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By enabling self-driving capabilities, autonomous vehicles (AVs) are revolutionizing transportation. Artificial intelligence (AI) integrating with AVs improves their perception, decision-making, and control systems. Despite AVs' remarkable growth and acceleration, two significant challenges emerge, i.e., distributed data and computational overhead. To address these challenges, we proposed a novel architecture combining split and federated learning (FL) for traffic sign classification in AVs. Our method distributes deep learning (DL) models across multiple AV systems, allowing efficient DL model training and usage. To mitigate computational overhead on vehicular clients, split learning partitions have been applied between the vehicular clients and a server. Additionally, FL advances this approach by simultaneously addressing data decentralization concerns and constructing a strong traffic sign detection model. It achieves this by training the model individually on each client and on that individual client's data and transmitting only crucial weight parameters of the DL model; it also adds a layer of security by avoiding the direct transfer of location-revealing image data. To send model weight parameters in a timely manner, we employed the staggering benefits of a 5G network, which improves the accuracy of applied DL models in each communication round. The proposed architecture offers efficient and real-time model training and utilization for accurate traffic sign identification in AVs by harnessing the capabilities of split learning, FL, and 5G networks. The experimental findings show that our architecture is effective, with a maximum accuracy of 99.54 % on the German Traffic Sign Research Benchmark (GTSRB) dataset with five client architectures, contributing to improving AV technology and developing intelligent transportation systems. Further, we analyze the performance of a 5G network in terms of phase noise and modulation; it is depicted from the experiment that the 5G network is showing noteworthy performance than the 4G network.

Introduction: The birth of a premature baby shocks parents a lot. Although fathers also experience high levels of anxiety, they have received little attention and research. In order to focus on family care, this study was conducted to determine the effect of the first skin contact of fathers with premature infants hospitalized on their anxiety in the neonatal intensive care unit. Methods: This study was a randomized clinical trial with two experimental and control groups. 72 fathers of premature infants admitted to the neonatal intensive care unit of Shohada-e-Kargar Hospital were selected. In the experimental group, fathers made skin-to-skin contact with their infants for half an hour. In the control group, no intervention was performed. The level of anxiety in the experimental and control groups before and after the intervention was determined using the Spielberger Anxiety Questionnaire. The collected data were analyzed using t-test and Chi-square statistical tests in SPSS 16 software. Results: The results showed that before the intervention in the experimental group, the mean scores of trait and state anxiety were 54.28±16.26 and 50.36±14.66, respectively, these mean scores in the control group, were 59.81±14.48 and 58.86±15.41, respectively, which was not statistically significant. However, after the intervention, the mean scores of trait (47.92±10.1) and state anxiety (48.47±9.7) of the experimental group compared to the mean scores of trait (57.72±14.65) and state anxiety (62.47±14.16) of the control group decreased, which showed a statistically significant difference (p

In this work, a lightweight compliant glove that detects scratching using data from microtubular stretchable sensors on each finger and an inertial measurement unit (IMU) on the palm through a machine learning model is presented: the SensorIsed Glove for Monitoring Atopic Dermatitis (SIGMA). SIGMA provides the user and clinicians with a quantifiable way of assaying scratch as a proxy to itch. With the quantitative information detailing scratching frequency and duration, the clinicians would be able to better classify the severity of itch and scratching caused by atopic dermatitis (AD) more objectively to optimise treatment for the patients, as opposed to the current subjective methods of assessments that are currently in use in hospitals and research settings. The validation data demonstrated an accuracy of 83% of the scratch prediction algorithm, while a separate 30 min validation trial had an accuracy of 99% in a controlled environment. In a pilot study with children (n = 6), SIGMA accurately detected 94.4% of scratching when the glove was donned. We believe that this simple device will empower dermatologists to more effectively measure and quantify itching and scratching in AD, and guide personalised treatment decisions.

