Retrieval Method Research Articles

Research on similarity retrieval method based on mass spectral entropy

Research on similarity retrieval method based on mass spectral entropy

Цифровое здравоохранение в медицинской реабилитации после инсульта: менеджмент телемедицинских технологий.

The development of digital technologies leads to a digital transformation of the healthcare system. Recently, digital healthcare has been developing dynamically, including its component part – telerehabilitation after a stroke. However, the novelty of technologies, their diversity, low awareness of doctors and healthcare organizers, and other reasons, do not allow healthcare organizations to use all their tools to manage organizational, managerial and medical problems of medical rehabilitation after a stroke. The aim of the study is to systematize information on telemedicine and cross-cutting digital technologies used in organizing medical rehabilitation of patients who have suffered a stroke, as well as the development of approaches to manage of these technologies to constantly improve the quality of healthcare and increase patient satisfaction. Materials and methods. The study used methods of information retrieval, analytical and synthetic research, etc. Results. The study showed that the development of technologies leads to an increase in the use of cross-cutting digital technologies in telemedicine rehabilitation of stroke patients. The results indicate the possibility of improving the quality and accessibility of medical care using telerehabilitation and cross-cutting technologies, expanding medical coverage, and improving therapeutic results in recovery of patients’ health. The PDCA cycle can be used to manage telerehabilitation and digital technologies. Findings. The introduction of digital technologies into telemedicine practice can reduce the workload of health workers and improve manageability with respect to a patient-centric approach. The use of quality management system principles and PDCA cycle can provide the standardization of telerehabilitation management, improve the quality of healthcare and patient satisfaction, contributing to the sustainable development of healthcare organizations.

Soil moisture retrieval over croplands using novel dual-polarization SAR vegetation index

Synthetic Aperture Radar (SAR) data, known for its high spatial resolution and all-weather observation capabilities, holds immense promise in soil moisture monitoring. The Water Cloud Model (WCM) is widely applied in soil moisture inversion using SAR data. However, the optical vegetation indices employed in traditional WCM cannot synchronize with SAR data, and the polarimetric scattering information contained in current SAR vegetation indices is incomplete, consequently compromising the accuracy of SAR-based soil moisture retrieval. Therefore, this study proposes a method for soil moisture retrieval over cropland using a novel dual-polarization SAR vegetation index. The method initially combines SAR data covariance elements with backscatter information to establish a polarization scattering contrast parameter (mcp). Then, based on mcp and incorporating the degree of polarization, a polarization scattering correlation contrast parameter (Rcp) is defined. Rcp integrates the distinctive features of scattering differences and polarization states. Building on Rcp, a novel dual-polarization SAR vegetation index (DRVIs) is introduced. Ultimately, DRVIs are utilized in the WCM to achieve surface soil moisture retrieval in cropland. This research conducts experiments in four crop cover areas of the Carman test site in Canada, namely soybean, wheat, canola, and corn. Under VV and VH polarizations, the overall correlation coefficients between measured in-situ data and SSM estimates reach 0.89 and 0.84, respectively. Compared to SSM estimates based on NDVI and LAI products, SSM estimates based on DRVIs exhibit a notable improvement in accuracy, with enhancements of approximately 7.2% and 12.7%, respectively. This novel DRVIs is poised to expand the utilization of SAR data in monitoring vegetation growth and soil moisture retrieval.

Ice Specimen Retrieval and Coring Method for Accreted Ice on Vertical Piles Subjected to Tidal Changes

Ice Specimen Retrieval and Coring Method for Accreted Ice on Vertical Piles Subjected to Tidal Changes

Deep hashing and attention mechanism-based image retrieval of osteosarcoma scans for diagnosis of bone cancer

BackgroundDue to its intricate nature and substantial data size, microscopic image data of osteosarcoma often present a significant obstacle to the effectiveness of conventional image retrieval methods. Therefore, this study investigates a new approach for medical image retrieval using advanced deep hashing techniques and attention mechanisms to address these challenges more effectively. MethodThe proposed algorithm significantly improves osteosarcoma cell microscopic image retrieval efficiency and accuracy using deep hashing and attention mechanisms. Image preprocessing includes adaptive histogram equalization and dataset augmentation to enhance quality and diversity. Feature extraction employs the WRN-AM model to map high-dimensional features to a low-dimensional hash code space, improving retrieval efficiency. Finally, similarity matching via Hamming distance allows rapid and precise identification of similar images. ResultsThe study shows notable advancements: the WRN-AM model achieves 93.2% classification accuracy and 97.09% mAP using 64-bit hash codes. These findings underscore the technique’s effective performance in extracting and categorizing diverse microscopic cell data efficiently and reliably. ConclusionsThis innovative approach provides a robust solution for retrieving and classifying microscopic data of osteosarcoma cells and other cell types, speeding up clinical diagnosis and medical research. It facilitates quicker access and analysis of patient image data, enhancing diagnostic precision and treatment planning for healthcare professionals. Concurrently, it supports researchers in leveraging medical image data more efficiently, fostering progress and innovation in the medical field.

