Quantitative Results Research Articles

Learning Framework for Joint Optimal Node Placement and Resource Management in Dynamic Fog Environment

Background: With recent improvements in fog computing, it is now feasible to offer faster response time and better service delivery quality; however, the impending challenge is to place the fog nodes within the environment optimally. A review of existing literature showcases that consideration of joint problems such as fog node placement and resource management are less reported. Irrespective of different available methodologies, it is noted that a learning scheme facilitates better capability to incorporate intelligence in the network device, which can act as an enabling technique for superior operation of fog nodes. Objective: The prime objective of the study is to introduce simplified and novel computational modelling toward the optimal placement of fog nodes with improved resource allocation mechanisms concerning bandwidth Method: Implemented in Python, the proposed scheme performs predictive operations using the Deep Deterministic Policy Gradient (DDPG) method. Markov modelling is used to frame the model. OpenAI Gym library is used for environment modelling, bridging communication between the environment and the learning agent. Result: Quantitative results indicate that the proposed scheme performs better than existing schemes by approximately 30%. Conclusion: The prime innovative approach introduced is the implementation of a reinforcement learning algorithm with a Markov chain towards enriching the predictive analytical capabilities of the controller system with faster service relaying operations a.

Multidimensional polynomial patterns over finite fields: Bounds, counting estimates and Gowers norm control

We examine multidimensional polynomial progressions involving linearly independent polynomials over finite fields, proving power saving bounds for sets lacking such configurations. This jointly generalises earlier results of Peluse (for the single dimensional case) and the author (for distinct degree polynomials). In contrast to the cases studied in the aforementioned two papers, a usual PET induction argument does not give Gowers norm control over multidimensional progressions that involve polynomials of the same degrees. The main challenge is therefore to obtain Gowers norm control, and we accomplish this for all multidimensional polynomial progressions with pairwise independent polynomials. The key inputs are: (1) a quantitative version of a PET induction scheme developed in ergodic theory by Donoso, Koutsogiannis, Ferré-Moragues and Sun, (2) a quantitative concatenation result for Gowers box norms in arbitrary finite abelian groups, motivated by (but different from) earlier results of Tao, Ziegler, Peluse and Prendiville; (3) an adaptation to combinatorics of the box norm smoothing technique, recently developed in the ergodic setting by the author and Frantzikinakis; and (4) a new version of the multidimensional degree lowering argument.

TQU-HG dataset and comparative study for hand gesture recognition of RGB-based images using deep learning

Hand gesture recognition has great applications in human-computer interaction (HCI), human-robot interaction (HRI), and supporting the deaf and mute. To build a hand gesture recognition model using deep learning (DL) with high results then needs to be trained on many data and in many different conditions and contexts. In this paper, we publish the TQU-HG dataset of large RGB images with low resolution (640×480) pixels, low light conditions, and fast speed (16 fps). TQU-HG dataset includes 60,000 images collected from 20 people (10 male, 10 female) with 15 gestures of both left and right hands. A comparative study with two branches: i) based on Mediapipe TML and ii) Based on convolutional neural networks (CNNs) (you only look once (YOLO); YOLOv5, YOLOv6, YOLOv7, YOLOv8, YOLO-Nas, single shot multiBox detector (SSD) VGG16, residual network (ResNet)18, ResNext50, ResNet152, ResNext50, MobileNet V3 small, and MobileNet V3 large), the architecture and operation of CNNs models are also introduced in detail. We especially fine-tune the model and evaluate it on TQU-HG and HaGRID datasets. The quantitative results of the training and testing are presented (F1-score of YOLOv8, YOLO-Nas, MobileNet V3 small, ResNet50 is 98.99%, 98.98%, 99.27%, 99.36%, respectively on the TQU-HG dataset and is 99.21%, 99.37%, 99.36%, 86.4%, 98.3%, respectively on the HaGRID dataset). The computation time of YOLOv8 is 6.19 fps on the CPU and 18.28 fps on the GPU.

