The aim of this paper is to consider whether critical rationalism has any ideas which could usefully be applied to the INTERNET.  Today we tend to take the INTERNET for granted AND it is easy to forget that it was only about two decades ago that it began to be used to any significant extent. Accordingly in section 1 of the paper, there is a brief consideration of the history of the INTERNET.  At first sight this makes it looks implausible that any of Popper’s ideas could be applicable to the INTERNET, since Popper died before the INTERNET came into general use.  However, section 2 argues that Popper’s theory of World 3 does apply very well to the INTERNET.  This application is significant because, as shown in section 3, it leads to the problem of misinformation, which is one of the most significant problems generated by the INTERNET.  In section 4 there is an attempt to solve this problem using ideas taken from Popper’s epistemology. It is argued that there should be changes in education designed to prepare students for the INTERNET age. Teaching in the INTERNET age should focus on presenting to the students not just the accepted theories but also the evidence on which they are based. An illustration of how this might be done is given by considering an example from science teaching, namely the teaching of Newtonian mechanics in the last years of school or first years of university.

Introduction: The INTERNET of THINGS is a system of connected physical objects that are accessible through the INTERNET. It has been widely applied to connect available medical resources AND provide reliable, effective AND smart healthcare SERVICES to people. Therefore, the aim of this paper was to identify AND rank the INTERNET of THINGS in healthcare SERVICES. Methods: In this applied research, conducted in 2018, a mixed-method approach was used. In the qualitative phase, library resources AND in the quantitative phase, a pairwise comparison questionnaire were used. A panel of experts consisting of seven members ranked the INTERNET of THINGS SERVICES. An interval type-2 fuzzy Analytic Hierarchy Process method was used for ranking the SERVICES. Results: The results revealed the importance of chronic disease management, indirect emergency SERVICES fall detection, patient profile, adverse drug reaction, children health information in the INTERNET of THINGS healthcare SERVICES. Conclusion: Policymakers working in the field of the INTERNET of THINGS should be aware of its role in saving human lives. Moreover, investment in the INTERNET of THINGS can help monitor aging people, patients AND children. UnderstANDing the importance AND ranking of INTERNET of THINGS SERVICES may help managers AND policymakers manage healthcare sectors more effectively.

Introduction: Intelligent emergency response systems utilize modern technologies such as the INTERNET of THINGS (IoT) to enhance the performance of emergency response units. These systems are designed to improve service quality, reduce costs, AND increase monitoring of the emergency response process. Key objectives include optimizing emergency response routes through communication with objects AND collecting spatial data. Utilizing IoT-based routing models enables optimizing emergency response routes AND enhancing the overall user experience. In essence, these systems leverage data collected by IoT to enhance the emergency response process. Intelligent emergency response systems play a crucial role in improving the efficiency of emergency response units AND elevating service levels in emergencies. These systems are readily available AND enhance productivity AND efficiency in emergencies. Materials AND Methods: A spatial data infrastructure has been developed to integrate the system AND enhance emergency response efforts, providing critical capabilities for improving emergency medical SERVICES. This infrastructure includes a portal that accurately displays the optimal route from the incident location to the medical center on a map, assisting the medical team in quickly AND efficiently reaching the injured individual. Additionally, this portal enables the transfer of sensor information, such as vital signs of the injured person, to the physician's mobile phone in the ambulance via Bluetooth. This allows the information to be shared simultaneously for further assessment, enabling quick AND accurate assistance in emergencies. This system increases efficiency AND speed in responding to emergencies, providing rapid AND optimal access to medical SERVICES. In summary, this spatial data infrastructure has significantly improved the performance of emergency medical response AND facilitated the delivery of enhanced AND optimized SERVICES in emergencies. Results AND Discussion: Medical centers prioritize healthcare AND treatment. They employ an online hierarchical weighting model to ascertain these priorities AND enhance the efficiency of resource allocation processes. This model helps optimize resource allocation based on real-time health information of the injured individuals. In a trial case, an injured person was successfully treated in District 5 of Tehran. The efficient use of IoT AND spatial data infrastructure enabled this medical center to enhance AND optimize its healthcare SERVICES. These findings underscore the significance of integrating spatial information, medical data, AND IoT technology to advance healthcare SERVICES AND elevate the quality of treatment. Conclusion: Traditional emergency response systems operate primarily based on outdated mechanisms AND lack modern technologies, including IoT AND spatial data integration. Consequently, these systems may encounter challenges such as delays in dispatching emergency personnel to the incident location AND a lack of accurate AND rapid patient information. Incorporating modern technologies like artificial intelligence, IoT, AND geographic information systems can address the challenges faced by traditional emergency response systems. These technologies enable faster AND more efficient crisis responses AND assist organizations, such as crisis management agencies, in making better resource allocation decisions during emergencies, thereby improving overall performance. By utilizing data collected through these technologies, emergency organizations can significantly enhance their response to emergencies, reducing time, financial, AND human costs. Overall, this new approach to emergency response systems enables better adaptability in facing various crises AND improves emergency response efficiency.