Secure fuzzy retrieval protocol for multiple datasets

With the diversification of data sources and the massive growth of datasets, data retrieval has become increasingly complex and time-consuming. In the traditional retrieval method, if a user wants to query multiple datasets, the general approach is to retrieve them one by one in order, which may lead to duplication of work and waste of resources. Private set intersection is a specific issue in secure multi-party computation. It allows several participants, each holding different sets, to jointly calculate the intersection of their sets without revealing any information other than the intersection. This method is naturally suitable for data fusion. In this work, we propose a secure fuzzy retrieval protocol for multiple datasets. First, we use private set intersection technology to fuse multiple datasets. Then, we perform secure retrieval based on this fused dataset, effectively avoiding the waste of resources caused by separate retrievals, thereby maximizing resource efficiency. It is worth mentioning that the protocol proposed in this paper can also be used for fuzzy retrieval to improve the user’s search experience. More importantly, the protocol can maximize privacy protection during the retrieval process, including strict protection of sensitive information such as retrieval keywords, ensuring that user data and query intentions will not be leaked during the entire retrieval process. Finally, we provide a rigorous security proof and demonstrate the effectiveness of the protocol through simulation experiments.

Enhancing Administrative Efficiency Through Archive Management at the Malalayang District Office, Manado City, Indonesia

Archives serve as the institutional memory, supporting administrative processes and ensuring efficient operations. However, initial observations at the Malalayang District Office revealed significant deficiencies in archive management. These included a lack of standardized procedures, inadequate retrieval methods, and insufficient facilities. To address these issues, this research aims to investigate the strategies implemented, challenges faced, and solutions adopted to improve archive management and streamline administrative processes at the Malalayang District Office. Employing a qualitative case study methodology, data were collected and analyzed through a systematic process of reduction, presentation, and conclusion drawing, followed by verification. The findings of this research offer valuable insights into effective archive management practices and their contribution to enhancing administrative efficiency.

Assessment of groundwater potential zones for hard rock area of sabi river basin using an integrated approach of remote sensing, GIS and AHP techniques

The study was conducted to assess groundwater potential zones (GWPZs) for hard rock area of Sabi river basin for the sustainable development. Remote Sensing (RS) and Geographic Information System (GIS) have emerged as crucial methods in retrieval, monitoring and conserving groundwater resources were used along with Analytical Hierarchy Process (AHP) to demarcate the GWPZs. In order to assess GWP in the study area, eight thematic maps: drainage density, geomorphology, geology, rainfall, land use/land cover, lineament density, slope and soil were prepared using RS and conventional data. The AHP was applied to assign the weightages for each thematic map and its sub classes. Then, the thematic layers were overlaid using the weighted overlay analysis means of ArcGIS 10.4 to assess the GWPZ map of Sabi basin. The Sabi basin was categorized into five classes of GWPZ as very good (0.01%), good (24.72%), moderate (56.54%), poor (10.45%) and very poor (8.28%). The accuracy of produced GWPZ map was assessed with well yield data by agreement scheme using ground water prospects map and by using ROC curve analysis through R-studio software which indicates the satisfactory GWPZ map prediction accuracy. This study provides valuable database in planning and development of groundwater recharging and to delineate locations of potential artificial recharge sites in the hard rock regions and semi -arid areas so that appropriate structures could be planned and constructed to manage aquifer recharge for sustainable groundwater management in the future.

