Inconsistency Resolution Research Articles

Whole genome–based comparative analysis of the genus Streptomyces reveals many misclassifications

Streptomyces species are experts in the production of bioactive secondary metabolites; however, their taxonomy has fallen victim of the tremendous interest shown by the scientific community, evident in the discovery of numerous synonymous in public repositories. Based on genomic data from NCBI Datasets and nomenclature from the LPSN database, we compiled a dataset of 600 Streptomyces species along with their annotations and metadata. To pinpoint the most suitable taxonomic classification method, we conducted a comprehensive assessment comparing multiple methodologies, including analysis of 16S rRNA, individual housekeeping genes, multilocus sequence analysis (MLSA), and Fast Average Nucleotide Identity (FastANI) on a subset of 409 species with complete data. Due to insufficient resolution of 16S rRNA and inconsistency observed in individual housekeeping genes, we performed a more in-depth analysis, comparing only FastANI and MLSA, which expanded our dataset to include 502 species. With FastANI validated as the preferred method, we conducted pairwise analysis on the entire dataset identifying 59 non-unique species among the 600, and subsequently refined the dataset to 541 unique species. Additionally, we collected data on 724 uncharacterized Streptomyces strains to investigate the uniqueness potential of the unannotated fraction of the Streptomyces genus. Utilizing FastANI, 289 strains could be successfully classified into one of the 541 Streptomyces species.Key points• Evaluation of taxonomic classification methods for Streptomyces species.• Whole genome analysis, specifically FastANI, has been chosen as preferred method.• Various reclassifications are proposed within the Streptomyces genus.Graphical abstract

Examining Participation of Expropriated Owners in Compulsory Land Acquisition for the Western Corridor Gas Infrastructure Project in Ghana

The participation of expropriated owners in compulsory land acquisition processes is necessary to maximise benefits, minimize the negative consequences and prevent actions or omissions associated with the implementation of the expropriation order. The procedure for compulsory land acquisition in Ghana starts with the government declaring an intention to acquire any parcel(s) of land within the territory of Ghana and ends with the resolution of inconsistencies and conflicts arising out of the acquisition. The main objective of the study was to investigate the extent of participation by expropriated owners of the Nzema East Municipality and the Ellembelle District in the compulsory land acquisition process for the Western Corridor Gas Infrastructure Project. Underpinned by the citizen participation theory, the study adopted the mixed methods research design. The quantitative data was collected through a survey from sampled expropriated persons in 24 affected communities of the study area while the qualitative data was obtained through focus group discussions and key informant interviews. The study revealed that the mode of engagement between the expropriated persons and the expropriators was through information sharing, thereby limiting the participation exercise to the first rung of the citizen's participation theory. As a recommendation, the Lands Commission should improve community participation in the expropriation order by ensuring information flow and feedback at any stage of the acquisition process.

Darcy: Automatic Architectural Inconsistency Resolution in Java

Darcy: Automatic Architectural Inconsistency Resolution in Java

Emission ensemble approach to improve the development of multi-scale emission inventories

Abstract. Many studies have shown that emission inventories are one of the inputs with the most critical influences on the results of air quality modelling. Comparing emission inventories among themselves is, therefore, essential to build confidence in emission estimates. In this work, we extend the approach of Thunis et al. (2022) to compare emission inventories by building a benchmark that serves as a reference for comparisons. This benchmark is an ensemble that is based on three state-of-the-art EU-wide inventories: CAMS-REG, EMEP and EDGAR. The ensemble-based methodology screens differences between inventories and the ensemble. It excludes differences that are not relevant and identifies among the remaining ones those that need special attention. We applied the ensemble-based screening to both an EU-wide and a local (Poland) inventory. The EU-wide analysis highlighted a large number of inconsistencies. While the origin of some differences between EDGAR and the ensemble can be identified, their magnitude remains to be explained. These differences mostly occur for SO2 (sulfur oxides), PM (particulate matter) and NMVOC (non-methane volatile organic carbon) for the industrial and residential sectors and reach a factor of 10 in some instances. Spatial inconsistencies mostly occur for the industry and other sectors. At the local scale, inconsistencies relate mostly to differences in country sectorial shares that result from different sectors/activities being accounted for in the two types of inventories. This is explained by the fact that some emission sources are omitted in the local inventory due to a lack of appropriate geographically allocated activity data. We identified sectors and pollutants for which discussion between local and EU-wide emission compilers would be needed in order to reduce the magnitude of the observed differences (e.g. in the residential and industrial sectors). The ensemble-based screening proved to be a useful approach to spot inconsistencies by reducing the number of necessary inventory comparisons. With the progressive resolution of inconsistencies and associated inventory improvements, the ensemble will improve. In this sense, we see the ensemble as a useful tool to motivate the community around a single common benchmark and monitor progress towards the improvement of regionally and locally developed emission inventories.

