Search Results (70)

Providing inclusive education and engaging all students in reading and writing activities presents an ongoing challenge for teachers, not necessarily resolved by implementing digital technology. This study addresses the need to better understand teacher competencies within the digitally infused classroom, specifically in relation to inclusive education and reading and writing practices. The study investigates the competencies and supportive strategies of middle school teachers who perceive themselves as successful in this area. The study employs the Delphi technique, using iterative surveys through which these teachers describe and rate aspects of their competencies and strategies. The results are analyzed through a modified version of the Technological, Pedagogical, and Content Knowledge (TPACK) framework, with particular attention to how teachers support students using their content knowledge and digital competency. Findings reveal a range of strategies and competency aspects related to both proactive accessibility and reactive individualization, using a variety of digital tools and text modalities. The teachers describe a dual orientation in their ability to curiously explore digital tools while simultaneously being able to critically appraise their usefulness. The findings contribute insights on what can support teachers when collaboratively developing knowledge of local practices and their agency in relation to available digital tools. Full article

Many researchers focus on improving reproductive health in sows and ensuring successful breeding by accurately identifying the optimal time of ovulation through estrus detection. One promising non-contact technique involves using computer vision to analyze temperature variations in thermal images of the sow’s vulva. However, variations in camera distance during dataset collection can significantly affect the accuracy of this method, as different distances alter the resolution of the region of interest, causing pixel intensity values to represent varying areas and temperatures. This inconsistency hinders the detection of the subtle temperature differences required to distinguish between estrus and non-estrus states. Moreover, failure to maintain a consistent camera distance, along with external factors such as atmospheric conditions and improper calibration, can distort temperature readings, further compromising data accuracy and reliability. Furthermore, without addressing distance variations, the model’s generalizability diminishes, increasing the likelihood of false positives and negatives and ultimately reducing the effectiveness of estrus detection. In our previously proposed methodology for estrus detection in sows, we utilized YOLOv8 for segmentation and keypoint detection, while monocular depth estimation was used for camera calibration. This calibration helps establish a functional relationship between the measurements in the image (such as distances between labia, the clitoris-to-perineum distance, and vulva perimeter) and the depth distance to the camera, enabling accurate adjustments and calibration for our analysis. Estrus classification is performed by comparing new data points with reference datasets using a three-nearest-neighbor voting system. In this paper, we aim to enhance our previous method by incorporating the mean pixel intensity of the region of interest as an additional factor. We propose a detailed four-step methodology coupled with two stages of evaluation. First, we carefully annotate masks around the vulva to calculate its perimeter precisely. Leveraging the advantages of deep learning, we train a model on these annotated images, enabling segmentation using the cutting-edge YOLOv9 algorithm. This segmentation enables the detection of the sow’s vulva, allowing for analysis of its shape and facilitating the calculation of the mean pixel intensity in the region. Crucially, we use monocular depth estimation from the previous method, establishing a functional link between pixel intensity and the distance to the camera, ensuring accuracy in our analysis. We then introduce a classification approach that differentiates between estrus and non-estrus regions based on the mean pixel intensity of the vulva. This classification method involves calculating Euclidean distances between new data points and reference points from two datasets: one for “estrus” and the other for “non-estrus”. The classification process identifies the five closest neighbors from the datasets and applies a majority voting system to determine the label. A new point is classified as “estrus” if the majority of its nearest neighbors are labeled as estrus; otherwise, it is classified as “non-estrus”. This automated approach offers a robust solution for accurate estrus detection. To validate our method, we propose two evaluation stages: first, a quantitative analysis comparing the performance of our new YOLOv9 segmentation model with the older U-Net and YOLOv8 models. Secondly, we assess the classification process by defining a confusion matrix and comparing the results of our previous method, which used the three nearest points, with those of our new model that utilizes five nearest points. This comparison allows us to evaluate the improvements in accuracy and performance achieved with the updated model. The automation of this vital process holds the potential to revolutionize reproductive health management in agriculture, boosting breeding success rates. Through thorough evaluation and experimentation, our research highlights the transformative power of computer vision, pushing forward more advanced practices in the field. Full article

