Retrieval Capabilities Research Articles

Factors influencing proxy online health information seeking among the elderly: A study from the perspective of the elderly with chronic illnesses.

With the ageing population in China and an increasing number of the elderly developing chronic illnesses, health services for the elderly have become a major concern. They have significant needs for health information (HI) such as online medical consultation, disease prevention, and medical insurance. To explore the influencing factors and their relative significance in proxy online health information (OHI) seeking among the elderly with chronic illnesses so as to provide a reference for enriching theoretical research and optimizing care for the elderly with chronic illness. Twenty-three elderly people with chronic illnesses participated in semi-structured in-depth interviews in this study. The interview transcripts were subject to three levels of coding (open, axial, and selective coding) based on grounded theory to determine the factors influencing proxy OHI seeking among the elderly and to understand how the main categories are interconnected. We used the interpretive structural modeling (ISM) process to analyze the hierarchy and associated pathways among the factors influencing proxy OHI seeking. From our analysis, we identified four main categories affecting proxy HI seeking: the health status, emotional needs, electronic HI literacy, and self-efficacy of the elderly. There were six subcategories that could be further subclassified into direct factors, key factors, and root factors, which constituted the hierarchical model of influencing factors. We found that proxy HI seeking among the elderly was directly triggered by self-efficacy and emotional needs following changes in their evaluation of health information and emotional needs, which originates from their perceptions of their health, concerns about their health, and information retrieval capability.

Creating a computer assisted ICD coding system: Performance metric choice and use of the ICD hierarchy

Objective:Machine learning methods hold the promise of leveraging available data and generating higher-quality data while alleviating the data collection burden on healthcare professionals. International Classification of Diseases (ICD) diagnoses data, collected globally for billing and epidemiological purposes, represents a valuable source of structured information. However, ICD coding is a challenging task. While numerous previous studies reported promising results in automatic ICD classification, they often describe input data specific model architectures, that are heterogeneously evaluated with different performance metrics and ICD code subsets.This study aims to explore the evaluation and construction of more effective Computer Assisted Coding (CAC) systems using generic approaches, focusing on the use of ICD hierarchy, medication data and a feed forward neural network architecture. Methods:We conduct comprehensive experiments using the MIMIC-III clinical database, mapped to the OMOP data model. Our evaluations encompass various performance metrics, alongside investigations into multitask, hierarchical, and imbalanced learning for neural networks. Results:We introduce a novel metric, ▪ , tailored to the ICD coding task, which offers interpretable insights for healthcare informatics practitioners, aiding them in assessing the quality of assisted coding systems. Our findings highlight that selectively cherry-picking ICD codes diminish retrieval performance without performance improvement over the selected subset. We show that optimizing for metrics such as NDCG and AUPRC outperforms traditional F1-based metrics in ranking performance. We observe that Neural Network training on different ICD levels simultaneously offers minor benefits for ranking and significant runtime gains. However, our models do not derive benefits from hierarchical or class imbalance correction techniques for ICD code retrieval. Conclusion:This study offers valuable insights for researchers and healthcare practitioners interested in developing and evaluating CAC systems. Using a straightforward sequential neural network model, we confirm that medical prescriptions are a rich data source for CAC systems, providing competitive retrieval capabilities for a fraction of the computational load compared to text-based models. Our study underscores the importance of metric selection and challenges existing practices related to ICD code sub-setting for model training and evaluation.

A Convolutional Neural Network and Attention-Based Retrieval of Temperature Profile for a Satellite Hyperspectral Microwave Sensor

