Volume Of Data Research Articles

Natural Language Processing (NLP) Application For Classifying and Managing Tacit Knowledge in Revolutionizing AI-Driven Library

The rapid evolution of technology has transformed library systems, with Natural Language Processing (NLP) emerging as a pivotal tool for enhancing knowledge management. This study aims to examine how NLP can improve the classification and management of tacit knowledge within AI-driven libraries, addressing the challenge of handling large volumes of unstructured data. The objective is to explore how NLP can optimize the retrieval, organization, and access to tacit knowledge, thus enhancing decision-making processes in libraries. The research adopts a conceptual design, synthesizing existing literature and theoretical models, including Information Processing Theory and Constructivist Theory, to propose a framework that integrates NLP with traditional knowledge management practices. Methodologies include a thorough review of recent advancements in NLP technologies and their applications within knowledge management systems. The study’s findings demonstrate that NLP significantly improves the accuracy and efficiency of knowledge retrieval by automating the processing of natural language data. This allows better access to tacit knowledge, supporting more informed decision-making. The outcomes of the study are twofold: it enhances existing knowledge management frameworks theoretically, and it provides practical insights for libraries to leverage NLP for greater operational efficiency and improved user experience. The study also underscores the need for future research on the real-world application of NLP and its ethical implications, such as data privacy and algorithmic bias.

Physics-Based Abnormal Trajectory Gap Detection

Given trajectories with gaps (i.e., missing data), we investigate algorithms to identify abnormal gaps in trajectories which occur when a given moving object did not report its location, but other moving objects in the same geographic region periodically did. The problem is important due to its societal applications, such as improving maritime safety and regulatory enforcement for global security concerns, such as illegal fishing, illegal oil transfers, and trans-shipments. The problem is challenging due to the difficulty of bounding the possible locations of the moving object during a trajectory gap, and the very high computational cost of detecting gaps in such a large volume of location data. The current literature on anomalous trajectory detection assumes linear interpolation within gaps, which may not be able to detect abnormal gaps since objects within a given region may have traveled away from their shortest path. In preliminary work, we introduced an abnormal gap measure that uses a classical space-time prism model to bound an object's possible movement during the trajectory gap and provided a scalable memoized gap detection algorithm (Memo-AGD). In this article, we propose a space time-aware gap detection (STAGD) approach to leverage space-time indexing and merging of trajectory gaps. We also incorporate a dynamic region merge-based (DRM) approach to efficiently compute gap abnormality scores. We provide theoretical proofs that both algorithms are correct and complete and also provide analysis of asymptotic time complexity. Experimental results on synthetic and real-world maritime trajectory data show that the proposed approach substantially improves computation time over the baseline technique.

Combined Drought Index Using High-Resolution Hydrological Models and Explainable Artificial Intelligence Techniques in Türkiye

We developed a combined drought index to better monitor agricultural drought events. To develop the index, different combinations of the temperature condition index, precipitation condition index, vegetation condition index, soil moisture condition index, gross primary productivity, and normalized difference water index were used to obtain a single drought severity index. To obtain more effective results, a mesoscale hydrologic model was used to obtain soil moisture values. The SHapley Additive exPlanations (SHAP) algorithm was used to calculate the weights for the combined index. To provide input to the SHAP model, crop yield was predicted using a machine learning model, with the training set yielding a correlation coefficient (R) of 0.8, while the test set values were calculated to be 0.68. The representativeness of the new index in drought situations was compared with established indices, including the Standardized Precipitation-Evapotranspiration Index (SPEI) and the Self-Calibrated Palmer Drought Severity Index (scPDSI). The index showed the highest correlation with an R-value of 0.82, followed by the SPEI with 0.7 and scPDSI with 0.48. This study contributes a different perspective for effective detection of agricultural drought events. The integration of an increased volume of data from remote sensing systems with technological advances could facilitate the development of significantly more efficient agricultural drought monitoring systems.

