Cutting-edge Tools Research Articles

Advancement in Paper-Based Electrochemical Biosensing and Emerging Diagnostic Methods.

The utilization of electrochemical detection techniques in paper-based analytical devices (PADs) has revolutionized point-of-care (POC) testing, enabling the precise and discerning measurement of a diverse array of (bio)chemical analytes. The application of electrochemical sensing and paper as a suitable substrate for point-of-care testing platforms has led to the emergence of electrochemical paper-based analytical devices (ePADs). The inherent advantages of these modified paper-based analytical devices have gained significant recognition in the POC field. In response, electrochemical biosensors assembled from paper-based materials have shown great promise for enhancing sensitivity and improving their range of use. In addition, paper-based platforms have numerous advantageous characteristics, including the self-sufficient conveyance of liquids, reduced resistance, minimal fabrication cost, and environmental friendliness. This study seeks to provide a concise summary of the present state and uses of ePADs with insightful commentary on their practicality in the field. Future developments in ePADs biosensors include developing novel paper-based systems, improving system performance with a novel biocatalyst, and combining the biosensor system with other cutting-edge tools such as machine learning and 3D printing.

Remote Patient Activity Monitoring System by Integrating IoT Sensors and Artificial Intelligence Techniques.

Even with the most cutting-edge tools, treating and monitoring patients-including children, elders, and suspected COVID-19 patients-remains a challenging activity. This study aimed to track multiple COVID-19-related vital indicators using a wearable monitoring device with an Internet of Things (IOT) focus. Additionally, the technology automatically alerts the appropriate medical authorities about any breaches of confinement for potentially contagious patients by tracking patients' real-time GPS data. The wearable sensor is connected to a network edge in the Internet of Things cloud, where data are processed and analyzed to ascertain the state of body function. The proposed system is built with three tiers of functionalities: a cloud layer using an Application Peripheral Interface (API) for mobile devices, a layer of wearable IOT sensors, and a layer of Android web for mobile devices. Each layer performs a certain purpose. Data from the IoT perception layer are initially collected in order to identify the ailments. The following layer is used to store the information in the cloud database for preventative actions, notifications, and quick reactions. The Android mobile application layer notifies and alerts the families of the potentially impacted patients. In order to recognize human activities, this work suggests a novel integrated deep neural network model called CNN-UUGRU which mixes convolutional and updated gated recurrent subunits. The efficiency of this model, which was successfully evaluated on the Kaggle dataset, is significantly higher than that of other cutting-edge deep neural models and it surpassed existing products in local and public datasets, achieving accuracy of 97.7%, precision of 96.8%, and an F-measure of 97.75%.

Beyond Predictive Learning Analytics Modelling and onto Explainable Artificial Intelligence with Prescriptive Analytics and ChatGPT

A significant body of recent research in the field of Learning Analytics has focused on leveraging machine learning approaches for predicting at-risk students in order to initiate timely interventions and thereby elevate retention and completion rates. The overarching feature of the majority of these research studies has been on the science of prediction only. The component of predictive analytics concerned with interpreting the internals of the models and explaining their predictions for individual cases to stakeholders has largely been neglected. Additionally, works that attempt to employ data-driven prescriptive analytics to automatically generate evidence-based remedial advice for at-risk learners are in their infancy. eXplainable AI is a field that has recently emerged providing cutting-edge tools which support transparent predictive analytics and techniques for generating tailored advice for at-risk students. This study proposes a novel framework that unifies both transparent machine learning as well as techniques for enabling prescriptive analytics, while integrating the latest advances in large language models for communicating the insights to learners. This work demonstrates a predictive modelling framework for identifying learners at risk of qualification non-completion based on a real-world dataset comprising ∼\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\sim $$\\end{document}7000 learners with their outcomes, covering 2018 - 2022. The study further demonstrates how predictive modelling can be augmented with prescriptive analytics on two case studies to generate human-readable prescriptive feedback for those who are at risk using ChatGPT.