As numerical weather forecasting advances, there is a growing demand for higher-quality atmospheric data. Hyperspectral instruments can capture more atmospheric information and increase vertical resolution, but there has been limited research into retrieval algorithms for obtaining hyperspectral microwaves in the future. This study proposes an atmospheric temperature profile detection algorithm based on Convolutional Neural Networks (CNN) and Local Attention Mechanisms for local feature extraction, applied to hyperspectral microwave sensors. The study utilizes the method of information entropy to extract more effective channels in the vicinities of 60 GHz, 118 GHz, and 425 GHz. The algorithm uses the brightness temperature as the input of the network. The algorithm addresses common issues encountered in conventional networks, such as overfitting, gradient explosion, and gradient vanishing. Additionally, this method isolates the three oxygen-sensitive frequency bands for modularized local feature extraction training, thereby avoiding abrupt changes in brightness temperature between adjacent frequency bands. More importantly, the algorithm considers the correlation between multiple channels and information redundancy, focusing on variations in local information. This enhances the effectiveness of hyperspectral microwave channel information extraction. We simulated the brightness temperatures of the selected channels through ARTS and divided them into training, validation, and test sets. The retrieval capability of the proposed method is validated on a test dataset, achieving a root mean square error of 1.46 K and a mean absolute error of 1.4 K for temperature profile. Detailed comparisons are also made between this method and other commonly used networks for atmospheric retrieval. The results demonstrate that the proposed method significantly improves the accuracy of temperature profile retrieval, particularly in capturing fine details, and is more adaptable to complex environments. The model also exhibits scalability, extending from one-dimensional (pressure level) to three-dimensional space. The error for each pressure level is controlled within 0.7 K and the average error is within 0.4 K, demonstrating effectiveness across different scales with impressive results. The computational efficiency and accuracy have both been improved when handling a large amount of radiation data.

Digital engineering audit management strategy based on machine learning combined with wireless communication network

In response to the evolving landscape of the modern era, the requirements for engineering audit have undergone significant changes. To achieve efficient audit tasks and obtain accurate and reliable results, the integration of machine learning and wireless network technology has become essential, leading to the emergence of digital and information-based audit modes. This paper focuses on the development of a digital audit system that combines engineering audit management fusion with machine learning and wireless network technology. Such an approach reflects the dynamic shift in internal audit functions and objectives, providing clear guidelines for the future of digital audit management. By harnessing the power of machine learning and wireless networks, the digital audit system effectively addresses challenges associated with data management, sharing, exchange, and security during the audit process. Through seamless integration, it enables comprehensive electronic and digital management of internal and audit business processes. This research explores the platform’s functionalities and its potential application, using actual audit data for analysis. The proposed digital audit system showcases superior real-time data querying performance, heightened accuracy in checks, and enhanced retrieval capabilities. The simulation results validate the system’s efficacy, highlighting its ability to deliver true and dependable audit outcomes. By embracing digital transformation, the engineering audit field can harness the potential of cutting-edge technologies, thus paving the way for a more efficient, reliable, and future-ready approach to audit management.

Resolution-enhanced multi-core fiber imaging learned on a digital twin for cancer diagnosis.

Deep learning enables label-free all-optical biopsies and automated tissue classification. Endoscopic systems provide intraoperative diagnostics to deep tissue and speed up treatment without harmful tissue removal. However, conventional multi-core fiber (MCF) endoscopes suffer from low resolution and artifacts, which hinder tumor diagnostics. We introduce a method to enable unpixelated, high-resolution tumor imaging through a given MCF with a diameter of around 0.65mm and arbitrary core arrangement and inhomogeneous transmissivity. Image reconstruction is based on deep learning and the digital twin concept of the single-reference-based simulation with inhomogeneous optical properties of MCF and transfer learning on a small experimental dataset of biological tissue. The reference provided physical information about the MCF during the training processes. For the simulated data, hallucination caused by the MCF inhomogeneity was eliminated, and the averaged peak signal-to-noise ratio and structural similarity were increased from 11.2dB and 0.20 to 23.4dB and 0.74, respectively. By transfer learning, the metrics of independent test images experimentally acquired on glioblastoma tissue ex vivo can reach up to 31.6dB and 0.97 with 14fps computing speed. With the proposed approach, a single reference image was required in the pre-training stage and laborious acquisition of training data was bypassed. Validation on glioblastoma cryosections with transfer learning on only 50 image pairs showed the capability for high-resolution deep tissue retrieval and high clinical feasibility.