Evaluation Method of Teaching Effect of Environmental Art and Design in Universities Based on Multi-Source Data Fusion Technology

The evaluation of teaching reform satisfaction is crucial for improving teaching quality. In the evaluation of satisfaction with teaching reforms in environmental art and design, issues such as the low reliability of evaluation indicator data can lead to unsatisfactory results. To address this, this paper proposes a new method for evaluating satisfaction with teaching reforms in environmental art and design based on multi-source data integration. By establishing principles for constructing a multi-source indicator system for evaluating satisfaction with teaching reforms in environmental art and design, a four-level multi-source indicator system was determined. This system mainly includes one first-level indicator, four second-level satisfaction evaluation indicators, and several third- and fourth-level evaluation indicators. The construction of the multi-source indicator system for evaluating satisfaction with teaching reforms in environmental art and design was completed. Using a four-part graph model and entropy weight calculation, the importance of the evaluation indicators for satisfaction with teaching reforms in environmental art and design was classified. The expert scoring method was introduced to correct the multi-source indicator data for evaluating satisfaction with teaching reforms in environmental art and design. Based on standardized indicator data, a penalty rule was added to the loss function of the multiple linear regression model to remove data noise, achieving pre-processing of the multi-source indicators for evaluating satisfaction with teaching reforms in environmental art and design. After completing the above process, first collect multi-source data from questionnaire surveys, student feedback, etc., and preprocess and map it to a unified ontology class. Subsequently, data features are extracted through non-negative matrix factorization, and the similarity between object types is calculated. Finally, based on the importance, redundancy, and interaction of data sources, an integrated quality model is designed to effectively integrate multiple sources of data, construct a satisfaction evaluation model, and output the score of satisfaction evaluation. The experimental results show that the proposed method has the highest satisfaction index, reaching about 0.99, indicating a high degree of recognition for the reform; when the amount of integrated data changes, the proposed method maintains the best integratison reliability. Even with an increase in data volume, its reliability only slightly decreases and stabilizes at a high level; meanwhile, the average absolute error of the proposed method is the lowest, consistently below 2.5%, indicating a high level of stability and accuracy in its evaluation results. The above results confirm that the proposed method can effectively evaluate the satisfaction of environmental art design teaching reform and improve teaching quality.

Whole body magnetic resonance imaging (WB MRI) in the diagnosis and evaluation of the effectiveness of treatment of metastatic prostate cancer: Met-RADS-P system standards

INTRODUCTION: Whole-body magnetic resonance imaging (WB MRI) including diffusion-weighted imaging (DWI) is increasingly used in clinical practice. For metastatic prostate cancer, the radiologic reporting and data system Met-RADS-P (METastasis Reporting and Data System for Prostate Cancer) has been proposed to determine the prevalence of the metastatic process and subsequently evaluate the response to specific treatment based on WB MRI data.OBJECTIVE: To present an overview of the methodological aspects of WB MRI in patients with metastatic prostate cancer in relation to the Met-RADS-P system, to outline its terminology and principles for assessing study results, and to discuss the possibilities of its clinical use.MATERIALS AND METHODS: A search was conducted for publications for the period from January 1, 2017 to December 1, 2023 in Russian and English in the electronic databases eLibrary, Medline, PubMed, using the keywords: «диффузионно-взвешенная томография всего тела», «метастатический рак предстательной железы», «Met-RADS-P», «Whole Body MRI, WB MRI», «Whole Body DWI, WB DWI», «metastatic prostate cancer», supplemented by examination of references in the retrieved articles.RESULTS: Based on the literature data and our own experience, WB MRI protocols in patients with metastatic prostate cancer are described, the algorithm of MR image analysis, the criteria for evaluating the response of metastatic foci in the skeleton, lymph nodes, and internal organs, and the response assessment categories according to the RAC (response assessment categories) scale, as well as summarize the results and discuss the prospects for the clinical use of Met-RADS-P.DISCUSSION: The few publications in the literature using the Met-RADS-P system primarily concern the assessment of its reliability (inter-reader agreement). The authors conclude that it is appropriate to use it in monitoring metastases in patients with castration-resistant prostate cancer, emphasizing the clinical importance of identifying discordant responses after therapy. They also note the obstacles to the widespread introduction of WB MRI imaging into clinical practice, including the duration of the examination procedure and the large volume of diagnostic data that requires significant time for analysis and reporting, which can be overcome by automating the assessment of MR images using artificial intelligence.CONCLUSION: The Met-RADS-P system systematizes the approach to performing and describing WB MRI in patients with advanced prostate cancer, allowing the technique to be reproduced on virtually any modern scanner, from the scan protocol to a standardized report of findings. This makes the WB MRI technique reproducible in a variety of medical settings, and therefore increases its value. Nevertheless, Met-RADS-P is a relatively new system and requires large multicenter clinical trials to confirm its efficacy and representativeness, as well as to level out its shortcomings.

