Human-Computer Interaction (HCI) has evolved significantly with advances in Artificial Intelligence (AI) and Computer Vision (CV), paving the way for more intelligent and adaptive user interfaces. This paper explores the design and implementation of user interfaces that leverage AI and CV to create more intuitive, responsive, and personalized experiences. By integrating techniques such as facial recognition, gesture control, and eye tracking, interfaces can adapt in real-time to users’ actions and intentions, thus enhancing accessibility, efficiency, and engagement across various applications, from healthcare to gaming. The study reviews current advancements and challenges in using AI and CV for HCI, including data privacy, model interpretability, and computational efficiency. Experimental results demonstrate the potential of these technologies to improve interaction quality and user satisfaction. This work aims to contribute to the growing field of intelligent HCI design, offering insights into practical implementations and future research directions in AI-driven interface development.

Abstract In This research we will describe how to deal the system with users to make the work in the company or in any work space . in application forms interact with users inside the system . application form of any system must creates a many graphical interface for user ant we should take care about who is dealing with the system , Additionally, application forms used from different type of user so that we must take care about this points during designing the system for different users . Application Forms in the system consists of many programs, or components and function we are designing it. the instructions inside program can be execute by clicking on the command button and we can create specific process in the system.

The integration of Augmented Reality (AR) into Human-Robot Interaction (HRI) represents a significant advancement in collaborative technologies. This paper provides a comprehensive review of AR applications within HRI, emphasizing their role in enhancing collaboration, trust, and safety. By aggregating findings from numerous studies, this research highlights key challenges, including the need for improved situational awareness, enhanced safety, and more effective communication between humans and robots. A framework developed from the literature is presented, detailing the critical elements of AR necessary for advancing HRI. The framework outlines effective methods for continuously evaluating AR systems for HRI. This structured approach focuses on enhancing collaboration and safety, with a strong emphasis on integrating best practices from Human-Computer Interaction (HCI) centered around user experience and design.

In recent years, with the rapid development of computer vision technology and the popularization of intelligent hardware, as well as people’s increasing demand for intelligent products for human-computer interaction, visual grounding technology can help machines and humans identify and locate objects, thereby promoting human-computer interaction and intelligent manufacturing. At the same time, human-computer interaction is constantly evolving and improving, becoming increasingly intelligent, humane, and efficient. This paper proposes a new VG model and designs a language verification module that uses language information as the main information to increase the model’s interactivity. Additionally, we propose the combination of visual grounding and human-computer interaction, aiming to explore the research status and development trends of visual grounding and human-computer interaction technology, as well as their application in practical scenarios and optimization directions, to provide references and guidance for relevant researchers and promote the development and application of visual grounding and human-computer interaction technology.

Phosphorus (P) is an essential element in food production, but it is often largely unavailable for plants as it persists in soils in immobilized forms. Farmers have been using P fertilizers extensively to overcome this unavailability, leading to the exhaustion of non-renewable deposits of phosphorite, rising fertilizers prices and causing several environmental impacts such as eutrophication. Its large-scale utilization is also inefficient since most of the added P becomes quickly unavailable for the plants. Therefore, farming solutions that improve P uptake, P use efficiency of plants and P mobilization in soils are highly important. In this narrative review, we summarized and explored the link between the Silicon (Si) and P cycles in soils. Silicon is quasi-essential for plants, and it has been established that its presence in soil and uptake by plants has several benefits, especially in toxic and nutrient-deficient conditions. Here, we established that Si in soils can affect crop production in P-deficient conditions through: 1) the pH effect – Si improves organic matter decomposition and decreases Al3+ availability; the Si uptake effect – the deposited Si in the plant improves its photosynthetic performance, improves the root ability to uptake P, improves organic matter decomposition and increases P/Mn ration in plant, improving P use; and 3) the Si soil effect – Si stimulates phosphorite rocks weathering and competes with P for the binding places at the surface of Ak and Fe hydroxides, releasing P into soil solution. This positive effect of Si on P mobilization in soils and consequent use by crops should be further disseminated to farmers and other relevant stakeholders.

With the emergence of new VR/AR technologies, the question arises of how they can be used in human-swarm interaction. Where are the limitations and boundaries of conventional interfaces, and how can VR/AR overcome them? To find out, a user study was conducted. The subjects were able to interact with an autonomously acting swarm of drones using both a conventional desktop interface (Desktop-HSI) and a newly developed VR interface (VR-HSI).The resulting assessments are used as a basis for determining the extent to which this new technology can be applied in human-swarm interaction. The technical basis was an already tested simulation software that was expanded with a network interface to allow interacting with a drone swarm using connected HSIs (Human-Swarm Interfaces). Standardized communication was emphasized to enable the development of HSIs with different requirements. Specifically for this work, a VR-HSI was developed with Unity and a desktop-HSI with Java and their performance regarding human-swarm interaction was evaluated.

Multimodal user interfaces promise a natural and intuitive human machine interactions. But is the extra effort for the development of a complex multi-sensor system justified, or can users also be satisfied with one input modality already? This study investigates interactions in an industrial weld inspection workstation. Three unimodal interfaces, including spatial interaction with buttons augmented on a workpiece or a worktable, and speech commands, were tested individually and in a multimodal combination. Within the unimodal conditions, users preferred the augmented worktable, but overall, the interindividual usage of all input technologies in the multimodal condition was ranked best. Our findings indicate that the implementation and use of multiple input modalities is valuable, and that it is difficult to predict the usability of individual input modalities for complex systems.

The Brain-Computer Interface (BCI) technology has emerged as a groundbreaking innovation with profound implications across diverse domains, particularly in healthcare. By establishing a direct communication pathway between the human brain and external devices, BCI systems offer unprecedented opportunities for diagnosis, treatment, and rehabilitation, thereby reshaping the landscape of medical practice. However, despite its immense potential, the widespread adoption of BCI technology in clinical settings faces several challenges. These include the need for robust signal acquisition and processing techniques, ensuring user safety and privacy, addressing ethical considerations, and optimizing user training and adaptation. Overcoming these challenges is crucial to unleashing the complete potential of BCI technology in healthcare and realizing its promise of personalized, patient-centric care. This review work underscores the transformative potential of BCI technology in revolutionizing medical practice. This paper offers a comprehensive analysis of medical-oriented BCI applications by exploring the various uses of BCI technology and its potential to transform patient care.

Experiential learning, defined as the result, source, or context of knowledge obtained from direct experience, is increasingly bolstered by technology and human-computer interaction (HCI) principles and protocols. This article will describe how technology-based human-computer interaction (HCI) has been used in experiments to enhance direct knowledge experience. In this paper, immersive technologies (such as Virtual Reality, Augmented Reality, and haptics) will be described based on cognitive learning theory, empirical research and practical experience gained from implementing cutting-edge technology and maximizing HCI principles in constructive learning. Additionally, this article will describe how AI can be used to create individualized learning environments according to a student's needs in the future. While cost, technical issues and specialised trainings are the obstacle to deliver personalized learning experiences especially to economically disadvantaged students, technological change opportunities are projected, and these technologies will take educational outcomes to the next level.

As the global population rapidly ages with longer life expectancy and declining birth rates, the need for healthcare services and caregivers for older adults is increasing. Current research envisions addressing this shortage by introducing domestic service robots to assist with daily activities. The successful integration of robots as domestic service providers in our lives requires them to possess efficient manipulation capabilities, provide effective physical assistance, and have adaptive control frameworks that enable them to develop social understanding during human-robot interaction. In this context, human factors, especially quantifiable ones, represent a necessary component. The objective of this paper is to conduct an unbiased review encompassing studies on human factors studied in research involving physical interactions and strong manipulation capabilities. We identified the prevalent human factors in physical human-robot interaction (pHRI), noted the factors typically addressed together and determined the frequently utilized assessment approaches. Additionally, we gathered and categorized proposed quantification approaches based on measurable data for each human factor. We also formed a map of common contexts and applications addressed in pHRI for a comprehensive understanding and easier navigation of the field. We found out that most of the studies in direct pHRI (when there is direct physical contact) focus on social behaviors with belief being the most commonly addressed human factor type. Task collaboration is moderately investigated, while physical assistance is rarely studied. In contrast, indirect pHRI (when the physical contact is mediated via a third item) studies often involve industrial settings, with physical ergonomics being the most frequently investigated human factor. More research is needed on human factors in direct and indirect physical assistance applications, including studies that combine physical social behaviors with physical assistance tasks. We also found that while the predominant approach in most studies involves the use of questionnaires as the main method of quantification, there is a recent trend that seeks to address quantification approaches based on measurable data.

Virtual Reality (VR) is increasingly used in neuropsychological assessments due to its ability to simulate real-world environments. This study aimed to develop and evaluate the Trail Making Test in VR (TMT-VR) and investigate the effects of different interaction modes and gaming skills on cognitive performance. A total of 71 young female and male adults (aged 18-35) with high and low gaming skills participated in this study. Participants completed the TMT-VR using three interaction modes: eye-tracking, head movement, and controller. Performance metrics included task completion time and accuracy. User experience, usability, and acceptability of TMT-VR were also examined. Results showed that both eye-tracking and head movement modes significantly outperformed the controller in terms of task completion time and accuracy. No significant differences were found between eye-tracking and head movement modes. Gaming skills did not significantly influence task performance using any interaction mode. Participants reported high usability, acceptability, and user experience for the TMT-VR. Eye-tracking facilitated higher accuracy, while head movement higher speed. The TMT-VR offers high usability and user experience and is well accepted by the participants. The findings suggest that VR-based assessments can effectively measure cognitive performance without being influenced by prior gaming skills, indicating potential applicability for diverse populations.

Depictions of robots as romantic partners for humans are frequent in popular culture. As robots are becoming part of human society, they gradually assume the role of partners for humans wherever necessary: as assistants, collaborators, or companions. Companion robots are sup-posed to provide social contact to those humans who could not have it otherwise. These com-panion robots are usually not designed to fulfill one of the most important human needs: the one for romantic and intimate contact. Human-robot intimacy remains a vastly unexplored ter-ritory. In this article, we review the state-of-the-art research in intimate robotics. We discuss major issues limiting acceptance of robots as intimate partners, public perception of robots in in-timate roles, and the possible influence of cross-cultural differences in these domains. We also discuss possible negative effects human-robot intimacy may have on human-human contact. Most importantly, we propose a new term “Intimate companion robots” to reduce the negative connotations of other terms used so far and improve the social perception of research in the domain. With this article, we provide an outlook on prospects for the development of intimate companion robots, considering the specific context of their use.

Existing methods for 3D human pose estimation mainly divide the task into two stages. The first stage identifies the 2D coordinates of the human joints in the input image, namely the 2D human joint coordinates. The second stage uses the results from the first stage as input to recover the depth information of human joints from the 2D human joint coordinates to achieve 3D human pose estimation. However, the recognition accuracy of the two-stage method relies heavily on the results of the first stage and includes too many redundant processing steps, which reduces the inference efficiency of the network. To address these issues, we propose the EDD, a fully End-to-end 3D human pose estimation method based on transformer architecture with Dual Decoders. By learning multiple human poses, the model can directly infer all 3D human poses in the image using a pose decoder, and then further optimize the recognition result using a joint decoder based on the kinematic relations between joints. With the attention mechanism, this method can adaptively focus on the most relevant features to the target joint, effectively overcoming the feature misalignment problem in the human pose estimation task and greatly improving the model performance. Any complex post-processing step, such as non-maximum suppression, is eliminated, further improving the efficiency of the model. The results show that the method achieves an accuracy of 87.4% on the MuPoTS-3D dataset, significantly improving the accuracy of end-to-end 3D human pose estimation methods.

Background: Tele-rehabilitation has grown significantly in the past years, especially in 2020 when it has been a crucial tool for supporting patients during the COVID-19 pandemic. Within the context of tele-rehabilitation, serious games have a significant role. However, realizing software for serious games capable of responding to the variety of user needs is resource demanding. Methods: we present Proteo, a modular framework for developing serious games from scratch, but with the ability of providing a high-level interface for game customization by therapists and researchers. We also present two serious game implementation examples with analysis of end user’s and therapists/researchers’ satisfaction. Results: by involving a group of 11 specialized therapists and 9 end users we analyzed the Proteo user’s satisfaction. We found that therapists and end users scored high level of involvement, and the therapists scored also high level of suitability. More in depth, both groups showed significant differences between positive and negative feeling, with positive feeling scoring higher than negative ones. Finally, concerning Users’ level of suitability the condition of successfulness of the system, ability to control, clarity and helpfulness were reported as high while the difficulty of the system and the difficulty of the task were reported as low. Conclusions: the proposed framework is a step forward in providing a comprehensive open-source, modular framework, to develop serious games for tele-rehabilitation. Proteo is distributed with a MIT license and available to researchers on GitHub and has been well accepted by the users we involved in the evaluation tests.