Synergizing intelligence and knowledge discovery: Hybrid black hole algorithm for optimizing discrete Hopfield neural network with negative based systematic satisfiability

<p>The current systematic logical rules in the Discrete Hopfield Neural Network encounter significant challenges, including repetitive final neuron states that lead to the issue of overfitting. Furthermore, the systematic logical rules neglect the impact on the appearance of negative literals within the logical structure, and most recent efforts have primarily focused on improving the learning capabilities of the network, which could potentially limit its overall efficiency. To tackle the limitation, we introduced a Negative Based Higher Order Systematic Logic to the network, imposing restriction on the appearance of negative literals within the clauses. Additionally, a Hybrid Black Hole Algorithm was proposed in the retrieval phase to optimize the final neuron states. This ensured that the optimized states achieved maximum diversity and reach global minima solutions with the lowest similarity index, thereby enhancing the overall performance of the network. The results illustrated that the proposed model can achieve up to 10,000 diversified and global solutions with an average similarity index of 0.09. The findings indicated that the optimized final neuron states are in optimal configurations. Based on the findings, the development of the new systematic SAT and the implementation of the Hybrid Black Hole algorithm to optimize the retrieval capabilities of DHNN to achieve multi-objective functions result in updated final neuron states with high diversity, high attainment of global minima solutions, and produces states with a low similarity index. Consequently, this proposed model could be extended for logic mining applications to tackle classification tasks. The optimized final neuron states will enhance the retrieval of high-quality induced logic, which is effective for classification and knowledge extraction.</p>

Deep Learning Algorithms for Personalized Services and Enhanced User Experience in Libraries

The integration of deep learning (DL) algorithms in library settings engenders a multitude of challenges and complexities, encompassing unintended ramifications, ethical quandaries, a dearth of specialized literature elucidating DL in library contexts, the intricacies of dataset selection and human intervention, and the inherent limitations when juxtaposed with the remarkable cognitive capabilities of the human brain. To surmount these hurdles and attain a profound comprehension of DL in library settings, a rigorous and comprehensive systematic literature review (SLR) becomes imperative. This study investigates the application of DL algorithms in examining user-seeking behaviour to provide personalized services and enhance user experience in libraries. Through a comprehensive literature review, the study aims to uncover the benefits, challenges, and implications of integrating DL algorithms for user behaviour analysis and personalized services in library environments. The investigation encompasses a systematic literature review, employing a meticulous search and screening process utilizing the Scopus database. DL algorithms enable tailored recommendations, resource suggestions, and personalized search outcomes, improving information retrieval and user-centric services. Ethical considerations and ongoing research are emphasized to address challenges and maximize the potential of DL algorithms in libraries. The integration of DL algorithms in libraries yields substantial benefits, including improved information retrieval capabilities, augmented resource recommendation systems, and the delivery of user-centric services. The paper offers valuable insights to researchers, practitioners, and stakeholders operating within this field.

Combined Retrieval of Oil Film Thickness Using Hyperspectral and Thermal Infrared Remote Sensing

An outdoor experiment was conducted to measure the thickness of oil films (0~3000 μm) using an airborne hyperspectral imager and thermal infrared imager, and the spectral response and thermal response of oil films of different thicknesses were analyzed. The classic support vector regression (SVR) model was used to retrieve the oil film thickness. On this basis, the suitable range for retrieving oil film thickness using hyperspectral and thermal infrared remote sensing was explored, and the decision-level fusion algorithm was developed to fuse the retrieval capabilities of hyperspectral and thermal infrared remote sensing for oil film thickness. The following conclusions can be drawn: (1) Based on airborne hyperspectral data and thermal infrared data, the retrieval accuracy of oil films of different thicknesses reached 154.31 μm and 116.59 μm, respectively. (2) The S185 hyperspectral data were beneficial for retrieving thicknesses greater than or equal to 400 μm, and the H20T thermal infrared data were beneficial for retrieving thicknesses greater than 500 μm. (3) The result of the decision-level fusion model based on a fuzzy membership degree was superior to those obtained using a single sensor (hyperspectral or thermal infrared), indicating that it can better integrate the retrieval results of hyperspectral and thermal infrared remote sensing for oil film thickness. Furthermore, the feasibility of using hyperspectral and thermal infrared remote sensing to detect water-in-oil emulsions of different thicknesses was investigated through spectral response and thermal response analysis.

