Specific Query Research Articles

The utility of point-of-care ultrasound in critical care nephrology.

Point-of-care ultrasonography (POCUS) is gaining heightened significance in critical care settings as it allows for quick decision-making at the bedside. While computerized tomography is still considered the standard imaging modality for many diseases, the risks and delays associated with transferring a critically ill patient out of the intensive care unit (ICU) have prompted physicians to explore alternative tools. Ultrasound guidance has increased the safety of invasive procedures in the ICU, such as the placement of vascular catheters and drainage of collections. Ultrasonography is now seen as an extension of the clinical examination, providing quick answers for rapidly deteriorating patients in the ICU. The field of nephrology is increasingly acknowledging the value of diagnostic point-of-care ultrasound (POCUS). By employing multi-organ POCUS, nephrologists can address specific queries that arise during the diagnosis and treatment of patients with acute kidney injury. This approach aids in ruling out hydronephrosis and offers immediate information on hemodynamics, thereby consolidating patient data and facilitating the development of personalized treatment strategies.

SketchQL: Video Moment Querying with a Visual Query Interface

Localizing video moments based on the movement patterns of objects is an important task in video analytics. Existing video analytics systems offer two types of querying interfaces based on natural language and SQL, respectively. However, both types of interfaces have major limitations. SQL-based systems require high query specification time, whereas natural language-based systems require large training datasets to achieve satisfactory retrieval accuracy. To address these limitations, we present SketchQL, a video database management system (VDBMS) for offline, exploratory video moment retrieval that is both easy to use and generalizes well across multiple video moment datasets. To improve ease-of-use, SketchQL features a visual query interface that enables users to sketch complex visual queries through intuitive drag-and-drop actions. To improve generalizability, SketchQL operates on object-tracking primitives that are reliably extracted across various datasets using pre-trained models. We present a learned similarity search algorithm for retrieving video moments closely matching the user's visual query based on object trajectories. SketchQL trains the model on a diverse dataset generated with a novel simulator, that enhances its accuracy across a wide array of datasets and queries. We evaluate SketchQL on four real-world datasets with nine queries, demonstrating its superior usability and retrieval accuracy over state-of-the-art VDBMSs.

Artificial Intelligence on Knowledge Management Systems for Businesses: A Systematic Literature Review

The fourth industrial revolution is forthcoming, bringing with it revolutionary alterations in connectivity, work dynamics, and everyday operations. The fundamental foundation of this transformation, artificial intelligence (AI) is at its core. The purpose of this research is to identify new and distinctive features of how artificial intelligence may improve and enhance business knowledge management systems. For this purpose, systematic literature review (SLR) was used as the research method and preferred reporting items for systematic reviews and meta-analyses (PRISMA) as an approach. Articles based on specific search queries from 2013 to 2023 were selected from major scientific databases such as Scopus, Web of Science, and ScienceDirect, with a focus on AI, knowledge management systems, and corporate organizations for the PRISMA. It covers four main research questions by utilizing 14 selected papers. The main findings of this research paper call attention to AI’s vital role in shaping the future of knowledge management which helps organizations make informed decisions and remain competitive and relevant in this ever-changing world of commerce. This study also explores the importance of data security in AI systems which need ethical considerations and responsible practices to address the vulnerabilities.

Adaptive and low-cost resource synchronization based on data distribution service in high dynamic networks

In disaster-stricken areas monitoring, management, search, and rescue operations, unmanned aerial vehicles (UAVs) play a crucial role in disaster management and emergency communication due to their high mobility. To efficiently coordinate and plan UAVs and their carried sensor and base station resources, synchronization is essential to establish consistency, laying the foundation for high-level demands. In such scenarios, synchronization relies on request–response (RR) or publish–subscribe (PS) forms of information exchange. Existing research in the field typically focuses on higher-level applications and selects either RR or PS synchronization, thereby overlooking the potential advantages that could be gained from combining both methods to meet synchronization requirements. We propose a resource synchronization method based on the Data Distribution Service (DDS) and a linear time complexity subscription mechanism tailored to specific query demands, which considers the pros and cons of the above two information exchange forms and the bottom-layer network topology. Experimental results using open-source simulation tools demonstrate that the proposed method adapts to scene requirements and decreases bandwidth by at least 21.2% and packet rate by at least 3.7% compared to different baseline methods across three topologies, while satisfying delay and query success rate requirements. Furthermore, the method maintains robust performance in the face of dynamic changes in network topology, showcasing its robustness.

