Search Results (1,267)

As stated by the Goal Orientation Theory, students want to achieve a goal for multiple reasons, with each having a different impact on academic performance. This framework encompasses a three-factor model of goal achievement: a mastery goal, a performance-avoidance (PAv) goal, and a performance-approach (PAp) goal. Students may experience elevated stress levels and burnout due to adopting an ineffective approach to goal achievement. This can lead to a loss of interest in studies and even physical and psychological exhaustion. In severe cases, this may result in students abandoning their studies early. This study aims to integrate these factors into a comprehensive model. A cross-sectional study comprising 1497 Italian university students examined the mediating role of academic burnout (professional efficacy, cynicism, and emotional exhaustion) in the association between achievement goals (mastery, PAv, and PAp goals) and the intention to drop out (ID). The questionnaires were administered from October 2022 to September 2023. Structural equation modeling was employed to evaluate the association between variables. The results of the mediation analysis indicate that cynicism and professional efficacy fully mediate the association between mastery and dropout. Cynicism (β = −0.28, p < 0.001) and professional efficacy (β = −0.17, p < 0.001) were both negatively associated with ID, while they partially mediate the association between PAv goals and ID (cynicism: β = 0.21, p ≤ 0.001; professional efficacy: β = 0.05, p ≤ 0.001), and between PAp goals and ID via professional efficacy (β = −0.04, p ≤ 0.001). This study contributes to the currently limited literature on the relationship between achievement goals, burnout, and ID in a sample of university students. The findings of this study may have useful implications for the application of interventions that impact students’ well-being and academic success, potentially limiting their possible dropout. Full article

Secure aggregation of local learning model parameters is crucial for achieving privacy-preserving federated learning. This paper presents a novel and practical aggregation method that effectively combines the advantages of masking-based aggregation with those of homomorphic encryption-based techniques. Each node conceals its local parameters using a randomly selected mask, independently chosen, thereby eliminating the need for additional computations to generate or exchange mask values with other nodes. Instead, each node homomorphically encrypts its random mask using its own encryption key. During each federated learning round, nodes send their masked parameters and the homomorphically encrypted mask to the federated learning server. The server then aggregates these updates in an encrypted state, directly calculating the average of actual local parameters across all nodes without the necessity to decrypt the aggregated result separately. To facilitate this, we introduce a new multi-key homomorphic encryption technique tailored for secure aggregation in federated learning environments. Each node uses a different encryption key to encrypt its mask value. Importantly, the ciphertext of each mask includes a partial decryption component from the node, allowing the collective sum of encrypted masks to be automatically decrypted once all are aggregated. Consequently, the server computes the average of the actual local parameters by simply subtracting the decrypted total sum of mask values from the cumulative sum of the masked local parameters. Our approach effectively eliminates the need for interactions between nodes and the server for mask generation and sharing, while addressing the limitation of a single key homomorphic encryption. Moreover, the proposed aggregation process completes the global model update in just two interactions (in the absence of dropouts), significantly simplifying the aggregation procedure. Utilizing the CKKS (Cheon-Kim-Kim-Song) homomorphic encryption scheme, our method ensures efficient aggregation without compromising security or accuracy. We demonstrate the accuracy and efficiency of the proposed method through varied experiments on MNIST data. Full article

Single-cell RNA sequencing (scRNA-seq) is a cutting-edge technique in molecular biology and genomics, revealing the cellular heterogeneity. However, scRNA-seq data often suffer from dropout events, meaning that certain genes exhibit very low or even zero expression levels due to technical limitations. Existing imputation methods for dropout events lack comprehensive evaluations in downstream analyses and do not demonstrate robustness across various scenarios. In response to this challenge, we propose a consensus clustering-based imputation (CCI) method. CCI performs clustering on each subset of data sampling across genes and summarizes clustering outcomes to define cellular similarities. CCI leverages the information from similar cells and employs the similarities to impute gene expression levels. Our comprehensive evaluations demonstrate that CCI not only reconstructs the original data pattern, but also improves the performance of downstream analyses. CCI outperforms existing methods for data imputation under different scenarios, exhibiting accuracy, robustness, and generalization. Full article

