Advances In Information Technology Research Articles

The use of telepathology in veterinary medicine: a scoping review.

Telepathology, as a subset of teleconsulting, is pathology interpretation performed at a distance. Telepathology is not a new phenomenon, but since ~2015, significant advances in information technology and telecommunications coupled with the pandemic have led to unprecedented sophistication, accessibility, and use of telepathology in human and veterinary medicine. Furthermore, telepathology can connect veterinary practices to distant laboratories and provide support for underserved animals and communities. Through our scoping review, we provide an overview of how telepathology is being used in veterinary medicine, identify gaps in the literature, and highlight future areas of research and service development. We searched MEDLINE, CAB Abstracts, and the gray literature, and included all relevant literature. Despite the widespread use of digital microscopy in large veterinary diagnostic laboratories, we identified a paucity of literature describing the use of telepathology in veterinary medicine, with a significant gap in studies addressing the validation of whole-slide imaging for primary diagnosis. Underutilization of telepathology to support postmortem examinations conducted in the field was also identified, which indicates a potential area for service development. The use of telepathology is increasing in veterinary medicine, and pathologists must keep pace with the changing technology, ensure the validation of innovative technologies, and identify novel uses to advance the profession.

Anomaly Detection in Smart Grid Application

The smart grid, a modernized electrical grid integrating advanced communication and information technologies, enables bidirectional flow of electricity and information, offering numerous benefits such as enhanced reliability, efficiency, and sustainability. However, the complexity and scale of the smart grid introduce new challenges, including the need for effective anomaly detection to ensure its secure and reliable operation. This research paper presents a comprehensive review of state-of-the-art anomaly detection techniques in smart grid applications. We explore various methodologies, including statistical methods, machine learning algorithms, and hybrid approaches, highlighting their strengths and limitations. Furthermore, we discuss the importance of accurate data preprocessing and feature selection to enhance anomaly detection performance. Through a comparative analysis, we demonstrate the effectiveness of different techniques in detecting anomalies in smart grid datasets. Our findings provide valuable insights for researchers and practitioners in developing robust anomaly detection systems for smart grid applications, ultimately contributing to the advancement of smart grid technology.

Pengembangan Modul Stock Fulfillment Online Transaction pada Aplikasi Distribution Center System menggunakan Framework Flask di PT.XYZ

The rapid advancement of information technology necessitates that every business adapts to keep pace with technological developments. PT. XYZ, a retail and distribution company, faced challenges with their outdated technology, specifically Oracle Forms. Oracle Forms incurred high maintenance costs and was limited to running on legacy systems. Consequently, PT.XYZ decided to create a Stock Fulfillment Online Transaction module using the Flask frameworks and Python. The development process followed the Agile Scrum methodology, including stages such as requirement analysis, sprint planning, iterative development, continuous testing, and final evaluation. The outcome of this application development demonstrates that using Flask frameworks and Python allows for an application that can operate across various platforms while reducing maintenance costs.

Improving care pathways through BPM and telemedicine: an Italian study

PurposeThe future increase of chronic diseases in the world requires new challenges in the health domain to improve patients' care from the point of view of the organizational processes, clinical pathways and technological solutions of digital health. For this reason, the present paper aims to focus on the study and application of well-known clinical practices and efficient organizational approaches through an innovative model (TALIsMAn) to support new care process redesign and digitalization for chronic patients.Design/methodology/approachIn addition to specific clinical models employed to manage chronic conditions such as the Population Health Management and Chronic Care Model, we introduce a Business Process Management methodology implementation supported by a set of e-health technologies, in order to manage Care Pathways (CPs) digitalization and procedures improvement.FindingsThis study shows that telemedicine services with advanced devices and technologies are not enough to provide significant changes in the healthcare sector if other key aspects such as health processes, organizational systems, interactions between actors and responsibilities are not considered and improved. Therefore, new clinical models and organizational approaches are necessary together with a deep technological change, otherwise, theoretical benefits given by telemedicine services, which often employ advanced Information and Communication Technology (ICT) systems and devices, may not be translated into effective enhancements. They are obtained not only through the implementation of single telemedicine services, but integrating them in a wider digital ecosystem, where clinicians are supported in different clinical steps they have to perform.Originality/valueThe present work defines a novel methodological framework based on organizational, clinical and technological innovation, in order to redesign the territorial care for people with chronic diseases. This innovative ecosystem applied in the Italian research project TALIsMAn is based on the concept of a continuum of care and digitalization of CPs supported by Business Process Management System and telemedicine services. The main goal is to organize the different socio-medical activities in a unique and integrated IT system that should be sustainable, scalable and replicable.