Integrating NoSQL, Hilbert Curve, and R*-Tree to Efficiently Manage Mobile LiDAR Point Cloud Data

The widespread use of Light Detection and Ranging (LiDAR) technology has led to a surge in three-dimensional point cloud data; although, it also poses challenges in terms of data storage and indexing. Efficient storage and management of LiDAR data are prerequisites for data processing and analysis for various LiDAR-based scientific applications. Traditional relational database management systems and centralized file storage struggle to meet the storage, scaling, and specific query requirements of massive point cloud data. However, NoSQL databases, known for their scalability, speed, and cost-effectiveness, provide a viable solution. In this study, a 3D point cloud indexing strategy for mobile LiDAR point cloud data that integrates Hilbert curves, R*-trees, and B+-trees was proposed to support MongoDB-based point cloud storage and querying from the following aspects: (1) partitioning the point cloud using an adaptive space partitioning strategy to improve the I/O efficiency and ensure data locality; (2) encoding partitions using Hilbert curves to construct global indices; (3) constructing local indexes (R*-trees) for each point cloud partition so that MongoDB can natively support indexing of point cloud data; and (4) a MongoDB-oriented storage structure design based on a hierarchical indexing structure. We evaluated the efficacy of chunked point cloud data storage with MongoDB for spatial querying and found that the proposed storage strategy provides higher data encoding, index construction and retrieval speeds, and more scalable storage structures to support efficient point cloud spatial query processing compared to many mainstream point cloud indexing strategies and database systems.

Electrogastrography measurement systems and analysis methods used in clinical practice and research: comprehensive review.

https://www.prisma-statement.org/.

Towards zero-shot object counting via deep spatial prior cross-modality fusion

Existing counting models predominantly operate on a specific category of objects, such as crowds and vehicles. The recent emergence of multi-modal foundational models, e.g., Contrastive Language-Image Pre-training (CLIP), has facilitated class-agnostic counting. This involves counting objects of any given class from a single image based on textual instructions. However, CLIP-based class-agnostic counting models face two primary challenges. Firstly, the CLIP model lacks sensitivity to location information. It generally considers global content rather than the fine-grain location of objects. Therefore, adapting the CLIP model directly is suboptimal. Secondly, these models generally freeze pre-trained vision and language encoders, while neglecting the potential misalignment in the constructed hypothesis space. In this paper, we address these two issues in a unified framework termed Deep Spatial Prior Interaction (DSPI) network. The DSPI leverages the spatial-awareness ability of large-scale pre-trained object grounding models, i.e., Grounding DINO, to incorporate spatial location as an additional prior for a specific query class. This enables the network to be more specifically focused on the precise location of the objects. Additionally, to align the feature space across different modalities, we tailor a meta adapter that extracts textual information into an object query. This serves as an instruction for cross-modality matching. These two modules collaboratively ensure the alignment of multi-modal representations while preserving their discriminative nature. Comprehensive experiments conducted on a diverse set of benchmarks verify the superiority of the proposed model. The code is available at https://github.com/jinyongch/DSPI.