Abstract Effective containment of the COVID-19 pandemic requires rapid and accurate detection of the pathogen. Polymerase chain reaction (PCR) remains the gold standard for COVID-19 confirmation. In this article, we report the performance of a cost-effective modular microfluidic reverse transcription (RT)-PCR and RT-loop mediated isothermal amplification (RT-LAMP) platform, Epidax®, for the point-of-care testing and confirmation of SARS-CoV-2. This platform is versatile and can be reconfigured either for screening using endpoint RT-PCR or RT-LAMP tests or for confirmatory tests using real-time RT-PCR. Epidax® is highly sensitive and detects as little as 1 RNA copy per µL for real-time and endpoint RT-PCR, while using only half of the reagents. We achieved comparable results with those of a commercial platform when detecting SARS-CoV-2 viruses from 81 clinical RNA extracts. Epidax® can also detect SARS-CoV-2 from 44 nasopharyngeal samples without RNA extraction by using a direct RT-PCR assay, which shortens the sample-to-answer time to an hour with minimal user steps. Furthermore, we validated the technology using an RT-LAMP assay on 54 clinical RNA extracts. Overall, our platform provides a sensitive, cost-effective, and accurate diagnostic solution for low-resource settings.

The aim of this study is to determine central macular thickness (CMT) and subfoveal choroid thickness (SFCT) in eyes with inferior oblique muscle overaction (IOOA) using spectral domain optical coherence tomography (SD-OCT) with enhanced depth imaging (EDI) mode, as well as macular layers using OCT segmentation software. In this retrospective study, OCT parameters collected from 42 eyes with unilateral IOOA were compared to those from 39 contralateral healthy eyes. The CMT and SFCT were measured using EDI SD-OCT, and each retinal layer was measured using SD-OCT in the central 1, 3, 6 mm areas identified by the Early Treatment Diabetic Retinopathy Study (ETDRS). Heidelberg segmentation was used to define the different layers of the macula. There were no morphological abnormalities in the macula in either eyes with unilateral IOOA or contralateral healthy eyes. The CMT, SFCT, and retinal layer thickness did not differ statistically between IOOA eyes and contralateral healthy eyes (P>0.05). Subgroups of IOOA eyes based on concomitant strabismus, which included IOOA only, IOOA plus esotropia, and IOOA plus exotropia, or by degree of IOOA, which included mild and severe forms, also revealed negligible differences in CMT, SFCT, and other retinal layer thickness (P=0.34, P=0.91, P>0.05, respectively) The total thickness of the ganglion cell and inner plexiform layer decreased non-significantly in severe IOOA compared to mild IOOA (P=0.059). Furthermore, the outer plexiform layer was non-significantly thicker in IOOA plus esotropia eyes (p=0.052). Despite the fact that IOOA had no discernible impact on CMT, SFCT, or macular layers, it is critical that this finding be confirmed by further large-scale studies comparing patients to healthy individuals. [Med-Science 2022; 11(2.000): 722-8]

AbstractIdentity is classically assumed a crucial part of one’s psychological state that guides how we behave and how we fit into society. Identity in discursive psychology is a dynamic construction which is attained through social interaction and discourse. The latter has definite role in construction of embodied identity. This paper shows the possibility of discursive psychology for understanding embodiment and identities in different research setting. Then, a second goal is to challenge the boundaries of embodiment and the limits of a Cartesian Model of bodies and identities. Discursive psychology is an analytic and theoretical approach to discourse, which focuses on speech and writing, believing that psychological phenomena should be understood in social contexts. This study is a qualitative method of data analysis. The interviewees are ten female bloggers between the age of twenty to thirty, from homogeneous areas in Tehran. The data are collected through blogger's interviews and live broadcasts on Instagram. The results demonstrate that the embodied identity are formed through every day and simple practices and that human physics plays a crucial role in the construction of embodied identity. It has also emphasized the significance of subject-object construction in identity work and the consequences of this discursive constructions that blur the boundary between self (subject) and bodies (object). Blurring this boundary could at times decrease the agency of the speaker, where physical or bodily processes are oriented as beyond the conscious or active control of the individual.

