Identification Software Research Articles

The Fault Diagnosis Method for Photovoltaic Modules Based on Machine Learning

Based on the urban natural gas pipeline accident statistics and semi-quantitative risk evaluation index system, this paper applies Bayesian network to establish a network model between various types of risk factors and the risk of natural gas pipeline failure. The EM algorithm was used to learn from the statistical accident data to obtain the parameters of the model. Based on the principle of evidential reasoning in reverse, the probability of occurrence of all risk indicators can be obtained when the probability of occurrence of urban natural gas pipeline accidents is 100%, the index weight is obtained by normalizing the occurrence probability. On this basis, this paper develops an efficient urban natural gas pipeline integrity risk identification and management software. The software can realize the basic data management of urban natural gas pipeline system, pipeline relative risk value calculation, pipeline risk level calculation and other functions, and the results are visualized. Finally, the practicability and effectiveness of the model and software are verified by a case of natural gas pipeline evaluation in a block.

Spectroscopic analysis of the strongly lensed SN Encore: constraints on cosmic evolution of Type Ia supernovae

ABSTRACT Strong gravitational lensing magnifies the light from a background source, allowing us to study these sources in detail. Here, we study the spectra of a $z = 1.95$ lensed Type Ia supernova (SN Ia) SN Encore for its brightest image A, taken 39 d apart. We infer the spectral age with template matching using the supernova identification (snid ) software and find the spectra to be at $29.0 \pm 5.0$ and $37.4 \pm 2.8$ rest-frame days post-maximum, respectively, consistent with separation in the observer frame after accounting for time dilation. Since SNe Ia measure dark energy properties by providing relative distances between low- and high-z SNe, it is important to test for the evolution of spectroscopic properties. Comparing the spectra to composite low-z SN Ia spectra, we find strong evidence of the similarity between the local sample and SN Encore. The line velocities of common SN Ia spectral lines, Si ii 6355 $\mathring{\rm A}$ and Ca ii near-infrared triplet, are consistent with the distribution for the low-z sample as well as other lensed SNe Ia, e.g. iPTF16geu ($z = 0.409$) and SN H0pe ($z = 1.78$). The consistency between the low-z sample and lensed SNe at high-z suggests no obvious cosmic evolution demonstrating their use as high-z distance indicators, though this needs to be confirmed/refuted via a larger sample. We also find that the spectra of SN Encore match the predictions for explosion models very well. With future large samples of lensed SNe Ia, e.g. with the Vera C. Rubin Observatory, spectra at such late phases will be important to distinguish between different explosion scenarios.

LSTM Recurrent Neural Network Based Method for Identification of PDC Drill Bit Operating Conditions and Its Application

Abstract PDC drill bit underground operating conditions identification is one of of the difficulties during the drilling operation. It is of great significant for the drilling improvement and accurate construction formation to accurately identify the complex operating conditions during drilling the operation such as PDC bit wearing and stick-slip vibration, etc. In this paper, data of relationship between torque, rate of penetration (ROP) signal and weight on bit (WOB), rev were obtained through lab simulation experiment, after which, a relationship database of torque, ROP signal and WOB, rev was established, a single-layer LSTM recurrent neural network (LSTM-RNN) was constructed, the number of neurons, batch size and optimizer type of LSTM layer were optimized. With BPTT algorithm, LSTM model was trained and tested on the training set containing 6000 groups of data, and achieved the goal of identifying the bit operation conditions against the input of WOB, torque, rev and ROP signal parameters. Based on this model, the PDC bit underground operating conditions identification software was compiled, and was connected to the logging unit to realize real-time identification of bit underground operating conditions, and field test application was successfully carried out in Mahu 1 Well Region. The research showed that the convergence speed was fast and the time cost was at the lowest when the number of neurons of LSTM model was 50, and that the optimized Adam optimizer model could meet the requirements of convergence speed and prediction accuracy with the lowest loss value. The classification accuracy of this model on the test set reached 94% and the accuracy of the prediction method was also verified by field test application. To sum up, the method of LSTM-RNN based PDC bit underground operating conditions real-time identification can better meet the requirements of accurate identification of PDC bit underground operating conditions in the practical drilling, and has guiding significance for the judgement of PDC bit operating conditions on site and formulation of scientific construction decisions.