As a pillar grain crop in China’s agriculture, the yield and quality of corn are directly related to food security and the stable development of the agricultural economy. Corn varieties from different regions have significant differences inblade, staminate and root cap characteristics, and these differences provide a basis for variety classification. However, variety characteristics may be mixed in actual cultivation, which increases the difficulty of identification. Deep learning classification research based on corn nodulation features can help improve classification accuracy, optimize planting management, enhance production efficiency, and promote the development of breeding and production technologies. In this study, we established a dataset of maize plants at the elongation stage containing 31,000 images of 40 different types, including corn leaves, staminates, and root caps, and proposed a DenXt framework model. Representative Batch Normalization (RBN) is introduced into the DenseNet-121 model to improve the generalization ability of the model, and the SE module and deep separable convolution are integrated to enhance the feature representation and reduce the computational complexity, and the Dropout regularization is introduced to further improve the generalization ability of the model and reduce the overfitting. The proposed network model achieves a classification accuracy of 97.79%, which outperforms VGG16, Mobilenet V3, ResNet50 and ConvNeXt image classification models in terms of performance. Compared with the original DenseNet 121 network model, the DenXt model improved the classification accuracy by 3.23% and reduced the parameter count by 32.65%. In summary, the new approach addresses the challenges of convolutional neural networks and provides easy-to-deploy lightweight networks to support corn variety recognition applications. Full article

Background/Objectives: One of the significant challenges in forensic casework is the analysis of samples with degraded or poorly concentrated genetic material. The utilisation of the GlobalFiler™ and Yfiler Plus™ kits has unquestionably enhanced the efficacy of genetic profiling in challenging samples, facilitating the analysis of alleles that were previously undetectable with alternative kits. Therefore, the main objective of this work was to verify the efficiency of these kits in analysing forensic samples, progressively reducing the amplification volumes. To further optimise genetic profiling, it was essential not only to assess the behaviour of the alleles but also to prevent allelic loss. Methods: A series of reaction volume reduction studies were conducted, evaluating the performance of genetic profiles in both controlled samples (positive controls) and low template DNA samples (0.01 ng/µL). Results: The results demonstrate that it is effective to obtain complete genetic profiles from the amplification of optimal samples in reduced volumes of 12, 6 or 3 µL with GlobalFiler™ and Yfiler™ Plus. Conclusions: The limiting factor in obtaining complete genetic profiles is the amount of DNA available, rather than the amplification volume. Furthermore, reducing the amplification volume from DNA extracts of low template DNA samples proportionally increases the number of allelic dropouts. Full article

Objective: To assess the effects of a two-week course of intensive impairment-oriented arm rehabilitation for chronic stroke survivors on motor function. Methods: An observational cohort study that enrolled chronic stroke survivors (≥6 months after stroke) with mild to severe arm paresis, who received a two-week course of impairment-oriented and technology-supported arm rehabilitation (1:1 participant–therapist setting), which was carried out daily (five days a week) for four hours. The outcome measures were as follows: the primary outcome was the arm motor function of the affected arm (mild paresis: BBT, NHPT; severe paresis: Fugl-Meyer arm motor score). The secondary outcomes were measures of finger strength, active ROM, spasticity, joint mobility/pain, somatosensation, emotional distress, quality of life, acceptability, and adverse events. Results: One hundred chronic stroke survivors (≥6 months after stroke) with mild to severe arm paresis were recruited. The training was acceptable (drop-out rate 3%; 3/100). The clinical assessment indicated improved motor function (SMD 0.42, 95% CI 0.36–0.49; n = 97), reduced spasticity/resistance to passive movement, and slightly improved joint mobility/pain and somatosensation. The technology-based objective measures corroborated the improved active range of motion for arm and finger joints, reduced finger spasticity/resistance to passive movement, and the increased amount of use in daily life, but there was no effect on finger strength. The patient’s emotional well-being and quality of life were positively influenced. Adverse events were reported by the majority of participants (51%, 49/97) and were mild. Conclusions: Structured intensive impairment-oriented and technology-supported arm rehabilitation can promote motor function among chronic stroke survivors with mild to severe arm paresis and is an acceptable and tolerable form of treatment when supervised and adjusted by therapists. Full article