Towards episode rules with non-overlapping frequency and targeted mining

Data mining has become a popular task due to the explosion of rich data. Frequent episode mining (FEM) is an effective technique for extracting valuable and crucial information from event sequences, playing a significant role in various fields such as market basket analysis, association analysis, and management sciences. A variety of FEM algorithms discover frequent episode rules using the frequency function and anti-monotony. Non-overlapping frequency works well for mining episode rules because it cuts down on unnecessary computations and still follows anti-monotonicity. Leveraging this technique, we propose the NONEPI+ algorithm for discovering rules that were previously overlooked. However, there are no algorithms that can be used to discover episode rules that contain target query rules. To fill the research gap in episode rule mining algorithms, we further propose a novel algorithm called TaER for targeted mining of episode rules, i.e., a set of rules containing specific query rules. TaER can successfully discover the episode rules that the users are more specifically interested in. It can provide direction for prediction tasks in many aspects, such as weather observation, network intrusion, e-commerce, and financial behavior prediction. All source code and datasets are available on GitHub https://github.com/DSI-Lab1/TaER.

Scientific Writing – ChatGPT Versus Real-time Output: Addressing Academician’s Concern

Background: The advent of ChatGPT, an artificial intelligence (AI) model, has introduced new challenges in educational practices, particularly in the realm of scientific writing at higher educational institutions. The AI is trained on extensive datasets to generate scientific texts. Many professors and academicians express concerns about the inclusion of AI chatbots in project execution, interpretation, and writing within specialized subject curricula at the undergraduate and master’s levels. Methods: To address these concerns, we employed the ChatGPT tool by posing a specific query “Gynecomastia and the risk of non-specific lung disease, along with associated risk factors for workers in the petrochemical industry”. We conducted a comparison between responses generated by ChatGPT and real-time output from master’s students, examining document-to-document variation on different dates. Results and Discussion: The AI chatbot failed to identify potential risk factors, in contrast to the student response, which highlighted alteration in neutrophil levels, lung architecture, high IgE, elevated CO2 levels, etc. The two responses did not align in terms of context understanding, language nuances (words and phrases), and knowledge limitations (real-time access to information, creativity, and originality of the query). A plagiarism check using the iThenticate software reported similarity indices of 11% and 14%, respectively, in document-to-document analyses. The concerns raised by academicians are not unfounded, and the apprehension regarding students utilizing ChatGPT in the future revolves around ethical considerations, the potential for plagiarism, and the absence of laws governing the use of AI in medical or scientific writing. Conclusion: While AI integration in the curriculum is feasible, it should be approached with a clear acknowledgement of its limitations and benefits. Emphasizing the importance of critical thinking and original work is crucial for students engaging with AI tools, addressing concerns related to ethics, plagiarism, and potential copyright infringement in medical or scientific writings.

Analysis of Food Security of Older Rural Indigenous People in Latin America and the Caribbean.

Food insecurity is a critical issue in the Americas, with severe impacts in the Caribbean, Mesoamerica, and South America, particularly affecting older adults in Indigenous and rural contexts where it intersects with poverty, gender, and ethnicity. This study aims to provide an in-depth understanding of the current research about food insecurity among older Indigenous adults in Latin America and the Caribbean. A comprehensive literature review was conducted, utilizing specific search queries and the population, intervention, comparison, and outcome (PICO) strategy across multiple databases to identify the pertinent studies. The findings indicate an increase in academic output on this topic since 2018, with significant emphasis on the interplay between climate change and food insecurity. The review highlights the importance of developing targeted food programs, reforming policies, and fostering collaboration between academia and local communities to implement practical interventions. Despite the growing body of literature, a notable research gap persists in rural areas of Latin America and the Caribbean. This study underscores the necessity of balancing the geographic distribution of research and emphasizes the preservation of cultural practices and the adaptation of public policies to support traditional food practices. It advocates for culturally sensitive interventions and interdisciplinary collaboration to formulate comprehensive strategies. The originality and value of this study lie in its focused analysis of older Indigenous adults, contributing crucial insights to the international literature on food security.