A Systematic Review of Software for Learning Analytics in Higher Education

Learning analytics (LA) is an important area of study in technology-enhanced learning that has emerged during the last decade. In earlier years, several systematic reviews have been conducted that focused on the theories behind LA or on empirical studies that utilized LA-based methods to improve learning and teaching processes in higher education. However, to date, there has been no systematic review of papers that have adopted a software perspective to report on the many forms of learning analytics software (LAS) that have been developed, despite these being used more frequently than before in higher education to support learning and teaching processes. To fill this gap, this paper presents a systematic review of LAS with the aim of critically scrutinizing the ways in which the use of interactive software in real-world settings may both support students in improving their academic performance and assist teachers in various pedagogical practices. A thematic analysis of 75 articles was conducted, resulting in the identification of three categories of LAS: at-risk student identification (ARSI) software; self-regulation software; and collaborative learning software. For each of these categories, we analyzed (i) the embedded functionality; (ii) the stakeholder (teacher and student) for which the functionality is intended; (iii) the analytical and visualization approaches implemented; and (iv) the limitations of the software that require future attention. Based on the findings of our review, we propose future directions for the development of learning analytics software.

Risk Evaluation and Software Development of Urban Natural Gas Pipelines Based on Bayesian Networks

Based on the urban natural gas pipeline accident statistics and semi-quantitative risk evaluation index system, this paper applies Bayesian network to establish a network model between various types of risk factors and the risk of natural gas pipeline failure. The EM algorithm was used to learn from the statistical accident data to obtain the parameters of the model. Based on the principle of evidential reasoning in reverse, the probability of occurrence of all risk indicators can be obtained when the probability of occurrence of urban natural gas pipeline accidents is 100%, the index weight is obtained by normalizing the occurrence probability. On this basis, this paper develops an efficient urban natural gas pipeline integrity risk identification and management software. The software can realize the basic data management of urban natural gas pipeline system, pipeline relative risk value calculation, pipeline risk level calculation and other functions, and the results are visualized. Finally, the practicability and effectiveness of the model and software are verified by a case of natural gas pipeline evaluation in a block.

Automatic Foreign Matter Segmentation System for Superabsorbent Polymer Powder: Application of Diffusion Adversarial Representation Learning

In current industries, sampling inspections of the quality of powders, such as superabsorbent polymers (SAPs) still are conducted via visual inspection. The size of samples and foreign matter are around 500 μm, making them difficult for humans to identify. An automatic foreign matter detection system for powder has been developed in the present study. The powder samples can be automatically delivered, distributed, and recycled, and images of them are captured through the hardware of the system, while the identification software of this system was developed based on diffusion adversarial representation learning (DARL). The background image is a foreign-matter-free powder image with an input image size of 1024 × 1024 × 3. Since DARL includes adversarial segmentation, a diffusion process, and synthetic image generation, the DARL model was trained using a diffusion block with the employment of a U-Net attention mechanism and a spatial-adaptation de-normalization (SPADE) layer through the adoption of a loss function from a vanilla generative adversarial network (GAN). This model was then compared with supervised models such as a fully convolutional network (FCN), U-Net, and DeepLABV3+, as well as with an unsupervised Otsu threshold segmentation. It should be noted that only 10% of the training samples were utilized for the DARL to learn and the intersection over union (IoU) of the DARL can reach up to 80.15%, which is much higher than the 59.00%, 53.47%, 49.39%, and 30.08% for the Otsu threshold segmentation, FCN, U-Net, and DeepLABV3+ models. Therefore, the performance of the model developed in the present study would not be degraded due to an insufficient number of samples containing foreign matter. In practical applications, there is no need to collect, label, and design features for a large number of foreign matter samples before using the developed system.

Futile recanalization in patients with basilar artery occlusion: assessment of the underlying etiology and the role of perfusion imaging

BackgroundFutile recanalization (FR) after endovascular therapy (EVT) is common in basilar artery occlusion (BAO). The purpose of this study was to investigate the predictors of FR in the posterior circulation...