Background: Accurate and reliable classification models play a major role in clinical decision-making processes for prostate cancer (PCa) diagnosis. However, existing methods often demonstrate limited performance, particularly when applied to small datasets and binary classification problems. Objectives: This study aims to design a fine-tuned deep learning (DL) model capable of classifying PCa MRI images with high accuracy and to evaluate its performance by comparing it with various DL architectures. Methods: In this study, a basic convolutional neural network (CNN) model was developed and subsequently optimized using techniques such as L2 regularization, Tanh activation, dropout, and early stopping to enhance its performance. Additionally, a pyramid-type CNN architecture was designed to simultaneously evaluate both fine details and broader structures by combining low- and high-resolution information through feature maps extracted from different CNN layers. This approach enabled the model to learn complex features more effectively. For performance comparison, the developed fine-tuned enhanced pyramid network (FT-EPN) model was benchmarked against models such as Vgg16, Vgg19, Resnet50, InceptionV3, Densenet121, and Xception, which were trained using transfer learning (TL) techniques. It was also compared to next-generation models such as vision transformer (ViT) and MaxViT-v2. Results: The developed fine-tuned model achieved an accuracy rate of 96.77%, outperforming pre-trained TL models and next-generation models like ViT and MaxViT-v2. Among the TL models, Vgg19 achieved the highest accuracy rate at 92.74%. In comparison, ViT achieved an accuracy of 93.55%, while MaxViT-v2 achieved an accuracy of 95.16%. Conclusions: This study presents an optimized FT-EPN model to enhance the performance of DL models for PCa classification, offering a reference solution for future research. This model provides significant advantages in terms of classification accuracy and simplicity and has been evaluated as an effective solution in clinical applications. Full article

Background/Objectives: The Addis Declaration on Immunization (ADI) is a historic pledge aiming at increasing political will to achieve universal access to immunization services and includes ten commitments to shape the future of immunization in Africa. Methods: To analyze African countries’ performance in achieving the fourth ADI commitment, a cross-sectional retrospective study was conducted including the 54 African Member States of the World Health Organization (WHO) out of 55 African Union (AU) Member States. The fourth ADI commitment aims at increasing the effectiveness and efficiency of immunization delivery systems and has four performance indicators. Results: The median percentage of districts with less than 10% of dropout rate between the first dose of diphtheria–tetanus–pertussis-containing vaccine (DTP1) and the third dose (DTP3) was 86.5%, ranging from 22% to 100%. Thirty-four countries (63%) recorded 80% or above of districts with less than 10% dropout rate between DTP1 and DTP3. Eleven countries (20.3%) and ten countries (18.5%) sustained 90% or above coverage for DTP3 and first dose of measles-containing vaccine (MCV1), respectively, in the past three years (2021–2023). Four countries (7.4%) had 44.5 skilled health workers per 10,000 people. Out of the 54 WHO Member States, 7 achieved at least three of the four indicators of the fourth ADI commitment (13%). Conclusions: It is critical, as a follow up to this study, to document best practices from the seven countries that achieved the fourth ADI commitment. Additionally, a deeper analysis of factors associated with achieving the ADI commitments is required. Full article

Recent advances in emotion recognition through Artificial Intelligence (AI) have demonstrated potential applications in various fields (e.g., healthcare, advertising, and driving technology), with electroencephalogram (EEG)-based approaches demonstrating superior accuracy compared to facial or vocal methods due to their resistance to intentional manipulation. This study presents a novel approach to enhance EEG-based emotion estimation accuracy by emphasizing temporal features and efficient parameter space exploration. We propose a model combining Long Short-Term Memory (LSTM) with an attention mechanism to highlight temporal features in EEG data while optimizing LSTM parameters through Particle Swarm Optimization (PSO). The attention mechanism assigned weights to LSTM hidden states, and PSO dynamically optimizes the vital parameters, including units, batch size, and dropout rate. Using the DEAP and SEED datasets, which serve as benchmark datasets for emotion estimation research using EEG, we evaluate the model’s performance. For the DEAP dataset, we conduct a four-class classification of combinations of high and low valence and arousal states. We perform a three-class classification of negative, neutral, and positive emotions for the SEED dataset. The proposed model achieves an accuracy of 0.9409 on the DEAP dataset, surpassing the previous state-of-the-art accuracy of 0.9100 reported by Lin et al. The model attains an accuracy of 0.9732 on the SEED dataset, recording one of the highest accuracies among the related research. These results demonstrate that integrating the attention mechanism with PSO significantly improves the accuracy of EEG-based emotion estimation, contributing to the advancement of emotion recognition technology. Full article