Automated Measurement of Ovary Development in Atlantic Salmon Using Deep Learning

Salmon breeding companies control the egg stripping period through environmental change, which triggers the need to identify the state of maturation. Ultrasound imaging of the salmon ovary is a proven non-invasive tool for this purpose; however, the process is laborious, and the interpretation of the ultrasound scans is subjective. Real-time ultrasound image segmentation of Atlantic salmon ovary provides an opportunity to overcome these limitations. However, several application challenges need to be addressed to achieve this goal. These challenges include the potential for false-positive and false-negative predictions, accurate prediction of attenuated lower ovary parts and resolution of inconsistencies in predicted ovary shape. We describe an approach designed to tackle these obstacles by employing targeted pre-training of a modified U-Net, capable of performing both segmentation and classification. In addition, a variational autoencoder (VAE) and generative adversarial network (GAN) were incorporated to rectify shape inconsistencies in the segmentation output. To train the proposed model, a data set of Atlantic salmon ovaries throughout two maturation periods was recorded. We then tested our model and compared its performance with that of conventional and novel U-Nets. The method was also tested in a salmon on-site ultrasound examination setting. The results of our application indicate that our method is able to efficiently segment salmon ovary with an average Dice score of 0.885 per individual in real-time. These results represent a competitive performance for this specific application, which enables us to design an automated system for smart monitoring of maturation state in Atlantic salmon.

Conspiracy or Incompetence? The Resolution of Inconsistencies in Discourses about Covid-19

This article shows that analyzing how incoherence is resolved in discourse makes it possible to identify clues revealing the “conspiracy” nature of a specific discourse and to distinguish it from a “non-conspiracy” one. It examines some of the main defining features of “conspiracism” in the scientific literature and, based on the analysis of three case studies drawn from interviews with people opposed to the French health policy during the Covid-19 crisis, shows how these features can be echoed in certain forms of resolution of incoherence in discourse. The study reveals that some elements of conspiracy rhetoric can function more as an occasional, or even opportunistic, strategy, than a total and systematic worldview.

Cryptic KMT2A/MLLT10 fusion detected by next-generation sequencing in a case of pediatric acute megakaryoblastic leukemia

KMT2A (11q23.3) gene rearrangements are found in acute leukemia and are associated with a poor or intermediate prognosis. MLLT10 is the fourth most common gene fusion partner for KMT2A. A reciprocal translocation t(10;11) is insufficient to produce an in-frame KMT2A/MLLT10 fusion, because the genes involved in the rearrangement have opposite transcriptional orientations. In order to bring KMT2A and MLLT10 into juxtaposition, complex rearrangements are required. Until now, conventional chromosome, fluorescence in situ hybridization (FISH), and reverse transcriptase-polymerase chain reaction (RT-PCR) studies have been used to detect KMT2A/MLLT10 fusions. However, conventional studies have limitations, such as poor and inconsistent resolution, when compared to next-generation sequencing (NGS). In this study, we report a pediatric patient with acute megakaryoblastic leukemia, in whom the cryptic KMT2A/MLLT10 fusion was not detected by KMT2A break-apart probe FISH and chromosome analysis, but detected by NGS. In this patient, NGS showed cryptic insertion of MLLT10 exons 9-24 into intron 9 of KMT2A, resulting in a KMT2A/MLLT10 fusion. Therefore, NGS is a valuable complementary option for the evaluation of structural aberrations, especially those with a cryptic size.

