication distance and high reliability requirements of referees in weightlifting events, this project uses TI's MSP430F5299 as its core processing chip. RS485 for long-distance communication. This system realizes a variety of judgments, arbitration supervision, system function setting, LED large screen display, PC data real-time transmission and other functions. Then, Fourier pyramid filtering method is used to extract human skeleton information, and SVM is used to classify human behavior to complete the construction of human behavior recognition model. The findings from the study indicate that the architectural approach detailed within this manuscript yields a higher degree of precision in identification, thereby offering judges an enhanced instrument for assessment.

MetaBIDx: a new computational approach to bacteria identification in microbiomes.

Objectives: This study introduces MetaBIDx, a computational method designed to enhance species prediction in metagenomic environments. The method addresses the challenge of accurate species identification in complex microbiomes, which is due to the large number of generated reads and the ever-expanding number of bacterial genomes. Bacterial identification is essential for disease diagnosis and tracing outbreaks associated with microbial infections. Methods: MetaBIDx utilizes a modified Bloom filter for efficient indexing of reference genomes and incorporates a novel strategy for reducing false positives by clustering species based on their genomic coverages by identified reads. The approach was evaluated and compared with several well-established tools across various datasets. Precision, recall, and F1-score were used to quantify the accuracy of species prediction. Results: MetaBIDx demonstrated superior performance compared to other tools, especially in terms of precision and F1-score. The application of clustering based on approximate coverages significantly improved precision in species identification, effectively minimizing false positives. We further demonstrated that other methods can also benefit from our approach to removing false positives by clustering species based on approximate coverages. Conclusion: With a novel approach to reducing false positives and the effective use of a modified Bloom filter to index species, MetaBIDx represents an advancement in metagenomic analysis. The findings suggest that the proposed approach could also benefit other metagenomic tools, indicating its potential for broader application in the field. The study lays the groundwork for future improvements in computational efficiency and the expansion of microbial databases.

A weighted two-stage sequence alignment framework to identify motifs from ChIP-exo data

In this study, we introduce TESA (weighted two-stage alignment), an innovative motif prediction tool that refines the identification of DNA-binding protein motifs, essential for deciphering transcriptional regulatory mechanisms. Unlike traditional algorithms that rely solely on sequence data, TESA integrates the high-resolution chromatin immunoprecipitation (ChIP) signal, specifically from ChIP-exonuclease (ChIP-exo), by assigning weights to sequence positions, thereby enhancing motif discovery. TESA employs a nuanced approach combining a binomial distribution model with a graph model, further supported by a "bookend" model, to improve the accuracy of predicting motifs of varying lengths. Our evaluation, utilizing an extensive compilation of 90 prokaryotic ChIP-exo datasets from proChIPdb and 167 H. sapiens datasets, compared TESA's performance against seven established tools. The results indicate TESA's improved precision in motif identification, suggesting its valuable contribution to the field of genomic research.

Securing electric transportation networks: A machine learning-driven cyber threat detection

The study examines the cybersecurity environment of electric transportation networks using a machine learning-based methodology. It analyzes the behaviors of electric vehicles, charging patterns, cyber threat occurrences, and the performance of machine learning models. An analysis of electric vehicle (EV) data shows that there are differences in battery capacity and distances covered, suggesting the presence of possible weaknesses across different cars. Cyber threat logs provide a comprehensive view of the various levels of threat severity and the time it takes to discover them, illustrating the ever-changing nature of cyber threats in the network. Machine learning models have varying performance; ML003 and ML005 exhibit excellent accuracy and precision in threat identification, whilst ML002 shows significantly lower metrics. These results highlight the need of implementing flexible cybersecurity solutions to handle different electric vehicle behaviors and effectively reduce cyber risks. This research emphasizes the need of using proactive threat detection tactics in order to effectively address high-severity attacks. It also highlights the need for ongoing improvement of machine learning models to strengthen network security. This study enhances our comprehension of cybersecurity obstacles in electric transportation networks, highlighting the crucial significance of machine learning-based analysis in strengthening network resilience against ever-changing cyber threats.

Improved cell-type identification and comprehensive mapping of regulatory features with spatial epigenomics 96-channel microfluidic platform.

Gene expression is subject to epigenetic regulation and is dependent upon cellular context. Spatial omics tools can provide insight into cellular context; however, development has centered on spatial transcriptomics and proteomics. Deterministic barcoding in tissue for spatial omics sequencing (DBiT-seq) was the first spatial epigenomics platform at the cellular level. Here we present a comparison of spatial epigenomic profiling on both 50-channel and 96-channel platforms. The new 96-channel microfluidics chip design greatly improved precision in cell typing and identification of regulatory elements by spatial-ATAC-seq. Spatial mapping reveals complexity of glial cell and neuronal localization within brain structures as well as cis-regulatory elements controlling cellular function. This technology streamlines spatial analysis of the epigenome and contributes a new layer of spatial omics to uncover the context dependent regulatory mechanisms underpinning development, disease, and normal cellular function.