P-048 Morphokinetic analysis and implantation prediction of embryos derived from oocytes microinjected with Artificial Intelligence (AI) real-time assessed sperm

Abstract Study question Is embryo morphokinetic development affected by sperm quality assessed real time during Intracytoplasmic Sperm Injection (ICSI)? Summary answer Embryos derived from oocytes microinjected with low-quality sperm, analyzed by an AI-tool, exhibit a sluggish pace and a diminished potential for successful implantation. What is known already The study of embryo morphokinetics provides insights into the temporal aspects of cell division, which are pivotal for successful implantation. Key parameters such as time of division to 3 cells (t3), time to division to 5 cells (t5), time between division to 3 cells and subsequent division to 4 cells (s2) and duration of cell cycle two, i.e. time between division to 2 cells and division to 3 cells (cc2) are closely associated with implantation potential. Monitoring these markers allows clinicians to select embryos with optimal developmental trajectories, improving the chances of successful implantation and ultimately leading to healthier pregnancies. Study design, size, duration That is a single-centre and non-intervention study of 60 ICSI cycles. Seminal samples were assessed real-time with Sperm identification (SiD) software (v1.0, IVF2.0, LTD). Microinjected oocytes were maintained in culture in time-lapse incubators (EmbryoScope or EmbryoScope Plus). Injected sperm and their corresponding oocytes were individually followed-up to assess early embryo morphokinetics: t3, t5, s2, cc2 (273 total blastocysts) in a four-month period. Participants/materials, setting, methods Sixty ICSI cycles (35 from own oocytes and 25 from egg donation) were studied.418 sperm were assessed with SiD and classified as ‘low’, ‘medium’, ‘good’ or ‘best’, according to their motility parameters. Early embryonic morphokinetics were studied focusing on t3, t5, s2 and cc2 times. Ranges related with best implantation potential were studied according to previous works of our group: t3(34-40h), t5 (45-55h), cc2 (9-12h) and s2 (≤0,76h). Main results and the role of chance No discernible differences were observed in the parameters t3 and t5 among embryos within each sperm quality group. However, significant differences were identified in s2 and cc2 metrics between embryos of distinct sperm quality groups. Specifically, oocytes subjected to microinjection with ‘best’ or ‘good’ sperm exhibited an optimal average cc2 length (10.49 ± 5.12), in stark contrast to embryos derived from oocytes microinjected with ‘medium’ or ‘low’ sperm, which manifested an acelerated developmental pace (8.73 ± 5.55) (p < 0.05). Furthermore, embryos from the ‘best’ and ‘good’ groups displayed significantly shorter s2 lengths (1.16 ± 1.98 and 1.03 ± 1.62) compared to embryos from the ‘low’ group (2.73 ± 3.96). The latter exhibited an extended duration between the 3-cell division and subsequent division to 4 cells (p < 0.05). These findings lead us to infer that embryos arising from oocytes microinjected with ‘best’ or ‘good’ sperm possess superior implantation potential compared to those originating from oocytes microinjected with ‘medium’ or ‘low’ sperm, as indicated by SiD scores. The latter cohort appears to exhibit an alterated morphokinetic development and diminished implantation potential. Limitations, reasons for caution This project is limited by its retrospective and single-centre nature. In addition, studying later embryo morphokinetics parameters could provide a better description of embryo development and its relation with sperm quality assessed real-time. Finally, sperm morphology should also be included in SiD software because of its relation with reproductive success. Wider implications of the findings Selection of high-quality sperm to microinject into the oocytes, supported by real-time analysis, could contribute to improved morphological and kinetic characteristics of embryos, potentially increasing the chances of success in assisted reproductive procedures. In addition, it provides an opportunity to reduce subjectivity and inter-user variability of ICSI procedure. Trial registration number Not applicable

Analysis of carbohydrates and glycoconjugates by matrix-assisted laser desorption/ionization mass spectrometry: An update for 2021-2022.