A Sociological study of the religious identity of young people in Central Asia and Europe (following the example of Kyrgyz Republic and the Republic of Poland)

The purpose of the article is to determine the situation in the field of religious preferences of young people in the Kyrgyz Republic and the Republic of Poland by studying the specific features and dynamics of the development of traditional beliefs in the Central Asian and European regions. The main methods used in the preparation of the work were the system-analytical method and the statistical method. The characteristic features of different areas of religion in Asia, as well as the origins and root causes of the formation and development of religious trends in Kyrgyzstan, are studied. The key moments in the history of the transformation of the Kyrgyz society in the XVIII-XXI centuries are outlined in the context of the influence of this process on the emergence of specific signs of local religious trends. Different approaches and concepts to the study of the young generation of the Central Asian Republic and Poland are evaluated by considering the qualitative and quantitative results of a number of sociological surveys. The results and conclusions of this work can be used as a basis for the development and implementation of a number of political and social reforms with an emphasis on raising awareness of the issue among the younger generation of Kyrgyzstan.

The Effect of Reflective Journal Writing on Moroccan EFL Master Students’ Reflective Thinking: A Mixed Methods Study

Reflective thinking is one of the transferable skills necessary for academic and professional success. Therefore, stimulating English as a foreign language (EFL) students’ reflective skills is undoubtedly important. Reflective journal writing is a pedagogical strategy proposed to help students build their reflective potential. Although previous research investigated the effect of this strategy on the development of students’ reflective skills, there are inconsistent conclusions regarding its effectiveness. Also, limited research has investigated this issue in relation to EFL students in the Moroccan context, specifically in higher education. The aim of this study is to evaluate the effectiveness of reflective journal writing in improving EFL master students reflective thinking. A convergent mixed methods approach associated with a quasi-experimental design was adopted. To collect quantitative and qualitative data, Kember, Leung, et al (2000) Reflective Thinking Questionnaire and reflective journal writing were used, respectively. A purposive sample of 69 EFL master students, including two intact groups, took part in the study. They were randomly assigned to experimental (n = 39) and control groups (n = 30). Quantitative results and qualitative findings revealed that the experimental group, after using reflective journals, has significantly developed reflective thinking compared to the control group. Also, a statistically significant change over time in the experimental group level of reflective thinking was observed. As it can be inferred, reflective journal writing is an effective strategy which could be used to help EFL students engage in a reflective reasoning. Accordingly, the results of this study encourage the formalization of reflective practice as a didactic approach to stimulate EFL students’ reflective thinking at the tertiary level.

Chemical Analysis of Exhaled Vape Emissions: Unraveling the Complexities of Humectant Fragmentation in a Human Trial Study.

Electronic cigarette smoking (or vaping) is on the rise, presenting questions about the effects of secondhand exposure. The chemical composition of vape emissions was examined in the exhaled breath of eight human volunteers with the high chemical specificity of complementary online and offline techniques. Our study is the first to take multiple exhaled puff measurements from human participants and compare volatile organic compound (VOC) concentrations between two commonly used methods, proton-transfer-reaction time-of-flight mass spectrometry (PTR-ToF-MS) and gas chromatography (GC). Five flavor profile groups were selected for this study, but flavor compounds were not observed as the main contributors to the PTR-ToF-MS signal. Instead, the PTR-ToF-MS mass spectra were overwhelmed by e-liquid thermal decomposition and fragmentation products, which masked other observations regarding flavorings and other potentially toxic species associated with secondhand vape exposure. Compared to the PTR-ToF-MS, GC measurements reported significantly different VOC concentrations, usually below those from PTR-ToF-MS. Consequently, PTR-ToF-MS mass spectra should be interpreted with caution when reporting quantitative results in vaping studies, such as doses of inhaled VOCs. Nevertheless, the online PTR-ToF-MS analysis can provide valuable qualitative information by comparing relative VOCs in back-to-back trials. For example, by comparing the mass spectra of exhaled air with those of direct puffs, we can conclude that harmful VOCs present in the vape emissions are largely absorbed by the participants, including large fractions of nicotine.

Cultivating the transfer landscape: Using a CRT framework to examine transfer receptivity at a Hispanic Serving Research Institution

AbstractWhen students of color transfer to Hispanic Serving Research Institutions (HSRI), they experience institutional barriers and stigmatization. Through 268 HSRI transfer students of color (TSOC) surveys and 12 interviews from four focus groups, we examined the role of stigmatization, campus relationships, and cultural strengths on TSOC's sense of belonging. Quantitative results showed greater stigmatization was associated with a lower sense of belonging, while stronger faculty and peer relationships and greater navigational and aspirational capital positively predicted a greater sense of belonging. Faculty and peer relationships mitigated the negative effects of stigmatization on sense of belonging. Qualitative results capture the intersectional marginalities experienced by our participants and how transfer receptivity was shaped by the prejudiced assumptions others have of TSOC, isolating white spaces, and a lack of transfer‐specific resources. We highlight the importance of campus relationships and students’ cultural strengths in navigating the transfer landscape and make institutional recommendations to cultivate transfer receptivity.

