Objective: This review aimed to assess the current use and acceptance of real-world data (RWD) and real-world evidence (RWE) in health technology assessment (HTA) process. It additionally aimed to discern stakeholders’ viewpoints concerning RWD and RWE in HTA and illuminate the obstacles, difficulties, prospects, and consequences associated with the incorporation of RWD and RWE into the realm of HTA.Methods: A comprehensive PRISMA-based systematic review was performed in July 2022 in PubMed/Medline, Scopus, IDEAS-RePEc, International HTA database, and Centre for Reviews and Dissemination with ad hoc supplementary search in Google Scholar and international organization websites. The review included pre-determined inclusion criteria while the selection of eligible studies, the data extraction process and quality assessment were carried out using standardized and transparent methods.Results: Twenty-nine (n = 29) studies were included in the review out of 2,115 studies identified by the search strategy. In various global contexts, disparities in RWD utilization were evident, with randomized controlled trials (RCTs) serving as the primary evidence source. RWD and RWE played pivotal roles, surpassing relative effectiveness assessments (REAs) and significantly influencing decision-making and cost-effectiveness analyses. Identified challenges impeding RWD integration into HTA encompassed limited local data access, complexities in non-randomized trial design, data quality, privacy, and fragmentation. Addressing these is imperative for optimal RWD utilization. Incorporating RWD/RWE in HTA yields multifaceted advantages, enhancing understanding of treatment efficacy, resource utilization, and cost analysis, particularly via patient registries. RWE complements assessments of advanced therapy medicinal products (ATMPs) and rare diseases. Local data utilization strengthens HTA, bridging gaps when RCT data is lacking. RWD aids medical device decision-making, cancer drug reassessment, and indirect treatment comparisons. Challenges include data availability, stakeholder acceptance, expertise, and privacy. However, standardization, training, collaboration, and guidance can surmount these barriers, fostering enhanced RWD utilization in HTA.Conclusion: This study highlights the intricate global landscape of RWD and RWE acceptance in HTA. Recognizing regional nuances, addressing methodological challenges, and promoting collaboration are pivotal, among others, for leveraging RWD and RWE effectively in healthcare decision-making.

This data contains the current status of the information education program promoted by the Nam-gu Office of Daegu Metropolitan City, and its purpose is to provide educational opportunities for improving citizens' digital capabilities and resolving the information gap, and to improve accessibility and convenience of educational information. The provided items include the course name, start date, end date, number of lecture days, lecture start time, lecture end time, course registration start date, lecture building, lecture room floor, lecture room address, latitude, and longitude. This data contributes to increasing the participation rate in information education and improving the efficiency of public service use by providing customized information centered on education demanders.

Introduction and Rationale: Due to our increasing understanding of the role the surrounding landscape plays in ecological processes, a detailed characterization of land cover, including both agricultural and natural habitats, is ever more important for both researchers and conservation practitioners. Unfortunately, in the United States, different types of land cover data are split across thematic datasets that emphasize agricultural or natural vegetation, but not both. To address this data gap and reduce duplicative efforts in geospatial processing, we merged two major datasets, the LANDFIRE National Vegetation Classification (NVC) and USDA-NASS Cropland Data Layer (CDL), to produce an integrated land cover map. Our workflow leveraged strengths of the NVC and the CDL to produce detailed rasters comprising both agricultural and natural land-cover classes. We generated these maps for each year from 2012-2021 for the conterminous United States, quantified agreement between input layers and accuracy of our merged product, and published the complete workflow necessary to update these data. In our validation analyses, we found that approximately 5.5% of NVC agricultural pixels conflicted with the CDL, but we resolved a majority of these conflicts based on surrounding agricultural land, leaving only 0.6% of agricultural pixels unresolved in our merged product. Contents: Spatial data

Attribute table for merged rasters

Technical validation data

Number and proportion of mismatched pixels Number and proportion of unresolved pixels Producer's and User's accuracy values and coverage of reference data Resources in this dataset:Resource Title: Attribute table for merged rasters. File Name: CombinedRasterAttributeTable_CDLNVC.csvResource Description: Raster attribute table for merged raster product. Class names and recommended color map were taken from USDA-NASS Cropland Data Layer and LANDFIRE National Vegetation Classification. Class values are also identical to source data, except classes from the CDL are now negative values to avoid overlapping NVC values. Resource Title: Number and proportion of mismatched pixels. File Name: pixel_mismatch_byyear_bycounty.csvResource Description: Number and proportion of pixels that were mismatched between the Cropland Data Layer and National Vegetation Classification, per year from 2012-2021, per county in the conterminous United States.Resource Title: Number and proportion of unresolved pixels. File Name: unresolved_conflict_byyear_bycounty.csvResource Description: Number and proportion of unresolved pixels in the final merged rasters, per year from 2012-2021, per county in the conterminous United States. Unresolved pixels are a result of mismatched pixels that we could not resolve based on surrounding agricultural land (no agriculture with 90m radius).Resource Title: Producer's and User's accuracy values and coverage of reference data. File Name: accuracy_datacoverage_byyear_bycounty.csvResource Description: Producer's and User's accuracy values and coverage of reference data, per year from 2012-2021, per county in the conterminous United States. We defined coverage of reference data as the proportional area of land cover classes that were included in the reference data published by USDA-NASS and LANDFIRE for the Cropland Data Layer and National Vegetation Classification, respectively. CDL and NVC classes with reference data also had published accuracy statistics. Resource Title: Data Dictionary. File Name: Data_Dictionary_RasterMerge.csv

This data set is time series electricity use data from rural households using off-grid energy systems in Kenya. As well as indicating lighting electricity use for a real-world use case, it can give insight into active occupancy times in the mornings and evenings. This can support estimation of load profiles for higher tiers of the Multi-tier Framework for energy access by adding in load profiles for additional appliances.

