Alignment Approach Research Articles

A New Approach for Formal and Coherent Ontology Alignment

The ontology alignment process is a big challenge especially in open environments like the semantic web. In this process, the construction of the global ontology corresponds to the application of a succession of operations of change (add new mappings). This is obviously a critical task because the new implementation of changes can make the global ontology incoherent. Inevitably, some mappings will be a source of contradictions in terms of ontology. In order to handle these contradictions, the authors propose in this study a new formal solution based on CTL model checking for a consistent ontology alignment. This model uses the Kripke structure which satisfies the CTL logic formula to model the behaviors of the ontology of alignment. To test the functional utility of the proposed approach, they applied it to several examples and the results showed that the proposed framework is working correctly and that inconsistencies are detected in CTL models.

Optimization-Based Strapdown Attitude Alignment for High-Accuracy Systems: Covariance Analysis With Applications

Strapdown inertial navigation usually relies on the extended Kalman filter (EKF) as the workhorse, which requires a proper initialization of the attitude. The optimization-based alignment (OBA) approach is widely employed to provide the coarse attitude information for the subsequent fine alignment or navigation stage by EKF. However, there still lacks a reliable quality index to assess the attitude accuracy of OBA. To tackle this problem, this article characterizes the OBA attitude error by vector measurement errors. For specific applications, such as the stationary alignment and odometer/GPS-aided in-motion alignment, the errors of vector measurements in OBA are further formulated in terms of raw sensor errors of gyroscopes, accelerometers, and odometer/GPS velocities. The covariance of attitude errors is analytically derived. Simulations and field experiments are performed to verify the covariance analysis.

A Bidirectional Active-Alignment Control System for LED Communication

LED-based free-space optical communication is becoming a promising alternative to the traditional acoustics mode of underwater communication because of its low cost, high data rate, and low power consumption. However, a fundamental challenge associated with the free space optical communication systems is to establish and maintain line-of-sight (LOS) between the two communicating agents. It becomes even more challenging when the agents are in motion, requiring an alignment system that enables each agent to point itself towards the LOS direction continuously. This paper presents a bi-directional active alignment control approach for the LED-based communication system of two robots, where the robots navigate in a three-dimensional (3D) space. We propose an extended Kalman filter (EKF) based alignment approach, where the estimates of azimuthal and elevation components of heading bias with the LOS are used to correct the alignment. We introduce and implement a new circular scanning technique on a two-degree-of-freedom (2DOF) rotational system mounted on each robot that enables consecutive independent measurements from a single photo-diode to satisfy the observability constraints of the EKF. Furthermore, we explore a synchronized alternating scheme to address the bidirectional nature of the problem. The scanning amplitude is further adjusted based on the EKF estimation covariance, to balance the trade-off between estimation accuracy and actuation effort. The presented results support the efficacy of the proposed method in the presence of slow relative motion between two robots, and demonstrate its superiority over an extremum-seeking (ES) based approach across a wide range of distances between the robots.

Balance Rehabilitation Approach by Bobath and Vojta Methods in Cerebral Palsy: A Pilot Study.

In cerebral palsy (CP) the basis for rehabilitation comes from neuroplasticity. One of the leading therapeutic approaches used in the management of CP is the NDT Bobath therapy and Vojta therapy consists in trying to program the ideal movement patterns for the age. The aim of our research was to analyze, from a functional point of view, the evolution of the biomechanical parameters characterizing the balance, in children with CP. The group of 12 subjects average age of 7 ± 3.28 years. The subject’s evaluation included a functional clinical evaluation by Berg pediatric scale and a biomechanical evaluation performed using the “Stabilometry footboard PoData 2.00” for evaluation the body weight distribution on the foot level. The rehabilitation program was developed based on two methods, NDT Bobath and Vojta. A 90-min physiotherapy session starts with a Vojta therapy activation, for 20 min. Between the two therapies there is a 10-min break, then the session continues with NDT Bobath exercises within the 3 physical exercises proposed for 60 min. 5 days per week, 6 months. The analysis of the data collected before and after the application of the rehabilitation program, regarding the using the Berg scale indicates a progress of 32.35%, (p = 0.0001 < 0.05) and the effect size is large. The evolution of the data that indicate the distribution of body weight at the level of the two lower limbs, at the two moments pre/post, evaluation. For left side a progress of 8.39%, (p = 0.027 < 0.05) but a small effect size of 0.86. For right side a progress of 10.36% (p = 0.027 < 0.05) and also a small effect size of 0.86. Analyzing the results, we find that there is a left-right rebalancing in most patients. The favorable results that were obtained by drawing up a physiotherapy program composed of the combination of the two Vojta and NDT Bobath methods are proof of the fact that both methods are based on the creation of a stimulating peripheral pressure, which, if maintained, generates an extended stereotyped motor response. A pattern of symmetrical muscle contraction is thus created and thus balance and postural control can be achieved. The left-right rebalancing, proven by the percentage distribution analysis of the weight at the lower segmental level, demonstrated that the body alignment approach through the Vojta method on the one hand and the inhibitory facilitating postures/exercises promoted by the NDT Bobath method, allows obtaining a symmetry.