The impact of automated writing evaluation on English as a foreign language learners' writing self‐efficacy, self‐regulation, anxiety, and performance

AbstractBackgroundWith the growing trend of integrating technology into teaching environments, using Automated Writing Evaluation (AWE) in writing instruction has been extensively studied over the last two decades. The studies on AWE mostly investigated its impact on students' writing proficiencies and revealed conflicting results. However, very few studies have investigated how the use of AWE impacts learners' writing‐specific psychological factors, such as self‐efficacy, self‐regulation, and anxiety, from an empirical perspective. Therefore, further experimental studies are needed in this regard.ObjectivesThis study aimed at examining how a combined automated‐teacher feedback condition in which the students received automated feedback on their sentence‐level errors alongside teacher feedback on content and organization impacted English as a Foreign Language (EFL) students' self‐efficacy, self‐regulation, anxiety, and writing performance, compared with a traditional teacher‐only feedback condition.MethodsThe study utilized a quasi‐experimental design in which two intact classes were randomly assigned to the experimental and control groups. During one semester (16 weeks), students in the experimental group received both automated and teacher feedback, referred to in this study as combined automated‐teacher feedback. In contrast, students in the control group received only teacher feedback. Two scales, a questionnaire and a focus group interview, were used to obtain the data. The data were analysed using both quantitative and qualitative techniques.Results and ConclusionThe quantitative results demonstrated no statistically significant difference between the two types of feedback procedures in promoting the use of self‐regulated writing strategies by the students. However, the use of combined automated‐teacher feedback was found to be more effective than conventional teacher‐only feedback in enhancing the students' writing self‐efficacy and writing performance. The two different feedback processes were shown to be ineffective at helping the students' writing anxiety, however. The qualitative data also showed that students had favourable opinions of their experiences with receiving automated and teacher comments together.ImplicationsConsidering the findings of this study regarding the impact of combined automated‐teacher feedback on students' writing self‐efficacy and writing performance, as well as positive student perceptions, it is suggested that this feedback model is used as an alternative to teacher‐only feedback in EFL writing instruction.

Digital literacy scale: Validity and reliability study with the rasch model

AbstractThe study aimed to develop a Likert-type measurement tool (Digital Literacy Scale, DLS) to determine the digital literacy levels of secondary school students. The validity and reliability of the developed measurement tool were verified using the Rasch model. The Rasch Model can estimate missing data and allow for small study groups (Rasch, Rasch, Probabilistic models for some intelligence and attainment tests, Danish Institute for Educational Research, 1960). Additionally, this model can verify the expected pattern of the measurement tool by calibrating it across participants and items. The content validity of DLS was ensured by expert opinion, and the construct validity was ensured by using the Rach model. In the content validity study, a 25-item pool was created for the draft DLS, and 5 items were removed from the draft DLS at this stage. Construct validity studies were carried out with the remaining 20 items using the classical test theory and item response theory (Rasch model). Exploratory Factor Analysis (EFA) and first-level Confirmatory Factor Analysis (CFA) were used within the scope of classical test theory. Then some Rasch assumptions such as dimensionlessness, local independence, monotonicity, and bias were tested for DLS. In the validity and reliability analysis of DLS, no items were eliminated, and the quantitative theoretical results were statistically confirmed. The analysis results showed that the minimum statistical values required for a good measurement tool were met. Accordingly, 20 valid and reliable compatible items that can be used to determine the digital literacy status of secondary school students were produced.

Some Solutions for Coping and Raising Awareness about Mental Health when Accessing Information about the Covid-19 Pandemic on Facebook

The study was conducted to explore the general mental health status of students when accessing information about the COVID-19 pandemic on the Facebook social media platform. The main methods used in the research were surveys through questionnaires and document analysis. The quantitative results highlight the mental health issues of students, thereby proposing groups of solutions to help students cope and raise awareness to protect mental health not only during social distancing periods due to the COVID-19 pandemic but also to equip them with knowledge for self-protection in the future.