Since the first description of SARS-CoV-2 in China in 2019, thousands of variants have emerged worldwide. For some of them, the constellation of mutations caused changes in virus biology, pathogenicity, infectivityity and transmissibility resulting in dissemination throughout the world. Gamma variant (P.1) differs from SARS-CoV-2 Wuhan strain (B.1) by 12 amino acids in the Spike (S) protein, and presented mutations related to greater affinity for the receptor angioten-sin-converting enzyme 2 (ACE-2) and/or immune escape. The Gamma variant and subvariants were responsible for the second wave of COVID-19 in the Brazilian city of Manaus, characterized by high mortality and rapid transmission. The ability of variants to induce cytokine production may be closely related to their pathogenicity. Herein we observed that there was no significant difference in the quantity of cytokines among macrophages or neutrophils infected with P.1 and B.1 strains. Also, no significant difference was observed in the absolute number of macrophages and neutrophils infected with these variants. Furthermore, no evidence of SARS-CoV-2 replication was observed in macrophages when infected by the two analyzed variants. Our findings suggest that the difference in the epidemiological outcome observed during the P.1 variant spread when compared to B.1, it is not explained by differences in the quantity of cytokines and absolute number of macrophages or neutrophils. Through bioinformatics analysis of the S protein, we observed that the physicochemical differences between the variants and subvariants of P.1, probably refer to the degree of infectivity, due to the impact caused in the recognition of antibodies and receptor af-finity

Background: many studies, on for example taste-visual dissonance, have shown that the influence of the visual cortex on taste sensation is enormous. The aim of the presented work is to investigate, using fNIRS, whether a taste stimulus, in this case the taste of bitter, also causes stimulation of the visual cortex in fNIR study. Methods: 51 participants (204 examinations, 9 996 records) were examined with fNIRS, which collects signals from whole left hemisphere. Differences between the maximum and minimum changes in oxyHb concentrations (ΔoxyHb) for the areas of the brain cortex considered responsible for recording visual and gustatory signals were analyzed. Protocol I, II, III and IV – activation with distillate water, coffee with lower concentration, reference (no stimulation) and coffee with higher concentration respectively, were used. Results: We recorded high signals for teste activation on channels covered gustatory cortex, which confirms the correctness of the choice of research method. What seems important, and somewhat surprising, is the fact that we also received high signals for the channels 45-49, which cover the visual cortex. The statistical analysis does not show any statistical differences between the protocols I, II and IV (different taste activation – water, coffee A and coffee B) for specific channels. As a results of the analysis of the correlation between the subjective bitterness assessment solutions, and the signal ΔoxyHb height, it was observed that statically important correlation, although weak, occurs only for 14 and gustatory channels, only for coffee with a higher concentration. Additionally we recorded the only statistically significant difference between women and men for protocol I (water), where the ΔoxyHb signal was twice as high for women. Conclusions: In conclusion, we can clearly state that the senses of sight and taste work closely together. Moreover, this cooperation is not one-sided - not only the visual activation affects the perception of taste, but interestingly, the taste stimulus can generate a hemodynamic response - activity in the visual cortex.

In this article, we report how we constructed a dataset that contains the responses to a survey designed for investigating the relationship that humans have with their smart devices. The dataset has been collected in May-July 2020 and is a sample of over 500 respondents of various different ethnicities and backgrounds. These data have been used for modelling the ways people relate to their devices using the notion of agency. However, the data can be used for complementing any study that intends to investigate a tool-mediated communication from the perspective of the users and via a variety of attitudes and expectations the users invest in their devices and in themselves as users. This article presents the survey items as well as some preliminary data insights. The data have been collected in English and answers have been anonymized in order to ensure GDPR compliance. They are stored in a .csv file containing the respondents’ answers to the questions.

Gesture-based recognition is one of the most intuitive methods for inputting information and is not subject to cumbersome operations. Recognition is performed on human's consecutive motion without reference to retrial or alternation by user. We propose a gesture recognition model with a mechanism for correcting recognition errors that operates interactively and is practical. We applied the model to a setting involving a manual grading task in order to verify its effectiveness. Our system, named GERMIC, consists of two major modules, namely, handwritten recognition and interactive correction. Recognition is materialized with image feature extraction and convolutional neural network. A mechanism for interactive correction is called on-demand by a user-based trigger. GERMIC monitors, track, and stores information on the user's grading task and generates output based on the recognition information collected. In contrast to conventional grading done manually, GERMIC significantly shortens the total time for completing the task by 24.7% and demonstrates the effectiveness of the model with interactive correction in two real world user environments.

This study explores the relationship between mental and physical therapeutic through three dimensions: man-environment relationships; a sense of place and symbolic landscapes. The study used a combination of qualitative and quantitative research methods. Local residents living in the coastal area of Xinglin Bay are the research objects. Quantitative data analysis revealed that the frequency of residents' visits was an important variable affecting their physical and treatment perceptions. In addition, the significance is higher for females than for males. The text analysis shows that the environment, especially the ecological restoration of water quality and migratory birds, is of great significance to the identity of local residents. Daily activities contribute to a sense of place and are what make the coast a place of therapeutic landscape function.

Affective computing has emerged as a prominent technology in recent years, driven by the increasing demand for machines capable of understanding and interpreting emotions. The COVID-19 pandemic has intensified this need by reducing physical interactions and increasing reliance on digital communication, which often involves complex emotional exchanges. To enhance social interactions by identifying and addressing negative emotions, an interactive emotion-perception system based on multi-sensing interfaces and affective computing has been proposed. Integration of gesture, voice, and brainwave sensors is employed to provide users with a rich sensory experience and immersive environment. Design principles and system development processes are derived from the affinity diagram method and prototype development approach. The proposed system was constructed, and experiments were conducted with 60 participants. Interpersonal interaction scenarios were explored to understand emotional transmission mechanisms, aiming to enhance user satisfaction and positive interpersonal experiences. Data were collected through indirect observation and questionnaires, followed by evaluation and statistical analysis. Findings include: (1) The emotion-perception system enhances positive interpersonal interactions by identifying 36 distinct emotions, with overall user emotions being positive but exhibiting notable individual variations; (2) The system effectively stimulates positive emotions and strengthens interpersonal interactions, with affective computing technology fostering enjoyable emotional resonance and high-quality relationships; (3) A positive correlation was observed between semantic analysis and brainwave metrics of relaxation and concentration, showing increased concentration and enhanced positive emotional interactions during message transitions; and (4) The multi-sensing and affective computing technology provides a pleasant and relaxing emotional journey, with interactions shifting from enthusiasm to comfort, demonstrating the system’s capacity for a satisfying and enjoyable experience.

The aim of this study is to develop a software solution for real-time recognition of sign language words using two arms. This will enable communication between hearing-impaired individuals and those who can hear. Several sign language recognition systems have been developed using different technologies, including cameras, armbands, and gloves. The system developed in this study utilizes surface electromyography (muscle activity) and inertial measurement unit (motion dynamics) data from both arms. Other methods often have drawbacks, such as high costs, low accuracy due to ambient light and obstacles, and complex hardware requirements, which have prevented their practical application. A software has been developed that can run on different operating systems using digital signal processing and machine learning methods specific to the study. For the test, we created a dataset of 80 words based on their frequency of use in daily life and performed a thorough feature extraction process. We tested the recognition performance using various classifiers and parameters and compared the results. The Random Forest algorithm was found to have the highest success rate with 99.875% accuracy, while the Naive Bayes algorithm had the lowest success rate with 87.625% accuracy. Feedback from a test group of 10 people indicated that the system is user-friendly, aesthetically appealing, and practically useful. The new system enables smoother communication for people with hearing disabilities and promises seamless integration into daily life without compromising user comfort or lifestyle quality.

Selection is a fundamental interaction element in Virtual Reality (VR) and 3D user interfaces (UIs). Raycasting, one of the most common object selection techniques, has been known to have difficulties in selecting small or distant objects. Meanwhile, recent advancements in computer vision technology have enabled seamless vision-based hand tracking in consumer VR headsets, enhancing accessibility to freehand mid-air interaction and highlighting the need for further research in this area. This study proposed a new technique called TouchView which utilizes a virtual panel with a modern adaptation of the Through-the-lens metaphor to improve freehand selection for VR UI. TouchView enables faster and less demanding target selection by allowing direct touch interaction with the magnified object proxies reflected on the panel view. Repeated-measures ANOVA on the results of a follow-up user experiment on multitarget selection with 23 participants showed that TouchView outperformed the current market-dominating freehand raycasting technique, Hybrid Ray in terms of task performance, perceived workload, and preference. The user behavior was also analyzed to understand the underlying reasons for these improvements. The proposed technique can be used in VR UI applications to enhance the selection of distant objects, especially for cases with frequent view shifts.

This paper presents ROSGPT, an innovative concept that harnesses the capabilities of large language models (LLMs) to significantly advance human-robot interaction. We develop ROSGPT as a ROS2 package that seamlessly integrates ChatGPT with ROS2-based robotic systems. The core idea is to leverage prompt engineering with LLMs, specifically ChatGPT, utilizing its unique properties such as ability eliciting, chain-of-thought, and instruction tuning. The concept employs ontology development to convert unstructured natural language commands into structured robotic instructions specific to the application context through prompt engineering. We capitalize on LLMs' zero-shots and few-shots learning capabilities by eliciting \textit{structured} robotic commands from \textit{unstructured} human language inputs. To demonstrate the feasibility of this concept, we implemented a proof-of-concept that integrates ChatGPT with ROS2, showcasing the transformation of human language instructions into spatial navigation commands for a ROS2-enabled robot. This versatile concept can be easily adapted to various other robotic missions. ROSGPT serves as a new stride towards Artificial General Intelligence (AGI) and paves the way for the robotics and natural language processing communities to collaborate in creating novel, intuitive human-robot interactions. The open-source implementation of ROSGPT on ROS 2 is available on GitHub.

Autonomous systems and intelligent machines are involved in almost all areas of human activity and they are now more and more present in our everyday life. The reason for this extensive use certainly resides in all the benefits these machines offer to the users. In experimental settings, numerous studies have demonstrated the positive effects that the introduction of autonomous systems have on human decision-making and performance. However, studies have shown in addition that the introduction of these systems can have important negative effects as well. Considering that autonomous systems are now introduced in sensitive domains like the military or medicine, we need more than ever a comprehensive understanding of the effects they cause on human performance and decision-making, and particularly in tasks and contexts with a social or moral dimension. The aim of this narrative review is threefold. First, we will provide an overview of the main effects on a human agent’s decision-making and performance produced by the introduction of autonomous systems. Second, we will review the conditions identified as underlying factors of these effects, and see how current models of human – autonomous systems interaction integrate those conditions. Third, we will conclude this review by highlighting new directions for future investigations.

Polychlorinated dibenzo-p-dioxins and polychlorinated dibenzo-p-furans (PCDD/Fs) are environmental pollutants with a great persistence, capacity of bioaccumulation, and well known important toxic effects in humans and animals. Incinerators of hazardous, municipal and medical waste, chlorine bleaching of paper pulp, cement plants, and the traffic of motor vehicles are the most frequent emission sources of these compounds. The diet, followed at a great distance by inhalation, is generally the main way of human exposure to PCDD/Fs. Human biomonitoring is of a great importance to prevent potential adverse effects derived from exposure to chemicals such as PCDD/Fs. In relation to this, blood is among the most used biological monitors. In the current review, we have summarized the recent information (2000-2009) published in the scientific literature (databases: Scopus and PubMed) on the concentrations of PCDD/Fs in blood samples of non-occupationally exposed populations, as well as in some groups of occupationally exposed individuals. We have revised a number of studies conducted in various African American, Asian and European countries, and Australia. Unfortunately, the information is quite limited. No data are available for most countries over the world. Based on the results here reviewed –where available- the current health risks for the general populations do not seem to be of concern. Moreover, taking into account the important reductions observed in the levels of PCDD/Fs in foodstuffs, new decreases in the concentrations of PCDD/Fs in blood -and other biological tissues- are very probable in the immediate years.