The use of matrix-assisted laser desorption/ionization (MALDI) mass spectrometry for the analysis of carbohydrates and glycoconjugates is a well-established technique and this review is the 12th update of the original article published in 1999 and brings coverage of the literature to the end of 2022. As with previous review, this review also includes a few papers that describe methods appropriate to analysis by MALDI, such as sample preparation, even though the ionization method is not MALDI. The review follows the same format as previous reviews. It is divided into three sections: (1) general aspects such as theory of the MALDI process, matrices, derivatization, MALDI imaging, fragmentation, quantification and the use of computer software for structural identification. (2) Applications to various structural types such as oligo- and polysaccharides, glycoproteins, glycolipids, glycosides and biopharmaceuticals, and (3) other general areas such as medicine, industrial processes, natural products and glycan synthesis where MALDI is extensively used. Much of the material relating to applications is presented in tabular form. MALDI is still an ideal technique for carbohydrate analysis, particularly in its ability to produce single ions from each analyte and advancements in the technique and range of applications show little sign of diminishing.

Characterize direct protein interactions with enrichable, cleavable and latent bioreactive unnatural amino acids

Latent bioreactive unnatural amino acids (Uaas) have been widely used in the development of covalent drugs and identification of protein interactors, such as proteins, DNA, RNA and carbohydrates. However, it is challenging to perform high-throughput identification of Uaa cross-linking products due to the complexities of protein samples and the data analysis processes. Enrichable Uaas can effectively reduce the complexities of protein samples and simplify data analysis, but few cross-linked peptides were identified from mammalian cell samples with these Uaas. Here we develop an enrichable and multiple amino acids reactive Uaa, eFSY, and demonstrate that eFSY is MS cleavable when eFSY-Lys and eFSY-His are the cross-linking products. An identification software, AixUaa is developed to decipher eFSY mass cleavable data. We systematically identify direct interactomes of Thioredoxin 1 (Trx1) and Selenoprotein M (SELM) with eFSY and AixUaa.

Evaluation of the accuracy of automated tooth segmentation of intraoral scans using artificial intelligence-based software packages

The accuracy of tooth segmentation in intraoral scans is crucial for performing virtual setups and appliance fabrication. Hence, the objective of this study was to estimate and compare the accuracy of automated tooth segmentation generated by the artificial intelligence of dentOne software (DIORCO Co, Ltd, Yongin, South Korea) and Medit Ortho Simulation software (Medit Corp, Seoul, South Korea). Twelve maxillary and mandibular pretreatment dental scan sets comprising 286 teeth were collected for this investigation from the archives of the Department of Orthodontics, Faculty of Dentistry, Alexandria University. The scans were imported as standard tessellation language files into both dentOne and Medit Ortho Simulation software. Automatic segmentation was run on each software. The number of successfully segmented teeth vs failed segmentations was recorded to determine the success rate of automated segmentation of each program. Evaluation of success and/or failure was based on the software's identification of the teeth and the quality of the segmentation. The mesiodistal tooth width measurements after segmentation using both tested software programs were compared with those measured on the unsegmented scan using Meshmixer software (Autodesk, San Rafael, Calif). The unsegmented scans served as the reference standard. A total of 288 teeth were examined. Successful identification rates were 99% and 98.3% for Medit and dentOne, respectively. Success rates of segmenting the lingual surfaces of incisors were significantly higher in Medit than in dentOne (93.7% vs 66.7%, respectively; P<0.001). DentOne overestimated the mesiodistal width of canines (0.11 mm, P= 0.032), premolars (0.22 mm, P< 0.001), and molars (0.14 mm, P= 0.043) compared with the reference standard, whereas Medit overestimated the mesiodistal width of premolars only (0.13 mm, P= 0.006). Bland-Altman plots showed that mesiodistal tooth width agreement limits exceeded 0.2 mm between each software and the reference standard. Both artificial intelligence-segmentation software demonstrated acceptable accuracy in tooth segmentation. There is a need for improvement in segmenting incisor lingual tooth surfaces in dentOne. Both software programs tended to overestimate the mesiodistal widths of segmented teeth, particularly the premolars. Artificial intelligence-segmentation needs to be manually adjusted by the operator to ensure accuracy. However, this still does not solve the problem of proximal surface reconstruction by the software.

Deep learning framework for peak detection at the intact level of therapeutic proteins.