A smartphone-based fluorescent biosensor with metal-organic framework biocomposites and cotton swabs for the rapid determination of tetrodotoxin in seafood

BackgroundTetrodotoxin (TTX) is a potent neurovirulent marine biotoxin that is present in puffer fish and certain marine animals. It is capable of causing severe neurotoxic symptoms and even death when consumed through contaminated seafood. Due to its high toxicity, developing an effective assay for TTX determination in seafood has significant benefits for food safety and human health. Currently, it remains challenging to achieve on-site determination of TTX in seafood. To facilitate mass on-site assays, more affordable technologies utilizing accessible equipment that require no skilled personnel are needed. ResultsA smartphone-based portable fluorescent biosensor is proposed for TTX determination by using metal-organic framework (MOF) biocomposites and cotton swabs. Oriented antibody (Ab)-decorated and fluorescent quantum dot (QD)-loaded MOF biocomposites (QD@MOF*Ab) are rapidly synthesized for binding targets and fluorescent responses by utilizing the tunability of zinc-based MOF. Moreover, facile Ab-immobilized household cotton swabs are utilized as TTX capture tools. TTX forms sandwich immune complexes with QD@MOF*Ab probes, achieving signal amplification. These probes are excited by a portable device to generate bright fluorescent signals, which can be detected by the naked eye, and TTX quantitative results are obtained using a smartphone. When observed with the naked eye, the limit of detection (LOD) is 0.4 ng/mL, while intelligent quantitation presents an LOD of 0.13 ng/mL at logarithmic concentrations of 0.2–400 ng/mL. SignificanceThis biosensor is convenient to use, and an easy-to-operate analysis is completed within 15 min, thus demonstrating excellent performance in terms of detection speed and portability. Furthermore, it successfully determines TTX contents in puffer fish and clam samples, demonstrating its potential for monitoring seafood. Herein, this work provides a favorable rapid sensing platform that is easily portable.

Deep-Pathfinder: a boundary layer height detection algorithm based on image segmentation

Abstract. A novel atmospheric layer detection approach has been developed based on deep learning techniques for image segmentation. Our proof of concept estimated the layering in the atmosphere, distinguishing between pollution-rich layers closer to the surface and cleaner layers aloft. Knowledge of the spatio-temporal development of atmospheric layers, such as the mixing boundary layer height (MBLH), is important for the dispersion of air pollutants and greenhouse gases, as well as for assessing the performance of numerical weather prediction systems. Existing lidar-based layer detection algorithms typically do not use the full resolution of the available data, require manual feature engineering, often do not enforce temporal consistency of the layers, and lack the ability to be applied in near-real time. To address these limitations, our Deep-Pathfinder algorithm represents the MBLH profile as a mask and directly predicts it from an image with backscatter lidar observations. Deep-Pathfinder was applied to range-corrected signal data from Lufft CHM15k ceilometers at five locations of the operational ceilometer network in the Netherlands. Input samples of 224 × 224 px were extracted, each covering a 45 min observation period. A customised U-Net architecture was developed with a nighttime indicator and MobileNetV2 encoder for fast inference times. The model was pre-trained on 19.4×106 samples of unlabelled data and fine-tuned using 50 d of high-resolution annotations. Qualitative and quantitative results showed competitive performance compared to two reference methods: the Lufft and STRATfinder algorithms, applied to the same dataset. Deep-Pathfinder enhances temporal consistency and provides near-real-time estimates at full spatial and temporal resolution. These properties make our approach valuable for application in operational networks, as near-real-time and high-resolution MBLH detection better meets the requirements of users, such as in aviation, weather forecasting, and air quality monitoring.

