Real-time Insights Research Articles

Precision medicine in breast cancer (Review).

Precision medicine in breast cancer is a revolutionary approach that customizes diagnosis and treatment based on individual and tumor characteristics, departing from the traditional one-size-fits-all approach. Breast cancer is diverse, with various subtypes driven by distinct genetic mutations. Understanding this diversity is crucial for tailored treatment strategies that target specific vulnerabilities in each tumor. Genetic testing, particularly for mutations in breast cancer gene (BRCA) DNA repair-associated genes, helps assess hereditary risks and influences treatment decisions. Molecular subtyping guides personalized treatments, such as hormonal therapies for receptor-positive tumors and human epidermal growth factor receptor 2 (HER2)-targeted treatments. Targeted therapies, including those for HER2-positive and hormone receptor-positive breast cancers, offer more effective and precise interventions. Immunotherapy, especially checkpoint inhibitors, shows promise, particularly in certain subtypes such as triple-negative breast cancer, with ongoing research aiming to broaden its effectiveness. Integration of big data and artificial intelligence enhances personalized treatment strategies, while liquid biopsies provide real-time insights into tumor dynamics, aiding in treatment monitoring and modification. Challenges persist, including accessibility and tumor complexity, but emerging technologies and precision prevention offer hope for improved outcomes. Ultimately, precision medicine aims to optimize treatment efficacy, minimize adverse effects and enhance the quality of life for patients with breast cancer.

Advancing Military Medical Planning in Large Scale Combat Operations: Insights From Computer Simulation and Experimentation in NATO's Vigorous Warrior Exercise 2024.

The ongoing conflict in Ukraine from Russian invasion presents a critical challenge to medical planning in the context of multi-domain battle against a peer adversary deploying conventional weapon systems. The potential escalation of preventable morbidity and mortality, reaching a scale unprecedented since World War II, underscores the paramount importance of effective phases of care from Point of Injury (PoI)/Point of Wounding (PoW) or Point of Exposure (PoE) to Role 1 (R1) and Role 2 (R2) echelons of care.The NATO Vigorous Warrior (VW) Live Exercise (LIVEX) serves as a strategic platform for NATO and its partners, providing an opportunity to challenge operational concepts, experiment, innovate life-saving systems, and foster best practices across the Alliance. This study delineates the strategic application of the VW LIVEX platform for the adaptation of the computational simulation software Simulation for the Assessment and Optimization of Medical Disaster Management (SIMEDIS) within the context of Large-Scale Combat Operations (LSCO). The SIMEDIS computer simulator plays a pivotal role by furnishing real-time insights into the evolving injury patterns of patients, employing an all-hazards approach. This simulator facilitates the examination of temporal shifts in medical timelines and the ramifications of resource scarcity against both morbidity and mortality outcomes. The VW LIVEX provides a unique opportunity for systematic validation to evaluate the results of the computer simulator in a realistic setting and identify gaps for future concepts of operations. We report the process and methodologies to be evaluated at the VW LIVEX in far forward and retrospective medical support operations. Using the SIMEDIS simulator, we can define battlefield scenarios for varied situations including artillery, drone strikes, and Chemical, Biological, Radiological, Nuclear, and explosive (CBRNe) attacks. Casualty health progressions versus time are dependent on each threat. Mortality is computed based on the concepts found in Tactical Combat Casualty Care (TCCC) of "self-aid"/"buddy-aid" factoring in the application or absence of definitive traumatic hemorrhage control and on the distribution policy of victims to medical treatment facilities through appropriate Command and Control (C2) ("Scoop and Run" versus "Stay and Play"). The number of medical supplies available along with the number of transport resources and personnel are set and are scalable, with their effect on both morbidity and mortality quantified.Concept of Medical Operations can be optimized and interoperability enhanced when shared data are provided to C2 for prospective medical planning with retrospective data. The SIMEDIS simulator determines best practices of medical management for a myriad of injury types and tactical/operational situations relevant to policy making and battlefield medical planning for LSCO. The VW LIVEX provides a Concept Development and Experimentation platform for SIMEDIS refinement and conclusive insights into medical planning to reduce preventable morbidity and mortality. Recommending further iterations of similar methodologies at other NATO LIVEXs for validation is crucial, as is information sharing across the Alliance and partners to ensure best practice standards are met.