Continual Observation of Joins under Differential Privacy

The problem of continual observation under differential privacy has been studied extensively in the literature. However, all existing works, with the exception of [28,51], have only studied the simple counting query and its derivatives. Join queries, which are arguably the most important class of queries in relational databases, have only been considered in [28,51], but the solutions offered there have two limitations: First, they only support a few specific graph pattern queries, which are special cases of joins. Second, they require hard degree/frequency constraints on the graph/database instance, and the privatized query answers have errors proportional to these constraints. In this paper, we propose a new differentially private mechanism for continual observation of joins that overcomes these two limitations. Our mechanism supports arbitrary joins and predicates, and do not require any constraints to be given in advance, even over an infinite stream. More importantly, it yields an error that is proportional to the actual maximum degree/frequencies in the graph/database instance at the current time of observation. Such an instance-specific utility guarantee is much preferred for the continual observation problem, where the database size and the query answer may change significantly over time.

Universal Relocalizer for Weakly Supervised Referring Expression Grounding

This article introduces the Universal Relocalizer, a novel approach designed for weakly supervised referring expression grounding. Our method strives to pinpoint a target proposal that corresponds to a specific query, eliminating the need for region-level annotations during training. To bolster the localization precision and enrich the semantic understanding of the target proposal, we devise three key modules: the category module, the color module, and the spatial relationship module. The category and color modules assign respective category and color labels to region proposals, enabling the computation of category and color scores. Simultaneously, the spatial relationship module integrates spatial cues, yielding a spatial score for each proposal to enhance localization accuracy further. By adeptly amalgamating the category, color, and spatial scores, we derive a refined grounding score for every proposal. Comprehensive evaluations on the RefCOCO, RefCOCO+, and RefCOCOg datasets manifest the prowess of the Universal Relocalizer, showcasing its formidable performance across the board.

Appraisal of clinical practice guidelines and consensus statements on obstetric anaesthesia: a systematic review using the AGREE II instrument

ObjectivesDespite the publication of hundreds of trials on obstetric anaesthesia, the management of these conditions remains suboptimal. We aimed to assess the quality and consistency of guidance documents for obstetric...

Evaluating ChatGPT's Utility in Medicine Guidelines Through Web Search Analysis.

With the rise of machine learning applications in health care, shifts in medical fields that rely on precise prognostic models and pattern detection tools are anticipated in the near future. Chat Generative Pretrained Transformer (ChatGPT) is a recent machine learning innovation known for producing text that mimics human conversation. To gauge ChatGPT's capability in addressing patient inquiries, the authors set out to juxtapose it with Google Search, America's predominant search engine. Their comparison focused on: 1) the top questions related to clinical practice guidelines from the American Academy of Family Physicians by category and subject; 2) responses to these prevalent questions; and 3) the top questions that elicited a numerical reply. Utilizing a freshly installed Google Chrome browser (version 109.0.5414.119), the authors conducted a Google web search (www.google.com) on March 4, 2023, ensuring minimal influence from personalized search algorithms. Search phrases were derived from the clinical guidelines of the American Academy of Family Physicians. The authors prompted ChatGPT with: "Search Google using the term '(refer to search terms)' and document the top four questions linked to the term." The same 25 search terms were employed. The authors cataloged the primary 4 questions and their answers for each term, resulting in 100 questions and answers. Of the 100 questions, 42% (42 questions) were consistent across all search terms. ChatGPT predominantly sourced from academic (38% vs 15%, p = 0.0002) and government (50% vs 39%, p = 0.12) domains, whereas Google web searches leaned toward commercial sources (32% vs 11%, p = 0.0002). Thirty-nine percent (39 questions) of the questions yielded divergent answers between the 2 platforms. Notably, 16 of the 39 distinct answers from ChatGPT lacked a numerical reply, instead advising a consultation with a medical professional for health guidance. Google Search and ChatGPT present varied questions and answers for both broad and specific queries. Both patients and doctors should exercise prudence when considering ChatGPT as a digital health adviser. It's essential for medical professionals to assist patients in accurately communicating their online discoveries and ensuing inquiries for a comprehensive discussion.