EFFICIENCY AND ACCURACY OF CONVOLUTIONAL AND FOURIER TRANSFORM LAYERS IN NEURAL NETWORKS FOR MEDICAL IMAGE CLASSIFICATION

In an era where information flow is moving at a rapid pace, image data processing is becoming increasingly important as technology advances, including in healthcare. Convolutional Neural Network (CNN) has been a common approach in image classification, but the larger the volume of data and the complexity of the task, the more expensive the computational cost of CNN. With the rapid growth in the amount of image data, efficiency in data processing is becoming increasingly important. In this study, the performance of neural network models using the convolution layer and Fourier transform layer in medical image data classification was compared. The results show that models with a Fourier transform layer tend to provide higher accuracy and better Area Under Curve (AUC) compared to models using a convolution layer. In addition, the model with the Fourier transform layer also shows faster execution time per epoch, which indicates efficiency in data processing. However, the convolution layer has an advantage in terms of model size, although it is not significantly different from the Fourier transform layer. In conclusion, the Fourier transform layer has an advantage in the classification of medical image data.

Kashif: A Chrome Extension for Classifying Arabic Content on Web Pages Using Machine Learning

Search engines are significant tools for finding and retrieving information. Every day, many new web pages in various languages are added. The threats of cyberattacks are expanding rapidly with this massive volume of data. The majority of studies on the detection of malicious websites focus on English-language websites. This necessitates more studies on malicious detection on Arabic-content websites. In this research, we aimed to investigate the security of Arabic-content websites by developing a detection tool that analyzes Arabic content based on artificial intelligence (AI) techniques. We contributed to the field of cybersecurity and AI by building a new dataset of 4048 Arabic-content websites. We created and conducted a comparative performance evaluation for four different machine-learning (ML) models using feature extraction and selection techniques: extreme gradient boosting, support vector machines, decision trees, and random forests. The best-performing model was then integrated into a Chrome plugin, created based on a random forest (RF) model, and utilized the features selected via the chi-square technique. This produced plugin tool attained an accuracy of 92.96% for classifying Arabic-content websites as phishing, suspicious, or benign. To our knowledge, this is the first tool designed specifically for Arabic-content websites.

Seasonal and Weekly Patterns of Korean Adolescents' Web Search Activity on Insomnia: Retrospective Study.