Application of Artificial Intelligence in Advanced Training and Education of Emergency Medicine Doctors: A Narrative Review

Emergency medicine (EM) demands continuous adaptation and refinement of training methodologies to equip healthcare professionals with the expertise to effectively manage complex and time-sensitive patient presentations. Artificial intelligence (AI), with its remarkable ability to process vast amounts of data, identify patterns, and make predictions, holds immense promise for enhancing the advanced training and education of EM physicians. This narrative review aims to discuss the potential of AI in transforming EM training and highlight the specific applications of AI in personalized learning, realistic simulations, data-driven decision support, and adaptive assessment, along with further exploring the benefits and challenges of AI-powered EM training. A comprehensive literature search was conducted using PubMed, MEDLINE, and Google Scholar to identify relevant studies focusing on AI applications in EM and EM training. The search terms included “artificial intelligence”, “emergency medicine”, “training”, “education”, “personalized learning”, “simulations”, “decision support”, and “assessment. Articles published in the past ten years were prioritized to ensure the inclusion of current advancements in the field. AI offers a plethora of opportunities to revolutionize EM training, including the following: Personalized learning: AI-powered systems can tailor educational content and pace to individual trainees’ needs, ensuring optimal instruction and knowledge acquisition. Realistic simulations: AI-powered simulations provide immersive experiences for trainees to practice clinical decision making under simulated pressure. Data-driven decision support: AI-powered systems analyze vast amounts of data to provide trainees with real-time recommendations and insights for informed clinical decisions. Adaptive assessment: AI-powered tools assess trainee progress dynamically, providing personalized feedback and identifying areas for improvement. Conclusions: AI integration into EM training holds immense promise for enhancing trainee learning and improving patient outcomes. By embracing AI, we can cultivate a new generation of EM physicians equipped to meet the ever-changing demands of this critical medical specialty.

A Scoping Review on the Implementation of Autonomous Learning at the University Level in Kuwait: PAAET as a Case Study

This paper is mainly a scoping review that explores the current body of knowledge related to the presence and growth of autonomous learning at the university level in Kuwait. The review was conducted to answer the research question “Can we implement autonomous learning at the university level in Kuwait?” To support this scoping review, a questionnaire consisting of eight opened-ended questions was designed that asked professors who teach in different colleges in The Public Authority for Applied Education and Learning (PAAET), one of the main and oldest universities in Kuwait, about their opinions on autonomous learning at the university level. The questionnaire served to allow the study to gather valuable primary data that contextualized the scoping review and added real time insights. One of the findings of the paper is that the initiation of the promotion of autonomous learning in Kuwait partially occurred because of the onset of the COVID-19 pandemic. The pandemic required universities to engage in digital and remote learning so students’ academic progress could stay on schedule as much as possible. This phase naturally developed some university students’ ability to study in their own time and complete their assignments without an excessive amount of prodding from teachers. However, the literature published after the pandemic does not present a state of widespread proficiency when it comes to university students’ autonomy. Another finding is that current research stressed the effectiveness of interventions that directly target students’ lack of autonomy. However, there is little knowledge about the frequency in which these interventions are recommended or implemented. From a practical perspective, university level teachers will need to improve their communication skills so they can learn how to connect with students as individual learners. In addition, teachers at the university level should develop a line of communication with secondary school teachers in Kuwait. University students’ lack of preparation for autonomous learning is a sign that they are not being properly developed as scholars in their younger years. Teachers at the university level must communicate with secondary teachers to identify where the current gaps in practice are.