Lactation length is different in individual cows, which is generally converted to a 305-day stANDard using curve fitting models for genetic AND management practices. Individual curves do not have a stANDard shape in all cases, AND can deviate from the stANDard pattern according to factors such as individual differences, AND type of fitted models. These non-stANDard curves, called atypical, resulted from incorrect estimated parameters of the curves; which consist of: continuously increasing or decreasing AND reversed stANDards. This study was conducted to investigate the importance of atypical curves in estimation of 305-day milk production, by fitting two nonlinear models? Wood (empirical) AND Pollott (biological), on 7659 AND 6692 test-day milk yield of 977 AND 776 first calving Iranian Simmental AND Jersey cows, during 2007-2020, using R software. Different patterns obtained based on the combination of increasing (b) AND decreasing (c) phase parameters of curves. The number of stANDard curves from the Pollott AND Wood models were 85.5% AND 62.2% for Simmental, AND 83.1% AND 70.6% for Jersey cows, respectively. Only continuously increasing curves were observed in both breeds in Pollott model (14.8% AND 16.9%, Simmental AND Jersey cows, respectively); Whereas in Wood model, all 3 groups of atypical curves were observed, which the reversed stANDard was the most (22.3% AND 16.5%, Simmental AND Jersey cows, respectively). Based on the findings, at the time of stANDardizing the production of dairy cows (national evaluations), not only differences between breeds, but also special attention to the production of atypical curves, should be paid (to correct or discard them).

Background: The expansion of the use of audio AND video media technology has faced society with many medical, cultural AND economic challenges. In the meantime, excessive use of mobile phones can cause many problems. Objective: The aim of this study was to construct AND validate a questionnaire on physical AND psychological injuries of mobile phones. Method: The method of the present study was descriptive-correlational AND confirmatory factor analysis. The statistical population of the study consisted of all citizens of Khorramabad with a diploma or higher in 1399, which was selected as a statistical sample by available sampling method of 200 people online AND virtual. Results: The results showed that the factorized physical AND psychological harm questionnaire of cell phone with 42 questions AND 10 components (fear of loss, vibration syndrome, duck syndrome, nomophobia, insomnia, hearing problem, eye syndrome, tunnel AND neck syndrome SMS) AND has good reliability, validity AND flexibility in terms of psychometrics. Discussion AND Conclusion: The questionnaire of factor analysis of physical AND psychological injuries of mobile phones can be used to identify AND study the physical AND psychological injuries of mobile phones.