A CNN-Based Layer-Adaptive GCPs Extraction Method for TIR Remote Sensing Images

Ground Control Points (GCPs) are of great significance for applications involving the registration and fusion of heterologous remote sensing images (RSIs). However, utilizing low-level information rather than deep features, traditional methods based on intensity and local image features turn out to be unsuitable for heterologous RSIs because of the large nonlinear radiation difference (NRD), inconsistent resolutions, and geometric distortions. Additionally, the limitations of current heterologous datasets and existing deep-learning-based methods make it difficult to obtain enough precision GCPs from different kinds of heterologous RSIs, especially for thermal infrared (TIR) images that present low spatial resolution and poor contrast. In this paper, to address the problems above, we propose a convolutional neural network-based (CNN-based) layer-adaptive GCPs extraction method for TIR RSIs. Particularly, the constructed feature extraction network is comprised of basic and layer-adaptive modules. The former is used to achieve the coarse extraction, and the latter is designed to obtain high-accuracy GCPs by adaptively updating the layers in the module to capture the fine communal homogenous features of the heterologous RSIs until the GCP precision meets the preset threshold. Experimental results evaluated on TIR images of SDGSAT-1 TIS and near infrared (NIR), short wave infrared (SWIR), and panchromatic (PAN) images of Landsat-8 OLI show that the matching root-mean-square error (RMSE) of TIS images with SWIR and NIR images could reach 0.8 pixels and an even much higher accuracy of 0.1 pixels could be reached between TIS and PAN images, which performs better than those of the traditional methods, such as SIFT, RIFT, and the CNN-based method like D2-Net.

Automated Debugging : Still a Dream ?

Software debugging is the process of finding and fixing incorrect statements in programs. The process of debugging takes a lot of time and is challenging. Therefore, the field of automated debugging, which is focused on automating the discovery and correction of a failure's underlying cause, has made huge progress in the past. By applying automated approaches to identify and correct any erroneous statements in a program, the cost of producing software may be significantly decreased while also improving the quality of the final product. The purpose of this paper is to shed light on the application of automated debugging in the current market scenario. Techniques like Delta Debugging, Path-based Weakest Preconditions, Fault Localization, and Program Slicing have been demonstrated to be quite effective in dealing with the identification and resolution of inconsistencies. This paper also aims to examine the question, "Is Automated Debugging still a dream? ". Key Words: Automated Debugging, Delta Debugging, Program Slicing.

A Sister-Group Comparison of Branching and Pedicellariae in Brittlestars (Echinodermata: Ophiuroidea).

Branching of arms and presence of pedicellariae are characters among ophiuroids found only in the order Euryalida (snakestars and basketstars). Family Asteronychidae has neither character; family Euryalidae has 2 small clades with branched arms; and family Gorgonocephalidae has all species with pedicellariae and 3 or 4 clades with branched arms. Despite the rare occurrence of these characters in the Ophiuroidea, they might be key adaptations within the Euryalida that have led to relatively high diversification. Sister-group comparison of the distribution of these 2 characters among taxa indicates that neither character alone explains diversity patterns within the order. In particular, branching restricted to the tips of arms seems not strongly adaptive, probably for the lack of integration of basal forks with the disc. On the other hand, 2 clades of gorgonocephalids with basal branching exceed their snakestar sister groups in numbers of species, indicating an advantage of branching within the family. Unfortunately, the analysis cannot benefit from statistics, for at least 5 independent comparisons are required for a one-tailed sign test. Because branching and pedicellariae are probably not independent variables, future sister-group comparisons should be done only within the Gorgonocephalidae once clade structure is better clarified with increased taxon sampling (10 currently missing genera) and resolution of intra-generic inconsistencies in the most recent cladograms available. Branching might confer upon gorgonocephalid basketstars a more efficient use of pedicellariae for upstream capture of zooplankton over their snakestar relatives as well as over the Euryalidae, which retain ancestral downstream capture by mucus-laden podia.

Evolutionary Computing to solve product inconsistencies in Software Product Lines

• Inconsistencies prevent and delay the configuration of an SPL product. • Evolutionary computation is proposed to resolve inconsistencies in SPL products. • ga-SPLIS (genetic algorithm-Software Product LIne Solution) is developed. • Results show the applicability of GA to resolve the SPL product inconsistencies. In Software Product Lines (SPLs), multiple design teams work collectively to configure products. Often, having multiple sub-designs leads to inconsistencies which prevent and delay the configuration of the final product at the end. Solutions are available to identify and resolve these inconsistencies for limited scale SPLs. For large scale and industrial SPLs, resolution of inconsistencies is still a potential research topic. In this paper, we propose, implement and validate an application of evolutionary computation, particularly genetic algorithms, to resolve inconsistencies in large scale SPL products. We label our approach ga -SPLIS (genetic algorithm-Software Product LIne Solution). Using a concrete experimental methodology with different feature set sizes and multiple standard performance metrics, we generate results which validate the applicability of ga -SPLIS for the SPL community.