Unlocking Insights: The Imperative of Real-Time Data Collection and Mapping Systems

In today's rapidly evolving landscape, the need for timely and accurate data is paramount for informed decision-making across various sectors. However, traditional data collection methods often fall short in providing real-time insights necessary for addressing dynamic challenges. This study examines the critical problem of outdated data and the imperative for adopting real-time data collection and mapping systems to overcome it. The aim of the article is to highlight the transformative potential of such systems in unlocking actionable insights for decision-makers. By leveraging advanced technologies like geospatial mapping, sensor networks, and mobile data collection apps, organizations can gather, process, and analyze data in near real-time. This enables them to monitor trends, detect anomalies, and respond promptly to emerging issues. Ultimately, the adoption of real-time data systems offers a solution to the problem of outdated data, empowering decision-makers with the timely information needed to navigate complex scenarios and drive positive outcomes

Cohort event monitoring of safety of COVID-19 vaccines: the Italian experience of the “ilmiovaccinoCOVID19 collaborating group”

Introduction: In 2021, the European Medicines Agency supported the “Covid Vaccine Monitor (CVM),” an active surveillance project spanning 13 European countries aimed at monitoring the safety of COVID-19 vaccines in general and special populations (i.e., pregnant/breastfeeding women, children/adolescents, immunocompromised people, and people with a history of allergies or previous SARS-CoV-2 infection). Italy participated in this project as a large multidisciplinary network called the “ilmiovaccinoCOVID19 collaborating group.”Methods: The study aimed to describe the experience of the Italian network “ilmiovaccinoCOVID19 collaborating group” in the CVM context from June 2021 to February 2023. Comprising about 30 partners, the network aimed to facilitate vaccinee recruitment. Participants completed baseline and follow-up questionnaires within 48 h from vaccination over a 6-month period. Analyses focused on those who completed both the baseline and the first follow-up questionnaire (Q1), exploring temporal trends, vaccination campaign correlation, and loss to follow-up. Characteristics of recruited vaccinees and vaccinee-reported adverse drug reactions (ADRs) were compared with passive surveillance data in Italy.Results: From June 2021 to November 2022, 22,384,663 first doses and 38,207,452 booster doses of COVID-19 vaccines were administered in Italy. Simultaneously, the study enrolled 1,229 and 2,707 participants for the first and booster doses, respectively. Of these, 829 and 1,879 vaccinees, respectively, completed both baseline and at least Q1 and were included in the analyses, with a significant proportion of them (57.8%/34.3%) belonging to special cohorts. Most vaccinees included in the analyses were women. Comirnaty® (69%) and Spikevax® (29%) were the most frequently administered vaccines. ADR rates following Comirnaty® and Spikevax® were higher after the second dose, particularly following Spikevax®. Serious ADRs were infrequent. Differences were observed in ADR characteristics between CVM and Italian passive surveillance.Conclusion: This study confirmed the favorable safety profile of COVID-19 vaccines, with findings consistent with pivotal clinical trials of COVID-19 vaccines, although different proportions of serious ADRs compared to spontaneous reporting were observed. Continuous evaluation through cohort event monitoring studies provides real-time insights crucial for regulatory responses. Strengthening infrastructure and implementing early monitoring strategies are essential to enhance vaccine safety assessment and prepare for future pandemics.