Evaluation of Five Satellite-Based Precipitation Products for Extreme Rainfall Estimations over the Qinghai-Tibet Plateau

The potential of satellite precipitation products (SPPs) in monitoring and mitigating hydrometeorological disasters caused by extreme rainfall events has been extensively demonstrated. However, there is a lack of comprehensive assessment regarding the performance of SPPs over the Qinghai-Tibet Plateau (QTP). Therefore, this research aimed to evaluate the effectiveness of five SPPs, including CMORPH, IMERG-Final, PERSIANN-CDR, TRMM-3B42V7, and TRMM-3B42RT, in identifying variations in the occurrence and distribution of intense precipitation occurrences across the QTP during the period from 2001 to 2015. To evaluate the effectiveness of the SPPs, a reference dataset was generated by utilizing rainfall measurements collected from 104 rainfall stations distributed across the QTP. Ten standard extreme precipitation indices (SEPIs) were the main focus of the evaluation, which encompassed parameters such as precipitation duration, amount, frequency, and intensity. The findings revealed the following: (1) Geographically, the SPPs exhibited better retrieval capability in the eastern and southern areas over the QTP, while displaying lower detection accuracy in high-altitude and arid areas. Among the five SPPs, IMERG-Final outperformed the others, demonstrating the smallest inversion error and the highest correlation. (2) In terms of capturing annual and seasonal time series, IMERG-Final performs better than other products, followed by TRMM-3B42V7. All products performed better during summer and autumn compared to spring and winter. (3) The statistical analysis revealed that IMERG-Final demonstrates exceptional performance, especially concerning indices related to precipitation amount and precipitation intensity. Moreover, it demonstrates a slight advantage in detecting the daily rainfall occurrences and occurrences of intense precipitation. On the whole, IMERG-Final’s ability to accurately detect extreme precipitation events on annual, seasonal, and daily scales is superior to other products for the QTP. It was also noted that all products overestimate precipitation events to some extent, with TRMM-3B42RT being the most overestimated.

Assessment of Artificial Intelligence Language Models and Information Retrieval Strategies for QA in Hematology

Introduction Large language models (LLMs) have gained popularity due to their natural language generation and interpretation capabilities. Integrating these models in medicine enables multiple tasks like summarizing medical histories, synthesizing literature, and suggesting diagnoses. Models like ChatGPT, GPT-4, and Med-PaLM2 (Singhal et al., 2023) have demonstrated their proficiency by achieving high scores in medical tests like the United States Medical Licensing Examination (USMLE) (Kung et al., 2023). However, LLMs may sometimes be inaccurate, providing unverified and erroneous information. In this study, we investigate the potential uses of LLMs in hematology, assessing their knowledge through hematology questions from the USMLE. Additionally, we propose augmenting LLMs with retrieval capabilities for medical guidelines in order to eliminate incorrect information. By extracting relevant information from specified medical documents, this approach holds the potential to streamline decision-making processes. Methods For comparative purposes, all experiments were conducted using both GPT 3.5-turbo and GPT-4 models. In a first step, we evaluated the general knowledge and performance of LLM in the field of hematology by testing it in a collected dataset of 127 question-answer pairs from the hematology section (covering various aspects of the field) of the USMLE. In a second step, we evaluated the proposed information retrieval framework using a set of 120 multiple-choice questions. These questions were specifically focused on the 4th revision of the World Health Organization classification of myeloid neoplasms and acute leukemia guidelines (subsequently called WHO 2017). By testing the framework on this domain-specific dataset, we aimed to assess its ability to extract specific clinical context and relevant information from complex clinical guidelines. Each question from the WHO 2017 guideline dataset was subjected to a comprehensive evaluation using two techniques. First, the questions were assessed using a zero-shot approach (the question together with the different options are directly posed to the model) to assess the LLM's capability to respond based on its own knowledge. Second, we employed our proposed retrieval information approach, enabling the system to conduct in-depth searches throughout the external documents (WHO 2017 guideline) to identify relevant (and similar) extracts about each question. Subsequently, the system provided answers based on the retrieved contexts from the document, thus facilitating more accurate and contextually informed responses. To achieve this, we created an embedding space containing the document's content and conducted a cosine-similarity search between a given question and all the content extracts from the document. The top three relevant extracts, based on similarity to the given question, were used as context for the LLM. Results In the evaluation of 127 hematology questions from the USMLE, GPT-3.5 in zero-shot mode achieved 63% accuracy, while GPT-4 demonstrated a higher accuracy rate of 82%. The evaluation of the WHO 2017 questions dataset revealed that the zero-shot approach achieved accuracy rates of 51% for GPT-3.5 and 71% for GPT-4. Incorporating information retrieval, retrieving the three most relevant extracts from the guidelines, substantially improved performance, with GPT-3.5 achieving 86% accuracy and GPT-4 demonstrating 97% accuracy. Conclusions LLMs have great potential, with current models showcasing substantial knowledge in hematology. However, ensuring their consistency and safety in responses is critical for their reliable application in medical settings (Thirunavukarasu et al., 2023). To address this, we demonstrated the benefits of information retrieval for question-answering in the field of hematology, significantly improving response reliability and accuracy by empowering LLMs to deliver more informed and contextually appropriate answers. The concept was effectively validated using the WHO 2017 guideline, and it can be effortlessly adapted to answer questions based on any set of hematology-related documents. Leveraging LLMs has the potential to significantly enhance the efficiency and effectiveness of clinical, educational, and research work in hematology.

