Search Results (342)

This narrative inquiry explores a vibrant classroom knowledge community in a Chinese normal university. By examining the teacher’s interactions, we analyze the community’s development through three perspectives: (1) the author’s narrative of the course outline, (2) the teacher’s narrative of classroom culture, and (3) students’ narratives of their growth. The author presents a student-centered model and seven steps for enacting the course, outlining the environment for cultivating the knowledge community. The teacher’s narrative reveals clues to his success, emphasizing his use of storytelling to foster the community and share educational ideas. Students’ narratives reflect their growth, validating the classroom as a safe space for development and language learning. The significance of this research is that the classroom knowledge community consisted of the teacher, his undergraduate students, and his post-graduates. The three layers existed because of this unrestrained character, devoid of conflicts of interest, created a safe place for students’ development. This research study adds to the literature on how knowledge communities form in school contexts. It focuses on a particular space and time and involves multiple layers of participants, which is prerequisite to the conceptualization of classroom knowledge community. This research has important implications for college language education. Full article

Introduction: This manuscript addresses the limitations of traditional orthognathic surgery in achieving both functional and aesthetic correction in patients with Class II malocclusion and severe mandibular retrusion. Current techniques often struggle to simultaneously address mandibular deficiency and inadequate transverse dimension, leading to unsatisfactory outcomes. Methods: Seven male patients underwent bimaxillary osteotomy with mandibular advancement. A novel surgical plate, Implate, was used, which was designed to facilitate precise osteotomy and stabilization. Pre-surgical planning included CBCT scanning, 3D modeling, and surgical simulation. Postoperative assessments included clinical examinations, CT and OPT scans. Results: Implate successfully addressed the challenges of conventional techniques, minimizing the formation of bony steps and achieving a more harmonious facial profile. The minimally invasive procedure, with careful periosteal and muscle management, contributed to stable outcomes, and no complications were reported. At the 6-month follow-up, OPT analysis showed a mean mandibular width increase of 18.1 ± 6.2 mm and vertical ramus height gains of 6.0 ± 3.1 mm (left) and 5.8 ± 1.7 mm (right). Conclusions: According to our preliminary experience, the integration of Implate into surgical practice offers a significant improvement in treating complex Class II malocclusions. By simultaneously correcting mandibular retrusion and width while minimizing complications, Implate provides a superior solution compared to traditional methods. This innovative approach highlights the potential of combining advanced surgical techniques with personalized 3D-printed implants to achieve optimal functional and aesthetic outcomes. Further prospective studies with controls and longer follow-up are needed to validate the efficacy and reproducibility of Implate in wider clinical use. Full article

Objectives: We aimed to identify clustering patterns of the device-measured physical activity (PA) levels (i.e., light PA and moderate-to-vigorous PA) and sedentary time (ST), screen time, sleep duration, and breakfast consumption of Spanish adolescents and their associations with the mode of commuting to and from schools (i.e., active and passive). Methods: A total of 151 adolescents aged 14.4 ± 0.6 years (53.64% girls) were included in this study. Participants wore an accelerometer device during seven consecutive days to measure PA levels and ST levels. Screen time, sleep duration, breakfast consumption, and the mode of commuting to and from school were self-reported by the participants. A two-step cluster analysis was performed to examine the different lifestyle behavior patterns (defined as data-driven groupings of daily behaviors identified through cluster analysis). Logistic regression models were used to determine the associations among the lifestyle behavior patterns and the mode of commuting to and from school. Results: The main characteristics of the three identified clusters were as follows: (active) high PA levels and low ST (38.4%); (inactive) high sleep duration and daily breakfast consumption, but low PA levels and high ST and screen time (37.2%); and (unhealthy) low PA levels and sleep duration, high ST and screen time, and usually skip breakfast (24.4%). No associations were found between these clusters and the mode of commuting to and from school (all, p > 0.05). Conclusions: Three different lifestyle behavior patterns were identified among Spanish adolescents, but no associations were found between these patterns and their mode of commuting to and from school. Full article

Accurate prediction of natural gas production is of great significance for optimizing development strategies, simplifying production management, and promoting decision-making. This paper utilizes partial differentiation to effectively capture the spatiotemporal characteristics of natural gas data and the advantages of grey prediction models. By introducing the fractional damping accumulation operator, a new fractional order partial grey prediction model is established. The new model utilizes partial capture of details and features in the data, improves model accuracy through fractional order accumulation, and extends the metadata of the classic grey prediction model from time series to matrix series, effectively compensating for the phenomenon of inaccurate results caused by data fluctuations in the model. Meanwhile, the principle of data accumulation is effectively expressed in matrix form, and the least squares method is used to estimate the parameters of the model. The time response equation of the model is obtained through multiplication transformation, and the modelling steps are elaborated in detail. Finally, the new model is applied to the prediction of natural gas production in Qinghai Province, China, selecting energy production related to natural gas production, including raw coal production, oil production, and electricity generation, as relevant variables. To verify the effectiveness of the new model, we started by selecting the number of relevant variables, divided them into three categories for analysis based on the number of relevant variables, and compared them with five other grey prediction models. The results showed that in the seven simulation experiments of the three types of experiments, the average relative error of the new model was less than 2%, indicating that the new model has strong stability. When selecting the other three types of energy production as related variables, the best effect was achieved with an average relative error of 0.3821%, and the natural gas production for the next nine months was successfully predicted. Full article