Shedding Light on Lithium-Sulfur Battery Dynamics: Real-Time Insights through in-Situ UV-Vis Spectroscopy on Modified Lab Equipment

In current post-lithium-ion research, the focus is directed toward Lithium-Sulfur (Li-S) batteries, motivated by their outstandingly high energy density (2640 Wh/kg) and the widespread availability of sulfur in the Earth's crust. Moreover, in-situ UV-Vis spectroscopy has proven to be instrumental, allowing for the real-time observation of alterations in the chemical composition of various battery components during operational cycles [1,2].Our current study delves into the influence of solvent selection, particularly donor and acceptor numbers, in formulating gel polymer electrolytes for Lithium-Sulfur batteries. Leveraging in-situ UV-Vis spectroscopy, our research provides real-time insights into dynamic interactions within the gel during battery operation, shedding light on evolving electrochemical processes. Systematically analyzing the impact of solvent properties on gel characteristics reveals a meaningful relationship, underscoring the importance of tailored solvent selection for optimizing gel performance. Moreover, this technique tracks polysulfide behavior, offering a comprehensive understanding of the gel's role in mitigating polysulfide shuttling. Notably, our study introduces a novel dimension by employing a custom-built in-situ UV-Vis cell featuring 3D-printed components, enhancing the versatility and applicability of these spectroscopic techniques in probing intricate interactions within gel polymer electrolytes. Moreover, by combining the UV-Vis results with Nuclear Magnetic Resonance and FTIR, we aim to advance fundamental knowledge and provide a pathway for optimizing battery performance and mitigating degradation mechanisms.References1- Patel, Manu UM, Rezan Demir‐Cakan, Mathieu Morcrette, Jean‐Marie Tarascon, Miran Gaberscek, and Robert Dominko. "Li‐S Battery Analyzed by UV/Vis in Operando Mode." ChemSusChem 6, no. 7 (2013): 1177-1181.2- He, Qi, Anna TS Freiberg, Manu UM Patel, Simon Qian, and Hubert A. Gasteiger. "Operando identification of liquid intermediates in lithium–sulfur batteries via transmission UV–vis spectroscopy." Journal of The Electrochemical Society 167, no. 8 (2020): 080508.

Operando Raman and in-Situ UV-Vis Spectroscopy Unveil the Impact of Solvent Donor and Acceptor Numbers on Gel Polymer Electrolytes in Lithium-Sulfur Batteries

Lithium-sulfur (Li-S) batteries boast a specific capacity five times greater than current Li-intercalation systems. Still, practical implementation faces challenges tied to liquid electrolytes, i.e., lithium metal dendrites formation, polysulfide redox-shuttle, and flammability [1]. Thus, we focus on developing biodegradable gel polymer electrolytes to overcome these issues, fostering eco-friendly, safer, and more effective energy storage solutions. Polycaprolactone (PCL) emerges as a promising GPE candidate due to its biodegradable nature and broad stability range (~5V vs Li0/Li+). Our current study investigates the influence of solvent selection, explicitly focusing on donor and acceptor numbers, in formulating gel polymer electrolytes for Lithium-Sulfur batteries. Leveraging operando Raman and in-situ UV-Vis spectroscopy, our research provides real-time insights into the dynamic interactions within the gel during battery operation, shedding light on the evolving electrochemical processes. Moreover, by systematically analyzing the impact of solvent properties on gel characteristics, we reveal a nuanced relationship, emphasizing the importance of tailored solvent selection for optimizing gel performance. Operando Raman spectroscopy enables the observation of bond formations and structural changes in the gel, while in-situ UV-Vis spectroscopy tracks the behavior of polysulfides, facilitating the identification of specific interactions and offering a comprehensive understanding of the gel's role in mitigating polysulfide shuttling. This work aims to advance our fundamental knowledge of gel polymer electrolytes and provide a pathway for optimizing battery performance and mitigating degradation mechanisms. Notably, our study introduces a novel dimension by employing a custom-built UV-Vis setup, enhancing the versatility and applicability of these spectroscopic techniques in probing the intricate interactions within gel polymer electrolytes.Reference1-Pervez, S. A.; Vinayan, B. P.; Cambaz, M. A.; Melinte, G.; Diemant, T.; Braun, T.; Karkera, G.; Behm, R. J.; Fichtner, M., Electrochemical and compositional characterization of solid interphase layers in an interface-modified solid-state Li–sulfur battery. Journal of Materials Chemistry A2020, 8 (32), 16451-16462.