Dual-granularity feature alignment for cross-modality person re-identification

RGB-infrared cross-modality person re-identification is a challenging image retrieval task due to the intra-class variations and cross-modality discrepancy. However, it is insufficient for existing methods to identify discriminative patterns with different granularities and align them across granularity and modalities in a synergistic manner. In this paper, we propose Dual-granularity Feature Alignment (DFA) approach for cross-modality re-ID, which accommodates dual-granularity feature extraction and cross-granularity feature alignment. More specifically, a feature extractor is introduced to generate discriminative patterns by dividing feature map into different granularities. Based on the above features, prototype learning is developed to assign different granularity features of the same identity to the same prototype and adaptively build the alignment across different granularities. To mine contextual relationships between training samples, a similarity inference is presented to enforce the similarity matrix closing to identity matrix. This allows model to find feature that conserves as much modality-shared information as possible while being least possible modality-specific information. Both modules work in a collaborative way to obtain the optimal intra-class compactness and inter-class separability for cross-modality image matching. Extensive experiments on several benchmarks demonstrate that our DAF surpasses the state-of-the-art methods. The code is available at https://github.com/PRIS-CV/DFA.

An intentional approach to the development and implementation of meaningful assessment in advanced radiation therapy practice curricula.

Creating meaningful assessment for advanced radiation therapy practice training programs is a challenge. This is because it requires a balance of formative and summative assessments, which meet the academic and professional needs of the practitioner, as well as the requirements of local service delivery, educational and professional standards. This paper discusses educational strategies and models used to integrate assessment into theoretical and clinical curricula, allowing practitioners to demonstrate higher order cognitive knowledge, advanced level clinical performance and attitudes/values associated with advanced practice. The discussion draws upon concepts of constructive alignment and programmatic approaches to assessment, which use Bloom's taxonomy, Benner's beginner to competent model of skill development, and Miller's pyramid of clinical competence. These models are analysed with respect to an advanced practice program in adaptive radiation therapy to provide context.

Nanostructured hafnium-doped strontium oxide film for homeotropic/homogeneous convertible liquid crystal alignment depending on the curing temperature

ABSTRACT A hafnium strontium oxide (HfSrO) liquid crystal (LC) alignment film was efficiently created through brush coating, and its ability to change the LC alignment direction was confirmed. A brush was applied to HfSrO sol coated on an indium-tin oxide substrate, after which the coating was solidified at various curing temperatures. It was confirmed that a directional micro/nanostructure was formed above 280°C due to the shear stresses caused by the movement of the brush hairs. Surface chemical changes were analyzed by using X-ray photoelectron spectroscopy and contact angle measurements. As the curing temperature increased, the prevalence of oxygen bonds increased and the contact angle decreased, thereby increasing the surface energy. The anisotropic boundary of the microgrooves and the van der Waals forces due to an increase in surface energy changed the alignment direction of LC molecules from vertical to horizontal, as verified through polarized optical microscopy and pretilt angle measurements. Thus, the efficiency of the brush-coating method, which dramatically simplifies the LC alignment film process, was confirmed. The homeotropic/homogeneous LC alignment property of the HfSrO film produced through brush coating depending on the curing temperature provides an innovative approach for LC alignment.

Fine Alignment of Thermographic Images for Robotic Inspection of Parts with Complex Geometries.