Plaid fabric image retrieval based on hand-crafted features and relevant feedback

Common fabric image retrieval methods ignore the diversity and dynamism of user demands, the results are determined by the query image and cannot be dynamically adjusted. To solve this problem, this study proposes a novel image retrieval method for plaid fabrics based on hand-crafted features and relevant feedback. First, local texture descriptors are extracted by the local binary pattern on the separated images which are processed by Fourier transform. Global texture descriptors are extracted by scale-invariant feature transform (SIFT) and vector of locally aggregated descriptors (VLAD). Second, color moments with image partitioning are extracted to characterize spatial color information of plaid fabric images. Third, the extracted features are fused by the weight allocation for similarity measurement. Finally, the relevant feedback based on meta learning is involved to realize personalized adjustment and optimization of retrieval results. An image retrieval database is built as the benchmark by collecting over 44, 000 plaid fabric samples from the factory. Experiments show that precision and recall at rank eight reach to 70.6% and 62.6%, respectively, and mAP reaches to 0.690. Results prove that the proposed strategy is feasible and effective, which can realize plaid fabric image retrieval fast and efficiently.

Parameter Adaptive Contrastive Hashing for multimedia retrieval

With the emergence of massive amounts of multi-source heterogeneous data on the Internet, quickly retrieving effective information from this extensive data has become a hot research topic. Due to the efficiency and speed of hash learning methods in multimedia retrieval, they have become a mainstream method for multimedia retrieval. However, unsupervised multimedia hash learning methods still face challenges with the difficulties of tuning due to the excessive number of hyperparameters and the lack of precise guidance on semantic similarity. To address these problems, we propose a Parameter Adaptive Contrastive Hashing (PACH) method for multimedia retrieval. The Fast Parameter Adaptive (FPA) module, combined with the powerful space exploration and dynamic optimization capabilities of reinforcement learning, designs a hot-plugging multimedia hashing method parameter adaptation module to solve for an approximate optimal combination of parameters. The Multimedia Contrastive Hashing (MCH) module comprehensively explores intra- and inter-modal semantic consistency of multimodal data, enriching the cross-modal semantic information of the hash codes. Comprehensive experiments were designed and compared with the latest hash learning methods, verifying the effectiveness and superiority of the PACH method. The code is available at https://github.com/YunfeiChenMY/PACH.

Publicly available continuously updated topic specific databases of randomised clinical trials: A scoping review.

Conducting systematic reviews of clinical trials is arduous and resource consuming. One potential solution is to design databases that are continuously and automatically populated with clinical trial data from harmonised and structured datasets. We aimed to map publicly available, continuously updated, topic-specific databases of randomised clinical trials (RCTs). We systematically searched PubMed, Embase, the preprint servers medRxiv, ArXiv, and Open Science Framework, and Google. We described seven features (access model, database architecture, data input sources, retrieval methods, data extraction methods, trial presentation, and export options) and narratively summarised the results. We did not register a protocol for this review. We identified 14 continuously updated clinical trial databases, seven related to COVID-19 (first active in 2020) and seven non-COVID databases (first active in 2009). All databases, except one, were publicly funded and accessible without restrictions. They mainly employed methods similar to those from static article-based systematic reviews and retrieved data from journal publications and trial registries. The COVID-19 databases and some non-COVID databases implemented semi-automated features of data import, which combined automated and manual data curation, whereas the non-COVID databases mainly relied on manual workflows. Most reported information was metadata, such as author names, years of publication, and link to publication or trial registry. Two databases included trial appraisal information (risk of bias assessments). Six databases reported aggregate group level results, but only one database provided individual participant data on request. We identified few continuously updated trial databases, and existing initiatives mainly employ methods known from static article -based reviews. The main limitation to create truly live evidence synthesis is the access and import of machine-readable and harmonised clinical trial data.

Knowledge enhanced edge-driven graph neural ranking for biomedical information retrieval

Neural networks used for information retrieval tend to capture textual matching signals between a query and a document. However, neural ranking models for biomedical information retrieval often struggle to semantically well match the query to the documents. The main reasons are that biomedical terms have many different representations and the fact description related to the query is non-consecutive and non-local in the documents. In this paper, we present an edge-driven graph neural ranking method for biomedical information retrieval by incorporating knowledge from medical databases. First, we propose to form an edge-driven graph by connecting some biomedical terms in the query and the document through different types of edges. Then, we design a novel way of knowledge integration to introduce knowledge related to biomedical terms into the graph and construct a knowledge-query-doc graph. Finally, a graph neural ranking model is applied to capture non-local and non-contiguous match signals between the query and the document. Experimental results show on the biomedical datasets that our method outperforms the advanced neural models. And further analysis shows that the knowledge integration method can well reduce the semantic gap between the query and the document, and our graph model can provide interpretation for matching between the query and the document.