Sleep deprivation in adolescents is a common but serious public health issue. Adolescents often have a progressive circadian delay and suffer from insufficient sleep during weekdays due to the school schedule. Temporal patterns in internet search activity data can provide relevant information for understanding the characteristic sleep problems of the adolescent population. We aimed to reveal whether adolescents exhibit distinct temporal seasonal and weekly patterns in internet search activity on insomnia compared to adults. We hypothesized that adolescents exhibit larger variations in the internet search volume for insomnia, particularly in association with the school schedule (e.g., academic vacations and weekends). We extracted the daily search volume for insomnia in South Korean adolescents (13-18 years old), adults (19-59 years old), and young adults (19-24 years old) during the years 2016-2019 using NAVER DataLab, the most popular search engine in South Korea. The daily search volume data for each group were normalized with the annual median of each group. The time series of the search volume was decomposed into slow fluctuation (over a year) and fast fluctuation (within a week) using fast Fourier transform. Next, we compared the normalized search volume across months in a year (slow fluctuation) and days in a week (fast fluctuation). In the annual trend, 2-way ANOVA revealed a significant (group) × (month) interaction (P<.001). Adolescents exhibited much greater seasonal variations across a year than the adult population (coefficient of variation=0.483 for adolescents vs 0.131 for adults). The search volume for insomnia in adolescents was notably higher in January, February, and August, which are academic vacation periods in South Korea (P<.001). In the weekly pattern, 2-way ANOVA revealed a significant (group) × (day) interaction (P<.001). Adolescents showed a considerably increased search volume on Sunday and Monday (P<.001) compared to adults. In contrast, young adults demonstrated seasonal and weekly patterns similar to adults. Adolescents demonstrate distinctive seasonal and weekly patterns in internet searches on insomnia (ie, increased search in vacation months and weekend-weekday transitions), which are closely associated with the school schedule. Adolescents' sleep concerns might be potentially affected by the disrupted daily routine and the delayed sleep phase during vacations and weekends. As we demonstrated, comparing various age groups in infodemiology and infoveillance data might be helpful in identifying distinctive features in vulnerable age groups.

Enhancing real‐time traffic volume prediction: A two‐step approach of object detection and time series modelling

AbstractA two‐step framework that integrates real‐time data collection with time series forecasting models for predicting traffic volume is proposed. In the first step, the framework utilizes live highway surveillance video data and YOLO‐v7 object detector to construct accurate traffic volume data. In the second step, an ARIMA–LSTM time series model is applied to forecast future traffic volumes. Experimental results show that YOLO‐v7 achieved a vehicle detection accuracy of over 93.30%, ensuring high precision in traffic volume data construction. The ARIMA–LSTM model demonstrated superior performance in traffic volume prediction, with a mean squared error of 87.97, root mean squared error of 10,388.57, and mean absolute error of 101.39. YOLO‐v7's detection speed of 7.8 ms per frame further validates the feasibility of real‐time data construction. The findings indicate that the combination of YOLO‐v7 for vehicle detection and ARIMA–LSTM for traffic prediction is highly effective, offering a significant reduction in training time compared to more complex deep learning models while maintaining high prediction accuracy. This research presents a unified solution for traffic data collection and prediction, enhancing transportation infrastructure planning and optimizing traffic flow. Future work will focus on extending the prediction intervals and further refining the models to improve performance.

PENGARUH VOLUME KENDARAAN TERHADAP KERUSAKAN JALAN PADA RUAS JALAN LEMBAR SELATAN - KEBUN AYU

Roads are an important factor in ensuring continued comfort and safety for all motorists, based on observations in the field, the condition of the South South Sheet crossing road to Kebun Ayu, which is approximately 5.7 km long, needs to be taken seriously and handled seriously by the government because the condition of the road has many potholes and bumps. The aim of this research is to determine the effect of vehicle volume on the level of road damage, and to determine the relationship between vehicle volume and the level of road damage on flexible pavement. This research is a case study research on the South South Sheet crossing road to Kebun Ayu. The data used in this research is secondary data such as road data and traffic volume data obtained from the West Lombok Regency Bina Marga service. Meanwhile, primary data obtained directly in the field includes vehicle volume data recorded during peak hours. The results of field data analysis showed that the road damage value (Nr) on the Southsheet – Kebun Ayu road was obtained with an average value (Nr) of 68.2; The traffic volume at peak hours based on passenger car units (pcu/hour) on the Kebun Ayu – South Sheet road is 560 pcu/hour, and in the South South Sheet - Kebun Ayu direction it is 546 pcu/hour. The equation for traffic volume analysis related to the value of road damage and time is Y = 0,025 X1 + 0,101 X2 + 25,74 with the magnitude of R2 or the correlation between variable x and variable y of 0.86 or 86%

Adaptive spatial down-sampling method based on object occupancy distribution for video coding for machines