The Draw-A-Person Intellectual Ability test for children, adolescents, and adults is a widely used tool in psychology for assessing intellectual ability. This test relies on human drawings for initial raw scoring, with the subsequent conversion of data into IQ ranges through manual procedures. However, this manual scoring and IQ assessment process can be time-consuming, particularly for busy psychologists dealing with a high caseload of children and adolescents. Presently, DAP-IQ screening continues to be a manual endeavor conducted by psychologists. The primary objective of our research is to streamline the IQ screening process for psychologists by leveraging deep learning algorithms. In this study, we utilized the DAP-IQ manual to derive IQ measurements and categorized the entire dataset into seven distinct classes: Very Superior, Superior, High Average, Average, Below Average, Significantly Impaired, and Mildly Impaired. The dataset for IQ screening was sourced from primary to high school students aged from 8 to 17, comprising over 1100 sketches, which were subsequently manually classified under the DAP-IQ manual. Subsequently, the manual classified dataset was converted into digital images. To develop the artificial intelligence-based models, various deep learning algorithms were employed, including Convolutional Neural Network (CNN) and state-of-the-art CNN (Transfer Learning) models such as Mobile-Net, Xception, InceptionResNetV2, and InceptionV3. The Mobile-Net model demonstrated remarkable performance, achieving a classification accuracy of 98.68%, surpassing the capabilities of existing methodologies. This research represents a significant step towards expediting and enhancing the IQ screening for psychologists working with diverse age groups. Full article

This article presents a large case study of fare evasion on bus lines in the city of Lisbon, Portugal, a common problem in dense urban areas. Focus is put on geographic factors, and an analysis is carried out using a generalized spatial two-step least-squares regression (GS2SLS). The large database, spanning one year of fare evasion reports, made it possible to segregate the analysis according to type of day (workday or weekend) and time period (rush hours, nighttime, etc.). Results show that indeed the type of day and time period lead to considerable differences between the seven models analyzed. It was found that the number of inspection actions is the strongest predictor of evasion, with geographic factors also playing a role in predicting fare evasion. Consideration of this spatial component made it possible to find moderate evidence for dissuasive effects of inspection actions in some models and of pockets of evasive tendencies in other models, which appear in the statistical error term. Interestingly, and contrary to other studies, age was found to have almost no influence on the propensity to evade fares. From a managerial point of view, this study highlights the importance of running inspection actions systematically and closely monitoring their outcomes. From a more theoretical standpoint, it suggests further research on geographic factors is needed to fully understand spatial patterns of evasion. Full article

Standard of Care (SOC) for locally advanced cervical cancer is represented by external beam radiation therapy concurrent with platinum-based chemotherapy and immunotherapy (cCIRT) followed by brachytherapy boost and immunotherapy maintenance. In some instances, it is impossible to perform brachytherapy due to patient and/or cancer issues. In these circumstances, an external beam boost could be delivered. Using a robotic arm LINAC, it is mandatory to use intramucosal implanted fiducials which are needed for tumour tracking. To avoid invasive procedures, we developed an original intravaginal 3D-printed universal device containing gold fiducials embedded within it. In this paper, we describe the step-by-step procedure that allowed us to obtain the utility model patent, including the in vivo test (feasibility, reproducibility, device compliance) on seven patients within the study protocol “STereotActic Radiotherapy Boost in locally Advanced Cervical carcinoma patientS” (STARBACS). Full article