Mapping Emotional Intelligence and Psychological Capital Research: A Bibliometric Analysis and Future Research Agenda

This study explores the scholarly literature on emotional intelligence (EI) and psychological capital (PsyCap) using a comprehensive bibliometric analysis to map the research landscape and propose future directions. Data was collected from the Scopus database using specific search queries related to EI and PsyCap, with inclusion and exclusion criteria established to refine the dataset. A total of 39 articles published between 2007 and 2023 were selected for analysis. Utilizing bibliometric techniques through VOSviewer, patterns of citation and collaboration within the field were quantified, capturing descriptive statistics related to citation data and performing network analysis on authors, countries, publications, citations, and keywords. Key documents, sources, authors, organizations, and countries contributing to the discourse on PsyCap and EI were identified, providing insights into the evolution and trends within this field. The analysis revealed longitudinal trends in research output and explored the intersectionality of EI and PsyCap with other individual differences and personality traits. The findings contribute to a deeper understanding of knowledge growth in positive organizational psychology, informing evidence-based practices, and guiding future research endeavors.

A Bibliometric Analysis of Study of Associations of Certain Genotypes with the Cardiovascular Form of Diabetic Neuropathy.

This bibliometric analysis explores the landscape of research on the associations between specific genotypes and the cardiovascular form of diabetic neuropathy. Diabetes mellitus (DM) is a major contributor to premature mortality, primarily due to increased susceptibility to cardiovascular diseases. The global prevalence of DM is rising, with projections indicating further increases. Diabetic neuropathy, a complication of DM, includes the cardiovascular subtype, posing challenges in diagnosis and management. Understanding the genetic basis of cardiovascular diabetic neuropathy is crucial for targeted therapeutic interventions. The study utilizes bibliometric analysis to synthesize existing literature, identify trends, and guide future research. The Scopus database was searched, applying inclusion criteria for English articles related to genotypes and cardiovascular diabetic neuropathy. The analysis reveals a dynamic field with a notable impact, collaborative efforts, and multidimensional aspects. Publication trends over 1997-2023 demonstrate fluctuating research intensity. Top journals, authors, and affiliations are highlighted, emphasizing global contributions. Keyword analysis reveals thematic trends, and citation analysis identifies influential documents. Limitations include database biases, incomplete metadata, and search query specificity. The urgent need to explore genetic factors in cardiovascular diabetic neuropathy aligns with the increasing global diabetes burden. This analysis provides a comprehensive overview, contributing to the broader discourse on diabetic neuropathy research.

Co-production processes for translation and validation of psychosocial assessments for older adults in aged care.

Given the diverse ethnic backgrounds of aged care clients, there is a critical requirement to translate psychosocial assessment tools into various languages to effectively evaluate social engagement and quality of life in older adults receiving aged care services. This study aimed to translate psychosocial tools into Turkish, Korean and Mandarin, the primary languages spoken by clients of an Australian community aged care provider. A co-development approach encompassing forward and backward translations of the Australian Community Participation Questionnaire and ICEpop CAPability measure for Older people tools, along with focus group discussions involving bilingual staff (n = 7) and clients (n = 16), was employed to ensure precision and cultural relevance. Multiple iterations were undertaken until linguistic, conceptual and scaling equivalence was achieved, with recorded sessions transcribed and analysed thematically. Cultural appropriateness significantly impacted the delivery of questions within the tools, emphasising translation challenges tied to specific queries. These difficulties included the lack of terms for unique places of worship, the use of outdated language (e.g., references to reading newspapers), and varying priorities in social and well-being matters between Western and Eastern/Asian cultures. Staff feedback identified that formal translated tool versions eased administration for culturally and linguistically diverse (CALD) clients, enabling them to independently interpret questions, resulting in improved questionnaire completion rates. Insights indicate the need for continued efforts in tailoring assessment tools to diverse cultural contexts to ensure accurate and meaningful data collection.