TPTE: Text-Guided Patch Token Exploitation for Unsupervised Fine-Grained Representation Learning

Recent advances in pre-trained vision-language models have successfully boosted the performance of unsupervised image representation in many vision tasks. Most of existing works focus on learning global visual features with Transformers and neglect detailed local cues, leading to suboptimal performance in fine-grained vision tasks. In this article, we propose a text-guided patch token exploitation framework to enhance the discriminative power of unsupervised representation by exploiting more detailed local features. Our text-guided decoder extracts local features with the guidance of texts or learned prompts describing discriminative object parts. We hence introduce a local-global relation distillation loss to promote the joint optimization of local and global features. The proposed method allows to flexibly extract either global or global-local features as the image representation. It significantly outperforms previous methods in fine-grained image retrieval and base-to-new fine-grained classification tasks. For instance, our Recall@1 metric surpasses the recent unsupervised retrieval method STML by 6.0% on the SOP dataset. The code is publicly available at https://github.com/maosnhehe/TPTE .

A Conceptual Design Method Based on Concept–Knowledge Theory and Large Language Models

Abstract To obtain innovative concepts in the design, designers often need to retrieve and use interdisciplinary knowledge. Concept–knowledge (C–K) theory emphasizes the role of knowledge and introduces the knowledge (K) space and concept (C) space, employing operators to transform the contents between these spaces. Some studies, based on this theory, have successfully provided designers with different forms of knowledge to stimulate concept generation. However, the amount of knowledge provided in these studies is limited, and they fail to offer convenient methods for knowledge retrieval and reasoning, making it challenging to meet the needs of conceptual design across different fields. This paper proposes an enhanced C–K method leveraging large language models (LLMs) to help designers retrieve knowledge and uncover potentially new concepts. Our method redefines the C space and K space within the context of LLMs, dividing the properties of concept into function, appearance, and technology, and requiring the knowledge to correspond to these properties, thereby facilitating a structured connection between concepts and knowledge. Based on this definition, we achieved flexible knowledge retrieval and concept ideation leveraging LLMs. We also conducted a case study on wearable devices to validate our method. The results showed that our method helped designers to retrieve professional knowledge and inspired them to create feasible and innovative concepts.

Mitigating the directional retrieval error of solar-induced chlorophyll fluorescence in the red band

Solar-induced chlorophyll fluorescence (SIF) is a promising tool to estimate gross primary production (GPP), but the retrieval of SIF is commonly noisy and highly sensitive to various interference factors. Particularly, the retrieval of SIF in the red band (RSIF) is more challenging than in the far-red SIF (FRSIF) due to the weaker fluorescence signal and the weaker absorption depth of oxygen at the red band compared with the far-red band. A comprehensive evaluation of all factors will allow a reproducible interpretation of SIF signals and advance the estimation of GPP from SIF. Recent studies have assessed the sensitivity of SIF retrieval to sensor characteristics, retrieval methods, and hardware specifications. However, none of these studies have systematically investigated the directional retrieval error of SIF resulting from the mismatch between irradiance measured above the canopy and the true irradiance reaching the canopy components viewed by a sensor. This study illustrated the effect of mismatched irradiance on the retrieval of RSIF using the commonly used standard 3FLD method based on SCOPE model simulations. The retrieval accuracy was highest in the hotspot direction, but it decreased as the observation direction was away from the hotspot. The relative root mean square error (RRMSE) was generally higher than 20 % in the forward directions. To reduce the retrieval error due to the mismatch effect, we proposed a modified 3FLD method (MFLD) by calculating the true irradiance reaching the canopy in a given direction based on geometric optical theory. The results showed that MFLD clearly improved the retrieval accuracy for RSIF, especially in the forward directions where RRMSE decreased by 10 % in most cases. For example, the RRMSE was reduced from 19.26 % to 5.50 % after mitigating the mismatch between the measured and actual solar irradiance, when the solar zenith angle was 40° and viewing zenith angle was 30° in the forward solar principal plane. Even at the nadir observation, the RRMSE was also reduced from 12.84 % to 5.64 %. In summary, MFLD can effectively mitigate the irradiance mismatch effect on the retrieval of RSIF. These results will improve our interpretation of the relationship between GPP and RSIF at different observation directions.