Seven wild-type lactic acid bacteria, belonging to Lactiplantibacillus plantarum and Lactococcus cremoris species, were isolated from beetroots and white mushrooms and evaluated for their safety and functional profile. Lc. cremoris isolates were sensitive to all antibiotics tested, while L. plantarum strains exhibited resistance in certain antibiotics. Among them, Lc. cremoris FBMS_5810 showed the highest cholesterol removal ability (51.89%) and adhesion capacity to Caco-2 cell lines (32.14%), while all plant origin strains exhibited strong antagonistic and inhibitory activity against foodborne pathogens, as well as high survival potential during an in vitro digestion model. Subsequently, freeze-dried immobilized Lc. cremoris FBMS_5810 cells on oat flakes were prepared with initial cell loads >8.5 log CFU/g, and the effect of trehalose as a cryoprotectant in cell viability during storage at room and refrigerated temperatures for up to 180 days was studied. A significant reduction in cell loads was observed in all cases studied. However, freeze-dried immobilized Lc. cremoris FBMS_5810 cells on oat flakes prepared using trehalose as a cryoprotectant stored at 4 °C exhibited the highest cell viability (8.75 log CFU/g) after 180 days. In the next step, functional breakfast cereals enriched with freeze-dried immobilized Lc. cremoris FBMS_5810 cells on oat flakes (produced with (MLT) or without (ML) trehalose) were developed and stored at room and refrigerated temperatures for 180 days. The initial cell levels ≥ 9.18 log CFU/g were achieved, while a significant decrease was recorded during storage in all cases. The maintenance of cell loads ≥ 7.75 log CFU/g was documented in the case of both ML and MLT samples stored at 4 °C; however, the presence of trehalose in MLT samples resulted in cell viability 7.52 log CFU/g after 180 days of storage at room temperature. Importantly, the functional breakfast cereals were accepted by the panel during the sensory evaluation. Full article

Natural water content affects many geotechnical parameters and geological properties of soils, which can reduce cohesion and friction, leading to potential failures in structures such as foundations, retaining walls, and slopes. Identification of the water content helps in designing effective drainage and water management systems to prevent flooding and erosion. In tunnel engineering, soil water content plays an important role as the stability of the tunnel face depends on it. This research solves the problem of classifying soil images depending on the natural water content by computer vision technology. First, laboratory soil tests were carried out, and the relationship between the amount of torque on the screw conveyor and the moisture content of the soil was established; photographs of the soil at different conditions were taken at each step of the experiment. Second, the resulting dataset after preprocessing was processed by convolutional neural network algorithms during deep learning; the transfer learning technique was used to obtain better results. As a result, seven algorithms were obtained that allow classifying the soil images, which can later be used to optimize the tunnel construction process. The best classification ability is demonstrated by the algorithm based on the DenseNet architecture (accuracy 0.9302 and loss 0.1980). The proposed model surpasses traditional approaches due to its increased automation and processing speed. Laboratory tests can be carried out only once for one type of soil in order to determine the boundaries of water content for classes labeling, after which only a cheap camera is required from the equipment to transmit new images for processing by the algorithm. Full article

This article focuses on the creation of a monitoring tool using routinely collected data from milk payment analyses. Milk samples were analyzed through Fourier Transform mid-infrared spectrometry every 1 to 3 days, and their compositions were predicted using machine learning models. Among the predicted parameters, fatty acid profiles appear to be effective indicators of animal status and management practices. In this research, these profiles were summarized using 31 fatty acids or groups of fatty acids. The methodology consists of four steps: hierarchical clustering to detect patterns in a Belgian spectral dataset (N = 774,781), interpretation of the identified seven clusters, development of predictive models applied to a Canadian dataset (N = 670,165), and validation using management information collected from Canadian farms. The identified clusters revealed significant relationships with feeding management strategies and temporal evolutions, highlighting the potential to develop automated alert systems that assist farmers and advisors in herd monitoring. Full article

Financial time-series forecasting presents a significant challenge due to the inherent volatility and complex patterns in market data. This study introduces a novel forecasting framework that integrates Variational Mode Decomposition (VMD) with a Cascaded Long Short-Term Memory (LSTM) network enhanced by an Attention mechanism. The primary objective is to enhance the predictive accuracy of the VIX, a key measure of market uncertainty, through advanced signal processing and deep learning techniques. VMD is employed as a preprocessing step to decompose financial time-series data into multiple Intrinsic Mode Functions (IMFs), effectively isolating short-term fluctuations from long-term trends. These decomposed features serve as inputs to a Cascaded LSTM model with an Attention mechanism, which enables the model to capture critical temporal dependencies, thereby improving forecasting performance. Experimental evaluations using VIX and S&P 500 data from January 2020 to December 2024 demonstrate the superior predictive capability of the proposed model compared to seven benchmark models. The results highlight the effectiveness of combining signal decomposition techniques with Attention-based deep learning architectures for financial market forecasting. This research contributes to the field by introducing a hybrid model that improves predictive accuracy, enhances robustness against market fluctuations, and underscores the importance of Attention mechanisms in capturing essential temporal dynamics. Full article