Background/Objectives: In maxillo-facial high-velocity complex war injuries, a rigid internal fixation is inappropriate, and external fixation is suitable with described benefits. This systematic review aimed to summarize the literature regarding the benefits, side effects and complications of external fixators in the management of mandibular war-related injuries. Methods: An electronic search was performed in the databases of PubMed and Google Scholar in December 2024. The title and abstracts from retrieved items were read by two reviewers to identify studies within the selection criteria. Included articles had to be published in English up to December 2024 and related to external fixators used in mandibular fracture war injuries. Results: The search strategy initially identified 445 studies through PubMed and 987 studies through Google Scholar. Following the application of inclusion criteria, 12 articles were selected for this review, describing the use of an external fixator for a mandibular fracture in a war injury. Conclusions: The external fixator offers effective treatment for severe mandibular fractures in war-related injuries with low rates of complications and high success rates. Where a manufacturer external fixator is not available, orthopedic external fixators and self-crafted external fixators are used. Full article

Background: The purpose of this study is to elucidate the potentially complex relationship between digital self-efficacy and students’ engagement with reading activities, given that the students are known to enjoy reading. Of particular interest were the roles of digital self-efficacy as potential regulators and/or moderators of students’ engagement with reading activities at school. Methods: The participants were fourth-grade students from the Kingdom of Saudi Arabia. The data came from the Progress in International Reading Literacy Study (PIRLS) in 2021. The main hypothesis put forth was that digital self-efficacy would play a significant role in regulating students’ engagement with reading activities. The prediction of students’ reading engagement at school may take two different directions: linear and nonlinear. Based on the linear model’s prediction, it was expected that increases in digital self-efficacy would be associated with increases in engagement with reading activities. Results: The opposite prediction was supported; that is, heightened self-efficacy led to an unexpected drop and unpredictability in students’ engagement with reading. Conclusions: A potential explanation is that enhanced self-efficacy in digital skills is associated with enhanced interest and activity in digitally related tasks, such as video games, and engagement with internet-type activities and social media platforms, which consume a significant amount of time. Another possible explanation is related to levels of overconfidence in students, and it is suggested that those enhanced ratings are not necessarily associated with enhanced outcomes. It is concluded that enhanced digital self-efficacy may not have the expected positive effects as individuals become complacent and cease their efforts after seeing prior successes in reading achievement. Full article

In elite basketball, closely contested games are often decided in the last minute, where a single possession can significantly alter the outcome. The objective of this study was to analyze the offensive patterns and effectiveness of successful play sequences executed during the last minute and overtime periods of the 2022–2023 EuroLeague men’s basketball season. Specifically, we examined how offensive strategies varied based on score conditions and team status (winning, losing, or tied) in games with a score difference of three points or fewer, representing a maximum one-possession margin. The sample consisted of 709 technical–tactical actions performed in one-possession games during the last minute and overtime periods of the men’s EuroLeague 2022–2023 season. An observational methodology was employed using the LINCE PLUS software with a purpose-designed observational instrument. Descriptive analyses and chi-square (χ²) tests were conducted using SPSS 25, and T-Pattern analysis was performed with Theme 6 software. Statistical significance was set at p < 0.05. The results showed that teams leading at the start of the possession executed a higher number of successful actions, primarily through free throws drawn from fouls committed by the opposing team. Additionally, plays involving few or no passes, executed within the first 17 s of possession, and completed by point guards or centers, were associated with higher success rates. While less frequent, fast breaks proved to be more effective than set offenses. Furthermore, the study highlights the role of elite playmakers in late-game situations, as their ability to read defenses and create scoring opportunities plays a crucial role in determining offensive success. The practical implications of this study can assist coaches in optimizing offensive strategies during high-pressure moments in elite men’s basketball by refining late-game decision-making and tactical planning. Full article