Impact of knowledge management, innovation and cloud computing on research

This Review article delves into the intricate relationship between knowledge management, innovation, and cloud computing, and their transformative impact on research across diverse fields. Drawing upon a wide range of literature, the article critically assesses the potential of these technologies in reshaping the landscape of scientific research, social science research, business and management, and education. The review highlights key insights and findings that showcase the immense possibilities of cloud-based solutions in revolutionizing research practices, including real-time data sharing, enhanced collaboration, and seamless access to cutting-edge tools and resources. However, the article also acknowledges the complex challenges that come with the adoption and implementation of cloud-based solutions, such as data security and privacy concerns, and the high costs associated with transitioning to these technologies. Despite these challenges, the article concludes that the advantages of these transformative technologies far outweigh the obstacles and that they offer unprecedented opportunities to transform research practices, spark innovation, and catalyze groundbreaking discoveries

A Review of Rapid Generation Advancement (RGA) in Crop Improvement

The use of the speed breeding method is widely considered to be the wave of the future in plant breeding. The term "speed breeding" is used to describe a rapid generational advancement technique that is used to minimise the time it takes from seed to seed, therefore reducing the length of a crop plant's typical life cycle. Plants that are not sensitive to light may have as many as six generations in a single year using this method, whereas other plants can only expect two or three generations every year. With this technique, the photoperiodic and temperature needs of crops produced in controlled-poly homes may be altered. This methodology, when combined with other cutting-edge tools like genome editing and high-throughput genotyping systems, may help breed new kinds of crops at a much quicker pace. Spacefaring food producers: NASA first conceived of this notion. Breeder's equation may be used to determine whether speed breeding is applicable to a certain crop. Light, photoperiodic regime, temperature, and humidity modification make up the backbone of the fast-breeding formula. Accelerated breeding, expedited genomic selection, improved transgenic and CRISPR-Cas9 pipelines, and the investigation of critically important agricultural plant physiological properties are just some of the numerous uses for this approach.

Driving Operational Excellence: Assessment of the Impact of Information Technology on Business Strategies and Business Processes Reengineering

Organizations have undergone transformation as a result of the modern, globally competitive business environment, which has compelled them to be inventive and adopt cutting-edge tools, techniques, and technology. The business procedure and available resources are seen as rivals by them. As a result, they continuously redesign, reorder, and upgrade their technology resources. In recent years, BPR has grown to be one of the most significant and well-liked change management strategies, garnering significant interest from academics and practitioners. This paper critically examines the how IT influence business strategies and further affect BPR and uses theory to explore extent to their interrelations drive operational excellence in organizations.

How Technology is Revolutionizing Sports Training and healthcare teaching

Technology has transformed sports training and healthcare teaching, enabling athletes and medical professionals to leverage cutting-edge tools and techniques to enhance their skills and knowledge. In sports training, wearable devices such as smartwatches and fitness trackers provide real-time feedback on athletes' performance, helping them optimize their training regimens and avoid injuries. Virtual and augmented reality technologies allow athletes to simulate game scenarios and improve their decision-making skills in a safe and controlled environment. In healthcare teaching, virtual and augmented reality, simulation software, and telemedicine platforms enable medical professionals to learn and practice complex procedures and techniques without needing physical patients. This can improve the quality and safety of care, reduce medical errors, and enhance the overall competence of healthcare professionals. In addition, technologies like electronic medical records, data analytics, and machine learning are being used to improve healthcare outcomes, from diagnosis and treatment to patient monitoring and follow-up care. By harnessing the power of technology, sports training, and healthcare teaching is being revolutionized, paving the way for more effective and efficient training and care for athletes and patients alike.