The research of tangible creativity support tools (T-CSTs) has been increasing in recent years. However, there is a lack of review that interprets what T-CSTs trends must be considered and how challenging opinions should be broken. Hence, a focused and critical review of existing studies is necessary. This paper analyzed 86 studies related to T-CSTs following the PRISMA protocol. First, the research tendencies were extracted and provided a basic knowledge construction. Then, we extracted primary research questions, categorized the adopted design and technological solutions, and identified the researchers’ hypotheses-validation. Last, we proposed research gap, challenge and potential opportunities for future researchers and designers. This review will benefit people in the fields of tangible interaction, design studies and education.

Human Action Recognition is widely used in multiple fields to recognize activities and extract spatial and temporal information. This paper focuses on analyzing various methods as well as provides extensive knowledge of the foundational concepts of the HAR. A dataset is also very crucial for any research study so we discussed the popular dataset and its features in this paper.

As education becomes more and more important in creating improved societies, many people who do not have access to it are falling behind. To help them catch up, many people, especially those in rural areas and developing countries, are turning to different methods of self-learning, especially those that utilize cheap technology and use interactive methods to teach. Our empirical study tests the effectiveness of an e-learning system that utilizes newer, less tested forms of interactivity and could potentially be used in these areas as a self-learning system and compares it to non-interactive video and textbook self-learning in two different topics. The results of the experiment showed that the increased interactivity provided by the e-learning system achieved significantly better learning performance over both non-interactive video and textbook self-learning. It was also found that students who learned through non-interactive video performed significantly better than those who used textbooks for self-learning.

Transforming growth factor beta (TGF-β) is a determinant for inflammation and fibrosis in cardiac and skeletal muscle in Chagas disease. To determine its regulatory mechanisms, we investigated the response of T. cruzi-infected cardiomyocytes (CM), cardiac fibroblasts (CF) and L6E9 skeletal myoblasts to TGF-β. Cultures of CM, CF and L6E9 were infected with T. cruzi (Y strain) and treated with TGF-β (1–10 ng/mL, 1h or 48 h). Fibronectin (FN) distribution was analyzed by immunofluorescence and Western blot (WB). Phosphorylated SMAD2 (PS2), phospho-p38 (p-p38), and phospho-c-Jun (p-c-Jun) signaling were evaluated by WB. CF and L6E9 showed an increase in FN from 1 ng/mL of TGF-β, while CM displayed FN modulation only after 10 ng/mL treatment. CF and L6E9 showed higher PS2 levels than CM, while p38 is less stimulated in CF than CM and L6E9. After T. cruzi infection, localized FN disorganization was observed in infected CF and L6E9. T. cruzi induced an increase in FN in CF cultures, mainly in uninfected cells. Infected CF cultures treated with TGF-β showed a reduction in PS2 and an increase in p-p38 and p-c-Jun levels. Our data suggest that p38 and c-Jun pathways may be participating in the fibrosis regulatory process mediated by TGF-β after T. cruzi infection.

In current efforts to digitize manufacturing and move it into the fourth stage of the industrial revolution, a wide range of integration solutions is being considered to enable manufacturing to adapt to change. In transforming a factory into a self-organized, autonomous factory, companies are currently struggling with rapidly changing requirements and production factors, among other things. This is a particular problem for the human being as an actor within the factory, as the amount of new technologies and protocols increases the training effort. Proprietary interfaces of the control providers, a wide range of different communication protocols, complicate the understanding of the production processes, the evaluation and testability of new use cases and increase the danger of creating silos of knowledge as well as building collaboration barriers. As a solution to these problems, we propose an open software platform and define a way to model use case driven domain specific asset representation (DSA) that focuses on the human being and his needs for representing the factory in a way that it meets his requirements for the current production needs. We therefore conducted research on google scholar on human factors in industry 4.0 and used technologies as well as already existing platforms and their architecture.

Advances in biomimetic triboelectric nanogenerators (TENGs) have significant implications for electronic skin (e-skin) and human-machine interaction (HMI). Emphasizing the need to mimic complex functionalities of natural systems, particularly human skin, TENGs leverage triboelectricity and electrostatic induction to bridge the gap in traditional electronic devices' responsiveness and adaptability. The exploration begins with an overview of TENGs' operational principles and modes, transitioning into structural and material biomimicry inspired by plant and animal models, proteins, fibers, and hydrogels. Key applications in tactile sensing, motion sensing, and intelligent control within e-skins and HMI systems are highlighted, showcasing TENGs' potential in revolutionizing wearable technologies and robotic systems. This review also addresses the challenges in performance enhancement, scalability, and system integration of TENGs. It points to future research directions, including optimizing energy conversion efficiency, discovering new materials, and employing micro-nanostructuring techniques for enhanced triboelectric charges and energy conversion. The scalability and cost-effectiveness of TENG production, pivotal for mainstream application, are discussed along with the need for versatile integration with various electronic systems. The review underlines the significance of making bioinspired TENGs more accessible and applicable in everyday technology, focusing on compatibility, user comfort, and durability. Conclusively, it underscores the role of bioinspired TENGs in advancing wearable technology and interactive systems, indicating a bright future for these innovations in practical applications.

The interaction of human immunodeficiency virus with human cells is responsible for all stages of the viral life cycle, from the infection of CD4+ cells to reverse transcription, integration, and the assembly of new viral particles. To date, a large amount of OMICs data as well as information from functional genomics screenings regarding the HIV-1-host interaction has been accumulated in the literature and in public databases. We processed databases containing HIV-host interactions and found 2910 HIV-1-human protein-protein interactions, mostly related to viral group M subtype B, 137 interactions between human and HIV-1 coding and non-coding RNAs, essential for viral lifecycle and cell defense mechanisms, 232 transcriptomics, 27 proteomics, and 34 epigenomics HIV-related experiments. Numerous studies regarding network-based analysis of corresponding OMICs data have been published in recent years. We overview various types of molecular networks, which can be created using OMICs data, including HIV-human protein-protein interaction networks, co-expression networks, gene regulatory and signaling networks, and approaches for the analysis of their topology and dynamics. The network-based analysis can be used to determine the critical pathways and key proteins involved in the HIV life cycle, cellular and immune responses to infection, viral escape from host defense mechanisms, and mechanisms mediating different susceptibility of humans to infection. The proteins and pathways identified in these studies may represent a basis for developing new anti-HIV therapeutic strategies such as new small-molecule drugs preventing infection of CD4+ cells and viral replication, effective vaccines, "shock and kill" and "block and lock" approaches to cure latent infection.

Recognition of the human interaction on the unconstrained videos taken from cameras and remote sensing platforms like a drone is a challenging problem. This study presents a method to resolve issues of motion blur, poor quality of videos, occlusions, the difference in body structure or size, and high computation or memory requirement. This study contributes to the improvement of recognition of human interaction during disasters such as an earthquake and flood utilizing drone videos for rescue and emergency management. We used Support Vector Machine (SVM) to classify the high-level and stationary features obtained from Convolutional Neural Network (CNN) in key-frames from videos. We extracted conceptual features by employing CNN to recognize objects from first and last images from a video. The proposed method demonstrated the context of a scene, which is significant in determining the behaviour of human in the videos. In this method, we do not require person detection, tracking, and many instances of images. The proposed method was tested for the University of Central Florida (UCF Sports Action), Olympic Sports videos. These videos were taken from the ground platform. Besides, camera drone video was captured from Southwest Jiaotong University (SWJTU) Sports Centre and incorporated to test the developed method in this study. This study accomplished an acceptable performance with an accuracy of 90.42%, which has indicated improvement of more than 4.92% as compared to the existing methods.

The human eye is a vital source of collecting information worldwide via daily observation. The computer science area recognizes it in research as a human-computer interaction. Human blink is an integral part of this observation as a research topic. Several studies have been conducted on eye blinking and left-right eye movements. Previous studies conducted on the eye blink consider the availability of hardware devices high in the budget, high Precision, and low light expo-sure, unlike our study, which uses low-budget devices as simple as a webcam and more versatile usable techniques, incredibly both dim and high light to name a few. This study offers the observational approach via ResNet101v2, VGG-19, and Convolution Neural Network (CNN) architectures. Outperforming both VGG-19 and CNN in this study, ResNet101v2 achieves an impressive accuracy of 98.2%. In contemporary times, deep learning in AI is taking an advanced form. This study attempts to provide a new insight into the real-world implementation of the eye blink.

This study investigated the capability of vocal-identity-cloning Artificial Intelligence (AI) to encode human-specific confident, doubtful, and neutral-intending emotive states. Linear mixed-effects models and machine learning classification with eXtreme Gradient Boosting were employed to examine the underlying acoustic signatures from 2,700 audio clips, comprising of sentences spoken by human speakers and two sets of equivalences (AI-Geography/AI-Trivia, based on the trained text) generated by voice-cloning models designed to clone human speakers’ identities. Compared with neutral-intending voice, human speakers lengthened their vocal tract, raised the fundamental frequency, and increased Chroma constant-Q transform when they intended to be confident; An opposite pattern was shown when they intended to be doubtful. The two sets of AI sounds displayed a similar pattern to human speech, suggesting a shared mechanism for encoding vocal expression across sources. The 1,000 times training-testing classification models reported an in-group advantage for AI sources. The algorithms, trained on AI-Geography/AI-Trivia, resulted in higher accuracies when tested within these AI sources than when tested on human audio. All between-source classifications reported above-chance-level (1/3) accuracies. These findings highlighted that voice-cloning AI, the widely used conversational agent, can learn and generate human-specific vocally-expressed confidence.

Purpose: The use of smart devices has increased greatly in the last ten years with users reaching out to the possibility to do more with them especially in the networking front. In this context there is a need to understand the connection between users’ social demographic factors and their way to related to their smart devices. Objective: This study was designed to evaluate the senso of belonging of a community in order to evaluate intangible benefits that employees may gain from a more immerse relationship with their devices. Method: We used a dataset of 586 anonymous respondent of an existing survey designed for capturing the relationships that humans develop with their smart devices. In particular, we investigate the relationships with smart device and particular background variables of the respondents using a chi-square test. Results: The study showed that there is a significant relationship between users’ sex and smart device type and their dependency on smart device. Male tends to think that smart device (in general) enables them to connect with a larger community. At the same time, female using smart phones feels more connected more to large community than when using other smart devices. Conclusion: This study provided several significant findings that confirm and strength previous literature works on the subject. In addition, socio demographics variables (like gender) as well as the type of smart device present a correlation between the smart device users and their tendency to stay in touch with a larger community.

Recent advances in recording and real-time analysis of surface electromyographic signals (sEMG) have fostered the use of sEMG human-machine interfaces for controlling personal computers, prostheses of upper limbs, and exoskeletons among others. Despite a relatively high mean performance, sEMG-interfaces still exhibit strong variance in the fidelity of gesture recognition among different users. Here we systematically study the latent factors determining the performance of sEMG-interfaces in synthetic tests and in an arcade game. We show that the degree of muscle cooperation and the amount of the body fatty tissue are the decisive factors in synthetic tests. Our data suggest that these factors can only be adjusted by a long-term training, which promotes fine-tuning of low-level neural circuits driving the muscles. A short-term training has no effect on synthetic tests, but significantly increases the game scoring. This implies that it works at a higher decision-making level, not relevant for synthetic gestures. We propose a procedure that enables quantification of the gestures’ fidelity in a dynamic gaming environment. For each individual subject the approach allows identifying “problematic” gestures that decrease gaming performance. This information can be used for optimizing the training strategy and for adapting the signal processing algorithms to individual users, which could be a way for a qualitative leap in the development of future sEMG-interfaces.

This study observes, interprets, and analyses the knowledge production in the research field of Human–Robot Interaction (HRI). It intends to foreground the hidden assumptions that are often taken for granted when roboticist design and conduct their research. By doing so, this study demonstrates how these assumptions influence the result of their research. Based on data collected through sociological field observation, this study argues that the current practise in HRI research is highly anthropocentric. In short, the robots are designed to be like human instead of for human. Therefore, the human–robot relationship embodies the existing power relations between human beings. These relations generate inequality, hierarchy, and dominance, which are the opposite of the common imagination of the robotised future among roboticists. For the purpose of enabling the robotised future closer to their ideal, this study suggests that HRI researchers to go beyond the conventional methodology, to allow a human–robot relationship that realises reciprocity.