Digital low-dropout (LDO) regulators are popular in research today as compact power supply solutions. This paper provides a unique approach to analyze digital LDO feedback mechanics and stability, to reduce voltage ripple and extend operating speed over the state-of-the-art. A novel error-subtracting counter is proposed to exponentially improve the response time of any digital LDO, to keep the loop stable outside the typical operating limits, and to increase power-supply rejection (PSR). This leverages the fact that digital LDOs are fundamentally one-bit relaxation oscillators in steady-state. Theory and simulations show how the analog-to-digital (ADC) and digital-to-analog converters (DAC) in these systems affect stability. When compromised, a digital LDO produces uncontrolled sub-clock oscillations at the output that the proposed error-subtracting counter removes. Full article

Remaining useful life (RUL) prediction plays a fundamental role in the prognostics and health management of mechanical equipment. Consequently, extensive research has been devoted to estimating the RUL of mechanical equipment. Owing to the development of modern advanced sensor technologies, a significant amount of monitoring data is recorded. Traditional methods, such as machine-learning-based methods and statistical-data-driven methods, are ineffective in matching when faced with big data thus leading to poor predictions. As a result, deep-learning-based methods are extensively utilized due to their efficient capability to excavate deep features and realize accurate predictions. However, most deep-learning-based methods only provide point estimations and ignore the prediction uncertainty. To address this limitation, this paper proposes a parallel prognostic network to sufficiently excavate the degradation features from multiple dimensions for more accurate RUL prediction. In addition, accurate calculation of model evidence is extremely difficult when dealing with big data so the Monte Carlo dropout is employed to infer the model weights under low computational cost and high scalability to obtain a probabilistic RUL prediction. Finally, the C-MAPSS aero-engine dataset is employed to validate the proposed dual-channel framework. The experimental results illustrate its superior prediction performance compared to other deep learning methods and the ability to quantify prediction uncertainty. Full article

The flourishing of university students is influenced by various factors that significantly impact their well-being and academic performance, with suboptimal levels being a serious concern. Global issues of high dropout rates and low levels of flourishing among university students have prompted this study to identify factors contributing to student flourishing and describe the characteristics of students who achieve it. The review followed a rigorous protocol, including a comprehensive search across multiple databases, screening based on pre-established criteria, quality assessment using the MMAT tool, data extraction using NVivo 12 version 12.6.0.959 (64-bit), and matrix synthesis to identify patterns and gaps in the literature. Results reveal that psychological factors, meaning and purpose, personal projects, social support, social relationships, and environmental factors influence student flourishing. Flourishing students exhibit emotional and psychological well-being (37.5%), positive social functioning (31.25%), achievement and competence (18.75%), and positive psychological functioning (12.5%). These findings, consistent with previous research and flourishing theory, suggest the need for a holistic approach to promoting student flourishing through targeted interventions and recognition of flourishing characteristics. This comprehensive mapping of factors and characteristics of student flourishing can guide theory development and practical implementation in universities. Future research should consider longitudinal studies, replication in different contexts, qualitative research, and exploration of additional factors. Full article