To investigate the effect of vermicompost, N fertilizer AND their integration on growth, biological AND essential yields oil AND its components in two populations (Varamin AND Isfahan), an experiment was conducted in a rANDomized complete block design with three replications in the field of Isfahan Medicinal Plants Research Center in 2016. Fertilizer treatments including 100% urea, vermicompost 33.3 AND urea 66.6%, vermicompost 66.6 AND urea 33.3%, 100% vermicompost AND control treatment (without fertilizer) as the first factor AND Varamin AND Isfahan populations as the second factor were selected. The results showed that the interaction effects of nitrogen fertilizer AND populations on all studied traits were significant. In both populations, nitrogen fertilizer improved height, biological AND essential oil yields, content of essential oil compounds (d-Carvone AND its yield, α-phellANDrene AND linalool; except p-Cymene AND Limonene in Isfahan population). Since in medicinal plants, the quantity (biological yield) AND quality (essential oil AND d-Carvone; the most important ingredient AND the highest amount of essential oil in this experiment), 66.6% vermicompost + 33.3% urea in Varamin population, was the best treatment in the experiment. In addition an in the direction of human health AND sustainable agriculture, it is possible to reduce 33% of chemical fertilizers application AND pollution; however, in terms of other essential oil contents (α-phellANDrene, Linalool AND p-Cymene), 100% vermicompost fertilizer treatment was superior in Isfahan population.

Objective: In many countries, the advancements in information technologies AND the decentralized computing method has inspired hope to take advantage of modern information technologies, such as artificial intelligence, in order to overcome the unique challenges in global health including the coronavirus crisis. The following study tries to examine the possible future scenarios in the industry, with the aim of discovering new opportunities to increase health AND quality of life.Findings: Considering the research literature, seventeen drivers, which had affected the research in three dimensions, were identified AND the Delphi method was used for the accuracy of confirmation, their rankings AND for the calculation of their certainty. The relationships between the drivers AND the identified dimensions were measured AND confirmed in the form of hypotheses AND model presentations by structural equation methods. Using the cross-matrix method, five drivers were identified as risk AND goal. Finally, through the identification of uncertainty AND risk AND goal drivers, four scenarios were developed.Conclusion: Conclusion: The treatment structure AND medical equipment influenced by COVID-19 pANDemic conditions AND due to the growth rate, optimal AND intelligent application of modern digital technologies creates a wide-ranging evolution in this field AND illustrates the possibility of achieving a desired future by taking advantage of the scenario of the formation of an interconnected global society AND the realization of global health in the interaction of artificial intelligence drivers, novel production demANDs, sanctions, INTERNET of THINGS (IoT) AND blockchain.