The impact of high-immersion virtual reality on EFL learners’ foreign language speaking anxiety: A mixed-method approach

Abstract High-immersion virtual reality (HiVR) attracts increasing attention among language learning researchers because of its potential to enhance language learning. Prior studies focused mainly on HiVR and linguistic knowledge acquisition, and little is known about HiVR and emotions in language learning. Foreign language speaking anxiety (FLSA) is a common emotion that inhibits language learning and use, so it is important to explore approaches to alleviate it. This study investigated the potential use of HiVR for coping with FLSA in which 140 Chinese EFL learners were randomly assigned to four groups (35 students each) with a different combination of learning environments (HiVR or classroom) and learning principles (situated learning or teacher-centred learning). Students’ pre- and post-test of FLSA levels within and among four groups were compared via t-tests and ANOVA. Participants’ descriptions of FLSA change and perceptions of the effects of HiVR on FLSA were integrated with quantitative results for analysis. The integration of analysis showed that although most students perceived HiVR as a useful tool for alleviating FLSA, it is difficult for them to apply the reduced anxiety experienced in HiVR to real-life situations. The statistical results also showed that HiVR did not influence students’ real-life FLSA significantly. Most participants reported that HiVR offered them an authentic environment and enjoyable learning activities, which engaged them in learning, but the use of avatars in HiVR sometimes created an obstacle to communication. Implications for using HiVR technology to enhance foreign language learning are provided.

Enhancing ferroelectric characterization at nanoscale: A comprehensive approach for data processing in spectroscopic piezoresponse force microscopy

Switching Spectroscopy Piezoresponse Force Microscopy (SSPFM) stands out as a powerful method for probing ferroelectric properties within materials subjected to incremental polarization induced by an external electric field. However, the dense data processing linked to this technique is a critical factor influencing the quality of obtained results. Furthermore, meticulous exploration of various artifacts, such as electrostatics, which may considerably influence the signal, is a key factor in obtaining quantitative results. In this paper, we present a global methodology for SSPFM data processing, accessible in open-source with a user-friendly Python application called PySSPFM. A ferroelectric thin film sample of potassium sodium niobate has been probed to illustrate the different aspects of our methodology. Our approach enables the reconstruction of hysteresis nano-loops by determining the PR as a function of applied electric field. These hysteresis loops are then fitted to extract characteristic parameters that serve as measures of the ferroelectric properties of the sample. Various artifact decorrelation methods are employed to enhance measurement accuracy, and additional material properties can be assessed. Performing this procedure on a grid of points across the surface of the sample enables the creation of spatial maps. Furthermore, different techniques have been proposed to facilitate post-treatment analysis, incorporating algorithms for machine learning (K-means), phase separation, and mapping cross correlation, among others. Additionally, PySSPFM enables a more in-depth investigation of the material by studying the nanomechanical properties during poling, through the measurement of the resonance properties of the cantilever–tip–sample surface system.

Sensitive and rapid identification of pathogens by droplet digital PCR in a cohort of septic patients: a prospective diagnostic study

Background There is a critical need for a rapid and sensitive pathogen detection method for septic patients. This study aimed to investigate the diagnostic efficacy of Digital droplet polymerase chain reaction (ddPCR) in identifying pathogens among suspected septic patients. Methods We conducted a prospective pilot diagnostic study to clinically validate the multiplex ddPCR panel in diagnosing suspected septic patients. A total of 100 sepsis episodes of 89 patients were included in the study. Results In comparison to blood culture, the ddPCR panel exhibited an overall sensitivity of 75.0% and a specificity of 69.7%, ddPCR yielded an additional detection rate of 17.0% for sepsis cases overall, with a turnaround time of 2.5 h. The sensitivity of ddPCR in the empirical antibiotic treatment and the non-empirical antibiotic treatment group were 78.6% versus 80.0% (p > 0.05). Antimicrobial resistance genes were identified in a total of 13 samples. Whenever ddPCR detected the genes beta-lactamase-Klebsiella pneumoniae carbapenemase (blaKPC) or beta-lactamase-New Delhi metallo (blaNDM), these findings corresponded to the cultivation of carbapenem-resistant gram-negative bacteria. Dynamic ddPCR monitoring revealed a consistent alignment between the quantitative ddPCR results and the trends observed in C-reactive protein and procalcitonin levels. Conclusions Compared to blood culture, ddPCR exhibited higher sensitivity for pathogen diagnosis in suspected septic patients, and it provided pathogen and drug resistance information in a shorter time. The quantitative results of ddPCR generally aligned with the trends seen in C-reactive protein and procalcitonin levels, indicating that ddPCR can serve as a dynamic monitoring tool for pathogen load in septic patients.