Two solar nano-grids (SONGs) were built in two rural communities in Kenya, as part of the Solar Nano-grids project (EPSRC ref: EP/L002612/1). One aspect of the SONGs were battery-charging systems, in which batteries could be charged at a central solar hub, and used in households to power lighting and mobile phone charging. For each battery the electricity use was recorded in real-time between July 2016 and November 2016 inclusive.

The data consist of separate demand (use of battery in the home for lighting) and charging (charging at the central hub) profiles in csv files, individually for each household. The data are half-hourly measurements of average power used for the household lighting system (3 3W LED bulbs with wiring and switches). There is data for 51 households, ranging in length from 3 days to 5 months. Note that the data set is solely electricity use for the household lighting system, and does not include electricity use via the USB port that was present for charging mobile phones. The households are anonymised and are numbered in order of ascending number of days of data.

The household battery packs were Li-ion with capacity 62 Wh, and the data were recorded using a FRDM K-64F mbed embedded in each. 13 post-processing steps were required to process the data gathered in raw form from the batteries into energy profiles for individual households (see reference below). These included: correcting the timestamps caused by time drift or recalibration of the RTCs, attributing batteries to the correct household, addressing logging disruptions and inconsistent logging frequencies, imposing limits on power and duration of use to remove non-representative battery use, and testing loading conditions to remove abnormal energy use. The gaps in the data and varying lengths of the data are caused by: technical challenges with the batteries, meaning that they required frequent repairing; issues with the RTC on the microcontroller being reset; difficulty in attributing data to the correct household. Between 18th July - 1st August (approx.), the charging hub was shut down and so there is a gap in all energy profiles.

Graphical representations of the data for each household, and further information about the solar nano-grids project, the energy data, and the processing steps involved, can be found in Clements, A F. Data-driven approaches enabling the design of community energy systems in the Global South. DPhil Thesis. Department of Engineering Science, University of Oxford. 2019.

Consumption of antibiotics in food animals is increasing worldwide and is approaching, if not already surpassing, the volume consumed by humans. It is often suggested that reducing the volume of antibiotics consumed by food animals could have public health benefits. Although this notion is widely regarded as intuitively obvious there is a lack of robust, quantitative evidence to either support or contradict the suggestion. As a first step towards addressing this knowledge gap, we develop a simple mathematical model for exploring the generic relationship between antibiotic consumption by food animals and levels of resistant bacterial infections in humans. We investigate the impact of restricting antibiotic consumption by animals and identify which model parameters most strongly determine that impact. Our results suggest that, for a wide range of scenarios, curtailing the volume of antibiotics consumed by food animals has, as a stand-alone measure, little impact on the level of resistance...

The software-defined data center (SDDC) industry is experiencing robust growth, with a market size of xx million in 2023 and a projected CAGR of 26.60% from 2025 to 2033. Key drivers of this growth include the increasing adoption of cloud computing, the need for greater agility and flexibility in data center operations, and the growing popularity of software-defined networking (SDN), software-defined storage (SDS), and software-defined computing (SDC). The SDDC industry is highly competitive, with major players such as IBM, Hewlett Packard Enterprise, Microsoft, NEC, Huawei, Oracle, Cisco, Dell EMC, VMware, and Citrix. These companies offer a wide range of SDDC solutions and services, including SDN, SDS, SDC, and managed services. The industry is also characterized by a growing number of startups that are developing innovative SDDC technologies and solutions. Key trends in the SDDC industry include the increasing adoption of hybrid cloud and multi-cloud environments, the growing use of artificial intelligence (AI) and machine learning (ML) in SDDC management, and the development of new SDDC security solutions. The Software Defined Data Centers (SDDC) industry is witnessing rapid growth, driven by the increasing adoption of cloud computing, virtualization, and the need for greater flexibility and agility in data center operations. The global SDDC market size is projected to reach USD 150.95 Billion by 2028, exhibiting a CAGR of 17.9% during the forecast period (2023-2028). Recent developments include: July 2022 -DartPoints, a cutting-edge digital infrastructure provider, has announced a groundbreaking technical collaboration with the University of South Carolina. DartPoints will deliver a customized Software-Defined Data Center (SDDC) solution to replace the university's existing data center., August 2022 - VMware Explore 2022, VMware Aria, a multi-cloud management portfolio, delivers a collection of end-to-end solutions for controlling the cost, performance, configuration, and delivery of infrastructure and cloud-native apps. VMware Aria is powered by VMware Aria Graph, a graph-based data store that captures the complexity of customers' multi-cloud environments., January 2023 - Rackspace Technology, a leading provider of end-to-end multi-cloud technology solutions, has launched Rackspace Technology Software-Defined Data Center (SDDC) Rackspace SDDC Enterprise, Rackspace SDDC Business, and Rackspace SDDC Flex. These new products will give enterprises specialized solutions to bridge the gap between the cloud and data centers.. Key drivers for this market are: Cost Reduction in Hardware and Other Resources is Driving the Growth of the Market.. Potential restraints include: Data Security While Deploying SDDC is a Major Challenge. Notable trends are: Software-Defined Storage to Dominate the Market.