Introduction of rolling motion at the tool-tip in metal cutting

In metal cutting, severe sliding contact at the tool-chip interface is unavoidable by a conventional cutting tool, which results in sloth motion of chip, higher chip thickness, formation of stagnation zone, higher power consumption, and poor surface finish. To overcome this limitation, rolling motion is introduced at the tool-chip interface by mounting a roller at the tip of a cutting tool – termed as Rotating Tip Cutting Tool (RTC-tool). The roller in RTC-tool rotates during cutting and establishes rolling-sliding contact. A model in-situ experimental configuration is used to study the plane strain flow characteristic of pure copper, notoriously known for higher chip thickness and power consumption, during cutting at low speed. The performance of RTC-tool is compared with sharp cutting-edge and blunt cutting-edge tools (fixed curvature and stationary-roller-tip tool). It is found that rolling motion at the tool-tip decreases the chip thickness and average plastic strain within the chip by about two times than sharp tool and more than two times than the blunt tools. Additionally, there is a significant improvement in surface roughness than the sharp tool. The digital image correlation techniques reveal that flow characteristics within the chip and near the tool-tip interface (retardation region) are influenced by the non-laminar plastic flow of materials. As opposed to the unstable retardation region and periodic cracking at the tool-tip of sharp tool, additional rolling motion at the tool-tip cuts the chip at the incipient stage, forms a stable retardation region, and increases the average velocity of the material in this region. Large retardation region and sloth motion of materials within the retardation region produce large chip thickness during cutting by blunt edge tool. As the chip thickness rather power consumption of the sharp tool is less than the blunt tool, force measurement in nearly orthogonal cutting configuration of RTC-tool compared with the sharp cutting edge. These tests are performed at moderate speed range in a lathe machine. The force measurement data and post-cutting characterization are aligned with the in-situ observations. As the frictional resistance of the roller controls the chip thickness further improvement in the performance of the RTC-tool is possible by reducing the frictional resistance of the roller.

Traditional vs. Automated Computer Image Analysis-A Comparative Assessment of Use for Analysis of Digital SEM Images of High-Temperature Ceramic Material.

Image analysis is a powerful tool that can be applied in scientific research, industry, and everyday life, but still, there is more room to use it in materials science. The interdisciplinary cooperation between materials scientists and computer scientists can unlock the potential of digital image analysis. Traditional image analysis used in materials science, manual or computer-aided, permits for the quantitative assessment of the coexisting components at the cross-sections, based on stereological law. However, currently used cutting-edge tools for computer image analysis can greatly speed up the process of microstructure analysis, e.g., via simultaneous extraction of quantitative data of all phases in an SEM image. The dedicated digital image processing software Aphelion was applied to develop an algorithm for the automated image analysis of multi-phase high-temperature ceramic material. The algorithm recognizes each phase and simultaneously calculates its quantity. In this work, we compare the traditional stereology-based methods of image analysis (linear and planimetry) to the automated method using a developed algorithm. The analysis was performed on a digital SEM microstructural image of high-temperature ceramic material from the Cu-Al-Fe-O system, containing four different phase components. The results show the good agreement of data obtained by classical stereology-based methods and the developed automated method. This presents an opportunity for the fast extraction of both qualitative and quantitative from the SEM images.

Study of the Moment of Drag and Lift on Different Air-foil Shapes and Thickness During Wind Tunnel Application: A Review

An experimental facility called a wind tunnel is used in aerodynamics to investigate how air behaves when it passes through solid things like wings or automobile bodies. Researchers can evaluate an object’s aerodynamic characteristics under many circumstances by producing a controlled airflow, including as variations in velocity, attack angle, or atmospheric pressure. The emergency of 3D computer simulation of the performance parameters of an airfoil which is characterised by optimisation and digital technology, are combined for easier determination of the aerodynamic characteristics of a chosen airfoil for better and effective lift and drag coefficient through computational simulations using software like ANSYS etc. The aim is to study the effect of lift and drag on different air-foil shapes and thicknesses at different angles of attack using experimental and wind tunnel applications for better validation. The study also reviewed work that cut across the effect of the different airfoil shapes and thickness in a wind tunnel experiment, drag force, lift force and numerical methods employed for wind tunnel experiment. This technological advancement is not without its difficulties and challenges, also discussed as possible solutions. The study further suggested integrating emerging technologies by using cutting-edge tools like machine learning and artificial intelligence to speed up the design and analysis of airfoil collaborations between academics and industry to ensure that airfoils foster design. Foster meets industrial standards and enables practical implementations.