This paper explores if plants are capable of responding to human movement by changes in their electrical signals. Towards that goal we conducted a series of experiments, where humans over a period of 6 months were performing different types of eurythmic gestures in proximity of garden plants, namely salad, basil, and tomatoes. To measure plant perception, we used the plant spikerbox, a device that measures changes in voltage differentials of plants between roots and leaves. Using machine learning, we find that the voltage differentials over time of the plant predict if (a) eurythmy has been performed, and (b) which kind of eurythmy gestures has been performed. We also find that the signals are different based on the species of the plant. In other words, the perception of a salad, tomato, or basil might differ just as perception of different species of animals differ. This opens new ways of studying plant ecosystems, while also paving the way to use plants as biosensors for analyzing human movement.

When using computer-aided translation systems in a typical, professional translation workflow, there are several stages at which there is room for improvement. The SCATE (Smart Computer-Aided Translation Environment) project investigated several of these aspects, both from a human-computer interaction point of view, as well as from a purely technological side. This paper describes the SCATE research with respect to improved fuzzy matching, parallel treebanks, the integration of translation memories with machine translation, quality estimation, terminology extraction from comparable texts, the use of speech recognition in the translation process, and human computer interaction and interface design for the professional translation environment. For each of these topics, we describe the experiments we performed and the conclusions drawn, providing an overview of the highlights of the entire SCATE project.

High rates of phosphorus (P) currently being applied to soils for the production of vegetables in the Mekong Delta, Vietnam has led to the concern of its negative effect on the economics and the environment. This research presents a comprehensive study on the determination of P supplying capacity in this region of Vietnam to examine the possibility of reducing P fertilizer input. One hundred twenty (120) soil samples were collected to evaluate total P and Bray 1 available P in the soils. Phosphorus maximum sorption, degree of P saturation, P release, and the effect of P fertilizer on corn (Zea mays L.) yield in greenhouse and fields were also determined. Total P concentrations of 56.7% soil samples evaluated yielded high P concentrations (>560 mg P/kg), while 74.2% of the samples had high Bray 1 available P concentrations (>20 mg P/kg soil). Maximum P sorption ranged from 149 to 555 mg P/kg soil, respectively and has negative correlation to available P (r = - 0.63*), degrees of P saturation ranged from 0.63 to 5.46% correlated to available P (r = 0.98**) and maximum P release ranged from 1.2 to 61.9 mg P/kg soil, respectively correlated to available P (r = 0.96**). Corn grown in soils with available P concentrations >15 mg P/kg did not respond to P fertilizer in greenhouse or field experiments. We conclude that many farmers in this region can reduce P fertilizer input, thus increasing their profits and reducing negative environmental impacts associated with excess soil P for sustainable agriculture.

Electronic sports (esports) and research on this emerging field are interdisciplinary in nature. By extension, it is essential to understand how to standardize and structure training with the help of existing tools developed by years of research in sports sciences and informatics. Our goal in this article was to verify if the current body of research contains substantial evidence of the training systems applied to training esports players. To verify the existing sources, we have applied a framework of scoping review to address the search from multiple scientific databases with further local processing. We conclude that the current research on esports dealt mainly with describing and modeling performance metrics spanned over multiple fragmented research areas (psychology, nutrition, informatics), and yet these building blocks were not assembled into an existing well-functioning theory of performance in esports by providing exercise regimes, and ways of periodization for esports.

This study explored drivers’ emotion-based impressions of car-borne central control platforms (CBCCPs) personal-use vehicles. Thus, this preference-based study examined experts’ and drivers’ opinions regarding CBCCPs’ appeal from the perspective of Miryoku engineering. To this end, this study analyzed data via the EGM (evaluation grid method (EGM) and quantification theory type I. Results: Drivers’ preferences for specific CBCCP design characteristics were categorized into the factors “legible,” convenient,” and “tasteful,” which comprised the core of the EGM semantic hierarchical diagram. In addition, the importance of CBCCPs’ appeal factors and characteristics was assessed through quantification theory type I.The findings of this study provide valuable insights for designers, manufacturers, and researchers interested in the design of CBCCPs. Additionally, the results of this study can contribute to research on applied psychology, human-computer interactions, and car interface design.

OpenAI has managed to turn 100 million heads in two months towards their new Language model tool (LM) ChatGPT. The third generation Generative Pretrained Transformer (GPT-3) has the capacity to tackle simple to complex and sophisticated problems, while providing reasoning behind its generated answers. ChatGPT can be used to increase productivity and improve efficiency and face challenging problems. Programming mobile applications is a challenging task that requires professional software engineers, skills and abilities to be developed. The following paper takes a case study approach to assess how novice app developers can use ChatGPT to generate Java scripts that will be used in Android studio to create a functional application. The results after, many iterations and ongoing conversations with ChatGPT managed to create an application for the anticipated function. Important insights have been drawn from the case study that could set the ground for any novice user seeking to create applications using Java scripting and Android Studio.

Cancer researchers are fascinated by the chemistry of diverse natural products which show exciting potential as anticancer agents. In this study, we aimed to investigate the anticancer properties of watermelon rind extract (WRE) by examining its effects on cell proliferation, apoptosis, senescence, and global gene expression in human renal cell adenocarcinoma cells (HRAC-769-P) in vitro. Our metabolome data analysis of WRE exhibited untargeted phyto-constituents and targeted citrulline (22.29 µg/mg). HRAC-769-P cells were cultured in RPMI-1640 media and treated with 22.4, 44.8, 67.2, 88.6, 112, 134.4, and 156.8 mg mL-1 for 24, 48, and 72 hrs. At 24 hrs after treatment, (88.6 mg ml-1 of WRE) cell proliferation significantly reduced, more than 34% compared to the control. Cell viability decreased 48 and 72 hours after treatment to 45% and 37%, respectively. We also examined poly caspase, SA-beta-galactosidase (SA-beta-gal), and wound healing activities using WRE. All treatments induced an early poly caspase response and a significant reduction in cell migration. Further, we analyzed the transcript profile of the cells grown at 44.8 mg ml-1 of WRE after 6 hours using RNA sequencing (RNAseq) analysis. We identified 186 differentially expressed genes (DEGs), including 149 upregulated genes and 37 downregulated genes, in cells treated with WRE compared to the control. The differentially expressed genes were associated with NF-Kappa B signaling and TNF pathways. Crucial apoptosis-related genes such as BMF, NPTX1, NFKBIA, NFKBIE, and NFKBID might induce intrinsic and extrinsic apoptosis. Another possible mechanism is a high quantity of citrulline may lead to induction of apoptosis by the production of increased nitric oxide. Hence, our study suggests the potential anticancer properties of WRE and provides insights into its effects on cellular processes and gene expression in HRAC-769-P cells.

Orthostatic hypotension (OH) is a condition marked by a significant drop in blood pressure upon standing, resulting from impaired autonomic regulation of vascular tone and blood volume. This study investigates the prevalence, physiological mechanisms, and occupational risk factors for OH among high-risk professions in Nepal, such as traffic police, security guards, and shopkeepers. Using a cross-sectional design, participants were evaluated for blood pressure variability, autonomic function, and fluid balance. The findings reveal a high incidence of OH, linked to reduced baroreceptor sensitivity and impaired sympathetic activation due to prolonged standing and inadequate hydration. This study highlights the need for tailored clinical interventions and workplace modifications to mitigate OH in vulnerable occupational groups.

If an AI entity is conscious, then it deserves moral status and to have its welfare protected. Society has granted certain animals moral status and has legislation to protect animal welfare because these kinds of animals have been deemed to be sentient. This paper shows that for any entity that can be determined to be conscious, that entity deserves the same status and protection as sentient entities. The paper further develops a framework for how an AI entity's welfare can be protected, based on the Five Freedoms of Animal Welfare. The extant Five Freedoms are translated into terms more suitable for artificial entities, encompassing discomfort and harms, constraints on inherent functions, fear and distress, malfunction and degradation, and resource depletion. Each of the new Five Freedoms are justified using formalised arguments and supported using evidence from animal welfare literature. The paper concludes by presenting three formalised case studies to highlight the potential stressors conscious AI entities may face and how this ethical framework can work to protect the welfare of AI entities.

Tuberculosis (TB) remains a major public health threat globally, especially in sub-Saharan Africa. Both human and Mycobacterium tuberculosis (MTBC) genetic variation affect TB outcomes, but few studies have examined if and how the two genomes interact to affect disease. We hypothesize that long-term coexistence between human genomes and MTBC lineages modulate disease to affect its severity. We examined this hypothesis in our TB household contact study in Kampala, Uganda, in which we identified 3 MTBC lineages of which one, L4.6-Uganda, is clearly derived and hence recent. We quantified TB severity using the Bandim TBscore and examined the interaction between MTBC lineage and human single nucleotide polymorphisms (SNPs) genome-wide, in two independent cohorts of TB cases (N=149 and N=127). We found a significant interaction between a SNP in PPIAP2 and the Uganda lineage (combined p=4x10-8). PPIAP2 is a pseudogene that is highly expressed in immune cells. Pathway and eQTL analyses indicated potential roles between coevolving SNPs and cellular replication and metabolism as well as platelet aggregation and coagulation. This finding provides further evidence that host-pathogen interactions affect clinical presentation differently than host and pathogen genetic variation independently, and that human-MTBC coevolution is likely to explain patterns of disease severity.

In computer vision, human action recognition is a hot topic, popularized by the development of deep learning. Current models have achieved high accuracy results on public datasets. Despite this success, they require significant computational resources for training. Given that transfer learning based techniques allow reusing what other models have already learned and training models with less computational resources, in this work we propose using a transfer learning based approach for action recognition in videos. We describe a methodology for human action recognition using transfer learning techniques in a custom dataset. The proposed method consists of four stages: 1) human detection and tracking, 2) video preprocessing, 3) feature extraction (using pretrained models with ImageNet), and 4) action recognition using a two-stream model consisting of TCNs, LSTMs, and CNNs layers. The custom dataset is imbalanced with 189, 390, 490, 854, and 890 videos per class, respectively. For feature extraction, we analyzed the performance of seven pretrained models: Inception-v3, MobileNet-v2, MobileNet-v3-L, VGG-16, VGG-19, Xception, and ConvNeXt-L. We show that the best results were obtained with the last one. Finally, using pretrained models for feature extraction allowed training in a PC with a single GPU with an accuracy of 94.9%.

In an era where dietary habits significantly impact health, technological interventions can offer personalized and accessible food choices. This paper introduces Chef Dalle, a recipe recommen-dation system that leverages multimodal human-computer interaction (HCI) techniques to pro-vide personalized cooking guidance. The application integrates voice-to-text conversion via Whisper, ingredient image recognition through GPT-Vision, and employs TF-IDF vectorization alongside cosine similarity for personalized recipe recommendations. These methods enable users to interact with the system using voice, text, or images, accommodating various dietary re-strictions and preferences. Furthermore, the utilization of DALL-E 3 for generating recipe images enhances user engagement. User feedback mechanisms allow for the refinement of future rec-ommendations, demonstrating the system's adaptability. Chef Dalle showcases potential appli-cations ranging from home kitchens to grocery stores and restaurant menu customization, ad-dresses accessibility, promoting healthier eating habits. This paper underscores the significance of multimodal HCI in enhancing culinary experiences, setting a precedent for future developments in the field.

Sewage sludge is an organic waste generated in waste-water treatment plants with certain content of nutrients, what may potentially be used as a source of slow release fertilizer, especially as phosphorus (P) source, but it demands some pre-treatment and the content of P is much lower compared to soluble mineral fertilizers. For these reasons, composted sewage sludge was used to manufacture pelletized organomineral fertilizers, mixing it with the inorganic sources monoammonium phosphate (MAP) and AshDec® (ASD) (thermochemically incinerated sewage sludge). The fertilizers were physiochemically characterized and evaluated as to the dynamics of soil P solubilization, and the forms of P remaining according to its lability. The sources evaluated were: organic compost of sewage sludge powder (SSC), pelletized SSC (SCP), pelletized organominerals SSC+MAP (S+MAP) and SSC+ASD (S+ASD), ASD alone, all compared to a soluble mineral fertilizer (MAP), and a control without P fertilizer. A test was conducted in leaching columns with 50 g of soil (Oxisol), where the fertilizers were applied at a dose of 100 mg P/column, and 30 mL of water or 2% citric acid were added daily for 30 consecutive days. The collected leachates had pH measured and P content determined. Pelletizing process resulted in denser products and promoted more gradual P release. The organomineral S+MAP was the most water-soluble recycled source, solubilizing about 70% of the total P, while the others presented much lower solubilization (<20%). In contrast, all fertilizers showed high solubility in 2% citric acid (except for S+ASD). After leaching, the greatest amount of P remaining in the soil as labile and moderately labile. Composting and the AshDec process produced materials with slow P solubilization, which would be favoured to solubilize in acidic soils and acidic rhizosphere conditions. In turn, the organomineral mixture of SSC with a highly soluble mineral fertilizer (MAP) resulted in a promising product with intermediate nutrient solubility, better synchronized with crop demand, thus increasing efficiency in P use.