Increasing the efficiency of the quality control phase in industrial production lines through automation is a rapidly growing trend. In non-destructive testing, active thermography techniques are known for their suitability to allow rapid non-contact and full-field inspections. The robotic manipulation of the thermographic instrumentation enables the possibility of performing inspections of large components with complex geometries by collecting multiple thermographic images from optimal positions. The robotisation of the thermographic inspection is highly desirable to improve assessment speed and repeatability without compromising inspection accuracy. Although integrating a robotic setup for thermographic data capture is not challenging, the application of robotic thermography has not grown significantly to date due to the absence of a suitable approach for merging multiple thermographic images into a single presentation. Indeed, such an approach must guarantee accurate alignment and consistent pixel blending, which is crucial to facilitate defect detection and sizing. In this work, an innovative inspection platform was conceptualised and implemented, consisting of a pulsed thermography setup, a six-axis robotic manipulator and an algorithm for image alignment, correction and blending. The performance of the inspection platform is tested on a convex-shaped specimen with artificial defects, which highlights the potential of the new combined approach. This work bridges a technology gap, making thermographic inspections more deployable in industrial environments. The proposed fine image alignment approach can find applicability beyond thermographic non-destructive testing.

A bidirectional multilayer contrastive adaptation network with anatomical structure preservation for unpaired cross-modality medical image segmentation

Multi-modal medical image segmentation has achieved great success through supervised deep learning networks. However, because of domain shift and limited annotation information, unpaired cross-modality segmentation tasks are still challenging. The unsupervised domain adaptation (UDA) methods can alleviate the segmentation degradation of cross-modality segmentation by knowledge transfer between different domains, but current methods still suffer from the problems of model collapse, adversarial training instability, and mismatch of anatomical structures. To tackle these issues, we propose a bidirectional multilayer contrastive adaptation network (BMCAN) for unpaired cross-modality segmentation. The shared encoder is first adopted for learning modality-invariant encoding representations in image synthesis and segmentation simultaneously. Secondly, to retain the anatomical structure consistency in cross-modality image synthesis, we present a structure-constrained cross-modality image translation approach for image alignment. Thirdly, we construct a bidirectional multilayer contrastive learning approach to preserve the anatomical structures and enhance encoding representations, which utilizes two groups of domain-specific multilayer perceptron (MLP) networks to learn modality-specific features. Finally, a semantic information adversarial learning approach is designed to learn structural similarities of semantic outputs for output space alignment. Our proposed method was tested on three different cross-modality segmentation tasks: brain tissue, brain tumor, and cardiac substructure segmentation. Compared with other UDA methods, experimental results show that our proposed BMCAN achieves state-of-the-art segmentation performance on the above three tasks, and it has fewer training components and better feature representations for overcoming overfitting and domain shift problems. Our proposed method can efficiently reduce the annotation burden of radiologists in cross-modality image analysis.

Controlling the Enantioselectivity in an Adaptable Ligand by Biomimetic Intramolecular Interlocking.

For the preparation of chiral drugs, both stereochemically stable and flexible catalysts in combination with chiral auxiliaries can be used. Here, chiral induction plays an important role in generating an enantiomerically pure catalyst. We demonstrate a successful approach to the spontaneous deracemization of tropos ligands for asymmetric catalysis. Three different constitutional isomers of a bisphosphinite ligand decorated with l-valine moieties (interaction units) linked to the flexible biphenyl system by a phenylene bridge for inducing a chiral switch were prepared. The substitution pattern's influence on the attached intermolecular recognition sites was systematically investigated. We can show that biomimetic intramolecular hydrogen bonding leads to a pronounced diastereoselective enrichment of one of the ligand stereoisomers. As a result, in the asymmetric Rh-catalyzed hydrogenation of prochiral olefins using these ligands, enantiomeric ratios of up to 95.8:4.2 (S) were obtained. Of particular note is the inversion of enantioselectivity relative to the previously reported BIBIPHOS-Rh catalyst due to the altered orientation of the biphenyl moiety from (Rax) to (Sax). The enantioselectivities achieved by appropriate intramolecular interlocking are remarkable for a tropos ligand/catalyst. The strategy presented here represents a powerful approach for the spontaneous alignment of tropos ligands, yielding high enantioselectivities in asymmetric catalysis.