High Prevalence of Subjective Minimizing Language in Clinical Trials of Hematologic Malignancies: Natural Language Processing (NLP) Validation Study and Systematic Review of Randomized Controlled Trials Presented at ASH 2009-2021

Background: Transparent and objective reporting of treatment toxicities in cancer clinical trials is critical to inform patient-centred, shared decision-making. Previous studies have shown that toxicity reporting is inconsistent and incomplete in randomized controlled trials (RCTs) presented at major conferences in both hematologic (Chin-Yee et al. 2022; Skorupski et al. 2022) and gastrointestinal malignancies (Yu et al. 2023). The objectives of this study were to validate an NLP-based algorithm to identify subjectively minimized toxicity language in conference abstracts, and to evaluate longitudinal changes in the prevalence of minimized language in RCTs presented at ASH. Methods: For NLP models, data from prior systematic reviews of RCTs presented at ASH 2017-2021 were used as development (RCTs in acute leukemia: Chin-Yee et al. 2022) and validation (multiple myeloma and lymphoma: Skorupski et al. 2022) datasets. Because subjective minimizing language usually exhibits limited variability, we adopted a dictionary-based approach that is highly interpretable. Two dictionaries were developed: the first to identify subjective minimizing toxicity language; the second to identify reporting of patient experiences through Patient-Reported Outcomes (PROs) or Quality-of-Life (QOL) measures. Primary minimizing terms were defined as: “tolerable”, “manageable”, “acceptable”, and “favorable”; secondary minimizing terms were: “feasible”, “safe”, and “limited” (Chin-Yee et al. 2022). The primary outcome was F1 score (summary statistic of accuracy and precision) for identification of primary minimizing language. Based on F1 score in the development set, we operationalized our dictionary to include 3 primary minimizing terms “tolerable”, “manageable”, and “acceptable” (including relevant variants), while dropping “favorable” and all secondary minimizing terms. Precision, recall, F1 score, and accuracy were calculated for both dictionaries in each dataset (see Table 1 for definitions). Validated dictionaries for minimizing terms and for PRO/QOL measures were subsequently applied in a systematic review of RCT abstracts at ASH from 2009-2021 (representing the available time period indexed in Embase) across 3 diseases (acute leukemia, myeloma, and lymphoma) to assess changes in use of subjective minimizing language and reporting of PROs or QOL measures over a priori defined 3 major time periods: earliest available, middle, and most recent. Study inclusion/exclusion criteria are described previously (Chin-Yee et al. 2022). Results: Study characteristics are reported in Table 1A for NLP development and validation sets.Our dictionary-based method showed a precision of 0.90, recall of 0.82, F1 of 0.86, and accuracy of 0.93 in the development set, values considered sufficient for validation of the NLP model. In the validation set, these values were 0.75, 0.75, 0.75, and 0.82, respectively. This NLP model was applied to evaluate RCTs presented at ASH from 2009-2021 across the 3 diseases (acute leukemia, myeloma, and lymphoma). Following abstract screening, inclusion criteria were met in 68 of 411, 82 of 443, and 82 of 581 acute leukemia, myeloma, and lymphoma RCTs, respectively. NLP-assessed subjective minimization was present in 89 (26.4%) of all studies from 2009-2021; and in 26 (22.0%) RCTs from 2009-2012, 29 (25.4%) RCTs from 2013-2016, 34 (32.3%) RCTs from 2017-2021 ( Table 1B). Time-series analysis and results on PROs and QOL measures will also be presented at ASH. Discussion: NLP provides a novel, systematic, and scalable approach for evaluating use of subjective minimizing language in clinical trials. Our model showed good accuracy in identifying primary minimizing terms in RCTs presented at ASH from 2017-2021. Applying this model across a broader time period (2009-2021) suggests that use of subjectively minimized toxicity language is common in RCTs at ASH, similar across lymphoid and myeloid malignancies, and potentially increasing over time. NLP could provide an effective means of evaluating conference abstracts on a large scale with the ultimate goal of improving the objectivity of toxicity reporting in clinical trials. In conclusion, our findings suggest that the use of minimizing language is common and may be increasing over time, a concerning finding that implies downplaying of treatment toxicity by investigators. ^co-first and #co-last authors