2022 AJP Editors’ Picks Showcase Innovation, Long-Term Studies

2022 <i>AJP</i> Editors’ Picks Showcase Innovation, Long-Term Studies

Investigation of groundwater flow direction using Geospatial technology: A case study in part of Port Harcourt, Rivers State, Nigeria

A geospatial technique was used in some parts of the Port Harcourt metropolis to establish the direction of groundwater movement. This method included the use of twelve carefully selected boreholes and a variety of cutting-edge tools, such as depth meters, handheld GPS, and measuring tape. The static water level in the boreholes and the ground surface elevation in relation to the average sea level of the region were two of the variables that were gathered. High-end software was used to analyze the data in order to create groundwater flow direction maps and 3D visualizations for displaying the whole research region based on the fluctuation in static water levels. This software included Microsoft Excel, ArcGIS 10.4, and ArcScene 10.4. Afterward, the main and minor flow directions were identified. The results showed that groundwater in the area moves from the north to the south, with group 4 having the largest hydraulic gradient and group 1 coming in second. On the other hand, groups 3 and 2 had the lowest hydraulic gradient. The main factors influencing groundwater flow were determined to be gravity and external pressure brought on by pumping. The research region's southern regions are more vulnerable to groundwater pollution, which may result from zones with higher hydraulic heads, according to the flow pattern in the aquifer system. As a result, it is advised that residential boreholes and municipal water wells be located on the study area's northern sides, whereas sanitary landfills and garbage sites should only be located in the southwest.

Towards Sim2Real Transfer of Autonomy Algorithms using AutoDRIVE Ecosystem

The engineering community currently encounters significant challenges in the development of intelligent transportation algorithms that can be transferred from simulation to reality with minimal effort. This can be achieved by robustifying the algorithms using domain adaptation methods and/or by adopting cutting-edge tools that help support this objective seamlessly. This work presents AutoDRIVE, an openly accessible digital twin ecosystem designed to facilitate synergistic development, simulation and deployment of cyber-physical solutions pertaining to autonomous driving technology; and focuses on bridging the autonomy-oriented simulation-to-reality (sim2real) gap using the proposed ecosystem. In this paper, we extensively explore the modeling and simulation aspects of the ecosystem and substantiate its efficacy by demonstrating the successful transition of two candidate autonomy algorithms from simulation to reality to help support our claims: (i) autonomous parking using probabilistic robotics approach; (ii) behavioral cloning using deep imitation learning. The outcomes of these case studies further strengthen the credibility of AutoDRIVE as an invaluable tool for advancing the state-of-the-art in autonomous driving technology.

Digital transformation under Covid-19: A Bibliometric Study and future research agenda

Digital transformation (DT) has been considered an essential enabler of operational and strategic change for any given organization across various industries. Based on the smart adoption and assimilation of cutting-edge tools and technologies, DT allows firms to develop new capabilities, including creating improved products and services, enhancing experiment and innovation abilities, becoming customers centric, and streamlining end-to-end business processes. No wonder some analysts believe that covid-19 has contributed to fostering DT trends across industries. Yet very few studies exist about the real impact of covid-19 on DT. This study aims at filling this knowledge gap using a bibliometric analysis of 441 documents collected from the Web of Science database. Two bibliometric tools, namely Bibliometrix and VOSViewer, were used for data analysis. It emerged that impact, innovation, and performance are among the top word growth and the most relevant words, while the top three relevant affiliations are based in the USA. Further insights are presented and discussed, followed by a future research agenda.

Utility of the Cerebral Organoid Glioma 'GLICO' Model for Screening Applications.