Introduction

The English Healthcare Chat Dataset is a rich collection of over 12,000 text-based conversations between customers and call center agents, focused on real-world healthcare interactions. Designed to reflect authentic language use and domain-specific dialogue patterns, this dataset supports the development of conversational AI, chatbots, and NLP models tailored for healthcare applications in English-speaking regions.

Participant & Chat Overview

Topic Diversity

The dataset captures a wide spectrum of healthcare-related chat scenarios, ensuring comprehensive coverage for training robust AI systems:

This variety helps simulate realistic healthcare support workflows and patient-agent dynamics.

Language Diversity & Realism

This dataset reflects the natural flow of English healthcare communication and includes:

These elements ensure the dataset is contextually relevant and linguistically rich for real-world use cases.

Conversational Flow & Structure

Conversations range from simple inquiries to complex advisory sessions, including:

Each conversation typically includes these structural components:

This structured flow mirrors actual healthcare support conversations and is ideal for training advanced dialogue systems.

Data Format & Structure

Available in JSON, CSV, and TXT formats, each conversation includes:

Applications

For questions about this data please contact ITOpenData@minneapolismn.gov2014 Minneapolis Community Technology Survey Data

Thanks to the 3,015 residents for their participation, the third year's results are in on a survey the City of Minneapolis conducted to understand how Minneapolis residents use computers, mobile devices and the Internet. Access to computers and the Internet, along with the skills to use these tools is critical as technology becomes more and more a part of our daily lives and is integrated in our economic, educational, health, and workforce systems. The results will inform priorities for the City’s digital inclusion initiatives, and help engage businesses, neighborhood and community groups, public sector partners, and funders to more effectively address community technology and economic development needs. In addition, the survey provides data to measure changes in the community over time.

The City of Minneapolis Information Technology Department contracted with National Research Center, Inc. (NRC) to conduct a survey of residents to inform the City’s efforts to overcome the digital equity gap between individuals and groups in their access to and use and knowledge of information and communication technologies. This is the third iteration of the Minneapolis Community Technology Survey; the first was conducted in 2012 and the second in 2013.Summary of Data Fields:Field 1 – Overall percentage of respondents who have lived in Minneapolis for 5 years or less by community and user levelField 2 – Overall percentage of foreign-born respondents by community and user levelField 3 – Overall percentage of respondents who rent their homes by community and user levelField 4 – Overall percentage of respondents who live in attached homes by community and user levelField 5 – Overall percentage of respondents living in households with three or more people by community and user levelField 6 – Overall percentage of respondents living in households with children under the age of 18 by community and user levelField 7 – Overall percentage of female respondents by community and user levelField 8 – Overall percentage of respondents aged 55 years or older by community and user levelField 9 – Overall percentage of respondents who are hispanic and/or any race other than white by community and user levelField 10 – Overall percentage of respondents who prefer to speak a language other than English at home by community and user levelField 11 – Overall percentage of respondents having annual household incomes of less than $50,000 by community and user levelField 12 – Overall percentage of respondents who do not work full- or part-time by community and user level

Field 13 – Overall percentage of respondents who do not have a 4-year degree by community and user level

Full data set (Raw data and data dictionary in Excel format)

The workbook has two tabs, the first is the data dictionary that is needed to translate the data; the second is the raw data.

See data summarized in a variety of formats at: http://www.minneapolismn.gov/it/inclusion/WCMS1P-118865

For additional details about the survey, the survey questionnaire, methodology and more, see: http://www.minneapolismn.gov/it/inclusion/WCMS1P-118865 or contact: Elise Ebhardt, 612-673-2026, City of Minneapolis IT Department

See also: 2012 and 2013 survey results

The City's IT Vision includes a component for addressing the digital divide in Minneapolis: All City residents, institutions and businesses will have the tools, skills and motivation to gain value from the digital society. Our residents and businesses need to be equipped to effectively compete with others around the world—to be smarter, more creative, more knowledgeable, and more innovative. Leveraging technology is a necessary ingredient of success.

Introduction

The dataset comprises over 12,000 chat conversations, each focusing on specific Healthcare related topics. Each conversation provides a detailed interaction between a call center agent and a customer, capturing real-life scenarios and language nuances.

Topic Diversity

The chat dataset covers a wide range of conversations on Healthcare topics, ensuring that the dataset is comprehensive and relevant for training and fine-tuning models for various Healthcare use cases. It offers diversity in terms of conversation topics, chat types, and outcomes, including both inbound and outbound chats with positive, neutral, and negative outcomes.