Reconstruction of Femtosecond Laser Pulses from FROG Traces by Convolutional Neural Networks

We report on the reconstruction of ultrashort laser pulses from computer-simulated and experimental second harmonic generation-frequency resolved optical gating (SHG-FROG) spectrograms. In order to retrieve the spectral amplitude and phase we use a convolutional neural network trained on simulated SHG-FROG spectrograms and the corresponding spectral-domain fields employed as labels for the network, which is a complex field encompassing the full information about the amplitude and phase. Our results show excellent retrieval capabilities of the neural network in case of the simulated pulses. Although trained only on computer generated data, the method shows promising results regarding experimentally measured pulses.

Structural optical design of multi-group, high zoom ratio optical systems

In this paper, we propose a method for the structural optical design of multi-group, high zoom ratio optical systems based on matrix optics and Particle Swarm Optimization (PSO) algorithm. This method effectively addresses the problems of initial structural calculation difficulties, large volume, and unsmooth zoom curves with large inflection points in high zoom ratio optical systems. To design the zoom optical system, the matrix equations of the multi-group zoom system are derived using geometric optics; and a mathematical model is built to solve the initial structural parameters of the zoom system based on the matrix. Then, a new fitness function is introduced as an indicator to iteratively optimize the initial structural parameters of the system in multi-dimensional space, considering factors such as maximum zoom ratio, total optical length, and full field of view angle. Finally, this method is validated in two complex optical system designs including a 20x four-group and a 35x five-group zoom optical system, showing strong retrieval capabilities. Independent of traditional trial-and-error investigations for solutions, our method facilitates the automatic intelligent design of complex optical systems.

Enhancing Cloud Communication Security: A Blockchain-Powered Framework with Attribute-Aware Encryption

The global production of information continuously increases in quantity and variety. However, the tools and technologies developed to handle such large volumes of data have not adequately met the security and privacy requirements. Existing cloud security systems, often managed by a trusted third party, are susceptible to various security risks. To address these challenges and ensure the protection of personal information, blockchain technology emerges as a crucial solution with substantial potential. This research uses the blockchain-powered attribute-aware encryption method to establish a real-time secure communication approach over the cloud. By employing attribute-based encryption technology, data owners can implement fine-grained search permissions for data users. The proposed solution incorporates accessible encryption technology to enable secure access to encrypted data and facilitate keyword searches on the blockchain. This study provides a functional comparison of recently developed attribute-based encryption algorithms. The access control strategy comprises two access tree types and a linear secret-sharing system, serving as the main components. The elliptic curve’s base field was set to 512b, and the bilinear pairing parameter type used was Type-A. This approach involves storing keywords on a remote server and encrypting them using attribute-based encryption. Furthermore, the encrypted data blockchain and the corresponding ciphertext are stored in the blockchain. Numerical experiments were conducted to evaluate the system’s key generation, trapdoor building, and keyword retrieval capabilities.

