Search Results (274)

Artificial intelligence (AI), specifically Generative Pre-trained Transformer (GPT) technology, has revolutionized various fields, including medicine and dentistry. The AI model ChatGPT, developed by OpenAI, mimics human language on a large scale, generating coherent and contextually appropriate responses. ChatGPT serves as an auxiliary resource for diagnosis and decision-making across various medical disciplines. This comprehensive narrative review aims to explore how ChatGPT can assist the dental sector, highlighting its potential to enhance various aspects of the discipline. This review includes a literature search on the application of ChatGPT in dentistry, with a focus on the differences between the free version, ChatGPT 3.5, and the more advanced subscription-based version, ChatGPT 4. Specifically, ChatGPT has proven to be effective in enhancing user interaction, providing fast and accurate information and improving the accessibility of knowledge. However, despite these advantages, several limitations are identified, including concerns regarding the accuracy of responses in complex scenarios, ethical considerations surrounding its use, and the need for improved training to handle highly specialized queries. In conclusion, while ChatGPT offers numerous benefits in terms of efficiency and scalability, further research and development are needed to address these limitations, particularly in areas requiring greater precision, ethical oversight, and specialized expertise. Full article

With the rapid development of IoT technology, securely querying sensitive data collected by devices within a specific range has become a focal concern for users. This paper proposes a privacy-preserving range query scheme based on quantum encryption, along with circuit simulations and performance analysis. We first propose a quantum private set similarity comparison protocol and then construct a privacy-preserving range query scheme for IoT environments. By leveraging the properties of quantum homomorphic encryption, the proposed scheme enables encrypted data comparisons, effectively preventing the leakage of sensitive data. The correctness and security analysis demonstrates that the designed protocol guarantees users receive the correct query results while resisting both external and internal attacks. Moreover, the protocol requires only simple quantum states and operations, and does not require users to bear the cost of complex quantum resources, making it feasible under current technological conditions. Full article

This article delves into Microsoft Azure’s cyber forensic capabilities, focusing on the unique challenges in cloud security incident investigation. Cloud services are growing in popularity, and Azure’s shared responsibility model, multi-tenant nature, and dynamically scalable resources offer unique advantages and complexities for digital forensics. These factors complicate forensic evidence collection, preservation, and analysis. Data collection, logging, and virtual machine analysis are covered, considering physical infrastructure restrictions and cloud data transience. It evaluates Azure-native and third-party forensic tools and recommends methods that ensure effective investigations while adhering to legal and regulatory standards. It also describes how AI and machine learning automate data analysis in forensic investigations, improving speed and accuracy. This integration advances cyber forensic methods and sets new standards for future innovations. Unified Audit Logs (UALs) in Azure are examined, focusing on how Azure Data Explorer and Kusto Query Language (KQL) can effectively parse and query large datasets and unstructured data to detect sophisticated cyber threats. The findings provide a framework for other organizations to improve forensic analysis, advancing cloud cyber forensics while bridging theoretical practices and practical applications, enhancing organizations’ ability to combat increasingly sophisticated cybercrime. Full article

In the digital era, the need for precise and efficient search operations is paramount as users increasingly rely on online resources to access specific information. However, search accuracy is often hindered by errors in user queries, such as incomplete or degraded input. Errors in search queries can reduce both the precision and speed of search results, making error correction a key factor in enhancing the user experience. This paper addresses the challenge of improving search performance through query error correction. We propose a novel methodology and architecture aimed at optimizing search results across thematic websites, such as those for universities, hospitals, or tourism agencies. The proposed solution leverages an intelligent model based on Gated Recurrent Units (GRUs) and Bahdanau Attention mechanisms to reconstruct erroneous or incomplete text in search queries. To validate our approach, we embedded the model in a prototype website consolidating data from multiple universities, demonstrating significant improvements in search accuracy and efficiency. Full article