Language Variety & Nuances

The conversations in this dataset capture the diverse language styles and expressions prevalent in Punjabi Healthcare interactions. This diversity ensures the dataset accurately represents the language used by Punjabi speakers in Healthcare contexts.

The dataset encompasses a wide array of language elements, including:

This linguistic authenticity ensures that the dataset equips researchers and developers with a comprehensive understanding of the intricate language patterns, cultural references, and communication styles inherent to Punjabi Healthcare interactions.

Conversational Flow and Interaction Types

The dataset includes a broad range of conversations, from simple inquiries to detailed discussions, capturing the dynamic nature of Healthcare customer-agent interactions.

Each of these conversations contains various aspects of conversation flow like:

This structured and varied conversational flow enables the creation of advanced NLP models that can effectively manage and respond to a wide range of customer service scenarios.

Data Format and Structure

The dataset is available in JSON, CSV, and TXT formats, with each conversation containing attributes like participant identifiers and chat

Gap-coding permits the use of continuous metric characters in cladistic analyses. Character means are converted to integer equivalents by placing character state divisions in the locations of phenetic breaks between specimen clusters, under the assumption that these breaks represent the locations of bottlenecks in character distributions. Similarities and differences between specimens from closely related species of cystoporate bryozoans were evaluated for the first time by converting continuous morphometric measurements into gap-coded binary and multistate characters and analyzing them cladistically, rather than just phenetically, across multiple species of Strotopora, Cliotrypa ramosa and Fistulipora compressa. Our results demonstrate that cladistic analysis of gap-coded morphological characters can be effective in resolving phylogenetic relationships at low taxonomic levels (within and among genera) while objectively highlighting both the morphological features that specimens (taxa) share and those characteristics that differentiate them. Differences in cystiphragm abundances and sizes, especially in the proximal portions of colonies, discriminate between species of Strotopora. Colony size and growth form, abundances and lengths of hemiphragms, and sizes of cystopores discriminate between Strotopora and the closely related genus Cliotrypa. Cladistic patterns indicate that Strotopora foveolata Ulrich is a valid species with Strotopora dermata as its junior subjective synonym. Fistulipora compressa is reassigned to the genus Strotopora whereas a decision on the taxonomic status of Cliotrypa ramosa requires a broader cladistic analysis of fistuliporine genera.

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Artificial Intelligence (AI) In Food And Beverage Industry Market Size 2025-2029

The artificial intelligence (AI) in food and beverage industry market size is forecast to increase by USD 32.2 billion, at a CAGR of 34.5% between 2024 and 2029.

The Artificial Intelligence (AI) market in the Food and Beverage industry is witnessing significant growth, driven by the rising demand for automation to enhance productivity and streamline operations. The integration of Industrial Internet of Things (IIoT) in food and beverage processing is a key trend, enabling real-time monitoring and predictive maintenance, leading to improved efficiency and quality. However, the lack of skilled personnel poses a significant challenge in implementing and managing AI technologies, necessitating investments in training and development programs.
Companies in the food and beverage sector seeking to capitalize on the opportunities presented by AI must focus on addressing this talent gap while also ensuring compliance with data security regulations and ethical considerations in the use of AI technologies. Effective collaboration between industry players, academia, and governments can help bridge the skills gap and foster innovation in the sector.

What will be the Size of the Artificial Intelligence (AI) In Food And Beverage Industry Market during the forecast period?

Explore in-depth regional segment analysis with market size data - historical 2019-2023 and forecasts 2025-2029 - in the full report.
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The food and beverage industry continues to experience dynamic market activities, driven by the integration of artificial intelligence (AI) technologies. From recipe development to production efficiency, AI applications span various sectors, shaping the industry's evolving landscape. Robotics and automation streamline processes, ensuring consistent product quality and reducing labor costs. Smart packaging with embedded sensors monitors food freshness and safety, enhancing consumer trust. AI-driven trend forecasting and social media marketing strategies help businesses stay competitive. Deep learning models optimize ingredient usage, improve demand forecasting, and enable personalized nutrition recommendations. Computer vision algorithms facilitate image recognition for food labeling regulations and allergen detection.

AI-powered sensory analysis refines flavor profiling and dietary recommendations. Sustainability reporting, precision fermentation, and food waste reduction are key areas where AI contributes to industry innovation. Business model development and supply chain management are optimized through AI-driven data analytics platforms and e-commerce solutions. AI's role in the food and beverage industry extends to food safety, consumer insights, and competitive landscape analysis. Food fraud detection and cloud-based solutions further enhance transparency and efficiency. The continuous integration of AI technologies promises a future of smart, sustainable, and personalized food production and delivery.

How is this Artificial Intelligence (AI) In Food And Beverage Industry Industry segmented?

The artificial intelligence (AI) in food and beverage industry industry research report provides comprehensive data (region-wise segment analysis), with forecasts and estimates in 'USD million' for the period 2025-2029, as well as historical data from 2019-2023 for the following segments.

Type

  Transportation and logistics
  Production planning
  Quality control
  Others


End-user

  Food processing industry
  Hotels and restaurants
  Beverage industry


Geography

  North America

    US
    Canada


  Europe

    France
    Germany
    Italy
    UK


  APAC

    China
    India
    Japan
    South Korea


  Rest of World (ROW)

.