Evaluating the performance of ChatGPT in clinical pharmacy: A comparative study of ChatGPT and clinical pharmacists.

To evaluate the performance of chat generative pretrained transformer (ChatGPT) in key domains of clinical pharmacy practice, including prescription review, patient medication education, adverse drug reaction (ADR) recognition, ADR causality assessment and drug counselling. Questions and clinical pharmacist's answers were collected from real clinical cases and clinical pharmacist competency assessment. ChatGPT's responses were generated by inputting the same question into the 'New Chat' box of ChatGPT Mar 23 Version. Five licensed clinical pharmacists independently rated these answers on a scale of 0 (Completely incorrect) to 10 (Completely correct). The mean scores of ChatGPT and clinical pharmacists were compared using a paired 2-tailed Student's t-test. The text content of the answers was also descriptively summarized together. The quantitative results indicated that ChatGPT was excellent in drug counselling (ChatGPT: 8.77 vs. clinical pharmacist: 9.50, P = .0791) and weak in prescription review (5.23 vs. 9.90, P = .0089), patient medication education (6.20 vs. 9.07, P = .0032), ADR recognition (5.07 vs. 9.70, P = .0483) and ADR causality assessment (4.03 vs. 9.73, P = .023). The capabilities and limitations of ChatGPT in clinical pharmacy practice were summarized based on the completeness and accuracy of the answers. ChatGPT revealed robust retrieval, information integration and dialogue capabilities. It lacked medicine-specific datasets as well as the ability for handling advanced reasoning and complex instructions. While ChatGPT holds promise in clinical pharmacy practice as a supplementary tool, the ability of ChatGPT to handle complex problems needs further improvement and refinement.

Bathymetry Inversion Using Attention-Based Band Optimization Model for Hyperspectral or Multispectral Satellite Imagery

Satellite-derived bathymetry enables the non-contact derivation of large-scale shallow water depths. Hyperspectral satellite images provide more information than multispectral satellite images, making them theoretically more effective and accurate for bathymetry inversion. This paper focuses on the use of hyperspectral satellite images (PRISMA) for bathymetry inversion and compares the retrieval capabilities of multispectral satellite images (Sentinel-2 and Landsat 9) in the southeastern waters of Molokai Island in the Hawaiian Archipelago and Yinyu Island in the Paracel Archipelago. This paper proposes an attention-based band optimization one-dimensional convolutional neural network model (ABO-CNN) to better utilize the increased spectral information from multispectral and hyperspectral images for bathymetry inversion, and this model is compared with a traditional empirical model (Stumpf model) and two deep learning models (feedforward neural network and one-dimensional convolutional neural network). The results indicate that the ABO-CNN model outperforms the above three models, and the root mean square errors of retrieved bathymetry using the PRISMA images are 1.43 m and 0.73 m in the above two study areas, respectively. In summary, this paper demonstrates that PRISMA hyperspectral imagery has superior bathymetry inversion capabilities compared to multispectral images (Sentinel-2 and Landsat 9), and the proposed deep learning model ABO-CNN is a promising candidate model for satellite-derived bathymetry using hyperspectral imagery. With the increasing availability of ICESat-2 bathymetric data, the use of a combination of the proposed ABO-CNN model and the ICEsat-2 data as the training data provides a practical approach for bathymetric retrieval applications.