Identification and physico-chemical characterization of microplastics in marine aerosols over the northeast Arabian Sea

Microplastics (MPs) in the atmosphere can undergo long-range transport from emission regions to pristine terrestrial and oceanic ecosystems. Due to their inherent toxic and hazardous characteristics, MPs pose serious risks to both human well-being and the equilibrium of ecosystem. The present study outlines the comprehensive characterization, spanning physical and chemical attributes of MPs associated with atmospheric aerosols. Total suspended particulates (TSPs) were collected on a quartz fibre filter by operating a high-volume sampler for 24 h during distinct years (March, 2016 and November, 2020) at a coastal location in the northeast Arabian Sea. Subsequent to the sampling, a series of techniques were applied including density separation. The assessment and scrutiny of the MPs was carried out using stereo-zoom microscopy with supplementary validation using advanced fluorescence microscopy for enhanced precision in identification. Our comparative assessment suggests peroxide treatment followed by density separation could be a robust procedure for the definitive identification and characterization of MPs in the atmosphere. Average total abundance of MPs was found to be 1.30 ± 0.14 n/m3 in 2016 and 1.46 ± 0.12 n/m3 in 2020 with fibres, fragments and films having similar relative contributions (41 %, 31 %, 28 % in 2016 and 40 %, 35 %, 25 % in 2020). Fibres were found to be dominant morphotype followed by fragments and films over the coastal region of the Arabian Sea. In order to unravel the detailed chemical nature of these MPs, spectral analysis using μ-FTIR was carried out. The outcome of the analysis showed prevailing polymers as polyvinyl chloride and polymethyl methacrylate (50545 %) as dominant polymers followed by polyester (15 %), styrene butyl methacrylate (11 %), and polyacetal (9 %). MPs present in the vicinity of the Arabian Sea have potential to supply nutrients and toxicants, consequently can contribute to the modulation of the surface water biogeochemical processes.

AI-Powered Renal Diet Support: Performance of ChatGPT, Bard AI, and Bing Chat.

Patients with chronic kidney disease (CKD) necessitate specialized renal diets to prevent complications such as hyperkalemia and hyperphosphatemia. A comprehensive assessment of food components is pivotal, yet burdensome for healthcare providers. With evolving artificial intelligence (AI) technology, models such as ChatGPT, Bard AI, and Bing Chat can be instrumental in educating patients and assisting professionals. To gauge the efficacy of different AI models in discerning potassium and phosphorus content in foods, four AI models-ChatGPT 3.5, ChatGPT 4, Bard AI, and Bing Chat-were evaluated. A total of 240 food items, curated from the Mayo Clinic Renal Diet Handbook for CKD patients, were input into each model. These items were characterized by their potassium (149 items) and phosphorus (91 items) content. Each model was tasked to categorize the items into high or low potassium and high phosphorus content. The results were juxtaposed with the Mayo Clinic Renal Diet Handbook's recommendations. The concordance between repeated sessions was also evaluated to assess model consistency. Among the models tested, ChatGPT 4 displayed superior performance in identifying potassium content, correctly classifying 81% of the foods. It accurately discerned 60% of low potassium and 99% of high potassium foods. In comparison, ChatGPT 3.5 exhibited a 66% accuracy rate. Bard AI and Bing Chat models had an accuracy rate of 79% and 81%, respectively. Regarding phosphorus content, Bard AI stood out with a flawless 100% accuracy rate. ChatGPT 3.5 and Bing Chat recognized 85% and 89% of the high phosphorus foods correctly, while ChatGPT 4 registered a 77% accuracy rate. Emerging AI models manifest a diverse range of accuracy in discerning potassium and phosphorus content in foods suitable for CKD patients. ChatGPT 4, in particular, showed a marked improvement over its predecessor, especially in detecting potassium content. The Bard AI model exhibited exceptional precision for phosphorus identification. This study underscores the potential of AI models as efficient tools in renal dietary planning, though refinements are warranted for optimal utility.