By Type Insights

The transportation and logistics segment is estimated to witness significant growth during the forecast period.

In the food and beverage industry, automation is becoming a key trend as players seek to optimize operations and improve production efficiency. This is particularly evident in intralogistics, where manufacturers, beverage wholesalers, breweries, and bottling plants are employing advanced technologies such as machine vision systems, robotics, and automation to streamline their warehousing and distribution processes. The need for flexibility and swift returns processing is also driving demand for these solutions. The transportation and logistics segment of the global AI market in food and beverage industry is poised for growth, with manufacturers investing in precision fermentation, deep learning models, and other advanced technologies to enhance their manufacturing processes.

The emergence of digitization and new business models is bringing about a paradigm shift in the industry. Food labeling regulations and product traceability are also major considerations for player

Introduction

The Vietnamese Healthcare Chat Dataset is a rich collection of over 10,000 text-based conversations between customers and call center agents, focused on real-world healthcare interactions. Designed to reflect authentic language use and domain-specific dialogue patterns, this dataset supports the development of conversational AI, chatbots, and NLP models tailored for healthcare applications in Vietnamese-speaking regions.

Participant & Chat Overview

Topic Diversity

The dataset captures a wide spectrum of healthcare-related chat scenarios, ensuring comprehensive coverage for training robust AI systems:

This variety helps simulate realistic healthcare support workflows and patient-agent dynamics.

Language Diversity & Realism

This dataset reflects the natural flow of Vietnamese healthcare communication and includes:

These elements ensure the dataset is contextually relevant and linguistically rich for real-world use cases.

Conversational Flow & Structure

Conversations range from simple inquiries to complex advisory sessions, including:

Each conversation typically includes these structural components:

This structured flow mirrors actual healthcare support conversations and is ideal for training advanced dialogue systems.

Data Format & Structure

Available in JSON, CSV, and TXT formats, each conversation includes:

International Journal of Engineering and Advanced Technology Publication fee - ResearchHelpDesk - International Journal of Engineering and Advanced Technology (IJEAT) is having Online-ISSN 2249-8958, bi-monthly international journal, being published in the months of February, April, June, August, October, and December by Blue Eyes Intelligence Engineering & Sciences Publication (BEIESP) Bhopal (M.P.), India since the year 2011. It is academic, online, open access, double-blind, peer-reviewed international journal. It aims to publish original, theoretical and practical advances in Computer Science & Engineering, Information Technology, Electrical and Electronics Engineering, Electronics and Telecommunication, Mechanical Engineering, Civil Engineering, Textile Engineering and all interdisciplinary streams of Engineering Sciences. All submitted papers will be reviewed by the board of committee of IJEAT. Aim of IJEAT Journal disseminate original, scientific, theoretical or applied research in the field of Engineering and allied fields. dispense a platform for publishing results and research with a strong empirical component. aqueduct the significant gap between research and practice by promoting the publication of original, novel, industry-relevant research. seek original and unpublished research papers based on theoretical or experimental works for the publication globally. publish original, theoretical and practical advances in Computer Science & Engineering, Information Technology, Electrical and Electronics Engineering, Electronics and Telecommunication, Mechanical Engineering, Civil Engineering, Textile Engineering and all interdisciplinary streams of Engineering Sciences. impart a platform for publishing results and research with a strong empirical component. create a bridge for a significant gap between research and practice by promoting the publication of original, novel, industry-relevant research. solicit original and unpublished research papers, based on theoretical or experimental works. Scope of IJEAT International Journal of Engineering and Advanced Technology (IJEAT) covers all topics of all engineering branches. Some of them are Computer Science & Engineering, Information Technology, Electronics & Communication, Electrical and Electronics, Electronics and Telecommunication, Civil Engineering, Mechanical Engineering, Textile Engineering and all interdisciplinary streams of Engineering Sciences. The main topic includes but not limited to: 1. Smart Computing and Information Processing Signal and Speech Processing Image Processing and Pattern Recognition WSN Artificial Intelligence and machine learning Data mining and warehousing Data Analytics Deep learning Bioinformatics High Performance computing Advanced Computer networking Cloud Computing IoT Parallel Computing on GPU Human Computer Interactions 2. Recent Trends in Microelectronics and VLSI Design Process & Device Technologies Low-power design Nanometer-scale integrated circuits Application specific ICs (ASICs) FPGAs Nanotechnology Nano electronics and Quantum Computing 3. Challenges of Industry and their Solutions, Communications Advanced Manufacturing Technologies Artificial Intelligence Autonomous Robots Augmented Reality Big Data Analytics and Business Intelligence Cyber Physical Systems (CPS) Digital Clone or Simulation Industrial Internet of Things (IIoT) Manufacturing IOT Plant Cyber security Smart Solutions – Wearable Sensors and Smart Glasses System Integration Small Batch Manufacturing Visual Analytics Virtual Reality 3D Printing 4. Internet of Things (IoT) Internet of Things (IoT) & IoE & Edge Computing Distributed Mobile Applications Utilizing IoT Security, Privacy and Trust in IoT & IoE Standards for IoT Applications Ubiquitous Computing Block Chain-enabled IoT Device and Data Security and Privacy Application of WSN in IoT Cloud Resources Utilization in IoT Wireless Access Technologies for IoT Mobile Applications and Services for IoT Machine/ Deep Learning with IoT & IoE Smart Sensors and Internet of Things for Smart City Logic, Functional programming and Microcontrollers for IoT Sensor Networks, Actuators for Internet of Things Data Visualization using IoT IoT Application and Communication Protocol Big Data Analytics for Social Networking using IoT IoT Applications for Smart Cities Emulation and Simulation Methodologies for IoT IoT Applied for Digital Contents 5. Microwaves and Photonics Microwave filter Micro Strip antenna Microwave Link design Microwave oscillator Frequency selective surface Microwave Antenna Microwave Photonics Radio over fiber Optical communication Optical oscillator Optical Link design Optical phase lock loop Optical devices 6. Computation Intelligence and Analytics Soft Computing Advance Ubiquitous Computing Parallel Computing Distributed Computing Machine Learning Information Retrieval Expert Systems Data Mining Text Mining Data Warehousing Predictive Analysis Data Management Big Data Analytics Big Data Security 7. Energy Harvesting and Wireless Power Transmission Energy harvesting and transfer for wireless sensor networks Economics of energy harvesting communications Waveform optimization for wireless power transfer RF Energy Harvesting Wireless Power Transmission Microstrip Antenna design and application Wearable Textile Antenna Luminescence Rectenna 8. Advance Concept of Networking and Database Computer Network Mobile Adhoc Network Image Security Application Artificial Intelligence and machine learning in the Field of Network and Database Data Analytic High performance computing Pattern Recognition 9. Machine Learning (ML) and Knowledge Mining (KM) Regression and prediction Problem solving and planning Clustering Classification Neural information processing Vision and speech perception Heterogeneous and streaming data Natural language processing Probabilistic Models and Methods Reasoning and inference Marketing and social sciences Data mining Knowledge Discovery Web mining Information retrieval Design and diagnosis Game playing Streaming data Music Modelling and Analysis Robotics and control Multi-agent systems Bioinformatics Social sciences Industrial, financial and scientific applications of all kind 10. Advanced Computer networking Computational Intelligence Data Management, Exploration, and Mining Robotics Artificial Intelligence and Machine Learning Computer Architecture and VLSI Computer Graphics, Simulation, and Modelling Digital System and Logic Design Natural Language Processing and Machine Translation Parallel and Distributed Algorithms Pattern Recognition and Analysis Systems and Software Engineering Nature Inspired Computing Signal and Image Processing Reconfigurable Computing Cloud, Cluster, Grid and P2P Computing Biomedical Computing Advanced Bioinformatics Green Computing Mobile Computing Nano Ubiquitous Computing Context Awareness and Personalization, Autonomic and Trusted Computing Cryptography and Applied Mathematics Security, Trust and Privacy Digital Rights Management Networked-Driven Multicourse Chips Internet Computing Agricultural Informatics and Communication Community Information Systems Computational Economics, Digital Photogrammetric Remote Sensing, GIS and GPS Disaster Management e-governance, e-Commerce, e-business, e-Learning Forest Genomics and Informatics Healthcare Informatics Information Ecology and Knowledge Management Irrigation Informatics Neuro-Informatics Open Source: Challenges and opportunities Web-Based Learning: Innovation and Challenges Soft computing Signal and Speech Processing Natural Language Processing 11. Communications Microstrip Antenna Microwave Radar and Satellite Smart Antenna MIMO Antenna Wireless Communication RFID Network and Applications 5G Communication 6G Communication 12. Algorithms and Complexity Sequential, Parallel And Distributed Algorithms And Data Structures Approximation And Randomized Algorithms Graph Algorithms And Graph Drawing On-Line And Streaming Algorithms Analysis Of Algorithms And Computational Complexity Algorithm Engineering Web Algorithms Exact And Parameterized Computation Algorithmic Game Theory Computational Biology Foundations Of Communication Networks Computational Geometry Discrete Optimization 13. Software Engineering and Knowledge Engineering Software Engineering Methodologies Agent-based software engineering Artificial intelligence approaches to software engineering Component-based software engineering Embedded and ubiquitous software engineering Aspect-based software engineering Empirical software engineering Search-Based Software engineering Automated software design and synthesis Computer-supported cooperative work Automated software specification Reverse engineering Software Engineering Techniques and Production Perspectives Requirements engineering Software analysis, design and modelling Software maintenance and evolution Software engineering tools and environments Software engineering decision support Software design patterns Software product lines Process and workflow management Reflection and metadata approaches Program understanding and system maintenance Software domain modelling and analysis Software economics Multimedia and hypermedia software engineering Software engineering case study and experience reports Enterprise software, middleware, and tools Artificial intelligent methods, models, techniques Artificial life and societies Swarm intelligence Smart Spaces Autonomic computing and agent-based systems Autonomic computing Adaptive Systems Agent architectures, ontologies, languages and protocols Multi-agent systems Agent-based learning and knowledge discovery Interface agents Agent-based auctions and marketplaces Secure mobile and multi-agent systems Mobile agents SOA and Service-Oriented Systems Service-centric software engineering Service oriented requirements engineering Service oriented architectures Middleware for service based systems Service discovery and composition Service level