Assessing the Chlorophyll-a Retrieval Capabilities of Sentinel 3A OLCI Images for the Monitoring of Coastal Waters in Algoa and Francis Bays, South Africa

Eleven level-2 Sentinel 3A OLCI images that were acquired between 29 March 2017 and 11 December 2017 were used to assess their ability to retrieve oceanic Chl-a concentrations in South Africa’s Algoa and St Francis Bays. This was done by applying a 7-colour gradient pallet in the SNAP software to produce oceanic Chl-a concentration maps on a scale ranging from 0.1–30 mg/m3. The validation of Sentinel’s Chl-a’s retrieval potentials was based on temporally corresponding in-situ data from eight stations. Comparative analysis of the image-based and in-situ Chl-a concentrations revealed statistically significant correlations (r 0.609–r 0.899, ᾶ 0.05) at five stations out of the eight that were used as sources of reference data. This finding is helpful as an objectively premised source of insights on how to sustainably utilise the oceanic resources at our disposal. It is also useful because it verifiably demonstrates that Sentinel images can be reliably used to retrieve usable information on Chl-a concentrations in lieu of the costly sea-surface-based in-situ measurements at appropriate temporal and spatial scales.

Comparative Study of the Atmospheric Gas Composition Detection Capabilities of FY-3D/HIRAS-I and FY-3E/HIRAS-II Based on Information Capacity

Fengyun-3E (FY-3E)/Hyperspectral Infrared Atmospheric Sounder-II (HIRAS-II) is an extension Fengyun-3D (FY-3D)/HIRAS-I. It is crucial to fully explore and analyze the detection capabilities of these two instruments for atmospheric gas composition. Based on the observed spectral data from the infrared hyperspectral detection instruments FY-3D/HIRAS-I and FY-3E/HIRAS-II, simulated radiance data and Jacobian matrices are obtained using the Rapid Radiative Transfer Model RTTOV (Radiative Transfer for TOVS (TIROS Operational Vertical Sounder)). By perturbing temperature (T), surface temperature (Tsurf), water vapor (H2O), ozone (O3), carbon dioxide (CO2), methane (CH4), carbon monoxide (CO), and nitrous oxide (N2O), the brightness temperature differences before and after the perturbations are calculated to analyze the sensitivity of temperature and various atmospheric gas components. The Improved Optimal Sensitivity Profile (OSP) algorithm is used to select the channels for atmospheric gas retrieval. The observation error covariance and background error covariance matrices are calculated, and then the information capacity is calculated, specifically the degrees of freedom for signal(DFS) and the entropy reduction (ER). Based on this, a comparative analysis is conducted on the information capacity of atmospheric water vapor and ozone components contained in the hyperspectral detection data from HIRAS-I and HIRAS-II instruments, respectively, to explore the retrieval capabilities of the two instruments for atmospheric gas components. We selected clear-sky data from the African oceanic region and the Chinese Yangtze River Delta terrestrial region for quantitative analysis of the information capacity of HIRAS-I and HIRAS-II. The results show that FY-3D/HIRAS-I and FY-3E/HIRAS-II exhibit different sensitivities to atmospheric gas components. In different experimental regions, temperature and water vapor show the most dramatic sensitivity changes, followed by ozone, methane, and nitrous oxide, while carbon monoxide and carbon dioxide exhibit the lowest variability. Regarding channel selection, HIRAS-II identifies more gas channels compared to HIRAS-I. The experiments concluded that HIRAS-II has a significantly higher information capacity than HIRAS-I, and the information capacity of atmospheric gas components varies across different experimental regions. Water vapor and ozone exhibit the highest information capacity, followed by nitrous oxide and methane, while carbon monoxide and carbon dioxide demonstrate the lowest capacity. The H2O ER (DFS) contained in FY-3E/HIRAS-II is 1.51 (0.35) higher than that in FY-3D/HIRAS-I, the O3 ER (DFS) in FY-3E/HIRAS-II is 1.51 (0.36) higher than that in FY-3D/HIRAS-I, while the N2O ER (DFS) in FY-3E/HIRAS-II is 0.17 (0.19) higher and the CH4 ER (DFS) is 0.07 (0.04) higher than that in FY-3D/HIRAS-I.