Purpose: Naegleria fowleri is the main etiologic agent implicated in primary amoebic meningoencephalitis (PAM). It is also known as the brain-eating amoeba because of the severe brain inflammation following infection, with a survival rate of about 5%. This review aims to identify Naegleria fowleri infections and evaluate patients’ progression. This literature review emphasizes the importance of rapid diagnosis and treatment of infected patients because only prompt initiation of appropriate therapy can lead to medical success. Compared to other articles of this kind, this one analyzes a large number of reported cases and all the factors that affected patients’ evolution. Materials and methods: Two independent reviewers used “Naegleria fowleri” and “case report” as keywords in the Clarivate Analytics—Web of Science literature review, obtaining 163 results. The first evaluation step was article title analysis. The two reviewers determined if the title was relevant to the topic. The first stage removed 34 articles, leaving 129 for the second stage. Full-text articles were evaluated after reading the abstract, and 77 were eliminated. This literature review concluded with 52 articles. Key findings: This review included 52 case report articles, 17 from the USA, eight from India, seven from China, four from Pakistan, two from the UK, and one each from Thailand, Korea, Japan, Italy, Iran, Norway, Turkey, Costa Rica, Zambia, Australia, Taiwan, and Venezuela, and Mexico. This study included 98 patients, with 17 women (17.4%) and 81 men (82.6%). The cases presented in this study show that waiting to start treatment until a diagnosis is confirmed can lead to rapid worsening and bad outcomes, especially since there is currently no drug that works very well as a treatment and the death rate is around 98%. Limitations: The lack of case presentation standardization may lead to incomplete case information in the review since the cases did not follow a writing protocol. The small number of global cases may also lead to misleading generalizations, especially about these patients’ treatment. Due to the small number of cases, there is no uniform sample of patients, making it difficult to determine the exact cause of infection. Full article

Understanding others correctly is of great importance for maintaining effective communication. Factors such as hearing difficulties or environmental noise can disrupt this process. Lip reading offers an effective solution to these challenges. With the growing success of deep learning architectures, research on lip reading has gained momentum. The aim of this study is to create a lip reading dataset for Turkish digit recognition and to conduct predictive analyses. The dataset has divided into two subsets: the face region and the lip region. CNN, LSTM, and 3DCNN-based models, including C3D, I3D, and 3DCNN+BiLSTM, were used. While LSTM models are effective in processing temporal data, 3DCNN-based models, which can process both spatial and temporal information, achieved higher accuracy in this study. Experimental results showed that the dataset containing only the lip region performed better; accuracy rates for CNN, LSTM, C3D, and I3D on the lip region were 67.12%, 75.53%, 86.32%, and 93.24%, respectively. The 3DCNN-based models achieved higher accuracy due to their ability to process spatio-temporal data. Furthermore, an additional 1.23% improvement was achieved through ensemble learning, with the best result reaching 94.53% accuracy. Ensemble learning, by combining the strengths of different models, provided a meaningful improvement in overall performance. These results demonstrate that 3DCNN architectures and ensemble learning methods yield high success in addressing the problem of lip reading in the Turkish language. While our study focuses on Turkish digit recognition, the proposed methods have the potential to be successful in other languages or broader lip reading applications. Full article

In spite of the development in technology and the recent innovations in dentistry, dental anxiety remains a common issue, and accurately assessing it is challenging due to reliance on patients’ self-reports, which are often biased. Hence, this study was undertaken to determine whether dental anxiety can be quantified objectively using the EDA parameters. EDA (skin conductance (SC), skin susceptance (SS), and skin potential (SP)) parameters and heart rate (HR) were recorded from 40 participants during two different sessions (baseline and anxiety). In addition, the Modified Dental Anxiety Scale (MDAS) scale was also used to record the level of anxiety. The physiological data from EDA and HR were compared with the subjective self-reports of anxiety provided on the MDAS to show whether higher EDA and HR readings correspond to higher scores on the MDAS. To elicit dental anxiety, participants were exposed to several film clips associated with dental treatment. EDA signals were compared between the two sessions for all clips and all EDA scores. SC and HR significantly (p < 0.05) increased during the anxiety session compared to the baseline session. The number of fluctuations per minute in the SC, SS, and SP notably increased during the anxiety session. The MDAS results revealed that the participants had dental anxiety when they were exposed to video clips of dental procedures. The study results imply that EDA parameters could be used as a useful tool to monitor dental anxiety, in particular in young children and non-verbal patients or those with intellectual disabilities, which may aid the dentist in the successful management of dental anxiety during treatment. The moment-to-moment EDA data on a patient’s anxiety levels throughout particular dental operations provides a clearer picture of anxiety variations than pre- or post-appointment surveys alone, in addition to offering unbiased tracking of dental anxiety levels over self-reports. This study seeks to encourage further research into the most effective EDA parameters for improving the management of dental anxiety. Full article