High-performance one-stage detector for SiC crystal defects based on convolutional neural network

SiC (silicon carbide), as the most important third-generation semiconductor material, has huge market prospects in numerous fields, such as 5G base stations and new energy vehicle charging piles. The identification of SiC crystal defects is essential for improving crystal quality. Currently, this study relies mainly on artificial methods to identify defects, which have significant limitations in terms of accuracy and efficiency. Thus, to quickly detect and classify different SiC crystal defects in complex scenarios, a convolutional neural network-based SiC crystal defect detection (SCDD-Net) model is presented for the first time in this study. SCDD-Net uses an improved online convolutional re-parameterization method that can effectively extract the features of SiC crystal defects and decrease the large training overhead. We devised a new spatial pyramid pooling module that, when combined with the global context block, enables the fast fusion of high-level crystal defects and underlying features. We also designed an anchor-based decoupling detection head network to identify smaller crystal defects. By collecting and processing more than 5300 high-quality microscopic images, we built a fine-grained labeled SiC crystal defect image dataset, SiC-Crystal-5K, for the first time. The experimental results show that the SCDD-Net has excellent detection accuracy compared to other state-of-the-art models. The mean average precision for high-resolution SiC crystal defect identification reached 99.53%, corresponding to a single-image detection speed of 102 fps. In addition to crystal-defect detection, the SCDD-Net model can be used as a general-purpose detector in a wide range of scenarios.

Convolution Neural Network Fusion Lock-In Thermography: A Debonding Defect Intelligent Determination Approach for Aviation Honeycomb Sandwich Composites (HSCs)

This report is on convolution neural network (CNN) fusion lock-in thermography, which can implement the intelligent identification of defects for aviation honeycomb sandwich composites (HSCs). First, HSCs specimens with subsurface delamination defects were fabricated and stimulated by halogen lamps according to sinusoidal modulation, and the defects were reliably inspected using lock-in thermography. The amplitude and phase images (commonly referred to as feature images) were obtained by using a digital lock-in correlation algorithm. Furthermore, these feature images were changed into gray or color-level image formalism datasets, which is pre-processed in ways including contrast enhancement, threshold segmentation as well as mosaic data augmentation. Finally, the four-layer feature pyramid structure and ransformer are combined and introduced to the popular YOLOv5 CNN model, and a YOLOLT CNN model is formed to realize the defect identification. The average precision (AP) in the defect identification of HSCs in complex environments (contains noise and other objects) reached 93.2% and achieved an average recognition speed of 0.6 s/image.

Investigating online activity in UK adolescent mental health patients: a feasibility study using a natural language processing approach for electronic health records

ObjectivesTo assess the feasibility of using a natural language processing (NLP) application for extraction of free-text online activity mentions in adolescent mental health patient electronic health records (EHRs).SettingThe Clinical Records...

High-Resolution Imaging Methods for Identification of Calcium Crystal Types in Osteoarthritis

The detection and differentiation of BCP and CPP crystals in calcified tissue is an important factor in the context of research and potential future treatment of osteoarthritis and chondrocalcinosis. Current standard methods originate from clinical practice and often lack precision in the correct identification of the calcium crystal type. In this work, a step-by-step guide for the use of the high-resolution imaging methods of tissue sections, Raman spectroscopy and scanning electron microscopy (SEM) in combination with energy-dispersive X-ray spectroscopy (EDS), for calcium crystal identification is presented. Sample preparation including Von Kossa staining, measurement and measurement parameters, data processing and data analysis methods are discussed and described. Furthermore, the different methods are compared to show advantages and disadvantages. Overall, Raman spectroscopy is a reasonable method from an economic point of view and regarding the time/effort required for acquiring highly reliable data in calcium crystal identification. Potentially, semi-quantitative results can be obtained with little effort and without the destruction of the respective test sample. The analysis/penetration depth during the Raman measurements, which is not precisely defined, poses a potential problem for accuracy. SEM can also be used for this task but requires more time, advanced technical knowledge and a pre-treatment of the samples using, e.g., gold sputtering, which may distort further analysis on the specific specimen. Therefore, this technique yields additional value compared to Raman spectroscopy only with additional research questions needed to be answered in the same sample, such as analysis of the sample topography or analysis of other unknown particles/deposits using EDS. The methods described in this manuscript are helpful for retrospective analyses in the context of research, but can also be used for potential future treatment strategies to discriminate between osteoarthritis and chondrocalcinosis patients.

Automatic meter classification of Kurdish poems.

Most of the classic texts in Kurdish literature are poems. Knowing the meter of the poems is helpful for correct reading, a better understanding of the meaning, and avoiding ambiguity. This paper presents a rule-based method for the automatic classification of the poem meter for the Central Kurdish language also known as Sorani. The metrical system of Kurdish poetry is divided into three classes quantitative, syllabic, and free verses. As the vowel length is not phonemic in the language, there are uncertainties in syllable weight and meter identification. The proposed method generates all the possible situations and then, by considering all lines of the input poem and the common meter patterns of Kurdish poetry, identifies the most probable meter type and pattern of the input poem. Evaluation of the method on a dataset from VejinBooks Kurdish corpus resulted in 97.3% of precision in meter type and 96.2% of precision in pattern identification.