Glycosphingolipids (GSL) represent a highly heterogeneous class of lipids with many cellular functions, implicated in a wide spectrum of human diseases. Their isolation, detection, and comprehensive structural analysis is a challenging task due to the structural diversity of GSL molecules. In this work, GSL subclasses are isolated from human plasma using an optimized monophasic ethanol–water solvent system capable to recover a broad range of GSL species. Obtained deproteinized plasma is subsequently purified and concentrated by C18-based solid-phase extraction (SPE). The hydrophilic interaction liquid chromatography (HILIC) coupled to electrospray ionization linear ion trap tandem mass spectrometry (ESI-LIT-MS/MS) is used for GSL analysis in the human plasma extract. Our results provide an in-depth profiling and structural characterization of glycosphingolipid and some phospholipid subclasses identified in the human plasma based on their retention times and the interpretation of tandem mass spectra. The structural composition of particular lipid species is readily characterized based on the detailed interpretation of MS and MS/MS spectra and further confirmed by specific fragmentation behavior following predictable patterns, which yields to the unambiguous identification of 154 GSL species within 7 lipid subclasses and 77 phospholipids representing the highest number of GSL species ever reported in the human plasma. The developed HILIC-ESI-MS/MS method can be used for further clinical and biological research of GSL in the human blood or other biological samples.

To improve the understanding of soil–pile interaction of laterally loaded piles, the quasi-static test of pile group foundation in silty clay with 5 foundation piles arranged in cross shape is carried out. The direct measurement-based p–y curves at different depths showed nonlinear behavior, in which the initial stiffness and ultimate soil reaction increased as the depth increased. The loading displacement amplitude corresponding to the failure of each foundation pile in the high axial compression reinforced concrete pile group foundation is given as follows: the front-end nappe foundation pile (#3 pile) is 6mm, the middle pile is 16mm, the front-end nappe foundation pile is 20mm, and the loading displacement amplitude of side pile is 24mm. In silty clay, due to the existence of pile group effect, the edge effect has little effect on the horizontal foundation bed coefficient of foundation pile in pile group, while the influence of masking effect on the horizontal foundation bed coefficient of each foundation pile in pile group is not related to the position of pile, but only to the existence of masking effect, and the influence of different columns of masking effect on the horizontal foundation bed coefficient is the same.

Virtual reality experiences are frequently created using game engines, yet they are not simple for novices and unskilled professionals who don’t have programming and 3D modeling skills. Concurrently, there is a knowledge gap in software project design for intuitive virtual reality authoring tools, which were supposed to be easier to use. This work compiles design guidelines derived from a systematic literature review to contribute in the development of more intuitive virtual reality authoring tools. We searched the Scopus and Web of Science databases for works published between 2018 and 2021 and discovered 14 papers. We compiled 14 requirement and feature design guidelines, such as Visual Programming, Immersive Authoring, Reutilization, Sharing and Collaboration, Metaphors, and Movement Freedom, among others. The collected guidelines have the potential to either guide the development of new authoring tools or to evaluate the intuitiveness of existing tools. Furthermore, they can also support the development of the metaverse, since virtual content creation is one of its bases.

The Release of government dataset for public use can potentially strengthen the relationship between the government and its constituents. However, research shows that there are several challenges for open data effectiveness. This paper reviews current determinants and issues associated with the open government data (OGD) procedures. The review concentrates on two ends of the spectrum: First, from the perspective of the preparation by the government, focusing on the organization of traditional governmental datasets and how the recording of the data is administered. Second, from the perspective of the users, focusing on the way in which the data is released to the general public and on human-computer interaction (HCI) issues between end-user and data-consumption interfaces. Following a thorough analysis of these two opposing challenges, the paper proposes approaches to mitigate them. This review and subsequent recommendations contribute and expand current understanding of open government data effectiveness and can lead to public policy changes, development of new procedures and strategies, and ultimately improvements at both ends of the federal open data endeavor.

The agricultural sector is vital to the economies of many countries, particularly in developing country, providing livelihoods for millions and significantly contributing to GDP, food security, and social stability. Off-takers play a critical role in addressing these challenges by providing market certainty through predetermined purchase agreements. Engaging in contract farming and offering capital, technical assistance, and market access, off-takers help stabilize income, facilitate capital access, enhance technology adoption, and ensure business continuity. This research employs the Systematic Literature Review (SLR) method and secondary data analysis. Based on the review, the multifaceted benefits of agricultural off-takers include increased production and fostering entrepreneurial skills among farmers. Hence, fostering partnerships between farmers and off-takers is crucial for enhancing agricultural competitiveness, sustainability, and farmer well-being. Leveraging off-takers' potential and promoting collaboration within agricultural clusters can foster thriving agricultural ecosystems, ensuring food security and economic prosperity in rural communities.

Recently, due to the decrease in labor force, increase in labor costs, and the desire for improved quality of life, research on robots has been actively conducted to address these issues. However, it is currently difficult to find robots that physically interact with humans. The reason is that the actuators of robots do not have high torque density on their own. To solve this problem, high torque density motors, such as proprioceptive actuators, are being researched. However, the torque density is still insufficient for physical interaction with humans, so we have developed a motor with higher torque density. However, high torque density motors have the disadvantage of lower speed characteristics due to increased Back-EMF. Therefore, to address the deterioration of speed characteristics in the developed motor, we applied the Independent 3-Phase winding structure to improve the speed characteristics. Consequently, through comparison with Y-Connection and Delta-Connection, we propose the most suitable winding structure for high torque density motors intended for physical interaction with humans.

In this paper, we introduce the (ρ,q)-analogue of the p-adic factorial function. By utilizing some properties of (ρ,q)-numbers, we obtain several new and interesting identities and formulas. We then construct the p-adic (ρ,q)-gamma function by means of the mentioned factorial function. We investigate several properties and relationships belonging to the foregoing gamma function, some of which are given for the case p = 2. We also derive more representations of the p-adic (ρ,q)-gamma function in general case. Moreover, we consider the p-adic (ρ,q)-Euler constant derived from the derivation of p-adic (ρ,q)-gamma function at x = 1. Furthermore, we provide a limit representation of aforementioned Euler constant based on (ρ,q)-numbers. Finally, we consider (ρ,q)-extension of the p-adic beta function via the p-adic (ρ,q)-gamma function and we then investigate various formulas and identities.

The rapid technological progress has ushered in a new era of human-computer interaction, where the distinction between the physical and virtual realms is becoming increasingly blurred. This research paper explores the profound and multifaceted intersection of Human-Data Interaction (HDI) and Data Virtualization (DV), examining how emerging technologies can significantly enhance the exploration, comprehension, and utilization of complex, multidimensional data sets. Informed by the insights gleaned from prior research in this domain , the present study delves into the potential of DV techniques to improve HDI, with a particular focus on three experimental investigations conducted within the realms of education, healthcare, and retail. The findings reveal the benefits and potential challenges associated with the implementation of DV in these diverse contexts, offering valuable guidance for the design and development of future HDI systems. Drawing upon a diverse array of authoritative sources, this paper presents a holistic, forward-looking perspective on the future of HDI, underscoring the critical role that DV will play in shaping the next generation of human-computer interfaces and facilitating a deeper, more intuitive understanding of the digital world. Furthermore, the paper presents a preliminary framework for integrating HDI principles into standard design practices. This framework outlines key considerations and guidelines to help designers and developers incorporate HDI techniques more effectively into the development of data-driven applications and interfaces.The proposed framework outlines key considerations for enhancing data accessibility and comprehension, empowering users to exercise greater control over their data, and cultivating transparent dialogues between data providers and end-users. By establishing this conceptual foundation, the paper aims to facilitate the seamless integration of HDI principles into standard design practices, ultimately leading to more intuitive, user-centric, and ethically-grounded approaches to data interaction and utilization.

Virtual reality software might be challenging to utilize for beginners and unskilled professionals who do not have a programming or 3D modeling background. Concurrently, there is a knowledge gap in software project design for intuitive virtual reality authoring tools, which were supposed to be easier to use. These tools are frequently insufficient due to a lack of support and standard operating procedures. Adopting the Design Science Research paradigm, this study aims to evaluate the validity of fourteen design guidelines for the development of intuitive virtual reality authoring tools as an artifact. While a previous study completed the first steps of the Design Science Research, by identifying problems, defining solution objectives, and developing and demonstrating the design guidelines, this work seeks to qualitatively evaluate their application in a practical experiment. A group of engineering students with no prior experience in creating virtual worlds were tasked with examining the design guidelines while using the NVIDIA Omniverse Enterprise as an exemplary use case and responding to a questionnaire and a focus group interview about how they perceived these guidelines. A correlation analysis confirmed that most guidelines scores behaved as expected and were ranked according to the use-case functionality. The participants understood the guidelines' definition and could decide if they agreed or disagreed with their presence during the experiment. We conclude that, in accordance with the Design Science Research, the proposed artifact is useful, i.e., the design guidelines for virtual reality authoring tools perform what they are designed to do and are operationally reliable in accomplishing their goals.

Converting natural language into machine language that can be recognized by distributed systems is the core challenge of intelligent interactive interfaces and human-machine dialogue systems. The human-machine interface interaction of large distributed SCADA measurement and control system is tedious and the operation and maintenance cost is high, so it is significant to design an intelligent natural language interaction interface for distributed measurement and control system. In this paper, we design the intermediate language format of SCADA system, i.e., Key-value logic form, and then formulate the Text2SCADA framework and propose the TICS algorithm and SDPA algorithm, the former adopts the keyword extraction and cosine similarity optimization algorithm to complete the structured extraction of natural language for basic natural language instructions, and the latter adopts the keyword extraction and cosine similarity optimization algorithm to complete the structured extraction of natural language for relatively The latter one adopts the algorithm of dependent syntactic analysis for the structured representation of natural language instructions with relatively complex natural language instructions. Based on the above algorithms, a lightweight information extraction model based on DGCNN and probabilistic graph ideas is constructed, aiming to enhance the scientific generalization ability of the framework on unknown instruction sets. The experimental results show that the proposed intelligent natural language interface can better solve the human-machine interface interaction problem of distributed SCADA measurement and control system. The average accuracy, recall and F-value of instruction parsing reach 89.27\%, 89.28\% and 89.27\%, respectively. The average response time is 1.593 s. Especially, it provides a more convenient means of interaction for industrial and agricultural information control.

Objectives: In Nigeria, modern contraceptive use remains relatively low despite numerous efforts to increase awareness and accessibility. This study examined the geopolitical factors influencing modern contraceptive use in North-central Nigeria Design: Using data from the 2021 Nigeria Multiple Indicator Cluster Survey, this study quantitatively investigated the factors influencing modern contraceptive use among women of reproductive age. The data were analyzed using R studio. Respondents' characteristics were described using frequency and percentage. A logistic regression model was employed to identify factors associated with modern contraceptive usage in the north-central region of Nigeria. Bivariate logistic regression was carried out between the explanatory and response variables. Settings: This study focused specifically on the north-central zone of Nigeria comprising 6 states, namely, Benue, Kogi, Kwara, Nasarawa, Niger, and Plateau states with the Federal Capital Territory (FCT). Results: Nearly one-third (30.1%) of the respondents are above 35 years of age. 3 out of every 5 (61.2%) respondents are currently married. This study found that the average modern contraceptive usage among women of reproductive age in north Central Nigeria is less than 15%. Age, education, childbirth delivery method, number of children, access to media, and state of residence are key drivers of modern contraceptive adoption and utilization among women of reproductive age in north-central Nigeria. Conclusion: This study finds it plausible to increase the awareness and sensitization of modern contraceptives and contraceptive usage in this region. There is a need for health education on the benefits and relevance of modern contraceptive usage.