Ultraviolet–Visible-Near InfraRed spectroscopy for assessing metal powder cross-contamination: A multivariate approach for a quantitative analysis

The last few years have seen an increasing use of spherical metals powders to produce bulk parts through metal forming technologies like Additive Manufacturing and Metal Injection Molding. This, coupled with the wide availability of metal powders, leads to a critical issue: contamination across different systems in different process steps. Consequently, it is necessary to find a new, fast, and reliable analysis sensible to tiny traces of contamination. This work evaluates the applicability of Ultraviolet–Visible-Near InfraRed (UV–Vis-NIR) spectroscopy, a technique providing information on powders’ reflectance, for studying contaminated powders. This work focuses on assessing 3 binary systems obtained from the cross-contamination of 3 components (A92618, C10200 and S31603) in a low contamination range (from 0.5 vol% to vol. 6%) and in a high contamination range (25 vol% and vol.50%). After the UV–Vis-NIR analysis, multivariate analysis has been used to obtain quantitative results. Results show that, as the contamination level increases in the binary system, the shape of spectra changes and becomes progressively more similar to the contaminant one. The chemometric analysis allows the detection of the contaminant type and its concentration percentage in the contaminated powder.

Fostering students' AI literacy development through educational games: AI knowledge, affective and cognitive engagement

AbstractBackgroundAs the significance of artificial intelligence (AI) continues to increase, there is a need for effective scaffolding and support for novice learners. Educators have encountered challenges in effectively scaffolding novice learners AI concepts, and providing appropriate motivational support. Research evidence has shown the potential of game‐based approaches to fostering secondary school students' AI literacy and motivation to learn AI.ObjectivesThis study developed an online platform TreasureIsland to gamify ebooks and investigated whether and how students playing with it can effectively enhance their AI literacy. This study aims to contribute an empirical and theoretical basis for AI literacy education and promote the use of gamification that would be broadly applied in other schools.MethodsA quasi‐experiment was conducted to evaluate the effects of the proposed gamified approach, which included a control group using an ebook with playful resources. To triangulate the quantitative results obtained from the pre and post‐test, focus group interviews were also conducted.ResultsThe platform was effective in improving students' motivation, self‐efficacy, career interest, and understanding of AI concepts and ethics, but did not enhance their confidence of using AI, and high cognition of applying, evaluating and creating AI. TreasureIsland players demonstrated significant improvement in all affective and cognitive domains, except for the ability to apply, evaluate, and create AI. Interviews revealed that the gamified approach could promote students' AI literacy by adhering to guidelines, including (1) creating a competitive and motivating learning environment through game mechanics, (2) providing scaffolding modules and feedback, and (3) visualising complex AI concepts via simulations. Feedback collected from the study suggested adding pedagogical elements such as flipped classrooms and project‐based learning in future research to improve the instructional design, and enable students to reach a higher level of cognition.ConclusionsThis study concludes that the use of gamification can provide affective and cognitive support and an enjoyable experience for fostering learners' AI literacy. It helps instructional designers and teachers enrich the pedagogical knowledge related to gamified platform and AI literacy.

Tiny machine learning empowers climbing inspection robots for real-time multiobject bolt-defect detection

Ensuring the structural integrity of steel construction is critical, necessitating effective methods for detecting bolt defects. Traditional inspection methods are reliable but require significant labor and struggle with the accessibility of complex structures. This research introduces a real-time bolt-defect detection system that integrates tiny machine learning (TinyML) with a magnetic climbing robot to enhance inspection efficiency and scope. The system employs the faster objects, more objects (FOMO) algorithm optimized for edge computing on microcontrollers. This approach enables accurate bolt identification, including normal, loose, and missing bolts. The average system accuracy is 82%, with precision and recall values from 0.57 to 0.89 and 0.67 to 0.87, respectively. The system demonstrates balanced detection, evidenced by an F1 score improvement of up to 62%. The FOMO model displays compelling performance on defect detection tasks, achieving an F1 score of approximately 75%, outperforming the MobileNetV2 Single Shot Multibox Detector with Feature Pyramid Networks Lite and You Only Look Once version 5 small. The efficiency of the FOMO model is highlighted by its low hardware requirements at less than 0.1 MB of flash memory and 893.8 KB of random access memory for the 32-bit floating-point data format and is reduced for the 8-bit integer data format, with inference times of 142 ms and 86 ms, respectively. These findings contrast with the higher resource demands and slower inference time of the compared models, indicating the suitability of the FOMO model for low-capacity microcontrollers and its feasibility for real-time applications. The performance analysis across scenarios confirms the high precision (average 0.77) and recall (average 0.76), validating the robustness of the model in diverse environmental conditions. The system offers an advancement over traditional bolt-defect detection methods in accuracy and efficiency by leveraging TinyML and a magnetic climbing robot, setting new benchmarks for real-time inspection technology. The quantitative results underscore the performance of the system, presenting a scalable, cost-effective solution for improving the safety and durability of steel structures.