Intelligent Diagnosis of Urban Underground Drainage Network: From Detection to Evaluation

Intelligent Diagnosis of Urban Underground Drainage Network: From Detection to Evaluation

Information and communication technology in smart agriculture: A scientometric review

SummaryAgriculture has been affected by several global trends raising the concern for food security. Agri‐food demands are amplifying due to the ever‐escalating population numbers. Thus, the notion of extending the use of smart innovative technology in managing agricultural practices has emerged rapidly over the last decade. Technological innovations have contributed significantly to shape modern agriculture as smart agriculture. Smart agriculture unfolds various benefits such as increased production, real time data and production insights and remote monitoring. The rising advancements in Information and Communication Technologies (ICT) have paved the way for researchers to use these technologies in managing agricultural practices, leading to greater benefits for farmers and society. These innovations are the key to establishing agriculture as a research discipline. The purpose of this article is to conduct a scientometric analysis to study the structure and evolution of research activities in the field of smart agriculture. The scientometric analysis aims to empirically map the scientific knowledge and identify any possible challenges in the field. This study performs elementary analysis to study publication growth over the years, impact analysis to assess the leading journals, authors, and countries, and articles analysis for findings patterns among the citations over the years and among the keyword‐based clusters. There has been a considerable increase of more than 200% in the number of publications from 2011 to 2022. However, around 60% of authors have contributed with a single publication. The findings of the study reveal the research trends and hot topics for future research fronts. Deep learning, digital agriculture, object detection, blockchain, and semantic segmentation have been identified as trending topics in smart agriculture. Comprehensively, an intellectual view of the agriculture domain is presented as a scientific field in this article.

Implementation of an Effective Course Registration Credit Unit Monitoring System

Automation of course registration is a major breakthrough with the advancement in information technology. Registration processes that take longer time most times eating deep into the semester and with laborious inputs from registration officers have evolved into a seamless and easy process due to the development of robust information systems. An information system requires accurate data for it to give a reliable output, so it is of the essence to be able to input accurate credit units of courses to the course registration system. Computing the credit units of courses by students and course registration officers is mostly manual, in this research, automation of this vital pre-process is implemented. The system is built using java programming language which makes it robust, simple and easy to use. It is a credit unit calculator that does not require being online before it can either be deployed or used. The system prompts the user to enter level, the semester, maximum credit load for the semester and their corresponding credit units. At each execution of the system the courses, corresponding credit units, total credit load for the semester is displayed provided the total credit units entered is not greater than the credit load for the semester, otherwise a prompt displays the excess credit units. Subsequent research can enhance the system to be able to send the output of the pre-registration document from the student directly to the registration officer, and also there should be a module embedded to the system that will enable the registration officer to perform the pre-registration document verification. A module to check double registration and determination if a given course is either first or second semester can also be added.

Implementation of an Effective Course Registration Credit Unit Monitoring System

Automation of course registration is a major breakthrough with the advancement in information technology. Registration processes that take longer time most times eating deep into the semester and with laborious inputs from registration officers have evolved into a seamless and easy process due to the development of robust information systems. An information system requires accurate data for it to give a reliable output, so it is of the essence to be able to input accurate credit units of courses to the course registration system. Computing the credit units of courses by students and course registration officers is mostly manual, in this research, automation of this vital pre-process is implemented. The system is built using java programming language which makes it robust, simple and easy to use. It is a credit unit calculator that does not require being online before it can either be deployed or used. The system prompts the user to enter level, the semester, maximum credit load for the semester and their corresponding credit units. At each execution of the system the courses, corresponding credit units, total credit load for the semester is displayed provided the total credit units entered is not greater than the credit load for the semester, otherwise a prompt displays the excess credit units. Subsequent research can enhance the system to be able to send the output of the pre-registration document from the student directly to the registration officer, and also there should be a module embedded to the system that will enable the registration officer to perform the pre-registration document verification. A module to check double registration and determination if a given course is either first or second semester can also be added

Study on One-Stop Student Community Education Management and Services in Higher Education Institutions

In the contemporary era of rapid advancements in information technology, the domain of higher education administration and services is also experiencing a new wave of transformation. Particularly concerning the management and provision of services to student communities, the limitations of traditional fragmented and departmentalized models are gradually becoming apparent. Against this backdrop, the emergence of a "one-stop" model for student community education management and services seeks to integrate a comprehensive range of support, encompassing academic guidance, psychological counseling, lifestyle amenities, and career planning, through a unified service platform. This model not only optimizes resource allocation and enhances service efficiency but, more importantly, significantly augments students' sense of attainment and satisfaction. Nevertheless, to realize this objective, universities must delve deeply into exploration and innovation across multiple dimensions, including administrative structures, information technology infrastructure, human resources, and student feedback.