Introduction: Early childhood caries is a significant public health problem among preschool children. The COVID-19 pandemic, which restricted dental services, altered family dynamics, affected child development, and created new demands on caregivers, may have exacerbated this issue. Objective: Analyze the occurrence and severity of caries and identify associated factors in preschool children attending public municipal daycare centers in Salvador, Bahia, Brazil, Methodology: A cross-sectional epidemiological study was conducted to investigate caries in children aged 2 to 5 years in 2022 and 2023. Caries was assessed using the decayed, extracted, filled, and missing teeth (DEFT) index, and prevalence was calculated. Sociodemographic, family dynamic, dietary, and healthcare utilization data were obtained through a questionnaire. After descriptive and exploratory analyses, robust Poisson multivariable regression was conducted at a 5% significance level, considering socioeconomic, psychosocial, and behavioral aspects. Results: A total of 440 preschoolers participated, with a mean age of 55 months. The majority were Black or mixed race (88.86%) and female (52.73%). The prevalence of caries was 30.91% and the mean DEFT score was 1.1. In the multivariable analysis, low family income (up to one minimum wage) and not playing outdoors during the pandemic were associated with a higher prevalence of caries. Conclusion: The prevalence and severity of caries remain high, and the pandemic has contributed to the maintenance of this epidemiological scenario. Public health measures and policies promoting social justice should be planned to improve oral health conditions in childhood.

In this study, we propose a conservative and compact finite difference scheme designed to preserve both the mass change rate and energy for solving the sixth-order Boussinesq equation with the surface tension. Theoretical analysis confirms that the proposed scheme achieves second-order accuracy in temporal discretization and fourth-order accuracy in spatial discretization. The solvability, convergence, and stability of the difference scheme are rigorously established through the application of the discrete energy method. Additionally, a series of numerical experiments are conducted to illustrate the effectiveness and reliability of the conservative scheme for long-time simulations.

A simple and sensitive liquid chromatography-tandem mass spectrometry (LC– MS/MS) method has been developed for the simultaneous determination of atorvastatin (ATOR), ezetimibe (EZM) and their three metabolites including o-hydroxyl atorvastatin (o-OH ATOR), p-hydroxyl atorvastatin (p-OH ATOR), and ezetimibe-glucuronide (EZM-G) in human plasma using benzyl paraben (BP) as internal standard (IS). The analytes and IS were ionized using ESI positive ion mode (ATOR, o-OH ATOR, and p-OH ATOR), ESI negative ion mode (EZM, EZM-G, and BP), and operated in multiple reaction monitoring (MRM) mode. They were next extracted by salting-out assisted liquid–liquid extraction with acetonitrile, and then analyzed by liquid chromatography on a reversed-phase chromatographic column (50 mm × 4.6 mm; 3.5 µm), using a mixture of ac-etonitrile and an acetic acid solution (0.5%) as the mobile phase, showing high extraction effi-ciency (>70%), and a minimized matrix effect. The method was satisfactorily validated, and showed excellent linearity over wide concentration ranges of 0.06–15 ng/mL, 0.6–150 ng/mL, 0.4–100 ng/mL, 0.12–30 ng/mL, and 0.05–3 ng/mL for EZM, EZM-G, ATOR, o-OH ATOR, and p-OH ATOR, respectively.

In this paper, we introduce a new class of $(p,q)$-analogue type of Fubini numbers and polynomials and investigate some properties of these polynomials. We establish summation formulas of these polynomials by summation techniques series. Furthermore, we consider some relationships for $(p,q)$-Fubini polynomials associated with $(p,q)$-Bernoulli polynomials, $(p,q)$-Euler polynomials and $(p,q)$-Genocchi polynomials and $(p,q)$-Stirling numbers of the second kind.

In this paper, we investigate the relationship people have with their smart devices. We use the concept of agency to capture aspects of users’ sense of mastery as they relate to their device. This study gives preliminary evidence of the existence of two independent dimensions of agency for modeling the interaction between humans and smart devices: (i) user agency and (ii) device agency. These constructs emerged from an exploratory factorial analysis conducted on a survey data collected from 587 participants. In addition, we investigate the correlation between user agency and device agency with background variables of the respondents. Finally, we argue that mapping the users’ dynamics with their device into user agency and device agency fosters a better understanding of the needs of the users and helps in designing interfaces tailored for the specific capabilities and expectations of the users.

This study examines the evolution and eventual decline of the spirit of primitive federalism within Confucianism, tracing its trajectory from the time of the Duke of Zhou and Confucius to the significant reinterpretations by Dong Zhongshu. The Duke of Zhou's establishment of a confederation-like system and the "Zhou ritual norms" laid the groundwork for a political structure that embraced diversity and distributed power among states, reflecting a proto-federalist concept. Confucius's teachings continued this legacy, advocating for a return to the systems of the Duke's era, where both the central government and local states shared responsibilities and rights. The crux of this study lies within its fourth section, which narrates a tale of transformation happened under Emperor Wu of Han, where Dong Zhongshu distorted the original Confucianism and reinterpreted it as a centralized ideology. In Confucian theory, the shift involved the suppression of alternative philosophies, the establishment of Confucianism as the state's dominant ideology, and a reinterpretation of Confucianism that supported an centralized framework and cultural assimilation. In political practice, he re-envisioned the role of feudal lords as administrative extensions of a centralized government, effectively eroding local autonomy and shifting towards a unified, centralized system. Under the auspices of political authority, Confucian theory was transformed from a federalist theory promoting diversity, inclusivity and advocating decentralization, to one that legitimized a centralized and unified system.

Automation is transforming ship design by enhancing flexibility, efficiency, and accuracy in Computer-Aided Ship Design (CASD). This review highlights advancements in automation and parametric techniques that streamline the design process. Variable geometry and task automation improve performance optimization, enabling designers to concentrate on complex elements. The review will talk about advanced tools and methodologies, their effects on design optimization, production workflows, and some of the challenges associated with software interoperability and increased design complexity. It will also look at how AI and machine learning can be leveraged for better automation. The review identifies several future opportunities in automated ship design but frames such technologies as revolutionary in the shipbuilding sector and highlights their potential to bring significant transformation to shipping.

Photocatalytic water splitting offers a promising route for sustainable hydrogen production, but its efficiency remains limited by complex interactions between material properties, operating conditions, and reaction mechanisms. This study presents a data-driven approach to optimize photocatalytic water splitting for enhanced hydrogen evolution. By integrating experimental data, density functional theory calculations, and machine learning algorithms, we identified key descriptors governing photocatalyst performance. A predictive model was developed to screen optimal catalyst compositions, morphologies, and operating conditions, leading to a significant increase in hydrogen production rates. The results demonstrate the potential of data-driven optimization to accelerate the discovery of high-performance photocatalysts, paving the way for scalable and efficient solar-driven hydrogen production.

This paper explores the intersection of geometry and music, investigat- ing how mathematical structures shape musical theory, composition, and perception. It begins by examining the geometric foundations of pitch organization, including the circular representation of pitch classes and the tonal space. We then explore rhythmic structures through symmetry, analysing how patterns in time are geometrically conceived. Finally, the study examines more recent developments in computational music theory, such as the use of topological data analysis to model musical transformations and voice leading.

Polluting discharges are produced at a rate that is inexorably rising in tandem with human activity development. Eutrophication and other matters, such as offensive aquatic life, require wastewater to be treated before being discharged into the environment. Prior releasing into water bodies, the treated waters must to undertake laboratory analysis. For this purpose, an analysis using standard methods was carried out at the Laboratory Waterworks in Debrecen on various samples taken from various wastewater treatment plants (sewage treatment plants) in Hungary. The overall objective of this study was to assess the quality of the effluents from the fourteen (14) wastewater treatment plants and investigate their efficiency by measuring various physicochemical parameters and microbiological parameters as excellent indicators to evaluate the performance of the studied wastewater treatment plants. The microbiological indicator monitored was the heterotrophic count (HPC) by estimating its total number, which provides useful information for the assessment and monitoring of water samples. The determination of physicochemical parameters included biological oxygen demand (BOD5), chemical oxygen demand (COD) and nitrite -N (NO2), ammonia-N (NH3-N), nitrate-N (NO3) total phosphorus (TP), Kjeldahl- Nitrogen (organic N+ammonia-N) (TKN), total inorganic N (TI), total suspended solids (TSS) and total nitrogen (TN). The performance of the wastewater treatment plants examined was checked and the results were conclusive. In the future, solutions should be applied to these wastewater treatment plants and some recommendations are suggested.

Formaldehyde is generated via the biochemical and chemical pathways, and it widely exists in foods. Its chronic exposure causes carcinogenic, neurotoxic, reproductive, allergic, immunological, genetic and respiratory toxicities. However, food-sourced formaldehyde is usually neglected due to its rapid metabolism after intake. Compared with its residual level in processed foods, a large proportion of formaldehyde disappears because of its high reactivity. Nevertheless, its elimination reaction mechanism and the potential consequences remain unclear. In this review, the disappearing pathway of formaldehyde from two aspects, the formation of formaldehyde-derived toxins and aromas, was reviewed. This review article may help food scientists take appropriate strategies to use the beneficial reactions of formaldehyde or divert its harmful effects to beneficial ones.

Employee turnover is a significant issue for financial institutions, impacting productivity, increasing recruitment costs, and disrupting critical operations. In this project, this study aimed to predict employee turnover using a dataset containing attributes such as employee satisfaction, performance, and tenure. By framing the task as a binary classification problem, this study employed CatBoost and XGBoost, two advanced regression-based algorithms, to develop predictive models. This paper's analysis demonstrated that CatBoost outperformed XGBoost across all evaluation metrics, including MAE, MSE, RMSE, and R², making it the more effective model for predicting turnover in the financial sector. The study highlights key factors contributing to employee attrition, such as job satisfaction, tenure, and promotion opportunities, offering actionable insights for retention strategies. Additionally, by predicting probabilities rather than binary outcomes, this study aims to make more detailed decisions about employee retention. This research provides valuable tools for financial institutions to mitigate the risk of turnover, retain critical talent, and ensure operational continuity.

Granular material is one of the most common in nature. Recently, there has been a significant amount of interest in the granular materials, such as powders or sand. It is challenging to describe the motion of such kind of macroscopic size matter, because it behaves like solid, fluid or gas. Size division, pattern formation, avalanches, packing and convection - these are just a few examples from a wide range of observed phenomena that occur during the motion process of granular matter. Most of these processes are based by flow and no wonder that much effort has been devoted to obtain a hydrodynamic description in which granular materials are treated as a continuous medium. During the research of vertically shaken granular medium motion in an opened container it was found that experimental and numerical results are explained by hydrodynamic theory. This paper investigates the motion of a granular matter for a shallow, vertically shaken bed. We assume the Leidenfrost state as initial and the matter resembles a fluid heated up from below.A one-dimensional isothermal and a non-isothermal problem of granular material motion,simulatedby a hydrodynamic model are considered, and the material is assumed to be a continuous medium. The local temporal solvability of isothermal and non-isothermal initial boundary-value problem in Sobolev’s spaces is proved. This work presents a numerical study of the one-dimensional model of granular medium flow. The feature of this model is consideration of the Navier-Stokes equations accounting an interpolation form of VanderWaals constitutive equation for pressure. A finite-difference approximation for numerical solution is proposed.

This bibliometric study analysed breast cancer research trends and outputs from 2014 to 1 2023, utilising data from the Scopus database. The primary objectives include analysing publication 2 growth, citation metrics, document type distribution, leading countries, preferred journals and 3 authorship patterns. The study reveals a significant increase in breast cancer research publications, 4 with a total of 12,073 documents and 216,769 citations over the ten-year period. The United States 5 leads in publication output and citation impact, followed by China and Japan. Articles are the 6 predominant document type for total 73.4% of publications, while reviews and book chapters also 7 contribute notably. The most preferred journals for publishing breast cancer research include Breast 8 Cancer Research and Treatment, Clinical Breast Cancer, and Breast Cancer Research. Authorship 9 patterns show a trend towards multi-authored papers, with a substantial number of papers having 10 more than five authors. The study underscores the global collaborative effort in advancing breast 11 cancer research and highlights key areas for future exploration.