Background/Objectives: By simultaneously integrating both genotyped and non-genotyped animals into genetic evaluation, the single-step genomic BLUP method enhanced the accuracy of genetic assessments. This study aimed to compare the increase in prediction reliability (R²) between restricted maximum likelihood (REML) and single-step genomic REML (ssGREML) in the Pura Raza Española (PRE) horse breed. Methods: The dataset comprised reproductive records for seven fertility traits from 47,502 females, with a total of 57,316 animals represented in the pedigree. A total of 4009 animals were genotyped using the EQUIGENE 90K SNP array, and 71,322 SNPs were retained for analysis after quality control. Genetic parameters were estimated using a multivariate model with the BLUPF90+ v2.60 software. Results: Heritability estimates were similar between REML and ssGREML, ranging from 0.07 for IF12 to 0.349 for ALF. An increase in R² was observed with ssGREML compared to REML across all traits, with overall gains ranging from 2.20% to 3.71%. Among genotyped animals, R² values ranged from 17.81% to 24.04%, while significantly lower values (0.80% to 2.34%) were observed in non-genotyped animals. Notably, individuals with low initial R² values under the REML approach exhibited the most significant gains using ssGREML. This improvement was particularly pronounced among stallions with fewer than 40 controlled foals. Conclusions: Our results demonstrated that incorporating genomic data improves the reliability of genetic evaluations for mare fertility in PRE horses. Full article

Denoising diffusion probabilistic models (DDPMs) have achieved remarkable success across various research domains. However, their high complexity when processing 3D images remains a limitation. To mitigate this, researchers typically preprocess data into 2D slices, enabling the model to perform segmentation in a reduced 2D space. This paper introduces DiffBTS, an end-to-end, lightweight diffusion model specifically designed for 3D brain tumor segmentation. DiffBTS replaces the conventional self-attention module in the traditional diffusion models by introducing an efficient 3D self-attention mechanism. The mechanism is applied between down-sampling and jump connections in the model, allowing it to capture long-range dependencies and global semantic information more effectively. This design prevents computational complexity from growing in square steps. Prediction accuracy and model stability are crucial in brain tumor segmentation; we propose the Edge-Blurring Guided (EBG) algorithm, which directs the diffusion model to focus more on the accuracy of segmentation boundaries during the iterative sampling process. This approach enhances prediction accuracy and stability. To assess the performance of DiffBTS, we compared it with seven state-of-the-art models on the BraTS 2020 and BraTS 2021 datasets. DiffBTS achieved an average Dice score of 89.99 and an average HD95 value of 1.928 mm on BraTS2021 and 86.44 and 2.466 mm on BraTS2020, respectively. Extensive experimental results demonstrate that DiffBTS achieves state-of-the-art performance in brain tumor segmentation, outperforming all competing models. Full article

Sintered porous materials present challenges for any modeling approach applied to simulate their damage evolution because of their complex microstructure, which is crucial for the initialization and propagation of microcracks. This paper presents discrete element simulations of the damage evolution of a NiAl-based material reconstructed by micro-CT imaging. A novel geometry reconstruction procedure based on micro-CT images and the adapted advancing front algorithm fills the solid phase using well-connected irregular and highly dense sphere packing, which directly represents the microstructure of the porous material. Uniaxial compression experiments were performed to identify the behavior of the NiAl sample and validate the discrete element model. Discrete element simulations based on micro-CT imaging revealed a realistic representation of the damage evolution and stress–strain dependency. The stress and strain of the numerically obtained curve peak differed from the experimentally measured values by 0.1% and 4.2%, respectively. The analysis of damage evolution was performed according to the time variation rate of the broken bond count. Investigation of the stress–strain dependencies obtained by using different values of the compression strain rate showed that the performed simulations approached the quasi-static state and achieved the acceptable accuracy within the limits of the available computational resources. The proposed stress scaling technique allowed a seven times increase of the size of the time step, which reduced the computing time by seven times. Full article

The distribution of moisture content in stacked tea leaves during processing significantly influences tea quality. Visualizing this moisture distribution is crucial for optimizing processing parameters. In this study, we utilized hyperspectral imaging (HSI) technology combined with machine learning algorithms to evaluate the moisture content and its distribution in the stacked tea leaves in West Lake Longjing and Tencha green tea products during the processing flow line. A spectral quantitative determination model was developed, achieving high accuracy (${\mathrm{R}}_{\mathrm{p}}^2$ > 0.940) The model demonstrated strong generalization ability, allowing it to predict moisture content in both types of tea. Through hyperspectral imaging, we visualized moisture distribution in seven key processing steps and observed that moisture content was non-uniform, with the leaf tips and petioles having higher moisture levels than the leaf surface. This study offers a novel solution for real-time moisture monitoring of stacked tea leaves in tea production, ensuring consistent product quality. Future research could focus on refining model transfer techniques and exploring additional tea varieties to further enhance the generalization of the approach. Full article