Background/Objectives: As long-term prescription opioid use is associated with increased morbidity and mortality, timely dose reduction of prescription opioids should be considered. However, most research has been conducted on patients using heroin. Given the differences between prescription and illicit opioid use, the aim of this review was to provide an overview of pharmacological strategies to reduce prescription opioid use or improve clinical outcomes for people who experience long-term prescription opioid use, including those with opioid use disorder. Methods: We conducted a systematic database search of PubMed, Embase, CINAHL, and the Cochrane Library. Outcomes included dose reduction, treatment dropout, pain, addiction, and outcomes relating to quality of life (depression, functioning, quality of life). Results: We identified thirteen studies (eight randomized controlled trials and five observational studies). Pharmacological strategies were categorized into two categories: (1) deprescribing (tapering) opioids or (2) opioid agonist treatment (OAT) with long-acting opioids. Tapering strategies decreased opioid dosage and had mixed effects on pain and addiction. OAT with buprenorphine or methadone led to improvements in pain relief and quality of life, with a slight (non-significant) preference for methadone in terms of treatment retention (RR = 1.10 [CI: 0.89–1.37]) but not for other outcomes. Most studies had high dropout rates and a serious risk of bias. Conclusions: Tapering reduced prescription opioid doses had mixed effects on pain. OAT improved clinical outcomes without dose reduction. Based on our review findings, there is no clear preference for either tapering or OAT. Tapering may be considered first as it reduces dependency, tolerance, and side effects, but is associated with adverse events and not always feasible. OAT can be a suitable alternative. Non-pharmacological interventions may facilitate tapering. Further research is needed to identify novel pharmacological strategies to facilitate opioid tapering. Registration: PROSPERO 2022 CRD42022323468. Full article

Maize is one of the most widely grown crops in Ethiopia and is a staple crop around the globe; however, common rust maize disease (CRMD) is becoming a serious problem and severely impacts yields. Conventional CRMD detection and treatment methods are time-consuming, expensive, and ineffective. To address these challenges, we propose a real-time deep-learning model that provides disease detection and pesticide dosage recommendations. In the model development process, we collected 5000 maize leaf images experimentally, with permission from Haramaya University, and increased the size of the dataset to 8000 through augmentation. We applied image preprocessing techniques such as image equalization, noise removal, and enhancement to improve model performance. Additionally, during training, we utilized batch normalization, dropout, and early stopping to reduce overfitting, improve accuracy, and improve execution time. The optimal model recognizes CRMD and classifies it according to scientifically established severity levels. For pesticide recommendations, the model was integrated with the Gradio interface, which provides real-time recommendations based on the detected disease type and severity. We used a convolutional neural network (CNN), specifically the ResNet50 model, for this purpose. To evaluate its performance, ResNet50 was compared with other state-of-the-art algorithms, including VGG19, VGG16, and AlexNet, using similar parameters. ResNet50 outperformed the other CNN models in terms of accuracy, precision, recall, and F-score, achieving over 97% accuracy in CRMD classification—surpassing the other algorithms by more than 2.5% in both experimental and existing datasets. The agricultural experts verified the accuracy of the recommendation system across different stages of the disease, and the system demonstrated 100% accuracy. Additionally, ResNet50 exhibited lower time complexity during model development. This study demonstrates the potential of ResNet50 models for improving maize disease management. Full article

Despite advances in pharmacological therapies, migraine patients are often drug resistant. Further therapeutic options in this field are, therefore, desirable. Recent studies have highlighted the efficacy of ketogenic diet (KD) on improving migraine, but data on their long-term efficacy and safety are lacking. In this study, we retrospectively evaluated the long-term effectiveness of the modified Atkins ketogenic diet (MAD) in episodic or chronic drug-resistant migraine patients. 52 patients diagnosed with episodic or chronic drug-resistant migraine under modified Atkins ketogenic diet (MAD) were evaluated. In total, 41 patients followed the diet for 6 months and 33 for 12 months. After both 6 and 12 months, frequency, length, and intensity of migraine episodes, as well as the number of medications significantly decreased with respect to the start of the diet. Body mass index, high sensitivity PCR, diastolic blood pressure, fasting plasma insulin and HOMA index were also significantly reduced both after 6 and 12 months. No major metabolic changes were observed during MAD treatment. In conclusion, KD has been shown to be effective and safe in the long-term treatment of drug-resistant migraine. A high dropout rate still remains an important factor, which often limits its use. Full article