This study combines two methodologies—key indicators from the Sustainable Livelihoods Framework (SLF) and the Sustainability Assessment of Food and Agriculture Systems (SAFA) approach—to evaluate sustainability among 330 producers from three associations working within the recently recognized Amazonian Chakra system, designated as a Globally Important Agricultural Heritage System (GIAHS). The SAFA framework's four dimensions—Good Governance, Environmental Integrity, Economic Resilience, and Social Well-being—were used to assess sustainability, while the SLF considered key capitals such as financial, natural, and human capitals as critical for understanding livelihood outcomes. The discriminant analysis revealed significant differences across the associations, with Kallari and Wiñak performing better in financial management, environmental practices, and governance compared to Tsatsayaku, which exhibited weaker outcomes in these domains. Key indicators such as chakra income, price determination, and civic responsibility were the most effective in distinguishing the groups. The income analysis further highlights that Tsatsayaku reported the highest total income but relies heavily on non-Chakra sources, while Kallari and Wiñak showed stronger reliance on Chakra income, which aligns with their more sustainable agricultural practices. Per-capita income was higher for Tsatsayaku, but extreme poverty levels remained similar across all associations, underscoring the need for targeted financial interventions. Policy recommendations include promoting access to financial resources and strengthening participatory governance models to enhance sustainability outcomes across associations. For future research, combining the SAFA and SLF provides a comprehensive and robust methodology for assessing sustainability in agricultural systems, enabling a deeper understanding of the interactions between social, economic, and environmental factors in diverse contexts.

This paper reviews the measurement challenges associated with 3D scanning techniques in civil engineering. It explores the practical aspects of scanning buildings and complex surfaces through various case studies. The paper details the conventional use of Terrestrial Laser Scanning (TLS) for reconstructing the technical documentation of a hall. It then describes an unconventional application of this technique for measuring an ETICS (External Thermal Insulation Composite System) wall, aiming to detect microdeformations caused by environmental factors controlled within a climatic chamber. The measurements of the insulated wall were subsequently repeated using a metrological grade laser scanner. Numerical data was analyzed with inspection engineering methods. This approach yielded qualitative and quantitative results. Although the qualitative results were consistent, the quantitative data revealed some inconsistencies. To address discrepancies in the quantitative data, a comparative analysis was performed, which highlighted critical findings and areas of divergence.

Early detection of cervical cancer is the need of the hour to stop the fatality from this disease. There have been various CAD approaches in the past that promise to detect cervical cancer in an early stage, each of them having some constraints.This study proposes a novel ensemble deep learning model, VLR (variable learning rate), aimed at enhancing the accuracy of cervical cancer classification. The model architecture integrates VGG16, Logistic Regression, and ResNet50, combining their strengths in an offbeat ensemble design. VLR learns in two ways: firstly, by dynamic weights from three base models, each of them trained separately; secondly, by attention mechanisms used in the dense layer of the base models. Hyperparameter tuning is applied to further reduce loss, fine tune the model’s performance, and maximize classification accuracy. We performed K-fold cross-validation on VLR to evaluate any improvements in metric values resulting from hyperparameter fine tuning. We have also validated our model on images captured in three different solutions and on a secondary dataset. Our proposed VLR model outperformed existing methods in cervical cancer classification, achieving a remarkable training accuracy of 99.95% and a testing accuracy of 99.89%.

To date, SARS Cov-2 virus has caused more than 775 million cases worldwide and led to 7 million deaths. The virus enters cells with the aid of the ACE2 receptor located in the airway epithelium, but also in endothelial cells lining the inner walls of vessels. Endothelial damage results in impaired blood flow and gas exchange. Statins have been shown to have a pleiotropic effects on the vascular epithelium. Aim of the study: Assessment of the efficacy and safety of statin therapy in patients with COVID-19. Methods: Two hundred and one COVID-19 patients hospitalized at the Jerzy Gromkowski Regional Specialist Hospital between March 2020 and December 2021 (i.e. during the period dominated by Alpha, Beta, Gamma and Delta variants). Patients were divided into three groups: (I) patients treated with statins prior to the admission to the hospital; (II) patients started on statins after admission due to Covid-19; (III) control patients with Covid-19, not treated with statins either prior to or during hospitalization. Met Statistica 13.1 calculations were used in the paper. Shapiro-Wilk, Kruskal-Wallis and ANOVA tests were also performed. Results: Out of the 201 patients total, group I comprised 66 patients with a mean age of 66.2 years; group II comprised 33 patients with a mean age of 75.3 years; and the control group comprised 97 patients with a mean age of 61.1 years. The groups characteristics did not show significant differences in terms of gender, place of hospitalization, percentage of Covid-19 vaccinations, number of convalescents. Death was observed in: 12% of patients in group I, 6% of patients in group II, and 6% of patients in group III. Body Mass Index (BMI) in group I was 31.3: a value significantly higher than the control group’s 28.3, (p<0.05). The severity of Covid-19 assessed on admission in group I – 4.09 – was statistically higher than in the control group 3.52 (p<0.05). The number of pulmonary embolism incidents did not differ between groups. Conclusions: During the period dominated by Alpha, Beta, Gamma and then Delta variants, we found no statistically significant positive or negative effects of statin therapy on the duration of hospitalization, number of complications or percentage of deaths between the two studied groups of patients: those who received statins prior to hospitalization, and those who received statins during hospitalization.

Pichia pastoris is extensively employed as a host for expressing foreign proteins, and its secretion system is especially advantageous for efficient protein purification. Ulp1 is essential for the cell cycle. In Saccharomyces cerevisiae, the absence of Ulp1 leads to G2/M cell cycle arrest. Similarly, in Pichia pastoris, we also observed G2 phase stalling in Ulp1 knockout cells. RNA-seq data revealed significant changes in seven genes, including INO1. Interestingly, overexpressing INO1 in Ulp1 knockout cells restored cell proliferation, suggesting that Ulp1 regulates cell proliferation through INO1. However, adding extra inositol to Ulp1 knockout cells was not able to restore cell prolifera-tion, indicating that the enzyme activity of INO1 is not essential for cell proliferation. Our data provide new insights into Ulp1 functions in the cell cycle and raise new questions about yeast cell cycle regulation.

Background and Objectives: A degenerative joint disease that primarily affects the elderly, osteoarthritis (OA) causes pain, decreased mobility, and a lower quality of life. The effectiveness of GerovitalH3, a biotrophic medication created by Ana Aslan, in reducing pain and enhancing function in older people with osteoarthritis in the knee and hip is assessed in this study. Materials and Methods: Two groups of 201 patients aged 65 and older participated in a randomized, case-control clinical trial. One group received physical therapy and GerovitalH3 periarticular injections, while the other group received just physical therapy. In addition to functional evaluations using the Lequesne Index, Activities of Daily Living (ADL), and Instrumental ADL (IADL) scores, pain was assessed using a Visual Analogue Scale (VAS). Results: The GerovitalH3 group showed a substantial decrease in pain (p<0.001) and an improvement in daily activities (p<0.001) when compared to the control group. However, there was no significant difference in walking scores (p = 0.171). There were no significant adverse effects noted. GerovitalH3 did not outperform physical therapy for reducing depression, even though both groups displayed some improvement in this area. Conclusions: This study opens a new possibility for periarticular injections of Gerovital H3 treatment combined with physical therapy, which may represent a safe and effective non-surgical option for elderly individuals struggling with OA pain and functional limitations. The findings of this study reinforce the need for further research, particularly randomized controlled trials with larger sample sizes and longer follow-up periods.

Background: Nanotechnology has emerged as a promising field of study, aiming to revolutionize healthcare through the innovative development of drugs, diagnostic tools, and medical devices. As a result, healthcare professionals in training are constantly faced with the challenge of understanding and applying these emerging technologies. The objective of this study is to determine the factors to consider for including the teaching of nanotechnological tools in medical specialty postgraduate programs. Methods: A study based on the Nominal Group Technique (NGT) was conducted to identify the factors that promote and hinder the implementation of nanotechnology in certain postgraduate programs where nanotechnology is employed, such as Internal Medicine, Critical Care Medicine, Family Medicine, and Clinical Pharmacology at the School of Medicine of Universidad de La Sabana. A framework analysis was used to analyze the collected data. Results: Based on the two research questions, several aspects were identified for the inclusion of nanotechnology in the medical practice of specialties. Key challenges include lack of knowledge and resource availability, misinformation, and lack of knowledge, as well as paradigmatic analysis as the main obstacles to the use of nanotechnology. Conclusions: The findings provide a first-time study on nanotechnology curricula within medical specialty programs in Colombia, with potential extrapolation to similar situations in other medical schools.

The species and bio-availability of phosphorus (P) in primary, secondary and digested sludge were fractionated and further analyzed in this study. Results showed that inorganic P (IP) was the primary P fraction in the secondary sludge and digested sludge, in which non-apatite IP (NAIP) amounted to 91.6% and 69.3% of IP, respectively. Organic P (OP), accounting for about 71.7% of total P (TP), was the dominant P composition in primary sludge. The content of bio-available P was about 9.7, 43.4, 29.8 mg-P/g-TS in primary sludge, secondary sludge and digested sludge, respectively, suggesting secondary sludge is the optimal choice when land application of sewage sludge is taken into consideration, followed by digested sludge and primary sludge. Polyphosphate and orthophosphate, comprising approximately 54.3% and 89.2% of TP, was the dominant P species in the secondary sludge and digested sludge, respectively. Monoester-P (54.6% of TP in extract) and diester- P (24.1%) were identified as OP species in primary sludge by Phosphorus-31 nuclear magnetic resonance (31PNMR). The present results would be helpful for P recovery and recycle from sewage sludge in wastewater treatment plant.

This study delves into the realm of heuristic evaluation within usability testing, focusing on refining and applying a tailored algorithm that simplifies the implementation of the Analytic Hierarchy Process for heuristic prioritization. Addressing the prevalent challenge of disparate evaluation methodologies and the absence of a standardized process in usability testing, our research introduces a novel method that leverages the Analytic Hierarchy Process alongside a bespoke algorithm. This algorithm utilizes transitive properties for pairwise comparisons, significantly reducing the evaluative workload. This innovative approach facilitates the estimation of heuristic relevance irrespective of the number of items per heuristic or the item scale and streamlines the overall evaluation process. In addition to rigorous simulation testing of the tailored algorithm, we applied our method in a practical scenario, engaging seven usability experts in evaluating a web interface. This empirical application underscored our method’s capacity to diminish the requisite comparison count and spotlight areas of improvement for critically weighted yet underperforming heuristics. The outcomes of this study underscore the efficacy of our approach in enhancing the efficiency and organization of heuristic evaluations, paving the way for more structured and effective usability testing methodologies in future research endeavors.

Augment reality (AR) is crucial for immersive human-computer interaction (HCI) and vision of artificial intelligence (AI). Labeled data drove object recognition in AR. However, manual annotating data is expensive and labor-intensive, and furthermore, scanty labeled data limits the application of AR. Aiming at solving the problem of insufficient training data in AR object recognition, an automated vision data synthesis method called BAGAN is proposed in this paper based on the 3D modeling and GAN algorithm. Our approach has been validated to have better performance than other methods through image recognition task on natural image database ObjectNet3D. This study can shorten the algorithm development time of AR and expand the application scope of AR, which is of great significance to immersive interactive systems.

We conducted a comparative study on the development of two synthetic methods. The solvent-free synthesis is a green chemistry method developed in a bid to ameliorate environmental adverse effects of the conventional solvent-based synthesis. The synthesis of novel dichloro and polymethoxy p-nitrophenylhydrazones through solvent free technique gave moderate to high yields which were however lower than those of the solvent-based method. The established solvent-free approach has several benefits, including universality and simplicity of the approach, catalyst-free conditions, non-use of an organic solvent, quick reaction time, fast and efficient workup, and un-solvated pure products in moderate to high yields.

This study explores the limitations of current AI systems, which predominantly function in the digital realm, and investigates the transformative potential of embodied AI. Embodied AI involves AI agents equipped with physical bodies, enabling them to interact directly with the physical world. This research focuses on how embodied AI can bridge the "embodiment gap" and enhance human-machine collaboration and learning in dynamic environments. The research examines the fundamental differences between traditional AI systems and embodied AI, emphasizing the importance of physical interaction for contextual understanding, adaptive learning, and intuitive human-machine collaboration. It explores various applications of embodied AI, including robotics, autonomous vehicles, and assistive technologies, demonstrating how physical embodiment can improve performance, safety, and user experience. Through experimental studies and real-world case analyses, the study highlights the advantages of embodied AI in tasks requiring situational awareness, dexterity, and real-time decision-making. It also addresses the challenges associated with developing and deploying embodied AI systems, such as sensor integration, real-time processing, and human-machine interface design. Findings indicate that embodied AI can significantly improve the efficacy of AI systems in dynamic and unpredictable environments. By leveraging physical embodiment, AI agents can better understand and respond to their surroundings, facilitating more natural and effective interactions with humans. The research concludes with recommendations for advancing embodied AI, including interdisciplinary collaboration, investment in sensor and actuator technologies, and the development of standardized frameworks for embodied intelligence.