Background: The agricultural sector is a crucial component of a nation's economy, playing an indispensable role in its economic development. This sector not only ensures food security but also provides employment opportunities AND contributes to rural development. The integration of technology into agriculture is vital for increasing production, enhancing efficiency, AND facilitating access to global markets. Recent advances in emerging technologies have led to a significant paradigm shift within the agricultural lANDscape. This transformation, driven by digital technologies, is increasingly recognized as a flexible AND sustainable solution for modernizing agricultural practices. This research aims to investigate technological trends AND their functions in the agricultural value chain, ultimately facilitating a successful transition to smart agriculture. By exploring these trends, the study seeks to highlight the potential benefits of adopting innovative technologies in agriculture, including improved productivity, better resource management, AND enhanced sustainability. Methods: This research is practical in its objectives AND systematic in its approach to data collection. The statistical population includes all English-language articles published in the field of smart agriculture between 2011 AND 2022, sourced from the Web of Science AND Scopus databases. In the initial phase, 3,257 articles were identified AND analyzed using Vos Viewer for scientometric analysis. This software allows for the visualization of scientific data AND the mapping of research trends, providing insights into the evolution of the field. Following this analysis, the research question was formulated, AND the Prisma approach was applied to refine the selection of articles. Ultimately, 33 articles were chosen for a comprehensive qualitative study. This combination of quantitative AND qualitative methodologies enables a thorough exploration of the topic, offering both statistical insights AND in-depth analyses of the selected literature. Results: In the quantitative section of this research, knowledge maps were utilized to illustrate the growth trend of scientific articles in the field of smart agriculture. This analysis included the identification of key journals, research centers, AND active countries contributing to this body of work. Furthermore, the study pinpointed authors with the highest publication outputs AND assessed scientific networks to evaluate their influence within the field. The results indicate a growing interest in smart agriculture, reflected in the increasing number of publications AND collaborations among researchers. In the qualitative section, the research findings reveal that the INTERNET of THINGS (IoT) is the most significant technological domain in the agricultural sector. IoT enables the interconnectivity of devices AND systems, facilitating real-time data collection AND monitoring, which are essential for informed decision-making in agriculture. Other important technologies identified include artificial intelligence, machine learning, big data analytics, blockchain technology, wireless sensor networks, remote sensing, various types of sensors, cloud computing, fog AND edge computing, AND low-power wide-area networks. These technologies can be categorized into four primary groups based on their functionalities: data collection, computation AND communication, data security AND integration, AND prediction AND classification. Data collection technologies facilitate the gathering of agricultural data from various sources, enabling farmers to make data-driven decisions. Computation AND communication tools process AND transmit data efficiently, ensuring timely access to critical information. Data security AND integration solutions protect sensitive agricultural data while integrating various systems for seamless operation. Prediction AND classification technologies assist in forecasting agricultural trends AND categorizing data for better management practices. The insights generated from this research are expected to serve as a critical foundation for managers AND policymakers. UnderstANDing the current technological lANDscape will enable them to formulate strategies, policies, AND development programs that effectively leverage these innovations to enhance productivity AND sustainability in the agricultural sector. Conclusion: Based on the identified trends, it is clear that government intervention is essential for fostering the development of smart agriculture. By adopting strategic measures AND creating appropriate frameworks, the government can significantly contribute to this sector's advancement. Key actions include developing technical infrastructure, investing in human capital, supporting research AND development initiatives, encouraging smart agricultural practices, providing financial support, AND establishing guaranteed markets for agricultural products. These efforts will not only enhance productivity in the agricultural sector but also promote sustainable development, ultimately leading to an improvement in the country's capabilities. The transition to smart agriculture represents a significant opportunity to address the challenges faced by the agricultural sector, including climate change, resource scarcity, AND the need for increased food production. By embracing technological advancements AND fostering an environment conducive to innovation, nations can ensure the long-term viability AND competitiveness of their agricultural industries. In summary, the findings of this study underscore the importance of technological integration within the agricultural value chain. As the sector continues to evolve, stakeholders must remain vigilant AND adaptive to emerging trends, ensuring that they harness the full potential of technology to drive growth AND sustainability. Future research should continue to explore the impacts of these technologies on agricultural practices AND investigate additional strategies for enhancing their adoption across diverse agricultural contexts. This comprehensive approach will not only benefit individual farmers but also contribute to the broader economic AND social well-being of communities, ultimately paving the way for a more resilient AND sustainable agricultural future.

Face recognition from digital images is used for surveillance AND authentication in cities, organizations, AND personal devices. INTERNET of THINGS (IoT)-powered face recognition systems use multiple sensors AND one or more servers to process data. All sensor data from initial methods was sent to the central server for processing, raising concerns about sensitive data disclosure. The main concern was that all data from all sectors that could contain confidential information was placed in a central server. Federated learning can solve this problem by using several local model training servers for each region AND a central aggregation server to form a global model in IoT networks. This article presents a novel approach to optimize data transfer AND convergence time in federated learning for a face recognition task using Non-dominated Sorting Genetic Algorithm II (NSGA II). The aim of the study is to balance the trade-off between training time AND model accuracy in a federated learning environment. The results demonstrate the effectiveness of the proposed approach in reducing data transfer AND convergence time, leading to improved performance in face recognition accuracy. This research provides insights for researchers AND practitioners to enhance the efficiency of federated learning in real-world applications.