HCNA: Hyperbolic Contrastive Learning Framework for Self-Supervised Network Alignment

Network alignment, or identifying the same entities (anchors) across multiple networks, has significant applications across diverse fields. Unsupervised approaches for network alignment, though popular, strictly assume that the anchor nodes’ structure and attributes remain consistent across different networks. However, in practice, strictly adhering to these constraints makes it difficult to deal with networks with high variance in the structural characteristics and inherent structural noises like missing nodes and edges, resulting in poor generalization. In order to handle these shortcomings, we propose HCNA: Hyperbolic Contrastive Learning Framework for Self-Supervised Network Alignment, a novel self-supervised contrastive learning model which learns from the multiple augmented views of each network, thereby making HCNA robust to the inherent multi-network characteristics. Furthermore, we propose multi-order hyperbolic graph convolution networks to generate node embedding for each network which can handle the hierarchical structure of networks. The main objective of HCNA is to obtain structure-preserving embeddings that are also robust to noises and variations for better alignment results. The major novelty lies in generating augmented multiple graph views for contrastive learning that are driven by real world network dynamics. Rigorous investigations on 4 real datasets show that HCNA consistently outperforms the baselines by at least 1–84% in terms of accuracy score. Furthermore, HCNA is also more resilient to structural and attributes noises, as evidenced by its adaptivity analysis on adversarial conditions.

Parallel protein multiple sequence alignment approaches: a systematic literature review

Parallel protein multiple sequence alignment approaches: a systematic literature review

An innovative active learning module on snow and climate modeling

The interaction between climate and the hydrologic cycle is complex due to intricate feedback mechanisms that can have multiple impacts on key hydrologic variables. Under a changing climate, it is becoming increasingly important for undergraduate engineering students to have a better understanding of climate and the hydrologic cycle to ensure future engineering systems are more climate resilient. One way of teaching undergraduate students about these key interactions between climate and the hydrologic cycle is through numerical models that mimic these relationships. However, this is difficult to do in an undergraduate engineering course because these models are complex, and it is not feasible to devote class time and resources to teaching students the knowledge base required to run and analyze these numerical models. In addition, the recent COVID-19 pandemic required a rapid change to flexible teaching methods that can be implemented in online, hybrid, or in-person courses. To overcome these limitations, a backward design and constructive alignment approach was used to develop an active learning module in the HydroLearn framework that allows students to explore the connection between snow processes and streamflow and how this will change under different climate scenarios using numerical models and analysis. This learning module provides learning activities and tools that help the student develop a basic knowledge of snow formation and terminology, snow measurements, numerical models of snow processes, and changes in snow and streamflow under future climate. This module is particularly innovative in that it uses Google Colabs and an interactive user interface to facilitate the students' active learning in an environment that is accessible for all students and is sustainable for continued use and adaptation. This paper describes the approach, best practices and lessons learned in developing and implementing this active learning module in a remote and in-person course. In addition, it presents the results from motivation and student self-assessment surveys and discusses opportunities for improvement and further implementation that have implications for the future of hydrologic education.

Building from Patient Experiences to Deliver Patient-Focused Healthcare Systems in Collaboration with Patients: A Call to Action

Patients’ experiences of their diagnosis, condition, and treatment (including the impact on their lives), and their experiences surrounding expectations of care, are becoming increasingly important in shaping healthcare systems that meet the evolving needs and priorities of different patient communities over time; this is an ongoing goal of all healthcare stakeholders. Current approaches that capture patient experiences with data are fragmented, resulting in duplication of effort, numerous requests for information, and increased patient burden. Application of patient experience data to inform healthcare decisions is still emerging and there remains an opportunity to align diverse stakeholders on the value of these data to strengthen healthcare systems. Given the collective value of understanding patient experiences across multiple stakeholder groups, we propose a more aligned approach to the collection of patient experience data. This approach is built on the principle that the patients’ experiences are the starting point, and not just something to be considered at the end of the process. It must also be based on meaningful patient engagement, where patients are collaborators and decision makers at each step, thereby ensuring their needs and priorities are accurately reflected. The resulting data and evidence should be made available for all stakeholders, to inform their decision making and healthcare strategies in ways that meet patient priorities. We call for multi-stakeholder collaboration that will deliver healthcare systems and interventions that are better centered around and tailored to patient experiences, and that will help address patients’ unmet needs.