In multi-objective optimization, standard evolutionary algorithms, such as NSGA-II, are computationally expensive, particularly when handling complex constraints. Constraint evaluations, often the bottleneck, require substantial resources. Pre-trained surrogate models have been used to improve computational efficiency, but they often rely heavily on the model’s accuracy and require large datasets. In this study, we use active learning to accelerate multi-objective optimization. Active learning is a machine learning approach that selects the most informative data points to reduce the computational cost of labeling data. It is employed in this study to reduce the number of constraint evaluations during optimization by dynamically querying new data points only when the model is uncertain. Incorporating machine learning into this framework allows the optimization process to focus on critical areas of the search space adaptively, leveraging predictive models to guide the algorithm. This reduces computational overhead and marks a significant advancement in using machine learning to enhance the efficiency and scalability of multi-objective optimization tasks. This method is applied to six challenging benchmark problems and demonstrates more than a 50% reduction in constraint evaluations, with varying savings across different problems. This adaptive approach significantly enhances the computational efficiency of multi-objective optimization without requiring pre-trained models. Full article

To address the deficiencies in privacy-preserving expressive query and verification mechanisms in outsourced key-value stores, we propose EKV-VBQ, a scheme designed to ensure verifiable Boolean queries over encrypted key-value data. We have integrated blockchain and homomorphic Xor operations and pseudo-random functions to create a secure and verifiable datastore, while enabling efficient encrypted Boolean queries. Additionally, we have designed a lightweight verification protocol using bilinear map accumulators to guarantee the correctness of Boolean query results. Our security analysis demonstrates that EKV-VBQ is secure against adaptive chosen label attacks (IND-CLA) and guarantees Integrity and Unforgeability under the bilinear q-strong Diffie–Hellman assumption. Our performance evaluations showed reduced server-side storage overhead, efficient proof generation, and a significant reduction in user-side computational complexity by a factor of log n. Finally, GPU-accelerated optimizations significantly enhance EKV-VBQ’s performance, reducing computational overhead by up to 50%, making EKV-VBQ highly efficient and suitable for deployment in environments with limited computational resources. Full article

Quantum-inspired algorithms represent an important direction in modern software information technologies that use heuristic methods and approaches of quantum science. This work presents a quantum approach for document search, retrieval, and ranking based on the Bell-like test, which is well-known in quantum physics. We propose quantum probability theory in the hyperspace analog to language (HAL) framework exploiting a Hilbert space for word and document vector specification. The quantum approach allows for accounting for specific user preferences in different contexts. To verify the algorithm proposed, we use a dataset of synthetic advertising text documents from travel agencies generated by the OpenAI GPT-4 model. We show that the “entanglement” in two-word document search and retrieval can be recognized as the frequent occurrence of two words in incompatible query contexts. We have found that the user preferences and word ordering in the query play a significant role in relatively small sizes of the HAL window. The comparison with the cosine similarity metrics demonstrates the key advantages of our approach based on the user-enforced contextual and semantic relationships between words and not just their superficial occurrence in texts. Our approach to retrieving and ranking documents allows for the creation of new information search engines that require no resource-intensive deep machine learning algorithms. Full article

Blockchain technology has been used with great effect in farm-to-fork traceability projects. However, this technology has a steep learning curve when it comes to its user interface. To minimize this difficulty, we created a solution based on a Large Language Model (LLM) conversational agent. Our implementation, starting with an existing knowledge base that is prepared and processed with an embedding model to be stored in a vector database, follows a Retrieval-Augmented Generation (RAG) approach. Other non-textual media like images and videos are aggregated with the embeddings to enrich the user experience. User queries are combined with a proximity search in the vector database and feed into an LLM that considers the conversation history with the user in its replies. Given the asynchronous nature of these models, we implemented a similarly asynchronous scheme using Server-Sent Events that deliver the models’ replies to a UI that supports multimodal media types such as images and videos by providing the visualization of these resources. The end solution allows users to interact with advanced technologies using a natural language interface; this in turn empowers food traceability projects to overcome their natural difficulty in engaging early adopters. Full article