ChainDash: An Ad-Hoc Blockchain Data Analytics System

The emergence of digital asset applications, driven by Web 3.0 and powered by blockchain technology, has led to a growing demand for blockchain-specific graph analytics to unearth the insights. However, current blockchain data analytics systems are unable to perform efficient ad-hoc graph analytics over both live and past time windows due to their inefficient data synchronization and slow graph snapshots retrieval capability. To address these issues, we propose ChainDash, a blockchain data analytics system that dedicates a highly-parallelized data synchronization component and a retrieval-optimized temporal graph store. By leveraging these techniques, ChainDash supports efficient ad-hoc graph analytics of smart contract activities over arbitrary time windows. In the demonstration, we showcase the interactive visualization interfaces of ChainDash, where attendees will execute customized queries for ad-hoc graph analytics of blockchain data.

Database System for Medical Record Keeping and Retrieval

The field of healthcare is producing a lot of information in the form of data and knowledge. Medical knowledge is increasing day by day. The data needs to be accessed for providing health services and for administrative, economic, and legal purposes. Most of the health sectors use a paper-based system, which due to its certain limitations can affect the quality of care. Modern medical systems observed the need for the use of medical databases. Medical Database System (MDS) is an advancing concept and is defined as a systematic collection of online-based health records in any health facility that helps to overcome the limitations of conventional paper-based systems. The problem remains in the existing methods of medical record keeping and retrieval, which are less efficient as well as accurate. Recent systems have problems in their systems, such as inaccuracies in data, inefficiencies in their workflows, and other potential security weaknesses. Thus, there is a need for a database system specifically designed for medical record keeping that can overcome these problems and can offer a reliable, secure, and efficient solution for storing and retrieving medical records. In this research, an online-based health record system is designed that can keep, manage, and retrieve medical health records. The system’s frontend, as well as backend, are designed on Microsoft Access 365. Microsoft Access 365 offers suitability in keeping medical records, cost-effectiveness, integration capabilities, user-friendly interface, and scalability which also support the objectives and requirements of our project. The system includes patient registration for reception, a questioner and data retrieval for cancer diagnosis, and a complete pharmacy system including multiple forms and ledgers as well as the reports to get the summary of data where desired. Furthermore, the system contains a login system to make it secure. Our proposed database system shows a very significant improvement over existing systems in terms of efficiency, scalability, security, retrieval capabilities, and societal impact in medical record keeping and retrieval with significant societal impacts. This system can further be improved in the future, expanding its design by including other hospital services, like laboratories, finance departments, and consultation appointments. Nowadays artificial intelligence is performing an excellent role in each domain. Implementing artificial intelligence in this database system can lead this database system to the next level.

Retrieval of atmospheric temperature and humidity profiles over a tropical coastal station from ground-based Microwave Radiometer using deep learning technique

Ground-based multi-frequency microwave radiometer Profiler (Microwave Radiometer Profiler-MRP) provides valuable information on the altitude distribution of atmospheric humidity and temperature in the troposphere (upto ∼10 km) with high temporal resolution (of ∼1 min) under all-weather condition. These information are vital inputs for characterization of the atmospheric boundary layer, convective cloud systems, local weather modification and for studying the atmospheric dynamics. Potential of such radiometers are well established over the mid-latitude region. Thiruvananthapuram, located in the Indian peninsular region, is one of the first stations in the equatorial region where such an instrument made continuously operational for seven years since 2010 at the tropical coastal station. The instrument measures the brightness temperatures (Tb in Kelvin) at Ka- and V-band frequencies with a temporal resolution of ∼1 min. A trained back-propagation neural network technique based on the radiosonde ascends data for more than one decade obtained by India Meteorological station at Thiruvananthapuram, was used to derive the atmospheric humidity and temperature profiles. This paper presents the implementation of another retrieval technique based on deep learning approach - batch normalization and neural network (BNN) to retrieve temperature and water vapour density (WVD) profiles from Tb values during clear sky conditions and validation of the retrieval accuracies. This inversion model, much faster in computation, consists of two hidden layers and the rectified linear unit (ReLU) is used as the activation function as it can overcome the problems of saturation and vanishing gradients. The Tbs observed by radiometer are corrected by reducing the bias between the simulated Tb, using forward radiative transfer model, and the observed Tb. The validation of retrieved profiles with the radiosonde demonstrates a good retrieval capability, showing a root-mean-square error of 1.8K for temperature, <2 g/m3 for WVD and ∼14% for RH.