Cross-city crash severity analysis with cost-sensitive transfer learning algorithm

Due to the difficulties of data collection in some cities and the relatively small portion of more severe crashes, this paper proposes a cost-sensitive transfer learning framework for more robust crash severity analysis. Specifically, to address the class imbalance issue, cost-sensitive learning method is adopted by assigning unequal cost values to different crash severity levels to obtain unbiased classification results. Moreover, due to the existence of city-irrelevant common crash contributing factors, cross-city crash severity analysis is developed, i.e., transferring common crash severity knowledge from other cities with transfer learning algorithm to assist crash severity modelling in target city that suffers from data scarcity problem. In this study, the crash datasets of Victorian Australia and Seattle are selected as the target and source domains respectively. To address the issue of heterogeneous explanatory features among these two datasets and extract the interpretable common crash severity knowledge, a feature alignment approach is proposed which can represent the cross-city data with unified feature representations on their original explanatory feature spaces. The two proposed models, i.e., cost-sensitive transfer logistic regression (CST-LR) and cost-sensitive transfer support vector machine (CST-SVM), have demonstrated better performance in comparison with twelve commonly used crash severity models, especially when target city crash data is scarce. The most significant crash contributing factors extracted by proposed model also show higher degree of consistency with the true contributing factors obtained from the target dataset, in comparison with the model built without transfer learning. The results could provide policy implications and counter measures for crash severity mitigation.

Ultra-rapid somatic variant detection via real-time targeted amplicon sequencing

Molecular markers are essential for cancer diagnosis, clinical trial enrollment, and some surgical decision making, motivating ultra-rapid, intraoperative variant detection. Sequencing-based detection is considered the gold standard approach, but typically takes hours to perform due to time-consuming DNA extraction, targeted amplification, and library preparation times. In this work, we present a proof-of-principle approach for sub-1 hour targeted variant detection using real-time DNA sequencers. By modifying existing protocols, optimizing for diagnostic time-to-result, we demonstrate confirmation of a hot-spot mutation from tumor tissue in ~52 minutes. To further reduce time, we explore rapid, targeted Loop-mediated Isothermal Amplification (LAMP) and design a bioinformatics tool—LAMPrey—to process sequenced LAMP product. LAMPrey’s concatemer aware alignment algorithm is designed to maximize recovery of diagnostically relevant information leading to a more rapid detection versus standard read alignment approaches. Using LAMPrey, we demonstrate confirmation of a hot-spot mutation (250x support) from tumor tissue in less than 30 minutes.

Crowd-sourced SfM: Best practices for high resolution monitoring of coastal cliffs and bluffs

Structure from motion (SfM) photogrammetry is an increasingly common technique for measuring landscape change over time by deriving 3D point clouds and surface models from overlapping photographs. Traditional change detection approaches require photos that are geotagged with a differential GPS (DGPS) location, which requires expensive equipment that can limit the ability of communities and researchers to perform frequent (i.e. daily, weekly, and/or monthly) surveys. Crowd-sourced photos can lower the barrier to entry and substantially increase the frequency of surveys, although such photos often lack accurate location information and can vary in quality. This paper presents a SfM approach for monitoring environmental change in high relief coastal environments that does not require all photos have DGPS location information and does not require field survey data. A 1.5 km section of coastal bluffs near the Elwha River Delta (Washington state) is used to demonstrate the efficacy of this approach. Photos of the bluff were collected with a digital SLR camera or phone camera while either on foot along the beach or from a boat as part of monitoring following removal of two large dams along the Elwha River during 2011–2013. Only 33% of photos had DGPS location information, whereas most photos had no location information or locations that were accurate to a couple of meters. All photos were processed using 3D, 4D, and fixed-floating (FF) SfM alignment methods and the resulting dense point clouds are used to compare the different alignment approaches with crowd-sourced photo sets. Results demonstrate that 4D and FF approaches are more likely to reconstruct and are more accurate than the 3D approach. While the 4D and FF have comparable accuracies, the FF approach is several orders of magnitude more efficient, as this method can leverage camera location information from relatively few photos to improve the accuracy of all aligned and derived products. Effectively utilizing crowd-sourced photos in SfM change detection can improve the frequency of surveying a landscape in a more cost-effective approach that also has potential for citizen-science engagement and communication. This is especially important for data-poor environments such as high-relief coastal cliffs and bluffs, where near-nadir imagery and LIDAR may fail to accurately capture near-vertical cliffs or bluff faces. Based on the analysis of different photo alignment and filtering approaches, we present suggested best practices for engaging citizen scientists in coastal cliff and bluff monitoring efforts through collecting photos amenable for SfM reconstruction.