Predictive modeling of combined cycle power plant performance using a digital twin-based neural ODE approach

This study presents a novel digital twin-based predictive modeling approach to enhance the operation, maintenance, and management of combined cycle power plants. The research aimed to accurately forecast the combined cycle power (CCP) output, a critical performance indicator, to support intelligent decision-making for plant operators and engineers. The proposed framework leverages a neural ordinary differential equation (NODE) model integrated within a digital twin architecture to capture the complex, nonlinear dynamics of combined cycle gas turbine unit (CCGU) system. The predictive model was trained and validated using multivariate time-series data collected from a CCGU, achieving high accuracy with an R2_score of 0.993, a mean absolute percentage error of 0.325 %, and a root mean squared error of 1.518. Importantly, the NODE model demonstrated superior forecasting capabilities compared to traditional machine learning techniques. The digital twin (DT) concept enables seamless real-time data integration, physics-based models, and advanced data analytics to facilitate comprehensive CCGU lifecycle management. This novel approach provides operators with reliable, real-time insights to optimize plant performance, reduce maintenance costs, and improve overall sustainability. The generalization of the NODE model to an independent CCGU unit validated its applicability across diverse power plant configurations, highlighting the originality and practical value of the research. The key contribution of this work is the development of a digital twin-based predictive modeling framework centered on the innovative use of neural ordinary differential equations to enhance the operation and maintenance of critical energy infrastructure. This research advances the state-of-the-art in intelligent asset management for the built environment.

Single-Walled Carbon Nanotubes As Fluorescent Probes for Supramolecular Self-Assembly Complexes

Semiconducting single-walled carbon nanotubes (SWCNTs) are distinguished by their fluorescence in the near-infrared range, which overlaps with the transparency window of biological samples, and they do not photobleach or blink. Utilizing their unique optical properties, SWCNTs can be rendered optical probes for the characterization and analysis of bio-inspired peptide-based hydrogels. Peptide hydrogels, formed through the supramolecular self-assembly of conjugated amino acids, provide versatile three-dimensional scaffolds for applications in cell culture, tissue engineering, and regenerative medicine. The inherent properties of these amino acids or peptides, notably their limited solubility in water, drive them to self-assemble into elongated fibrillary nanostructures, ultimately creating self-supporting hydrogel matrices. We integrate near-infrared fluorescent SWCNTs into these peptide hydrogels as dynamic fluorescent probes, offering real-time insights into the morphological and structural evolution of the hydrogels. This approach not only enhances our understanding of the self-assembly mechanisms at play but also opens new avenues in supramolecular science. The integration of SWCNTs into the supramolecular structures showcases their potential as a powerful tool for long-term, non-invasive monitoring, with immense promise for numerous biomedical applications.

An All-in-One Array of Pressure Sensors and sEMG Electrodes for Scoliosis Monitoring.

Scoliosis often occurs in adolescents and seriously affects physical development and health. Traditionally, medical imaging is the most common means of evaluating the corrective effect of bracing during treatment. However, the imaging approach falls short in providing real-time feedback, and the optimal corrective force remains unclear, potentially slowing the patient's recovery progress. To tackle these challenges, an all-in-one integrated array of pressure sensors and sEMG electrodes based on hierarchical MXene/chitosan/polydimethylsiloxane (PDMS)/polyurethane sponge and MXene/polyimide (PI) is developed. Benefiting from the microstructured electrodes and the modulus enhancement of PDMS, the sensor demonstrates a high sensitivity of 444.3 kPa-1 and a broad linear detection range (up to 81.6kPa). With the help of electrostatic attraction of chitosan and interface locking of PDMS, the pressure sensor achieves remarkable stability of over 100000 cycles. Simultaneously, the sEMG electrodes offer exceptional stretchability and flexibility, functioning effectively at 60% strain, which ensures precise signal capture for various human motions. After integrating the developed all-in-one arrays into a commercial scoliosis brace, the system can accurately categorize human motion and predict Cobb angles aided by deep learning. This study provides real-time insights into brace effectiveness and patient progress, offering new ideas for improving the efficiency of scoliosis treatment.