As the performance of machine vision continues to improve, it is being used in various industrial fields to analyze and generate massive amounts of video data. Although the demand for and consumption of video data by machines has increased significantly, video coding for machines needs to be improved. It is therefore necessary to consider a new codec that differs from conventional codecs based on the human visual system (HVS). Spatial down-sampling plays a critical role in video coding for machines because it reduces the volume of the video data to be processed while maintaining the shape of the data’s features that are important for the machine to reference when processing the video. An effective method of determining the intensity of spatial down-sampling as an efficient coding tool for machines is still in the early stages. Here, we propose a method of determining an optimal scale factor for spatial down-sampling by collecting and analyzing information on the number of objects and the ratio of the area occupied by the object within a picture. We compare the data reduction ratio to the machine accuracy error ratio (DRAER) to evaluate the performance of the proposed method. By applying the proposed method, the DRAER was found to be a maximum of 21.40 dB and a minimum of 11.94 dB. This shows that video coding gain for the machines could be achieved through the proposed method while maintaining the accuracy of machine vision tasks.

The Influence of Big Data Analytics on Supply Chain Operations Efficiency

This paper aims to validate the feasibility of establishing a risk assessment system. Delphi and Entropy Weighting for Indicator Weighting was conducted by experts in the field of economics on the assessment data of Haier's supply chain indicators to analyze and derive a risk assessment system based on Haier's supply chain indicators. With a comprehensive analysis of risk assessment indicators using the Delphi and entropy weighting methods, the feasibility of the evaluation system for supply chain risk analysis in the context of big data gets demonstrated. The establishment of this assessment system will help Haier to analyze and respond to supply chain risks in the future, and will solve the related problems in avoiding supply chain market risk. These problems encompass difficulties related to managing substantial volumes of data and handling uncertainties in demand and supply. The template makes relevant conclusions and suggestions based on analysis, which could have effect on supply chain risk circumvention in a certain extent.

Smart Supervision of Public Expenditure: A Review on Data Capture, Storage, Processing, and Interoperability with a Case Study from Colombia

Effective fiscal control and monitoring of public management are critical for preventing and mitigating corruption, which in turn, enhances government performance and benefits citizens. Given the vast amounts of data involved in government operations, applying advanced data analysis methods is essential for strengthening fiscal oversight. This paper explores data management strategies aimed at enhancing fiscal control, beginning with a bibliometric study to underscore the relevance of this research. The study reviews existing data capture techniques that facilitate fiscal oversight, addresses the challenges of data storage in terms of its nature and the potential for contributing to this goal, and discusses data processing methods that yield actionable insights for analysis and decision-making. Additionally, the paper deals with data interoperability, emphasizing the importance of these practices in ensuring accurate and reliable analysis, especially given the diversity and volume of data within government operations. Data visualization is highlighted as a crucial component, enabling the detection of anomalies and promoting informed decision-making through clear and effective visual representations. The research concludes with a case study on the modernization of fiscal control in Colombia, focusing on the identification of user requirements for various data-related processes. This study provides valuable insights for modern audit and fiscal control entities, emphasizing that data capture, storage, processing, interoperability, and visualization are integral to the effective supervision of public expenditure. By ensuring that public funds are managed with transparency, accountability, and efficiency, the research advances the literature by addressing both the technological aspects of data management and the essential process improvements and human factors required for successful implementation.

Impact of World Cerebral Palsy Day on Public Interest in Brazil: Evidence from Internet Search Data

ABSTRACT Aim This study investigated the impact of the World Cerebral Palsy Day (WCPD) campaign on the public interest using Google Trends Analysis data in Brazil. Methods Google Trends was used to collect Relative Search Volume (RSV) data for “cerebral palsy” from 2004 to 2011 (control years) and 2012 to 2022 (WCPD years). RSV during the 4 weeks around WCPD (period of interest) was compared with the rest of the year (control period) in each timeframe. Regional RSV, search queries, and main topics were also investigated. Results RSV increased by 62.22% from pre-campaign to campaign period. During the WCPD years, a 21.36% RSV increase occurred in campaign weeks, with an average difference of 12.16 (95% CI: 1.74, 22.58); notably in in the last five years in the southeast 9.47 (95% CI: 2.93, 16.01) and south 8.66 (95% CI: 1.66, 15.66) macro-regions. Conclusion The campaign has fulfilled its role, but targeting more vulnerable areas could further amplify its impact.