Towards the development of an electrochemical random access DNA memory (e-RADM)

As a result of the exponentially growing amount of information being produced, new data storage solutions are required. DNA has attracted significant attention as a potential data storage medium thanks to several intrinsic properties, such as ultrahigh physical information density with up to 2 bits per nucleotide. Current DNA-based memories rely on sequencing strategies for data recovery. However, sequencing all DNA strands for data retrieval would be very time consuming and thus result in high levels of latency. Therefore, random access strategies are required to make DNA-based data storage a viable alternative. Here, we present our first steps towards the development of a compartmentalized electrochemical random access DNA memory (e-RADM) using cascade reactions controlled by DNA nanostructures immobilized on gold microelectrode arrays which will be triggered when a specific information retrieval query is put into the system. Electrodes containing the desired information can then be identified by Square Wave Voltammetry.Graphical abstract

Traceable mixnets

We introduce the notion of traceable mixnets. In a traditional mixnet, multiple mix-servers jointly permute and decrypt a list of ciphertexts to produce a list of plaintexts, along with a proof of correctness, such that the association between individual ciphertexts and plaintexts remains completely hidden. However, in many applications, the privacy-utility tradeoff requires answering some specific queries about this association, without revealing any information beyond the query result. We consider queries of the following types: a) given a ciphertext in the mixnet input list, whether it encrypts one of a given subset of plaintexts in the output list, and b) given a plaintext in the mixnet output list, whether it is a decryption of one of a given subset of ciphertexts in the input list. Traceable mixnets allow the mix-servers to jointly prove answers to the above queries to a querier such that neither the querier nor a threshold number of mix-servers learn any information beyond the query result. Further, if the querier is not corrupted, the corrupted mix-servers do not even learn the query result. We first comprehensively formalise these security properties of traceable mixnets and then propose a construction of traceable mixnets using novel distributed zero-knowledge proofs (ZKPs) of set membership and of a statement we call reverse set membership. Although set membership has been studied in the single-prover setting, the main challenge in our distributed setting lies in making sure that none of the mix-servers learn the association between ciphertexts and plaintexts during the proof. We implement our distributed ZKPs and show that they are faster than state-of-the-art by at least one order of magnitude.

Middle school educators’ experiences in a cross-institutional professional development model for enhancing writing instruction

PurposeThis paper explores a five-year case example of two educators engaged in practice-based professional development (PBPD) for the Self-Regulated Strategy Development (SRSD) model in a middle school. It examines the transformative effects and challenges of improving writing instruction, the activities involved and alternative PBPD delivery methods. Highlighting a collaborative effort between an institute of higher education (IHE), a middle school and ThinkSRSD, a PBPD for SRSD developer, the example underscores the long-term benefits and innovative insights into engaging with PBPD for SRSD over multiple years.Design/methodology/approachThe case involves analyzing survey data collected over five years. These surveys, which included specific SRSD-related queries and open-ended questions, were instrumental in assessing the evolution of the educators’ perceptions regarding SRSD and their engagement with PBPD. Additionally, the paper details PBPD activities as documented in a research journal, providing a comprehensive account of the developmental process.FindingsThrough a cross-institutional partnership, two middle school general educators participated in PBPD for SRSD for 30 h across five years. Their engagement with PBPD progressed from initial introduction and implementation to facilitating PBPD for SRSD among peers and at the national level. Over time, the most consistently enacted SRSD action was “memorize it,” while actions such as “discuss it,” “support it” and “independent performance” showed greater variability. Both educators consistently praised SRSD and sought continued PBPD engagement over the five years.Originality/valueOur case example is the first five-year analysis of PBPD for SRSD among general middle school educators, highlighting the benefits and challenges of adopting evidence-based writing instruction. Our example emphasizes the need for continuous and focused professional development in areas crucial for student success, including self-regulation, prewriting strategies and techniques for fostering independent performance. Moreover, the two middle school educators’ critical feedback is invaluable for refining PBPD for SRSD. This work also enriches professional development schools (PDS) literature by offering effective strategies to support middle school teachers in developing a vibrant writing community, a cornerstone for student advancement in writing.