Application of Q-learning algorithm based on heterogeneous sensor networks in higher education teaching management

With the rapid advancement of information technology and online technologies, it has become imperative for all colleges and universities to address the challenge of leveraging modern management methods to enhance management efficiency, level, and practices. In the realm of educational institution management, the learning management system stands as the cornerstone of contemporary campus management and the entire campus computer network. In this article, the Q-learning algorithm, specifically applied to a heterogeneous cellular network, is employed to investigate the issues pertaining to teaching management in colleges and universities.The design and implementation of the university teaching management system mainly uses heterogeneous cellular networks to establish a physical model of the Q-learning algorithm, and then in the Rayleigh channel, the coverage factor and the system rate expression are derived in a closed form, and in a closed form Derive the energy efficiency expression, and then optimize the density of the basic micro-station algorithm to maximize performance. In the process of system design, this paper uses an algorithm to analyze the current research status of university teaching management, then proposes the research purpose and subject content, and analyzes the university's teaching management function and potential future functional requirements. When designing the university learning management system, it describes in detail the overall design concept, key technologies, system structure, and operating system structure and system topology. This research mainly focuses on the research of campus information management and Q learning algorithm. Based on the cellular network, a large number of campus management cases were analyzed and data fitting at the same time, and a set of Q-learning algorithm teaching management system based on heterogeneous cellular network was designed.

Sparse sensing to dense assessment: Incorporating spatial autocorrelation for assessing flood impacts.

A rapid and comprehensive assessment of flood impacts is critical to assist emergency managers in conducting effective relief operations. With advances in information technologies, various types of sensors have been widely used to assess flood impacts promptly as they are capable of providing rapid flood impact information. However, sensor-driven approaches are limited in the provision of a comprehensive impact assessment as sensors are often sparsely distributed. In this research, the authors integrate the sparse flood impact information obtained from sensors and the spatial autocorrelation of flood-impacted areas, in order to achieve a rapid and comprehensive flood impact assessment. To achieve such a purpose, a systematic approach is proposed to (1) extract flood impact information from sparsely distributed sensors; (2) model the spatial autocorrelation of flood-impacted areas based on flood evolution and geography principles; (3) learn the parameters of the spatial autocorrelation model through a gradient descent method; (4) infer the flood impacts of sensor-uncovered areas based on the sparsely sensed impacts and the modeled spatial autocorrelation. To illustrate the proposed approach, we studied flood impacts on Highways in Houston, Texas during Hurricane Harvey. Results show that the spatial autocorrelation model presents a decent generalization capability in inferring the probability of neighboring highway blocks having the same flood impacts. Compared to purely sensor-driven approaches, the proposed approach is capable of greatly extending the coverage of flood impact assessment while maintaining the nearly same accuracy.

AAGCN: a graph convolutional neural network with adaptive feature and topology learning

In recent years, there has been a growing prevalence of deep learning in various domains, owing to advancements in information technology and computing power. Graph neural network methods within deep learning have shown remarkable capabilities in processing graph-structured data, such as social networks and traffic networks. As a result, they have garnered significant attention from researchers.However, real-world data often face challenges like data sparsity and missing labels, which can hinder the performance and generalization ability of graph convolutional neural networks. To overcome these challenges, our research aims to effectively extract the hidden features and topological information of graph convolutional neural networks. We propose an innovative model called Adaptive Feature and Topology Graph Convolutional Neural Network (AAGCN). By incorporating an adaptive layer, our model preprocesses the data and integrates the hidden features and topological information with the original data’s features and structure. These fused features are then utilized in the convolutional layer for training, significantly enhancing the expressive power of graph convolutional neural networks.To evaluate the effectiveness of the adaptive layer in the AAGCN model, we conducted node classification experiments on real datasets. The results validate its ability to address data sparsity and improve the classification performance of graph convolutional neural networks.In conclusion, our research primarily focuses on addressing data sparsity and missing labels in graph convolutional neural networks. The proposed AAGCN model, which incorporates an adaptive layer, effectively extracts hidden features and topological information, thereby enhancing the expressive power and classification performance of these networks.

Adoption by clinicians of electronic order communications in NHS secondary care: a descriptive account

BackgroundDue to the rapid advancement in information technology, changes to communication modalities are increasingly implemented in healthcare. One such modality is Computerised Provider Order Entry (CPOE) systems which replace paper,...