Deep learning pipeline for automated cell profiling from cyclic imaging

Cyclic fluorescence microscopy enables multiple targets to be detected simultaneously. This, in turn, has deepened our understanding of tissue composition, cell-to-cell interactions, and cell signaling. Unfortunately, analysis of these datasets can be time-prohibitive due to the sheer volume of data. In this paper, we present CycloNET, a computational pipeline tailored for analyzing raw fluorescent images obtained through cyclic immunofluorescence. The automated pipeline pre-processes raw image files, quickly corrects for translation errors between imaging cycles, and leverages a pre-trained neural network to segment individual cells and generate single-cell molecular profiles. We applied CycloNET to a dataset of 22 human samples from head and neck squamous cell carcinoma patients and trained a neural network to segment immune cells. CycloNET efficiently processed a large-scale dataset (17 fields of view per cycle and 13 staining cycles per specimen) in 10 min, delivering insights at the single-cell resolution and facilitating the identification of rare immune cell clusters. We expect that this rapid pipeline will serve as a powerful tool to understand complex biological systems at the cellular level, with the potential to facilitate breakthroughs in areas such as developmental biology, disease pathology, and personalized medicine.

Analysis and Research on Intelligent Logistics Data under Internet of Things and Blockchain

ABSTRACT To explore the security performance of logistics data transmission in an Intelligent Logistics System (ILS) under the Internet of Things (IoT) and Blockchain (BC), IoT is introduced to optimize the logistics transportation process. First, BC is adopted to improve the ILS under IoT, and ILS under IoT combined with BC is established. Next, an analysis of the relationship between the information service provider and the transportation provider in the logistics transaction process is conducted to perform data analysis and to establish an incentive mechanism for logistics transportation under BC. Finally, the simulation analysis of the constructed model is implemented. The results reveal that the constructed model is highly efficient. It can effectively solve the traceability problem of logistics transaction information, and the calculation cost is stable within 1,000 ms. In the comparative analysis of system transmission performance, the model exhibits outstanding results in transmission latency, consistently maintaining an average delay of approximately 350 ms. This stability is primarily attributed to the seamless integration of BC technology, which optimizes data packet transmission paths and mitigates waiting time. Furthermore, the system efficiently manages high data volumes while upholding robust data processing capabilities, owing to the optimization of smart contracts and decentralized storage mechanisms. As for energy consumption, the model notably reduces overall energy usage within the logistics network by curtailing unnecessary data transmission and computation, particularly in mobile nodes and wireless communication channels. The proposed algorithm excels across various transmission metrics, including transmission delay, data successful acceptance rate, network throughput, and energy consumption are all shown to be the best transmission performance of the proposed algorithm, in which the data successful acceptance rate is close to 1. As a result, the developed ILS model not only guarantees low latency performance but also demonstrates high data transmission security, facilitating more efficient and precise real-time information transmission. In this study, within the ILS framework, the relationship between information service providers and transportation service providers is established through BC, and an incentive mechanism is constructed. However, some shortcomings in this incentive mechanism are recognized. For instance, during the initial stages of BC integration, there are issues of information asymmetry concerning factors such as credit, transportation capacity, and effort level. Moreover, the open access policy of couriers in the model raises concerns, as the access policy itself constitutes sensitive information, which may result in privacy breaches. Hence, as part of future work, the introduction of attribute encryption schemes is planned to obfuscate access policies, thereby safeguarding the privacy of logistics personnel’s access policies.

DrugReAlign: a multisource prompt framework for drug repurposing based on large language models.