The Full Process Data Engineering Services market is experiencing robust growth, driven by the increasing demand for data-driven decision-making across diverse sectors. The convergence of Big Data, Artificial Intelligence (AI), and the Internet of Things (IoT) is fueling this expansion, as organizations grapple with ever-larger and more complex datasets. The market is segmented by application (Business Intelligence, AI, IoT) and by type of service (ETL, ELT, EL), with ETL currently holding the largest market share due to its established presence and familiarity. However, ELT is rapidly gaining traction due to its ability to handle the velocity and volume of modern data streams more efficiently. Major cloud providers like AWS, Azure, Google Cloud, and others are significantly impacting the market, offering scalable and cost-effective solutions. Geographic distribution reveals North America and Europe as dominant regions, though the Asia-Pacific region shows significant growth potential, driven by increasing digitalization and adoption of cloud-based services in emerging economies like India and China. Competition is fierce, with established players like IBM, Microsoft, and Oracle competing against agile cloud-native companies. Future growth will be influenced by factors such as advancements in data management technologies, increasing cybersecurity concerns, and the ongoing skills gap in data engineering. The forecast period of 2025-2033 anticipates sustained expansion, propelled by continued digital transformation across various industries. The adoption of advanced analytics, real-time data processing, and the increasing use of data for operational efficiency will contribute to market growth. However, challenges remain, including data integration complexities, the need for skilled data engineers, and the costs associated with implementing and maintaining data engineering infrastructure. Addressing these challenges through strategic partnerships, investment in training, and development of robust data governance frameworks will be crucial for sustained market growth. The competitive landscape will likely see further consolidation, with larger players acquiring smaller firms to expand their service offerings and geographical reach. The ongoing development and refinement of automation tools within data engineering will streamline processes and reduce operational costs, impacting market dynamics.

Effective hydrologic-hydraulic model development such as U.S. Environmental Protection Agency’s Storm Water Management Model (SWMM) depends on the data availability and data completeness of as-built stormwater infrastructure data. The infrastructure data gaps affect accurate process representation in model causing output uncertainty, error and bias, which further affect model construction, parameterization and its reliable use. However, complete stormwater infrastructure data are often not available due to data sharing restrictions or data gaps occurring from errors of omission (i.e., infrastructure components not being recorded) and error of commission (i.e., assignment of incorrect data). This algorithm, created in R, fills the missing stormwater infrastructure attribute-values data in accordance with the available design standards and modeling practice. It can be adopted to fill missing stormwater infrastructure attributes data for any size of SWMM model. This algorithm can also be implemented to randomly sample, using Monte Carlo sampling approach, the effects of missing attribute-values for different parameters of conduits and junctions such as diameter, roughness and depth.

For details about this work readers are referred to:

1). Shrestha, A., Mascaro, G., & Garcia, M. (2022). Effects of stormwater infrastructure data completeness and model resolution on urban flood modeling. Journal of Hydrology, 607, 127498. https://doi.org/10.1016/j.jhydrol.2022.127498 2). Shrestha, A. (2022). Advances in Urban Flood Management: Addressing Data Uncertainty, Data Gaps and Adaptation Planning (Doctoral dissertation, Arizona State University). https://search.proquest.com/openview/b79c1eb133e93ea0a07b6147fe7feff6/1?pq-origsite=gscholar&cbl=18750&diss=y

For GitHub link to this repository, readers are referred to: 1). https://github.com/ashish-shrs/filling_missing_data_for_swmm/tree/main

The USGS Geomagnetism Program operates a network of magnetic observatories that collect vector and scalar magnetometer data for use in Earth main-field modeling, geophysics research, space physics research, and space weather hazard assessment and mitigation. Until mid-2011, only 1-minute time resolution magnetic field measurements were archived with the INTERMAGNET consortium following international magnetic observatory standards. 1-second time resolution magnetic field measurements, which had already been collected by all the USGS observatories for up to almost a decade prior, started being archived with INTERMAGNET on June 13, 2011, or July 27, 2012 in the case of the more recently constructed Deadhorse (DED) magnetic observatory. This data release contains 1-second time resolution magnetic field measurements collected up through the end of 2012, after which time 1-second data from USGS magnetic observatories may be obtained from INTERMAGNET. There is some overlap between data in this release and those data archived with INTERMAGNET. Any discrepancies that may exist between these two data sources should resolve in favor of INTERMAGNET. SHU-specific notes: - there are significant gaps in the 2003 data, most notably in late November through December - there are significant gaps in the 2004 data, most notably in January and February - some filenames originally possessing a ".sec" extension were renamed to ".raw"

1.Increasing demand for benefits provided by riverine ecosystems threatens their sustainable provision. The ecosystem service concept is a promising avenue to inform riverine ecosystem management, but several challenges have prevented the application of this concept.