Proactive ransomware prevention in pervasive IoMT via hybrid machine learning

Advancements in information and communications technology (ICT) have fundamentally transformed computing, notably through the internet of things (IoT) and its healthcare-focused branch, the internet of medical things (IoMT). These technologies, while enhancing daily life, face significant security risks, including ransomware. To counter this, the authors present a scalable, hybrid machine learning framework that effectively identifies IoMT ransomware attacks, conserving the limited resources of IoMT devices. To assess the effectiveness of their proposed solution, the authors undertook an experiment using a state-of-the-art dataset. Their framework demonstrated superiority over conventional detection methods, achieving an impressive 87% accuracy rate. Building on this foundation, the framework integrates a multi-faceted feature extraction process that discerns between benign and malign actions, with a subsequent in-depth analysis via a neural network. This advanced analysis is pivotal in precisely detecting and terminating ransomware threats, offering a robust solution to secure the IoMT ecosystem.

A comparative study of feature selection and feature extraction methods for financial distress identification

Financial distress identification remains an essential topic in the scientific literature due to its importance for society and the economy. The advancements in information technology and the escalating volume of stored data have led to the emergence of financial distress that transcends the realm of financial statements and its’ indicators (ratios). The feature space could be expanded by incorporating new perspectives on feature data categories such as macroeconomics, sectors, social, board, management, judicial incident, etc. However, the increased dimensionality results in sparse data and overfitted models. This study proposes a new approach for efficient financial distress classification assessment by combining dimensionality reduction and machine learning techniques. The proposed framework aims to identify a subset of features leading to the minimization of the loss function describing the financial distress in an enterprise. During the study, 15 dimensionality reduction techniques with different numbers of features and 17 machine-learning models were compared. Overall, 1,432 experiments were performed using Lithuanian enterprise data covering the period from 2015 to 2022. Results revealed that the artificial neural network (ANN) model with 30 ranked features identified using the Random Forest mean decreasing Gini (RF_MDG) feature selection technique provided the highest AUC score. Moreover, this study has introduced a novel approach for feature extraction, which could improve financial distress classification models.

Production Chain Organization in the Digital Age: Information Technology Use and Vertical Integration in U.S. Manufacturing

Advances in information technology (IT) may affect the organizational design of production. Exploiting the rapid diffusion of the internet in the United States, we assess the sensitivity of production chain organization to this innovation in IT access and use. Extending theories of the firm that recognize the importance of downstream transfers (selling as opposed to sourcing) and plural governance in organizational design, we predict IT-driven shifts in downstream vertical integration. In a detailed panel of Census Bureau data for over 5,600 manufacturing plants, we observe the extent of a production unit’s downstream transactions within the firm alongside concurrent sales to external customers—a mix we refer to as plural selling. Our main finding is that the use of the internet for external coordination precipitated a significant decline in downstream vertical integration across the manufacturing sector. Instrumental variables estimation points to a causal relationship but also heterogeneous treatment effects. Key drivers of plural organization, such as complementarities and constraints across differently governed transactions, help explain such heterogeneity, as does concurrent use of internal production management IT. Our study is the first study to leverage a plural governance framework and large-scale microdata to understand how U.S. production chain organization shifted in response to this rapid and far-reaching technological change. This paper was accepted by David Simchi-Levi, information systems. Funding: This research was performed at the Atlanta, Boston, and Cornell (supported by the Cornell Center for the Social Sciences) Federal Statistical Research Data Centers [Project 1069 (CBDRB-FY22-279)]. Support for the Research Data Centers network from the National Science Foundation [Grant ITR-0427889] is gratefully acknowledged, as is support from the Social Sciences and Humanities Research Council of Canada. Supplemental Material: The online appendix is available at https://doi.org/10.1287/mnsc.2019.01586 .

Analysis and Design of Breeding Management Information System in Poultry Farms

Breeding management is indispensable in business development in the field of poultry farming where companies are required to keep up with the development of the digitalization era. Companies must be able to take advantage of opportunities in advances in information technology in obtaining accurate, fast and reliable information so that management can make decisions. PT. Peternakan Ayam Manggis has been using manual methods in recording the results of production or the process of breeding chicken farms. in making records using office software, namely excel, so that errors often occur and are often not realtime in making records. Records generated by poultry production include depletion, quarantine, culling, feed and egg production. in processing data generated manually can make it difficult for management to make decisions. The purpose of this research is to design an application that can be used by companies to obtain information during the breeding process.The research method in developing this application uses waterfall and design role analysis with data collection and using UML modeling. This information system application produces information about Breeding management including depletion, quarantine, feed, egg production, movement of chickens from one cage to another, culling. this information system application uses a database and application blackbox testing is carried out so that the application runs as needed.