Humour and creativity of younger school-age children

The purpose of this paper is to draw attention to the rarely undertaken topic of humour in the work of early childhood education pupils. In the first part of the article the author defines the categories of children’s creativity and humour and points out the similarities and connections between them. The author considers the cognitive theory of inconsistency resolution to be the most useful for the undertaken considerations. In the second part, the researcher presents the place of humour in children’s creativity analysed from four perspectives: personological (taking into account the characteristics of the creators), processual (analysing the course of the creative process), attributive (concerning the product) and ecological (analysing the social conditions of creativity). It was showed that although humour is perceived as both a form of creativity and an important generator of creative ideas, it is not appreciated by early childhood education teachers and rarely appears in the creativity of children at a younger school age.

Instability of remote sensing ecological index and its optimisation for time frequency and scale

Evaluating regional ecological environmental quality (EEQ) using remote sensing is important for achieving sustainable development. The remote sensing ecological index (RSEI) is a typical EEQ evaluation model used to comprehensively reflect regional ecological quality. However, owing to its complete dependence on remote sensing image information, the RSEI also has inherent issues, including unstable time series and inconsistent resolutions of its four sub-indices. To address these problems, this study used the Google Earth Engine (GEE) to propose an optimisation notion for the harmonic analysis of time series (HANTS) coupled random forest (RF) model and determined the accuracy and image quality of the optimised RSEI (RSEIo) for the Yangtze River Basin (YRB). Based on the findings, HANTS could fill the gaps in the images, effectively reducing the noise and discrete degrees of the four indices and thereby improving the stability of the RSEI. The correlation coefficient (R) between the RSEI and RSEIo was 0.93, with a root mean square error (RMSE) of 0.13, indicating that RSEIo is reliable. The image quality assessment (based on contrast and information entropy) indicates that HANTS combined with the RF model can produce RSEIo images with higher definitions and richer information at a constant spatial resolution. Further, the pixel-by-pixel coefficient of variation evaluation indicates that the RSEIo image was highly stabilised, yielding higher numbers of effective RSEIo images without changing the temporal resolution. Compared with traditional RSEI calculations, the optimisation proposal herein could highlight ecological differences caused by topographic changes in the YRB, which would produce an RSEI closer to actual surface conditions. Further, this proposed method could be used to obtain more detailed ecological information at a constant spatiotemporal resolution, thereby meeting the needs of long-term ecological monitoring in large-scale regions.

A novel grid generation method based on multi-resolution data fusion for 2D shallow water models

Aiming at resolving the grid problems caused by the inconsistent resolution requirements when simulating overland flows using the 2D shallow water equations, a novel grid generation method based on multi-resolution data fusion is developed in this work. This method is able to not only reduce the computational burden associated with uniform structured grids but also ensure the simulation accuracy of the hydrodynamic model by reproducing the so-called small-scale effect. The efficiency of the method is assessed using different cases. Theoretical and laboratory cases demonstrate that fused non-uniform structured grids can reproduce hydrographs without appreciable accuracy losses. In addition, a high simulation accuracy (NRMSE ≤ 10.40 %, R2 ≥ 0.87) is achieved in the simulation of a real flood event. The performance of this method is very promising in terms of the large-scale flood simulation accuracy, and it significantly reduces the data requirements and computational burden with globally fine uniform grids.

Adiabatic theorem independent of the quantum representations: Resolution of the Marzlin-Sanders inconsistency

We construct the adiabatic theorem independent of the quantum representations. The quantum-representation-independent geometrical phase, adiabatic condition, and fidelity of the adiabatic approximation are derived. As an example, we apply the quantum-representation-independent adiabatic theorem to provide a thorough resolution of the Marzlin-Sanders inconsistency. We show that the Marzlin-Sanders inconsistency results from the wrong identification of the time-dependent Hamiltonian as the instantaneous energy operator and the quantum representation dependence of the Marzlin-Sanders transformation.

SA-Q

Sentiment analysis has received constant research attention due to its usefulness and importance in different applications. However, despite the research advances in this field, most current tools suffer in prediction quality due to the inconsistencies in their results, i.e., intra- and inter-tool inconsistencies. This demonstration proposes a system for the evaluation of sentiment analysis quality namely SA-Q. The system allows the evaluation of inconsistency in sentiment analysis tools, the resolution of the inconsistency using state-of-the-art methods and the recommendation of relevant sentiment analysis tool for any type of data set provided by the attendees. It allows the attendees to compare the tools. Moreover, we demonstrate that SA-Q evaluates the consistency of tools on two levels (intra-tool and inter-tool). Through various scenarios, we showcase the challenges of inconsistency resolution, demonstrate the usefulness of the proposed system and the recommendations that can be given to the attendees for their datasets. We demonstrate that SA-Q system has practical utility in many areas of industrial applications for better decision making. This demonstration shows promising research areas for data management, NLP, and machine learning communities by adopting and drawing inspiration from truth inference methods to create more robust tools and improve the tool's scalability.