In today's rapidly advancing world, more deliberate development and implementation of energy‐efficient thermal management systems is emergent. In industrial and engineering contexts, the regulation of heat and mass transfer phenomena is heavily swayed by factors such as heat and mass fluxes, and wall temperature, and concentration levels. In cryogenic containers, understanding heat flux and wall temperature is essential for maintaining ultra‐low temperatures over extended periods. In this context, our paper embarks on modeling the transient dynamics of a radiant Casson fluid adjacent to a steeped plate, which is featured by its emission of both heat and mass fluxes. The plate is under two thermal conditions, namely uniform wall temperature (UWT) and uniform heat flux (UHF), which are crucial in modulating both upward and downward heat transport processes. The Laplace transform (LT) technique yields close‐form solutions for the model's governing equations. The distribution of flow and salient physical quantities are graphed and elucidated in response to intricate physical parameters. A comparative graphical analysis of UWT and UHF scenarios reveals stark differences. Notably, the fluid's flow rate appears considerably amplified in the UWT scenario compared to the UHF one. Furthermore, the UHF setting profoundly influences the heat and mass transportation within the fluid's flow realm. A pronounced Casson parameter tends to thin out the velocity profile. Concurrently, a heightened radiation parameter results in a reduction in fluid temperature. The theoretical framework explored in our study is not just an academic exercise but holds tangible applicability across varied sectors. This spans from rocket chamber cooling, where fine‐tuned heat flux conditions are paramount for safeguarding operations, to industries like cosmetics and food processing, which demand meticulous thermal regulation to ensure product excellence. [ABSTRACT FROM AUTHOR]

The environment and agricultural output are being challenged by weeds and crops. Innovative advancements in alternative weed control techniques that seek to reduce the need on herbicides have been spurred by the growing demand for sustainable weed control methods. Having weed recognition that is sufficiently successful is a hurdle to the implementation of these methods for selective in-crop application. Deep learning has shown remarkable promise in a variety of vision tasks, leading to the development of several effective image-based weeds and crops identification systems. This study looks at the newest developments in deep learning techniques for pixel-wise semantic segmentation of identifying crops and weeds. The hardest problem is semantic segmentation-based weeds and crops recognition, which needs to be solved for smart farming to work well. The goal is to give each pixel of a picture its own class name. Deep learning for smart farming has a number of useful applications, one of the most important of which is identifying the exact position of crops and weeds on farms. There are already quite complex systems for separating weeds and crops, with millions of factors that need more time to train. To get around these problems, we suggest an AgriResUpNet design based on deep learning. It is a carefully put together mix of the U-Net and residual learning frameworks. To see how well the suggested model works on the crop and weed GitHub dataset in terms of pixel accuracy, precision, f1-sore, and IoU measures. The suggested network is tested and compared with other cutting-edge networks using a crop and weed dataset that is open to the public. Also, the AgriResUpNet model gave an IoU of 97.58% for weeds and 94.82% for crops. In addition, the data show that the AgriResUpNet can easily find both crops and weeds. This suggests that this design is better for finding weeds early in the growing season. It was shown in experiments and comparisons that the suggested network does better than existing designs in intersection over union (IoU) and F1-score. [ABSTRACT FROM AUTHOR]

This paper focuses on demonstrating the shear‐driven convective flow of an ionic optically thin fluid in a narrow channel formed by two vertical parallel plates subject to a Hall electric field. The Hall electric field induces Hall currents, amending the flow dynamics of the ionic fluid. The setup involves a stationary left wall and a right wall that either undergoes impulsive motion (IM) or accelerated motion (AM), which initiates the fluid flow. A unified closed‐form solution for flow‐regulating equations is derived by harnessing the Laplace transform (LT) approach. The upshots of cardinal parameters on the velocity components and temperature distributions, shear stresses, and rate of heat transfer (RHT) are elucidated via graphics for both IM and AM scenarios. The graphs reveal that an intensification in the Hall parameter notably boosts the velocity components in both IM and AM cases. The primary and secondary velocities are consistently higher for IM than AM. The magnitude of shear stresses at the moving wall is always greater for IM than AM. Additionally, the shear stresses at the moving wall are notably greater for IM than AM, and the RHT at the moving wall reduces as the radiation parameter amplifies. The significant findings of this research have potential applications in electromagnetic propulsion systems, like plasma or ion thrusters, commonly employed in propelling spacecraft. [ABSTRACT FROM AUTHOR]