Determinants and Factors of Physical Activity After Oncology Treatments (DEFACTO) in Metropolitan France: Protocol of a Mixed Methods Study and Intervention.

While the scientific community widely recognizes the benefits of physical activity (PA) in oncology supportive care, cancer survivors who have undergone chemo- or radio-immunotherapy treatments struggle to meet PA recommendations. This underscores the importance of identifying factors influencing active lifestyle adoption and maintenance and proposing a multilevel model (micro-, meso-, and macrolevel) to better understand facilitators and barriers. Currently, no socioecological model explains an active lifestyle in the posttreatment phase of breast, colorectal, prostate, and lung cancers. The objective is to identify factors influencing an active lifestyle in cancer survivorship and assess the feasibility of an individualized program targeting an active lifestyle. The objectives will be addressed in 3 stages. Stage 1 aims to elucidate factors associated with the active lifestyle of cancer survivors. Stage 2 involves developing an explanatory model based on previously identified factors to create a tailored health education program for an active lifestyle after oncology treatments. Stage 3 aims to evaluate the feasibility and potential effects of this personalized health education program after its national implementation. First, the exploration of factors influencing PA (stage 1) will be based on a mixed methods approach, using an explanatory sequential design and multilevel analysis. The quantitative phase involves completing a questionnaire from a socioecological perspective. Subsequently, a subset of respondents will engage in semistructured interviews to aid in interpreting the quantitative results. This phase aims to construct a model of the factors influencing an active lifestyle and develop an individualized 12-week program based on our earlier findings (stage 2). In stage 3, we will implement our multicenter, multimodal program for 150 physically inactive and sedentary cancer survivors across metropolitan France. Program feasibility will be evaluated. Measured PA level by connected device and multidimensional variables such as declared PA and sedentary behaviors, PA readiness, motivation, PA preferences, PA knowledge and skills, and barriers and facilitators will be assessed before and during the program and 52 weeks afterward. The institutional review board approved the mixed methods study (phase 1) in April 2020, and the intervention (phase 3) was approved in March 2022. Recruitment and data collection commenced in April 2022, with intervention implementation concluded in May 2023. Data collection and full analysis are expected to be finalized by July 2024. The Determinants and Factors of Physical Activity After Oncology Treatments (DEFACTO) study seeks to enhance our understanding, within our socioecological model, of factors influencing an active lifestyle among cancer survivors and to assess whether a tailored intervention based on this model can support an active lifestyle. ClinicalTrials.gov NCT05354882; https://www.clinicaltrials.gov/study/NCT05354882. DERR1-10.2196/52274.

Nature of science and domain-specificity: investigating the coverage of nature of science in physics, chemistry and biology curricula across grade levels

ABSTRACT The paper investigates the coverage of nature of science (NOS) in different science subject curricula to understand how they emphasise various aspects of NOS across grade levels in secondary science education. Although various science curriculum analyses exist in the literature, there is a paucity of investigations that have combined the elements of domain-specificity and learning progression in relation to NOS curriculum content. Furthermore, a holistic and inclusive account of NOS is used to examine the cognitive, epistemic, social and institutional aspects of NOS. Physics, chemistry and biology high school curricula from Turkey are investigated. Each subject curriculum is analysed and compared using an analytical framework guided by the Family Resemblance Approach. Mixed methods approaches are used to summarise the quantitative and qualitative results. Findings suggest an overemphasis on the cognitive-epistemic aspects of NOS, considerable variation across science subject areas and non-uniform progression of NOS themes across grade levels. Analysis of curricula incorporating the domain-specific and learning progression elements of NOS is likely to enhance understanding of how to design robust curricula in different science subjects and to improve the teaching and learning of NOS. Implications for curriculum reform are discussed.