In this paper, we present the implementation of an artificial intelligence health assistant designed to complement a previously built eHealth data acquisition system for helping both patients and medical staff. The assistant allows users to query medical information in a smarter, more natural way, respecting patient privacy and using secure communications through a chat style interface based on the Matrix decentralized open protocol. Assistant responses are constructed locally by an interchangeable large language model (LLM) that can form rich and complete answers like most human medical staff would. Restricted access to patient information and other related resources is provided to the LLM through various methods for it to be able to respond correctly based on specific patient data. The Matrix protocol allows deployments to be run in an open federation; hence, the system can be easily scaled. Full article

Many patients infected with the SARS-CoV-2 virus (COVID-19) continue to experience symptoms for weeks to years as sequelae of the initial infection, referred to as “Long COVID”. Although many studies have described the incidence and symptomatology of Long COVID, there are little data reporting the potential burden of Long COVID on surgical departments. A previously constructed database of survey respondents who tested positive for COVID-19 was queried, identifying patients reporting experiencing symptoms consistent with Long COVID. Additional chart review determined whether respondents had a surgical or non-routine invasive procedure on or following the date of survey completion. Outcomes from surgeries on patients reporting Long COVID symptoms were compared to those from asymptomatic patients. A total of 17.4% of respondents had surgery or a non-routine invasive procedure in the study period. A total of 48.8% of these patients reported experiencing symptoms consistent with Long COVID. No statistically significant differences in surgical outcomes were found between groups. The results of this analysis demonstrate that Long COVID does not appear to have created a significant burden of surgical disease processes on the healthcare system despite the wide range of chronic symptoms and increased healthcare utilization by this population. This knowledge can help guide surgical operational resource allocation as a result of the pandemic and its longer-term sequelae. Full article

Monitoring the psychophysical conditions of drivers is crucial for ensuring road safety. However, achieving real-time monitoring within a vehicle presents significant challenges due to factors such as varying lighting conditions, vehicle vibrations, limited computational resources, data privacy concerns, and the inherent variability in driver behavior. Analyzing driver states using visible spectrum imaging is particularly challenging under low-light conditions, such as at night. Additionally, relying on a single behavioral indicator often fails to provide a comprehensive assessment of the driver’s condition. To address these challenges, we propose a system that operates exclusively in the far-infrared spectrum, enabling the detection of critical features such as yawning, head drooping, and head pose estimation regardless of the lighting scenario. It integrates a channel fusion module to assess the driver’s state more accurately and is underpinned by our custom-developed and annotated datasets, along with a modified deep neural network designed for facial feature detection in the thermal spectrum. Furthermore, we introduce two fusion modules for synthesizing detection events into a coherent assessment of the driver’s state: one based on a simple state machine and another that combines a modality encoder with a large language model. This latter approach allows for the generation of responses to queries beyond the system’s explicit training. Experimental evaluations demonstrate the system’s high accuracy in detecting and responding to signs of driver fatigue and distraction. Full article

Phytoplasmas are small, intracellular bacteria that infect a vast range of plant species, causing significant economic losses and impacting agriculture and farmers’ livelihoods. Early and rapid diagnosis of phytoplasma infections is crucial for preventing the spread of these diseases, particularly through early symptom recognition in the field by farmers and growers. A symptom database for phytoplasma infections can assist in recognizing the symptoms and enhance early detection and management. In this study, nearly 35,000 phytoplasma sequence entries were retrieved from the NCBI nucleotide database using the keyword “phytoplasma” and information on phytoplasma disease-associated plant hosts and symptoms was gathered. A total of 945 plant species were identified to be associated with phytoplasma infections. Subsequently, links to symptomatic images of these known susceptible plant species were manually curated, and the Phytoplasma Disease Symptom Database (iPhyDSDB) was established and implemented on a web-based interface using the MySQL Server and PHP programming language. One of the key features of iPhyDSDB is the curated collection of links to symptomatic images representing various phytoplasma-infected plant species, allowing users to easily access the original source of the collected images and detailed disease information. Furthermore, images and descriptive definitions of typical symptoms induced by phytoplasmas were included in iPhyDSDB. The newly developed database and web interface, equipped with advanced search functionality, will help farmers, growers, researchers, and educators to efficiently query the database based on specific categories such as plant host and symptom type. This resource will aid the users in comparing, identifying, and diagnosing phytoplasma-related diseases, enhancing the understanding and management of these infections. Full article