Leveraging IoT, AI, and ML for Enhanced Decision-Making in Karnataka’s Smart Citie

The rapid urbanization in Karnataka, characterized by increasing population and infrastructure demands, necessitates innovative solutions to ensure sustainable and efficient urban management. Leveraging the Internet of Things (IoT), Artificial Intelligence (AI), and Machine Learning (ML) offers significant potential to enhance the decision-making capabilities of policy makers in Karnataka’s smart cities. This research paper investigates the effectiveness of these technologies in improving urban governance, focusing on real-time data acquisition, predictive analytics, and informed policy decisions. AI and ML are crucial in the analysis and interpretation of the vast amounts of data generated by IoT devices. AI algorithms process this data to identify patterns, anomalies, and trends, while ML models predict future scenarios based on historical data. For instance, predictive analytics can forecast traffic congestion, energy demand, and potential public health crises, allowing policy makers to deploy preemptive measures. In smart city initiatives, AI-driven insights ensure that resources are allocated efficiently, urban planning is optimized, and public services are enhanced. In conclusion, the integration of IoT, AI, and ML holds transformative potential for enhancing decision-making processes in Karnataka’s smart cities. By providing real-time data, predictive insights, and efficient resource management tools, these technologies enable policy makers to address urban challenges proactively and sustainably.

Fabrication of high-resolution, flexible, laser-induced graphene sensors via stencil masking

The advent of wearable sensing platforms capable of continuously monitoring physiological parameters indicative of health status have resulted in a paradigm shift for clinical medicine. The accessibility and adaptability of such portable, unobtrusive devices enables proactive, personalized care based on real-time physiological insights. While wearable sensing platforms exhibit powerful capabilities for continuously monitoring physiological parameters, device fabrication often requires specialized facilities and technical expertise, restricting deployment opportunities and innovation potential. The recent emergence of rapid prototyping approaches to sensor fabrication, such as laser-induced graphene (LIG), provides a pathway for circumventing these barriers through low-cost, scalable fabrication. However, inherent limitations in laser processing restrict the spatial resolution of LIG-based flexible electronic devices to the minimum laser spot size. For a CO2 laser–a commonly reported laser for device production–this corresponds to a feature size of ∼120 μm. Here, we demonstrate a facile, low-cost stencil-masking technique to reduce the minimum resolvable feature size of a LIG-based device from 120 ± 20 μm to 45 ± 3 μm when fabricated by CO2 laser. Characterization of device performance reveals this stencil-masked LIG (s-LIG) method yields a concomitant improvement in electrical properties, which we hypothesize is the result of changes in macrostructure of the patterned LIG. We showcase the performance of this fabrication method via production of common sensors including temperature and multi-electrode electrochemical sensors. We fabricate fine-line microarray electrodes not typically achievable via native CO2 laser processing, demonstrating the potential of the expanded design capabilities. Comparing microarray sensors made with and without the stencil to traditional macro LIG electrodes reveals the s-LIG sensors have significantly reduced capacitance for similar electroactive surface areas. Beyond improving sensor performance, the increased resolution enabled by this metal stencil technique expands capabilities for scalable fabrication of high-performance wearable sensors in low-resource settings without reliance on traditional fabrication pathways.