While automated peak detection functionalities are available in commercially accessible software, achieving optimal true positive rates frequently necessitates visual inspection and manual adjustments. In the initial phase of this study, hetero-variants (glycoforms) of a monoclonal antibody were distinguished using liquid chromatography-mass spectrometry, revealing discernible peaks at the intact level. To comprehensively identify each peak (hetero-variant) in the intact-level analysis, a deep learning approach utilizing convolutional neural networks (CNNs) was employed in the subsequent phase of the study. In the current case study, utilizing conventional software for peak identification, five peaks were detected using a 0.5 threshold, whereas seven peaks were identified using the CNN model. The model exhibited strong performance with a probability area under the curve (AUC) of 0.9949, surpassing that of partial least squares discriminant analysis (PLS-DA) (probability AUC of 0.8041), and locally weighted regression (LWR) (probability AUC of 0.6885) on the data acquired during experimentation in real-time. The AUC of the receiver operating characteristic curve also illustrated the superior performance of the CNN over PLS-DA and LWR.

Molecular characterization of coxsackievirus A24 variants isolated from an outbreak of acute hemorrhagic conjunctivitis

PurposeAcute Hemorrhagic conjunctivitis (AHC) is associated with CVA24v. Recently there was a severe outbreak of conjunctivitis in months of July and August 2023 in India. This study emphasizes the identification of the distinct mutations in the CVA24v strains, which were isolated during the AHC outbreak and could have potentially played a role in the high transmission of AHC in India during the 2023 outbreak. MethodsA total of 71 conjunctivitis patients aged 1–75 years comprising 47 males and 24 females who attended Ophthalmology department of a tertiary care hospital of easternIndia were studied.RNA was extracted from all conjunctival swab samples and converted into cDNA. Subsequently, the viral 5′ UTR was amplified and the PCR positive samples were subjected to sequencing. The newly isolated viral 5′ UTR sequences were aligned with other worldwide sequences using the Clustal W tool to conduct mutational analysis. A phylogenetic tree was built using the MEGA software for viral genotype identification. ResultsAll of the current outbreak strains belonged to genotype IV of CVA24v. The present outbreak strains formed a distinct clade in the phylogenetic tree and were different from previously reported Indian strains. Two persistent mutations, specifically in domain IV (T213C) and domain V (C475T), were exclusively detected within the internal ribosome entry site (IRES) of the 5′ UTR of the current strains causing the outbreak. These two alterations have previously been shown to impact the virulence of another enterovirus (CV B3), but they have not been described in CVA24v until now. ConclusionFinding of the present study highlights the possibility and the significance of the aforementioned two mutations in enhancing the transmissibility of the newer CVA24v strains. Hence, these two distinct mutations should be investigated further for developing antiviral therapies to combat future AHC outbreaks associated with CVA24v.

SNP-SVant: A Computational Workflow to Predict and Annotate Genomic Variants in Organisms Lacking Benchmarked Variants.

Whole-genome sequencing is widely used to investigate population genomic variation in organisms of interest. Assorted tools have been independently developed to call variants from short-read sequencing data aligned to a reference genome, including single nucleotide polymorphisms (SNPs) and structural variations (SVs). We developed SNP-SVant, an integrated, flexible, and computationally efficient bioinformatic workflow that predicts high-confidence SNPs and SVs in organisms without benchmarked variants, which are traditionally used for distinguishing sequencing errors from real variants. In the absence of these benchmarked datasets, we leverage multiple rounds of statistical recalibration to increase the precision of variant prediction. The SNP-SVant workflow is flexible, with user options to tradeoff accuracy for sensitivity. The workflow predicts SNPs and small insertions and deletions using the Genome Analysis ToolKit (GATK) and predicts SVs using the Genome Rearrangement IDentification Software Suite (GRIDSS), and it culminates in variant annotation using custom scripts. A key utility of SNP-SVant is its scalability. Variant calling is a computationally expensive procedure, and thus, SNP-SVant uses a workflow management system with intermediary checkpoint steps to ensure efficient use of resources by minimizing redundant computations and omitting steps where dependent files are available. SNP-SVant also provides metrics to assess the quality of called variants and converts between VCF and aligned FASTA format outputs to ensure compatibility with downstream tools to calculate selection statistics, which are commonplace in population genomics studies. By accounting for both small and large structural variants, users of this workflow can obtain a wide-ranging view of genomic alterations in an organism of interest. Overall, this workflow advances our capabilities in assessing the functional consequences of different types of genomic alterations, ultimately improving our ability to associate genotypes with phenotypes. © 2024 The Authors. Current Protocols published by Wiley Periodicals LLC. Basic Protocol: Predicting single nucleotide polymorphisms and structural variations Support Protocol 1: Downloading publicly available sequencing data Support Protocol 2: Visualizing variant loci using Integrated Genome Viewer Support Protocol 3: Converting between VCF and aligned FASTA formats.