Highly accurate quantification of allelic gene expression for population and disease genetics.

Analysis of allele-specific gene expression (ASE) is a powerful approach for studying gene regulation, particularly when sample sizes are small, such as for rare diseases, or when studying the effects of rare genetic variation. However, detection of ASE events relies on accurate alignment of RNA sequencing reads, where challenges still remain, particularly for reads containing genetic variants or those that align to many different genomic locations. We have developed the Personalised ASE Caller (PAC), a tool that combines multiple steps to improve the quantification of allelic reads, including personalized (i.e., diploid) read alignment with improved allocation of multimapping reads. Using simulated RNA sequencing data, we show that PAC outperforms standard alignment approaches for ASE detection, reducing the number of sites with incorrect biases (>10%) by ∼80% and increasing the number of sites that can be reliably quantified by ∼3%. Applying PAC to real RNA sequencing data from 670 whole-blood samples, we show that genetic regulatory signatures inferred from ASE data more closely match those from population-based methods that are less prone to alignment biases. Finally, we use PAC to characterize cell type–specific ASE events that would be missed by standard alignment approaches, and in doing so identify disease relevant genes that may modulate their effects through the regulation of gene expression. PAC can be applied to the vast quantity of existing RNA sequencing data sets to better understand a wide array of fundamental biological and disease processes.

Approaches of material selection, alignment and methods of fabrication for natural fiber polymer composites: A review

The recent superiority of the composite materials is cautiously focusing on environmental adoption of natural fiber composites. The major source of the natural fiber materials covered in the globe, especially natural fibers, is plant-based, animal-based and mineral-based. Eco friendly based material can save the environment and recycling of the material is possible, as well as important criteria. Hence engineers ultimately focused on natural fiber polymer matrix materials to save the environment, pollution control, plastic manipulation, etc. The literature work was studied to identify natural fiber material possession. The major goal of the present review was to identify material characterization and appropriate application, mainly offering to enhance mechanical properties, flexural strength, electrical properties, thermal properties etc. The major consequence of the natural fiber is hydrophilic treatment. There is poor interfacial adhesion between the addition/filling substances and poor mechanical characteristics. All of these shortcomings constitute a critical issue. This review presents numerous sorts of natural and synthetic polymers, natural fibres such as jute, ramie, banana, pineapple leaf fibre, and kenaf, etc.; short and long fibre loading methods, fibre fillers in micro and nanoparticle, American society of testing and materials (ASTM) standard plate dimensions, fabrication methods such as hand lay-up process, spray lay-up process, vacuumed-bag, continuous pultrusion, and pulforming process, etc.; industries and home appliances such as automotive parts, building construction, sports kits, domestic goods, and electronic devices. The review lists various material combinations, fibre loading, fillers, and matrix that can aid in the improvement of material properties and the reduction of failures during mechanical testing of composites.

English–Korean speech translation corpus (EnKoST‐C): Construction procedure and evaluation results

AbstractWe present an English–Korean speech translation corpus, named EnKoST‐C. End‐to‐end model training for speech translation tasks often suffers from a lack of parallel data, such as speech data in the source language and equivalent text data in the target language. Most available public speech translation corpora were developed for European languages, and there is currently no public corpus for English–Korean end‐to‐end speech translation. Thus, we created an EnKoST‐C centered on TED Talks. In this process, we enhance the sentence alignment approach using the subtitle time information and bilingual sentence embedding information. As a result, we built a 559‐h English–Korean speech translation corpus. The proposed sentence alignment approach showed excellent performance of 0.96 f‐measure score. We also show the baseline performance of an English–Korean speech translation model trained with EnKoST‐C. The EnKoST‐C is freely available on a Korean government open data hub site.