2.We quantitatively assess the field of riverine ecosystem services’ progress in meeting these challenges. We highlight conceptual and methodological gaps, which have impeded integration of the ecosystem service concept into management.

3.Across 89 relevant studies, 33 unique riverine ecosystem services were evaluated, for a total of 404 ecosystem service quantifications. Studies quantified between one and 23 ecosystem services, although the majority (55%) evaluated three or less. Among studies that quantified more than one service, 58% assessed interactions between services. Most studies (71%) did not include stakeholders in their quantification protocols, and 34% developed future scenarios of ecosystem service provision. Almost half (45%) conducted monetary valuation, using 16 methods. Only 9% did not quantify or discuss uncertainties associated with service quantification. The indicators and methods used to quantify the same type of ecosystem service varied. Only 3% of services used indicators of capacity, flow, and demand in concert.

4.Our results suggest indicators, data sources, and methods for quantifying riverine ecosystem services should be more clearly defined and accurately represent the service they intend to quantify. Furthermore, more assessments of multiple services across diverse spatial extents and of riverine service interactions are needed, with better inclusion of stakeholders. Addressing these challenges will help riverine ecosystem service science inform river management.

5.Synthesis and applications. The ecosystem service concept has great potential to inform riverine ecosystem management and decision making processes. However, this review of riverine ecosystem service quantification uncovers several remaining research gaps, impeding effective use of this tool to manage riverine ecosystems. We highlight these gaps and point to studies showcasing methods that can be used to address them.

The USGS Geomagnetism Program operates a network of magnetic observatories that collect vector and scalar magnetometer data for use in Earth main-field modeling, geophysics research, space physics research, and space weather hazard assessment and mitigation. Until mid-2011, only 1-minute time resolution magnetic field measurements were archived with the INTERMAGNET consortium following international magnetic observatory standards. 1-second time resolution magnetic field measurements, which had already been collected by all the USGS observatories for up to almost a decade prior, started being archived with INTERMAGNET on June 13, 2011, or July 27, 2012 in the case of the more recently constructed Deadhorse (DED) magnetic observatory. This data release contains 1-second time resolution magnetic field measurements collected up through the end of 2012, after which time 1-second data from USGS magnetic observatories may be obtained from INTERMAGNET. There is some overlap between data in this release and those data archived with INTERMAGNET. Any discrepancies that may exist between these two data sources should resolve in favor of INTERMAGNET. FRD-specific notes: - there are significant gaps in the 2006 data, most notably in January, February, and March - there are significant gaps in the 2007 data, most notably in July, August, September, october, November, and December - some filenames originally possessing with a ".sec" extension were renamed to ".raw"

We are opening data on information education conducted to resolve the information gap of the information-deprived class. The opened data provides information such as the course name, training period, number of students, level and period, application period, number of course training days, operating autonomous body, and address, and provides information on 1,112 courses of information education operated in 16 districts and counties including Yeongdo-gu 1, Buk-gu 4, Busanjin-gu 6, Sasang-gu 10, Geumjeong-gu 12, Jung-gu 22, Nam-gu 23, Suyeong-gu 30, Haeundae-gu 40, Saha-gu 41, Dong-gu 46, Yeonje-gu 50, Gangseo-gu 109, Seo-gu 29, Gijang-gun 230, and Dongrae-gu 279. Receive information education necessary for real life using computers and mobile devices, such as YouTube, computer basics, smartphone education, mobile usage, ITQ certification, artificial intelligence, and administrative service use. *As of May 19, 2025

The global market for digital services in cardiovascular and cerebrovascular health is experiencing robust growth, driven by the increasing prevalence of cardiovascular diseases, rising adoption of wearable technology, and the expanding availability of remote patient monitoring solutions. The market, estimated at $15 billion in 2025, is projected to exhibit a Compound Annual Growth Rate (CAGR) of 15% from 2025 to 2033, reaching approximately $50 billion by 2033. Key drivers include the growing geriatric population, technological advancements leading to more accurate and accessible diagnostic tools, and increasing demand for convenient and cost-effective healthcare solutions. The healthcare industry is a major segment, followed by the insurance and pharmaceutical sectors, which benefit from improved risk stratification and patient management. Personal health management services constitute a significant portion of the market, fueled by the rising consumer preference for self-monitoring and proactive health management. Competitive landscape is dynamic, with established players like Philips and Omron, alongside innovative tech companies like Apple and AliveCor, vying for market share through continuous product development and strategic partnerships. The market's growth is also shaped by several trends. The integration of artificial intelligence (AI) and machine learning (ML) in diagnostic tools and predictive analytics is enhancing the accuracy and efficiency of cardiovascular risk assessment. Furthermore, the increasing adoption of telehealth and remote monitoring platforms facilitates better patient care and reduces healthcare costs. However, challenges persist, including data privacy and security concerns, regulatory hurdles in data usage and interoperability across different platforms, and the digital literacy gap among certain populations, particularly in developing countries. Addressing these challenges will be crucial to fully realizing the potential of digital services in improving cardiovascular and cerebrovascular health outcomes globally. North America currently holds the largest market share due to advanced healthcare infrastructure and high technology adoption rates, but Asia-Pacific is expected to witness significant growth in the coming years.