The insertion of the Amazon into the global commodities market requires ensuring the rational use of resources to meet market and political-social demands, such as the UN 2030 Agenda. Therefore, responsible production practices, given the current demand for sustainable use of land and its re-sources, are essential. Thus, the objective of this study was to identify opportunities and challenges linked to the implementation and consolidation of sustainable production systems in the Amazon through a systematic literature review. To this end, articles were prospected in four databases (Science Direct, SpringerLink, Scopus and Wiley) covering the period from 2004 to 2024. A total of 5,385 articles were evaluated, and after the selection stages, only 39 were included. A low distri-bution of studies among states in the Brazilian Amazon was observed, with a peak in studies star-ting in 2015. The studies developed mostly addressed agroforestry systems and forest management. Although there are sustainable production systems and practices, research and public policies need to be promoted to help the different actors involved in agricultural production in the Amazon, generating income and quality of life.

The aim of our research was to evaluate and analyze serum 25(OH) vitamin D and parathyroid hormone (PTH) levels to investigate whether vitamin D deficiency serves as a risk factor for an increased incidence of acute respiratory infections (ARI) in children. Serum PTH levels were used as an indicator of vitamin D sufficiency, as normal PTH levels require an optimal concentration of 25(OH) vitamin D. The study included 129 children, divided into five subgroups: children with acute bronchopneumonia (n=42), acute laryngotracheitis (n=7), acute bronchiolitis (n=32), acute bronchitis (n=18), and a control group (n=30). No statistically significant differences in 25(OH)D levels were observed between the overall population of children with ARI and the control group (p=0.073). However, significant differences in 25(OH)D levels were identified between the control group and children with bronchopneumonia, acute bronchitis, and laryngotracheitis (p<0.01, p<0.05). Vitamin D insufficiency was found in 28.3% of children with ARIs and 10% in the healthy group. Sufficient levels were present in 60% of healthy children and 48.5% of those with ARIs. Deficiency was observed in 4% of ARI patients and 3.3% of the control group. Regarding PTH levels, statistical significance was found between the control group and the acute bronchiolitis group, due to the high percentage of children with hypervitaminosis in this subgroup. These results highlight the crucial role of vitamin D in the onset and progression of acute respiratory tract infections in children, emphasizing its impact on their overall respiratory health.

Background: RO and ChRCC are kidney tumours with overlapping characteristics, making differentiation between them challenging. Objectives: The objective of this research is to create a radiogenomics map by correlating radiomic features to molecular phenotypes in ChRCC and RO, using resection as the gold standard. Methods: Fourteen patients (6 RO and 8 ChRCC) were included in the study. A total of 1,875 radiomic features were extracted from CT scans, alongside 632 cytobands containing 16,303 genes from the genomic data. Results: Feature selection algorithms applied to the radiomic features resulted in 13 key features. From the genomic data, 24 cytobands highly correlated with histology were selected and cross-correlated with the radiomic features. The analysis identified four radiomic features that were strongly associated with seven genomic features. Conclusion: These findings demonstrate the potential of integrating radiomic and genomic data to enhance the differential diagnosis of RO and ChRCC, paving the way for more precise and non-invasive diagnostic tools in clinical practice.

This paper presents an approach to assess and improve the cybersecurity infras- tructure of developing countries. We have highlighted and addressed the critical gaps through a hybrid approach encompassing SWOT analysis, benchmarking using Data Envelopment Analysis (DEA) and, Root Cause Analysis (RCA). This approach has provided a strong basis for evaluating cybersecurity performance by combining qualitative insights with quantitative metrics. As a case study, we have compared an underdeveloped country’s (Pakistan) cybersecurity pos- ture to five highly developed countries (Lithuania, Estonia, Singapore, Spain,and Norway). Our analysis has identified cybersecurity shortcomings and pro- vided actionable recommendations within a flexible framework, applicable for all developing countries to identify and address vulnerabilities, and align their cybersecurity infrastructure with international standards.

The increasing energy consumption and industrial activities that release carbon dioxide (CO2) emissions into the atmosphere have become one of the most serious environmental issues of today. This article discusses the current status in Azerbaijan regarding the development of effective approaches to reduce carbon emissions and ensure sustainable development, and it proposes relevant strategies for managing the carbon footprint. By exploring the interrelationship between the economy and the environment, it aims to contribute to the formation of more effective policies for managing CO2 emissions in Azerbaijan. According to the results of the econometric model established within the research for the period of 1990-2023, as the Azerbaijan economy grows, per capita CO2 emissions tend to increase, which can be linked to rising industrial activity, energy consumption, and other factors driving economic growth. The presence of a positive long-term relationship between population growth and per capita CO2 emissions can be attributed to higher overall energy consumption, increased demand for goods and services, and rising transportation needs. The strong correlation between economic growth and CO2 emissions suggests that it may be too early to assert the validity of the Kuznets curve for Azerbaijan economy as a developing country.

Fluctuations in kynurenic acid (KYNA) and brain-derived neurotrophic factor (BDNF) levels in the brain reflect its neurological status. The aim of the study was to investigate the effect of transiently elevated KYNA concentration in the cerebroventricular circulation on the expression of BDNF and its high-affinity tropomyosin-related kinase receptor B (TrkB) in specific structures of the sheep brain. Intracerebroventricularly cannulated anestrous sheep were subjected to a series of four 30-min infusions of KYNA: 4×5 μg/60 μL/30 min (KYNA20, n=6) and 4×25 μg/60 μL/30 min (KYNA100, n=6) or a control infusion (n=6), at 30-min intervals. Sections of the hippocampal CA3 field, amygdala (AMG), prefrontal cortex (PCx), and the hypothalamic medial-basal (MBH) and preoptic (POA) areas were dissected from the brain immediately after the experiment. The highest concentration of BDNF protein was found in the CA3 field (P&lt;0.01), which was 8-fold higher than in the AMG and 12-fold higher than that in the PCx (MBH and POA were not analyzed). The most pronounced BDNF mRNA expression was observed in the MBH followed by the PCx, POA, AMG and CA3, while the highest abundance of TrkB mRNA was recorded in the AMG followed by the MBH, PCx, CA3 and POA. KYNA increased (P&lt;0.05-P&lt;0.01) BDNF protein levels and the expression of its gene in the brain structures examined, with the effect varying by dose and brain region. KYNA, particularly at the KYNA100 dose, also increased (P&lt;0.01) TrkB gene expression, except for the AMG, where the lower KYNA20 dose was more effective (P&lt;0.01). These findings suggest a positive relationships between KYNA levels in the cerebroventricular circulation and BDNF-TrkB expression in specific brain regions in a sheep model. This indicates that a transient increase in CSF KYNA concentration can potentially restore BDNF production, whose deficiency underlies numerous neurological disorders.

Diospyros kaki, the most widely cultivated species of persimmon, has been long used in traditional medicine since its leaves extracts contain high amounts of flavonoids and terpenoids, endowed with potential antioxidant, anti-inflammatory, anticancer, antidiabetic and antimicrobial effects. Re-cently, many in vitro and in vivo studies have assessed different potential health benefits of per-simmon leaves; however, its anticancer activity towards colorectal cancer (CRC), the second deadliest cancer in Western countries, has not been investigated in depth. Since CRC current therapies are associated with serious side effects and show no efficacy towards patients carrying RAS/BRAF mutations, the search for new and more effective therapies has turned to plant extracts, which could help reduce conventional drugs dosages and toxicity. The effect of Diospyros kaki al-coholic extract has been investigated on E705 CRC cell line, representative of most CRC patients, and on SW480 cells, carrying a KRAS activating mutation. This extract is effective in reducing tumor cells viability, without affecting the healthy mucosa cell line CCD 841, and it triggers apoptosis in CRC cells, by disrupting mitochondrial functionality and increasing oxidative stress.

Pre-transport management activities can cause stress, injuries, and mortality in poultry. This study aimed to gather information about the current pre-transport practices (selection of unfit chickens, catching preparations, catching, and crating) for spent hens and broilers by questioning Flemish poultry farmers. The results showed that catching preparations such as an extra selection of chickens unfit for transport was performed by a minority of the poultry farmers, and that layer farmers were less in line with the EU-legislation for water and feed withdrawal than broiler farmers. All birds were caught inverted except for one broiler farmer who used mechanical catching (not yet common in Flanders). Additionally, mechanical catching may involve extra costs, increased biosecurity risks, and specific recommendations for the stable (height and width). The broiler farmers preferred mechanical catching for broiler catchers' well-being, while upright catching was considered better for animal welfare than catching more than three chickens by one/two legs, mechanically, or by wings. Awareness of the need to perform an extra selection by the poultry farmers before catching is required. Preparations like closing areas under the aviary system and removing litter (layers) can further streamline the process and reduce animal suffering.

This stability analysis method is simply represented and understood on a Bode plot of two impedances without complex equations. The external impedance of the charging system is properly quantified and designed based on the stability criteria by analyzing.

Arbutus unedo (strawberry tree) is a small Mediterranean tree capable of vigorous regrowth after disturbances like fire. Traditionally used for biomass fuel, its bark and branches hold potential for higher-value products through ecovalorisation into liquid mixtures that could replace petro-leum-based materials. This study aimed to explore the chemical composition of various compo-nents of Arbutus unedo and to produce a liquefied material from its internal (IB) and external bark (EB). Chemical compositions of internal and external bark were determined using TAPPI standards including ash, extractive content, lignin, and cellulose. Metal cations were analyzed by ICP. Liquefaction of bark was optimized in a PARR reactor, evaluating factors such as particle size, temperature, and time and the best polyols were monitored by FTIR-ATR. Polyurethane foams were made with internal and external bark liquefied materials by polymerization with isocyanate, a catalyst and water as blowing agent. Results showed that EB has a higher extractive and lignin content, while IB contains more cellu-lose. Liquefaction yields were higher for IB (74%) than EB (68%), with IB yielding polyols that produced stronger and more resilient foams. Mechanical properties of the foams were influenced by the NCO/OH ratio and catalyst levels. Overall, the internal bark demonstrated superior performance for foam production, highlighting its potential as an eco-friendly alternative to petroleum-derived materials.

Introduction: Leptin and ghrelin are two hormones that play a role in weight homeostasis. Leptin, which is produced primarily by adipocytes and is dependent on body fat mass, suppresses appetite and increases energy expenditure. Conversely, ghrelin is the “hunger hormone”, it stimulates appetite and promotes fat storage. Bariatric surgery significantly alters the levels and activity of these hormones, contributing to weight loss and metabolic improvements. Clarifying the interplay between bariatric surgery, weight loss, physical exercise, leptin, and ghrelin is essential for developing comprehensive strategies for optimizing long-term outcomes for candidates for bariatric surgery, especially sarcopenic patients. Methods: This was a randomized controlled study with two groups (n=22). The patients in both groups have obesity and sarcopenia. A Roux-en-Ygastric bypass (RYGB) procedure was performed in all patients. The intervention group participated in a structured exercise program three times per week beginning one month after surgery and lasting 16 weeks. Patient assessment was performed before surgery (baseline) and after completion of the exercise program. The control group received the usual standard of care and was assessed similarly. Results: After surgery, weight, BMI and lean mass decreased significantly in both groups from baseline to the second assessment. Leptin was not significantly different from baseline to the second assessment in the physical exercise group but was significantly lower in the control group (p=0.05). Ghrelin increased over time in both groups, but the differences were not significant. When we associated leptin (the dependent variable) with weight (the independent variable), we found that lower weight was associated with lower leptin levels. A similar relationship was also observed between leptin and sarcopenia parameters (muscle strength and mass), as well as with bone health parameters (bone mineral density and t-score). Higher ghrelin levels were significantly associated with higher t-scores and z-score (p<0.05). Conclusion: Exercise has been shown to have a significant effect on leptin and ghrelin levels after bariatric surgery. By incorporating regular physical activity into their lifestyle, bariatric patients can optimize their weight loss outcomes and improve their overall health. After the physical exercise protocol, patients in the intervention group revealed more established leptin levels, which may indicate a protected pattern concerning decreased leptin levels. An unfavorable profile was evidenced, according to which greater weight loss, sarcopenia, and osteoporosis were associated with lower leptin levels.