Drug repurposing is a promising approach in the field of drug discovery owing to its efficiency and cost-effectiveness. Most current drug repurposing models rely on specific datasets for training, which limits their predictive accuracy and scope. The number of both market-approved and experimental drugs is vast, forming an extensive molecular space. Due to limitations in parameter size and data volume, traditional drug-target interaction (DTI) prediction models struggle to generalize well within such a broad space. In contrast, large language models (LLMs), with their vast parameter sizes and extensive training data, demonstrate certain advantages in drug repurposing tasks. In our research, we introduce a novel drug repurposing framework, DrugReAlign, based on LLMs and multi-source prompt techniques, designed to fully exploit the potential of existing drugs efficiently. Leveraging LLMs, the DrugReAlign framework acquires general knowledge about targets and drugs from extensive human knowledge bases, overcoming the data availability limitations of traditional approaches. Furthermore, we collected target summaries and target-drug space interaction data from databases as multi-source prompts, substantially improving LLM performance in drug repurposing. We validated the efficiency and reliability of the proposed framework through molecular docking and DTI datasets. Significantly, our findings suggest a direct correlation between the accuracy of LLMs' target analysis and the quality of prediction outcomes. These findings signify that the proposed framework holds the promise of inaugurating a new paradigm in drug repurposing.

The O-GlcNAc database: introducing new features and tools developed from community feedback.

O-GlcNAc is a reversible post-translational modification found on serine and threonine residues of nucleocytoplasmic proteins. Four years ago, we released the O-GlcNAc Database( oglcnac.mcw.edu ), a comprehensive catalog of O-GlcNAcylated proteins that has become one of the most cited resources in the field, with hundreds of unique users per month. We are now presenting an updated O-GlcNAc Database, which includes nearly 20,000 O-GlcNAcylated proteins and 48 species, marking substantial growth in data volume and scope. This paper presents the most noteworthy features implemented over the last year, often originating from feedback from the O-GlcNAc community. Among these features, we provide a brief overview of the database content, introduce our new protein viewer mode, and discuss the implementation of subcellular localization information and its applications in the O-GlcNAc score. We also provide an interface to use CytOVS, a tool designed to evaluate and sort O-GlcNAcome datasets derived from MS experiments. In conclusion, this new and improved O-GlcNAc Database represents a significant advancement in providing a comprehensive and expanded resource for researchers in the field of O-GlcNAc biology.

A Digital Twin of a University Campus from an Urban Sustainability Approach: Case Study in Madrid (Spain)

The development of geographic information systems has grown significantly over the past decade. Simultaneously, the concept of smart cities based on the management of large volumes of data has also spread worldwide. The digital twin concept has recently been incorporated into the technological domain of urban management. However, currently, phases such as technological integration, standardization, data and process interconnection, the development of tools and utilities, professional training, and the application of digital urban development in real-world situations are converging. This paper presents the experience developed on a university campus, detailing each of the phases carried out from the initial design to a fully operational pilot phase model. The article highlights the importance of certain aspects to consider in each phase, demonstrating that there are barriers and limitations and at the same time, great strengths and opportunities in applying the digital twin model in urban management, considering aspects such as mobility, accessibility, energy management, and involving students and university administrators in the process.

Mobility-Assisted Digital Twin Network Optimization over Industrial Internet of Things

Many real-world networks for the Industrial Internet of Things (IIoT) have diverse connectivity characteristics and real-time constraints imposed by industrial processing. In the context of digital twin networks (DTNs), a large number of IIoT devices may access the network and have a tremendous volume of data. A crucial element of these IIoT devices is mobility, which cannot be effectively solved because the number of IIoT devices connected is extremely large. IIoT devices in DTNs suffer from poor data transmission and link quality because of their mobility. In this paper, device-to-device (D2D) communication-based mobility-assisted digital twin networks are proposed, where edge computing is introduced to design an efficient mapping between the physical and virtual space. Then, we propose the architecture of data transmission for the D2D network to maximize the data rate for reliable connectivity among multiple mobile nodes based on IIoT. A Markov decision process (MDP) is formulated to maximize the data rate for multiple mobile nodes while maintaining the D2D communication link quality. The simulation results demonstrate the superiority of the proposed scheme over other comparable models.