Introduction: Today, one of the best tools available for value chain engineering is applying the practical lessons of business models. By analyzing a business model, the processes of development, presentation and receipt of value by a company can be improved. Although many companies operating in different industries of the country have increasingly turned to the analysis of their business models, the issue is moving forward at a lower speed in the pharmaceutical industry. Numerous new business models have been developed or renovated in today’s changing business environment. The development and renovation of new business models are not a matter of choice but a necessity in order to survive in an environment whose rules of the game are constantly changing. In such a situation, the greatest challenge posed to organizations is developing a framework through which an accurate understanding of the dimensions and components of their business models can be achieved and a point of reference can be offered to compare their business models with those of the competing companies to enable innovation and outperformance. The main focus of this study is to find a practical framework for pharmaceutical companies so as to identify and compare the key features of their business models with those of active competitors, hence to understand the strengths and positive qualities of other models and overcoming their weaknesses.In this study, the latest scientific findings about the business models of pharmaceutical companies are presented, and the common types of these business models are identified using taxonomy (experimental typology) to achieve a more comprehensive and accurate view of the dynamics of this business model in companies.Methodology: The statistical population of this study included all the Iranian companies producing human drugs that have had a competitive product in the growth stage. The study was conducted in two phases, content analysis and quantitative phase. Initially, semi-structured interviews during the content analysis phase revealed a comprehensive framework for the dimensions and components of the business model of Iranian pharmaceutical companies, and then, through quantitative surveys and in-person questionnaires, the business model of each pharmaceutical company was identified. The sampling method in the qualitative part of the research was judged after 19 theoretical saturation interviews, and the quantitative research became available. By using the PASS software, an optimal sample of 40 companies was adopted. In this study, the empirical classification (taxonomy) approach, rather than the ideal classification (typology), was used to identify various business models. In clustering methods, since the data are classified irrespective of any presupposition or predetermined framework, the fuzzy c-means (FCM) clustering algorithm serves as the most appropriate tool to attain the typological objective of this study.Results and Discussion: Finally, by fuzzy clustering with the MATLAB software, four distinct variants of the business model were identified for the pharmaceutical companies, each having significant differences in the key features of the business models with the other types. These four types included Diverse Effectiveness, Concentrated Effectiveness, Diverse Effectiveness, Concentrated Effectiveness. The identified types have specific similarities and differences in each business model dimension and component which can be concisely compared as follows based on their key features including concentration on quality rather than on efficiency, extent of relationships in various areas such as sales, marketing, and raw material supply.Conclusion: The competitive pharmaceutical products in Iran can be categorized in four distinct types in terms of the characteristics of their business model. These models have different characteristics in terms of the amount of value proposition, the processes of shaping the value architecture, the mechanisms for interaction with different groups of customers and value acquisition methods. Finally, these types of business models were named based on their proprietary features as: diversified effective, focused effective, diversified efficient and focused efficient. The first business model type was labeled “diversified effective” as it is greatly focused on quality and extensive relationships with various stakeholders of the value chain. Companies such as CinnaGen, Exir, SobhanDarou, DarouPakhsh, Osvah, and Samisaz had the greatest degree of membership in this business model type. The second type was labeled “diversified efficient” as it is highly concentrated on cost control and extensive relationship maintenance with the value chain stakeholders. Companies such as ParsDarou, TehranShimi, AlborzDarou, TehranDarou, and Actoverco had the greatest degree of membership in this business model type. The third type was labeled “focused efficient” and included such pharmaceutical companies as Hakim, CaspianTamin, Sepidaj, Loghman, and Jalinous which had the greatest degree of membership in this business model type. Finally, the fourth type was labeled “focused effective” and involved companies such as Shafa, Ronak, DinehIran, and Modava with the greatest degree of membership therein.