Odor source localization (OSL) technology allows autonomous agents like mobile robots to localize a target odor source in an unknown environment. This is achieved by an OSL navigation algorithm that processes an agent’s sensor readings to calculate action commands to guide the robot to locate the odor source. Compared to traditional ‘olfaction-only’ OSL algorithms, our proposed OSL algorithm integrates vision and olfaction sensor modalities to localize odor sources even if olfaction sensing is disrupted by non-unidirectional airflow or vision sensing is impaired by environmental complexities. The algorithm leverages the zero-shot multi-modal reasoning capabilities of large language models (LLMs), negating the requirement of manual knowledge encoding or custom-trained supervised learning models. A key feature of the proposed algorithm is the ‘High-level Reasoning’ module, which encodes the olfaction and vision sensor data into a multi-modal prompt and instructs the LLM to employ a hierarchical reasoning process to select an appropriate high-level navigation behavior. Subsequently, the ‘Low-level Action’ module translates the selected high-level navigation behavior into low-level action commands that can be executed by the mobile robot. To validate our algorithm, we implemented it on a mobile robot in a real-world environment with non-unidirectional airflow environments and obstacles to mimic a complex, practical search environment. We compared the performance of our proposed algorithm to single-sensory-modality-based ‘olfaction-only’ and ‘vision-only’ navigation algorithms, and a supervised learning-based ‘vision and olfaction fusion’ (Fusion) navigation algorithm. The experimental results show that the proposed LLM-based algorithm outperformed the other algorithms in terms of success rates and average search times in both unidirectional and non-unidirectional airflow environments. Full article

This paper provides an in-depth analysis of a novel methodology to enhance the commissioning processes of robotic production lines in the automotive sector, with a particular emphasis on the implementation of offline programming (OLP) methods. The proposed innovative methodology, verified within the automotive industry, introduces a systematic, iterative process for calibrating and aligning the local user coordinate system (UCS) with high-precision external measurements, ensuring minimal discrepancy between simulated and actual robot paths. A significant contribution of this study is an original adjustment of the numerical algorithm applying a closed-form solution to the absolute orientation problem where unit quaternions are used to establish a UCS and evaluate positioning errors. The experimental validation study draws from 485 measurement datasets gathered across more than 300 robot stations, with each dataset comprising at least six measured point pairs, using readings from both internal robot positioning systems and a Leica AT403 laser tracker, aligned with nominal tooling values. This approach addresses discrepancies between simulated and actual environments, and our findings show an 83.51% success rate for direct implementation of simulated robot path programs. This result underscores the effectiveness of the proposed method and demonstrates the accuracy of the developed numerical algorithm, providing a reliable measure of real OLP implementation effectiveness in the automotive sector. This method further streamlines multi-robot station setup through centralized UCS alignment, significantly reducing commissioning time and enhancing efficiency in both the assembly and commissioning stages of robotized production lines. The proposed methodology facilitates precise alignment in the commissioning stage and highlights the need for synchronized simulation updates, robust offline programming practices, and regular kinematic error verification to further enhance OLP accuracy. Full article

RNA molecules within ejaculated sperm can be characterized through whole-transcriptome sequencing, enabling the identification of pivotal transcripts that may influence reproductive success. However, the profiling of sperm transcriptomes through next-generation sequencing has several limitations impairing the identification of functional transcripts. In this study, we explored the nature of the RNA sequences present in the sperm transcriptome of two livestock species, cattle and horses, using RNA sequencing (RNA-seq) technology. Through processing of transcriptomic data derived from bovine and equine sperm cell preparations, low mapping rates to the reference genomes were observed, mainly attributed to the presence of ribosomal RNA and bacteria in sperm samples, which led to a reduced sequencing depth of RNAs of interest. To explore the presence of bacteria, we aligned the unmapped reads to a complete database of bacterial genomes and identified bacteria-associated transcripts which were characterized. This analysis examines the limitations associated with sperm transcriptome profiling by reporting the nature of the RNA sequences among which bacterial RNA was found. These findings can aid researchers in understanding spermatozoal RNA-seq data and pave the way for the identification of molecular markers of sperm performance. Full article