Glioblastoma, a grade IV astrocytoma, is regarded as the most aggressive primary brain tumour with an overall median survival of 16.0 months following the standard treatment regimen of surgical resection, followed by radiotherapy and chemotherapy with temozolomide. Despite such intensive treatment, the tumour almost invariably recurs. This poor prognosis has most commonly been attributed to the initiation, propagation, and differentiation of cancer stem cells. Despite the unprecedented advances in biomedical research over the last decade, the current in vitro models are limited at preserving the inter- and intra-tumoural heterogeneity of primary tumours. The ability to understand and manipulate complex cancers such as glioblastoma requires disease models to be clinically and translationally relevant and encompass the cellular heterogeneity of such cancers. Therefore, brain cancer research models need to aim to recapitulate glioblastoma stem cell function, whilst remaining amenable for analysis. Fortunately, the recent development of 3D cultures has overcome some of these challenges, and cerebral organoids are emerging as cutting-edge tools in glioblastoma research. The opportunity to generate cerebral organoids via induced pluripotent stem cells, and to perform co-cultures with patient-derived cancer stem cells (GLICO model), has enabled the analysis of cancer development in a context that better mimics brain tissue architecture. In this article, we review the recent literature on the use of patient-derived glioblastoma organoid models and their applicability for drug screening, as well as provide a potential workflow for screening using the GLICO model. The proposed workflow is practical for use in most laboratories with accessible materials and equipment, a good first pass, and no animal work required. This workflow is also amenable for analysis, with separate measures of invasion, growth, and viability.

Blockchain-Based Internet of Things: Review, Current Trends, Applications, and Future Challenges

Advances in technology always had an impact on our lives. Several emerging technologies, most notably the Internet of Things (IoT) and blockchain, present transformative opportunities. The blockchain is a decentralized, transparent ledger for storing transaction data. By effectively establishing trust between nodes, it has the remarkable potential to design unique architectures for most enterprise applications. When it first appeared as a platform for anonymous cryptocurrency trading, such as Bitcoin, on a public network platform, blockchain piqued the interest of researchers. The chain is completed when each block connects to the previous block. The Internet of Things (IoT) is a network of networked devices that can exchange data and be managed and controlled via unique identifiers. Automation, wireless sensor networks, embedded systems, and control systems are just a few of the well-known technologies that power the IoT. Converging advancements in real-time analytics, machine learning, commodity sensors, and embedded systems demonstrate the rapid expansion of the IoT paradigm. The Internet of Things refers to the global networking of millions of networked smart gadgets that gather and exchange data. Integrating the IoT and blockchain technology would be a significant step toward developing a reliable, secure, and comprehensive method of storing data collected by smart devices. Internet-enabled devices in the IoT can send data to private blockchain networks, creating immutable records of all transaction history. As a result, these networks produce unchangeable logs of all transactions. This research looks at how blockchain technology and the Internet of Things interact to understand better how devices can communicate with one another. The blockchain-enabled Internet of Things architecture proposed in this article is a useful framework for integrating blockchain technology and the Internet of Things using the most cutting-edge tools and methods currently available. This article discusses the principles of blockchain-based IoT, consensus methods, reviews, difficulties, prospects, applications, trends, and communication between IoT nodes in an integrated framework.

The Impact of Climate Change on Water Resources: Lessons from Villages of Tonk District of Rajasthan, India