OrthoRefine: automated enhancement of prior ortholog identification via synteny

BackgroundIdentifying orthologs continues to be an early and imperative step in genome analysis but remains a challenging problem. While synteny (conservation of gene order) has previously been used independently and in combination with other methods to identify orthologs, applying synteny in ortholog identification has yet to be automated in a user-friendly manner. This desire for automation and ease-of-use led us to develop OrthoRefine, a standalone program that uses synteny to refine ortholog identification.ResultsWe developed OrthoRefine to improve the detection of orthologous genes by implementing a look-around window approach to detect synteny. We tested OrthoRefine in tandem with OrthoFinder, one of the most used software for identification of orthologs in recent years. We evaluated improvements provided by OrthoRefine in several bacterial and a eukaryotic dataset. OrthoRefine efficiently eliminates paralogs from orthologous groups detected by OrthoFinder. Using synteny increased specificity and functional ortholog identification; additionally, analysis of BLAST e-value, phylogenetics, and operon occurrence further supported using synteny for ortholog identification. A comparison of several window sizes suggested that smaller window sizes (eight genes) were generally the most suitable for identifying orthologs via synteny. However, larger windows (30 genes) performed better in datasets containing less closely related genomes. A typical run of OrthoRefine with ~ 10 bacterial genomes can be completed in a few minutes on a regular desktop PC.ConclusionOrthoRefine is a simple-to-use, standalone tool that automates the application of synteny to improve ortholog detection. OrthoRefine is particularly efficient in eliminating paralogs from orthologous groups delineated by standard methods.

Optimizing additive manufacturing: Minimizing support structures through constraint-based design

Support structures are critical in Additive Manufacturing (AM) but pose challenges due to increased time and material costs for post-processing. This study introduces a novel constraint-based strategy to refine structural parameters while reducing reliance on support structures. By integrating this technique with topology optimization, component angulations are calibrated to align with AM constraints, enabling optimal designs without the need for support. Implementation of this constraint was done using MATLAB, transferring models to Cura software for support identification during printing. To validate the results, stress distribution was scrutinized in the ABAQUS environment, revealing a significant reduction in stress levels due to the proposed methodology, consistent with existing studies on stress reduction phenomena. Moreover, the study demonstrates a substantial decrease in material volume usage, leading to cost savings and promoting sustainable manufacturing practices. This reduction in material usage also enhances structural integrity, minimizing the risk of failure and improving overall performance.

Performance of different automatic photographic identification software for larvae and adults of the European fire salamander.

For many species, population sizes are unknown despite their importance for conservation. For population size estimation, capture-mark-recapture (CMR) studies are often used, which include the necessity to identify each individual, mostly through individual markings or genetic characters. Invasive marking techniques, however, can negatively affect the individual fitness. Alternatives are low-impact techniques such as the use of photos for individual identification, for species with stable distinctive phenotypic traits. For the individual identification of photos, a variety of different software, with different requirements, is available. The European fire salamander (Salamandra salamandra) is a species in which individuals, both at the larval stage and as adults, have individual specific patterns that allow for individual identification. In this study, we compared the performance of five different software for the use of photographic identification for the European fire salamander: Amphibian & Reptile Wildbook (ARW), AmphIdent, I3S pattern+, ManderMatcher and Wild-ID. While adults can be identified by all five software, European fire salamander larvae can currently only be identified by two of the five (ARW and Wild-ID). We used one dataset of European fire salamander larval pictures taken in the laboratory and tested this dataset in two of the five software (ARW and Wild-ID). We used another dataset of European fire salamander adult pictures taken in the field and tested this using all five software. We compared the requirements of all software on the pictures used and calculated the False Rejection Rate (FRR) and the Recognition Rate (RR). For the larval dataset (421 pictures) we found that the ARW and Wild-ID performed equally well for individual identification (99.6% and 100% Recognition Rate, respectively). For the adult dataset (377 pictures), we found the best False Rejection Rate in ManderMatcher and the highest Recognition Rate in the ARW. Additionally, the ARW is the only program that requires no image pre-processing. In times of amphibian declines, non-invasive photo identification software allowing capture-mark-recapture studies help to gain knowledge on population sizes, distribution, movement and demography of a population and can thus help to support species conservation.