Entropy and institutional theory

PurposeOnce introduced and conceptualized as a factor that causes erosion and decay of social institutions and subsequent deinstitutionalization, the notion of entropy is at odds with predictions of institutional isomorphism and seems to directly contradict the tendency toward ever-increasing institutionalization. The purpose of this paper is to offer a resolution of this theoretical inconsistency by revisiting the meaning of entropy and reconceptualizing institutionalization from an information-theoretic point of view.Design/methodology/approachIt is a theoretical paper that offers an information perspective on institutionalization.FindingsA mistaken understanding of the nature and role of entropy in the institutional theory is caused by conceptualizing it as a force that counteracts institutional tendencies and acts in opposite direction. Once institutionalization and homogeneity are seen as a product of natural tendencies in the organizational field, the role of entropy becomes clear. Entropy manifests itself at the level of information processing and corresponds with increasing uncertainty and the decrease of the value of information. Institutionalization thus can be seen as a special case of an increase in entropy and a decrease of knowledge. Institutionalization is a state of maximum entropy.Originality/valueIt is explained why institutionalization and institutional persistence are what to be expected in the long run and why information entropy contributes to this tendency. Contrary to the tenets of the institutional work perspective, no intentional efforts of individuals and collective actors are needed to maintain institutions. In this respect, the paper contributes to the view of institutional theory as a theory of self-organization.

Methods Used to Compare Narrative Clinical Practice Guidelines: A Scoping Review.

Guideline-based clinical decision support systems (CDSSs) need the most recent evidence for reliable performance, making the provision of regularly updated clinical practice guidelines (CPGs) a major issue. Some international guidelines are renewed in short intervals and can be used for checking the status of given national guidelines with regard to the most recent evidence. Considering the volume of medical data and the number of CPGs published, computerized comparison of clinical guidelines can be an effective method. We performed a scoping review to evaluate the methods used for comparing two CPGs. We searched for methods for extracting CPG components and for methods used for comparing CPGs at different levels of abstraction. In each case, computerized and semi-computerized methods were recognized. Expert knowledge has yet a determinant role for assessing the comparisons, this role being more prominent for the extraction of semantic rules and the resolution of inconsistencies.

Knowledge integration and decision support for accelerated discovery of antibiotic resistance genes

We present a machine learning framework to automate knowledge discovery through knowledge graph construction, inconsistency resolution, and iterative link prediction. By incorporating knowledge from 10 publicly available sources, we construct an Escherichia coli antibiotic resistance knowledge graph with 651,758 triples from 23 triple types after resolving 236 sets of inconsistencies. Iteratively applying link prediction to this graph and wet-lab validation of the generated hypotheses reveal 15 antibiotic resistant E. coli genes, with 6 of them never associated with antibiotic resistance for any microbe. Iterative link prediction leads to a performance improvement and more findings. The probability of positive findings highly correlates with experimentally validated findings (R2 = 0.94). We also identify 5 homologs in Salmonella enterica that are all validated to confer resistance to antibiotics. This work demonstrates how evidence-driven decisions are a step toward automating knowledge discovery with high confidence and accelerated pace, thereby substituting traditional time-consuming and expensive methods.

A measurement of the cosmic expansion within our lifetime

The most exciting future observation in cosmology will feature a monitoring of the cosmic expansion in real time, unlike anything that has ever been attempted before. This campaign will uncover crucial physical properties of the various constituents in the universe, and perhaps answer a simpler question concerning whether or not the cosmic expansion is even accelerating. An unambiguous yes/no response to this query will significantly impact cosmology, of course, but also the standard model of particle physics. Here, we discuss—in a straightforward way—how to understand the so-called ‘redshift drift’ sought by this campaign, and why its measurement will help us refine the standard-model parameters if the answer is ‘yes.’ A ‘no’ answer, on the other hand, could be more revolutionary, in the sense that it might provide a resolution of several long-standing problems and inconsistencies in our current cosmological models. An outcome of zero redshift drift, for example, would obviate the need for a cosmological constant and render inflation completely redundant.