Climate change is a major concern in Rajasthan’s water-stressed state. Climate change has a direct impact on the water resources in many parts of the state. In comparison to the rest of the country, the state has a larger portion of desert or arid lands (58% of the total geographical area) and limited water resources. Therefore, any adverse climate change conditions will likely have a catastrophic impact on the state’s already vulnerable water resource system. Attempts have been made in this study to understand the state’s climate conditions and highlight isolated past events that suggest climate change may occur in the state. It is emphasised that climate change modelling for various types of climate is required. To develop optimal water resource management strategies for different zones, cutting-edge tools such as remote sensing and geospatial tools must be used. The study’s overarching goal is to comprehend the impact of climate change on the study area’s water resources. The study’s objectives are as follows: (1) to determine the effects of climate change on the water resources of the study location, (2) to investigate the nature and impact of climate change-induced drought disasters in the study area, (3) to identify methods and strategies for mitigating potential risks and vulnerabilities due to climatic variations and (4) determine the scope of research and policy action in response to identified current and future vulnerabilities, as well as the anticipated effects of climate change. The descriptive study used both quantitative and qualitative data collection methods. A total of 100 respondents were chosen from the villages of Rajasthan’s Tonk district for quantitative data collection and 05 in-depth interviews with various stakeholders were conducted for qualitative analysis. The study revealed that indigenous knowledge systems have allowed various communities in the area to live in harmony with their environments for generations and that their traditional knowledge systems are essential tools in environment conservation and natural disaster management. Again, according to the study, the majority of respondents believe that climate change is a cause of drought disasters, which should be addressed through effective participatory research. The qualitative interviews made known themes such as stakeholder participation, climate modelling, the impacts of climate change on key development sectors, adaptation needs, initiatives, opportunities, and so on. The findings imply that climate change is one of the critical reasons for the occurrence of drought disasters in the study area and the state as a whole; drought and climate change have severely impacted water resources. To conclude, the study emphasises the emerging need for research, policy, and action to mitigate the impact of climate change on water resources and related areas.

Artificial Intelligence: A Primer for the Radiologists

AbstractArtificial intelligence (AI) has revolutionized almost every sphere of life today by providing cutting-edge tools aimed at improving the quality of life. The term AI refers to any operating system or a software that mimics human intelligence and performs functions like the human mind with minimal human intervention. The present review article focuses on the basics of AI and the terminology used in the field of AI. Flowcharts and figures to facilitate easy understanding of its impact and its potential applications have also been provided. It is meant to serve as a primer for the beginner.

Understanding Local Adaptation to Prepare Populations for Climate Change

Abstract Adaptation within species to local environments is widespread in nature. Better understanding this local adaptation is critical to conserving biodiversity. However, conservation practices can rely on species’ trait averages or can broadly assume homogeneity across the range to inform management. Recent methodological advances for studying local adaptation provide the opportunity to fine-tune efforts for managing and conserving species. The implementation of these advances will allow us to better identify populations at greatest risk of decline because of climate change, as well as highlighting possible strategies for improving the likelihood of population persistence amid climate change. In the present article, we review recent advances in the study of local adaptation and highlight ways these tools can be applied in conservation efforts. Cutting-edge tools are available to help better identify and characterize local adaptation. Indeed, increased incorporation of local adaptation in management decisions may help meet the imminent demands of managing species amid a rapidly changing world.

Shoreline Change Analysis along Rivers and Deltas: A Systematic Review and Bibliometric Analysis of the Shoreline Study Literature from 2000 to 2021

Globally, coastal zones, rivers and riverine areas, and deltas carry enormous values for ecosystems, socio-economic, and environmental perspectives. These often highly populated areas are generally significantly different from interior hinterlands in terms of population density, economic activities, and geophysical and ecological processes. Geospatial technologies are widely used by scholars from multiple disciplines to understand the dynamic nature of shoreline changes globally. In this paper, we conduct a systematic literature review to identify and interpret research patterns and themes related to shoreline change detection from 2000 to 2021. Two databases, Web of Science and Scopus, were used to identify articles that investigate shoreline change analysis using geospatial technique such as remote sensing and GIS analysis capabilities (e.g., the Digital Shoreline Analysis System (DSAS). Between the years 2000 and 2021, we initially found 1622 articles, which were inspected for suitability, leading to a final set of 905 articles for bibliometric analysis. For systematic analysis, we used Rayyan—a web-based platform used for screening literature. For bibliometric network analysis, we used the CiteSpace, Rayyan, and VOSviewer software. The findings of this study indicate that the majority of the literature originated in the USA, followed by India. Given the importance of protecting the communities living in the riverine areas, coastal zones, and delta regions, it is necessary to ask new research questions and apply cutting-edge tools and technology, such as machine learning approach and GeoAI, to fill the research gaps on shoreline change analysis. Such approaches could include, but are not limited to, centimeter level accuracy with high-resolution satellite imagery, the use of unmanned aerial vehicles (UAV), and point cloud data for both local and global level shoreline change and analysis.