Exploring individual's public trust in the NHS Test and Trace System – A pragmatic reflexive thematic analysis

ContextDigital contact tracing uses automated systems and location technology embedded on smartphone software for efficient identification of individuals exposed to COVID-19. Such systems are only effective with high compliance, yet compliance is mediated by public trust in the system. This work explored the perception of individual's trust and expectation of the broader Test and Trace system in the United Kingdom (UK) with the upcoming release of the National Health Service's (NHS) COVID-19 app as a case example. MethodsTwelve adults underwent online semi-structured interviews in August 2020, prior to public availability of the COVID-19 app. Pragmatic reflexive thematic analysis was applied inductively to explore common themes between participants, using an organic and recursive process (Braun & Clarke, 2019). ResultsThemes highlighted features of the technology that would be perceived to be trustworthy (Theme 1), and concerns relating to i) whether users would comply with a T&T system (Theme 2) and ii) how a T&T system would handle user's personal data (Theme 3). Two further themes built on aspects of automation within a T&T system and its impact on trust (Theme 4) and how the media altered perceptions of the T&T system (Theme 5). ConclusionsParticipants outlined the need for different user requirements that could be built into the NHS COVID-19 app that would support increased adherence. Concurrently, participants raised questions surrounding personal data and privacy of their data, plus the level of automated versus manual tasks, which impacted perception of trust in the app and wider system. Additionally, themes highlighted that T&T systems do not happen within a vacuum, but within a pre-existing environment influenced by variables such as the media and perception of other's compliance to T&T. ImplicationsSince it's roll-out, controversies surrounding the UK T&T system include concerns about privacy, stigma and uptake. Considering the current piece of work, which anticipated similar concerns prior to public access to COVID-19 app, engaging with the public may have been an important step in improving the perception and compliance with the app. Principles fundamental to patient and public involvement (PPI) and Responsible Research and Innovation (RRI) such as the inclusion of the public in the early development of research and aligning the outcomes of research and innovation with broader societal values and expectations would have been well-applied to this system and should be applied to future autonomous systems requiring high public uptake.

Underwater advertisement call of the threatened Telmatobius rubigo (Anura: Telmatobiidae)

Abstract Underwater Passive Acoustic Monitoring (UPAM) has been used infrequently to register subsurface vocal behaviour in anurans. This study describes the underwater advertisement call of Telmatobius rubigo based on recordings made with UPAM technique. Telmatobius rubigo call is a low-frequency trill composed of 6 to 22 pulses with amplitude modulation. The spectral and temporal characters showed differences from other Telmatobius species. The UPAM, combined with automated call identification software, showed effectiveness in monitoring the calling behaviour of this aquatic Andean frog in their natural environment. UPAM and soundscape/riverscape analysis should be incorporated as a non-invasive tool for monitoring threatened aquatic species.

Detection of Source Code Plagiarism Utilizing an Approach Based on Machine Learning

Academic institutions, which often publish papers and journals, are ideal testing grounds for the efficacy of counterfeit detection methods. Plagiarism occurs when someone uses the words of another writer without giving that writer proper credit. The proliferation of freeware text editors and the increasing availability of scientific materials online have made the detection of plagiarism a pressing concern; however, the detection of plagiarism in the source code presents a particularly difficult problem. Plagiarism detection algorithms for identification systems and software source code have been the subject of numerous academic investigations. The proposed method combines TF-IDF transformations with K-means clustering to achieve a 99.2% accuracy rate when detecting instances of plagiarism in the source code. This is because it groups similar lines of code together. On the other hand, in comparison to the outcomes produced by the random forest algorithm, the ones that it generates are significantly better. The performance of the MOSS system that was already in place was inferior to that of the system that was used for 90% and 80% of the training set. When contrasting the results, some parameters for evaluation that are considered include precision, recall, and F-measure. The proposed system is implemented in Jupyter Notebook 7 and Python. Also, graphic user interface is designed and implemented to give user friendly experience to the users.