Electric buses as an air pollution and meteorological observation network: Methodology and preliminary results

Many local agencies in the United States and other countries are tasked to install air pollution monitoring systems of highly accurate sensors that have high acquisition, operating, and maintenance costs. The need for expanded coverage of air quality measurements across Salt Lake County (SLCO), Utah is being met by mounting air quality and temperature sensors on an expanding fleet of battery electric buses (BEBs). Monitoring air quality from a mobile sensor network provides real-time insights into air pollution patterns at high temporal and spatial resolution. Mobile measurements contribute to assessing residents' exposure to air pollution, facilitating the implementation of cost-effective public health policies and highlighting disparities. The Electric Bus Air Quality Observation Project was launched in SLCO during July 2021 and has collected millions of observations to date. A BEB traveling at typical traffic speeds (~10 m s−1) can provide multiple measurements along city block lengths of up to ~200 m. With careful analysis that factors in the time response of the differing sensors, variability from block-to-block may be attributed to fine-scale factors (e.g., pollution and heat sources, tree shading and urban vegetation, etc.). Preliminary findings showcase the value of increased coverage and resolution. During an extreme heat event in July 2023, both the morning and afternoon temperature readings showed differences of over 6.5 °C (12 °F), primarily as an east-west gradient with similar gradients in ozone. We conclude that temperature and pollutant concentration readings, at fine spatial and temporal resolutions, will facilitate future health studies and equitable policy and mitigation strategies. Our study demonstrates that our partnerships established with governmental, non-profit, and transit agencies facilitate the successful transfer of research and development to operational real-time mobile air quality monitoring.

Unveiling the Future of Li-Ion Batteries: Real-Time Insights into the Synthesis of Advanced Layered Cathode Materials

Unveiling the Future of Li-Ion Batteries: Real-Time Insights into the Synthesis of Advanced Layered Cathode Materials

Analysis of Intelligent Biomass Energy in Energy Monitoring and Consumption Optimization in Physical Education Teaching

Amidst the focus on sustainability and technological advancements, this study explores how intelligent biomass energy can boost physical education efficiency through energy monitoring and consumption optimization. Based on experiments and data analysis, it highlights intelligent biomass energy's potential to provide sustainable support, fulfil teaching needs, improve efficiency, and reduce waste. The paper introduces key concepts of intelligent biomass energy for physical education, offering guidance on its application for energy monitoring and optimization. Analyzing students' physiological data, establishes a scientific teaching foundation, enabling real-time insights and personalized programs tailored to individual differences. The paper also explores the potential application of intelligent biomass energy in physical education, anticipating an increased role with ongoing technological advancements. Offering innovative perspectives for reform and innovation, the study provides valuable insights into the development and implementation of renewable energy technology. The introduction of intelligent biomass energy supports real-time energy consumption monitoring, fostering scientific teaching and optimizing effectiveness. This enhances the scientific and efficient aspects of physical education but also aids students in understanding their physical condition and sports performance, laying a solid foundation for future endeavours.

Ethical Implications of Artificial Intelligence in the Global Healthcare Sector: Trends, Perspectives and Future Developments

This research paper examines the ethical implications of AI in healthcare, covering the benefits and risks of using AI in healthcare services and provision. The paper highlights the applications of AI in healthcare, which can improve efficiency and accuracy of providing healthcare services by health professionals. The benefits of AI cover reducing the need for human intervention and increasing productivity through automation, delivering personalised experiences by recommendations, assisting with informed decision-making by providing real-time data analysis and insights, predicting outcomes or identifying potential threats, improving healthcare and overall customer satisfaction. The paper highlights the ethical implications of the use of AI in healthcare, including privacy and security, bias and discrimination, transparency and explainability, responsibility and accountability, informed consent and human interaction and empathy. The paper recommends that as AI becomes more prevalent in healthcare, establishing clear guidelines for responsible use, and maintaining the importance of human interaction and empathy in patient care, enhances healthcare outcomes while safeguarding patient rights and welfare. Continued research and development of the ethical implications of AI in healthcare for low-income countries as further work can promote the ethical use of AI in healthcare worldwide. Keywords: Artificial Intelligence, Ethics, Ethical Implication, Healthcare, Privacy Journal Reference Format: Adeyemi, Deborah A., Amedior, Nutifafa Cudjoe, Longe Bankole-Philips, Akintunwa Fred, 5Omotosho Seyi & Longe, Olumide B. (2023): Ethical Implications of Artificial Intelligence in the Global Healthcare Sector: Trends, Perspectives and Future Developments. Social Informatics, Business, Politics, L:aw, Environmental Sciences & Technology Journal. Vol. 9, No. 3. Pp 41-53 www.isteams/socialinformaticsjournal. dx.doi.org/10.22624/AIMS/SIJ/V9N3P3