The impact of healthcare information technology on reducing medication errors: A review of recent advances

This review explores the impact of HIT on reducing medication errors, encompassing recent advances in electronic health records (EHRs), clinical decision support systems (CDSS), bar-coding technology, telemedicine, and mobile health applications. By synthesizing evidence from scholarly literature, case studies, and real-world implementations, this paper delineates the multifaceted benefits, challenges, and future prospects of integrating HIT into medication management processes. Understanding the evolving role of HIT in error reduction is pivotal for healthcare stakeholders to optimize patient outcomes and enhance the quality and safety of medication administration. In the dynamic realm of modern healthcare, medication errors loom as a formidable challenge, imperiling patient safety, compromising care quality, and inflating healthcare expenditures. However, amidst this backdrop of concern, healthcare information technology (HIT) emerges as a beacon of hope, offering potent remedies to counteract these errors. This review embarks on a journey to unravel the profound impact of HIT on ameliorating medication errors, delving into recent strides across various fronts including electronic health records (EHRs), clinical decision support systems (CDSS), bar-coding technology, telemedicine, and mobile health applications. Drawing upon a rich tapestry of scholarly literature, real-world case studies, and practical implementations, this paper meticulously examines the multifaceted landscape of HIT's influence on medication management. Through a comprehensive synthesis of evidence, we uncover the diverse benefits, confront the inherent challenges, and illuminate the promising vistas that emerge from the integration of HIT into medication-related processes. At the core of this exploration lies an urgent call to comprehend the evolving role of HIT in error reduction, recognizing its pivotal significance for healthcare stakeholders. By assimilating insights from disparate sources, we endeavor to empower stakeholders with the knowledge necessary to optimize patient outcomes, fortify the fabric of care delivery, and foster a culture of safety and excellence in medication administration. In essence, this review serves as a guiding compass amidst the labyrinth of healthcare challenges, illuminating pathways towards safer, more efficient, and more effective medication management practices. As we navigate this terrain, let us remain steadfast in our commitment to harness the transformative power of HIT, thereby ushering in a new era of enhanced patient safety, elevated care quality, and sustainable healthcare systems. In the contemporary healthcare landscape, medication errors represent a significant concern, posing risks to patient safety, quality of care, and overall healthcare costs. However, advancements in healthcare information technology (HIT) offer promising solutions to mitigate these errors.

Future of Medical Equipment Technology

Advances in manufacturing and digital information technology have sparked the Industrial Revolution 4.0. UDIs and 6G telemedicine networks are reshaping the future of healthcare technology as part of the Fourth Industrial Revolution (4IR), which is now underway. With the help of UDIs, device recalls and post-market surveillance may be done more accurately and efficiently, and studies using UDI data can be conducted to help doctors make better clinical decisions. The development of intelligent healthcare systems is currently a hot topic, and wireless connectivity is a critical component. The goal of the 6G vision is to transform the globe into a secure and completely linked digital society through the use of communication technologies with intelligence. Stentrode, digital pacemaker, insulin pumps and coronary intervention with drug-eluting stents are some of the new methods that will improve human life. As technology continues to evolve, so will our methods of self-care. In the future, patients will continue to seek out companies that use technological innovation in medicine to empower them to take charge of their own health. The healthcare system is evolving to become more proactive, personalised, and easy for you, which makes it easier than ever to live longer and perform better.

Electronic medical record system for school dental hygienists: a preventive focus

Relevance. Clinical documentation remains a cornerstone of medical practice. Over the last decade, there has been a notable evolution in the standards for medical documentation and the speed of processing due to advancements in healthcare information technology, including electronic medical record systems. Despite the Ministry of Health of Russia endorsing specific documentation procedures for healthcare organizations, pediatric preventive care dental services have not yet been provided with an appropriate electronic documentation format.Materials and methods. The development of the electronic medical record system for dental hygienists employed Qt Creator software (http://www.qt.io/), using the C++ programming language framework (http://www.isocpp.org/). The system's efficacy was evaluated through testing across the Omsk region over the period 2020-2021.Results. The project resulted in the successful creation of an electronic medical record system designed for dental hygienists.Conclusion. The new electronic medical record system is set to greatly improve the effectiveness of primary preventive care for dental specialists.