The problem of reading comprehension at the university level has not been sufficiently explored in the field of education research. Understanding written texts is an essential prerequisite for academic success during university. Consequently, reading comprehension challenges can influence the dropout rate at the university level. On the other hand, research has been conducted from a cognitive perspective, without considering affective variables such as reading motivation and its relationship with linguistic and psychological variables. In this study, five questionnaires were administered to a sample of 65 teaching students from different disciplines. The questionnaires dealt with linguistic dimensions such as reading comprehension, at lexical, textual, and discursive levels; psychological aspects such as reading motivation; and cognitive aspects such as working memory. The results obtained through bivariate and mediation analyses show the mediating role of vocabulary in reading comprehension and other cognitive and affective variables. In this context, working memory has a positive indirect effect on more explicit processing tasks in reading comprehension of long texts associated with discourse comprehension. Finally, intrinsic motivation has a positive indirect effect on reading fluency and also on the comprehension of short texts. It also correlates positively with vocabulary tasks. These results are relevant in order to establish a reading profile of university students who have reading comprehension problems, as found in this study. Full article

Narrow-diameter implants (NDI) serve as a solution for treating limited bone volume in the anterior mandible. This study aimed to evaluate the one-year clinical outcomes of various NDIs in the mandibular incisor area after immediate loading in partially edentulous patients. This single-center, prospective, single-blinded, randomized controlled trial study included 21 patients, with 7 patients in each of the following groups: control (BLT NC SLActive^®; Straumann), experimental group 1 (CMI IS-III Active^® S-Narrow; Neobiotech), and experimental group 2 (CMI IS-III Active^® Narrow; Neobiotech). Using full digital flow, two fixtures were placed in each patient and immediately provisionalized on the day of surgery. Evaluations encompassed periapical radiographs, implant stability quotient (ISQ), implant stability test (IST) readings, per-implant soft tissue health, patient satisfaction surveys, and esthetic score assessments. Definitive prostheses were delivered twelve weeks post-surgery (CRiS, number: KCT0007300). Following exclusions due to low stability values (n = 2), fixture failure (n = 5), and voluntary withdrawal (n = 1), the implant success rate for patients completing all clinical protocols stood at 100%. The resulting patient failure rates in the control, experimental group 1, and experimental group 2 were 50.0%, 42.9%, and 14.3%, respectively. There were no significant differences between the groups in terms of marginal bone loss, soft tissue health, patient satisfaction, and esthetic scores. Narrow implants showed superior clinical outcomes, followed by S-Narrow and Straumann implants. Calculated one-year survival rates at the implant level were 66.7% for the control group, 85.7% for experimental group 1, and 100% for experimental group 2. All three types of NDIs showed acceptable clinical and radiographic results during the year-long observation period. Full article

In this comprehensive review, we explore the significant role that nanopore sequencing technology plays in the study of plant organellar genomes, particularly mitochondrial and chloroplast DNA. To date, the application of nanopore sequencing has led to the successful sequencing of over 100 plant mitochondrial genomes and around 80 chloroplast genomes. These figures not only demonstrate the technology’s robustness but also mark a substantial advancement in the field, highlighting its efficacy in decoding the complex and dynamic nature of these genomes. Nanopore sequencing, known for its long-read capabilities, significantly surpasses traditional sequencing techniques, especially in addressing challenges like structural complexity and sequence repetitiveness in organellar DNA. This review delves into the nuances of nanopore sequencing, elaborating on its benefits compared to conventional methods and the groundbreaking applications it has fostered in plant organellar genomics. While its transformative impact is clear, the technology’s limitations, including error rates and computational requirements, are discussed, alongside potential solutions and prospects for technological refinement. Full article

Human cytomegalovirus (HCMV) is a pathogen with high prevalence in the general population that is responsible for high morbidity and mortality in immunocompromised individuals and newborns, while remaining mainly asymptomatic in healthy individuals. The HCMV genome is 236,000 nucleotides long and encodes approximately 200 genes in more than 170 open reading frames, with the highest rate of genetic polymorphisms occurring in the envelope glycoproteins. HCMV infection is treated with antiviral drugs such as ganciclovir, valganciclovir, cidofovir, foscarnet, letermovir and maribavir targeting viral enzymes, DNA polymerase, kinase and the terminase complex. One of the obstacles to successful therapy is the emergence of drug resistance, which can be tested phenotypically or by genotyping using Sanger sequencing, which is a widely available but less sensitive method, or next-generation sequencing performed in samples with a lower viral load to detect minority variants, those representing approximately 1% of the population. The prevalence of drug resistance depends on the population tested, as well as the drug, and ranges from no mutations detected to up to almost 50%. A high prevalence of resistance emphasizes the importance of testing the patient whenever resistance is suspected, which requires the development of more sensitive and rapid tests while also highlighting the need for alternative therapeutic targets, strategies and the development of an effective vaccine. Full article