Combined query embroidery image retrieval based on enhanced CNN and blend transformer

Embroidery images carry rich historical information and are an important form of embroidery art. In the field of combination query image retrieval, how to efficiently retrieve the embroidery image information required by users has become a current research challenge. In recent years, convolutional neural networks (CNNs) have achieved significant success in image feature extraction, but they tend to focus on local information, making it easy to ignore global context information when processing such textured embroidery images. Therefore, we propose a combination query retrieval method for embroidery images. First, we propose Blend-Transformer, which introduces Group External Attention (GEA). GEA can integrate feature information from three different dimensions, effectively capturing the local and global context information of embroidery images. Second, we propose Enhanced CNN, which introduces Shuffle Attention (SA), regrouping the reference image features extracted by CNN and reaggregating them by channel to enhance the richness of embroidery image feature information. Through experiments on the TCE-S and ICR2020 standard datasets, we verify the excellent performance of the proposed algorithm in embroidery image retrieval. Our method fills the gap in embroidery image retrieval research and provides a new perspective for the protection of embroidery art.

Supervised Contrastive Learning for 3D Cross-Modal Retrieval

Interoperability between different virtual platforms requires the ability to search and transfer digital assets across platforms. Digital assets in virtual platforms are represented in different forms or modalities, such as images, meshes, and point clouds. The cross-modal retrieval of three-dimensional (3D) object representations is challenging due to data representation diversity, making common feature space discovery difficult. Recent studies have been focused on obtaining feature consistency within the same classes and modalities using cross-modal center loss. However, center features are sensitive to hyperparameter variations, making cross-modal center loss susceptible to performance degradation. This paper proposes a new 3D cross-modal retrieval method that uses cross-modal supervised contrastive learning (CSupCon) and the fixed projection head (FPH) strategy. Contrastive learning mitigates the influence of hyperparameters by maximizing feature distinctiveness. The FPH strategy prevents gradient updates in the projection network, enabling the focused training of the backbone networks. The proposed method shows a mean average precision (mAP) increase of 1.17 and 0.14 in 3D cross-modal object retrieval experiments using ModelNet10 and ModelNet40 datasets compared to state-of-the-art (SOTA) methods.

Technical Feasibility Analysis of Digital Twin Technology in the Field of Urban Disaster Prevention

Abstract. In recent years, disasters have occurred frequently around the world, causing great harm to national finances and people's safety. There is still a lack of research on digital disaster prevention technology, but with the development of technologies such as artificial intelligence and computer vision, it has gradually become possible to use digital twin technology in urban disaster prevention. This paper is mainly based on digital twin related technologies, briefly introduces the concept and importance of digital twins, and the applicable fields, and then discusses the mode of data interaction between real entities and twins and the feasibility of digital disaster prevention, so as to prevent and simulate urban disasters and derive strategies for dealing with disasters. This paper reviews the existing research on digital twin technology through the method of literature retrieval and analysis. By analyzing technologies such as the Internet of Things, sensors, three-dimensional laser technology, urban monitoring technology, satellite remote sensing technology, drone remote sensing technology, mobile device technology, lidar technology, and oblique photography, a systematic review of critical analysis is obtained, and a macro method of data interaction and modeling for digital twin technology for disaster prevention is obtained, thereby deriving the feasibility of disaster prevention based on digital twin systems.

Impacts of Agricultural Practices on Groundwater Quality and its Retrieval Methods and Solutions: A Critical Review

Impacts of Agricultural Practices on Groundwater Quality and its Retrieval Methods and Solutions: A Critical Review

Enhanced sperm isolation via bulk acoustic waves for high-throughput motility screening

The increasing infertility rate has become a worrying global challenge in recent years. According to the report of the World Health Organization, the male factor is responsible for over half of infertility cases, which includes the lack of desirable characteristics in sperm motility, morphology, and DNA integrity. In recent years, it has been shown that clinical methods including density gradient centrifugation cause damage to sperm DNA and besides being invasive, they are costly and time-consuming. In contrast, microfluidics has been used as a promising and non-invasive approach to manipulate biological cells. Here, by using the microvortices created by the oscillation of the bubbles caused by the bulk acoustic waves, we were able to trap sperms with less motility. In contrast, the highly motile sperms overcame the force of the microvortices and were guided to the outlet pool by following the channel boundaries. As a result, over 50% and 44% improvement in sperm progressive motility and viability, respectively, as well as 40% improvement in DNA integrity, were observed in the analysis of sperms retrieved from the output pool. In addition to being fast and non-invasive, the proposed device benefits from an easy method for sperms retrieval and does not require any preprocessing of the raw sperm sample.