E-SDAPONIKS: A Cloud-Based Aquaponics Management System with Data Analytics

This study endeavors to create an advanced aquaponics management system, elevating monitoring capabilities through the integration of environmental sensors. By harnessing the power of the Internet of Things (IoT) and cloud-based technology, users gain unprecedented control over devices while seamlessly recording and analyzing system data. Complementing this, a user-friendly mobile application provides realtime insights into feed consumption, water parameters, and harvest schedules, enhancing operational efficiency. The Agile Methodology serves as the framework for this project, enabling a flexible and iterative approach to hardware and software development. Key hardware components include temperature and humidity sensors, electrical conductivity sensors, and pH sensors, essential for comprehensive monitoring. Facilitating seamless integration, an ESP32 microcontroller manages these sensors, boasting built-in Bluetooth and Wi-Fi modules to facilitate connectivity with the mobile application. In assessing project performance, adherence to ISO 25010 requirements and IEEE standards validates the system’s robustness and reliability.

Wildlife Tracker using Deep Learning

Wildlife tracker using deep learning is a project aimed at developing a system that can automatically detect wild animals using deep learning techniques. Conservationists and researchers face challenges in accurately and efficiently detecting and monitoring wildlife populations in vast and often remote habitats. Traditional methods of wildlife monitoring, such as manual surveys or camera traps, are labour-intensive, time-consuming, and may not provide real-time insights into population dynamics or threats facing wildlife species. The proposed system aims to address these challenges by developing an automated animal detection system using the YOLOv8 object detection model. The system will be trained on a diverse dataset of wildlife images, encompassing various species and environmental conditions. Upon deployment, the system will analyse input images and accurately identify and localize animals within the scene in real-time. Through this approach, the system will provide conservationists and researchers with timely and actionable information for monitoring wildlife populations, assessing habitat health, and implementing targeted conservation interventions. Evaluation of the system's performance will involve metrics such as detection accuracy, precision, recall, and processing speed, ensuring reliable and efficient wildlife detection capabilities. Overall, the proposed system seeks to enhance wildlife conservation efforts by leveraging advanced technology to improve wildlife monitoring and management practices and provide real-time alert.

The Effect of Smart Grid Technology on Energy Consumption Management in Canada

Purpose: The aim of the study was to evaluate the effect of smart grid technology on energy consumption management in Canada. Methodology: This study adopted a desk methodology. A desk study research design is commonly known as secondary data collection. This is basically collecting data from existing resources preferably because of its low cost advantage as compared to a field research. Our current study looked into already published studies and reports as the data was easily accessed through online journals and libraries. Findings: Smart grid technology has significantly enhanced energy consumption management in Canada. By integrating advanced metering infrastructure and real-time data analytics, smart grids optimize energy distribution, reduce peak demand, and enhance grid reliability. This technology enables better demand-response programs and promotes renewable energy integration, leading to more efficient energy use and reduced environmental impact. Unique Contribution to Theory, Practice and Policy: Diffusion of innovations theory, technology acceptance model (TAM) & the theory of planned behavior (TPB) may be used to anchor future studies on the effect of smart grid technology on energy consumption management in Canada. Smart grid technology enables more efficient energy usage by providing consumers with real-time data and insights into their consumption patterns. The vast amounts of data generated by smart grids necessitate robust policies to protect consumer privacy and ensure data security. Policymakers should establish clear regulations governing the collection, storage, and use of smart grid data.