Chemotherapy poses both physical and psychological challenges for patients, prompting many to seek answers independently through online resources. This study investigates German Google search behavior regarding chemotherapy-related terms using Google AdWords data from September 2018 to September 2022 to gain insights into patient concerns and needs. A total of 1461 search terms associated with “chemotherapy” were identified, representing 1,749,312 to 28,958,400 search queries. These terms were categorized into four groups based on frequency and analyzed. Queries related to “adjuvant” and “neoadjuvant” chemotherapy, as well as “immunotherapy”, suggest potential confusion among patients. Breast cancer emerged as the most searched tumor type, with hair loss, its management, and dermatological issues being the most searched side effects. These findings underscore the role of search engines such as Google in facilitating access to healthcare information and provide valuable insights into patient thoughts and needs. Healthcare providers can leverage this information to deliver patient-centric care and optimize treatment outcomes. Full article

The Fengjing pig is one of the local pig breed resources in China and has many excellent germplasm characteristics. However, research on its genome is lacking. To explore the degree of genetic diversity of the Fengjing pig and to deeply explore its excellent traits, this study took Fengjing pigs as the research object and used the Beadchip Array Infinium iSelect-96|XT KPS_PorcineBreedingChipV2 for genotyping. We analyzed the genetic diversity, relatedness, inbreeding coefficient, and population structure within the Fengjing pig population. Our findings revealed that the proportion of polymorphic markers (P_N) was 0.469, and the effective population size was 6.8. The observed and expected heterozygosity were 0.301 and 0.287, respectively. The G-matrix results indicated moderate relatedness within the population, with certain individuals exhibiting closer genetic relationships. The NJ evolutionary tree classified Fengjing boars into five family lines. The average inbreeding coefficient based on ROH was 0.318, indicating a high level of inbreeding. GWAS identified twenty SNPs significantly associated with growth traits (WW, 2W, and 4W) and reproductive traits (TNB and AWB). Notably, WNT8B, RAD21, and HAO1 emerged as candidate genes influencing 2W, 4W, and TNB, respectively. Genes such as WNT8B were verified by querying the PigBiobank database. In conclusion, this study provides a foundational reference for the conservation and utilization of Fengjing pig germplasm resources and offers insights for future molecular breeding efforts in Fengjing pigs. Full article

Imaging for prostate cancer defines the extent of disease. Guidelines recommend against imaging low-risk prostate cancer patients with a computed tomography (CT) scan or bone scan due to the low probability of metastasis. We reviewed imaging performed for men diagnosed with low-risk prostate cancer across the Pennsylvania Urologic Regional Collaborative (PURC), a physician-led data sharing and quality improvement collaborative. The data of 10 practices were queried regarding the imaging performed in men diagnosed with prostate cancer from 2015 to 2022. The cohort included 13,122 patients with 3502 (27%) low-risk, 2364 (18%) favorable intermediate-risk, 3585 (27%) unfavorable intermediate-risk, and 3671 (28%) high-risk prostate cancer, based on the AUA guidelines. Amongst the low-risk patients, imaging utilization included pelvic MRI (59.7%), bone scan (17.8%), CT (16.0%), and PET-based imaging (0.5%). Redundant imaging occurred in 1022 patients (29.2%). There was variability among the PURC sites for imaging used in the low-risk patients, and iterative education reduced the need for CT and bone scans. Approximately 15% of low-risk patients had staging imaging performed using either a CT or bone scan, and redundant imaging occurred in almost one-third of men. Such data underscore the need for continued guideline-based education to optimize the stewardship of resources and reduce unnecessary costs to the healthcare system. Full article